1

A Multistage Passive Islanding Detection Methodfor Synchronous-Based Distributed Generation

Abdullah Sawas, Student Member, IEEE, Wei Lee Woon, Member, IEEE,V. Ravikumar Pandi, Member, IEEE and Hatem Zeineldin, Member, IEEE

Abstract—A novel, multistage approach to passive islandingdetection is presented which uses a decision-tree-like classificationalgorithm. The novelty of the proposed method is centered onthe way in which features are passed to subsequent stages of thedecision tree (DT). A traditional DT uses a single feature set whilein the modified algorithm, feature sets extracted using differentsized time windows are passed to successive stages of the tree.This provides two important advantages: (i) cases which can beeasily determined as either islanding or non-islanding events areflagged as soon as possible without waiting for the full featureset to become available (ii) because the algorithm allows for theuse of different sized time windows, features are analyzed intime-scales which fit their natural patterns of temporal evolution.The proposed classifier is trained and tested using a database offeature vectors obtained via PSCAD/EMTDC simulations (basedon an IEEE 34-bus distribution system) which were carefullydesigned to reflect a variety of commonly encountered events andload profiles. As mentioned, one of the key requirements for theproposed algorithm was that “easy” cases should be flagged assoon as possible; this property was confirmed by the observationthat most events (≈79%) were detected within 10-20ms, while atthe same time retaining a very high detection rate over all cases(> 99%).

Index Terms—Islanding detection, non-detection zone, decisiontree, distributed generation.

I. INTRODUCTION

A. Islanding Detection

ADVANCEMENTS in distributed generation technologiesand the movement towards a deregulated electricity

market have increased the usage of distributed generators(DG). Despite its advantages, distributed generation raisesseveral safety and control issues related to the restructuring ofconventional generation systems [1]. One of these issues is theislanding phenomena where parts of the grid are disconnectedfrom the main utility and remain energized by the local DGunits. It is essential for the control unit in the DG to be awareof such islanding situations. Therefore, the protection relay hasto be equipped with an islanding detection algorithm which iscapable of correctly determining the current state of the DG’sconnection to the grid.

Towards this end, many islanding detection approacheshave been proposed and studied. As a baseline, the IEEE1547-2003 standard [2] prescribes the basic requirements for

A. Sawas and W. L. Woon are with the Department of Computing andInformation Science, Masdar Institute of Science and Technology, Abu Dhabi,UAE.

V. Ravikumar Pandi and H. Zeineldin are with the Department of ElectricalPower Engineering, Masdar Institute of Science and Technology, Abu Dhabi,UAE.

the detection method implemented by the protection relays.Islanding detection can be conducted locally at the PCC orremotely at the utility side by means of advanced communi-cation techniques. Local methods can generally be groupedinto active and passive methods depending on their operatingmechanism [3]. When the detection method fails to identify anislanding case within the preset time limit, the incident eventis said to fall within the non-detection zone (NDZ) of themethod [4]. Many research efforts have focused on reducingthe size of the NDZ in passive schemes by carefully settingthe thresholds on various parameters [5].

An active method operates by intentionally injecting pertur-bations into the grid at the PCC and then relies on the mainutility to maintain the system in a stable state. Once the DGbecomes islanded, the perturbations will result in significantdrifts in some system parameters which will in turn causethe protection relay to trip. Active schemes are deemed tohave smaller NDZs compared to passive schemes. The maindisadvantage of active schemes is the instability introduced bythe injected perturbations and the subsequent degradation ofpower quality [6].

In contrast, a passive detection schemes uses only themeasurements made at the PCC. A relay equipped with a pas-sive detection algorithm observes various local measurementparameters then makes the decision to trip based on prede-fined thresholds. Typical passive methods include over/undervoltage and over/under frequency protection (OVP/UVP andOFP/UFP) [7], the rate of change of system parameters likefrequency (ROCOF) [8], rate of change of active power(ROCOP) [9], and the rate of change of frequency with power[10]. The change in voltage angle has also been used as aparameter in Vector Surge Relays (VSRs) [11]. The use ofthe voltage unbalance to detect islanding was studied in [12].More sophisticated signal processing techniques such as thediscrete wavelet transform have also been applied to achievefaster detection [13].

The application of intelligent algorithms in detecting is-landing events is described in [14]- [16]. For example, in[14], the use of the naı̈ve-Bayes classifier to classify ESPRITcoefficients was proposed. Even though this method resultedin good classification accuracy, it used a single time window of100ms duration. In [15], a decision tree (DT)-based approachwhich used 11 features was proposed for use with a systemwith synchronous-based DG. The decision making times werein the range of 45 to 50 ms, and the misclassification rate was0% for non-islanding events and 16% for islanding events.Finally, in [16], a comparison was presented of the use of

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DTs, support vector machines (SVM) and probabilistic neuralnetworks (PNN) for islanding detection. As with the otherstudies, the classifiers were trained using features extractedfrom time windows of a single length, though in this case thefeature of choice was the complex discrete wavelet transform.The results of the study showed that the DT classifier achievedthe highest classification accuracy of the three, at 80%.

B. Motivation and Novelty

A common feature of the examples listed above (and, as faras we know, of all related studies) is that they are based onfeature vectors that are derived from time windows of uniformlength. This subsequently implies that all potential islandingevents should be classified within a common time period.

While this assumption simplifies the feature extraction andclassification process, it is certainly not a self-evident fact. Asa counterpoint, we put forward the following two postulations:

1) Many commonly encountered power system events aresimple in that they can be easily classified as either is-landing or non-islanding occurrences. If this is true, thenit should be possible to quickly identify and flag theseevents accordingly, thus avoiding unnecessary delays inthe decision making process.

2) The temporal evolution of islanding-related featuresoccur over different time scales. As such, extractingfeatures from uniformly sized time windows is akin totaking a “one-size-fits-all” approach. If this is true, thenusing variable-sized time windows may result in higheraccuracy rates compared to traditional methods.

In this paper, we seek to test the validity of these two state-ments by developing and testing a novel, multistage islandingdetection method on a system incorporating synchronous-based DG. The proposed method is based on a decisiontree-like (DTL) algorithm that operates on features derivedfrom a series of time windows of increasing length. Whiletraditional DTs use a single stationary set of features, theapproach presented uses features extracted from the differenttime windows as input to successive stages in the tree.

A number of ways can be conceived of for training such atree, but for this study a simple greedy algorithm is describedwhere the feature subsets to be used in each stage, alongwith their corresponding threshold values, are determined bymaximizing the number of events classified during each timewindow.

The proposed approach is tested on the standard IEEE34-bus radial distribution system that is modelled usingPSCAD/EMTDC software. A comprehensive training databasethat covers a wide variety of loads, real and reactive powermismatches and different scenarios encompassing both island-ing and non-islanding events has been generated and is used totrain and test the DTL classifier. Finally, the results obtainedfor different set of training and testing data size are compared.

The organization of the rest of this paper is as follows:Section II describes the test system considered and simulatedcases used to generate the classification database. Section IIIintroduces the DTL classifier method and proposed approach.The classification accuracy results of proposed approach and

Fig. 1. IEEE 34-Bus System with a synchronous-based DG unit

its related discussion are presented in Section IV. Finally,conclusions are drawn in Section V.

II. SYSTEM MODELING AND SIMULATIONS

In this study, the IEEE 34-bus distribution network withsynchronous-based distributed generator (DG) is modeledusing PSCAD/EMTDC simulations. A set of events corre-sponding to islanding and non-islanding cases are simulatedusing various loading conditions at different locations. Sixteenfeatures were extracted based on the values of voltage, powerand frequency measured at the point of common coupling(PCC).

A. System under study

The schematic diagram of the IEEE 34-bus distributionnetwork is shown in Fig. 1. The distribution system is con-nected with the grid through a 69kV/24.9kV transformer. Asynchronous-based DG rated for 1MVA, operating at unitypower factor, is connected at node 848 through a 24.9 kV/6.6kV transformer. This DG is equipped with an IEEE Type-1 exciter model. Loads modeled in this system are of thestatic type load model mentioned in [17]. The mathematicalrepresentation of the static load characteristics is as follows:

P = P0

(V

V0

)NP(1 +Kpf∆f) (1)

Q = Q0

(V

V0

)NQ(1 +Kqf∆f) (2)

where V0, P0, and Q0 are the nominal load’s voltage, active,and reactive power respectively. ∆f is the deviation in fre-quency from the rated value (f−f0). NP and NQ are valuesbetween 0 and 2 which determine the load’s characteristic(constant power, constant current or constant impedance whenthe values are 0, 1, or 2 respectively). The constants Kpf andKqf shape the load’s response for frequency deviation [17].

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In addition to load variation, two system loading conditionsare considered: normal loading and light loading; the latterwas simulated by disconnecting the link between nodes 834and 860, which removes all subsequent loads.

B. Simulated cases

Table I summarizes the simulated events. As can be seen,two major categories of events were simulated, namely “is-landing” and “non-islanding”. Islanding events refer to caseswhere the DG is isolated from the main utility and suppliespower to the local loads. IEEE Standard 1547 mandates thatthe detection method be tested on various mismatches betweenthe power generated and the demands of these local loads. Tosatisfy this requirement, a variety of local load combinationswere designed in an islanding case by the real and reactivepower mismatches in the typical static load types. As shownin the table, the total number of islanding events simulated are5906.

TABLE ISUMMARY OF SIMULATED EVENTS

Event description Number of events Event classRLC loads with active andreactive power mismatch

5,906 islanding

Single phase short circuitat various locations

620 non-islanding

Three phase short circuitat various locations

628 non-islanding

Capacitor switching atvarious locations

1,260 non-islanding

Load switching at variouslocations

3,151 non-islanding

Non-linear load switchingat various locations

800 non-islanding

Motor switching at vari-ous locations

630 non-islanding

For the non-islanding category, disturbances which occurduring the normal operation of the system, but which mightresemble islanding events, were simulated. Five main types ofnon-islanding events have been simulated. These were single-phase and three-phase short circuits faults taking place atvarious locations and lasting for 100ms duration before beingcleared; Capacitor switching events at various location withtotal capacity of 12.29 µF , 8.29 µF and 5.29 µF ; RLC loadrated between 0.3MW and 1.4MW switching events at variouslocations and different power factor from 0.90 to 0.98; Non-linear load switching; induction motor switching at differentlocations. A total of 7089 non-islanding events were generated,as is shown in table I.

The typical voltage and frequency waveforms measured atthe PCC due to an islanding event with active power mismatchare shown in Fig. 2, while the corresponding waveformsfor non-islanding events related to load switching, capacitorswitching, single and three phase short circuits are shown inFig. 3. It can be clearly seen that the islanding event has min-imal deviation and is hence not detectable using OVP/UVP orOFP/UFP protective relays. Hence, more intelligent methodsfor islanding detection are important for the DG units.

Fig. 4. Detailed steps involving dataset generation

TABLE IILIST OF THE POTENTIAL FEATURES COLLECTED AT THE PCC

V F P Q V− V+ V0 THDV

δV δF δP δQ δV− δV+ δV0 δTHDV

C. Feature extraction

Many features have been proposed in the literature. Someof these where simple while others required the applicationof complex signal processing techniques. Fig. 4 shows thedetailed steps involved in the dataset generation. The mea-surements made at the point of common coupling (PCC) isused to extract the feature vectors corresponding to the eventsimulated. The features over 10ms, 20ms, ... , 50ms timingwindows are estimated and stored in five different matricesalong with the target vectors as shown in the figure. In thisstudy, the following features are considered as potential pre-dictors of islanding: DG units active and reactive power (P andQ), rated voltage (V ), terminal frequency (F ), the negative,positive and zero sequence (V−, V+, V0) voltage componentsand the total harmonic distortion of voltage (THDV ).

THDV =

√V 22 + V 2

3 + · · ·+ V 2h

V1(3)

where h is the number of harmonics, and Vi is the voltagemagnitude of ith harmonic order. In addition to the abovefeatures, the derivative of each signal have been also includedin the set of features. A summary of the whole feature set islisted in Table II. Derivatives were taken by incorporating morethan two samples via splitting the respective time window intotwo parts then taking the difference between the average ofeach part divided by the total window size. For example, thederivative of frequency measurement is expressed below as in(4):

δF =2

n

n/2∑i=1

Fi −n∑

i=n/2+1

Fi

/w (4)

4

0.5 1 1.5 2 2.50.98

1

1.02

1.04

1.06

1.08

time (sec)

volta

ge (

per

unit)

+0.5%+2%

−2%

−0.5%

−10%

(a)

0.5 1 1.5 2 2.559.5

59.75

60

60.25

60.5

60.75

61

61.25

61.5

time (sec)

freq

uenc

y (H

z)

−10%

−2%

−0.5%

+0.5%+2%

(b)

Fig. 2. Plots of the system (a) voltage, and (b) frequency waveforms at PCC due to an islanding events occurred at 1.0 s for the following mismatches inactive power supplied: -10%, -2%, -0.5%, +0.5%, and +2% while the system is carrying an RLC load with power factor of 0.98 supplied by a single DG atbus 832

0.5 1 1.5 2 2.50.4

0.5

0.6

0.7

0.8

0.9

1

1.1

time (sec)

volta

ge (

per

unit)

Load Switching

Capacitor Switching

Single Phase Short Circuit

Three Phase Short Circuit

(a)

0.5 1 1.5 2 2.5

59.8

59.9

60

60.1

60.2

time (sec)

freq

uenc

y (H

z)Capacitor Switching

Three Phase Short Circuit Single Phase Short Circuit

Load Switching

(b)

Fig. 3. Plots of the system (a) voltage, and (b) frequency waveforms at PCC due to disturbances (non-islanding events) occurred at 1.0 s at the bus 812described as follow: (1.4 MW, pf=0.5) load switching, single and three-phase short circuit faults for 100ms, and 12.29 µF capacitors bank switching with asingle DG at bus 832

where n is the number of samples in the respected window,w is the window size in seconds, and Fi is the ith sample ofthe frequency signal.

III. TRAINING THE DTL CLASSIFIER

As explained previously, the proposed method consists of aseries of classification stages which act on features extractedfrom time windows of different lengths. At each stage, eventsfalling outside the pre-set decision boundaries are classifiedas either islanding or non-islanding, while events that fallwithin the boundaries (i.e. the NDZ of that stage) are passedto successive stages. So the problem of training the DTLclassifier translates to the process of identifying the optimalfeature (or features) to be used in each stage, setting therespective thresholds and associating these with the correctclass labels.

It would be possible to conceive of a variety of schemes forperforming these operations, but whatever the exact algorithmused, it should be designed to reduce the overall classification

time by minimizing the number of events falling within theNDZ at each stage. For this study, a greedy training algorithmwas designed which explicitly satisfied the above requirement.As will be seen, the proposed algorithm is easy to implement,yet resulted in performance that was very efficient.

The steps of the proposed training algorithm are as follows:

1) Let Γ = {f1, f2, ..., fM} be a set of M features availablein the database of all time windows or stages.

2) Set the feature number i = 1.3) Set the lower bound (LBi) of the selected feature fi as

the minimum of the value in the database and move itforward to next value if it is of the same type of event.Repeat until an event of a different type is encountered.Similarly set the upper bound (UBi) of the selectedfeature to the maximum value for fi, and graduallyreduce this value until a change of event is encountered.The final values of lower and upper bound will be asshown in Fig. 5.

4) Calculate the number of training data points (Ni) that

5

δQ

−800 −600 −400 −200 0 200 400 600 800

LBi1 UBi1

+ islandingx non-islanding

Fig. 5. Setting the upper and lower bounds for δQ at label switching points

fall beyond these boundary values and which can beclassified as either an islanding or non-islanding event.The remaining (N−

i ) data points have feature values thatfall between the lower and upper bounds, and can notbe classified using feature f1 at this stage.

5) Set i← i+ 1. If i ≤ M , the size of the feature vector,repeat from step 3, otherwise continue to the next step.

6) Select the feature fi which classifies the largest numberof data points f∗i = arg maxiNi.

7) If N−i = 0 or the number of features used reaches the

predefined maximum feature vector size per stage, thenproceed to the next step, otherwise redo steps 2-6 on theremaining N−

i data points.8) If N−

i > 0 and the current time window is less than themaximum time scale considered, then go to step 2 torepeat the above procedures using a longer time window,otherwise continue to the next step.

9) Report the selected features, thresholds and associatedclass labels for each stage

IV. RESULTS AND DISCUSSION

The proposed algorithm is implemented in Matlab andtested on the feature dataset extracted from the IEEE 34-busPSCAD simulation. The mechanism by which the proposedislanding detection approach discriminates between differentkinds of events can be visually depicted by means of thenon-detection zone (NDZ) graph. At each stage, the selectedfeatures progressively eliminate successfully classified subsetsof the training dataset. Remaining cases are passed on tosubsequent stages and the procedure is repeated. This isillustrated in Fig. 6 where it can be seen that islanding eventsfalling within region A cannot be correctly classified usingonly bounds on the two features THDV and δQ. Thus, anevent falling into that area will be passed to the next stagewhere a new set of feature bounds has to be identified. For thecase where two features are used in each stage, the completelist of selected features along with the corresponding lowerand upper thresholds are shown in Fig. 7 as a decision tree.The labels non and isl denotes the nonislanding and islandingevents respectively.

As can also be seen in Fig. 7, two separate collections offeatures are used, the first is extracted from the 0−10ms timewindow and the second from the 10 − 20ms time window.From this figure, it can be seen that some decisions are madeafter 10ms while others are deferred to the 20ms point. As hasbeen explained in earlier sections, this segregation of the dataset into different stages allows for “easier” cases to be detected

0.005 0.010 0.015 0.020 0.025 0.030 0.035−400

−300

−200

−100

0

100

200

300

400

THDV

δQ

LBi1 UBi1

LBi2

UBi2

A

+ islandingx non-islanding

Fig. 6. Lower and upper bounds of the features THDV and δQ. Only casesthat are out of these bounds are detected, while area A is a NDZ within theboundary.

THDV

non

> 0.0230

δQ

non

> 223.7855

δV−

non

> 110.9528

δP

non

> −36.3170

isl

−36.3170 >

isl

−8.1167 >

isl

−30.7904 >

non

0.0006 >

10ms

20ms

Fig. 7. A DTL classifier uses multiple time windows to detect islanding.(label ”non” and “isl” denotes non-islanding and islanding mode)

much sooner (10ms) while decisions on more difficult caseswill be postponed until more information becomes available.

A. Case I

DTL classifiers using feature subsets of size {1, 2, 3, 4}were trained then tested using data sets corresponding totime windows ranging from 10ms to 50ms. The database israndomly divided using stratified sampling into two segments:6,500 cases which were used to train the model, and a further6,495 cases which were reserved for testing.

As described in section III, the feature sets are ranked basedon the number of training events excluded by the feature

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δV+

isl

> 100.60

δV−

non

> 129.45

V−

isl

> 0.019

F

isl

> 60.00

V−

non

> 0.0045

isl

0.0045 >

non

59.96 >

non

0.0014 >

isl

−8.247 >

non

−237.3 >

10ms

20ms

30ms

40ms

50ms

(a) one-feature per stage

THDV

non

> 0.023

δQ

non

> 223.78

δTHDV

isl

> 15.39

δV−

non

> −30.72

isl

−30.72 >

non

−25.41 >

isl

−30.79 >

non

0.0006 >

10ms

20ms

(b) two-features per stage

THDV

non

> 0.023

δV+

isl

> 9.20

δF

isl

> 3.20

δQ

non

> −17.93

isl

−17.93 >

isl

−13.069 >

non

−243.85 >

non

0.0006 >

10ms

20ms

(c) three-features per stage

THDV

non

> 0.023

δV+

isl

> 9.20

δF

isl

> 3.22

δV−

non

> −0.216

δQ

non

> −21.94

isl

−21.94 >

isl

−8.68 >

isl

−13.07 >

non

−243.85 >

non

0.0006 >

10ms

20ms

(d) four-features per stage

Fig. 8. Decision trees for feature sets of size M = 1, 2, 3, and 4 per stage

thresholds. Table III shows the top ranked, pairwise featuresets obtained after the first stage of the training algorithm.The sets are sorted in descending order based on the columnNi1 + Ni1,i2 which represents the number of cases correctlyclassified (excluded) using thresholds on the joint featurepairs fi1 and fi2 . If two sets have the same value of jointlyexcluded cases, the number of the cases excluded by only thefirst feature is used for sorting then the number of the casesexcluded by the second feature. This is to give more priorityto the feature fi1 so as to get minimal number of features.

From the table III, it can be seen that almost 79% of theevents can be classified at this first stage (5130 out of 6500events). Thus validates our original motivation that the DTLwould be able to discriminate between most islanding or non-islanding events within a very short period of time (10ms).

TABLE IIITHE NUMBER OF CASES CLASSIFIED BY SETS OF TWO FEATURES OUT

OF 6,500 CASES

fi1 fi2 Ni1 +Ni1,i2 Ni1 Ni1,i2THDV δQ 5130 1624 3506THDV δV 4816 1624 3192THDV δV+ 4626 1624 3002V+ δF 4493 2880 1613δV δV+ 4293 2520 1773δV+ δV− 4264 3086 1178V+ δP 4237 2880 1357V δV0 4066 2859 1207δV+ δV0 4008 3086 922V δF 3939 2859 1080

The structures of the DTLs produced using the proposedtraining algorithm are shown in Fig. 8 (a, b, c, and d) forfeature sets of size 1, 2, 3 and 4 respectively. As can be seen,the output of the DTL classifiers vary somewhat in size and

the features selected. When only one feature per time windowis used, the resulting tree has 5 decision levels. This is becausethe predictive capability of one feature is less than that of acombination of two or more features, which also necessitatesthe use of more stages. However, the tree produced usingsubsets of size 4 is composed of 5 level of rules, thoughthese are contained within two DTL stages. This points toa tradeoff between the complexity of each layer, determinedby the number of decision rules used, and the time taken tocorrectly classify a case, which is a function of the numberof stages in the DTL. More studies are needed to determinethe optimum balance but in general, having some “tunability”in the system was perceived to be a good thing as it suggeststhat the proposed approach can be customized to suit differentapplications and requirements.

It can also be observed that the trees which use 2, 3, and4 feature subsets have the same root node THDV , and thetrees which use 3 and 4 feature subsets share a common setof features for the first three levels, which were THDV , δV+and δF . From this we can infer that this group of features hasthe best predictive capability at this time scale. It can also benoticed, from Fig. 8(c) and (d), that the DTL needs only onefeature in the 10 − 20ms window to classify the remainingcases, even though it is allowed to have up to 3 and 4 featuresrespectively.

Another interesting observation can be made by comparingthe trees depicted in Fig. 8(b) and (d). Here, it can be seen thatin tree (b), four features are used: two of which were extractedfrom the 0− 10ms window and the other two from the 10−20ms window; tree (d), on the other hand, uses four featuresextracted from the 0−10ms window but these are incapable ofclassifying all the cases and a fifth feature is required from the20ms window. This clearly shows the time dependant nature

7

1 2 3 4

0

0.2

0.4

0.6

Feature set size

Error(%

)10%

20%

30%

40%

50%

Fig. 9. Classification error when using different size feature sets for 10%,20%, 30%, 40% and 50% of data set size

of the best feature vector combination. It is worthy to notethat the number of training cases detected within 20ms forthe subset of one feature, two features, three features and fourfeatures are 3096, 5103, 5893 and 6363 respectively. Thus, interms of the above, the four features per stage leads to thedetection of more cases in less time, but will result in lessaccuracy (as explained in the next section).

B. Case II

Finally, to test the classification accuracy of the proposedmethod, training sets consisting of 10%, 20%, 30%, 40%, and50% of the total full dataset were used to train the classifier,and the rest used for testing.

The feature sets used and classification errors of the re-sulting decision trees are shown in Table IV. As can beseen there are slight changes in the feature sets obtainedusing the different sizes of training sets. Fig. 9 shows theerrors corresponding to each feature subset size and datasubset. For all other combinations there were small numbersof misclassified cases but the misclassification rates wereinvariably very low (> 1% for all cases, and > 0.1% in 14out of 20 cases).

Feature sets of size 2 and 3 achieved lower classification er-rors, averaging 0.092% and 0.058% respectively while featuresets of size 1 and 4 had 0.21% and 0.17% misclassificationrates respectively. Based on the above results, the three fea-tures per stage achieves the best average accuracy and thusmaking it the most effective feature subset size for detectingislanding for the case under study.

V. CONCLUSION

In this paper a novel, multistage islanding detection ap-proach based on the decision tree-like classifier is proposedand tested on a system which incorporates a synchronous-based DG. The proposed islanding detection approach wastested on a standard IEEE 34-bus radial distribution test systemwith a variety of disturbances, including a wide range ofactive and reactive power mismatches with constant RLCloads in islanding cases, short circuits, capacitor switching,

TABLE IVFEATURE SETS AND CLASSIFICATION ERROR WHEN USING DIFFERENT

SUBSETS OF DATA FOR BUILDING THE TREES

Feature setsize

10% 20% 30% 40% 50%

1

δV+ δV+ δV+ δV+ δV+

δV− δV− δV− δV− δV−V− V− V− V− V−V− F F F FF V− V V V−

Error (%) 0.69% 0.25% 0.07% 0.02% 0.02%

2

THDV THDV THDV THDV THDV

δQ δQ δQ δQ δQF Q δTHDV δTHDV δTHDV

δQ δV− δV− δV− δV−Error (%) 0.17% 0.26% 0.01% 0.00% 0.02%

3

THDV THDV THDV THDV THDV

δV+ δV+ δV+ δV+ δV+

V− δF δF δF δFδQ δQ δQ δQ δQ

Error (%) 0.08% 0.05% 0.05% 0.05% 0.06%

4

THDV THDV THDV THDV THDV

δV+ δV+ δV+ δV+ δV+

δF δF δF δF δFδV− δV− δV− δV− δV−Q δQ δQ δQ δQ

Error (%) 0.51% 0.22% 0.05% 0.05% 0.02%

load switching, and motor switching under fully-loaded andlightly-loaded system conditions in non-islanding cases. Inthis passive islanding detection approach, each stage uses adifferent feature set collected over a range of time windows.An event is classified when it falls outside the thresholds setat the respective stage, failing which the decision is deferredto the next stage and hence time window. Also, the resultsindicate that the use of multiple features in the detectionmethod is more effective when different time windows areconsidered for the various groups of features to reduce theNDZ in the problem.

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Abdullah Sawas received the B.Sc. degree in infor-matics engineering from Aleppo University, Syria,in 2008 and M.Sc. degree in computing and infor-mation science from the Masdar Institute of Scienceand Technology, United Arab Emirates, in 2011.

His research interests are applied machine learn-ing in distributed and renewable energy systems.

Wei Lee Woon (M’08) received the B.Eng. degreein electronic engineering (Hons.) from the Universityof Manchester Institute of Science and Technology,Manchester, U.K. (now merged with the Universityof Manchester), in 1997, and the Ph.D. degree fromthe Neural Computing Research Group, Aston Uni-versity, Birmingham, U.K., in 2002.

Upon graduation, he joined the Malaysia Uni-versity of Science and Technology (MUST) as anAssistant Professor, where he served until 2007. Hesubsequently joined the Masdar Institute of Science

and Technology, Abu Dhabi, United Arab Emirates. He has also worked as aVisiting Researcher at the Massachusetts Institute of Technology, Cambridge,and at the RIKEN Brain Science Institute, Tokyo, Japan. His research interestsinclude technology mining, analysis of distributed generation systems, andEEG signal analysis.

V. Ravikumar Pandi (M’10) received the B.E., de-gree from Madurai Kamaraj University, Tamil Nadu,India, in 2003, the M. Tech., degree from AnnamalaiUniversity, Tamil Nadu, India, in 2005, and thePh.D. degree from Indian Institute of TechnologyDelhi, New Delhi, India, in 2010. Currently he isworking as a Post Doctoral Researcher with theMasdar Institute of Science and Technology, AbuDhabi, Unite Arab Emirates. His research interestincludes power system optimization, power quality,protection, transmission pricing, renewable energy,

artificial intelligence and evolutionary algorithms.

Hatem H. Zeineldin (M’06) received the B.Sc.and M.Sc. degrees in electrical engineering fromCairo University, Cairo, Egypt, in 1999 and 2002,respectively, and the Ph.D. degree in electrical andcomputer engineering from the University of Water-loo, Waterloo, ON, Canada.

He was with Smith and Andersen Electrical En-gineering Inc., where he was involved with projectsinvolving distribution system design, protection, anddistributed generation. He then was a Visiting Pro-fessor at the Massachusetts Institute of Technology

(MIT), Cambridge. Currently, he is an Associate Professor with the MasdarInstitute of Science and Technology, Abu Dhabi, United Arab Emirates. Hiscurrent interests include power system protection, distributed generation, andderegulation.