Mohd Syukri Ali *a, Non-member Ab Halim Abu Bakar *, Non-member Tan ChiaKwang *, Non-member Hamzah Arof **, Non-member Hazlie Mokhlis **, Non-member

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1 IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING IEEJ Trans 218 Published online in Wiley Online Library (wileyonlinelibrary.com). DOI:1.12/tee.226 High Impedance Fault Detection and Identification Based on Pattern Recognition of Phase Displacement Computation Paper Mohd Syukri Ali *a, Non-member Ab Halim Abu Bakar *, Non-member Tan ChiaKwang *, Non-member Hamzah Arof **, Non-member Hazlie Mokhlis **, Non-member This paper proposes a new algorithm for high-impedance-fault (HIF) detection based on phase displacement computation (PDC). The PDC is calculated between the measured and reference three-phase voltage signals. There are two stages in this algorithm. In the first stage, the pattern of the PDC is analyzed to detect the occurrence of an event. In the second stage, the peak of the PDC is used as a feature to distinguish between HIF and non-hif events. Subsequently, an automatic HIF classification algorithm based on predefined indices is proposed to perform event identification and HIF detection. Different types of HIF events and non-hif events, such as load, motor starting, and capacitor, have been simulated in PSCAD/EMTDC. The proposed algorithm is able to classify the events accurately for both single and multiple events occurrence. 218 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. Keywords: high impedance fault; distribution system; phase displacement computation Received 9 January 217; Revised 28 March Introduction The increase in nonlinear loads has introduced more disturbances into power systems than ever before. The disturbances may come from load, motor starting, and capacitor. These disturbances increase the harmonic content and distort the voltage profile. Fortunately, these disturbances have not caused cascading outages, which lead to power blackout. High-impedance faults (HIFs) show a similar effect on the voltage profile as the other disturbances. It is detrimental to the power system, but the low fault current produced by HIF does not trigger the conventional protection relay or fuse, making the detection and identification of an HIF event a difficult task. However, it is crucial to detect HIF immediately, as it can lead to a major problem such as power outage if it is left to proliferate. Several techniques have been proposed to detect the occurrence of HIFs. These techniques can be divided into mechanical and electrical methods. In mechanical methods, a low-impedance path is used to detect the broken conductor to create a short-circuit that can trigger the protection relay [1]. However, this technique cannot be applied if the cause of the HIF is not related to conductor break. Therefore, the electrical detection is preferable. In electrical methods, the measured voltage and current signals are analyzed to discover telltale features that indicate the occurrence of HIFs. The electrical detection methods can be classified into timedomain and frequency-domain approaches. In time-domain HIF detection, the variation in the magnitude of voltage and current signals over time is observed, wherea in frequency-domain HIF a Correspondence to: Mohd Syukri Ali. mosba86@yahoo.com.my *UM Power Energy Dedicated Advanced Centre (UMPEDAC), University of Malaya, 5999 Kuala Lumpur, Malaysia **Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 563 Kuala Lumpur, Malaysia (V) waveform Voltage waveform (V) Voltage Time (s) Time (s) Faulted waveform Reference waveform Faulted waveform Reference waveform Fig. 1. Faulted and reference voltage. Comparison between faulted and reference voltage. Enlarged view of the faulted and reference voltages detection, the characteristic of the signal within a frequency range is analyzed. Several techniques have been proposed using timedomain HIF detection as presented in Refs. [2 5]. In Ref. [3], four algorithm-based relays, namely proportional relaying, ratio ground relaying, second-order harmonic current relaying, and third-order harmonic current relaying, are used. 218 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

2 M. S. ALI ET AL. Time (s) Table I. Voltage magnitude Faulted waveform (V) Reference waveform (V) Simulation Measure 3-phase voltage waveform Calculate the phase angle difference End Moving window process ing process Calculating the index Voltage waveform (V) V ref Point B Y=mX+C Point A Faulted waveform Reference waveform (t 1 ) (t new ) (t 2 ) Time (s) Fig. 2. Illustration of faulted and reference waveforms Frequency-domain techniques based on the arcing phenomenon associated with the HIF events have also been reported. HIFrelated arcing produces high-frequency components that are useful to differentiate HIF from other events. In Refs. [6 1], Fourier transform (FT) is utilized to extract fundamental, even harmonic, odd harmonic, and inter harmonic frequency components of the voltage during an HIF event. Besides FT, wavelet transform (WT) has been widely used because of its capability to provide both time and frequency localization [11 2]. Usually, WT is used to extract important features from the signal. Then, a classifier or pattern recognition method is utilized to classify the event based on the features. These pattern recognition methods include artificial neural network (ANN) [13,14], fuzzy system (FS) [16,21], nearest neighbor rule (NNR) [17], Bayes classifier [18], moving window approach [12] and deterministic logic scheme [15]. In Ref. [22], the energy and randomness algorithms are proposed to identify the HIF event. Multiple algorithms are used to increase the dependability and security of the system. Besides that, the nature of the arcing fault due to HIF is also considered based on the arc confidence level of the generator. In this paper, a new approach based on phase displacement computation (PDC) is proposed to detect and discriminate HIF from non-hif events such as capacitor, motor starting, and load. It calculates the phase displacement between the reference voltage signal (healthy signal) and faulted voltage signal (unhealthy signal). The peak changes of PDC can be used as an indicator to discriminate the events. As such, this paper presents an automatic HIF classification algorithm based on the peak changes of PDC. Two types of indices, namely detection and differentiation indices, are developed to detect a possible HIF event and subsequently classify it as an HIF or a non-hif event. 2. Proposed Method The proposed method detects and classifies HIF and non-hif events based on the PDC of the three-phase voltage waveform. The PDC is calculated between the measured and reference voltage waveforms. While the reference voltage waveform is purely sinusoidal, the measured waveform is not as clean since it contains Fig. 3. Flowchart of the proposed HIF detection and classification method anomaly caused by the event. Details of the proposed method and its PDC calculation are given below Phase displacement computation When a fault occurs in a network, there will be a slight distortion in the signal phasor as shown in Fig. 1. The differences between the reference and the faulted waveforms can be observed clearly in Fig. 1 and. Figure 1 shows an example of a few cycles of the reference and faulted voltage waveforms. Figure 1 shows an enlarged view of the circled part in Fig. 1, showing the onset of fault. The distortion along the waveform in Fig. 1 can be quantified using the PDC method as follows. Table I shows the voltage magnitude before and after the fault occurrence. The fault is applied to the system at t = 1 s. As shown in the table, the voltage is distorted after the fault is applied. Figure 2 shows the plots of the faulted and reference waveforms just before and after the fault from Table I. As shown, the voltage starts to deviate at t = 1 s. This distortion can be represented by the PDC. The PDC is calculated based on the time difference between the faulted and reference waveforms where the voltage magnitude for both waveforms is the same. As such, the time (t new )atwhich the faulted waveform has the same magnitude as the reference waveform will first be computed. Initially, the algorithm will continuously track for differences in magnitude between the reference and captured voltage waveforms throughout the samples. Once the difference in magnitude is detected at t 2, the magnitude of the reference waveform at Point A will be traced to the same magnitude of the faulted waveform at Point B to obtain t new. The computation of t new isbasedonthe assumption that consecutive samples in the voltage waveforms are connected through a straight line. As such, t new is calculated from the straight line equation as follows: Y = mx + C (1) where X is time, Y is voltage magnitude, m is gradient of the straight line, and C is the y-axis intercept of the straight line. Therefore t new = V ref C (2) m Finally, the phase angle difference can be calculated between both waveforms as follows: diff = t new t 2 36 (3) T where T = time over one full cycle = 1/f, f = frequency. 2 IEEJ Trans (218)

3 HIGH IMPEDANCE FAULT DETECTION AND IDENTIFICATION Fig. 4. The 132/11 kv distribution Network 3 IEEJ Trans (218)

4 M. S. ALI ET AL. Absolute PDC ( ) Sample Absolute PDC ( ) Sample Absolute PDC ( ) Sample Absolute PDC ( ) Sample (d) Fig. 5. Absolute PDC during HIF event. Phase-A, Phase-B, Phase-C, (d) Summation for all phases Absolute PDC ( ) Sample Fig. 6. Moving window Fig. 8. Smoothed PDC Moving window PDC ( ) Window data Fig. 7. Summed PDC 2.2. Phase displacement computation for HIF detection and classification Figure 3 shows the flowchart of the proposed HIF detection and classification method. After measuring the three-phase voltage waveforms, the phase displacement is calculated using the time difference between the measured and reference voltage waveforms. Two processes, namely moving window and summing, are necessary to filter the effect of noise and smoothing the phase displacement data. Finally, an automatic HIF classification algorithm based on new indices is introduced. 3. Modeling and Simulation A simple 132/11 kv distribution network consisting of 18 buses is developed using PSCAD/EMTDC software as shown in Fig. 4. The frequency of the system is 5 Hz and the sampling frequency is 4 khz (8samples per one full cycle). The network data such as line length and cable types are described in the Appendix. Various types of disturbances such as HIF, capacitor, motor starting, and load are simulated and introduced at BUS2. The three-phase voltage waveform is measured at the main substation at BUS Power system events To validate the effectiveness of the proposed technique, different sets of cases are simulated. The events are as follows: HIF: different types of HIF, such as single line-to-earth fault (SLEF), double line-to-earth fault (LLEF), double line fault (LLF), and three phase-to-earth fault (LLLEF) have been simulated with different high impedance fault values. In this study, 5 and 1 are chosen to represent the high impedance fault value. Load : the loads of.3 MW/.24MVaR,.75 MW/.36MVaR, and 1.5 MW/.9MVaR were switched into the system. Motor starting: the motor with the rating of.6431, 1.245, and 1.29 MW are injected into the system. Capacitor : the capacitor bank with the rating of.5, 1.5, and 2.5MVaR is connected into the system. All of the above events are introduced at BUS2 at t = 1. s when the system is stable. The process starts by measuring the three-phase voltage waveform at BUS1. The measured voltage is compared with the reference voltage to calculate the PDC. The process of PDC calculation is described below: Figure 5 shows an example of absolute PDC obtained using (3) during an HIF event. The phase angle difference between the reference and faulted waveforms is calculated before and after the event. It can be observed that the absolute PDC is constantly zero before the event but fluctuates after the events. The fluctuation of 4 IEEJ Trans (218)

5 HIGH IMPEDANCE FAULT DETECTION AND IDENTIFICATION SLEF (5 Ω) 115 SLEF (1 Ω) LLEF (5 Ω) LLF (5 Ω) LLLEF (5 Ω) LLEF (1 Ω) LLF (1 Ω) LLLEF (1 Ω) 11 Fig. 9. Patterns of smoothed PDC for HIF event. SLEF, LLEF, LLF, (d) LLLEF (d) the absolute PDC value indicates an anomaly that has occurred in the system. Figure 5 shows the absolute PDC of voltage waveform for phase A, B, and C, respectively. Figure 5(d) shows the sum of absolute PDCs of the three phases. To reduce sensitivity toward noise, the data points in Fig. 5(d) are summed by a moving window in which each window contains 8 samples (one full cycle). The position of the window is shifted point by point consecutively as illustrated in Fig. 6. The result is displayed in Fig. 7. As can be seen in Fig. 7, the moving window PDC is oscillatory. Thus, another summing window is applied to smoothen the data further. In this step, every 4 consecutive data are summed, and the result is shown in Fig. 8. Based on the pattern of the smoothed PDC, the occurrence of an HIF event is detected Pattern of phase displacement Events such as HIF, load, motor starting, and capacitor are simulated, and the patterns of their smoothed PDC are analyzed to determine whether the events are HIF or non-hif HIF event (BUS2) Figure 9 (d) shows the patterns of smoothed PDC for different types of HIF events with fault impedance values of 5 and 1. Although the magnitudes are different, it can be seen that the patterns are similar. Once an HIF occurs, the smoothed PDC starts to increase until it reaches a plateau without dip, overshoot, or fluctuation Load event (BUS2) Figure 1 shows the changes in the smoothed PDCs when loads are added to the system. It can be seen that the smoothed PDCs increase and then decrease before reaching a constant value. The overshoot before the dip differentiates this event from HIF events Motor starting event (BUS2) Figure 11 shows the smoothed PDCs associated with motor starting events. It can be seen that the overshoot is very obvious. A small fluctuation is detected after the first dip Capacitor event (BUS2) Finally, the smooth PDCs of capacitor with ratings of.5, 1.5, 5 IEEJ Trans (218)

6 M. S. ALI ET AL Fig. 1. Pattern of smoothed PDC for load event..3 MW/.24 MVaR,.75 MW/.36 MVaR, 1.5 MW/.9 MVaR Fig. 11. Pattern of smoothed PDC for starting motor events MW, MW, 1.29MW 6 IEEJ Trans (218)

7 HIGH IMPEDANCE FAULT DETECTION AND IDENTIFICATION Fig. 12. Pattern of smoothed PDC of capacitor event..5 MVaR, 1.5 MVaR, 2.5 MVaR. Table II. Consecutive events occurring in the system Figure 15 t = 1s t = 1.2 s t = 1.4 s Motor starting HIF fault Capacitor HIF fault Load HIF fault (d) Load Capacitor (e) Motor starting Load Capacitor (f) Motor starting Load HIF fault 2.5MVaR are shown in Fig. 12. It can be seen that the overshoot and the dip that follows are pronounced Sensitivity studies Based on the observation above, HIF and non-hif events can be differentiated by the rise pattern of the smoothed PDC. However, it is crucial to investigate the robustness of the proposed technique when more than one event occurs in the system. For this purpose, several events are simulated consecutively one after the other as shown in Table II. The first event occurs at t = 1 s, the second event at t = 1.2 s, and the third event at t = 1.4 s. The events are simulated at BUS2, and the smoothed PDCs of the events are given below. Figure 13 shows the smoothed PDC of motor starting followed by an HIF. As shown in the figure, a sharp peak is followed by a fall of the PDC, indicating that the event is a non- HIF event. The onset of the second event is marked by the rise of the smoothed PDC to a plateau, signifying that an HIF has occurred. In Fig. 13(e), three events occur consecutively, which are motor starting, load, and capacitor. The events are simulated at t = 1, 1.2, and 1.4 s, respectively. It is observed that there is a noticeable overshoot for all events and the sharp peaks are obvious for the first and the third events. Thus, the proposed method has successfully identified all events as non HIF HIF detection and differentiation index To simplify the detection and classification of the HIF and non-hif events, two indices are developed. The first is the detection index (D-index), which is used to detect the occurrence of an event. The second is the identification index (Id-index), which is used to differentiate HIF from non-hif events. Figure 14 shows an example of smoothed PDC of a load event. The increase in PDC value indicates that an event has occurred in the system. D-index, which indicates the occurrence of an event, is calculated as follows. First, the difference between two consecutive Smoothed PDC data, G(1) G(2), is calculated. If the difference is negative, the value is stored and the difference between the next two data, G(2) G(3), is calculated. If it is still negative, the value is stored and the subtraction step is carried out for G(3) G(4). This step is repeated until a positive difference is obtained. Suppose the subtraction step is repeated n times before the first positive difference occurs. Then, the D-index is given by n i=1 G(i) G(i + 1) D index = (4) n The value of D-index is used to identify the anomaly caused by an HIF event and normal fluctuation caused by a non-hif event. Besides, it can be used to differentiate the event with the steadystate condition. Based on trial and error, the threshold value of D-index is set to 2. If the value is greater than 2, it is just a normal fluctuation; otherwise, it indicates the occurrence of an HIF event in the system. Unfortunately, the D-index is not able to tell whether the event is HIF or non-hif. It is noted that the smoothed PDC for HIF events rises to the steady-state value without noticeable overshoot. Other events such as load, motor starting, and capacitor overshoot the steady-state value or form a separate peak before rising again to reach the steady-state value. As such, the Idindex is developed to differentiate between HIF and non HIF events by detecting the presence of the peak before the smoothed 7 IEEJ Trans (218)

8 M. S. ALI ET AL (d) (e) (f) Fig. 13. Pattern of the smoothed PDC. Motor starting HIF fault, Capacitor HIF fault, Load HIF fault, (d) Load capacitor, (e) Motor starting load-capacitor, (f) Motor starting load HIF fault G2 G1 G3 G4 G5 G Fig. 14. Smoothed PDC of a load event PDC reaches its steady-state value. Referring to Fig. 14, the Id-index is only calculated if there exists a slope change at any smoothed PDC datum, G(i), such that (G(i 1) G(i)) < and (G(i) G(i + 1)) >. In other words, if two consecutive differences in smoothed PDC change sign from negative to positive, then the peak is expected to occur at that position. The Id-index is calculated as follows: Id index = G(i) G(i + 3) G(i) (5) Table III. D-index and Id-index calculation Summed PDC Difference in summed PDC value value between two adjacent groups G(1)-G(2) = Step G(2)-G(3) = G(3)-G(4) = Step G(4)-G(5) = G(5)-G(6) = G(6)-G(7) = Step G(7)-G(8) = G(8)-G(9) = G(9)-G(1) = 8.41 From the results, it is found that the best threshold that separates HIF from non-hif events for the Id-index is.1. If the Id-index falls below the threshold, an HIF event is detected. Otherwise, only a non-hif event such as capacitor, load, or motor starting is detected. Table III shows the steps in in the calculation of the D-index and Id-index for the event in Fig. 14, while Fig. 15 shows the flowchart of the steps in the whole process. Step 1 : D-index. = ( )/2. = IEEJ Trans (218)

9 HIGH IMPEDANCE FAULT DETECTION AND IDENTIFICATION D(k)=; Id(k)=; k=1; count=; G=group data i=1:length(g); first event 1-13 second event Start checking from 14 and upward 2 X(i)=G(i)-G(i+1) If X(i)< D(k)=D(k)+X(i) count=count+1 Yes If X(i)> Fig. 17. Transition and steady-state period X(i-1)>? Table IV. Capacitor event analysis No j=i D(k)=D(k)/count Id(k)=[G(j)-G(j+3)]/G(j) BUS2 Capacitor BUS9 Q(MVaR) D-index Id-index D-index Id-index D(k)<-2? No k=k+1; D(k)=; Id(k)=; count=; Yes Id(k)>.1? Yes Non-HIF event No Fig. 15. HIF detection and identification flowchart Step 2 : Conditions to initiate Id-index fulfilled. Step 3 : Id-index. = ( )/ = HIF occurs END 3.4. Transition and steady-state period of event It is noted that the smoothed PDC waveform fluctuates after the occurrence of an event before it stabilizes to its steady-state value. The volatile period before the steady-state condition is known as the transition period. Likewise, the steady-state condition is achieved when there is no obvious oscillation in the smoothed PDC. Figure 16 shows the transition periods of different types of events. As shown in the figure, each event has different transition period. HIF events have a shorter transition period as compared to non-hif events. It is noted that non-hif events always oscillate before reaching the steady state. It is observed that they require a maximum transition period of 13 consecutive samples from the start of an event to the steady-state condition. As such, if two events occur within 13 consecutive data, only the first Capacitor Load transition steady state period transition period steady state period Starting motor 9 HIF (A-G Fault) transition period steady state period transition period steady state period 6 15 (d) Fig. 16. Transition and steady-state period for event. Capacitor, Load s, Motor starting, (d) HIF 9 IEEJ Trans (218)

10 M. S. ALI ET AL. Table V. Load event analysis BUS2 Load BUS9 P/Q (three-phase) D-index Id-index D-index Id-index.3/ / / Table VI. Motor starting event analysis Motor starting BUS2 BUS9 P/Q (three-phase) D-index Id-index D-index Id-index / / / event is detected. The other event is considered as a fluctuation within the transition period as shown in Fig. 16. Therefore, two events are simulated with at least 14 data apart as shown in Fig Results To investigate the effectiveness of the proposed automatic HIF classification algorithm, the same case studies as in Section 3 is repeated. In this study, the events are simulated at two different locations, which are at BUS2 and BUS9. Tables IV VII show the results of the proposed automatic HIF classification algorithm. The second column is the detection index (D-index) and the third column is the identification index (Id-index). In the proposed algorithm, D-index is monitored to detect the occurrence of events in the system and to differentiate them from normal fluctuation. An event is considered to occur if the value of D-index is less than 2. When the occurrence of an event is detected, Id-index is checked to determine whether it is an HIF or non-hif event. If the value of the Id-index is less than.1, it is considered as an HIF event. Otherwise, it is a non-hif event. It can be observed that the developed algorithm Table VII. HIF event analysis BUS2 HIF BUS9 Fault type D-index Id-index D-index Id-index A-E (5 ) A-E (1 ) A-B-E (5 ) A-B-E (1 ) B-C (5 ) B-C (1 ) A-B-C-E (5 ) A-B-C-E (1 ) had successfully classified the HIF and non-hif events in all case studies based on the Id-index. To evaluate the robustness of the proposed algorithm, the occurrence of multiple consecutive events as in Section is repeated, and the results are shown in Tables VIII and IX. Based on the results, it can be seen that all the HIF events have been successfully identified by the proposed algorithm. There are several studies that have been conducted to detect and identify the HIF event as shown in Table X. Basically, all the previous techniques were able to discriminate between the HIF and non-hif events with an accuracy >97%. However, in this proposed method, the accuracy obtained was 1%. Other than that, the proposed method is capable of identifying the HIF event, although multiple events were occurring consecutively. Besides, the HIF event can be differentiated from the other three non-hif events which have a similar voltage profile. 5. Conclusion This paper proposed a new algorithm using PDC data of threephase voltage signals to detect and discriminate HIF from non-hif events. The PDC data are calculated between the measured and reference three-phase voltage signals. Two indices introduced in this method are the detection index (D-index) and the identification index, (Id-index). D-index is used to detect the occurrence of an event in the system. Then, Id-index is applied to classify the event between HIF and non-hif events. The proposed algorithm successfully detects and discriminates various HIF and non-hif events. Case study Event #1 Event #2 Event #3 Table VIII. Mix event analysis (BUS2) D-index #1 Id-index #1 D-index #2 BUS2 Id-index #2 D-index #3 Id-index #3 1 Motor starting HIF fault (AEF) Capacitor HIF fault (ABEF) 3 Load HIF fault (AB) Load Capacitor Motor starting Load Capacitor Motor starting Load HIF fault (ABCEF) IEEJ Trans (218)

11 HIGH IMPEDANCE FAULT DETECTION AND IDENTIFICATION Case study Event #1 Event #2 Event #3 Table IX. Mix event analysis (BUS9) D-index #1 Id-index #1 D-index #2 BUS9 Id-index #2 D-index #3 Id-index #3 1 Motor starting HIF fault (AEF) Capacitor HIF fault (ABEF) Load HIF fault (AB) Load Capacitor Motor starting Load Capacitor Motor starting Load HIF fault (ABCEF) Techniques used in other similar paper Table X. Comparison of the proposed method with other similar methods Capacitor Type of event presented by each of the techniques Non-HIF Load Motor starting HIF Accuracy Subsequent events [11] WT Yes No No Yes No [12] DWT Yes No No Yes No [14] DWT and ANN Yes Yes No Yes 99% Yes [16] WT, principal component analysis, fuzzy Yes Yes No Yes 98.33% No inference system and GA [18] WT and statistical pattern recognition Yes Yes No Yes 97.6% No [17] DWT, frequency range and RMS conversion Yes No No Yes 97.48% No Proposed method Yes Yes Yes Yes 1% Yes A. Appendix Table A1. 18-bus system, cable data, source data, (d) Transformer Delta-Wye data 18-bus system Node From To Length (km) Type of cable Pi Section # Pi Section # Pi Section # Pi Section # Pi Section # Pi Section # Pi Section # Pi Section # Pi Section # Pi Section # Pi Section # Pi Section # Pi Section # Pi Section # Pi Section # Pi Section # Pi Section #1 Cable data Positive sequence (p.u/km) Zero sequence (p.u/km) Type of cable R X R X Pi Section # Pi Section # Table A1. Continued Source data Source: 132 kv 5 Hz 1 MVA R + =.1 p.u X + =.4 p.u R =.1 p.u X =.4 p.u (d) Transformer Delta-Wye data 132/11 kv 3 MVA R + =.14 p.u X + =.994 p.u Earth resistance = 4 References (1) Adamiak M, Wester C, Thakur M, Jensen C. High Impedance fault detection on distribution feeders. GE Industrial Solutions. (2) Calhoun H, Bishop MT, Eichler CH, Lee RE. Development and testing of an electro-mechanical relay to detect fallen distribution conductors. IEEE Power Engineering Review 1982; PER-2(6): (3) Ching-Lien H, Hui-Yung C, Ming-Tong C. Algorithm comparison for high impedance fault detection based on staged fault test. IEEE Transactions on Power Delivery 1988; 3(4): (4) Sharaf AM, Abu-Azab SIA. Smart relaying scheme for high impedance faults in distribution and utilization networks. Canadian Conference in Electrical and Computer Engineering, 2. (5) Lee RE, Bishop MT. Performance testing of the ratio ground relay on a four-wire distribution feeder. IEEE Transactions on Power Apparatus and Systems 1983; PAS-12(9): (6) Zanjani MGM, Kargar HK. High impedance fault detection of distribution network by phasor measurement units. Proceedings of 17th Conference in Electrical Power Distribution Networks (EPDC), IEEJ Trans (218)

12 M. S. ALI ET AL. (7) Tripathi A, Kori AK. High impedance fault detection on rural electric distribution and power quality control systems. International Journal on Emerging Technologies 213; 4(2): (8) Yong S, Rovnyak SM. Decision tree-based methodology for high impedance fault detection. IEEE Transactions on Power Delivery 24; 19(2): (9) Lazkano A, et al. High impedance arcing fault detector for three-wire power distribution networks. 1th Mediterranean Electrotechnical Conference, 2. (1) Aucoin BM, Russell BD. Distribution high impedance fault detection utilizing high frequency current components. IEEE Power Engineering Review 1982; PER-2(6): (11) Shinde VB, Hase SG. Identification of L-G Fault Transients & Capacitors Switching Transients by using wavelet. International Journal of Advancement in Electronics and Computer Engineering 212; 1(2): (12) Akorede MF, Katende J. Wavelet transform based algorithm for high- impedance faults detection in distribution feeders. European Journal of Scientific Research 21; 41(2): (13) Ghaffarzadeh N, Vahidi B. A new protection scheme for high impedance fault detection using wavelet packet transform. Advances in Electrical and Computer Engineering 21; 1(3):17 2. (14) Vahidi B, Ghaffarzadeh N, Hosseinian SH, Ahadi SM. An approach to detection of high impedance fault using discrete wavelet transform and artificial neural networks. Simulation 21; 86(4): (15) Michalik M et al. High-impedance fault detection in distribution networks with use of wavelet-based algorithm. IEEE Transactions on Power Delivery 26; 21(4): (16) Haghifam M, Sedighi AR, Malik OP. Development of a fuzzy inference system based on genetic algorithm for high-impedance fault detection. IEEE Proceedings-Generation, Transmission and Distribution 26; 153(3): (17) Lai TM et al. High-impedance fault detection using discrete wavelet transform and frequency range and RMS conversion. IEEE Transactions on Power Delivery 25; 2(1): (18) Sedighi AR et al. High impedance fault detection based on wavelet transform and statistical pattern recognition. IEEE Transactions on Power Delivery 25; 2(4): (19) Shyh-Jier H, Cheng-Tao H. High-impedance fault detection utilizing a Morlet wavelet transform approach. IEEE Transactions on Power Delivery 1999; 14(4): (2) David CTW, Xia Y. A novel technique for high impedance fault identification. IEEE Transactions on Power Delivery 1998; 13(3): (21) Jota FG, Jota PRS. High-impedance fault identification using a fuzzy reasoning system. IEE Proceedings-Generation, Transmission and Distribution 1998; 145(6): (22) Benner CL, Russell BD. Practical high-impedance fault detection on distribution feeders. IEEE Transactions on Industry Applications 1997; 33(3): Ab Halim Abu Bakar (Non-member) received the B.Sc. degree in electrical engineering in 1976 from Southampton University, UK, and the M.Eng. and Ph.D. degrees from the University of Technology Malaysia, Malaysia, in 1996 and 23, respectively. He has 3 years of utility experience in Malaysia before joining academia. Currently he is a Senior Lecturer with the UM Power Energy Dedicated Advanced Centre (UMPEDAC), University of Malaya, Malaysia. Dr. Halim is a Member of the IEEE, CIGRE, and IET and a Chartered Engineer. His research interests include power system protection and power system transients. Tan ChiaKwang (Non-member) received the Ph.D. degree in electrical engineering from Universiti Tenaga Nasional, Malaysia. He was an engineer with the Tenaga Nasional Berhad (TNB) and was deputed to TNB Research Sdn Bhd for more than 2 years. He is currently a Senior Lecturer with the UM Power Energy Dedicated Advanced Centre (UMPEDAC), University of Malaya, Malaysia. His research interests include power system study, power system protection, and power quality. Hamzah Arof (Non-member) received the B.Eng. degree from the University of Michigan, USA, and the Ph.D. degree in electrical engineering in 1997 from the University of Wales, UK. Currently he is a Senior Lecturer with the Department of Electrical Engineering, University of Malaya, Malaysia. His current research interests include signal processing, pattern recognition, and electrical power generation. Hazlie Mokhlis (Non-member) received the B. Eng. degree in electrical engineering in 1999 and the M.Eng.Sc. degree in 22, both from the University of Malaya (UM), Malaysia, and the Ph.D. degree from the University of Manchester, UK, in 29. Currently he is a Lecturer with the Department of Electrical Engineering, UM. His research interests include distribution automation and power system protection. Mohd Syukri Ali (Non-member) was born in Malacca, Malaysia, in He received the B.Eng. degree in telecommunication engineering in 29, and M.Eng.Sc. degree in electrical engineering in 213, both from the University of Malaya, Malaysia. 12 IEEJ Trans (218)