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Research ArticleFuzzy Logic Controller Based Distributed GenerationIntegration Strategy for Stochastic Performance Improvement
Jagdish Prasad Sharma1 and H Ravishankar Kamath2
1Mewar University Chittorgarh Rajasthan India2Malwa Institute of Technology Indore India
Correspondence should be addressed to Jagdish Prasad Sharma jpsharmacsebgmailcom
Received 15 July 2016 Accepted 12 October 2016
Academic Editor Mamun B Ibne Reaz
Copyright copy 2016 J P Sharma and H R Kamath This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited
In the restructured environment distributed generation (DG) is considered as a very promising option due to a high initial capitalcost of conventional plants environmental concerns and power shortage Apart from the above distributed generation (DG) hasalso abilities to improve performance of feeder Most of the distribution feeders have radial structure which compel to observethe impact of distributed generations on feeder performance having different characteristics and composition of time varyingstatic ZIP load models Two fuzzy-based expert system is proposed for selecting and ranking the most appropriated periods toan integration of distributed generations with a feeder Madami type fuzzy logic controller was developed for sizing of distributedgeneration whereas Sugeno type fuzzy logic controller was developed for the DG location Input parameters for Madami fuzzylogic controller are substation reserve capacity feeder power loss to load ratio voltage unbalance and apparent power imbalancesDG output survivability index and node distance from substation are chosen as input to Sugeno type fuzzy logic controller Thestochastic performance of proposed fuzzy expert systems was evaluated on a modified IEEE 37 node test feeder with 15 minutescharacteristics time interval varying static ZIP load models
1 Introduction
In the restructured environment feasibility study of dis-tributed generation (DG) integration to existing grid is akey interesting area of research Deployment of distributedgeneration (DG) in distribution system brings technical aswell as financial benefits to utilities The positive benefits areloss reduction reliability enhancement and power qualityimprovement and negative effects are increased fault leveland false operation of the feeder Most of the distributionfeeders have radial structure and are designed to operate witha single source along the feeder To compensate rapid growthof load demand and proliferation of electronic device avail-able options for utilities are network extension substationcapacity augmentation and DG integration Apart from theabove the majority of the existing radial distribution feedersare lengthy over and nonuniform loading which resulted inan excessive voltage drop poor voltage profile male trippingof protection devices and power loss DG integration to
existing grid is well suited option to utilities due to lack offinancial resources and long-term implementation of gridextension work As DG installation greatly influences theperformance of distribution feeder the optimal location andsizing of distributed generation (DG) are an active researchinterest which is needed to harness maximum benefits fromthe DG
Several researchers have used different optimizationmethods such as analytical numerical and heuristic forthe sake of power loss minimization cost reduction profitmaximization and environmental emission reduction Barinet al have used a fuzzy-based expert system for choosingand ranking of the most appropriated periods to integratedistribution generation with an existing distribution network[1] A fuzzy-based powermanagement technique is employedto schedule power dispatching for microgridutility gridTheproposed power management technique is subjected to a setof constraints including weather conditions load-sheddinghours and peak pricing hours [2] In order to mitigate
Hindawi Publishing CorporationAdvances in Electrical EngineeringVolume 2016 Article ID 9760538 13 pageshttpdxdoiorg10115520169760538
2 Advances in Electrical Engineering
demand and avoid power outage in peak hour two fuzzy logiccontroller are simulated for optimal location and sizing ofdistributed generation on IEEE 13 test feeder [3]
A fuzzy logic method is deployed for optimal DGplacement subjected to minimize total power loss constraintwhereas a new analytical method is used for DG sizing Theeffectiveness of DG on system voltage profile and branchpower losses is carried out on IEEE 69 and IEEE 33 radialfeeder [4] Manjili and Rajaee proposed fuzzy controllerfor energy management and cost reduction microgrid Theamount of power exchange from storage unit is based on theload demand renewable generation rate and electricity price[5]
Metia and Ghosh presented optimal location of DGunits with a 33-bus system based on the available amountof DG using fuzzy logic [6] A firefly based algorithm foroptimal location and capacity of CHP technology DG or aphotovoltaic DG is implemented on IEEE 37-node feederwith the objectives of profit maximization [7] Harmonysearch algorithm is utilized to determine appropriate size ofshunt capacitors with real power losses and installation costof shunt capacitors whereas the location of shunt capacitorsis identified using voltage stability index [8] Padma Lalithaet al presented a fuzzy approach for finding optimal DGlocations and a PSO algorithm for optimal DG sizes on IEEE33 node feeder [9] A probabilistic fuzzy solution is proposedto identify vulnerable nodes for the optimal reconfigurationproblem [10] Fuzzy expert system employed for optimalcapacitor placement and sizing for 35 buses with multilevelof loads [11]
Arabali et al carried out a stochastic framework tooptimal sizing and reliability analysis for a hybrid powersystem having wind power photovoltaic (PV) and energystorage system The stochastic nature of wind solar irradia-tion and photovoltaic (PV) power and load are stochasticallymodelled using ARMA [12] Soroudi and Ehsan investigatedthe impact of an uncertain power production of distributedgenerations (DGs) on active losses of distribution feederUncertainty in wind speed and gas turbines is modelled bya Weibull probability distribution function (PDF) and fuzzyrespectively [13]
Sharma and Ravishankar Kamath have presented voltageassessment indices for modified IEEE 37 node test feederhaving time varying composite voltage sensitive load [14]Performance indices to assess feeder performance of modi-fied IEEE 37 node test feeder were developed with the help offorward-backward sweep method and two port parametersrepresentation of feeder components [15]
The objective of this paper is to integrate DG usingtwo fuzzy logic controllers One fuzzy logic controller isused to determine the sizing of DG on the basis of feederperformance parameter such as substation reserve capacityfeeder power loss to load ratio voltage unbalance andapparent power imbalance Another fuzzy logic controller isused to choose the DG location node on the basis of DGoutput survivability index andnode distance from substationare chosen as input
2 Feeder Performance Indices
The quality of power supply for modified IEEE 37 nodetest feeder is evaluated to develop performance indices andthese performance indices are substation reserve capacityvoltage unbalance factor and feeder power loss to load ratiobranch loading voltage deviation and power factor [15] Itis observed that the said substation transformer of feederis highly overloaded between 35 and 71 characteristics timeinterval which could be relived with solar PV penetration atthe feeder Modified feeder has also accounted for stochasticcharacteristics and composition of voltage sensitive loadmodels These load models are categorized into residen-tial commercial and industrial consumers Each categoryconsumers are appliances with constant power constantcurrent and constant impedance in random propositionsThe participation of real and reactive power load exponentsfor the different type categories of consumers is characterizedby the following equations [15]
119875119896119886119887119888(ℎ) = 1198621119896
119886119887119888(ℎ) + 1198622119896
119886119887119888(ℎ) lowast1003816100381610038161003816100381610038161003816100381610038161003816
119881119896119886119887119888(ℎ)119881119873119886119887119888
1003816100381610038161003816100381610038161003816100381610038161003816
2
+ 1198623119896119886119887119888(ℎ) lowast1003816100381610038161003816100381610038161003816100381610038161003816
119881119896119886119887119888(ℎ)119881119873119886119887119888
1003816100381610038161003816100381610038161003816100381610038161003816
119876119896119886119887119888(ℎ) = 1198631119896
119886119887119888(ℎ) + 1198632119896
119886119887119888(ℎ) lowast1003816100381610038161003816100381610038161003816100381610038161003816
119881119896119886119887119888(ℎ)119881119873119886119887119888
1003816100381610038161003816100381610038161003816100381610038161003816
2
+ 1198633119896119886119887119888(ℎ) lowast1003816100381610038161003816100381610038161003816100381610038161003816
119881119896119886119887119888(ℎ)119881119873119886119887119888
1003816100381610038161003816100381610038161003816100381610038161003816
(1)
Calculation of ZIP loads compositions is shown inAppendix Tomeet smart grid implementation criterion per-formance indices are evaluated for 15 minutes characteristicstime interval for the whole day In these days distributiongeneration integration is a common practice to meet outcontinuously increased demandThe load and renewable DGgeneration probabilistic nature are considered in this study Itis observed from load flow solution that feeder is subjected tooverloading during 8AM to 6PMTherefore the fuzzy expertsystem is developed in a way that DG operated between theabove periods The photovoltaic DG system under differentpower factor scenario is considered for investigation
3 Proposed Fuzzy Expert System
The proposed fuzzy logic controller for DG integrationimplies Mamdani and Sugeno fuzzy inference system TheDG sizing is determined using Mamdani type fuzzy logiccontroller on the basis of feeder performance parametersuch as substation reserve capacity feeder power loss toload ratio voltage unbalance and apparent power imbalancewhereas Sugeno type fuzzy logic controller is used to choosethe DG location on the basis of DG output survivabilityindex and node distance from the substation The fuzzy-based expert system is tested using the MATLAB fuzzylogic tool box undermulti-rules-based decision andmultisetsconsiderations as in Figure 1
Advances in Electrical Engineering 3
TOD
SCRI
FLLR
UPQ
VUF
Input for Mamdani typefuzzy logic controller
Mamdani type
Distance
Input for Sugeno typefuzzy logic controller
Sugeno typefuzzy logiccontroller
fuzzy logic controller
round
Rounding
DG sizing
DG locationfunction
Survivability Index
Figure 1 Fuzzy logic controller for DG integration
TOD
PowerGAP
FLLR
UPQ
VUF
FCLperformancenew(mamdani)
PDGOUTPUT
Figure 2 Mamdani type fuzzy logic controller for DG sizing
Inference process forMamdani type fuzzy logic controllerhas MIN-MAX method of aggregation and SOM of thedefuzzification process as standard settings The other settingfor Sugeno type fuzzy logic controller remains the sameexcept for defuzzification method named as whatever [16]
31 Mamdani Type Fuzzy Logic Controller for DG SizingUnbalance in voltage and current increased apparent powerimbalance feeder loss voltage deviation and neutral currentIncreased neutral current in substation transformer leadsto communication interference equipment overloading andfalse operation of the protective system Apart from theabove apparent power imbalance is a more appropriateapproach to reactive power compensation The 15-minutecharacteristics time interval substation reserve capacity(SRCI) feeder power loss to load ratio (FLLR) voltage unbal-ance factor (VUF) and apparent power imbalance (APBI)indices are the five inputs to the Mamdani type inference sys-tem which are computed from the load flow solution over aneach 15-minute time interval for the whole day [15] ProposedMamdani type inference as shown in Figure 2 has a set of 15rules which involve heuristic rules for determining the size ofDG in the fuzzification process In the fuzzification processthese inputs are converted into logic form in accordance withthe associated membership functions
Membership function plots
TOD1 TOD2 TOD3 TOD4
Plot points 181
1
05
0
Input variable ldquoTODrdquo9080706050403020100
181
Figure 3 Membership function of TOD
Four different times of day (TOD) in terms of 15-minutemetering time interval is taken for the whole day andrepresented by four triangles membership function curvesas shown in Figure 3 In Figure 4 the power demand gap isdescribed by Z Gauss and S shape membership functionsTheZ shapemembership function represents the demand gapless than 05MW and demand gap greater than 05MW isrepresented by an S shape membership curve
Triangle membership function is used for medium valueFLLR UPQ and VUF whereas trapezoidal membershipfunctions are considered for the low and high of the abovethree variables Their graphical representations of member-ship function are depicted in Figures 5 6 and 7 respectively
4 Advances in Electrical Engineering
Low Medium HighMembership function plots Plot points
1
05
0
181
0604020 08 1minus04minus06minus08 minus02minus1
Input variable ldquoPowerGAPrdquo
Figure 4 Membership function of power demand gap
Low Medium HighMembership function plots Plot points
1
05
0
181
005 006 007 008 009 01004
Input variable ldquoFLLRrdquo
Figure 5 Membership function of FLLR
DG sizing is shown with three trapezoidal membershipfunctions in Figure 8 Low DG output is assumed to bebetween 0 and 01MW whereas medium DG output hasranged between 012 and 04 The DG output is consideredbetween 04 and 1MW Surface view of fuzzy logic controllerrules for DG sizing is shown in Figure 9
32 Sugeno Type Fuzzy Logic Controller for DGLocation Thispaper proposes a fuzzy approach to predict vulnerability ofthe node using survivability index The proposed survivabil-ity index (SI) is computed by equation (2) below for eachnodeusing voltage stability margin (VSI) and voltage deviationindex (VDI) corresponds to 90 percentile over each 15-minute time interval for the whole day [14 15]
SI = min (VSIℎ119886119887119888) lowast 075 +max (VDIℎ
119886119887119888) lowast 025 (2)
Table 1 shows the survivability index of top 15 nodes fromthe list of nodes in or near vulnerability andTable 1 is depictedin the Appendix The membership function for vulnerablenodes is represented in Figure 12
Sugeno type fuzzy logic controller as shown in Figure 10has crisp input parameters such as node distance from thesubstation DG output and vulnerable node Node distance isdepicted by three intersecting trapezoidal curves in Figure 11The low distance corresponds to the range of 0 to 3300feet and medium distance is between 3400 and 6000 feetHigh distance is considered between 6000 and 8000 feetThe output of Sugeno type fuzzy logic controller has con-stant functions as shown in Figure 13 where the distributedgeneration can be added according to the demand from thecustomer premises The proposed Sugeno type inference hasa set of 09 rules to determine the location of DG as shownFigure 14
Low Medium HighMembership function plots Plot points
1
05
0
181
01 015 02 025 03005
Input variable ldquoUPQrdquo
Figure 6 Membership function of UPQ
Table 1 Survivability index of top 15 nodes
S number Node Distance (Feet) Survivability index
1 775 4930 08816722 709 4930 0880713 708 5250 08899384 733 5570 0905285 734 6130 09183966 737 6770 09292167 738 7170 0933718 711 7570 09361159 741 7970 093687210 732 5570 089094811 731 5530 088071212 710 6650 092179613 735 6850 092308314 736 7930 092193415 740 7770 0937371
Figures 15 and 16 show the inputs of Madami andSugeno fuzzy logic controller respectively whereas outputsof Madami and Sugeno fuzzy logic controller are depicted inFigure 17 Node 734 is found at the optimum DG locationwith 440KW per Phase capacity to be operational between37 and 73 characteristics time interval
4 Results and Analysis
The proposed algorithm has been implemented in MATLABand evaluated on modified unbalance IEEE 37 node testfeeder In this study total optimum DG capacity of 440KWper phase is considered at node 734 and the DG operatingtime interval very much resembles solar photovoltaic DG Inthis paper constant power factorDGmodel is considered andhas constant 119875dg active power at a pfdg constant power factorTo keep constant power factor required 119876dg reactive powerof the DG is computed by following equation [17]
119876dg = 119875dg tan (cosminus1 (119875dg)) (3)
In the base case the performance indices are computedwithout any DG integration for the whole day The base loss
Advances in Electrical Engineering 5
LowMedium
HighMembership function plots Plot points
1
05
0
181
01 02 03 04 05 06 07 08 09 10
Input variable ldquoVUFrdquo
Figure 7 Membership function of VUF
Low Medium High
Output variable ldquoPDGOUTPUTrdquo1000900800700600500400300200100
Membership function plots Plot points
1
05
00
181
Figure 8 Membership function of DG output
PDG
OU
TPU
T
105
0minus05
minus1 TODPowerGAP
9080706050403020100
0
100
200
300
400
Figure 9 Surface view of fuzzy logic controller rules for DG sizing
PGOUTPUT
Distance
Surviablityindex
FCL2rule(sugeno)
DGNodeSelection
f(u)
Figure 10 Sugeno type fuzzy logic controller for DG location
of feeder is depicted in Figure 18 For the base case the dailyphase voltage profile for all buses is shown in Figures 19ndash21The least voltage in Phase A Phase-B and Phase C is foundon nodes 34 33 and 31 respectively These nodes can be usedfor shunt compensation
The impacts of distributed generation on various indicesare detailed as follows
Figure 22 reveals that DG operation at 095 power factorleading has the highest substation reserve capacity whereas
1
05
0
Low Medium HighMembership function plots Plot points 181
Input variable ldquoDistancerdquo800070006000500040003000200010000
Figure 11 Membership function of distance
1
05
0
Membership function plots Plot points 181
01 02 03 04 05 06 07 08 09 10
Input variable ldquoSurviablityindexrdquo
Low Medium High
Figure 12 Membership function of survivability index
Membership function plots plot Points 181
Output variable ldquoDGNodeSelectionrdquo
775709708733734737738711
741732731710735736740
Figure 13 Membership function of DG location
6 Advances in Electrical Engineering
Distance PGOUTPUT
10009008007006005004003002001000 010002000
3000400050006000
70008000720722724726728730732
DG
Nod
eSele
ctio
n
Figure 14 Surface view of Sugeno type fuzzy logic controller rules
2000
minus200minus400minus600minus800
008
007
006
005
03
025
02
015
0908070605
Input to Mamdani Controller
FLLR
UPQ
VUF
80 90706050403020100
80 90706050403020100
80 90706050403020100
80 90706050403020100
Figure 15 Inputs to Mamdani fuzzy logic controller
the base case has the lowest substation reserve capacitybetween 35 and 71 characteristics time interval DG operationat 095 lagging power factor has the reverse effect on loadrelief
It is observed from Figures 23 24 and 25 that the highestFLLR for all phases is found in DG operation at 095 powerfactor lag Lowest reduction in FLLR in Phase-A Phase-Band Phase-C is found inDG 095 power factor (lag) base andDG 099 power factor (lead) case respectively
In Figures 26 27 and 28 it is observed that DG operationat 095 power factor leading showed highest BCLI reductionfor all phases the DG operation at 099 power factor leadinggot the second reduction in BCLI and then the DG operationat unity power factor and DG operation at 095 power factorlagging the next Base case showed a branch overloading
1173 and 115 at 53 and 55 characteristics time interval forPhase-A and C respectively
As shown in Figure 29 there is no significant impact ofDG placement over apparent power imbalance Figures 3031 and 32 reveal the same impact of DG placement on voltagedeviation for all phases which was observed in the case ofBCLI
As shown in Figures 33 34 and 35 DG operation at 095power factor lagging showed minimum power factor (MPF)for all phasesMaximumpower factor for Phase-A andPhase-C occurred in the DG operation at 095 power factor leadingwhile maximum power factor for Phase-B occurred in DGoperation at unity power factor
In Figure 36 base case showed a highest voltage unbal-ance factor (VUF) the DG operation at 095 power factor
Advances in Electrical Engineering 7
80007000600050004000300020001000
500
Distance
Distance
09409309209109
089088
Inputs of Sugeno logic controller
Survivability index
Survivability index
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
50403020100 90807060
400300200100
0
Figure 16 Inputs to Sugeno fuzzy controller
732734
730728726724722720
450400350300250200150100500
DG Node
DG Node
Simulink output of Madami and Sugeno logic controller
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
Figure 17 Simulink outputs of fuzzy logic controller
lagging got the second maximum VUF and then the DGoperation at unity power factor and DG operation at 099power factor leading the next DG operation at 095 powerfactor lagging showed a maximum VUF of 05177at 6 charac-teristics time interval
5 Conclusion
The stochastic behavior of DG integrated distribution feederis a challenging problem for operations and planning aspectsA fuzzy expert system is proposed to determine optimal
8 Advances in Electrical Engineering
Tota
l fee
der l
oss (
KW)
253035404550556065
20 9010 50 60 70 8030 40Time in 15-minute interval
X 6Y 2522
X 52Y 5946
Figure 18 Feeder loss for base case
1
098
096
094
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number0
102030
40100806040200
(483409452)
Time in 15-minute interval
Figure 19 Phase voltage profile of Phase-A
1
099
098
097
096
095
0995
099
0985
098
0975
097
(56330966)
Phas
e vol
tage
pro
file
Node number010203040 100806040200
Time in 15-minute interval
Figure 20 Phase voltage profile of Phase-B
0995
099
0985
098
0975
097
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number010203040
100806040200
0965
(48310962)
Time in 15-minute interval
Figure 21 Phase voltage profile of Phase-C
Advances in Electrical Engineering 9
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 9010 80Time in 15-minute interval
minus02
0
02
04
06
08
SRCI
(PU
)
X 35X 35
X 35X 35
Y 0546Y 04836
Y 04282Y 02816
X 73X 73
X 73
X 73
Y 05441Y 04819Y 04268Y 02824
Figure 22 DG impact of substation reserve capacity index
FLLR
-Pha
se-A
(PU
)
minus002minus001
0001002003004005006007008
20 30 40 50 60 70 80 9010Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35
X 35
X 46
Y 007636
Y 004391
Y 002575
(720077)
Figure 23 DG impact of feeder loss to load ratio for Phase-A
X 35
X 35
Y 01209 Y 0124
Y 00771
Y 004061
X 73
X 71
X 56
Y 007568
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
80 9040 50 60 7020 3010Time in 15-minute interval
minus0020
002004006008
01012014
FLLR
-Pha
se-B
(PU
)
Figure 24 DG impact of feeder loss to load ratio for Phase-B
location and size of fixed PQ DG integration to a modifiedunbalance IEEE 37 feeder In this paper impact of DGintegration on performance indices has been demonstratedunder at different power factor under stochastic environ-ment To investigate clear effect of DG operation for making
FLLR
-Pha
se-C
(PU
)
001
0015
002
0025
20 30 40 50 60 70 8010 90Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35Y 002184
X 34Y 001736 X 56
Y 001862
X 73Y 002224
X 74Y 001754
X 70Y 001425
Figure 25 DG impact of feeder loss to load ratio for Phase-C
BCLI
-Pha
se-A
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
(3507117)
(3508666)(531173)
X 35Y 05765
X 75Y 1105
X 73Y 05746
20 30 40 50 60 70 9010 80Time in 15-minute interval
040506070809
11112
Figure 26 DG impact on branch loading index of Phase-A
BCLI
-Pha
se-B
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
8030 40 50 60 7020 9010Time in 15-minute interval
040506070809
111
X 34Y 08137
X 35Y 06739X 35Y 05325X 35
Y 04917X 35Y 04641
X 75Y 08246
X 73Y 0678
X 73Y 05316 X 73
Y 04906X 73Y 04628
Figure 27 DG impact on branch loading index of Phase-B
useful decision support voltage regulator of the above feederis removed Comparative results obtained with different fixedPQ DG operating scenario are discussed and it is found thatthe results obtained by DG operation at 095 power factorleading are more suitable to improve performance of feeder
10 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
9030 40 50 60 70 802010Time in 15-minute interval
X 34Y 1045
X 35Y 04945
X 74Y 1057
X 70Y 06878
X 71Y 04907
(55115)
040506070809
11112
BCLI
minusPha
seminusC
(PU
)
Figure 28 DG impact on branch loading index of Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
160180200220240260280300
Unb
alan
ce fe
eder
pow
er(K
VA)
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 6Y 1768
X 82Y 2656
X 82Y 2391
(422813)
Figure 29 DG impact on apparent powers unbalance index of feeder
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
VD
I-Ph
ase-
A (P
U)
03040506070809
111
X 34Y 09884
X 35Y 04341
X 35Y 02874
X 73Y 04457X 73Y 02871
(551084)
Figure 30 DG impact on maximum voltage deviation Phase-A
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
02
03
04
05
06
07
VD
I-Ph
ase-
B (P
U)
X 34Y 06145
X 35Y 03889X 35Y 03379
X 35Y 03156 X 35
Y 0287
X 75Y 06252
X 73Y 03959X 73Y 03411
X 73Y 03189X 73
Y 02904
(5206797)
Figure 31 DG impact on maximum voltage deviation Phase-B
Advances in Electrical Engineering 11
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
X 34Y 06916
X 35Y 03766
X 55Y 07598 X 74
Y 0695
X 71Y 03784
02030405060708
VD
I-Ph
ase-
C (P
U)
Figure 32 DG impact on maximum voltage deviation Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
MPF
-Pha
se-A
(PU
)
X 73Y 05634
(3509594)(340917)
(72minus09757)0
02
04
06
08
1
Figure 33 DG impact on minimum power factor of Phase-A
X 73X 73X 73
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 35Y 0779X 35
Y 0779X 35Y 06893
X 35Y 03432
X 73Y 07722X 73Y 07344X 73
Y 06813
X 73Y 0327302
03040506070809
1
MPF
-Pha
se-B
(PU
)
Figure 34 DG impact on minimum power factor of Phase-B
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 34Y 08142
X 35Y 06906
X 35Y 04764
X 35Y minus01302
X 74Y 08142
X 73Y 0692
X 73Y 04777
X 73Y minus01228
(5507574)
minus02
002040608
1
MPF
-Pha
se-C
(PU
)
Figure 35 DG impact of minimum power factor of Phase-C
12 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
Volta
ge U
nbal
ance
fact
or (
)
X 55Y 08497
X 48Y 06419
X 35Y 05578
X 55Y 06498
X 73Y 05576(605177)
05055
06065
07075
08085
09
Figure 36 DG impact of voltage unbalance factor of feeder
Appendix
A Survivability Index of Top 15 Nodes
(see Table 1)
B ZIP Load Compositions Calculation
To find ZIP load composition at each bus 119896 the followingequations were utilized to compute ZIP load composition ateach bus 119896 for each ℎ characteristics time interval
1198621119896119886119887119888(ℎ) = 08 lowast 119875119868119896
119886119887119888(ℎ) + 06 lowast 119875119862119896
119886119887119888(ℎ) + 08
lowast 119875119877119896119886119887119888(ℎ)
1198622119896119886119887119888(ℎ) = 02 lowast 119875119868119896
119886119887119888(ℎ) + 04 lowast 119875119862119896
119886119887119888(ℎ) + 019
lowast 119875119877119896119886119887119888(ℎ)
1198623119896119886119887119888(ℎ) = 0 lowast 119875119868119896
119886119887119888(ℎ) + 0 lowast 119875119862119896
119886119887119888(ℎ) + 001
lowast 119875119877119896119886119887119888(ℎ)
1198631119896119886119887119888(ℎ) = 08 lowast 119876119868119896
119886119887119888(ℎ) + 06 lowast 119876119862119896
119886119887119888(ℎ) + 08
lowast 119876119877119896119886119887119888(ℎ)
1198632119896119886119887119888(ℎ) = 02 lowast 119876119868119896
119886119887119888(ℎ) + 04 lowast 119876119862119896
119886119887119888(ℎ) + 019
lowast 119876119877119896119886119887119888(ℎ)
1198633119896119886119887119888(ℎ) = 0 lowast 119876119868119896
119886119887119888(ℎ) + 0 lowast 119876119862119896
119886119887119888(ℎ) + 001
lowast 119876119877119896119886119887119888(ℎ)
(B1)
Nomenclature
119875119896119886119887119888(ℎ) 119876119896
119886119887119888(ℎ) Total real and reactive power
load at bus 119896 during ℎ119881119873119886119887119888 119881
119896
119886119887119888(ℎ) Nominal and three-phase
voltage at 119896 node
1198621119896119886119887119888(ℎ) 1198622119896
119886119887119888(ℎ) 1198623119896
119886119887119888(ℎ) ZIP active load composition
at bus 119896 during ℎ1198631119896119886119887119888(ℎ)1198632119896
119886119887119888(ℎ)1198633119896
119886119887119888(ℎ) ZIP reactive load
composition at bus 119896 duringℎ
119875119868119896119886119887119888(ℎ) 119875119877119896
119886119887119888(ℎ) 119875119862119896
119886119887119888(ℎ) Active power for industrial
residential and commercialload connected at bus 119896during ℎ
119876119868119896119886119887119888(ℎ) 119876119877119896
119886119887119888(ℎ) 119876119862119896
119886119887119888(ℎ) Reactive power for
industrial residential andcommercial load connectedat bus 119896 during ℎ
ℎ 15-minute characteristicstime interval
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
References
[1] A Barin C U Brazil L Martins and E S A Brazil ldquoFuzzybased expert system for renewable energy managementrdquo inProceedings of the CIREDWorkshop no 0020 Rome Italy 2014
[2] N Hashmi and S A Khan ldquoPower energy management fora grid-connected PV system using rule-base fuzzy logicrdquo inProceedings of the 3rd International Conference on ArtificialIntelligence Modelling amp Simulation (AIMS rsquo15) pp 31ndash36 KotaKinabalu Malaysia December 2015
[3] A R Kashfi and M E El-Hawary ldquoIntegration of distributedgeneration inmedium voltage distribution network using fuzzylogic controller for demand side managementrdquo in Proceedingsof the Electrical Power and Energy Conference (EPEC rsquo14) pp254ndash259 Calgary Canada November 2014
[4] S K Injeti and N P Kumar ldquoPlanning and operation of activeradial distribution networks for improved voltage stability andloss reductionrdquoWorld Journal of Modelling and Simulation vol8 no 3 pp 211ndash222 2012
Advances in Electrical Engineering 13
[5] Y Manjili and A Rajaee ldquoFuzzy Control of Electricity StorageUnit for EnergyManagement of Micro-Gridsrdquo httpacewaco-ngorgAssetsdocFuzzy20Control20of20Electricity20Storage20Unit20for20Energy20Management20of20Micro-Grids Mexico20WAC202012pdf
[6] A Metia and S Ghosh ldquoFuzzy based DG allocation for LossMinimization in a Radial Distribution Systemrdquo Balkan Journalof Electrical amp Computer Engineering vol 3 no 3 pp 115ndash1232015
[7] E A Mohamed M M Othman and Y G Hegazy ldquoOpti-mal sizing and placement of distributed generators for profitmaximization using firefly algorithmrdquo in Proceedings of theInternational Conference on Artificial Intelligence Energy andManufacturing Engineering (ICAEME rsquo14) pp 6ndash10 2014
[8] K Muthukumar and S Jayalalitha ldquoOptimal reactive powercompensation by shunt capacitor sizing using harmony searchalgorithm in unbalanced radial distribution system for powerloss minimizationrdquo International Journal on Electrical Engineer-ing and Informatics vol 5 no 4 pp 474ndash491 2013
[9] M Padma Lalitha V C Veera Reddy and N Sivarami ReddyldquoApplication of fuzzy and ABC algorithm for DG placement forminimum loss in radial distribution systemrdquo Iranian Journal ofElectrical and Electronic Engineering vol 6 no 4 pp 248ndash2572010
[10] M S Thomas R Ranjan and R Roma ldquoProbabilistic fuzzyapproach to assess RDS vulnerability and plan corrective actionusing feeder reconfigurationrdquo Energy and Power Engineeringvol 4 no 5 pp 330ndash338 2012
[11] P N Si and S S Win ldquoFuzzy algorithm for capacitor allocationand sizing in radial distribution system to reduce lossesrdquoInternational Journal of Science Engineering and TechnologyResearch vol 3 no 12 2014
[12] A Arabali M Ghofrani M Etezadi-Amoli and M S FadalildquoStochastic performance assessment and sizing for a hybridpower system of SolarWindEnergy Storagerdquo IEEE Transac-tions on Sustainable Energy vol 5 no 2 pp 363ndash371 2014
[13] A Soroudi and M Ehsan ldquoA possibilistic-probabilistic tool forevaluating the impact of stochastic renewable and controllablepower generation on energy losses in distribution networksmdashacase studyrdquo Renewable and Sustainable Energy Reviews vol 15no 1 pp 794ndash800 2011
[14] J P Sharma and H Ravishankar Kamath ldquoStochastic voltageassessment of unbalance radial feederrdquo Journal of ElectricalSystems vol 11 no 3 pp 258ndash270 2015
[15] J P Sharma and H R Kamath ldquoPerformance analysis ofunbalance radial feeder with time varying composite loadrdquoJournal of Power and Energy Engineering vol 3 no 5 pp 56ndash702015
[16] The MathWorks Inc Types of Fuzzy Interefernece Systems TheMathWorks Inc 1994 1994ndash2014 httpwwwmathworkscomhelpfuzzytypes-of-fuzzyinference-systemshtml
[17] J-H Teng ldquoModelling distributed generations in three-phasedistribution load flowrdquo IET Generation Transmission and Dis-tribution vol 2 no 3 pp 330ndash340 2008
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Active and Passive Electronic Components
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RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
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Navigation and Observation
International Journal of
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DistributedSensor Networks
International Journal of
2 Advances in Electrical Engineering
demand and avoid power outage in peak hour two fuzzy logiccontroller are simulated for optimal location and sizing ofdistributed generation on IEEE 13 test feeder [3]
A fuzzy logic method is deployed for optimal DGplacement subjected to minimize total power loss constraintwhereas a new analytical method is used for DG sizing Theeffectiveness of DG on system voltage profile and branchpower losses is carried out on IEEE 69 and IEEE 33 radialfeeder [4] Manjili and Rajaee proposed fuzzy controllerfor energy management and cost reduction microgrid Theamount of power exchange from storage unit is based on theload demand renewable generation rate and electricity price[5]
Metia and Ghosh presented optimal location of DGunits with a 33-bus system based on the available amountof DG using fuzzy logic [6] A firefly based algorithm foroptimal location and capacity of CHP technology DG or aphotovoltaic DG is implemented on IEEE 37-node feederwith the objectives of profit maximization [7] Harmonysearch algorithm is utilized to determine appropriate size ofshunt capacitors with real power losses and installation costof shunt capacitors whereas the location of shunt capacitorsis identified using voltage stability index [8] Padma Lalithaet al presented a fuzzy approach for finding optimal DGlocations and a PSO algorithm for optimal DG sizes on IEEE33 node feeder [9] A probabilistic fuzzy solution is proposedto identify vulnerable nodes for the optimal reconfigurationproblem [10] Fuzzy expert system employed for optimalcapacitor placement and sizing for 35 buses with multilevelof loads [11]
Arabali et al carried out a stochastic framework tooptimal sizing and reliability analysis for a hybrid powersystem having wind power photovoltaic (PV) and energystorage system The stochastic nature of wind solar irradia-tion and photovoltaic (PV) power and load are stochasticallymodelled using ARMA [12] Soroudi and Ehsan investigatedthe impact of an uncertain power production of distributedgenerations (DGs) on active losses of distribution feederUncertainty in wind speed and gas turbines is modelled bya Weibull probability distribution function (PDF) and fuzzyrespectively [13]
Sharma and Ravishankar Kamath have presented voltageassessment indices for modified IEEE 37 node test feederhaving time varying composite voltage sensitive load [14]Performance indices to assess feeder performance of modi-fied IEEE 37 node test feeder were developed with the help offorward-backward sweep method and two port parametersrepresentation of feeder components [15]
The objective of this paper is to integrate DG usingtwo fuzzy logic controllers One fuzzy logic controller isused to determine the sizing of DG on the basis of feederperformance parameter such as substation reserve capacityfeeder power loss to load ratio voltage unbalance andapparent power imbalance Another fuzzy logic controller isused to choose the DG location node on the basis of DGoutput survivability index andnode distance from substationare chosen as input
2 Feeder Performance Indices
The quality of power supply for modified IEEE 37 nodetest feeder is evaluated to develop performance indices andthese performance indices are substation reserve capacityvoltage unbalance factor and feeder power loss to load ratiobranch loading voltage deviation and power factor [15] Itis observed that the said substation transformer of feederis highly overloaded between 35 and 71 characteristics timeinterval which could be relived with solar PV penetration atthe feeder Modified feeder has also accounted for stochasticcharacteristics and composition of voltage sensitive loadmodels These load models are categorized into residen-tial commercial and industrial consumers Each categoryconsumers are appliances with constant power constantcurrent and constant impedance in random propositionsThe participation of real and reactive power load exponentsfor the different type categories of consumers is characterizedby the following equations [15]
119875119896119886119887119888(ℎ) = 1198621119896
119886119887119888(ℎ) + 1198622119896
119886119887119888(ℎ) lowast1003816100381610038161003816100381610038161003816100381610038161003816
119881119896119886119887119888(ℎ)119881119873119886119887119888
1003816100381610038161003816100381610038161003816100381610038161003816
2
+ 1198623119896119886119887119888(ℎ) lowast1003816100381610038161003816100381610038161003816100381610038161003816
119881119896119886119887119888(ℎ)119881119873119886119887119888
1003816100381610038161003816100381610038161003816100381610038161003816
119876119896119886119887119888(ℎ) = 1198631119896
119886119887119888(ℎ) + 1198632119896
119886119887119888(ℎ) lowast1003816100381610038161003816100381610038161003816100381610038161003816
119881119896119886119887119888(ℎ)119881119873119886119887119888
1003816100381610038161003816100381610038161003816100381610038161003816
2
+ 1198633119896119886119887119888(ℎ) lowast1003816100381610038161003816100381610038161003816100381610038161003816
119881119896119886119887119888(ℎ)119881119873119886119887119888
1003816100381610038161003816100381610038161003816100381610038161003816
(1)
Calculation of ZIP loads compositions is shown inAppendix Tomeet smart grid implementation criterion per-formance indices are evaluated for 15 minutes characteristicstime interval for the whole day In these days distributiongeneration integration is a common practice to meet outcontinuously increased demandThe load and renewable DGgeneration probabilistic nature are considered in this study Itis observed from load flow solution that feeder is subjected tooverloading during 8AM to 6PMTherefore the fuzzy expertsystem is developed in a way that DG operated between theabove periods The photovoltaic DG system under differentpower factor scenario is considered for investigation
3 Proposed Fuzzy Expert System
The proposed fuzzy logic controller for DG integrationimplies Mamdani and Sugeno fuzzy inference system TheDG sizing is determined using Mamdani type fuzzy logiccontroller on the basis of feeder performance parametersuch as substation reserve capacity feeder power loss toload ratio voltage unbalance and apparent power imbalancewhereas Sugeno type fuzzy logic controller is used to choosethe DG location on the basis of DG output survivabilityindex and node distance from the substation The fuzzy-based expert system is tested using the MATLAB fuzzylogic tool box undermulti-rules-based decision andmultisetsconsiderations as in Figure 1
Advances in Electrical Engineering 3
TOD
SCRI
FLLR
UPQ
VUF
Input for Mamdani typefuzzy logic controller
Mamdani type
Distance
Input for Sugeno typefuzzy logic controller
Sugeno typefuzzy logiccontroller
fuzzy logic controller
round
Rounding
DG sizing
DG locationfunction
Survivability Index
Figure 1 Fuzzy logic controller for DG integration
TOD
PowerGAP
FLLR
UPQ
VUF
FCLperformancenew(mamdani)
PDGOUTPUT
Figure 2 Mamdani type fuzzy logic controller for DG sizing
Inference process forMamdani type fuzzy logic controllerhas MIN-MAX method of aggregation and SOM of thedefuzzification process as standard settings The other settingfor Sugeno type fuzzy logic controller remains the sameexcept for defuzzification method named as whatever [16]
31 Mamdani Type Fuzzy Logic Controller for DG SizingUnbalance in voltage and current increased apparent powerimbalance feeder loss voltage deviation and neutral currentIncreased neutral current in substation transformer leadsto communication interference equipment overloading andfalse operation of the protective system Apart from theabove apparent power imbalance is a more appropriateapproach to reactive power compensation The 15-minutecharacteristics time interval substation reserve capacity(SRCI) feeder power loss to load ratio (FLLR) voltage unbal-ance factor (VUF) and apparent power imbalance (APBI)indices are the five inputs to the Mamdani type inference sys-tem which are computed from the load flow solution over aneach 15-minute time interval for the whole day [15] ProposedMamdani type inference as shown in Figure 2 has a set of 15rules which involve heuristic rules for determining the size ofDG in the fuzzification process In the fuzzification processthese inputs are converted into logic form in accordance withthe associated membership functions
Membership function plots
TOD1 TOD2 TOD3 TOD4
Plot points 181
1
05
0
Input variable ldquoTODrdquo9080706050403020100
181
Figure 3 Membership function of TOD
Four different times of day (TOD) in terms of 15-minutemetering time interval is taken for the whole day andrepresented by four triangles membership function curvesas shown in Figure 3 In Figure 4 the power demand gap isdescribed by Z Gauss and S shape membership functionsTheZ shapemembership function represents the demand gapless than 05MW and demand gap greater than 05MW isrepresented by an S shape membership curve
Triangle membership function is used for medium valueFLLR UPQ and VUF whereas trapezoidal membershipfunctions are considered for the low and high of the abovethree variables Their graphical representations of member-ship function are depicted in Figures 5 6 and 7 respectively
4 Advances in Electrical Engineering
Low Medium HighMembership function plots Plot points
1
05
0
181
0604020 08 1minus04minus06minus08 minus02minus1
Input variable ldquoPowerGAPrdquo
Figure 4 Membership function of power demand gap
Low Medium HighMembership function plots Plot points
1
05
0
181
005 006 007 008 009 01004
Input variable ldquoFLLRrdquo
Figure 5 Membership function of FLLR
DG sizing is shown with three trapezoidal membershipfunctions in Figure 8 Low DG output is assumed to bebetween 0 and 01MW whereas medium DG output hasranged between 012 and 04 The DG output is consideredbetween 04 and 1MW Surface view of fuzzy logic controllerrules for DG sizing is shown in Figure 9
32 Sugeno Type Fuzzy Logic Controller for DGLocation Thispaper proposes a fuzzy approach to predict vulnerability ofthe node using survivability index The proposed survivabil-ity index (SI) is computed by equation (2) below for eachnodeusing voltage stability margin (VSI) and voltage deviationindex (VDI) corresponds to 90 percentile over each 15-minute time interval for the whole day [14 15]
SI = min (VSIℎ119886119887119888) lowast 075 +max (VDIℎ
119886119887119888) lowast 025 (2)
Table 1 shows the survivability index of top 15 nodes fromthe list of nodes in or near vulnerability andTable 1 is depictedin the Appendix The membership function for vulnerablenodes is represented in Figure 12
Sugeno type fuzzy logic controller as shown in Figure 10has crisp input parameters such as node distance from thesubstation DG output and vulnerable node Node distance isdepicted by three intersecting trapezoidal curves in Figure 11The low distance corresponds to the range of 0 to 3300feet and medium distance is between 3400 and 6000 feetHigh distance is considered between 6000 and 8000 feetThe output of Sugeno type fuzzy logic controller has con-stant functions as shown in Figure 13 where the distributedgeneration can be added according to the demand from thecustomer premises The proposed Sugeno type inference hasa set of 09 rules to determine the location of DG as shownFigure 14
Low Medium HighMembership function plots Plot points
1
05
0
181
01 015 02 025 03005
Input variable ldquoUPQrdquo
Figure 6 Membership function of UPQ
Table 1 Survivability index of top 15 nodes
S number Node Distance (Feet) Survivability index
1 775 4930 08816722 709 4930 0880713 708 5250 08899384 733 5570 0905285 734 6130 09183966 737 6770 09292167 738 7170 0933718 711 7570 09361159 741 7970 093687210 732 5570 089094811 731 5530 088071212 710 6650 092179613 735 6850 092308314 736 7930 092193415 740 7770 0937371
Figures 15 and 16 show the inputs of Madami andSugeno fuzzy logic controller respectively whereas outputsof Madami and Sugeno fuzzy logic controller are depicted inFigure 17 Node 734 is found at the optimum DG locationwith 440KW per Phase capacity to be operational between37 and 73 characteristics time interval
4 Results and Analysis
The proposed algorithm has been implemented in MATLABand evaluated on modified unbalance IEEE 37 node testfeeder In this study total optimum DG capacity of 440KWper phase is considered at node 734 and the DG operatingtime interval very much resembles solar photovoltaic DG Inthis paper constant power factorDGmodel is considered andhas constant 119875dg active power at a pfdg constant power factorTo keep constant power factor required 119876dg reactive powerof the DG is computed by following equation [17]
119876dg = 119875dg tan (cosminus1 (119875dg)) (3)
In the base case the performance indices are computedwithout any DG integration for the whole day The base loss
Advances in Electrical Engineering 5
LowMedium
HighMembership function plots Plot points
1
05
0
181
01 02 03 04 05 06 07 08 09 10
Input variable ldquoVUFrdquo
Figure 7 Membership function of VUF
Low Medium High
Output variable ldquoPDGOUTPUTrdquo1000900800700600500400300200100
Membership function plots Plot points
1
05
00
181
Figure 8 Membership function of DG output
PDG
OU
TPU
T
105
0minus05
minus1 TODPowerGAP
9080706050403020100
0
100
200
300
400
Figure 9 Surface view of fuzzy logic controller rules for DG sizing
PGOUTPUT
Distance
Surviablityindex
FCL2rule(sugeno)
DGNodeSelection
f(u)
Figure 10 Sugeno type fuzzy logic controller for DG location
of feeder is depicted in Figure 18 For the base case the dailyphase voltage profile for all buses is shown in Figures 19ndash21The least voltage in Phase A Phase-B and Phase C is foundon nodes 34 33 and 31 respectively These nodes can be usedfor shunt compensation
The impacts of distributed generation on various indicesare detailed as follows
Figure 22 reveals that DG operation at 095 power factorleading has the highest substation reserve capacity whereas
1
05
0
Low Medium HighMembership function plots Plot points 181
Input variable ldquoDistancerdquo800070006000500040003000200010000
Figure 11 Membership function of distance
1
05
0
Membership function plots Plot points 181
01 02 03 04 05 06 07 08 09 10
Input variable ldquoSurviablityindexrdquo
Low Medium High
Figure 12 Membership function of survivability index
Membership function plots plot Points 181
Output variable ldquoDGNodeSelectionrdquo
775709708733734737738711
741732731710735736740
Figure 13 Membership function of DG location
6 Advances in Electrical Engineering
Distance PGOUTPUT
10009008007006005004003002001000 010002000
3000400050006000
70008000720722724726728730732
DG
Nod
eSele
ctio
n
Figure 14 Surface view of Sugeno type fuzzy logic controller rules
2000
minus200minus400minus600minus800
008
007
006
005
03
025
02
015
0908070605
Input to Mamdani Controller
FLLR
UPQ
VUF
80 90706050403020100
80 90706050403020100
80 90706050403020100
80 90706050403020100
Figure 15 Inputs to Mamdani fuzzy logic controller
the base case has the lowest substation reserve capacitybetween 35 and 71 characteristics time interval DG operationat 095 lagging power factor has the reverse effect on loadrelief
It is observed from Figures 23 24 and 25 that the highestFLLR for all phases is found in DG operation at 095 powerfactor lag Lowest reduction in FLLR in Phase-A Phase-Band Phase-C is found inDG 095 power factor (lag) base andDG 099 power factor (lead) case respectively
In Figures 26 27 and 28 it is observed that DG operationat 095 power factor leading showed highest BCLI reductionfor all phases the DG operation at 099 power factor leadinggot the second reduction in BCLI and then the DG operationat unity power factor and DG operation at 095 power factorlagging the next Base case showed a branch overloading
1173 and 115 at 53 and 55 characteristics time interval forPhase-A and C respectively
As shown in Figure 29 there is no significant impact ofDG placement over apparent power imbalance Figures 3031 and 32 reveal the same impact of DG placement on voltagedeviation for all phases which was observed in the case ofBCLI
As shown in Figures 33 34 and 35 DG operation at 095power factor lagging showed minimum power factor (MPF)for all phasesMaximumpower factor for Phase-A andPhase-C occurred in the DG operation at 095 power factor leadingwhile maximum power factor for Phase-B occurred in DGoperation at unity power factor
In Figure 36 base case showed a highest voltage unbal-ance factor (VUF) the DG operation at 095 power factor
Advances in Electrical Engineering 7
80007000600050004000300020001000
500
Distance
Distance
09409309209109
089088
Inputs of Sugeno logic controller
Survivability index
Survivability index
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
50403020100 90807060
400300200100
0
Figure 16 Inputs to Sugeno fuzzy controller
732734
730728726724722720
450400350300250200150100500
DG Node
DG Node
Simulink output of Madami and Sugeno logic controller
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
Figure 17 Simulink outputs of fuzzy logic controller
lagging got the second maximum VUF and then the DGoperation at unity power factor and DG operation at 099power factor leading the next DG operation at 095 powerfactor lagging showed a maximum VUF of 05177at 6 charac-teristics time interval
5 Conclusion
The stochastic behavior of DG integrated distribution feederis a challenging problem for operations and planning aspectsA fuzzy expert system is proposed to determine optimal
8 Advances in Electrical Engineering
Tota
l fee
der l
oss (
KW)
253035404550556065
20 9010 50 60 70 8030 40Time in 15-minute interval
X 6Y 2522
X 52Y 5946
Figure 18 Feeder loss for base case
1
098
096
094
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number0
102030
40100806040200
(483409452)
Time in 15-minute interval
Figure 19 Phase voltage profile of Phase-A
1
099
098
097
096
095
0995
099
0985
098
0975
097
(56330966)
Phas
e vol
tage
pro
file
Node number010203040 100806040200
Time in 15-minute interval
Figure 20 Phase voltage profile of Phase-B
0995
099
0985
098
0975
097
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number010203040
100806040200
0965
(48310962)
Time in 15-minute interval
Figure 21 Phase voltage profile of Phase-C
Advances in Electrical Engineering 9
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 9010 80Time in 15-minute interval
minus02
0
02
04
06
08
SRCI
(PU
)
X 35X 35
X 35X 35
Y 0546Y 04836
Y 04282Y 02816
X 73X 73
X 73
X 73
Y 05441Y 04819Y 04268Y 02824
Figure 22 DG impact of substation reserve capacity index
FLLR
-Pha
se-A
(PU
)
minus002minus001
0001002003004005006007008
20 30 40 50 60 70 80 9010Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35
X 35
X 46
Y 007636
Y 004391
Y 002575
(720077)
Figure 23 DG impact of feeder loss to load ratio for Phase-A
X 35
X 35
Y 01209 Y 0124
Y 00771
Y 004061
X 73
X 71
X 56
Y 007568
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
80 9040 50 60 7020 3010Time in 15-minute interval
minus0020
002004006008
01012014
FLLR
-Pha
se-B
(PU
)
Figure 24 DG impact of feeder loss to load ratio for Phase-B
location and size of fixed PQ DG integration to a modifiedunbalance IEEE 37 feeder In this paper impact of DGintegration on performance indices has been demonstratedunder at different power factor under stochastic environ-ment To investigate clear effect of DG operation for making
FLLR
-Pha
se-C
(PU
)
001
0015
002
0025
20 30 40 50 60 70 8010 90Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35Y 002184
X 34Y 001736 X 56
Y 001862
X 73Y 002224
X 74Y 001754
X 70Y 001425
Figure 25 DG impact of feeder loss to load ratio for Phase-C
BCLI
-Pha
se-A
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
(3507117)
(3508666)(531173)
X 35Y 05765
X 75Y 1105
X 73Y 05746
20 30 40 50 60 70 9010 80Time in 15-minute interval
040506070809
11112
Figure 26 DG impact on branch loading index of Phase-A
BCLI
-Pha
se-B
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
8030 40 50 60 7020 9010Time in 15-minute interval
040506070809
111
X 34Y 08137
X 35Y 06739X 35Y 05325X 35
Y 04917X 35Y 04641
X 75Y 08246
X 73Y 0678
X 73Y 05316 X 73
Y 04906X 73Y 04628
Figure 27 DG impact on branch loading index of Phase-B
useful decision support voltage regulator of the above feederis removed Comparative results obtained with different fixedPQ DG operating scenario are discussed and it is found thatthe results obtained by DG operation at 095 power factorleading are more suitable to improve performance of feeder
10 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
9030 40 50 60 70 802010Time in 15-minute interval
X 34Y 1045
X 35Y 04945
X 74Y 1057
X 70Y 06878
X 71Y 04907
(55115)
040506070809
11112
BCLI
minusPha
seminusC
(PU
)
Figure 28 DG impact on branch loading index of Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
160180200220240260280300
Unb
alan
ce fe
eder
pow
er(K
VA)
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 6Y 1768
X 82Y 2656
X 82Y 2391
(422813)
Figure 29 DG impact on apparent powers unbalance index of feeder
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
VD
I-Ph
ase-
A (P
U)
03040506070809
111
X 34Y 09884
X 35Y 04341
X 35Y 02874
X 73Y 04457X 73Y 02871
(551084)
Figure 30 DG impact on maximum voltage deviation Phase-A
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
02
03
04
05
06
07
VD
I-Ph
ase-
B (P
U)
X 34Y 06145
X 35Y 03889X 35Y 03379
X 35Y 03156 X 35
Y 0287
X 75Y 06252
X 73Y 03959X 73Y 03411
X 73Y 03189X 73
Y 02904
(5206797)
Figure 31 DG impact on maximum voltage deviation Phase-B
Advances in Electrical Engineering 11
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
X 34Y 06916
X 35Y 03766
X 55Y 07598 X 74
Y 0695
X 71Y 03784
02030405060708
VD
I-Ph
ase-
C (P
U)
Figure 32 DG impact on maximum voltage deviation Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
MPF
-Pha
se-A
(PU
)
X 73Y 05634
(3509594)(340917)
(72minus09757)0
02
04
06
08
1
Figure 33 DG impact on minimum power factor of Phase-A
X 73X 73X 73
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 35Y 0779X 35
Y 0779X 35Y 06893
X 35Y 03432
X 73Y 07722X 73Y 07344X 73
Y 06813
X 73Y 0327302
03040506070809
1
MPF
-Pha
se-B
(PU
)
Figure 34 DG impact on minimum power factor of Phase-B
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 34Y 08142
X 35Y 06906
X 35Y 04764
X 35Y minus01302
X 74Y 08142
X 73Y 0692
X 73Y 04777
X 73Y minus01228
(5507574)
minus02
002040608
1
MPF
-Pha
se-C
(PU
)
Figure 35 DG impact of minimum power factor of Phase-C
12 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
Volta
ge U
nbal
ance
fact
or (
)
X 55Y 08497
X 48Y 06419
X 35Y 05578
X 55Y 06498
X 73Y 05576(605177)
05055
06065
07075
08085
09
Figure 36 DG impact of voltage unbalance factor of feeder
Appendix
A Survivability Index of Top 15 Nodes
(see Table 1)
B ZIP Load Compositions Calculation
To find ZIP load composition at each bus 119896 the followingequations were utilized to compute ZIP load composition ateach bus 119896 for each ℎ characteristics time interval
1198621119896119886119887119888(ℎ) = 08 lowast 119875119868119896
119886119887119888(ℎ) + 06 lowast 119875119862119896
119886119887119888(ℎ) + 08
lowast 119875119877119896119886119887119888(ℎ)
1198622119896119886119887119888(ℎ) = 02 lowast 119875119868119896
119886119887119888(ℎ) + 04 lowast 119875119862119896
119886119887119888(ℎ) + 019
lowast 119875119877119896119886119887119888(ℎ)
1198623119896119886119887119888(ℎ) = 0 lowast 119875119868119896
119886119887119888(ℎ) + 0 lowast 119875119862119896
119886119887119888(ℎ) + 001
lowast 119875119877119896119886119887119888(ℎ)
1198631119896119886119887119888(ℎ) = 08 lowast 119876119868119896
119886119887119888(ℎ) + 06 lowast 119876119862119896
119886119887119888(ℎ) + 08
lowast 119876119877119896119886119887119888(ℎ)
1198632119896119886119887119888(ℎ) = 02 lowast 119876119868119896
119886119887119888(ℎ) + 04 lowast 119876119862119896
119886119887119888(ℎ) + 019
lowast 119876119877119896119886119887119888(ℎ)
1198633119896119886119887119888(ℎ) = 0 lowast 119876119868119896
119886119887119888(ℎ) + 0 lowast 119876119862119896
119886119887119888(ℎ) + 001
lowast 119876119877119896119886119887119888(ℎ)
(B1)
Nomenclature
119875119896119886119887119888(ℎ) 119876119896
119886119887119888(ℎ) Total real and reactive power
load at bus 119896 during ℎ119881119873119886119887119888 119881
119896
119886119887119888(ℎ) Nominal and three-phase
voltage at 119896 node
1198621119896119886119887119888(ℎ) 1198622119896
119886119887119888(ℎ) 1198623119896
119886119887119888(ℎ) ZIP active load composition
at bus 119896 during ℎ1198631119896119886119887119888(ℎ)1198632119896
119886119887119888(ℎ)1198633119896
119886119887119888(ℎ) ZIP reactive load
composition at bus 119896 duringℎ
119875119868119896119886119887119888(ℎ) 119875119877119896
119886119887119888(ℎ) 119875119862119896
119886119887119888(ℎ) Active power for industrial
residential and commercialload connected at bus 119896during ℎ
119876119868119896119886119887119888(ℎ) 119876119877119896
119886119887119888(ℎ) 119876119862119896
119886119887119888(ℎ) Reactive power for
industrial residential andcommercial load connectedat bus 119896 during ℎ
ℎ 15-minute characteristicstime interval
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
References
[1] A Barin C U Brazil L Martins and E S A Brazil ldquoFuzzybased expert system for renewable energy managementrdquo inProceedings of the CIREDWorkshop no 0020 Rome Italy 2014
[2] N Hashmi and S A Khan ldquoPower energy management fora grid-connected PV system using rule-base fuzzy logicrdquo inProceedings of the 3rd International Conference on ArtificialIntelligence Modelling amp Simulation (AIMS rsquo15) pp 31ndash36 KotaKinabalu Malaysia December 2015
[3] A R Kashfi and M E El-Hawary ldquoIntegration of distributedgeneration inmedium voltage distribution network using fuzzylogic controller for demand side managementrdquo in Proceedingsof the Electrical Power and Energy Conference (EPEC rsquo14) pp254ndash259 Calgary Canada November 2014
[4] S K Injeti and N P Kumar ldquoPlanning and operation of activeradial distribution networks for improved voltage stability andloss reductionrdquoWorld Journal of Modelling and Simulation vol8 no 3 pp 211ndash222 2012
Advances in Electrical Engineering 13
[5] Y Manjili and A Rajaee ldquoFuzzy Control of Electricity StorageUnit for EnergyManagement of Micro-Gridsrdquo httpacewaco-ngorgAssetsdocFuzzy20Control20of20Electricity20Storage20Unit20for20Energy20Management20of20Micro-Grids Mexico20WAC202012pdf
[6] A Metia and S Ghosh ldquoFuzzy based DG allocation for LossMinimization in a Radial Distribution Systemrdquo Balkan Journalof Electrical amp Computer Engineering vol 3 no 3 pp 115ndash1232015
[7] E A Mohamed M M Othman and Y G Hegazy ldquoOpti-mal sizing and placement of distributed generators for profitmaximization using firefly algorithmrdquo in Proceedings of theInternational Conference on Artificial Intelligence Energy andManufacturing Engineering (ICAEME rsquo14) pp 6ndash10 2014
[8] K Muthukumar and S Jayalalitha ldquoOptimal reactive powercompensation by shunt capacitor sizing using harmony searchalgorithm in unbalanced radial distribution system for powerloss minimizationrdquo International Journal on Electrical Engineer-ing and Informatics vol 5 no 4 pp 474ndash491 2013
[9] M Padma Lalitha V C Veera Reddy and N Sivarami ReddyldquoApplication of fuzzy and ABC algorithm for DG placement forminimum loss in radial distribution systemrdquo Iranian Journal ofElectrical and Electronic Engineering vol 6 no 4 pp 248ndash2572010
[10] M S Thomas R Ranjan and R Roma ldquoProbabilistic fuzzyapproach to assess RDS vulnerability and plan corrective actionusing feeder reconfigurationrdquo Energy and Power Engineeringvol 4 no 5 pp 330ndash338 2012
[11] P N Si and S S Win ldquoFuzzy algorithm for capacitor allocationand sizing in radial distribution system to reduce lossesrdquoInternational Journal of Science Engineering and TechnologyResearch vol 3 no 12 2014
[12] A Arabali M Ghofrani M Etezadi-Amoli and M S FadalildquoStochastic performance assessment and sizing for a hybridpower system of SolarWindEnergy Storagerdquo IEEE Transac-tions on Sustainable Energy vol 5 no 2 pp 363ndash371 2014
[13] A Soroudi and M Ehsan ldquoA possibilistic-probabilistic tool forevaluating the impact of stochastic renewable and controllablepower generation on energy losses in distribution networksmdashacase studyrdquo Renewable and Sustainable Energy Reviews vol 15no 1 pp 794ndash800 2011
[14] J P Sharma and H Ravishankar Kamath ldquoStochastic voltageassessment of unbalance radial feederrdquo Journal of ElectricalSystems vol 11 no 3 pp 258ndash270 2015
[15] J P Sharma and H R Kamath ldquoPerformance analysis ofunbalance radial feeder with time varying composite loadrdquoJournal of Power and Energy Engineering vol 3 no 5 pp 56ndash702015
[16] The MathWorks Inc Types of Fuzzy Interefernece Systems TheMathWorks Inc 1994 1994ndash2014 httpwwwmathworkscomhelpfuzzytypes-of-fuzzyinference-systemshtml
[17] J-H Teng ldquoModelling distributed generations in three-phasedistribution load flowrdquo IET Generation Transmission and Dis-tribution vol 2 no 3 pp 330ndash340 2008
International Journal of
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Active and Passive Electronic Components
Control Scienceand Engineering
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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RotatingMachinery
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Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
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DistributedSensor Networks
International Journal of
Advances in Electrical Engineering 3
TOD
SCRI
FLLR
UPQ
VUF
Input for Mamdani typefuzzy logic controller
Mamdani type
Distance
Input for Sugeno typefuzzy logic controller
Sugeno typefuzzy logiccontroller
fuzzy logic controller
round
Rounding
DG sizing
DG locationfunction
Survivability Index
Figure 1 Fuzzy logic controller for DG integration
TOD
PowerGAP
FLLR
UPQ
VUF
FCLperformancenew(mamdani)
PDGOUTPUT
Figure 2 Mamdani type fuzzy logic controller for DG sizing
Inference process forMamdani type fuzzy logic controllerhas MIN-MAX method of aggregation and SOM of thedefuzzification process as standard settings The other settingfor Sugeno type fuzzy logic controller remains the sameexcept for defuzzification method named as whatever [16]
31 Mamdani Type Fuzzy Logic Controller for DG SizingUnbalance in voltage and current increased apparent powerimbalance feeder loss voltage deviation and neutral currentIncreased neutral current in substation transformer leadsto communication interference equipment overloading andfalse operation of the protective system Apart from theabove apparent power imbalance is a more appropriateapproach to reactive power compensation The 15-minutecharacteristics time interval substation reserve capacity(SRCI) feeder power loss to load ratio (FLLR) voltage unbal-ance factor (VUF) and apparent power imbalance (APBI)indices are the five inputs to the Mamdani type inference sys-tem which are computed from the load flow solution over aneach 15-minute time interval for the whole day [15] ProposedMamdani type inference as shown in Figure 2 has a set of 15rules which involve heuristic rules for determining the size ofDG in the fuzzification process In the fuzzification processthese inputs are converted into logic form in accordance withthe associated membership functions
Membership function plots
TOD1 TOD2 TOD3 TOD4
Plot points 181
1
05
0
Input variable ldquoTODrdquo9080706050403020100
181
Figure 3 Membership function of TOD
Four different times of day (TOD) in terms of 15-minutemetering time interval is taken for the whole day andrepresented by four triangles membership function curvesas shown in Figure 3 In Figure 4 the power demand gap isdescribed by Z Gauss and S shape membership functionsTheZ shapemembership function represents the demand gapless than 05MW and demand gap greater than 05MW isrepresented by an S shape membership curve
Triangle membership function is used for medium valueFLLR UPQ and VUF whereas trapezoidal membershipfunctions are considered for the low and high of the abovethree variables Their graphical representations of member-ship function are depicted in Figures 5 6 and 7 respectively
4 Advances in Electrical Engineering
Low Medium HighMembership function plots Plot points
1
05
0
181
0604020 08 1minus04minus06minus08 minus02minus1
Input variable ldquoPowerGAPrdquo
Figure 4 Membership function of power demand gap
Low Medium HighMembership function plots Plot points
1
05
0
181
005 006 007 008 009 01004
Input variable ldquoFLLRrdquo
Figure 5 Membership function of FLLR
DG sizing is shown with three trapezoidal membershipfunctions in Figure 8 Low DG output is assumed to bebetween 0 and 01MW whereas medium DG output hasranged between 012 and 04 The DG output is consideredbetween 04 and 1MW Surface view of fuzzy logic controllerrules for DG sizing is shown in Figure 9
32 Sugeno Type Fuzzy Logic Controller for DGLocation Thispaper proposes a fuzzy approach to predict vulnerability ofthe node using survivability index The proposed survivabil-ity index (SI) is computed by equation (2) below for eachnodeusing voltage stability margin (VSI) and voltage deviationindex (VDI) corresponds to 90 percentile over each 15-minute time interval for the whole day [14 15]
SI = min (VSIℎ119886119887119888) lowast 075 +max (VDIℎ
119886119887119888) lowast 025 (2)
Table 1 shows the survivability index of top 15 nodes fromthe list of nodes in or near vulnerability andTable 1 is depictedin the Appendix The membership function for vulnerablenodes is represented in Figure 12
Sugeno type fuzzy logic controller as shown in Figure 10has crisp input parameters such as node distance from thesubstation DG output and vulnerable node Node distance isdepicted by three intersecting trapezoidal curves in Figure 11The low distance corresponds to the range of 0 to 3300feet and medium distance is between 3400 and 6000 feetHigh distance is considered between 6000 and 8000 feetThe output of Sugeno type fuzzy logic controller has con-stant functions as shown in Figure 13 where the distributedgeneration can be added according to the demand from thecustomer premises The proposed Sugeno type inference hasa set of 09 rules to determine the location of DG as shownFigure 14
Low Medium HighMembership function plots Plot points
1
05
0
181
01 015 02 025 03005
Input variable ldquoUPQrdquo
Figure 6 Membership function of UPQ
Table 1 Survivability index of top 15 nodes
S number Node Distance (Feet) Survivability index
1 775 4930 08816722 709 4930 0880713 708 5250 08899384 733 5570 0905285 734 6130 09183966 737 6770 09292167 738 7170 0933718 711 7570 09361159 741 7970 093687210 732 5570 089094811 731 5530 088071212 710 6650 092179613 735 6850 092308314 736 7930 092193415 740 7770 0937371
Figures 15 and 16 show the inputs of Madami andSugeno fuzzy logic controller respectively whereas outputsof Madami and Sugeno fuzzy logic controller are depicted inFigure 17 Node 734 is found at the optimum DG locationwith 440KW per Phase capacity to be operational between37 and 73 characteristics time interval
4 Results and Analysis
The proposed algorithm has been implemented in MATLABand evaluated on modified unbalance IEEE 37 node testfeeder In this study total optimum DG capacity of 440KWper phase is considered at node 734 and the DG operatingtime interval very much resembles solar photovoltaic DG Inthis paper constant power factorDGmodel is considered andhas constant 119875dg active power at a pfdg constant power factorTo keep constant power factor required 119876dg reactive powerof the DG is computed by following equation [17]
119876dg = 119875dg tan (cosminus1 (119875dg)) (3)
In the base case the performance indices are computedwithout any DG integration for the whole day The base loss
Advances in Electrical Engineering 5
LowMedium
HighMembership function plots Plot points
1
05
0
181
01 02 03 04 05 06 07 08 09 10
Input variable ldquoVUFrdquo
Figure 7 Membership function of VUF
Low Medium High
Output variable ldquoPDGOUTPUTrdquo1000900800700600500400300200100
Membership function plots Plot points
1
05
00
181
Figure 8 Membership function of DG output
PDG
OU
TPU
T
105
0minus05
minus1 TODPowerGAP
9080706050403020100
0
100
200
300
400
Figure 9 Surface view of fuzzy logic controller rules for DG sizing
PGOUTPUT
Distance
Surviablityindex
FCL2rule(sugeno)
DGNodeSelection
f(u)
Figure 10 Sugeno type fuzzy logic controller for DG location
of feeder is depicted in Figure 18 For the base case the dailyphase voltage profile for all buses is shown in Figures 19ndash21The least voltage in Phase A Phase-B and Phase C is foundon nodes 34 33 and 31 respectively These nodes can be usedfor shunt compensation
The impacts of distributed generation on various indicesare detailed as follows
Figure 22 reveals that DG operation at 095 power factorleading has the highest substation reserve capacity whereas
1
05
0
Low Medium HighMembership function plots Plot points 181
Input variable ldquoDistancerdquo800070006000500040003000200010000
Figure 11 Membership function of distance
1
05
0
Membership function plots Plot points 181
01 02 03 04 05 06 07 08 09 10
Input variable ldquoSurviablityindexrdquo
Low Medium High
Figure 12 Membership function of survivability index
Membership function plots plot Points 181
Output variable ldquoDGNodeSelectionrdquo
775709708733734737738711
741732731710735736740
Figure 13 Membership function of DG location
6 Advances in Electrical Engineering
Distance PGOUTPUT
10009008007006005004003002001000 010002000
3000400050006000
70008000720722724726728730732
DG
Nod
eSele
ctio
n
Figure 14 Surface view of Sugeno type fuzzy logic controller rules
2000
minus200minus400minus600minus800
008
007
006
005
03
025
02
015
0908070605
Input to Mamdani Controller
FLLR
UPQ
VUF
80 90706050403020100
80 90706050403020100
80 90706050403020100
80 90706050403020100
Figure 15 Inputs to Mamdani fuzzy logic controller
the base case has the lowest substation reserve capacitybetween 35 and 71 characteristics time interval DG operationat 095 lagging power factor has the reverse effect on loadrelief
It is observed from Figures 23 24 and 25 that the highestFLLR for all phases is found in DG operation at 095 powerfactor lag Lowest reduction in FLLR in Phase-A Phase-Band Phase-C is found inDG 095 power factor (lag) base andDG 099 power factor (lead) case respectively
In Figures 26 27 and 28 it is observed that DG operationat 095 power factor leading showed highest BCLI reductionfor all phases the DG operation at 099 power factor leadinggot the second reduction in BCLI and then the DG operationat unity power factor and DG operation at 095 power factorlagging the next Base case showed a branch overloading
1173 and 115 at 53 and 55 characteristics time interval forPhase-A and C respectively
As shown in Figure 29 there is no significant impact ofDG placement over apparent power imbalance Figures 3031 and 32 reveal the same impact of DG placement on voltagedeviation for all phases which was observed in the case ofBCLI
As shown in Figures 33 34 and 35 DG operation at 095power factor lagging showed minimum power factor (MPF)for all phasesMaximumpower factor for Phase-A andPhase-C occurred in the DG operation at 095 power factor leadingwhile maximum power factor for Phase-B occurred in DGoperation at unity power factor
In Figure 36 base case showed a highest voltage unbal-ance factor (VUF) the DG operation at 095 power factor
Advances in Electrical Engineering 7
80007000600050004000300020001000
500
Distance
Distance
09409309209109
089088
Inputs of Sugeno logic controller
Survivability index
Survivability index
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
50403020100 90807060
400300200100
0
Figure 16 Inputs to Sugeno fuzzy controller
732734
730728726724722720
450400350300250200150100500
DG Node
DG Node
Simulink output of Madami and Sugeno logic controller
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
Figure 17 Simulink outputs of fuzzy logic controller
lagging got the second maximum VUF and then the DGoperation at unity power factor and DG operation at 099power factor leading the next DG operation at 095 powerfactor lagging showed a maximum VUF of 05177at 6 charac-teristics time interval
5 Conclusion
The stochastic behavior of DG integrated distribution feederis a challenging problem for operations and planning aspectsA fuzzy expert system is proposed to determine optimal
8 Advances in Electrical Engineering
Tota
l fee
der l
oss (
KW)
253035404550556065
20 9010 50 60 70 8030 40Time in 15-minute interval
X 6Y 2522
X 52Y 5946
Figure 18 Feeder loss for base case
1
098
096
094
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number0
102030
40100806040200
(483409452)
Time in 15-minute interval
Figure 19 Phase voltage profile of Phase-A
1
099
098
097
096
095
0995
099
0985
098
0975
097
(56330966)
Phas
e vol
tage
pro
file
Node number010203040 100806040200
Time in 15-minute interval
Figure 20 Phase voltage profile of Phase-B
0995
099
0985
098
0975
097
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number010203040
100806040200
0965
(48310962)
Time in 15-minute interval
Figure 21 Phase voltage profile of Phase-C
Advances in Electrical Engineering 9
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 9010 80Time in 15-minute interval
minus02
0
02
04
06
08
SRCI
(PU
)
X 35X 35
X 35X 35
Y 0546Y 04836
Y 04282Y 02816
X 73X 73
X 73
X 73
Y 05441Y 04819Y 04268Y 02824
Figure 22 DG impact of substation reserve capacity index
FLLR
-Pha
se-A
(PU
)
minus002minus001
0001002003004005006007008
20 30 40 50 60 70 80 9010Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35
X 35
X 46
Y 007636
Y 004391
Y 002575
(720077)
Figure 23 DG impact of feeder loss to load ratio for Phase-A
X 35
X 35
Y 01209 Y 0124
Y 00771
Y 004061
X 73
X 71
X 56
Y 007568
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
80 9040 50 60 7020 3010Time in 15-minute interval
minus0020
002004006008
01012014
FLLR
-Pha
se-B
(PU
)
Figure 24 DG impact of feeder loss to load ratio for Phase-B
location and size of fixed PQ DG integration to a modifiedunbalance IEEE 37 feeder In this paper impact of DGintegration on performance indices has been demonstratedunder at different power factor under stochastic environ-ment To investigate clear effect of DG operation for making
FLLR
-Pha
se-C
(PU
)
001
0015
002
0025
20 30 40 50 60 70 8010 90Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35Y 002184
X 34Y 001736 X 56
Y 001862
X 73Y 002224
X 74Y 001754
X 70Y 001425
Figure 25 DG impact of feeder loss to load ratio for Phase-C
BCLI
-Pha
se-A
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
(3507117)
(3508666)(531173)
X 35Y 05765
X 75Y 1105
X 73Y 05746
20 30 40 50 60 70 9010 80Time in 15-minute interval
040506070809
11112
Figure 26 DG impact on branch loading index of Phase-A
BCLI
-Pha
se-B
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
8030 40 50 60 7020 9010Time in 15-minute interval
040506070809
111
X 34Y 08137
X 35Y 06739X 35Y 05325X 35
Y 04917X 35Y 04641
X 75Y 08246
X 73Y 0678
X 73Y 05316 X 73
Y 04906X 73Y 04628
Figure 27 DG impact on branch loading index of Phase-B
useful decision support voltage regulator of the above feederis removed Comparative results obtained with different fixedPQ DG operating scenario are discussed and it is found thatthe results obtained by DG operation at 095 power factorleading are more suitable to improve performance of feeder
10 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
9030 40 50 60 70 802010Time in 15-minute interval
X 34Y 1045
X 35Y 04945
X 74Y 1057
X 70Y 06878
X 71Y 04907
(55115)
040506070809
11112
BCLI
minusPha
seminusC
(PU
)
Figure 28 DG impact on branch loading index of Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
160180200220240260280300
Unb
alan
ce fe
eder
pow
er(K
VA)
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 6Y 1768
X 82Y 2656
X 82Y 2391
(422813)
Figure 29 DG impact on apparent powers unbalance index of feeder
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
VD
I-Ph
ase-
A (P
U)
03040506070809
111
X 34Y 09884
X 35Y 04341
X 35Y 02874
X 73Y 04457X 73Y 02871
(551084)
Figure 30 DG impact on maximum voltage deviation Phase-A
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
02
03
04
05
06
07
VD
I-Ph
ase-
B (P
U)
X 34Y 06145
X 35Y 03889X 35Y 03379
X 35Y 03156 X 35
Y 0287
X 75Y 06252
X 73Y 03959X 73Y 03411
X 73Y 03189X 73
Y 02904
(5206797)
Figure 31 DG impact on maximum voltage deviation Phase-B
Advances in Electrical Engineering 11
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
X 34Y 06916
X 35Y 03766
X 55Y 07598 X 74
Y 0695
X 71Y 03784
02030405060708
VD
I-Ph
ase-
C (P
U)
Figure 32 DG impact on maximum voltage deviation Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
MPF
-Pha
se-A
(PU
)
X 73Y 05634
(3509594)(340917)
(72minus09757)0
02
04
06
08
1
Figure 33 DG impact on minimum power factor of Phase-A
X 73X 73X 73
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 35Y 0779X 35
Y 0779X 35Y 06893
X 35Y 03432
X 73Y 07722X 73Y 07344X 73
Y 06813
X 73Y 0327302
03040506070809
1
MPF
-Pha
se-B
(PU
)
Figure 34 DG impact on minimum power factor of Phase-B
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 34Y 08142
X 35Y 06906
X 35Y 04764
X 35Y minus01302
X 74Y 08142
X 73Y 0692
X 73Y 04777
X 73Y minus01228
(5507574)
minus02
002040608
1
MPF
-Pha
se-C
(PU
)
Figure 35 DG impact of minimum power factor of Phase-C
12 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
Volta
ge U
nbal
ance
fact
or (
)
X 55Y 08497
X 48Y 06419
X 35Y 05578
X 55Y 06498
X 73Y 05576(605177)
05055
06065
07075
08085
09
Figure 36 DG impact of voltage unbalance factor of feeder
Appendix
A Survivability Index of Top 15 Nodes
(see Table 1)
B ZIP Load Compositions Calculation
To find ZIP load composition at each bus 119896 the followingequations were utilized to compute ZIP load composition ateach bus 119896 for each ℎ characteristics time interval
1198621119896119886119887119888(ℎ) = 08 lowast 119875119868119896
119886119887119888(ℎ) + 06 lowast 119875119862119896
119886119887119888(ℎ) + 08
lowast 119875119877119896119886119887119888(ℎ)
1198622119896119886119887119888(ℎ) = 02 lowast 119875119868119896
119886119887119888(ℎ) + 04 lowast 119875119862119896
119886119887119888(ℎ) + 019
lowast 119875119877119896119886119887119888(ℎ)
1198623119896119886119887119888(ℎ) = 0 lowast 119875119868119896
119886119887119888(ℎ) + 0 lowast 119875119862119896
119886119887119888(ℎ) + 001
lowast 119875119877119896119886119887119888(ℎ)
1198631119896119886119887119888(ℎ) = 08 lowast 119876119868119896
119886119887119888(ℎ) + 06 lowast 119876119862119896
119886119887119888(ℎ) + 08
lowast 119876119877119896119886119887119888(ℎ)
1198632119896119886119887119888(ℎ) = 02 lowast 119876119868119896
119886119887119888(ℎ) + 04 lowast 119876119862119896
119886119887119888(ℎ) + 019
lowast 119876119877119896119886119887119888(ℎ)
1198633119896119886119887119888(ℎ) = 0 lowast 119876119868119896
119886119887119888(ℎ) + 0 lowast 119876119862119896
119886119887119888(ℎ) + 001
lowast 119876119877119896119886119887119888(ℎ)
(B1)
Nomenclature
119875119896119886119887119888(ℎ) 119876119896
119886119887119888(ℎ) Total real and reactive power
load at bus 119896 during ℎ119881119873119886119887119888 119881
119896
119886119887119888(ℎ) Nominal and three-phase
voltage at 119896 node
1198621119896119886119887119888(ℎ) 1198622119896
119886119887119888(ℎ) 1198623119896
119886119887119888(ℎ) ZIP active load composition
at bus 119896 during ℎ1198631119896119886119887119888(ℎ)1198632119896
119886119887119888(ℎ)1198633119896
119886119887119888(ℎ) ZIP reactive load
composition at bus 119896 duringℎ
119875119868119896119886119887119888(ℎ) 119875119877119896
119886119887119888(ℎ) 119875119862119896
119886119887119888(ℎ) Active power for industrial
residential and commercialload connected at bus 119896during ℎ
119876119868119896119886119887119888(ℎ) 119876119877119896
119886119887119888(ℎ) 119876119862119896
119886119887119888(ℎ) Reactive power for
industrial residential andcommercial load connectedat bus 119896 during ℎ
ℎ 15-minute characteristicstime interval
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
References
[1] A Barin C U Brazil L Martins and E S A Brazil ldquoFuzzybased expert system for renewable energy managementrdquo inProceedings of the CIREDWorkshop no 0020 Rome Italy 2014
[2] N Hashmi and S A Khan ldquoPower energy management fora grid-connected PV system using rule-base fuzzy logicrdquo inProceedings of the 3rd International Conference on ArtificialIntelligence Modelling amp Simulation (AIMS rsquo15) pp 31ndash36 KotaKinabalu Malaysia December 2015
[3] A R Kashfi and M E El-Hawary ldquoIntegration of distributedgeneration inmedium voltage distribution network using fuzzylogic controller for demand side managementrdquo in Proceedingsof the Electrical Power and Energy Conference (EPEC rsquo14) pp254ndash259 Calgary Canada November 2014
[4] S K Injeti and N P Kumar ldquoPlanning and operation of activeradial distribution networks for improved voltage stability andloss reductionrdquoWorld Journal of Modelling and Simulation vol8 no 3 pp 211ndash222 2012
Advances in Electrical Engineering 13
[5] Y Manjili and A Rajaee ldquoFuzzy Control of Electricity StorageUnit for EnergyManagement of Micro-Gridsrdquo httpacewaco-ngorgAssetsdocFuzzy20Control20of20Electricity20Storage20Unit20for20Energy20Management20of20Micro-Grids Mexico20WAC202012pdf
[6] A Metia and S Ghosh ldquoFuzzy based DG allocation for LossMinimization in a Radial Distribution Systemrdquo Balkan Journalof Electrical amp Computer Engineering vol 3 no 3 pp 115ndash1232015
[7] E A Mohamed M M Othman and Y G Hegazy ldquoOpti-mal sizing and placement of distributed generators for profitmaximization using firefly algorithmrdquo in Proceedings of theInternational Conference on Artificial Intelligence Energy andManufacturing Engineering (ICAEME rsquo14) pp 6ndash10 2014
[8] K Muthukumar and S Jayalalitha ldquoOptimal reactive powercompensation by shunt capacitor sizing using harmony searchalgorithm in unbalanced radial distribution system for powerloss minimizationrdquo International Journal on Electrical Engineer-ing and Informatics vol 5 no 4 pp 474ndash491 2013
[9] M Padma Lalitha V C Veera Reddy and N Sivarami ReddyldquoApplication of fuzzy and ABC algorithm for DG placement forminimum loss in radial distribution systemrdquo Iranian Journal ofElectrical and Electronic Engineering vol 6 no 4 pp 248ndash2572010
[10] M S Thomas R Ranjan and R Roma ldquoProbabilistic fuzzyapproach to assess RDS vulnerability and plan corrective actionusing feeder reconfigurationrdquo Energy and Power Engineeringvol 4 no 5 pp 330ndash338 2012
[11] P N Si and S S Win ldquoFuzzy algorithm for capacitor allocationand sizing in radial distribution system to reduce lossesrdquoInternational Journal of Science Engineering and TechnologyResearch vol 3 no 12 2014
[12] A Arabali M Ghofrani M Etezadi-Amoli and M S FadalildquoStochastic performance assessment and sizing for a hybridpower system of SolarWindEnergy Storagerdquo IEEE Transac-tions on Sustainable Energy vol 5 no 2 pp 363ndash371 2014
[13] A Soroudi and M Ehsan ldquoA possibilistic-probabilistic tool forevaluating the impact of stochastic renewable and controllablepower generation on energy losses in distribution networksmdashacase studyrdquo Renewable and Sustainable Energy Reviews vol 15no 1 pp 794ndash800 2011
[14] J P Sharma and H Ravishankar Kamath ldquoStochastic voltageassessment of unbalance radial feederrdquo Journal of ElectricalSystems vol 11 no 3 pp 258ndash270 2015
[15] J P Sharma and H R Kamath ldquoPerformance analysis ofunbalance radial feeder with time varying composite loadrdquoJournal of Power and Energy Engineering vol 3 no 5 pp 56ndash702015
[16] The MathWorks Inc Types of Fuzzy Interefernece Systems TheMathWorks Inc 1994 1994ndash2014 httpwwwmathworkscomhelpfuzzytypes-of-fuzzyinference-systemshtml
[17] J-H Teng ldquoModelling distributed generations in three-phasedistribution load flowrdquo IET Generation Transmission and Dis-tribution vol 2 no 3 pp 330ndash340 2008
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
4 Advances in Electrical Engineering
Low Medium HighMembership function plots Plot points
1
05
0
181
0604020 08 1minus04minus06minus08 minus02minus1
Input variable ldquoPowerGAPrdquo
Figure 4 Membership function of power demand gap
Low Medium HighMembership function plots Plot points
1
05
0
181
005 006 007 008 009 01004
Input variable ldquoFLLRrdquo
Figure 5 Membership function of FLLR
DG sizing is shown with three trapezoidal membershipfunctions in Figure 8 Low DG output is assumed to bebetween 0 and 01MW whereas medium DG output hasranged between 012 and 04 The DG output is consideredbetween 04 and 1MW Surface view of fuzzy logic controllerrules for DG sizing is shown in Figure 9
32 Sugeno Type Fuzzy Logic Controller for DGLocation Thispaper proposes a fuzzy approach to predict vulnerability ofthe node using survivability index The proposed survivabil-ity index (SI) is computed by equation (2) below for eachnodeusing voltage stability margin (VSI) and voltage deviationindex (VDI) corresponds to 90 percentile over each 15-minute time interval for the whole day [14 15]
SI = min (VSIℎ119886119887119888) lowast 075 +max (VDIℎ
119886119887119888) lowast 025 (2)
Table 1 shows the survivability index of top 15 nodes fromthe list of nodes in or near vulnerability andTable 1 is depictedin the Appendix The membership function for vulnerablenodes is represented in Figure 12
Sugeno type fuzzy logic controller as shown in Figure 10has crisp input parameters such as node distance from thesubstation DG output and vulnerable node Node distance isdepicted by three intersecting trapezoidal curves in Figure 11The low distance corresponds to the range of 0 to 3300feet and medium distance is between 3400 and 6000 feetHigh distance is considered between 6000 and 8000 feetThe output of Sugeno type fuzzy logic controller has con-stant functions as shown in Figure 13 where the distributedgeneration can be added according to the demand from thecustomer premises The proposed Sugeno type inference hasa set of 09 rules to determine the location of DG as shownFigure 14
Low Medium HighMembership function plots Plot points
1
05
0
181
01 015 02 025 03005
Input variable ldquoUPQrdquo
Figure 6 Membership function of UPQ
Table 1 Survivability index of top 15 nodes
S number Node Distance (Feet) Survivability index
1 775 4930 08816722 709 4930 0880713 708 5250 08899384 733 5570 0905285 734 6130 09183966 737 6770 09292167 738 7170 0933718 711 7570 09361159 741 7970 093687210 732 5570 089094811 731 5530 088071212 710 6650 092179613 735 6850 092308314 736 7930 092193415 740 7770 0937371
Figures 15 and 16 show the inputs of Madami andSugeno fuzzy logic controller respectively whereas outputsof Madami and Sugeno fuzzy logic controller are depicted inFigure 17 Node 734 is found at the optimum DG locationwith 440KW per Phase capacity to be operational between37 and 73 characteristics time interval
4 Results and Analysis
The proposed algorithm has been implemented in MATLABand evaluated on modified unbalance IEEE 37 node testfeeder In this study total optimum DG capacity of 440KWper phase is considered at node 734 and the DG operatingtime interval very much resembles solar photovoltaic DG Inthis paper constant power factorDGmodel is considered andhas constant 119875dg active power at a pfdg constant power factorTo keep constant power factor required 119876dg reactive powerof the DG is computed by following equation [17]
119876dg = 119875dg tan (cosminus1 (119875dg)) (3)
In the base case the performance indices are computedwithout any DG integration for the whole day The base loss
Advances in Electrical Engineering 5
LowMedium
HighMembership function plots Plot points
1
05
0
181
01 02 03 04 05 06 07 08 09 10
Input variable ldquoVUFrdquo
Figure 7 Membership function of VUF
Low Medium High
Output variable ldquoPDGOUTPUTrdquo1000900800700600500400300200100
Membership function plots Plot points
1
05
00
181
Figure 8 Membership function of DG output
PDG
OU
TPU
T
105
0minus05
minus1 TODPowerGAP
9080706050403020100
0
100
200
300
400
Figure 9 Surface view of fuzzy logic controller rules for DG sizing
PGOUTPUT
Distance
Surviablityindex
FCL2rule(sugeno)
DGNodeSelection
f(u)
Figure 10 Sugeno type fuzzy logic controller for DG location
of feeder is depicted in Figure 18 For the base case the dailyphase voltage profile for all buses is shown in Figures 19ndash21The least voltage in Phase A Phase-B and Phase C is foundon nodes 34 33 and 31 respectively These nodes can be usedfor shunt compensation
The impacts of distributed generation on various indicesare detailed as follows
Figure 22 reveals that DG operation at 095 power factorleading has the highest substation reserve capacity whereas
1
05
0
Low Medium HighMembership function plots Plot points 181
Input variable ldquoDistancerdquo800070006000500040003000200010000
Figure 11 Membership function of distance
1
05
0
Membership function plots Plot points 181
01 02 03 04 05 06 07 08 09 10
Input variable ldquoSurviablityindexrdquo
Low Medium High
Figure 12 Membership function of survivability index
Membership function plots plot Points 181
Output variable ldquoDGNodeSelectionrdquo
775709708733734737738711
741732731710735736740
Figure 13 Membership function of DG location
6 Advances in Electrical Engineering
Distance PGOUTPUT
10009008007006005004003002001000 010002000
3000400050006000
70008000720722724726728730732
DG
Nod
eSele
ctio
n
Figure 14 Surface view of Sugeno type fuzzy logic controller rules
2000
minus200minus400minus600minus800
008
007
006
005
03
025
02
015
0908070605
Input to Mamdani Controller
FLLR
UPQ
VUF
80 90706050403020100
80 90706050403020100
80 90706050403020100
80 90706050403020100
Figure 15 Inputs to Mamdani fuzzy logic controller
the base case has the lowest substation reserve capacitybetween 35 and 71 characteristics time interval DG operationat 095 lagging power factor has the reverse effect on loadrelief
It is observed from Figures 23 24 and 25 that the highestFLLR for all phases is found in DG operation at 095 powerfactor lag Lowest reduction in FLLR in Phase-A Phase-Band Phase-C is found inDG 095 power factor (lag) base andDG 099 power factor (lead) case respectively
In Figures 26 27 and 28 it is observed that DG operationat 095 power factor leading showed highest BCLI reductionfor all phases the DG operation at 099 power factor leadinggot the second reduction in BCLI and then the DG operationat unity power factor and DG operation at 095 power factorlagging the next Base case showed a branch overloading
1173 and 115 at 53 and 55 characteristics time interval forPhase-A and C respectively
As shown in Figure 29 there is no significant impact ofDG placement over apparent power imbalance Figures 3031 and 32 reveal the same impact of DG placement on voltagedeviation for all phases which was observed in the case ofBCLI
As shown in Figures 33 34 and 35 DG operation at 095power factor lagging showed minimum power factor (MPF)for all phasesMaximumpower factor for Phase-A andPhase-C occurred in the DG operation at 095 power factor leadingwhile maximum power factor for Phase-B occurred in DGoperation at unity power factor
In Figure 36 base case showed a highest voltage unbal-ance factor (VUF) the DG operation at 095 power factor
Advances in Electrical Engineering 7
80007000600050004000300020001000
500
Distance
Distance
09409309209109
089088
Inputs of Sugeno logic controller
Survivability index
Survivability index
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
50403020100 90807060
400300200100
0
Figure 16 Inputs to Sugeno fuzzy controller
732734
730728726724722720
450400350300250200150100500
DG Node
DG Node
Simulink output of Madami and Sugeno logic controller
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
Figure 17 Simulink outputs of fuzzy logic controller
lagging got the second maximum VUF and then the DGoperation at unity power factor and DG operation at 099power factor leading the next DG operation at 095 powerfactor lagging showed a maximum VUF of 05177at 6 charac-teristics time interval
5 Conclusion
The stochastic behavior of DG integrated distribution feederis a challenging problem for operations and planning aspectsA fuzzy expert system is proposed to determine optimal
8 Advances in Electrical Engineering
Tota
l fee
der l
oss (
KW)
253035404550556065
20 9010 50 60 70 8030 40Time in 15-minute interval
X 6Y 2522
X 52Y 5946
Figure 18 Feeder loss for base case
1
098
096
094
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number0
102030
40100806040200
(483409452)
Time in 15-minute interval
Figure 19 Phase voltage profile of Phase-A
1
099
098
097
096
095
0995
099
0985
098
0975
097
(56330966)
Phas
e vol
tage
pro
file
Node number010203040 100806040200
Time in 15-minute interval
Figure 20 Phase voltage profile of Phase-B
0995
099
0985
098
0975
097
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number010203040
100806040200
0965
(48310962)
Time in 15-minute interval
Figure 21 Phase voltage profile of Phase-C
Advances in Electrical Engineering 9
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 9010 80Time in 15-minute interval
minus02
0
02
04
06
08
SRCI
(PU
)
X 35X 35
X 35X 35
Y 0546Y 04836
Y 04282Y 02816
X 73X 73
X 73
X 73
Y 05441Y 04819Y 04268Y 02824
Figure 22 DG impact of substation reserve capacity index
FLLR
-Pha
se-A
(PU
)
minus002minus001
0001002003004005006007008
20 30 40 50 60 70 80 9010Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35
X 35
X 46
Y 007636
Y 004391
Y 002575
(720077)
Figure 23 DG impact of feeder loss to load ratio for Phase-A
X 35
X 35
Y 01209 Y 0124
Y 00771
Y 004061
X 73
X 71
X 56
Y 007568
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
80 9040 50 60 7020 3010Time in 15-minute interval
minus0020
002004006008
01012014
FLLR
-Pha
se-B
(PU
)
Figure 24 DG impact of feeder loss to load ratio for Phase-B
location and size of fixed PQ DG integration to a modifiedunbalance IEEE 37 feeder In this paper impact of DGintegration on performance indices has been demonstratedunder at different power factor under stochastic environ-ment To investigate clear effect of DG operation for making
FLLR
-Pha
se-C
(PU
)
001
0015
002
0025
20 30 40 50 60 70 8010 90Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35Y 002184
X 34Y 001736 X 56
Y 001862
X 73Y 002224
X 74Y 001754
X 70Y 001425
Figure 25 DG impact of feeder loss to load ratio for Phase-C
BCLI
-Pha
se-A
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
(3507117)
(3508666)(531173)
X 35Y 05765
X 75Y 1105
X 73Y 05746
20 30 40 50 60 70 9010 80Time in 15-minute interval
040506070809
11112
Figure 26 DG impact on branch loading index of Phase-A
BCLI
-Pha
se-B
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
8030 40 50 60 7020 9010Time in 15-minute interval
040506070809
111
X 34Y 08137
X 35Y 06739X 35Y 05325X 35
Y 04917X 35Y 04641
X 75Y 08246
X 73Y 0678
X 73Y 05316 X 73
Y 04906X 73Y 04628
Figure 27 DG impact on branch loading index of Phase-B
useful decision support voltage regulator of the above feederis removed Comparative results obtained with different fixedPQ DG operating scenario are discussed and it is found thatthe results obtained by DG operation at 095 power factorleading are more suitable to improve performance of feeder
10 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
9030 40 50 60 70 802010Time in 15-minute interval
X 34Y 1045
X 35Y 04945
X 74Y 1057
X 70Y 06878
X 71Y 04907
(55115)
040506070809
11112
BCLI
minusPha
seminusC
(PU
)
Figure 28 DG impact on branch loading index of Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
160180200220240260280300
Unb
alan
ce fe
eder
pow
er(K
VA)
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 6Y 1768
X 82Y 2656
X 82Y 2391
(422813)
Figure 29 DG impact on apparent powers unbalance index of feeder
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
VD
I-Ph
ase-
A (P
U)
03040506070809
111
X 34Y 09884
X 35Y 04341
X 35Y 02874
X 73Y 04457X 73Y 02871
(551084)
Figure 30 DG impact on maximum voltage deviation Phase-A
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
02
03
04
05
06
07
VD
I-Ph
ase-
B (P
U)
X 34Y 06145
X 35Y 03889X 35Y 03379
X 35Y 03156 X 35
Y 0287
X 75Y 06252
X 73Y 03959X 73Y 03411
X 73Y 03189X 73
Y 02904
(5206797)
Figure 31 DG impact on maximum voltage deviation Phase-B
Advances in Electrical Engineering 11
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
X 34Y 06916
X 35Y 03766
X 55Y 07598 X 74
Y 0695
X 71Y 03784
02030405060708
VD
I-Ph
ase-
C (P
U)
Figure 32 DG impact on maximum voltage deviation Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
MPF
-Pha
se-A
(PU
)
X 73Y 05634
(3509594)(340917)
(72minus09757)0
02
04
06
08
1
Figure 33 DG impact on minimum power factor of Phase-A
X 73X 73X 73
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 35Y 0779X 35
Y 0779X 35Y 06893
X 35Y 03432
X 73Y 07722X 73Y 07344X 73
Y 06813
X 73Y 0327302
03040506070809
1
MPF
-Pha
se-B
(PU
)
Figure 34 DG impact on minimum power factor of Phase-B
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 34Y 08142
X 35Y 06906
X 35Y 04764
X 35Y minus01302
X 74Y 08142
X 73Y 0692
X 73Y 04777
X 73Y minus01228
(5507574)
minus02
002040608
1
MPF
-Pha
se-C
(PU
)
Figure 35 DG impact of minimum power factor of Phase-C
12 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
Volta
ge U
nbal
ance
fact
or (
)
X 55Y 08497
X 48Y 06419
X 35Y 05578
X 55Y 06498
X 73Y 05576(605177)
05055
06065
07075
08085
09
Figure 36 DG impact of voltage unbalance factor of feeder
Appendix
A Survivability Index of Top 15 Nodes
(see Table 1)
B ZIP Load Compositions Calculation
To find ZIP load composition at each bus 119896 the followingequations were utilized to compute ZIP load composition ateach bus 119896 for each ℎ characteristics time interval
1198621119896119886119887119888(ℎ) = 08 lowast 119875119868119896
119886119887119888(ℎ) + 06 lowast 119875119862119896
119886119887119888(ℎ) + 08
lowast 119875119877119896119886119887119888(ℎ)
1198622119896119886119887119888(ℎ) = 02 lowast 119875119868119896
119886119887119888(ℎ) + 04 lowast 119875119862119896
119886119887119888(ℎ) + 019
lowast 119875119877119896119886119887119888(ℎ)
1198623119896119886119887119888(ℎ) = 0 lowast 119875119868119896
119886119887119888(ℎ) + 0 lowast 119875119862119896
119886119887119888(ℎ) + 001
lowast 119875119877119896119886119887119888(ℎ)
1198631119896119886119887119888(ℎ) = 08 lowast 119876119868119896
119886119887119888(ℎ) + 06 lowast 119876119862119896
119886119887119888(ℎ) + 08
lowast 119876119877119896119886119887119888(ℎ)
1198632119896119886119887119888(ℎ) = 02 lowast 119876119868119896
119886119887119888(ℎ) + 04 lowast 119876119862119896
119886119887119888(ℎ) + 019
lowast 119876119877119896119886119887119888(ℎ)
1198633119896119886119887119888(ℎ) = 0 lowast 119876119868119896
119886119887119888(ℎ) + 0 lowast 119876119862119896
119886119887119888(ℎ) + 001
lowast 119876119877119896119886119887119888(ℎ)
(B1)
Nomenclature
119875119896119886119887119888(ℎ) 119876119896
119886119887119888(ℎ) Total real and reactive power
load at bus 119896 during ℎ119881119873119886119887119888 119881
119896
119886119887119888(ℎ) Nominal and three-phase
voltage at 119896 node
1198621119896119886119887119888(ℎ) 1198622119896
119886119887119888(ℎ) 1198623119896
119886119887119888(ℎ) ZIP active load composition
at bus 119896 during ℎ1198631119896119886119887119888(ℎ)1198632119896
119886119887119888(ℎ)1198633119896
119886119887119888(ℎ) ZIP reactive load
composition at bus 119896 duringℎ
119875119868119896119886119887119888(ℎ) 119875119877119896
119886119887119888(ℎ) 119875119862119896
119886119887119888(ℎ) Active power for industrial
residential and commercialload connected at bus 119896during ℎ
119876119868119896119886119887119888(ℎ) 119876119877119896
119886119887119888(ℎ) 119876119862119896
119886119887119888(ℎ) Reactive power for
industrial residential andcommercial load connectedat bus 119896 during ℎ
ℎ 15-minute characteristicstime interval
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
References
[1] A Barin C U Brazil L Martins and E S A Brazil ldquoFuzzybased expert system for renewable energy managementrdquo inProceedings of the CIREDWorkshop no 0020 Rome Italy 2014
[2] N Hashmi and S A Khan ldquoPower energy management fora grid-connected PV system using rule-base fuzzy logicrdquo inProceedings of the 3rd International Conference on ArtificialIntelligence Modelling amp Simulation (AIMS rsquo15) pp 31ndash36 KotaKinabalu Malaysia December 2015
[3] A R Kashfi and M E El-Hawary ldquoIntegration of distributedgeneration inmedium voltage distribution network using fuzzylogic controller for demand side managementrdquo in Proceedingsof the Electrical Power and Energy Conference (EPEC rsquo14) pp254ndash259 Calgary Canada November 2014
[4] S K Injeti and N P Kumar ldquoPlanning and operation of activeradial distribution networks for improved voltage stability andloss reductionrdquoWorld Journal of Modelling and Simulation vol8 no 3 pp 211ndash222 2012
Advances in Electrical Engineering 13
[5] Y Manjili and A Rajaee ldquoFuzzy Control of Electricity StorageUnit for EnergyManagement of Micro-Gridsrdquo httpacewaco-ngorgAssetsdocFuzzy20Control20of20Electricity20Storage20Unit20for20Energy20Management20of20Micro-Grids Mexico20WAC202012pdf
[6] A Metia and S Ghosh ldquoFuzzy based DG allocation for LossMinimization in a Radial Distribution Systemrdquo Balkan Journalof Electrical amp Computer Engineering vol 3 no 3 pp 115ndash1232015
[7] E A Mohamed M M Othman and Y G Hegazy ldquoOpti-mal sizing and placement of distributed generators for profitmaximization using firefly algorithmrdquo in Proceedings of theInternational Conference on Artificial Intelligence Energy andManufacturing Engineering (ICAEME rsquo14) pp 6ndash10 2014
[8] K Muthukumar and S Jayalalitha ldquoOptimal reactive powercompensation by shunt capacitor sizing using harmony searchalgorithm in unbalanced radial distribution system for powerloss minimizationrdquo International Journal on Electrical Engineer-ing and Informatics vol 5 no 4 pp 474ndash491 2013
[9] M Padma Lalitha V C Veera Reddy and N Sivarami ReddyldquoApplication of fuzzy and ABC algorithm for DG placement forminimum loss in radial distribution systemrdquo Iranian Journal ofElectrical and Electronic Engineering vol 6 no 4 pp 248ndash2572010
[10] M S Thomas R Ranjan and R Roma ldquoProbabilistic fuzzyapproach to assess RDS vulnerability and plan corrective actionusing feeder reconfigurationrdquo Energy and Power Engineeringvol 4 no 5 pp 330ndash338 2012
[11] P N Si and S S Win ldquoFuzzy algorithm for capacitor allocationand sizing in radial distribution system to reduce lossesrdquoInternational Journal of Science Engineering and TechnologyResearch vol 3 no 12 2014
[12] A Arabali M Ghofrani M Etezadi-Amoli and M S FadalildquoStochastic performance assessment and sizing for a hybridpower system of SolarWindEnergy Storagerdquo IEEE Transac-tions on Sustainable Energy vol 5 no 2 pp 363ndash371 2014
[13] A Soroudi and M Ehsan ldquoA possibilistic-probabilistic tool forevaluating the impact of stochastic renewable and controllablepower generation on energy losses in distribution networksmdashacase studyrdquo Renewable and Sustainable Energy Reviews vol 15no 1 pp 794ndash800 2011
[14] J P Sharma and H Ravishankar Kamath ldquoStochastic voltageassessment of unbalance radial feederrdquo Journal of ElectricalSystems vol 11 no 3 pp 258ndash270 2015
[15] J P Sharma and H R Kamath ldquoPerformance analysis ofunbalance radial feeder with time varying composite loadrdquoJournal of Power and Energy Engineering vol 3 no 5 pp 56ndash702015
[16] The MathWorks Inc Types of Fuzzy Interefernece Systems TheMathWorks Inc 1994 1994ndash2014 httpwwwmathworkscomhelpfuzzytypes-of-fuzzyinference-systemshtml
[17] J-H Teng ldquoModelling distributed generations in three-phasedistribution load flowrdquo IET Generation Transmission and Dis-tribution vol 2 no 3 pp 330ndash340 2008
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
Advances in Electrical Engineering 5
LowMedium
HighMembership function plots Plot points
1
05
0
181
01 02 03 04 05 06 07 08 09 10
Input variable ldquoVUFrdquo
Figure 7 Membership function of VUF
Low Medium High
Output variable ldquoPDGOUTPUTrdquo1000900800700600500400300200100
Membership function plots Plot points
1
05
00
181
Figure 8 Membership function of DG output
PDG
OU
TPU
T
105
0minus05
minus1 TODPowerGAP
9080706050403020100
0
100
200
300
400
Figure 9 Surface view of fuzzy logic controller rules for DG sizing
PGOUTPUT
Distance
Surviablityindex
FCL2rule(sugeno)
DGNodeSelection
f(u)
Figure 10 Sugeno type fuzzy logic controller for DG location
of feeder is depicted in Figure 18 For the base case the dailyphase voltage profile for all buses is shown in Figures 19ndash21The least voltage in Phase A Phase-B and Phase C is foundon nodes 34 33 and 31 respectively These nodes can be usedfor shunt compensation
The impacts of distributed generation on various indicesare detailed as follows
Figure 22 reveals that DG operation at 095 power factorleading has the highest substation reserve capacity whereas
1
05
0
Low Medium HighMembership function plots Plot points 181
Input variable ldquoDistancerdquo800070006000500040003000200010000
Figure 11 Membership function of distance
1
05
0
Membership function plots Plot points 181
01 02 03 04 05 06 07 08 09 10
Input variable ldquoSurviablityindexrdquo
Low Medium High
Figure 12 Membership function of survivability index
Membership function plots plot Points 181
Output variable ldquoDGNodeSelectionrdquo
775709708733734737738711
741732731710735736740
Figure 13 Membership function of DG location
6 Advances in Electrical Engineering
Distance PGOUTPUT
10009008007006005004003002001000 010002000
3000400050006000
70008000720722724726728730732
DG
Nod
eSele
ctio
n
Figure 14 Surface view of Sugeno type fuzzy logic controller rules
2000
minus200minus400minus600minus800
008
007
006
005
03
025
02
015
0908070605
Input to Mamdani Controller
FLLR
UPQ
VUF
80 90706050403020100
80 90706050403020100
80 90706050403020100
80 90706050403020100
Figure 15 Inputs to Mamdani fuzzy logic controller
the base case has the lowest substation reserve capacitybetween 35 and 71 characteristics time interval DG operationat 095 lagging power factor has the reverse effect on loadrelief
It is observed from Figures 23 24 and 25 that the highestFLLR for all phases is found in DG operation at 095 powerfactor lag Lowest reduction in FLLR in Phase-A Phase-Band Phase-C is found inDG 095 power factor (lag) base andDG 099 power factor (lead) case respectively
In Figures 26 27 and 28 it is observed that DG operationat 095 power factor leading showed highest BCLI reductionfor all phases the DG operation at 099 power factor leadinggot the second reduction in BCLI and then the DG operationat unity power factor and DG operation at 095 power factorlagging the next Base case showed a branch overloading
1173 and 115 at 53 and 55 characteristics time interval forPhase-A and C respectively
As shown in Figure 29 there is no significant impact ofDG placement over apparent power imbalance Figures 3031 and 32 reveal the same impact of DG placement on voltagedeviation for all phases which was observed in the case ofBCLI
As shown in Figures 33 34 and 35 DG operation at 095power factor lagging showed minimum power factor (MPF)for all phasesMaximumpower factor for Phase-A andPhase-C occurred in the DG operation at 095 power factor leadingwhile maximum power factor for Phase-B occurred in DGoperation at unity power factor
In Figure 36 base case showed a highest voltage unbal-ance factor (VUF) the DG operation at 095 power factor
Advances in Electrical Engineering 7
80007000600050004000300020001000
500
Distance
Distance
09409309209109
089088
Inputs of Sugeno logic controller
Survivability index
Survivability index
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
50403020100 90807060
400300200100
0
Figure 16 Inputs to Sugeno fuzzy controller
732734
730728726724722720
450400350300250200150100500
DG Node
DG Node
Simulink output of Madami and Sugeno logic controller
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
Figure 17 Simulink outputs of fuzzy logic controller
lagging got the second maximum VUF and then the DGoperation at unity power factor and DG operation at 099power factor leading the next DG operation at 095 powerfactor lagging showed a maximum VUF of 05177at 6 charac-teristics time interval
5 Conclusion
The stochastic behavior of DG integrated distribution feederis a challenging problem for operations and planning aspectsA fuzzy expert system is proposed to determine optimal
8 Advances in Electrical Engineering
Tota
l fee
der l
oss (
KW)
253035404550556065
20 9010 50 60 70 8030 40Time in 15-minute interval
X 6Y 2522
X 52Y 5946
Figure 18 Feeder loss for base case
1
098
096
094
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number0
102030
40100806040200
(483409452)
Time in 15-minute interval
Figure 19 Phase voltage profile of Phase-A
1
099
098
097
096
095
0995
099
0985
098
0975
097
(56330966)
Phas
e vol
tage
pro
file
Node number010203040 100806040200
Time in 15-minute interval
Figure 20 Phase voltage profile of Phase-B
0995
099
0985
098
0975
097
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number010203040
100806040200
0965
(48310962)
Time in 15-minute interval
Figure 21 Phase voltage profile of Phase-C
Advances in Electrical Engineering 9
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 9010 80Time in 15-minute interval
minus02
0
02
04
06
08
SRCI
(PU
)
X 35X 35
X 35X 35
Y 0546Y 04836
Y 04282Y 02816
X 73X 73
X 73
X 73
Y 05441Y 04819Y 04268Y 02824
Figure 22 DG impact of substation reserve capacity index
FLLR
-Pha
se-A
(PU
)
minus002minus001
0001002003004005006007008
20 30 40 50 60 70 80 9010Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35
X 35
X 46
Y 007636
Y 004391
Y 002575
(720077)
Figure 23 DG impact of feeder loss to load ratio for Phase-A
X 35
X 35
Y 01209 Y 0124
Y 00771
Y 004061
X 73
X 71
X 56
Y 007568
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
80 9040 50 60 7020 3010Time in 15-minute interval
minus0020
002004006008
01012014
FLLR
-Pha
se-B
(PU
)
Figure 24 DG impact of feeder loss to load ratio for Phase-B
location and size of fixed PQ DG integration to a modifiedunbalance IEEE 37 feeder In this paper impact of DGintegration on performance indices has been demonstratedunder at different power factor under stochastic environ-ment To investigate clear effect of DG operation for making
FLLR
-Pha
se-C
(PU
)
001
0015
002
0025
20 30 40 50 60 70 8010 90Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35Y 002184
X 34Y 001736 X 56
Y 001862
X 73Y 002224
X 74Y 001754
X 70Y 001425
Figure 25 DG impact of feeder loss to load ratio for Phase-C
BCLI
-Pha
se-A
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
(3507117)
(3508666)(531173)
X 35Y 05765
X 75Y 1105
X 73Y 05746
20 30 40 50 60 70 9010 80Time in 15-minute interval
040506070809
11112
Figure 26 DG impact on branch loading index of Phase-A
BCLI
-Pha
se-B
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
8030 40 50 60 7020 9010Time in 15-minute interval
040506070809
111
X 34Y 08137
X 35Y 06739X 35Y 05325X 35
Y 04917X 35Y 04641
X 75Y 08246
X 73Y 0678
X 73Y 05316 X 73
Y 04906X 73Y 04628
Figure 27 DG impact on branch loading index of Phase-B
useful decision support voltage regulator of the above feederis removed Comparative results obtained with different fixedPQ DG operating scenario are discussed and it is found thatthe results obtained by DG operation at 095 power factorleading are more suitable to improve performance of feeder
10 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
9030 40 50 60 70 802010Time in 15-minute interval
X 34Y 1045
X 35Y 04945
X 74Y 1057
X 70Y 06878
X 71Y 04907
(55115)
040506070809
11112
BCLI
minusPha
seminusC
(PU
)
Figure 28 DG impact on branch loading index of Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
160180200220240260280300
Unb
alan
ce fe
eder
pow
er(K
VA)
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 6Y 1768
X 82Y 2656
X 82Y 2391
(422813)
Figure 29 DG impact on apparent powers unbalance index of feeder
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
VD
I-Ph
ase-
A (P
U)
03040506070809
111
X 34Y 09884
X 35Y 04341
X 35Y 02874
X 73Y 04457X 73Y 02871
(551084)
Figure 30 DG impact on maximum voltage deviation Phase-A
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
02
03
04
05
06
07
VD
I-Ph
ase-
B (P
U)
X 34Y 06145
X 35Y 03889X 35Y 03379
X 35Y 03156 X 35
Y 0287
X 75Y 06252
X 73Y 03959X 73Y 03411
X 73Y 03189X 73
Y 02904
(5206797)
Figure 31 DG impact on maximum voltage deviation Phase-B
Advances in Electrical Engineering 11
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
X 34Y 06916
X 35Y 03766
X 55Y 07598 X 74
Y 0695
X 71Y 03784
02030405060708
VD
I-Ph
ase-
C (P
U)
Figure 32 DG impact on maximum voltage deviation Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
MPF
-Pha
se-A
(PU
)
X 73Y 05634
(3509594)(340917)
(72minus09757)0
02
04
06
08
1
Figure 33 DG impact on minimum power factor of Phase-A
X 73X 73X 73
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 35Y 0779X 35
Y 0779X 35Y 06893
X 35Y 03432
X 73Y 07722X 73Y 07344X 73
Y 06813
X 73Y 0327302
03040506070809
1
MPF
-Pha
se-B
(PU
)
Figure 34 DG impact on minimum power factor of Phase-B
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 34Y 08142
X 35Y 06906
X 35Y 04764
X 35Y minus01302
X 74Y 08142
X 73Y 0692
X 73Y 04777
X 73Y minus01228
(5507574)
minus02
002040608
1
MPF
-Pha
se-C
(PU
)
Figure 35 DG impact of minimum power factor of Phase-C
12 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
Volta
ge U
nbal
ance
fact
or (
)
X 55Y 08497
X 48Y 06419
X 35Y 05578
X 55Y 06498
X 73Y 05576(605177)
05055
06065
07075
08085
09
Figure 36 DG impact of voltage unbalance factor of feeder
Appendix
A Survivability Index of Top 15 Nodes
(see Table 1)
B ZIP Load Compositions Calculation
To find ZIP load composition at each bus 119896 the followingequations were utilized to compute ZIP load composition ateach bus 119896 for each ℎ characteristics time interval
1198621119896119886119887119888(ℎ) = 08 lowast 119875119868119896
119886119887119888(ℎ) + 06 lowast 119875119862119896
119886119887119888(ℎ) + 08
lowast 119875119877119896119886119887119888(ℎ)
1198622119896119886119887119888(ℎ) = 02 lowast 119875119868119896
119886119887119888(ℎ) + 04 lowast 119875119862119896
119886119887119888(ℎ) + 019
lowast 119875119877119896119886119887119888(ℎ)
1198623119896119886119887119888(ℎ) = 0 lowast 119875119868119896
119886119887119888(ℎ) + 0 lowast 119875119862119896
119886119887119888(ℎ) + 001
lowast 119875119877119896119886119887119888(ℎ)
1198631119896119886119887119888(ℎ) = 08 lowast 119876119868119896
119886119887119888(ℎ) + 06 lowast 119876119862119896
119886119887119888(ℎ) + 08
lowast 119876119877119896119886119887119888(ℎ)
1198632119896119886119887119888(ℎ) = 02 lowast 119876119868119896
119886119887119888(ℎ) + 04 lowast 119876119862119896
119886119887119888(ℎ) + 019
lowast 119876119877119896119886119887119888(ℎ)
1198633119896119886119887119888(ℎ) = 0 lowast 119876119868119896
119886119887119888(ℎ) + 0 lowast 119876119862119896
119886119887119888(ℎ) + 001
lowast 119876119877119896119886119887119888(ℎ)
(B1)
Nomenclature
119875119896119886119887119888(ℎ) 119876119896
119886119887119888(ℎ) Total real and reactive power
load at bus 119896 during ℎ119881119873119886119887119888 119881
119896
119886119887119888(ℎ) Nominal and three-phase
voltage at 119896 node
1198621119896119886119887119888(ℎ) 1198622119896
119886119887119888(ℎ) 1198623119896
119886119887119888(ℎ) ZIP active load composition
at bus 119896 during ℎ1198631119896119886119887119888(ℎ)1198632119896
119886119887119888(ℎ)1198633119896
119886119887119888(ℎ) ZIP reactive load
composition at bus 119896 duringℎ
119875119868119896119886119887119888(ℎ) 119875119877119896
119886119887119888(ℎ) 119875119862119896
119886119887119888(ℎ) Active power for industrial
residential and commercialload connected at bus 119896during ℎ
119876119868119896119886119887119888(ℎ) 119876119877119896
119886119887119888(ℎ) 119876119862119896
119886119887119888(ℎ) Reactive power for
industrial residential andcommercial load connectedat bus 119896 during ℎ
ℎ 15-minute characteristicstime interval
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
References
[1] A Barin C U Brazil L Martins and E S A Brazil ldquoFuzzybased expert system for renewable energy managementrdquo inProceedings of the CIREDWorkshop no 0020 Rome Italy 2014
[2] N Hashmi and S A Khan ldquoPower energy management fora grid-connected PV system using rule-base fuzzy logicrdquo inProceedings of the 3rd International Conference on ArtificialIntelligence Modelling amp Simulation (AIMS rsquo15) pp 31ndash36 KotaKinabalu Malaysia December 2015
[3] A R Kashfi and M E El-Hawary ldquoIntegration of distributedgeneration inmedium voltage distribution network using fuzzylogic controller for demand side managementrdquo in Proceedingsof the Electrical Power and Energy Conference (EPEC rsquo14) pp254ndash259 Calgary Canada November 2014
[4] S K Injeti and N P Kumar ldquoPlanning and operation of activeradial distribution networks for improved voltage stability andloss reductionrdquoWorld Journal of Modelling and Simulation vol8 no 3 pp 211ndash222 2012
Advances in Electrical Engineering 13
[5] Y Manjili and A Rajaee ldquoFuzzy Control of Electricity StorageUnit for EnergyManagement of Micro-Gridsrdquo httpacewaco-ngorgAssetsdocFuzzy20Control20of20Electricity20Storage20Unit20for20Energy20Management20of20Micro-Grids Mexico20WAC202012pdf
[6] A Metia and S Ghosh ldquoFuzzy based DG allocation for LossMinimization in a Radial Distribution Systemrdquo Balkan Journalof Electrical amp Computer Engineering vol 3 no 3 pp 115ndash1232015
[7] E A Mohamed M M Othman and Y G Hegazy ldquoOpti-mal sizing and placement of distributed generators for profitmaximization using firefly algorithmrdquo in Proceedings of theInternational Conference on Artificial Intelligence Energy andManufacturing Engineering (ICAEME rsquo14) pp 6ndash10 2014
[8] K Muthukumar and S Jayalalitha ldquoOptimal reactive powercompensation by shunt capacitor sizing using harmony searchalgorithm in unbalanced radial distribution system for powerloss minimizationrdquo International Journal on Electrical Engineer-ing and Informatics vol 5 no 4 pp 474ndash491 2013
[9] M Padma Lalitha V C Veera Reddy and N Sivarami ReddyldquoApplication of fuzzy and ABC algorithm for DG placement forminimum loss in radial distribution systemrdquo Iranian Journal ofElectrical and Electronic Engineering vol 6 no 4 pp 248ndash2572010
[10] M S Thomas R Ranjan and R Roma ldquoProbabilistic fuzzyapproach to assess RDS vulnerability and plan corrective actionusing feeder reconfigurationrdquo Energy and Power Engineeringvol 4 no 5 pp 330ndash338 2012
[11] P N Si and S S Win ldquoFuzzy algorithm for capacitor allocationand sizing in radial distribution system to reduce lossesrdquoInternational Journal of Science Engineering and TechnologyResearch vol 3 no 12 2014
[12] A Arabali M Ghofrani M Etezadi-Amoli and M S FadalildquoStochastic performance assessment and sizing for a hybridpower system of SolarWindEnergy Storagerdquo IEEE Transac-tions on Sustainable Energy vol 5 no 2 pp 363ndash371 2014
[13] A Soroudi and M Ehsan ldquoA possibilistic-probabilistic tool forevaluating the impact of stochastic renewable and controllablepower generation on energy losses in distribution networksmdashacase studyrdquo Renewable and Sustainable Energy Reviews vol 15no 1 pp 794ndash800 2011
[14] J P Sharma and H Ravishankar Kamath ldquoStochastic voltageassessment of unbalance radial feederrdquo Journal of ElectricalSystems vol 11 no 3 pp 258ndash270 2015
[15] J P Sharma and H R Kamath ldquoPerformance analysis ofunbalance radial feeder with time varying composite loadrdquoJournal of Power and Energy Engineering vol 3 no 5 pp 56ndash702015
[16] The MathWorks Inc Types of Fuzzy Interefernece Systems TheMathWorks Inc 1994 1994ndash2014 httpwwwmathworkscomhelpfuzzytypes-of-fuzzyinference-systemshtml
[17] J-H Teng ldquoModelling distributed generations in three-phasedistribution load flowrdquo IET Generation Transmission and Dis-tribution vol 2 no 3 pp 330ndash340 2008
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
6 Advances in Electrical Engineering
Distance PGOUTPUT
10009008007006005004003002001000 010002000
3000400050006000
70008000720722724726728730732
DG
Nod
eSele
ctio
n
Figure 14 Surface view of Sugeno type fuzzy logic controller rules
2000
minus200minus400minus600minus800
008
007
006
005
03
025
02
015
0908070605
Input to Mamdani Controller
FLLR
UPQ
VUF
80 90706050403020100
80 90706050403020100
80 90706050403020100
80 90706050403020100
Figure 15 Inputs to Mamdani fuzzy logic controller
the base case has the lowest substation reserve capacitybetween 35 and 71 characteristics time interval DG operationat 095 lagging power factor has the reverse effect on loadrelief
It is observed from Figures 23 24 and 25 that the highestFLLR for all phases is found in DG operation at 095 powerfactor lag Lowest reduction in FLLR in Phase-A Phase-Band Phase-C is found inDG 095 power factor (lag) base andDG 099 power factor (lead) case respectively
In Figures 26 27 and 28 it is observed that DG operationat 095 power factor leading showed highest BCLI reductionfor all phases the DG operation at 099 power factor leadinggot the second reduction in BCLI and then the DG operationat unity power factor and DG operation at 095 power factorlagging the next Base case showed a branch overloading
1173 and 115 at 53 and 55 characteristics time interval forPhase-A and C respectively
As shown in Figure 29 there is no significant impact ofDG placement over apparent power imbalance Figures 3031 and 32 reveal the same impact of DG placement on voltagedeviation for all phases which was observed in the case ofBCLI
As shown in Figures 33 34 and 35 DG operation at 095power factor lagging showed minimum power factor (MPF)for all phasesMaximumpower factor for Phase-A andPhase-C occurred in the DG operation at 095 power factor leadingwhile maximum power factor for Phase-B occurred in DGoperation at unity power factor
In Figure 36 base case showed a highest voltage unbal-ance factor (VUF) the DG operation at 095 power factor
Advances in Electrical Engineering 7
80007000600050004000300020001000
500
Distance
Distance
09409309209109
089088
Inputs of Sugeno logic controller
Survivability index
Survivability index
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
50403020100 90807060
400300200100
0
Figure 16 Inputs to Sugeno fuzzy controller
732734
730728726724722720
450400350300250200150100500
DG Node
DG Node
Simulink output of Madami and Sugeno logic controller
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
Figure 17 Simulink outputs of fuzzy logic controller
lagging got the second maximum VUF and then the DGoperation at unity power factor and DG operation at 099power factor leading the next DG operation at 095 powerfactor lagging showed a maximum VUF of 05177at 6 charac-teristics time interval
5 Conclusion
The stochastic behavior of DG integrated distribution feederis a challenging problem for operations and planning aspectsA fuzzy expert system is proposed to determine optimal
8 Advances in Electrical Engineering
Tota
l fee
der l
oss (
KW)
253035404550556065
20 9010 50 60 70 8030 40Time in 15-minute interval
X 6Y 2522
X 52Y 5946
Figure 18 Feeder loss for base case
1
098
096
094
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number0
102030
40100806040200
(483409452)
Time in 15-minute interval
Figure 19 Phase voltage profile of Phase-A
1
099
098
097
096
095
0995
099
0985
098
0975
097
(56330966)
Phas
e vol
tage
pro
file
Node number010203040 100806040200
Time in 15-minute interval
Figure 20 Phase voltage profile of Phase-B
0995
099
0985
098
0975
097
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number010203040
100806040200
0965
(48310962)
Time in 15-minute interval
Figure 21 Phase voltage profile of Phase-C
Advances in Electrical Engineering 9
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 9010 80Time in 15-minute interval
minus02
0
02
04
06
08
SRCI
(PU
)
X 35X 35
X 35X 35
Y 0546Y 04836
Y 04282Y 02816
X 73X 73
X 73
X 73
Y 05441Y 04819Y 04268Y 02824
Figure 22 DG impact of substation reserve capacity index
FLLR
-Pha
se-A
(PU
)
minus002minus001
0001002003004005006007008
20 30 40 50 60 70 80 9010Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35
X 35
X 46
Y 007636
Y 004391
Y 002575
(720077)
Figure 23 DG impact of feeder loss to load ratio for Phase-A
X 35
X 35
Y 01209 Y 0124
Y 00771
Y 004061
X 73
X 71
X 56
Y 007568
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
80 9040 50 60 7020 3010Time in 15-minute interval
minus0020
002004006008
01012014
FLLR
-Pha
se-B
(PU
)
Figure 24 DG impact of feeder loss to load ratio for Phase-B
location and size of fixed PQ DG integration to a modifiedunbalance IEEE 37 feeder In this paper impact of DGintegration on performance indices has been demonstratedunder at different power factor under stochastic environ-ment To investigate clear effect of DG operation for making
FLLR
-Pha
se-C
(PU
)
001
0015
002
0025
20 30 40 50 60 70 8010 90Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35Y 002184
X 34Y 001736 X 56
Y 001862
X 73Y 002224
X 74Y 001754
X 70Y 001425
Figure 25 DG impact of feeder loss to load ratio for Phase-C
BCLI
-Pha
se-A
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
(3507117)
(3508666)(531173)
X 35Y 05765
X 75Y 1105
X 73Y 05746
20 30 40 50 60 70 9010 80Time in 15-minute interval
040506070809
11112
Figure 26 DG impact on branch loading index of Phase-A
BCLI
-Pha
se-B
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
8030 40 50 60 7020 9010Time in 15-minute interval
040506070809
111
X 34Y 08137
X 35Y 06739X 35Y 05325X 35
Y 04917X 35Y 04641
X 75Y 08246
X 73Y 0678
X 73Y 05316 X 73
Y 04906X 73Y 04628
Figure 27 DG impact on branch loading index of Phase-B
useful decision support voltage regulator of the above feederis removed Comparative results obtained with different fixedPQ DG operating scenario are discussed and it is found thatthe results obtained by DG operation at 095 power factorleading are more suitable to improve performance of feeder
10 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
9030 40 50 60 70 802010Time in 15-minute interval
X 34Y 1045
X 35Y 04945
X 74Y 1057
X 70Y 06878
X 71Y 04907
(55115)
040506070809
11112
BCLI
minusPha
seminusC
(PU
)
Figure 28 DG impact on branch loading index of Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
160180200220240260280300
Unb
alan
ce fe
eder
pow
er(K
VA)
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 6Y 1768
X 82Y 2656
X 82Y 2391
(422813)
Figure 29 DG impact on apparent powers unbalance index of feeder
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
VD
I-Ph
ase-
A (P
U)
03040506070809
111
X 34Y 09884
X 35Y 04341
X 35Y 02874
X 73Y 04457X 73Y 02871
(551084)
Figure 30 DG impact on maximum voltage deviation Phase-A
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
02
03
04
05
06
07
VD
I-Ph
ase-
B (P
U)
X 34Y 06145
X 35Y 03889X 35Y 03379
X 35Y 03156 X 35
Y 0287
X 75Y 06252
X 73Y 03959X 73Y 03411
X 73Y 03189X 73
Y 02904
(5206797)
Figure 31 DG impact on maximum voltage deviation Phase-B
Advances in Electrical Engineering 11
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
X 34Y 06916
X 35Y 03766
X 55Y 07598 X 74
Y 0695
X 71Y 03784
02030405060708
VD
I-Ph
ase-
C (P
U)
Figure 32 DG impact on maximum voltage deviation Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
MPF
-Pha
se-A
(PU
)
X 73Y 05634
(3509594)(340917)
(72minus09757)0
02
04
06
08
1
Figure 33 DG impact on minimum power factor of Phase-A
X 73X 73X 73
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 35Y 0779X 35
Y 0779X 35Y 06893
X 35Y 03432
X 73Y 07722X 73Y 07344X 73
Y 06813
X 73Y 0327302
03040506070809
1
MPF
-Pha
se-B
(PU
)
Figure 34 DG impact on minimum power factor of Phase-B
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 34Y 08142
X 35Y 06906
X 35Y 04764
X 35Y minus01302
X 74Y 08142
X 73Y 0692
X 73Y 04777
X 73Y minus01228
(5507574)
minus02
002040608
1
MPF
-Pha
se-C
(PU
)
Figure 35 DG impact of minimum power factor of Phase-C
12 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
Volta
ge U
nbal
ance
fact
or (
)
X 55Y 08497
X 48Y 06419
X 35Y 05578
X 55Y 06498
X 73Y 05576(605177)
05055
06065
07075
08085
09
Figure 36 DG impact of voltage unbalance factor of feeder
Appendix
A Survivability Index of Top 15 Nodes
(see Table 1)
B ZIP Load Compositions Calculation
To find ZIP load composition at each bus 119896 the followingequations were utilized to compute ZIP load composition ateach bus 119896 for each ℎ characteristics time interval
1198621119896119886119887119888(ℎ) = 08 lowast 119875119868119896
119886119887119888(ℎ) + 06 lowast 119875119862119896
119886119887119888(ℎ) + 08
lowast 119875119877119896119886119887119888(ℎ)
1198622119896119886119887119888(ℎ) = 02 lowast 119875119868119896
119886119887119888(ℎ) + 04 lowast 119875119862119896
119886119887119888(ℎ) + 019
lowast 119875119877119896119886119887119888(ℎ)
1198623119896119886119887119888(ℎ) = 0 lowast 119875119868119896
119886119887119888(ℎ) + 0 lowast 119875119862119896
119886119887119888(ℎ) + 001
lowast 119875119877119896119886119887119888(ℎ)
1198631119896119886119887119888(ℎ) = 08 lowast 119876119868119896
119886119887119888(ℎ) + 06 lowast 119876119862119896
119886119887119888(ℎ) + 08
lowast 119876119877119896119886119887119888(ℎ)
1198632119896119886119887119888(ℎ) = 02 lowast 119876119868119896
119886119887119888(ℎ) + 04 lowast 119876119862119896
119886119887119888(ℎ) + 019
lowast 119876119877119896119886119887119888(ℎ)
1198633119896119886119887119888(ℎ) = 0 lowast 119876119868119896
119886119887119888(ℎ) + 0 lowast 119876119862119896
119886119887119888(ℎ) + 001
lowast 119876119877119896119886119887119888(ℎ)
(B1)
Nomenclature
119875119896119886119887119888(ℎ) 119876119896
119886119887119888(ℎ) Total real and reactive power
load at bus 119896 during ℎ119881119873119886119887119888 119881
119896
119886119887119888(ℎ) Nominal and three-phase
voltage at 119896 node
1198621119896119886119887119888(ℎ) 1198622119896
119886119887119888(ℎ) 1198623119896
119886119887119888(ℎ) ZIP active load composition
at bus 119896 during ℎ1198631119896119886119887119888(ℎ)1198632119896
119886119887119888(ℎ)1198633119896
119886119887119888(ℎ) ZIP reactive load
composition at bus 119896 duringℎ
119875119868119896119886119887119888(ℎ) 119875119877119896
119886119887119888(ℎ) 119875119862119896
119886119887119888(ℎ) Active power for industrial
residential and commercialload connected at bus 119896during ℎ
119876119868119896119886119887119888(ℎ) 119876119877119896
119886119887119888(ℎ) 119876119862119896
119886119887119888(ℎ) Reactive power for
industrial residential andcommercial load connectedat bus 119896 during ℎ
ℎ 15-minute characteristicstime interval
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
References
[1] A Barin C U Brazil L Martins and E S A Brazil ldquoFuzzybased expert system for renewable energy managementrdquo inProceedings of the CIREDWorkshop no 0020 Rome Italy 2014
[2] N Hashmi and S A Khan ldquoPower energy management fora grid-connected PV system using rule-base fuzzy logicrdquo inProceedings of the 3rd International Conference on ArtificialIntelligence Modelling amp Simulation (AIMS rsquo15) pp 31ndash36 KotaKinabalu Malaysia December 2015
[3] A R Kashfi and M E El-Hawary ldquoIntegration of distributedgeneration inmedium voltage distribution network using fuzzylogic controller for demand side managementrdquo in Proceedingsof the Electrical Power and Energy Conference (EPEC rsquo14) pp254ndash259 Calgary Canada November 2014
[4] S K Injeti and N P Kumar ldquoPlanning and operation of activeradial distribution networks for improved voltage stability andloss reductionrdquoWorld Journal of Modelling and Simulation vol8 no 3 pp 211ndash222 2012
Advances in Electrical Engineering 13
[5] Y Manjili and A Rajaee ldquoFuzzy Control of Electricity StorageUnit for EnergyManagement of Micro-Gridsrdquo httpacewaco-ngorgAssetsdocFuzzy20Control20of20Electricity20Storage20Unit20for20Energy20Management20of20Micro-Grids Mexico20WAC202012pdf
[6] A Metia and S Ghosh ldquoFuzzy based DG allocation for LossMinimization in a Radial Distribution Systemrdquo Balkan Journalof Electrical amp Computer Engineering vol 3 no 3 pp 115ndash1232015
[7] E A Mohamed M M Othman and Y G Hegazy ldquoOpti-mal sizing and placement of distributed generators for profitmaximization using firefly algorithmrdquo in Proceedings of theInternational Conference on Artificial Intelligence Energy andManufacturing Engineering (ICAEME rsquo14) pp 6ndash10 2014
[8] K Muthukumar and S Jayalalitha ldquoOptimal reactive powercompensation by shunt capacitor sizing using harmony searchalgorithm in unbalanced radial distribution system for powerloss minimizationrdquo International Journal on Electrical Engineer-ing and Informatics vol 5 no 4 pp 474ndash491 2013
[9] M Padma Lalitha V C Veera Reddy and N Sivarami ReddyldquoApplication of fuzzy and ABC algorithm for DG placement forminimum loss in radial distribution systemrdquo Iranian Journal ofElectrical and Electronic Engineering vol 6 no 4 pp 248ndash2572010
[10] M S Thomas R Ranjan and R Roma ldquoProbabilistic fuzzyapproach to assess RDS vulnerability and plan corrective actionusing feeder reconfigurationrdquo Energy and Power Engineeringvol 4 no 5 pp 330ndash338 2012
[11] P N Si and S S Win ldquoFuzzy algorithm for capacitor allocationand sizing in radial distribution system to reduce lossesrdquoInternational Journal of Science Engineering and TechnologyResearch vol 3 no 12 2014
[12] A Arabali M Ghofrani M Etezadi-Amoli and M S FadalildquoStochastic performance assessment and sizing for a hybridpower system of SolarWindEnergy Storagerdquo IEEE Transac-tions on Sustainable Energy vol 5 no 2 pp 363ndash371 2014
[13] A Soroudi and M Ehsan ldquoA possibilistic-probabilistic tool forevaluating the impact of stochastic renewable and controllablepower generation on energy losses in distribution networksmdashacase studyrdquo Renewable and Sustainable Energy Reviews vol 15no 1 pp 794ndash800 2011
[14] J P Sharma and H Ravishankar Kamath ldquoStochastic voltageassessment of unbalance radial feederrdquo Journal of ElectricalSystems vol 11 no 3 pp 258ndash270 2015
[15] J P Sharma and H R Kamath ldquoPerformance analysis ofunbalance radial feeder with time varying composite loadrdquoJournal of Power and Energy Engineering vol 3 no 5 pp 56ndash702015
[16] The MathWorks Inc Types of Fuzzy Interefernece Systems TheMathWorks Inc 1994 1994ndash2014 httpwwwmathworkscomhelpfuzzytypes-of-fuzzyinference-systemshtml
[17] J-H Teng ldquoModelling distributed generations in three-phasedistribution load flowrdquo IET Generation Transmission and Dis-tribution vol 2 no 3 pp 330ndash340 2008
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
Advances in Electrical Engineering 7
80007000600050004000300020001000
500
Distance
Distance
09409309209109
089088
Inputs of Sugeno logic controller
Survivability index
Survivability index
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
50403020100 90807060
400300200100
0
Figure 16 Inputs to Sugeno fuzzy controller
732734
730728726724722720
450400350300250200150100500
DG Node
DG Node
Simulink output of Madami and Sugeno logic controller
Per Phase DG Capacity
50403020100 90807060
50403020100 90807060
Figure 17 Simulink outputs of fuzzy logic controller
lagging got the second maximum VUF and then the DGoperation at unity power factor and DG operation at 099power factor leading the next DG operation at 095 powerfactor lagging showed a maximum VUF of 05177at 6 charac-teristics time interval
5 Conclusion
The stochastic behavior of DG integrated distribution feederis a challenging problem for operations and planning aspectsA fuzzy expert system is proposed to determine optimal
8 Advances in Electrical Engineering
Tota
l fee
der l
oss (
KW)
253035404550556065
20 9010 50 60 70 8030 40Time in 15-minute interval
X 6Y 2522
X 52Y 5946
Figure 18 Feeder loss for base case
1
098
096
094
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number0
102030
40100806040200
(483409452)
Time in 15-minute interval
Figure 19 Phase voltage profile of Phase-A
1
099
098
097
096
095
0995
099
0985
098
0975
097
(56330966)
Phas
e vol
tage
pro
file
Node number010203040 100806040200
Time in 15-minute interval
Figure 20 Phase voltage profile of Phase-B
0995
099
0985
098
0975
097
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number010203040
100806040200
0965
(48310962)
Time in 15-minute interval
Figure 21 Phase voltage profile of Phase-C
Advances in Electrical Engineering 9
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 9010 80Time in 15-minute interval
minus02
0
02
04
06
08
SRCI
(PU
)
X 35X 35
X 35X 35
Y 0546Y 04836
Y 04282Y 02816
X 73X 73
X 73
X 73
Y 05441Y 04819Y 04268Y 02824
Figure 22 DG impact of substation reserve capacity index
FLLR
-Pha
se-A
(PU
)
minus002minus001
0001002003004005006007008
20 30 40 50 60 70 80 9010Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35
X 35
X 46
Y 007636
Y 004391
Y 002575
(720077)
Figure 23 DG impact of feeder loss to load ratio for Phase-A
X 35
X 35
Y 01209 Y 0124
Y 00771
Y 004061
X 73
X 71
X 56
Y 007568
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
80 9040 50 60 7020 3010Time in 15-minute interval
minus0020
002004006008
01012014
FLLR
-Pha
se-B
(PU
)
Figure 24 DG impact of feeder loss to load ratio for Phase-B
location and size of fixed PQ DG integration to a modifiedunbalance IEEE 37 feeder In this paper impact of DGintegration on performance indices has been demonstratedunder at different power factor under stochastic environ-ment To investigate clear effect of DG operation for making
FLLR
-Pha
se-C
(PU
)
001
0015
002
0025
20 30 40 50 60 70 8010 90Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35Y 002184
X 34Y 001736 X 56
Y 001862
X 73Y 002224
X 74Y 001754
X 70Y 001425
Figure 25 DG impact of feeder loss to load ratio for Phase-C
BCLI
-Pha
se-A
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
(3507117)
(3508666)(531173)
X 35Y 05765
X 75Y 1105
X 73Y 05746
20 30 40 50 60 70 9010 80Time in 15-minute interval
040506070809
11112
Figure 26 DG impact on branch loading index of Phase-A
BCLI
-Pha
se-B
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
8030 40 50 60 7020 9010Time in 15-minute interval
040506070809
111
X 34Y 08137
X 35Y 06739X 35Y 05325X 35
Y 04917X 35Y 04641
X 75Y 08246
X 73Y 0678
X 73Y 05316 X 73
Y 04906X 73Y 04628
Figure 27 DG impact on branch loading index of Phase-B
useful decision support voltage regulator of the above feederis removed Comparative results obtained with different fixedPQ DG operating scenario are discussed and it is found thatthe results obtained by DG operation at 095 power factorleading are more suitable to improve performance of feeder
10 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
9030 40 50 60 70 802010Time in 15-minute interval
X 34Y 1045
X 35Y 04945
X 74Y 1057
X 70Y 06878
X 71Y 04907
(55115)
040506070809
11112
BCLI
minusPha
seminusC
(PU
)
Figure 28 DG impact on branch loading index of Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
160180200220240260280300
Unb
alan
ce fe
eder
pow
er(K
VA)
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 6Y 1768
X 82Y 2656
X 82Y 2391
(422813)
Figure 29 DG impact on apparent powers unbalance index of feeder
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
VD
I-Ph
ase-
A (P
U)
03040506070809
111
X 34Y 09884
X 35Y 04341
X 35Y 02874
X 73Y 04457X 73Y 02871
(551084)
Figure 30 DG impact on maximum voltage deviation Phase-A
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
02
03
04
05
06
07
VD
I-Ph
ase-
B (P
U)
X 34Y 06145
X 35Y 03889X 35Y 03379
X 35Y 03156 X 35
Y 0287
X 75Y 06252
X 73Y 03959X 73Y 03411
X 73Y 03189X 73
Y 02904
(5206797)
Figure 31 DG impact on maximum voltage deviation Phase-B
Advances in Electrical Engineering 11
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
X 34Y 06916
X 35Y 03766
X 55Y 07598 X 74
Y 0695
X 71Y 03784
02030405060708
VD
I-Ph
ase-
C (P
U)
Figure 32 DG impact on maximum voltage deviation Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
MPF
-Pha
se-A
(PU
)
X 73Y 05634
(3509594)(340917)
(72minus09757)0
02
04
06
08
1
Figure 33 DG impact on minimum power factor of Phase-A
X 73X 73X 73
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 35Y 0779X 35
Y 0779X 35Y 06893
X 35Y 03432
X 73Y 07722X 73Y 07344X 73
Y 06813
X 73Y 0327302
03040506070809
1
MPF
-Pha
se-B
(PU
)
Figure 34 DG impact on minimum power factor of Phase-B
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 34Y 08142
X 35Y 06906
X 35Y 04764
X 35Y minus01302
X 74Y 08142
X 73Y 0692
X 73Y 04777
X 73Y minus01228
(5507574)
minus02
002040608
1
MPF
-Pha
se-C
(PU
)
Figure 35 DG impact of minimum power factor of Phase-C
12 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
Volta
ge U
nbal
ance
fact
or (
)
X 55Y 08497
X 48Y 06419
X 35Y 05578
X 55Y 06498
X 73Y 05576(605177)
05055
06065
07075
08085
09
Figure 36 DG impact of voltage unbalance factor of feeder
Appendix
A Survivability Index of Top 15 Nodes
(see Table 1)
B ZIP Load Compositions Calculation
To find ZIP load composition at each bus 119896 the followingequations were utilized to compute ZIP load composition ateach bus 119896 for each ℎ characteristics time interval
1198621119896119886119887119888(ℎ) = 08 lowast 119875119868119896
119886119887119888(ℎ) + 06 lowast 119875119862119896
119886119887119888(ℎ) + 08
lowast 119875119877119896119886119887119888(ℎ)
1198622119896119886119887119888(ℎ) = 02 lowast 119875119868119896
119886119887119888(ℎ) + 04 lowast 119875119862119896
119886119887119888(ℎ) + 019
lowast 119875119877119896119886119887119888(ℎ)
1198623119896119886119887119888(ℎ) = 0 lowast 119875119868119896
119886119887119888(ℎ) + 0 lowast 119875119862119896
119886119887119888(ℎ) + 001
lowast 119875119877119896119886119887119888(ℎ)
1198631119896119886119887119888(ℎ) = 08 lowast 119876119868119896
119886119887119888(ℎ) + 06 lowast 119876119862119896
119886119887119888(ℎ) + 08
lowast 119876119877119896119886119887119888(ℎ)
1198632119896119886119887119888(ℎ) = 02 lowast 119876119868119896
119886119887119888(ℎ) + 04 lowast 119876119862119896
119886119887119888(ℎ) + 019
lowast 119876119877119896119886119887119888(ℎ)
1198633119896119886119887119888(ℎ) = 0 lowast 119876119868119896
119886119887119888(ℎ) + 0 lowast 119876119862119896
119886119887119888(ℎ) + 001
lowast 119876119877119896119886119887119888(ℎ)
(B1)
Nomenclature
119875119896119886119887119888(ℎ) 119876119896
119886119887119888(ℎ) Total real and reactive power
load at bus 119896 during ℎ119881119873119886119887119888 119881
119896
119886119887119888(ℎ) Nominal and three-phase
voltage at 119896 node
1198621119896119886119887119888(ℎ) 1198622119896
119886119887119888(ℎ) 1198623119896
119886119887119888(ℎ) ZIP active load composition
at bus 119896 during ℎ1198631119896119886119887119888(ℎ)1198632119896
119886119887119888(ℎ)1198633119896
119886119887119888(ℎ) ZIP reactive load
composition at bus 119896 duringℎ
119875119868119896119886119887119888(ℎ) 119875119877119896
119886119887119888(ℎ) 119875119862119896
119886119887119888(ℎ) Active power for industrial
residential and commercialload connected at bus 119896during ℎ
119876119868119896119886119887119888(ℎ) 119876119877119896
119886119887119888(ℎ) 119876119862119896
119886119887119888(ℎ) Reactive power for
industrial residential andcommercial load connectedat bus 119896 during ℎ
ℎ 15-minute characteristicstime interval
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
References
[1] A Barin C U Brazil L Martins and E S A Brazil ldquoFuzzybased expert system for renewable energy managementrdquo inProceedings of the CIREDWorkshop no 0020 Rome Italy 2014
[2] N Hashmi and S A Khan ldquoPower energy management fora grid-connected PV system using rule-base fuzzy logicrdquo inProceedings of the 3rd International Conference on ArtificialIntelligence Modelling amp Simulation (AIMS rsquo15) pp 31ndash36 KotaKinabalu Malaysia December 2015
[3] A R Kashfi and M E El-Hawary ldquoIntegration of distributedgeneration inmedium voltage distribution network using fuzzylogic controller for demand side managementrdquo in Proceedingsof the Electrical Power and Energy Conference (EPEC rsquo14) pp254ndash259 Calgary Canada November 2014
[4] S K Injeti and N P Kumar ldquoPlanning and operation of activeradial distribution networks for improved voltage stability andloss reductionrdquoWorld Journal of Modelling and Simulation vol8 no 3 pp 211ndash222 2012
Advances in Electrical Engineering 13
[5] Y Manjili and A Rajaee ldquoFuzzy Control of Electricity StorageUnit for EnergyManagement of Micro-Gridsrdquo httpacewaco-ngorgAssetsdocFuzzy20Control20of20Electricity20Storage20Unit20for20Energy20Management20of20Micro-Grids Mexico20WAC202012pdf
[6] A Metia and S Ghosh ldquoFuzzy based DG allocation for LossMinimization in a Radial Distribution Systemrdquo Balkan Journalof Electrical amp Computer Engineering vol 3 no 3 pp 115ndash1232015
[7] E A Mohamed M M Othman and Y G Hegazy ldquoOpti-mal sizing and placement of distributed generators for profitmaximization using firefly algorithmrdquo in Proceedings of theInternational Conference on Artificial Intelligence Energy andManufacturing Engineering (ICAEME rsquo14) pp 6ndash10 2014
[8] K Muthukumar and S Jayalalitha ldquoOptimal reactive powercompensation by shunt capacitor sizing using harmony searchalgorithm in unbalanced radial distribution system for powerloss minimizationrdquo International Journal on Electrical Engineer-ing and Informatics vol 5 no 4 pp 474ndash491 2013
[9] M Padma Lalitha V C Veera Reddy and N Sivarami ReddyldquoApplication of fuzzy and ABC algorithm for DG placement forminimum loss in radial distribution systemrdquo Iranian Journal ofElectrical and Electronic Engineering vol 6 no 4 pp 248ndash2572010
[10] M S Thomas R Ranjan and R Roma ldquoProbabilistic fuzzyapproach to assess RDS vulnerability and plan corrective actionusing feeder reconfigurationrdquo Energy and Power Engineeringvol 4 no 5 pp 330ndash338 2012
[11] P N Si and S S Win ldquoFuzzy algorithm for capacitor allocationand sizing in radial distribution system to reduce lossesrdquoInternational Journal of Science Engineering and TechnologyResearch vol 3 no 12 2014
[12] A Arabali M Ghofrani M Etezadi-Amoli and M S FadalildquoStochastic performance assessment and sizing for a hybridpower system of SolarWindEnergy Storagerdquo IEEE Transac-tions on Sustainable Energy vol 5 no 2 pp 363ndash371 2014
[13] A Soroudi and M Ehsan ldquoA possibilistic-probabilistic tool forevaluating the impact of stochastic renewable and controllablepower generation on energy losses in distribution networksmdashacase studyrdquo Renewable and Sustainable Energy Reviews vol 15no 1 pp 794ndash800 2011
[14] J P Sharma and H Ravishankar Kamath ldquoStochastic voltageassessment of unbalance radial feederrdquo Journal of ElectricalSystems vol 11 no 3 pp 258ndash270 2015
[15] J P Sharma and H R Kamath ldquoPerformance analysis ofunbalance radial feeder with time varying composite loadrdquoJournal of Power and Energy Engineering vol 3 no 5 pp 56ndash702015
[16] The MathWorks Inc Types of Fuzzy Interefernece Systems TheMathWorks Inc 1994 1994ndash2014 httpwwwmathworkscomhelpfuzzytypes-of-fuzzyinference-systemshtml
[17] J-H Teng ldquoModelling distributed generations in three-phasedistribution load flowrdquo IET Generation Transmission and Dis-tribution vol 2 no 3 pp 330ndash340 2008
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
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Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
8 Advances in Electrical Engineering
Tota
l fee
der l
oss (
KW)
253035404550556065
20 9010 50 60 70 8030 40Time in 15-minute interval
X 6Y 2522
X 52Y 5946
Figure 18 Feeder loss for base case
1
098
096
094
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number0
102030
40100806040200
(483409452)
Time in 15-minute interval
Figure 19 Phase voltage profile of Phase-A
1
099
098
097
096
095
0995
099
0985
098
0975
097
(56330966)
Phas
e vol
tage
pro
file
Node number010203040 100806040200
Time in 15-minute interval
Figure 20 Phase voltage profile of Phase-B
0995
099
0985
098
0975
097
1
099
098
097
096
095
Phas
e vol
tage
pro
file
Node number010203040
100806040200
0965
(48310962)
Time in 15-minute interval
Figure 21 Phase voltage profile of Phase-C
Advances in Electrical Engineering 9
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 9010 80Time in 15-minute interval
minus02
0
02
04
06
08
SRCI
(PU
)
X 35X 35
X 35X 35
Y 0546Y 04836
Y 04282Y 02816
X 73X 73
X 73
X 73
Y 05441Y 04819Y 04268Y 02824
Figure 22 DG impact of substation reserve capacity index
FLLR
-Pha
se-A
(PU
)
minus002minus001
0001002003004005006007008
20 30 40 50 60 70 80 9010Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35
X 35
X 46
Y 007636
Y 004391
Y 002575
(720077)
Figure 23 DG impact of feeder loss to load ratio for Phase-A
X 35
X 35
Y 01209 Y 0124
Y 00771
Y 004061
X 73
X 71
X 56
Y 007568
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
80 9040 50 60 7020 3010Time in 15-minute interval
minus0020
002004006008
01012014
FLLR
-Pha
se-B
(PU
)
Figure 24 DG impact of feeder loss to load ratio for Phase-B
location and size of fixed PQ DG integration to a modifiedunbalance IEEE 37 feeder In this paper impact of DGintegration on performance indices has been demonstratedunder at different power factor under stochastic environ-ment To investigate clear effect of DG operation for making
FLLR
-Pha
se-C
(PU
)
001
0015
002
0025
20 30 40 50 60 70 8010 90Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35Y 002184
X 34Y 001736 X 56
Y 001862
X 73Y 002224
X 74Y 001754
X 70Y 001425
Figure 25 DG impact of feeder loss to load ratio for Phase-C
BCLI
-Pha
se-A
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
(3507117)
(3508666)(531173)
X 35Y 05765
X 75Y 1105
X 73Y 05746
20 30 40 50 60 70 9010 80Time in 15-minute interval
040506070809
11112
Figure 26 DG impact on branch loading index of Phase-A
BCLI
-Pha
se-B
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
8030 40 50 60 7020 9010Time in 15-minute interval
040506070809
111
X 34Y 08137
X 35Y 06739X 35Y 05325X 35
Y 04917X 35Y 04641
X 75Y 08246
X 73Y 0678
X 73Y 05316 X 73
Y 04906X 73Y 04628
Figure 27 DG impact on branch loading index of Phase-B
useful decision support voltage regulator of the above feederis removed Comparative results obtained with different fixedPQ DG operating scenario are discussed and it is found thatthe results obtained by DG operation at 095 power factorleading are more suitable to improve performance of feeder
10 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
9030 40 50 60 70 802010Time in 15-minute interval
X 34Y 1045
X 35Y 04945
X 74Y 1057
X 70Y 06878
X 71Y 04907
(55115)
040506070809
11112
BCLI
minusPha
seminusC
(PU
)
Figure 28 DG impact on branch loading index of Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
160180200220240260280300
Unb
alan
ce fe
eder
pow
er(K
VA)
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 6Y 1768
X 82Y 2656
X 82Y 2391
(422813)
Figure 29 DG impact on apparent powers unbalance index of feeder
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
VD
I-Ph
ase-
A (P
U)
03040506070809
111
X 34Y 09884
X 35Y 04341
X 35Y 02874
X 73Y 04457X 73Y 02871
(551084)
Figure 30 DG impact on maximum voltage deviation Phase-A
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
02
03
04
05
06
07
VD
I-Ph
ase-
B (P
U)
X 34Y 06145
X 35Y 03889X 35Y 03379
X 35Y 03156 X 35
Y 0287
X 75Y 06252
X 73Y 03959X 73Y 03411
X 73Y 03189X 73
Y 02904
(5206797)
Figure 31 DG impact on maximum voltage deviation Phase-B
Advances in Electrical Engineering 11
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
X 34Y 06916
X 35Y 03766
X 55Y 07598 X 74
Y 0695
X 71Y 03784
02030405060708
VD
I-Ph
ase-
C (P
U)
Figure 32 DG impact on maximum voltage deviation Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
MPF
-Pha
se-A
(PU
)
X 73Y 05634
(3509594)(340917)
(72minus09757)0
02
04
06
08
1
Figure 33 DG impact on minimum power factor of Phase-A
X 73X 73X 73
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 35Y 0779X 35
Y 0779X 35Y 06893
X 35Y 03432
X 73Y 07722X 73Y 07344X 73
Y 06813
X 73Y 0327302
03040506070809
1
MPF
-Pha
se-B
(PU
)
Figure 34 DG impact on minimum power factor of Phase-B
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 34Y 08142
X 35Y 06906
X 35Y 04764
X 35Y minus01302
X 74Y 08142
X 73Y 0692
X 73Y 04777
X 73Y minus01228
(5507574)
minus02
002040608
1
MPF
-Pha
se-C
(PU
)
Figure 35 DG impact of minimum power factor of Phase-C
12 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
Volta
ge U
nbal
ance
fact
or (
)
X 55Y 08497
X 48Y 06419
X 35Y 05578
X 55Y 06498
X 73Y 05576(605177)
05055
06065
07075
08085
09
Figure 36 DG impact of voltage unbalance factor of feeder
Appendix
A Survivability Index of Top 15 Nodes
(see Table 1)
B ZIP Load Compositions Calculation
To find ZIP load composition at each bus 119896 the followingequations were utilized to compute ZIP load composition ateach bus 119896 for each ℎ characteristics time interval
1198621119896119886119887119888(ℎ) = 08 lowast 119875119868119896
119886119887119888(ℎ) + 06 lowast 119875119862119896
119886119887119888(ℎ) + 08
lowast 119875119877119896119886119887119888(ℎ)
1198622119896119886119887119888(ℎ) = 02 lowast 119875119868119896
119886119887119888(ℎ) + 04 lowast 119875119862119896
119886119887119888(ℎ) + 019
lowast 119875119877119896119886119887119888(ℎ)
1198623119896119886119887119888(ℎ) = 0 lowast 119875119868119896
119886119887119888(ℎ) + 0 lowast 119875119862119896
119886119887119888(ℎ) + 001
lowast 119875119877119896119886119887119888(ℎ)
1198631119896119886119887119888(ℎ) = 08 lowast 119876119868119896
119886119887119888(ℎ) + 06 lowast 119876119862119896
119886119887119888(ℎ) + 08
lowast 119876119877119896119886119887119888(ℎ)
1198632119896119886119887119888(ℎ) = 02 lowast 119876119868119896
119886119887119888(ℎ) + 04 lowast 119876119862119896
119886119887119888(ℎ) + 019
lowast 119876119877119896119886119887119888(ℎ)
1198633119896119886119887119888(ℎ) = 0 lowast 119876119868119896
119886119887119888(ℎ) + 0 lowast 119876119862119896
119886119887119888(ℎ) + 001
lowast 119876119877119896119886119887119888(ℎ)
(B1)
Nomenclature
119875119896119886119887119888(ℎ) 119876119896
119886119887119888(ℎ) Total real and reactive power
load at bus 119896 during ℎ119881119873119886119887119888 119881
119896
119886119887119888(ℎ) Nominal and three-phase
voltage at 119896 node
1198621119896119886119887119888(ℎ) 1198622119896
119886119887119888(ℎ) 1198623119896
119886119887119888(ℎ) ZIP active load composition
at bus 119896 during ℎ1198631119896119886119887119888(ℎ)1198632119896
119886119887119888(ℎ)1198633119896
119886119887119888(ℎ) ZIP reactive load
composition at bus 119896 duringℎ
119875119868119896119886119887119888(ℎ) 119875119877119896
119886119887119888(ℎ) 119875119862119896
119886119887119888(ℎ) Active power for industrial
residential and commercialload connected at bus 119896during ℎ
119876119868119896119886119887119888(ℎ) 119876119877119896
119886119887119888(ℎ) 119876119862119896
119886119887119888(ℎ) Reactive power for
industrial residential andcommercial load connectedat bus 119896 during ℎ
ℎ 15-minute characteristicstime interval
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
References
[1] A Barin C U Brazil L Martins and E S A Brazil ldquoFuzzybased expert system for renewable energy managementrdquo inProceedings of the CIREDWorkshop no 0020 Rome Italy 2014
[2] N Hashmi and S A Khan ldquoPower energy management fora grid-connected PV system using rule-base fuzzy logicrdquo inProceedings of the 3rd International Conference on ArtificialIntelligence Modelling amp Simulation (AIMS rsquo15) pp 31ndash36 KotaKinabalu Malaysia December 2015
[3] A R Kashfi and M E El-Hawary ldquoIntegration of distributedgeneration inmedium voltage distribution network using fuzzylogic controller for demand side managementrdquo in Proceedingsof the Electrical Power and Energy Conference (EPEC rsquo14) pp254ndash259 Calgary Canada November 2014
[4] S K Injeti and N P Kumar ldquoPlanning and operation of activeradial distribution networks for improved voltage stability andloss reductionrdquoWorld Journal of Modelling and Simulation vol8 no 3 pp 211ndash222 2012
Advances in Electrical Engineering 13
[5] Y Manjili and A Rajaee ldquoFuzzy Control of Electricity StorageUnit for EnergyManagement of Micro-Gridsrdquo httpacewaco-ngorgAssetsdocFuzzy20Control20of20Electricity20Storage20Unit20for20Energy20Management20of20Micro-Grids Mexico20WAC202012pdf
[6] A Metia and S Ghosh ldquoFuzzy based DG allocation for LossMinimization in a Radial Distribution Systemrdquo Balkan Journalof Electrical amp Computer Engineering vol 3 no 3 pp 115ndash1232015
[7] E A Mohamed M M Othman and Y G Hegazy ldquoOpti-mal sizing and placement of distributed generators for profitmaximization using firefly algorithmrdquo in Proceedings of theInternational Conference on Artificial Intelligence Energy andManufacturing Engineering (ICAEME rsquo14) pp 6ndash10 2014
[8] K Muthukumar and S Jayalalitha ldquoOptimal reactive powercompensation by shunt capacitor sizing using harmony searchalgorithm in unbalanced radial distribution system for powerloss minimizationrdquo International Journal on Electrical Engineer-ing and Informatics vol 5 no 4 pp 474ndash491 2013
[9] M Padma Lalitha V C Veera Reddy and N Sivarami ReddyldquoApplication of fuzzy and ABC algorithm for DG placement forminimum loss in radial distribution systemrdquo Iranian Journal ofElectrical and Electronic Engineering vol 6 no 4 pp 248ndash2572010
[10] M S Thomas R Ranjan and R Roma ldquoProbabilistic fuzzyapproach to assess RDS vulnerability and plan corrective actionusing feeder reconfigurationrdquo Energy and Power Engineeringvol 4 no 5 pp 330ndash338 2012
[11] P N Si and S S Win ldquoFuzzy algorithm for capacitor allocationand sizing in radial distribution system to reduce lossesrdquoInternational Journal of Science Engineering and TechnologyResearch vol 3 no 12 2014
[12] A Arabali M Ghofrani M Etezadi-Amoli and M S FadalildquoStochastic performance assessment and sizing for a hybridpower system of SolarWindEnergy Storagerdquo IEEE Transac-tions on Sustainable Energy vol 5 no 2 pp 363ndash371 2014
[13] A Soroudi and M Ehsan ldquoA possibilistic-probabilistic tool forevaluating the impact of stochastic renewable and controllablepower generation on energy losses in distribution networksmdashacase studyrdquo Renewable and Sustainable Energy Reviews vol 15no 1 pp 794ndash800 2011
[14] J P Sharma and H Ravishankar Kamath ldquoStochastic voltageassessment of unbalance radial feederrdquo Journal of ElectricalSystems vol 11 no 3 pp 258ndash270 2015
[15] J P Sharma and H R Kamath ldquoPerformance analysis ofunbalance radial feeder with time varying composite loadrdquoJournal of Power and Energy Engineering vol 3 no 5 pp 56ndash702015
[16] The MathWorks Inc Types of Fuzzy Interefernece Systems TheMathWorks Inc 1994 1994ndash2014 httpwwwmathworkscomhelpfuzzytypes-of-fuzzyinference-systemshtml
[17] J-H Teng ldquoModelling distributed generations in three-phasedistribution load flowrdquo IET Generation Transmission and Dis-tribution vol 2 no 3 pp 330ndash340 2008
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
Advances in Electrical Engineering 9
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 9010 80Time in 15-minute interval
minus02
0
02
04
06
08
SRCI
(PU
)
X 35X 35
X 35X 35
Y 0546Y 04836
Y 04282Y 02816
X 73X 73
X 73
X 73
Y 05441Y 04819Y 04268Y 02824
Figure 22 DG impact of substation reserve capacity index
FLLR
-Pha
se-A
(PU
)
minus002minus001
0001002003004005006007008
20 30 40 50 60 70 80 9010Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35
X 35
X 46
Y 007636
Y 004391
Y 002575
(720077)
Figure 23 DG impact of feeder loss to load ratio for Phase-A
X 35
X 35
Y 01209 Y 0124
Y 00771
Y 004061
X 73
X 71
X 56
Y 007568
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
80 9040 50 60 7020 3010Time in 15-minute interval
minus0020
002004006008
01012014
FLLR
-Pha
se-B
(PU
)
Figure 24 DG impact of feeder loss to load ratio for Phase-B
location and size of fixed PQ DG integration to a modifiedunbalance IEEE 37 feeder In this paper impact of DGintegration on performance indices has been demonstratedunder at different power factor under stochastic environ-ment To investigate clear effect of DG operation for making
FLLR
-Pha
se-C
(PU
)
001
0015
002
0025
20 30 40 50 60 70 8010 90Time in 15-minute interval
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
X 35Y 002184
X 34Y 001736 X 56
Y 001862
X 73Y 002224
X 74Y 001754
X 70Y 001425
Figure 25 DG impact of feeder loss to load ratio for Phase-C
BCLI
-Pha
se-A
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
(3507117)
(3508666)(531173)
X 35Y 05765
X 75Y 1105
X 73Y 05746
20 30 40 50 60 70 9010 80Time in 15-minute interval
040506070809
11112
Figure 26 DG impact on branch loading index of Phase-A
BCLI
-Pha
se-B
(PU
)
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
8030 40 50 60 7020 9010Time in 15-minute interval
040506070809
111
X 34Y 08137
X 35Y 06739X 35Y 05325X 35
Y 04917X 35Y 04641
X 75Y 08246
X 73Y 0678
X 73Y 05316 X 73
Y 04906X 73Y 04628
Figure 27 DG impact on branch loading index of Phase-B
useful decision support voltage regulator of the above feederis removed Comparative results obtained with different fixedPQ DG operating scenario are discussed and it is found thatthe results obtained by DG operation at 095 power factorleading are more suitable to improve performance of feeder
10 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
9030 40 50 60 70 802010Time in 15-minute interval
X 34Y 1045
X 35Y 04945
X 74Y 1057
X 70Y 06878
X 71Y 04907
(55115)
040506070809
11112
BCLI
minusPha
seminusC
(PU
)
Figure 28 DG impact on branch loading index of Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
160180200220240260280300
Unb
alan
ce fe
eder
pow
er(K
VA)
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 6Y 1768
X 82Y 2656
X 82Y 2391
(422813)
Figure 29 DG impact on apparent powers unbalance index of feeder
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
VD
I-Ph
ase-
A (P
U)
03040506070809
111
X 34Y 09884
X 35Y 04341
X 35Y 02874
X 73Y 04457X 73Y 02871
(551084)
Figure 30 DG impact on maximum voltage deviation Phase-A
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
02
03
04
05
06
07
VD
I-Ph
ase-
B (P
U)
X 34Y 06145
X 35Y 03889X 35Y 03379
X 35Y 03156 X 35
Y 0287
X 75Y 06252
X 73Y 03959X 73Y 03411
X 73Y 03189X 73
Y 02904
(5206797)
Figure 31 DG impact on maximum voltage deviation Phase-B
Advances in Electrical Engineering 11
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
X 34Y 06916
X 35Y 03766
X 55Y 07598 X 74
Y 0695
X 71Y 03784
02030405060708
VD
I-Ph
ase-
C (P
U)
Figure 32 DG impact on maximum voltage deviation Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
MPF
-Pha
se-A
(PU
)
X 73Y 05634
(3509594)(340917)
(72minus09757)0
02
04
06
08
1
Figure 33 DG impact on minimum power factor of Phase-A
X 73X 73X 73
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 35Y 0779X 35
Y 0779X 35Y 06893
X 35Y 03432
X 73Y 07722X 73Y 07344X 73
Y 06813
X 73Y 0327302
03040506070809
1
MPF
-Pha
se-B
(PU
)
Figure 34 DG impact on minimum power factor of Phase-B
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 34Y 08142
X 35Y 06906
X 35Y 04764
X 35Y minus01302
X 74Y 08142
X 73Y 0692
X 73Y 04777
X 73Y minus01228
(5507574)
minus02
002040608
1
MPF
-Pha
se-C
(PU
)
Figure 35 DG impact of minimum power factor of Phase-C
12 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
Volta
ge U
nbal
ance
fact
or (
)
X 55Y 08497
X 48Y 06419
X 35Y 05578
X 55Y 06498
X 73Y 05576(605177)
05055
06065
07075
08085
09
Figure 36 DG impact of voltage unbalance factor of feeder
Appendix
A Survivability Index of Top 15 Nodes
(see Table 1)
B ZIP Load Compositions Calculation
To find ZIP load composition at each bus 119896 the followingequations were utilized to compute ZIP load composition ateach bus 119896 for each ℎ characteristics time interval
1198621119896119886119887119888(ℎ) = 08 lowast 119875119868119896
119886119887119888(ℎ) + 06 lowast 119875119862119896
119886119887119888(ℎ) + 08
lowast 119875119877119896119886119887119888(ℎ)
1198622119896119886119887119888(ℎ) = 02 lowast 119875119868119896
119886119887119888(ℎ) + 04 lowast 119875119862119896
119886119887119888(ℎ) + 019
lowast 119875119877119896119886119887119888(ℎ)
1198623119896119886119887119888(ℎ) = 0 lowast 119875119868119896
119886119887119888(ℎ) + 0 lowast 119875119862119896
119886119887119888(ℎ) + 001
lowast 119875119877119896119886119887119888(ℎ)
1198631119896119886119887119888(ℎ) = 08 lowast 119876119868119896
119886119887119888(ℎ) + 06 lowast 119876119862119896
119886119887119888(ℎ) + 08
lowast 119876119877119896119886119887119888(ℎ)
1198632119896119886119887119888(ℎ) = 02 lowast 119876119868119896
119886119887119888(ℎ) + 04 lowast 119876119862119896
119886119887119888(ℎ) + 019
lowast 119876119877119896119886119887119888(ℎ)
1198633119896119886119887119888(ℎ) = 0 lowast 119876119868119896
119886119887119888(ℎ) + 0 lowast 119876119862119896
119886119887119888(ℎ) + 001
lowast 119876119877119896119886119887119888(ℎ)
(B1)
Nomenclature
119875119896119886119887119888(ℎ) 119876119896
119886119887119888(ℎ) Total real and reactive power
load at bus 119896 during ℎ119881119873119886119887119888 119881
119896
119886119887119888(ℎ) Nominal and three-phase
voltage at 119896 node
1198621119896119886119887119888(ℎ) 1198622119896
119886119887119888(ℎ) 1198623119896
119886119887119888(ℎ) ZIP active load composition
at bus 119896 during ℎ1198631119896119886119887119888(ℎ)1198632119896
119886119887119888(ℎ)1198633119896
119886119887119888(ℎ) ZIP reactive load
composition at bus 119896 duringℎ
119875119868119896119886119887119888(ℎ) 119875119877119896
119886119887119888(ℎ) 119875119862119896
119886119887119888(ℎ) Active power for industrial
residential and commercialload connected at bus 119896during ℎ
119876119868119896119886119887119888(ℎ) 119876119877119896
119886119887119888(ℎ) 119876119862119896
119886119887119888(ℎ) Reactive power for
industrial residential andcommercial load connectedat bus 119896 during ℎ
ℎ 15-minute characteristicstime interval
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
References
[1] A Barin C U Brazil L Martins and E S A Brazil ldquoFuzzybased expert system for renewable energy managementrdquo inProceedings of the CIREDWorkshop no 0020 Rome Italy 2014
[2] N Hashmi and S A Khan ldquoPower energy management fora grid-connected PV system using rule-base fuzzy logicrdquo inProceedings of the 3rd International Conference on ArtificialIntelligence Modelling amp Simulation (AIMS rsquo15) pp 31ndash36 KotaKinabalu Malaysia December 2015
[3] A R Kashfi and M E El-Hawary ldquoIntegration of distributedgeneration inmedium voltage distribution network using fuzzylogic controller for demand side managementrdquo in Proceedingsof the Electrical Power and Energy Conference (EPEC rsquo14) pp254ndash259 Calgary Canada November 2014
[4] S K Injeti and N P Kumar ldquoPlanning and operation of activeradial distribution networks for improved voltage stability andloss reductionrdquoWorld Journal of Modelling and Simulation vol8 no 3 pp 211ndash222 2012
Advances in Electrical Engineering 13
[5] Y Manjili and A Rajaee ldquoFuzzy Control of Electricity StorageUnit for EnergyManagement of Micro-Gridsrdquo httpacewaco-ngorgAssetsdocFuzzy20Control20of20Electricity20Storage20Unit20for20Energy20Management20of20Micro-Grids Mexico20WAC202012pdf
[6] A Metia and S Ghosh ldquoFuzzy based DG allocation for LossMinimization in a Radial Distribution Systemrdquo Balkan Journalof Electrical amp Computer Engineering vol 3 no 3 pp 115ndash1232015
[7] E A Mohamed M M Othman and Y G Hegazy ldquoOpti-mal sizing and placement of distributed generators for profitmaximization using firefly algorithmrdquo in Proceedings of theInternational Conference on Artificial Intelligence Energy andManufacturing Engineering (ICAEME rsquo14) pp 6ndash10 2014
[8] K Muthukumar and S Jayalalitha ldquoOptimal reactive powercompensation by shunt capacitor sizing using harmony searchalgorithm in unbalanced radial distribution system for powerloss minimizationrdquo International Journal on Electrical Engineer-ing and Informatics vol 5 no 4 pp 474ndash491 2013
[9] M Padma Lalitha V C Veera Reddy and N Sivarami ReddyldquoApplication of fuzzy and ABC algorithm for DG placement forminimum loss in radial distribution systemrdquo Iranian Journal ofElectrical and Electronic Engineering vol 6 no 4 pp 248ndash2572010
[10] M S Thomas R Ranjan and R Roma ldquoProbabilistic fuzzyapproach to assess RDS vulnerability and plan corrective actionusing feeder reconfigurationrdquo Energy and Power Engineeringvol 4 no 5 pp 330ndash338 2012
[11] P N Si and S S Win ldquoFuzzy algorithm for capacitor allocationand sizing in radial distribution system to reduce lossesrdquoInternational Journal of Science Engineering and TechnologyResearch vol 3 no 12 2014
[12] A Arabali M Ghofrani M Etezadi-Amoli and M S FadalildquoStochastic performance assessment and sizing for a hybridpower system of SolarWindEnergy Storagerdquo IEEE Transac-tions on Sustainable Energy vol 5 no 2 pp 363ndash371 2014
[13] A Soroudi and M Ehsan ldquoA possibilistic-probabilistic tool forevaluating the impact of stochastic renewable and controllablepower generation on energy losses in distribution networksmdashacase studyrdquo Renewable and Sustainable Energy Reviews vol 15no 1 pp 794ndash800 2011
[14] J P Sharma and H Ravishankar Kamath ldquoStochastic voltageassessment of unbalance radial feederrdquo Journal of ElectricalSystems vol 11 no 3 pp 258ndash270 2015
[15] J P Sharma and H R Kamath ldquoPerformance analysis ofunbalance radial feeder with time varying composite loadrdquoJournal of Power and Energy Engineering vol 3 no 5 pp 56ndash702015
[16] The MathWorks Inc Types of Fuzzy Interefernece Systems TheMathWorks Inc 1994 1994ndash2014 httpwwwmathworkscomhelpfuzzytypes-of-fuzzyinference-systemshtml
[17] J-H Teng ldquoModelling distributed generations in three-phasedistribution load flowrdquo IET Generation Transmission and Dis-tribution vol 2 no 3 pp 330ndash340 2008
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
10 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
9030 40 50 60 70 802010Time in 15-minute interval
X 34Y 1045
X 35Y 04945
X 74Y 1057
X 70Y 06878
X 71Y 04907
(55115)
040506070809
11112
BCLI
minusPha
seminusC
(PU
)
Figure 28 DG impact on branch loading index of Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
160180200220240260280300
Unb
alan
ce fe
eder
pow
er(K
VA)
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 6Y 1768
X 82Y 2656
X 82Y 2391
(422813)
Figure 29 DG impact on apparent powers unbalance index of feeder
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
VD
I-Ph
ase-
A (P
U)
03040506070809
111
X 34Y 09884
X 35Y 04341
X 35Y 02874
X 73Y 04457X 73Y 02871
(551084)
Figure 30 DG impact on maximum voltage deviation Phase-A
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
02
03
04
05
06
07
VD
I-Ph
ase-
B (P
U)
X 34Y 06145
X 35Y 03889X 35Y 03379
X 35Y 03156 X 35
Y 0287
X 75Y 06252
X 73Y 03959X 73Y 03411
X 73Y 03189X 73
Y 02904
(5206797)
Figure 31 DG impact on maximum voltage deviation Phase-B
Advances in Electrical Engineering 11
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
X 34Y 06916
X 35Y 03766
X 55Y 07598 X 74
Y 0695
X 71Y 03784
02030405060708
VD
I-Ph
ase-
C (P
U)
Figure 32 DG impact on maximum voltage deviation Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
MPF
-Pha
se-A
(PU
)
X 73Y 05634
(3509594)(340917)
(72minus09757)0
02
04
06
08
1
Figure 33 DG impact on minimum power factor of Phase-A
X 73X 73X 73
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 35Y 0779X 35
Y 0779X 35Y 06893
X 35Y 03432
X 73Y 07722X 73Y 07344X 73
Y 06813
X 73Y 0327302
03040506070809
1
MPF
-Pha
se-B
(PU
)
Figure 34 DG impact on minimum power factor of Phase-B
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 34Y 08142
X 35Y 06906
X 35Y 04764
X 35Y minus01302
X 74Y 08142
X 73Y 0692
X 73Y 04777
X 73Y minus01228
(5507574)
minus02
002040608
1
MPF
-Pha
se-C
(PU
)
Figure 35 DG impact of minimum power factor of Phase-C
12 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
Volta
ge U
nbal
ance
fact
or (
)
X 55Y 08497
X 48Y 06419
X 35Y 05578
X 55Y 06498
X 73Y 05576(605177)
05055
06065
07075
08085
09
Figure 36 DG impact of voltage unbalance factor of feeder
Appendix
A Survivability Index of Top 15 Nodes
(see Table 1)
B ZIP Load Compositions Calculation
To find ZIP load composition at each bus 119896 the followingequations were utilized to compute ZIP load composition ateach bus 119896 for each ℎ characteristics time interval
1198621119896119886119887119888(ℎ) = 08 lowast 119875119868119896
119886119887119888(ℎ) + 06 lowast 119875119862119896
119886119887119888(ℎ) + 08
lowast 119875119877119896119886119887119888(ℎ)
1198622119896119886119887119888(ℎ) = 02 lowast 119875119868119896
119886119887119888(ℎ) + 04 lowast 119875119862119896
119886119887119888(ℎ) + 019
lowast 119875119877119896119886119887119888(ℎ)
1198623119896119886119887119888(ℎ) = 0 lowast 119875119868119896
119886119887119888(ℎ) + 0 lowast 119875119862119896
119886119887119888(ℎ) + 001
lowast 119875119877119896119886119887119888(ℎ)
1198631119896119886119887119888(ℎ) = 08 lowast 119876119868119896
119886119887119888(ℎ) + 06 lowast 119876119862119896
119886119887119888(ℎ) + 08
lowast 119876119877119896119886119887119888(ℎ)
1198632119896119886119887119888(ℎ) = 02 lowast 119876119868119896
119886119887119888(ℎ) + 04 lowast 119876119862119896
119886119887119888(ℎ) + 019
lowast 119876119877119896119886119887119888(ℎ)
1198633119896119886119887119888(ℎ) = 0 lowast 119876119868119896
119886119887119888(ℎ) + 0 lowast 119876119862119896
119886119887119888(ℎ) + 001
lowast 119876119877119896119886119887119888(ℎ)
(B1)
Nomenclature
119875119896119886119887119888(ℎ) 119876119896
119886119887119888(ℎ) Total real and reactive power
load at bus 119896 during ℎ119881119873119886119887119888 119881
119896
119886119887119888(ℎ) Nominal and three-phase
voltage at 119896 node
1198621119896119886119887119888(ℎ) 1198622119896
119886119887119888(ℎ) 1198623119896
119886119887119888(ℎ) ZIP active load composition
at bus 119896 during ℎ1198631119896119886119887119888(ℎ)1198632119896
119886119887119888(ℎ)1198633119896
119886119887119888(ℎ) ZIP reactive load
composition at bus 119896 duringℎ
119875119868119896119886119887119888(ℎ) 119875119877119896
119886119887119888(ℎ) 119875119862119896
119886119887119888(ℎ) Active power for industrial
residential and commercialload connected at bus 119896during ℎ
119876119868119896119886119887119888(ℎ) 119876119877119896
119886119887119888(ℎ) 119876119862119896
119886119887119888(ℎ) Reactive power for
industrial residential andcommercial load connectedat bus 119896 during ℎ
ℎ 15-minute characteristicstime interval
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
References
[1] A Barin C U Brazil L Martins and E S A Brazil ldquoFuzzybased expert system for renewable energy managementrdquo inProceedings of the CIREDWorkshop no 0020 Rome Italy 2014
[2] N Hashmi and S A Khan ldquoPower energy management fora grid-connected PV system using rule-base fuzzy logicrdquo inProceedings of the 3rd International Conference on ArtificialIntelligence Modelling amp Simulation (AIMS rsquo15) pp 31ndash36 KotaKinabalu Malaysia December 2015
[3] A R Kashfi and M E El-Hawary ldquoIntegration of distributedgeneration inmedium voltage distribution network using fuzzylogic controller for demand side managementrdquo in Proceedingsof the Electrical Power and Energy Conference (EPEC rsquo14) pp254ndash259 Calgary Canada November 2014
[4] S K Injeti and N P Kumar ldquoPlanning and operation of activeradial distribution networks for improved voltage stability andloss reductionrdquoWorld Journal of Modelling and Simulation vol8 no 3 pp 211ndash222 2012
Advances in Electrical Engineering 13
[5] Y Manjili and A Rajaee ldquoFuzzy Control of Electricity StorageUnit for EnergyManagement of Micro-Gridsrdquo httpacewaco-ngorgAssetsdocFuzzy20Control20of20Electricity20Storage20Unit20for20Energy20Management20of20Micro-Grids Mexico20WAC202012pdf
[6] A Metia and S Ghosh ldquoFuzzy based DG allocation for LossMinimization in a Radial Distribution Systemrdquo Balkan Journalof Electrical amp Computer Engineering vol 3 no 3 pp 115ndash1232015
[7] E A Mohamed M M Othman and Y G Hegazy ldquoOpti-mal sizing and placement of distributed generators for profitmaximization using firefly algorithmrdquo in Proceedings of theInternational Conference on Artificial Intelligence Energy andManufacturing Engineering (ICAEME rsquo14) pp 6ndash10 2014
[8] K Muthukumar and S Jayalalitha ldquoOptimal reactive powercompensation by shunt capacitor sizing using harmony searchalgorithm in unbalanced radial distribution system for powerloss minimizationrdquo International Journal on Electrical Engineer-ing and Informatics vol 5 no 4 pp 474ndash491 2013
[9] M Padma Lalitha V C Veera Reddy and N Sivarami ReddyldquoApplication of fuzzy and ABC algorithm for DG placement forminimum loss in radial distribution systemrdquo Iranian Journal ofElectrical and Electronic Engineering vol 6 no 4 pp 248ndash2572010
[10] M S Thomas R Ranjan and R Roma ldquoProbabilistic fuzzyapproach to assess RDS vulnerability and plan corrective actionusing feeder reconfigurationrdquo Energy and Power Engineeringvol 4 no 5 pp 330ndash338 2012
[11] P N Si and S S Win ldquoFuzzy algorithm for capacitor allocationand sizing in radial distribution system to reduce lossesrdquoInternational Journal of Science Engineering and TechnologyResearch vol 3 no 12 2014
[12] A Arabali M Ghofrani M Etezadi-Amoli and M S FadalildquoStochastic performance assessment and sizing for a hybridpower system of SolarWindEnergy Storagerdquo IEEE Transac-tions on Sustainable Energy vol 5 no 2 pp 363ndash371 2014
[13] A Soroudi and M Ehsan ldquoA possibilistic-probabilistic tool forevaluating the impact of stochastic renewable and controllablepower generation on energy losses in distribution networksmdashacase studyrdquo Renewable and Sustainable Energy Reviews vol 15no 1 pp 794ndash800 2011
[14] J P Sharma and H Ravishankar Kamath ldquoStochastic voltageassessment of unbalance radial feederrdquo Journal of ElectricalSystems vol 11 no 3 pp 258ndash270 2015
[15] J P Sharma and H R Kamath ldquoPerformance analysis ofunbalance radial feeder with time varying composite loadrdquoJournal of Power and Energy Engineering vol 3 no 5 pp 56ndash702015
[16] The MathWorks Inc Types of Fuzzy Interefernece Systems TheMathWorks Inc 1994 1994ndash2014 httpwwwmathworkscomhelpfuzzytypes-of-fuzzyinference-systemshtml
[17] J-H Teng ldquoModelling distributed generations in three-phasedistribution load flowrdquo IET Generation Transmission and Dis-tribution vol 2 no 3 pp 330ndash340 2008
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
Advances in Electrical Engineering 11
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
X 34Y 06916
X 35Y 03766
X 55Y 07598 X 74
Y 0695
X 71Y 03784
02030405060708
VD
I-Ph
ase-
C (P
U)
Figure 32 DG impact on maximum voltage deviation Phase-C
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 8010 90Time in 15-minute interval
MPF
-Pha
se-A
(PU
)
X 73Y 05634
(3509594)(340917)
(72minus09757)0
02
04
06
08
1
Figure 33 DG impact on minimum power factor of Phase-A
X 73X 73X 73
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 35Y 0779X 35
Y 0779X 35Y 06893
X 35Y 03432
X 73Y 07722X 73Y 07344X 73
Y 06813
X 73Y 0327302
03040506070809
1
MPF
-Pha
se-B
(PU
)
Figure 34 DG impact on minimum power factor of Phase-B
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
X 34Y 08142
X 35Y 06906
X 35Y 04764
X 35Y minus01302
X 74Y 08142
X 73Y 0692
X 73Y 04777
X 73Y minus01228
(5507574)
minus02
002040608
1
MPF
-Pha
se-C
(PU
)
Figure 35 DG impact of minimum power factor of Phase-C
12 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
Volta
ge U
nbal
ance
fact
or (
)
X 55Y 08497
X 48Y 06419
X 35Y 05578
X 55Y 06498
X 73Y 05576(605177)
05055
06065
07075
08085
09
Figure 36 DG impact of voltage unbalance factor of feeder
Appendix
A Survivability Index of Top 15 Nodes
(see Table 1)
B ZIP Load Compositions Calculation
To find ZIP load composition at each bus 119896 the followingequations were utilized to compute ZIP load composition ateach bus 119896 for each ℎ characteristics time interval
1198621119896119886119887119888(ℎ) = 08 lowast 119875119868119896
119886119887119888(ℎ) + 06 lowast 119875119862119896
119886119887119888(ℎ) + 08
lowast 119875119877119896119886119887119888(ℎ)
1198622119896119886119887119888(ℎ) = 02 lowast 119875119868119896
119886119887119888(ℎ) + 04 lowast 119875119862119896
119886119887119888(ℎ) + 019
lowast 119875119877119896119886119887119888(ℎ)
1198623119896119886119887119888(ℎ) = 0 lowast 119875119868119896
119886119887119888(ℎ) + 0 lowast 119875119862119896
119886119887119888(ℎ) + 001
lowast 119875119877119896119886119887119888(ℎ)
1198631119896119886119887119888(ℎ) = 08 lowast 119876119868119896
119886119887119888(ℎ) + 06 lowast 119876119862119896
119886119887119888(ℎ) + 08
lowast 119876119877119896119886119887119888(ℎ)
1198632119896119886119887119888(ℎ) = 02 lowast 119876119868119896
119886119887119888(ℎ) + 04 lowast 119876119862119896
119886119887119888(ℎ) + 019
lowast 119876119877119896119886119887119888(ℎ)
1198633119896119886119887119888(ℎ) = 0 lowast 119876119868119896
119886119887119888(ℎ) + 0 lowast 119876119862119896
119886119887119888(ℎ) + 001
lowast 119876119877119896119886119887119888(ℎ)
(B1)
Nomenclature
119875119896119886119887119888(ℎ) 119876119896
119886119887119888(ℎ) Total real and reactive power
load at bus 119896 during ℎ119881119873119886119887119888 119881
119896
119886119887119888(ℎ) Nominal and three-phase
voltage at 119896 node
1198621119896119886119887119888(ℎ) 1198622119896
119886119887119888(ℎ) 1198623119896
119886119887119888(ℎ) ZIP active load composition
at bus 119896 during ℎ1198631119896119886119887119888(ℎ)1198632119896
119886119887119888(ℎ)1198633119896
119886119887119888(ℎ) ZIP reactive load
composition at bus 119896 duringℎ
119875119868119896119886119887119888(ℎ) 119875119877119896
119886119887119888(ℎ) 119875119862119896
119886119887119888(ℎ) Active power for industrial
residential and commercialload connected at bus 119896during ℎ
119876119868119896119886119887119888(ℎ) 119876119877119896
119886119887119888(ℎ) 119876119862119896
119886119887119888(ℎ) Reactive power for
industrial residential andcommercial load connectedat bus 119896 during ℎ
ℎ 15-minute characteristicstime interval
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
References
[1] A Barin C U Brazil L Martins and E S A Brazil ldquoFuzzybased expert system for renewable energy managementrdquo inProceedings of the CIREDWorkshop no 0020 Rome Italy 2014
[2] N Hashmi and S A Khan ldquoPower energy management fora grid-connected PV system using rule-base fuzzy logicrdquo inProceedings of the 3rd International Conference on ArtificialIntelligence Modelling amp Simulation (AIMS rsquo15) pp 31ndash36 KotaKinabalu Malaysia December 2015
[3] A R Kashfi and M E El-Hawary ldquoIntegration of distributedgeneration inmedium voltage distribution network using fuzzylogic controller for demand side managementrdquo in Proceedingsof the Electrical Power and Energy Conference (EPEC rsquo14) pp254ndash259 Calgary Canada November 2014
[4] S K Injeti and N P Kumar ldquoPlanning and operation of activeradial distribution networks for improved voltage stability andloss reductionrdquoWorld Journal of Modelling and Simulation vol8 no 3 pp 211ndash222 2012
Advances in Electrical Engineering 13
[5] Y Manjili and A Rajaee ldquoFuzzy Control of Electricity StorageUnit for EnergyManagement of Micro-Gridsrdquo httpacewaco-ngorgAssetsdocFuzzy20Control20of20Electricity20Storage20Unit20for20Energy20Management20of20Micro-Grids Mexico20WAC202012pdf
[6] A Metia and S Ghosh ldquoFuzzy based DG allocation for LossMinimization in a Radial Distribution Systemrdquo Balkan Journalof Electrical amp Computer Engineering vol 3 no 3 pp 115ndash1232015
[7] E A Mohamed M M Othman and Y G Hegazy ldquoOpti-mal sizing and placement of distributed generators for profitmaximization using firefly algorithmrdquo in Proceedings of theInternational Conference on Artificial Intelligence Energy andManufacturing Engineering (ICAEME rsquo14) pp 6ndash10 2014
[8] K Muthukumar and S Jayalalitha ldquoOptimal reactive powercompensation by shunt capacitor sizing using harmony searchalgorithm in unbalanced radial distribution system for powerloss minimizationrdquo International Journal on Electrical Engineer-ing and Informatics vol 5 no 4 pp 474ndash491 2013
[9] M Padma Lalitha V C Veera Reddy and N Sivarami ReddyldquoApplication of fuzzy and ABC algorithm for DG placement forminimum loss in radial distribution systemrdquo Iranian Journal ofElectrical and Electronic Engineering vol 6 no 4 pp 248ndash2572010
[10] M S Thomas R Ranjan and R Roma ldquoProbabilistic fuzzyapproach to assess RDS vulnerability and plan corrective actionusing feeder reconfigurationrdquo Energy and Power Engineeringvol 4 no 5 pp 330ndash338 2012
[11] P N Si and S S Win ldquoFuzzy algorithm for capacitor allocationand sizing in radial distribution system to reduce lossesrdquoInternational Journal of Science Engineering and TechnologyResearch vol 3 no 12 2014
[12] A Arabali M Ghofrani M Etezadi-Amoli and M S FadalildquoStochastic performance assessment and sizing for a hybridpower system of SolarWindEnergy Storagerdquo IEEE Transac-tions on Sustainable Energy vol 5 no 2 pp 363ndash371 2014
[13] A Soroudi and M Ehsan ldquoA possibilistic-probabilistic tool forevaluating the impact of stochastic renewable and controllablepower generation on energy losses in distribution networksmdashacase studyrdquo Renewable and Sustainable Energy Reviews vol 15no 1 pp 794ndash800 2011
[14] J P Sharma and H Ravishankar Kamath ldquoStochastic voltageassessment of unbalance radial feederrdquo Journal of ElectricalSystems vol 11 no 3 pp 258ndash270 2015
[15] J P Sharma and H R Kamath ldquoPerformance analysis ofunbalance radial feeder with time varying composite loadrdquoJournal of Power and Energy Engineering vol 3 no 5 pp 56ndash702015
[16] The MathWorks Inc Types of Fuzzy Interefernece Systems TheMathWorks Inc 1994 1994ndash2014 httpwwwmathworkscomhelpfuzzytypes-of-fuzzyinference-systemshtml
[17] J-H Teng ldquoModelling distributed generations in three-phasedistribution load flowrdquo IET Generation Transmission and Dis-tribution vol 2 no 3 pp 330ndash340 2008
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
12 Advances in Electrical Engineering
Base casePQ at 099 lead pfPQ at 095 lead pf
PQ at unity pfPQ at 095 lag pf
20 30 40 50 60 70 80 9010Time in 15-minute interval
Volta
ge U
nbal
ance
fact
or (
)
X 55Y 08497
X 48Y 06419
X 35Y 05578
X 55Y 06498
X 73Y 05576(605177)
05055
06065
07075
08085
09
Figure 36 DG impact of voltage unbalance factor of feeder
Appendix
A Survivability Index of Top 15 Nodes
(see Table 1)
B ZIP Load Compositions Calculation
To find ZIP load composition at each bus 119896 the followingequations were utilized to compute ZIP load composition ateach bus 119896 for each ℎ characteristics time interval
1198621119896119886119887119888(ℎ) = 08 lowast 119875119868119896
119886119887119888(ℎ) + 06 lowast 119875119862119896
119886119887119888(ℎ) + 08
lowast 119875119877119896119886119887119888(ℎ)
1198622119896119886119887119888(ℎ) = 02 lowast 119875119868119896
119886119887119888(ℎ) + 04 lowast 119875119862119896
119886119887119888(ℎ) + 019
lowast 119875119877119896119886119887119888(ℎ)
1198623119896119886119887119888(ℎ) = 0 lowast 119875119868119896
119886119887119888(ℎ) + 0 lowast 119875119862119896
119886119887119888(ℎ) + 001
lowast 119875119877119896119886119887119888(ℎ)
1198631119896119886119887119888(ℎ) = 08 lowast 119876119868119896
119886119887119888(ℎ) + 06 lowast 119876119862119896
119886119887119888(ℎ) + 08
lowast 119876119877119896119886119887119888(ℎ)
1198632119896119886119887119888(ℎ) = 02 lowast 119876119868119896
119886119887119888(ℎ) + 04 lowast 119876119862119896
119886119887119888(ℎ) + 019
lowast 119876119877119896119886119887119888(ℎ)
1198633119896119886119887119888(ℎ) = 0 lowast 119876119868119896
119886119887119888(ℎ) + 0 lowast 119876119862119896
119886119887119888(ℎ) + 001
lowast 119876119877119896119886119887119888(ℎ)
(B1)
Nomenclature
119875119896119886119887119888(ℎ) 119876119896
119886119887119888(ℎ) Total real and reactive power
load at bus 119896 during ℎ119881119873119886119887119888 119881
119896
119886119887119888(ℎ) Nominal and three-phase
voltage at 119896 node
1198621119896119886119887119888(ℎ) 1198622119896
119886119887119888(ℎ) 1198623119896
119886119887119888(ℎ) ZIP active load composition
at bus 119896 during ℎ1198631119896119886119887119888(ℎ)1198632119896
119886119887119888(ℎ)1198633119896
119886119887119888(ℎ) ZIP reactive load
composition at bus 119896 duringℎ
119875119868119896119886119887119888(ℎ) 119875119877119896
119886119887119888(ℎ) 119875119862119896
119886119887119888(ℎ) Active power for industrial
residential and commercialload connected at bus 119896during ℎ
119876119868119896119886119887119888(ℎ) 119876119877119896
119886119887119888(ℎ) 119876119862119896
119886119887119888(ℎ) Reactive power for
industrial residential andcommercial load connectedat bus 119896 during ℎ
ℎ 15-minute characteristicstime interval
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
References
[1] A Barin C U Brazil L Martins and E S A Brazil ldquoFuzzybased expert system for renewable energy managementrdquo inProceedings of the CIREDWorkshop no 0020 Rome Italy 2014
[2] N Hashmi and S A Khan ldquoPower energy management fora grid-connected PV system using rule-base fuzzy logicrdquo inProceedings of the 3rd International Conference on ArtificialIntelligence Modelling amp Simulation (AIMS rsquo15) pp 31ndash36 KotaKinabalu Malaysia December 2015
[3] A R Kashfi and M E El-Hawary ldquoIntegration of distributedgeneration inmedium voltage distribution network using fuzzylogic controller for demand side managementrdquo in Proceedingsof the Electrical Power and Energy Conference (EPEC rsquo14) pp254ndash259 Calgary Canada November 2014
[4] S K Injeti and N P Kumar ldquoPlanning and operation of activeradial distribution networks for improved voltage stability andloss reductionrdquoWorld Journal of Modelling and Simulation vol8 no 3 pp 211ndash222 2012
Advances in Electrical Engineering 13
[5] Y Manjili and A Rajaee ldquoFuzzy Control of Electricity StorageUnit for EnergyManagement of Micro-Gridsrdquo httpacewaco-ngorgAssetsdocFuzzy20Control20of20Electricity20Storage20Unit20for20Energy20Management20of20Micro-Grids Mexico20WAC202012pdf
[6] A Metia and S Ghosh ldquoFuzzy based DG allocation for LossMinimization in a Radial Distribution Systemrdquo Balkan Journalof Electrical amp Computer Engineering vol 3 no 3 pp 115ndash1232015
[7] E A Mohamed M M Othman and Y G Hegazy ldquoOpti-mal sizing and placement of distributed generators for profitmaximization using firefly algorithmrdquo in Proceedings of theInternational Conference on Artificial Intelligence Energy andManufacturing Engineering (ICAEME rsquo14) pp 6ndash10 2014
[8] K Muthukumar and S Jayalalitha ldquoOptimal reactive powercompensation by shunt capacitor sizing using harmony searchalgorithm in unbalanced radial distribution system for powerloss minimizationrdquo International Journal on Electrical Engineer-ing and Informatics vol 5 no 4 pp 474ndash491 2013
[9] M Padma Lalitha V C Veera Reddy and N Sivarami ReddyldquoApplication of fuzzy and ABC algorithm for DG placement forminimum loss in radial distribution systemrdquo Iranian Journal ofElectrical and Electronic Engineering vol 6 no 4 pp 248ndash2572010
[10] M S Thomas R Ranjan and R Roma ldquoProbabilistic fuzzyapproach to assess RDS vulnerability and plan corrective actionusing feeder reconfigurationrdquo Energy and Power Engineeringvol 4 no 5 pp 330ndash338 2012
[11] P N Si and S S Win ldquoFuzzy algorithm for capacitor allocationand sizing in radial distribution system to reduce lossesrdquoInternational Journal of Science Engineering and TechnologyResearch vol 3 no 12 2014
[12] A Arabali M Ghofrani M Etezadi-Amoli and M S FadalildquoStochastic performance assessment and sizing for a hybridpower system of SolarWindEnergy Storagerdquo IEEE Transac-tions on Sustainable Energy vol 5 no 2 pp 363ndash371 2014
[13] A Soroudi and M Ehsan ldquoA possibilistic-probabilistic tool forevaluating the impact of stochastic renewable and controllablepower generation on energy losses in distribution networksmdashacase studyrdquo Renewable and Sustainable Energy Reviews vol 15no 1 pp 794ndash800 2011
[14] J P Sharma and H Ravishankar Kamath ldquoStochastic voltageassessment of unbalance radial feederrdquo Journal of ElectricalSystems vol 11 no 3 pp 258ndash270 2015
[15] J P Sharma and H R Kamath ldquoPerformance analysis ofunbalance radial feeder with time varying composite loadrdquoJournal of Power and Energy Engineering vol 3 no 5 pp 56ndash702015
[16] The MathWorks Inc Types of Fuzzy Interefernece Systems TheMathWorks Inc 1994 1994ndash2014 httpwwwmathworkscomhelpfuzzytypes-of-fuzzyinference-systemshtml
[17] J-H Teng ldquoModelling distributed generations in three-phasedistribution load flowrdquo IET Generation Transmission and Dis-tribution vol 2 no 3 pp 330ndash340 2008
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
Advances in Electrical Engineering 13
[5] Y Manjili and A Rajaee ldquoFuzzy Control of Electricity StorageUnit for EnergyManagement of Micro-Gridsrdquo httpacewaco-ngorgAssetsdocFuzzy20Control20of20Electricity20Storage20Unit20for20Energy20Management20of20Micro-Grids Mexico20WAC202012pdf
[6] A Metia and S Ghosh ldquoFuzzy based DG allocation for LossMinimization in a Radial Distribution Systemrdquo Balkan Journalof Electrical amp Computer Engineering vol 3 no 3 pp 115ndash1232015
[7] E A Mohamed M M Othman and Y G Hegazy ldquoOpti-mal sizing and placement of distributed generators for profitmaximization using firefly algorithmrdquo in Proceedings of theInternational Conference on Artificial Intelligence Energy andManufacturing Engineering (ICAEME rsquo14) pp 6ndash10 2014
[8] K Muthukumar and S Jayalalitha ldquoOptimal reactive powercompensation by shunt capacitor sizing using harmony searchalgorithm in unbalanced radial distribution system for powerloss minimizationrdquo International Journal on Electrical Engineer-ing and Informatics vol 5 no 4 pp 474ndash491 2013
[9] M Padma Lalitha V C Veera Reddy and N Sivarami ReddyldquoApplication of fuzzy and ABC algorithm for DG placement forminimum loss in radial distribution systemrdquo Iranian Journal ofElectrical and Electronic Engineering vol 6 no 4 pp 248ndash2572010
[10] M S Thomas R Ranjan and R Roma ldquoProbabilistic fuzzyapproach to assess RDS vulnerability and plan corrective actionusing feeder reconfigurationrdquo Energy and Power Engineeringvol 4 no 5 pp 330ndash338 2012
[11] P N Si and S S Win ldquoFuzzy algorithm for capacitor allocationand sizing in radial distribution system to reduce lossesrdquoInternational Journal of Science Engineering and TechnologyResearch vol 3 no 12 2014
[12] A Arabali M Ghofrani M Etezadi-Amoli and M S FadalildquoStochastic performance assessment and sizing for a hybridpower system of SolarWindEnergy Storagerdquo IEEE Transac-tions on Sustainable Energy vol 5 no 2 pp 363ndash371 2014
[13] A Soroudi and M Ehsan ldquoA possibilistic-probabilistic tool forevaluating the impact of stochastic renewable and controllablepower generation on energy losses in distribution networksmdashacase studyrdquo Renewable and Sustainable Energy Reviews vol 15no 1 pp 794ndash800 2011
[14] J P Sharma and H Ravishankar Kamath ldquoStochastic voltageassessment of unbalance radial feederrdquo Journal of ElectricalSystems vol 11 no 3 pp 258ndash270 2015
[15] J P Sharma and H R Kamath ldquoPerformance analysis ofunbalance radial feeder with time varying composite loadrdquoJournal of Power and Energy Engineering vol 3 no 5 pp 56ndash702015
[16] The MathWorks Inc Types of Fuzzy Interefernece Systems TheMathWorks Inc 1994 1994ndash2014 httpwwwmathworkscomhelpfuzzytypes-of-fuzzyinference-systemshtml
[17] J-H Teng ldquoModelling distributed generations in three-phasedistribution load flowrdquo IET Generation Transmission and Dis-tribution vol 2 no 3 pp 330ndash340 2008
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of