Voltage Control Scheme Using Fuzzy Logic

7/25/2019 Voltage Control Scheme Using Fuzzy Logic

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Abstract--One of the most important operational requirements

for any electrical power network for both distribution and

transmission level is voltage control. Many studies have been

carried out to improve or develop new voltage control techniques

to facilitate safe connection of distributed generation. In Saudi

Arabia, due to environmental, economical and development

perspectives a wide integration of photovoltaic (PV) generation in

distribution network is expected in the near future. This

development in the network may cause voltage regulation

problems due to the interface with the existing conventional

control system. Therefore, a new control system with PVs should

be developed. This paper introduces a new voltage control

scheme for residential area networks in Saudi Arabia based on

Fuzzy Logic concept (FL). The structure of two implementations

of FL controller to regulate the voltage by setting the on-load tap

changing transformer is proposed. In order to confirm the

validity of the proposed methods, simulations are carried out for

a realistic distribution network with real data for load and solar

radiation. Results showing the performance of each

implementation are presented and discussed.

Index Terms-- Distribution System, ETAP, Fuzzy logic

controller, Grid Connected, MATLAB, Photovoltaic Systems,

Saudi Arabia, Solar radiation, Voltage control

I. INTRODUCTION

raditionally, the distribution network of the power system

is a passive network with a radial configuration.

Electricity flows one way from a substation to a largedistribution network. During normal operation or planning

period, a steady-state analysis of voltage regulation, system

losses, protection coordination, power quality, and system

reliability must be performed to ensure proper operation

within appropriate operating voltage range. Each utility has its

own operation and planning criteria depending on distribution

system characteristics and design criteria.

Currently, in Saudi Arabia, the exploitation of solar energy

as an alternative source of electric power is being considered

because of the abundant amount of irradiation and long hours

of sunshine. One way to achieve this is by using Grid-

Connected

Photovoltaic systems (GCPV) on domestic dwellings directlyconnected to the distribution network. This means that in the

This work was supported by the Ministry of High Education In Saudi

Arabia under Grant U208. The support of the Saudi Cultural Bureau in

London for sponsoring the fact-finding trip to Saudi Arabia to obtain the

required data is gratefully acknowledged.R. A. Shalwala is with the Department of Engineering, University of

Leicester, Leicester, UK (e-mail: [email protected]).

J. A. M. Bleijs is with the Department of Engineering, University ofLeicester, Leicester, UK (e-mail:[email protected])

2010 IEEE

future the system performance will be affected by PV

generators. According to [1-3] distributed generation has both

advantages and disadvantages for the system.

In this paper two implementations of fuzzy logic technique

have been used to maintain the voltage in a residential area

network with high penetration of PV generators in Saudi

Arabia. The ETAP simulation package has been used for

power flow calculation and the MATLAB software package

has been used to design the fuzzy logic controller.

II. POWER FLOW CALCULATIONS

Generally, distribution utilities deliver electric energy to

their customers within an appropriate voltage range to meet

customer requirements. For a radial configuration the bus

voltage, voltage drop, power flow, and power loss can be

calculated by using a simplified model such as the two-bus

system as shown in Fig. 1 [4].

Fig. 1. Model of a two-bus distribution system.

The model consists of a short distance line represented by a

series connection of resistance (R) and inductive reactance

(X). In this case, real and reactive power ( transferbetween bus #1 and bus #2 is described by (1) and (2). cos cos (1)

sin sin (2)Where:is the voltage angle at bus #1 is the voltage angle at bus #2is the admittance anglePower loss between bus #1 and bus #2 is given by eq.(3):

||

(3)In addition, the voltage at bus #2 and the voltage drop

between theses buses can be calculated in terms of the voltage

at bus #1 by using (4) and (5), respectively. (4) (5)

In a typical distribution system there are many scenarios to

be considered, and to handle calculation in a large system,

power system simulation software is required. In this paper,

the power systems simulation package ETAP is used for

evaluating of steady-state performance under different load

and PV generation conditions.

R. A. Shalwala, Student Member, IEEE,and J. A. M. Bleijs,Member, IEEE

Voltage Control Scheme Using Fuzzy Logic

for Residential Area Networks with PV

Generators in Saudi Arabia

T

Bus#1 Bus#2

|| || 1/ ||12


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III. SYSTEM MODELING

The following model of a real distribution network in a

residential area will be used as a base case in this paper

(Fig.2). The distribution network starts from the Station bus at

110 kV, through a step-down 110/11 kV power transformer at

each primary substation (ISK11, RSF04, and ISK10)

connecting to 3 branches. Each branch includes a number of

secondary substations (labeled as I1, I2,I9, R1,....R7,S1....and

S9), connecting to a customer feeder through another step-

down11/0.38 kV transformer (see detail in Fig. 2).

Fig. 2. Test residential network with 3 branches

There are 6 load nodes tapped off from each feeder. Each

branch is equipped with on-load tap changing transformer

(LTCT) that has the ability of changing the voltage level of

the branch at the main substation bus in small steps (0.5% of

nominal voltages), adjusted by the automatic voltage

controller (AVC). The main branches can be interconnected

through normally-open circuit breakers in the case of outage

of one of the 110/11 kV transformers.

Since this study is concentrating on the effect of GCPV on

the voltage regulation in the network under normal operation

(no faults), the longest branch which is ISK11 will be the most

sensitive because it will have the largest variations for thedifferent load and irradiation scenarios. The line parameters of

this branch are shown in Table I.

A. Load Conditions

Real load data for the selected residential area has been

collected from the Saudi Electricity Company in the Western

Region (SEC-WR) who has also provided details of the

transformers and lines of the network. Based on this

information a detailed model of the distribution network has

been created in ETAP.

TABLEI

LINE PARAMETERS OF ISK11

From Bus To Bus Lenghth (m) Impedance (/km)

ISK11 I1 1875 0.128+j0.1344

I1 I2 15 0.128+j0.1344

I2 I3 327 0.128+j0.1344

I3 I4 153 0.128+j0.1344

I4 I5 513 0.128+j0.1344

I5 I6 396 0.128+j0.1344

I6 I7 132 0.128+j0.1344

I7 I8 648 0.128+j0.1344

I8 I9 255 0.128+j0.1344

Fig. 3 shows the average daily load profile for this area

during each month. This area includes 5000 residential

properties.

Fig. 3. Average daily load of a residential area.

A considerable part of the load is due to air conditioning

(A/C) systems and in general the load reaches its maximum

between noon and 16:00 h in summer. This type of load can

reach 65 per cent of the total load during summer and since

the AC systems are motor-driven, this reduces the power

factor (PF) of the total load to approximately 0.85. The

minimum load is equal to about 30% of summer peak load.

The following conditions of each consumer load in the

network will be considered in this research:

1-

Extreme load (based on the maximum capacity of

customer circuit breaker)

2-

Peak load (Maximum Summer load)

3-

Normal load (Annual Average load)4- Light load (30% of peak load)

B. PV Generators

PV generators are connected to the grid through power-

electronic inverters. The current generation of PV inverters

operate at unity power factor. So, their behavior during steady

state is similar to that of a Synchronous Generator (SG) with

unity power factor. Therefore, a SG with unity power factor is

used in ETAP to represent the PV generator.

Since in this research Building Integrating photovoltaic

(BIPV) system will be used to address the effect of such

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.080.0

90.0

100.0

0 2 4 6 8 10 12 14 16 18 20 22 24

Laod(MW)

Time (hour)

Average Daily load for a residential AreaJan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Average

Peak Load

Light Load

Average Load


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system on voltage regulation, the available

selected buildings must be known. The aver

area for PV installation on houses in a reside

100m. This figure comes from about 29 diresidential houses in Saudi Arabia.

The solar radiation data for this study h

from King Abdul-Aziz City for Science

(KACST) which has 40 stations around the

the solar radiation every 5 minutes. The mon

radiation that was recorded at Jeddah meteo

year 2002 is shown in Fig. 4.

Fig 4. Monthly average solar radiation, Jed

Fig. 4 shows clearly the large amount

between 9:00 to 15:00 throughout the yea

value of 400 W/m^2 and maximum valu

W/m^2.

In this study, concentrating PV modules

efficiency has been assumed. Also, a 90%

inverter is considered in designing the PV

to increase the penetration level. The output

generator which will be delivered to the

network can be estimated as:

Based on the previous information and

range of power that can be generated using

system between 9:00 to 15:00 for each

between 14.4 kW to 36 kW. So, the followi

PV will be considered in this research:

1-

Max PV = 36 kW

2- Minimum midday PV = 14.4 kW

3- No PV

IV. IMPACT OF PVON PRESENT VOLTA

At each branch of Fig. 2 the voltage lev

the AVR, which estimates the voltage drop o

measuring the branch current at the mai

However, this method assumes that the powe

from the main substation bus (where the

flowing to the end of the branch. The prese

feeders makes the power flow bi-directiona

connected to feeders are carrying most or al

then the voltage profile along the feeders d

0

100

200

300

400

500

600

700

800

900

1000

1100

0 2 4 6 8 10 12 14 16 18 20 22

SolarRadiation(W/m^2)

Time (hour)

Average daily Solar Radiation

Max Radiation

Minimum Midday

Radiation

No Radiation

roof area of the

ge available roof

tial area is about

fferent designs of

as been obtained

and Technology

ountry recording

hly average solar

rology station for

ah 2002.

f solar radiation

r with minimum

e of about 1000

(CPV) with 40%

efficiency of the

enerator in order

power of the PV

customer or the

(6)

y using (6), the

concentrating PV

single house is

ng conditions for

E CONTROL

l is regulated by

ver the branch by

substation end.

r is unidirectional

AVR is located)

nce of GCPV on

l, and if the PVs

l the branch load,

pends mainly on

the PVs location, power generated,

This creates an unpredictable and u

the voltage level of all nodes mig

acceptable limits. To illustrate such

the worse scenarios at light load a

case the PVs, connected to feeders

carrying the most of the branch l

from the LTCT will be very low.

assume that the branch load is at th

will adjust its voltage level to 1.

shows the voltage profile of each fe

Fig. 5.Voltage profile of all feeders co

It can be concluded that the co

technique cannot properly adjust th

with various PVs connected to the

on measuring the branch current at

which is no longer a good indica

Therefore, a new technique must be

coordination between PVs and the

larger penetration and better voltage

V. SCENARIOSAND

In order to proof that the adjust

sufficient to keep the voltage level a

permissible level, the system has

possible scenarios of load con

combinations. It has been assumed t

to each node in the system with equ

assumed to be same for all house

scenarios more realistic a further 3

load feeders are also considered.

The standard deviation for all vo

from the nominal is used to det

position of LTC (-5.0% to +5.0% in

order to reduce the number of tap chresults are rounded to the nearest

5.0%,-2.5%,0%,+2.5%,+5%). This

for the tap changer and reduce the

due to changing the LTC setting.

for all scenarios.

The worse cases are shown in F

with the preferred position of tap ch

The simulations show that both b

for all scenarios improve the voltag

the allowable limits for all customer

24

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

949596979899

100101102103104105106

1 2 3 4 5

Voltage%ofNominal

Customer Nodes

Voltage profile ( Light load, Ma

and the PV power factor.

controlled situation where

t or might not be within

problem, consider one of

nd maximum PV. In this

long ISK11 in Fig. 2, are

oad. The current flowing

The AVR will therefore

e minimum level; hence it

0 p.u. (0% Tap). Fig. 5

der in this case.

nnected to ISK11 branch.

ventional voltage control

e voltage level of feeders

, since it depends mainly

the main substation end,

tion of the feeder status.

developed to facilitate the

TCT for safe integration,

control.

SSUMPTION

ment of the LTC only is

long the branch within the

been simulated for all

ditions, PV status and

hat the PVs are connected

al power. Also, the load is

s. However, to make the

random conditions of the

ltage nodes in the system

ermine the best possible

0.5% steps). However, in

anging operations the bestpreferred position of (-

ill increase the life time

isturbances in the system

able II shows the results

g. 6, and Fig. 7, together

nger.

est and preferred position

e level and keep it within

s in the branch.

6

PV&0% tap)

I1

I2

I3

I4

I5

I6

I7

I8

I9

Upper limit

Lower Limit


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TABLE IIBEST &PREFERRED POSITION OF TAP CHANGER FO

Scenario Load PVAverage

V(p.u.)

ALNPV Average No 98.19 +

ALAPV Average Min 100.12 0

ALMPV Average Max 102.71 -

XLNPV Extreme No 94.39 +

XLAPV Extreme Min 96.51 +

XLMPV Extreme Max 99.33 +

LLNPV Light No 99.56 +

LLAPV Light Min 101.43 -

LLMPV Light Max 103.94 -

NLMPV No Max 104.35 -

PLNPV Peak No 96.32 +

PLAPV Peak Min 98.34 +

PLMPV Peak Max 101.04 -

R1LNPN Random1 No 97.48 +

R1LAPV Random1 Min 99.44 +

R1LMPV Random1 Max 102.07 -

R2LNPV Random2 No 96.92 +



R3LNPV Random3 No 97.33 +



Fig. 6. Voltage profile of XLNPV scenario with pre

Fig. 7. Voltage profile of LLMPVscenario with prefer

949596979899

100101102103104105106

1 2 3 4 5Voltage%ofNominal

Customer Nodes

( Extreme load, No PV & +5%

949596979899

100101102103104105106

1 2 3 4 5 6Voltage%ofNominal

Customer Nodes

( Light load, Max PV & -5%tap)

ALL SCENARIOS

est Preferred

.0% +2.5%

.0% 0.0%

.5% -2.5%

.0% +5.0%

.5% +2.5%

.5% 0.0%

.5% 0.0%

.5% -2.5%

.0% -5.0%

.5% -5.0%

.5% +5.0%

.0% +2.5%

.0% 0.0%

.5% +2.5%

.5% 0.0%

.0% -2.5%

.0% +2.5%

.0% 0.0%

.5% -2.5%

.5% +2.5%

.5% 0.0%

.0% -2.5%

erred position of tap

red position of tap

VI. FUZZY LOGIC

Fuzzy Logic Control (FLC)

conventional controllers when ther

model of the system to be controlle

input-output linguistic variables and

of desirable control outcomes ca

features might include user-specifie

analogous to a control range, clos

conditions if desired, and the ability

off between energy costs and intlogic controllers consist of a set

based on fuzzy implications and t

providing an algorithm, they con

strategy based on expert knowledge

strategy [5]. Just as fuzzy logic ca

"computing with words rather tha

can be described simply as "control

equations". Therefore, in contrast t

other expert systems, fuzzy log

representation of imprecise huma

way, with approximate terms and

the use of precise statements and

them more robust, more compact, a

1stImplementation of FLC

Fig. 8 shows the straight forward

controller based on the numerical so

changer position at each scenario

simulated in MATLAB software.

one input, the average customer vo

preferred tap changer setting.

Fig. 8. 1stImplementati

1) The membership of input and

Input: Average voltage (AvgV) of al

Very High (VH), High (H), Normal

Low (VL) as shown in Fig.9.

Fig. 9. Input membership function for

Output: Tap changer setting (TC); V

Normal (N), Low (L) and Very low

6

tap)

I1

I2

I3

I4

I5

I6

I7

I8

I9

Upper limit

Lower Limit

I1

I2

I3

I4

I5

I6

I7

I8

I9

Upper limit

Lower Limit

Allcusto

mersvoltages Centralized

system

Average Voltage

Avr V Fuzzy Lo

Controll

Input variable Avg

ONTROL

offers an alternative to

is no available accurate

d. By suitable selection of

a rule base, a broad range

n be achieved. Possible

overall control 'tightness'

er adherence to set point

to explicitly set the trade-

rior environment. Fuzzyf linguistic control rules

e rules of inference. By

ert the linguistic control

into an automatic control

n be described simply as

numbers" fuzzy control

with sentences rather than

mathematical models or

c controllers allow the

knowledge in a logical

alues, rather than forcing

xact values, thus making

d simpler [6].

application of fuzzy logic

lution for the preferred tap

. This control system is

he controller consists of

ltage, and one output, the

on of FLC

utput signals:

l customers in the branch;

(N), Low (L) and Very

1stimplementation of FLC

ery High (VH), High (H),

(VL) as shown in Fig.10.

Tap position

+5.0%

+5.0%

+2.5%

0.0%

-2.5%

gic

er

V


5/6

Fig. 10. Output membership function for 1

st

imple

2) The control rules:TABLEIII

RULE TABLE FOR 1ST IMPLEMENTATION O

Input: AvgV VH H N L

Output: TC VL L N H

Fig. 11 shows the difference between

calculated and proposed FLC setting for the t

Fig. 11. Numerical and Fuzzy Logic setting

The FLC gives almost the same results

solutions. The small differences in Fig. 9 ar

range of variation in average voltage is

numerical calculation (scenarios only). The

this way of control is that it is independent o

parameters and can be applied to various tHowever, it needs the installation of a digital

customer to send the voltage values to a cent

a communication network. This make

prohibitively expensive. Also, if all requi

method are available, there are many techniq

can be used based on look-up tables for th

and then set the preferred tap changer pos

does not give any advantages of using FLC.

B. 2ndImplementation of FLC

In order to find a better solution than prev

for controlling the voltage in the branch

communication network and take advant

features, the correlation between the localthe system and the preferred setting have

based on engineering sense. Table

measurements of active power (P) and reac

ISK11. It is quite clear that there is a po

between the load of the branch and the react

ISK11. So, as the load increases the reactiv

and vice versa. On the other hand the

correlation between the PV generation and

flow at ISK11. According to the new relatio

in Fig. 12 can be set up.

-5%

-4%

-3%

-2%-1%

0%

1%

2%

3%

4%

5%

94 95 96 97 98 99 100 101 102 103 104 105

TapchangerSetting

Average Costomer voltages

entation of FLC

F FLC

VL

VH

the numerically

ap changer.

for LTC

as the numerical

because the full

not used in the

ain advantage of

f branch and line

pes of networks.voltmeter at each

alized system via

s this solution

rements for this

ues other than FL

average voltage

ition which may

iously introduced

without using a

ge of the FLC

measurements into be established

IV shows the

ive power (Q) at

sitive correlation

ve power flow at

power increases

e is a negative

the active power

ships, the system

TABLEIVPOWER FLOW MEASUREMENTS @ISK11

Scenario P@ISK11(kW) Q@ISK

ALNPV 805 479

ALAPV -915 486

ALMPV -3382 638

XLNPV 2444 1549

XLAPV -670 1480

XLMPV -1859 1540

LLNPV 200 99

LLAPV -1502 130

LLMPV -3946 315

NLMPV -4134 208

PLNPV 1619 1004

PLAPV -127 974

PLMPV -2624 1082

R1LNPN 1122 691

R1LAPV -609 683

R1LMPV -3088 818

R2LNPV 1327 824

R2LAPV -411 805

R2LMPV -2899 927

R3LNPV 1168 720

R3LAPV -565 709

R3LMPV -3046 841

Fig. 12. Fuzzy Logic controlle

where:

1) The membership of input and

Input1: Reactive power (Q) @ ISK1

loadhigh Q (HL), average load

light loadlight Q (LL) as shown i

Fig. 13. Input membership function 1 for

Input2: Active power (P) @ ISK11;

Minimum PVmedium P (MinPV

light P (MaxPV) as shown in Fig.14

Numerical

Fuzzy

Q @ ISK11

P @ ISK11 Fuzzy Logic

Controller

OR SCENARIOS USING ETAP

1(kVar) Preferred tap

+2.5%

0.0%

-2.5%

+5.0%

+2.5%

0.0%

0.0%

-2.5%

-5.0%

-5.0%

+5.0%

+2.5%

0.0%

+2.5%

0.0%

-2.5%

+2.5%

0.0%

-2.5%

+2.5%

0.0%

-2.5%

implementation 2

utput signals:

1; extreme and peak

medium Q (ML) and

Fig.13.

2nd implementation of FLC

no PVhigh P (NoPV),

and maximum Power

.

Tap position

+5.0%

+5.0%

+2.5%

0.0%

-2.5%


6/6

Fig. 14. Input membership function 2 for 2nd imple

Output: Tap changer setting (TC); Very High

Normal (N), Low (L), Very low (VL) as sho

Fig. 15. Output membership function for 2nd imple

2) The control rules:TABLEV

RULE TABLE FOR 2NDIMPLEMENTATION O

PPV

QLoadNo PV Min PV

HL VH H

ML H N

LL N L

Fig. 16 shows the tap changer settings for p

and Q using the proposed FLC.

Fig. 16. Fuzzy Logic set for LTC ased on power

The controller gives the preferred tap ch

all scenarios when the values of P and

scenario have been used as an input to the

key benefit of this implementation is that all

taken locally and there is no need for remot

This makes this solution simple and cheaother control technique. In order to optimize

membership for the input has to be tuned b

preferred position numerically for the pow

the transition region.

VII. CONCLUSIONS

Two methods of implementation the o

controller for setting the tap changer positi

network were investigated in this paper. It h

both proposed FLCs have the ability to im

profile of distribution network and kee

permissible limits.

-5.0%

-2.5%

0.0%

2.5%

5.0%

50

200

350

500

650

800

950

1100

1250

1400

1550

Tapchan

gersetting

Reactive Power Q(kVar)

entation of FLC

(VH), High (H),

n in Fig.15.

entation of FLC

F FLC

Max PV

N

L

VL

ssible range of P

flow @ ISK11

nger position for

value of each

this system. The

easurements are

communication.

compared withthis solution, the

determining the

r flow values in

f a fuzzy logic

on in distribution

s been found that

rove the voltage

p it within the

The first implementation is as

costs due to hardware cost and

communication infrastructure but i

networks since it is independent on t

The second implementation sho

control the LTCT based on the po

transformer itself. The main advant

is that all measurements are taken l

for remote communication with

system. However, the main drawba

depends on the network par

characteristics. So, for each network

based on analysis of the network

results are encouraging and warrant

the fuzzy logic concept in such prob

VIII. REFERE[1]

Davis, W. Murray, Distributed ResourSignificant Advantages Over Central

Power Systems Part I, Proceedings

Society Transmission and Distribution2002, pp. 54-61.

[2]

Davis, Murray W., Distributed Resour

Significant Advantages Over CentralPower Systems Part II, Proceedings

Society Transmission and Distribution

2002, pp 62-69.[3]

H.B. Puttgen, P.R. MacGregor, F.C. L

Semantic Hype or the Dawn of a

Magazine, IEEE Vol. 1, Issue 1, Jan-Fe

[4]

N. Jenkins, R. Allan, P. Crossley, D. Ki

generation: The Institution of Electrical

[5]

C.C. Lee, "Fuzzy Logic in Control SParts I & II,"IEEE Transactions on Sys

20, No. 2, March/April 1990, pp. 404-4

[6]

A.I. Dounis, M. Bruant, M. Santamo"Comparison of conventional and fuzz

quality in buildings,", Journal of Intel

pp.131-140

IX. ACKNOWLE

The authors would like to thanks

solar irradiance data, and SEC-WR

providing the utility grid data.

X. BIOGRAP

Raed A. Shalwala

King AbdulAziz U

in 2002 and theNottingham, Notti

electrical engineeri

Ph.D. degree at UU.K.

His research interes

and operation, re

distribution network

Johannes (Hans)

Electrical andEindhoven Unive

Netherlands, in 19

College in Londonon integration o

generators. He wa

Imperial College inLecturer in Electric

of Engineering at t

he teaches Electrical Machines and Power

covers a wide range of subjects in Renewabl

Storage, from electrical generators and po

and advanced controllers.

-4150

-2610

-1070

470

2010

sociated with significant

the need to widespread

can be applied to many

he network parameters.

ws a novel technique to

er flow information at the

ge of this implementation

cally and there is no need

other information in the

k of this method is that it

meters and the load

the FLC need to be set up

oad data. In general, the

further investigation using

lems.

CESce Electric Power Systems Offer

Station Generation and T&D

f the IEEE Power Engineering

Conference, Vol. 1, Jul 21-25

ce Electric Power Systems Offer

Station Generation and T&Df the IEEE Power Engineering

Conference, Vol. 1, Jul 21-25

mbert, Distributed Generation:

ew Era?, Power and Energy

b 2003, pp. 22 29.

schen, and G. Strbac,Embedded

Engineers, London , UK, 1995.

stems: Fuzzy Logic Controller,tems, Man and Cybernetics, Vol.

5.

ris, G. Guaraccino, P. Michel,control of indoor of indoor air

igent and Fuzzy Systems, 1996,

GMENT

KACST for providing the

main office in Jeddah for

IES

received the B.S. degree from

iversity, Jeddah, Saudi Arabia,

.S. degree from University ofgham, U.K., in 2006, both in

g. He is currently pursuing the

iversity of Leicester, Leicester,

ts are in power system planning

ewable energy resources and

.

leijsreceived his MSc degree in

lectronic Engineering fromrsity of Technology, The

2. In 1983 he joined Imperial

s a Research Associate workingwind turbines with diesel

awarded a PhD degree from

1990. Since 1991 he has been al Engineering in the Department

e University of Leicester, where

Systems. His field of research

Energy Conversion and Energy

er electronics to power systems

Documents

Voltage Control Scheme Using Fuzzy Logic