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New ANN-Based Algorithms for Detecting HIFs in Multigrounded MV Networks Marek Michalik, Mirosław Łukowicz, Waldemar Rebizant, Senior Member, IEEE, Seung-Jae Lee, Member, IEEE, and Sang-Hee Kang, Member, IEEE
Abstract—Application of two new ANN-based algorithms for arcing high impedance fault (HIF) detection in multigrounded medium-voltage (MV) distribution networks is presented in this paper. The paper provides an evaluation of two new structures of artificial neural networks (ANNs) that may be used for reliable HIF detection in multigrounded as well as isolated, compensated, and grounded via small resistance distribution grids. The results obtained by use of both neural nets are presented. The performance was tested using data obtained from staged HIFs in real MV network as well as from Electromagnetic Transients Program–Alternative Transients Program simulations. A small number of necessary neurons in developed ANNs, short measuring sliding data window, and easy interpretation of obtained output signals are the main advantages of the proposed approach. Satisfactory results of ANN performance were observed for all examined HIF cases in which the ground fault current was greater than 16 A. The selected ANNs of best performance show high reliability and immunity to transients resulting from switching operations in protected feeders and from capacitor bank switching. Index Terms—Artificial neural networks (ANNs), high impedance faults (HIFs), medium-voltage (MV) networks, transient analysis.
I. INTRODUCTION
H
IGH impedance fault (HIF) occurs when a phase conductor makes electrical contact with any object in its vicinity, resulting in short-circuit current of magnitude that is often below the detection threshold of the protective relays. Such a situation is often observed when a conductor is down or it has contact with a tree branch. Although the existence of an HIF in a medium-voltage (MV) network does not endanger electrical equipment, it should be detected and eliminated to minimize an electric shock hazard and to ensure the reliability of an electric power supply. Since the HIF current magnitude is
Manuscript received June 14, 2006; revised January 16, 2007. This work was supported in part by the Korea Ministry of Science and Technology and in part by the Korea Science and Engineering Foundation. Paper no. TPWRD-003312006. M. Michalik, M. Łukowicz, and W. Rebizant are with the Institute of Electrical Power Engineering, Wroclaw University of Technology, Wroclaw 50-370, Poland (e-mail:
[email protected];
[email protected];
[email protected];
[email protected]). S.-J. Lee and S.-H. Kang are with the Next Generation Power Technology Center, Myongji University, Myongji 449-728, Korea (e-mail:
[email protected];
[email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TPWRD.2007.911146
of levels comparable with load currents, its detection in distribution networks still presents a difficult protection problem. An ideal expected detection method should be reliable and secure. It should detect all HIFs and distinguish between faulty and healthy feeders. Several techniques for HIF detection have been reported so far. These are harmonics component methods based on the lowfrequency energy components [1], Kalman filtering approach [2], low-order current harmonics ratio [3], [4], high-frequency harmonics of the feeder neutral line current [5], discrete wavelet transform [6], and continuous Morlet wavelet transform [7]. Another approach is based on artificial neural networks (ANNs) [8]–[10] or ANNs supported by either wavelet technique used for preprocessing of relaying signals [11] or a rule-based system [12]. It has been reported so far that approaches based on ANN techniques structures of utilized neural networks are typical multilayer nonlinear ones. Multilayer perceptrons composed of two hidden layers can practically be used to solve any pattern classification or mapping problem. The question is only what type and how large the ANN should be to realize the required task. Concerning artificial neural cells, nonlinearity is their strong point; however, in some applications, certain disadvantages of such nets may appear. For example, any unexpected increase of input signals beyond the accepted level in the training stage often leads to an erroneous output signal or decision. Moreover, the explanation or interpretation of such ANN performance and the decision-making process is difficult, if possible, at all. The HIF detecting algorithms investigated herein are based on linear ANNs that are used for analyzing higher harmonic components induced in the MV network during arcing fault and routine switching operations. Two structures of ANNs proposed for faulty feeder detection are described in the following sections. Since both proposed ANNs are linear and composed of only three or five neurons, it is easy to interpret the algorithm operation. The suppression of high-frequency components in multigrounded distribution system is more effective compared to unearthed (isolated) or earthed via Petersen coil MV radial networks. The results reported herein lead to the expectation that the proposed algorithms will, in the authors’ opinion, do well when applied to HIF detection and faulty feeder discrimination in the latter grids. Since the used ANNs are linear, the amount of necessary data for training as well as the time consumption of the training stage will be insignificant in comparison to the training of large dimension nonlinear perceptrons. Required data for training can
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be mainly gained from the simulations of the appropriate model of the grid and HIF arc and, additionally, from field tests in the grid to be protected, if available in a sufficient number. The performance of developed algorithms was tested in two steps. In the first step, the algorithms were tested on data obtained from the EMTP model of a typical multigrounded MV network. In the second step, the data recorded during staged HIF faults on the line operating in the real and live multigrounded MV network were used. These data were obtained by courtesy of KEPCo. Based on the records of currents and voltages from the staged faults, the essential parameters of the modeled network (linear and nonlinear loads) were adjusted to obtain the same higher harmonics content in the simulated voltage and current waveforms as in the respective records from the staged ground fault experiments in prefault conditions. The development of the EMTP model was found to be necessary since some data relevant to the algorithm performance verification were not available from live experiments for obvious reasons. It particularly involves currents in healthy and loaded feeders as well as currents in faulty loaded ones (the feeder on which the real HIFs were staged was unloaded). The results of both algorithms’ performance obtained in each stage of the investigation are presented further. II. HIF DETECTING ALGORITHM BASED ON ANNS WITH LINEAR FILTRATION A. Concept of Algorithm I—With ANN Imitating Measurement of Instantaneous Apparent Power The algorithms presented herein are based on two types of neural networks. The main concept of both approaches was decision making regarding the state of the feeders based on the direction (sign) of zero-sequence active power of the harmonic component generated by arcing faults. In the first approach, the authors’ intention was to construct a scheme with possibly a simple structure of ANN being able to estimate the instantaneous power of appropriate harmonic component of zero-sequence voltage and current of the feeder to be protected and then averaging it to obtain active power. The Algorithm I with a neural network as shown in Fig. 1 was designed to make a decision based on the averaged value of the signal expressed by equation (1) , , and are instantaneous apparent where power, zero-sequence voltage, and current of the harmonic of order measured at the relaying point, respectively. The mean is the active power, with a positive sign for faulty value of feeders and a negative sign for healthy ones. The outlined situation is by all means the ideal one since the filtration carried out and yields two signals comby ANN with weights posed of some set of harmonics, from which some are gained and the other are suppressed. In addition, the applied filters introduce phase shifts for each component of the input signals. As the final result, the multiplication is performed on two signals composed of original zero-sequence voltage and current harmonics. Since the multiplication of neurons’ output signals
Fig. 1. Scheme of HIF detecting and feeder state classification ANN algorithm based on the ANN imitating an instantaneous power measurement.
is not commonly utilized in ANN structures, the appropriate method of weights and bias modification for the described here ANN is included in the Appendix. An appropriate combination of voltage and current harmonics can create periodic signals of positive or negative pulse duration. It is very likely that the ANN weights will be adequately tuned during training that the output signal of ANN in Algorithm I will stay above or under threshold constituted by the bias, which enables the final decision-making. The aforementioned interpretation of the proposed ANN operation is very general and the exact explanation of obtained weights can be possible by spectral analyses of zero-sequence voltage and current signals before and after applied filtration (Fig. 1). To conclude what components of input signals are essential for decision making, the comparison of signal spectra before and after filtration is necessary. It is worthwhile underlining that the harmonic order , for which the decision process is performed, was not defined arbitrarily as in the approach presented in [13]. The filters realized in ANN are tuned automatically during training to a frequency component (band) that carries the largest amount of information necessary for HIF detection. B. Concept of Algorithm II—With ANN Imitating the Measurement of Active Power The inspiration for development of the neural-network structure applied in Algorithm II was the idea of direct calculation of active power of appropriately chosen harmonics in zero-sequence voltage and currents measured at the relaying points. The scheme of the algorithm is shown in Fig. 2. The ANN is comprised of four linear neurons that work as linear filters. Two filters process the zero-sequence voltage and the other two process the zero-sequence current. As a result of filtration, the orthogonal components of voltage and current signals appear that can be used to compute active power as follows: (2) , , , and are orthogonal components where of harmonic of order in zero-sequence voltage and current, respectively. Since the carried out research [13] revealed that the fault location determines the characteristic relation of phase shift between the appropriate harmonic components in zero-sequence voltage and zero-sequence current in faulty and healthy
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Fig. 2. Scheme of the HIF detecting and feeder state classification ANN algorithm based on the ANN imitating active power measurement.
feeders, it is highly probable that the ANN of the structure as given in Fig. 2 mimicking operation described by (2) should correctly detect faulty lines in MV distribution systems. Moreover, it is expected that the response of Algorithm II should be more stable and the decisions more reliable when compared to those obtained with the use of Algorithm I.
Fig. 3. Basic diagram of the modeled MV network.
III. MULTIGROUNDED MV NETWORK MODEL Since the algorithms reported in this paper are based on ANNs of predetermined structures, the only parameter that is to be set is the length of the sliding measurement data window. The investigations were carried out for lengths of 10, 20, 40, 80, 120, and 166 samples for a 10-kHz sampling frequency of relaying signals and 60-Hz fundamental frequency of the MV system. That corresponds to 1, 2, 4, 8, 12, and 16.6 ms (full cycle). To collect the adequate number of training and testing data, a multigrounded MV network that corresponds to the real MV network has been modeled in ATP/EMTP. Its simplified diagram is shown in Fig. 3. It was assumed that the line in which the real HIF was staged was a part of the network so the model includes 11-km-long unloaded overhead line marked on the diagram as the “Test Feeder.” The line was used to fit the unknown parameters of the network modeled in order to obtain the same content of higher harmonics in prefault and faulty conditions that were observed in the real network. The overhead lines of the MV network were modeled as a chain of -circuits, each one representing 0.4 km of line. The -cells parameters were calculated from the geometrical configuration of the lines that are shown in Fig. 4. Each -cell was grounded at its end so that the grounding frequency was 2.5 grounds/km with a typical electrode resistance of 25 . Loads in the feeders were assumed to be evenly distributed along each line and were represented as Y circuits with a solidly , parameters calculated grounded neutral point and with for power factor . The load data for each line modeled are shown in Table I. Since a small asymmetry of phase voltages ( 5%) was seen in phase voltages recorded in the live network, the same asymmetry was introduced into the network model by making the loads slightly asymmetrical, with phase A
Fig. 4. Geometrical data of feeder wires configuration.
TABLE I FEEDER LOADS DATA
(in which the HIFs were staged) as the least loaded and phase C as the most loaded one. The MV network modeled was supplied by a typical 45-MVA, 154-kV/22.9-kV transformer unit (short-circuit voltage 14.9%) in Y/Y connection, solidly grounded at the HV side and via 1- coupling resistance at the MV side. Based on analyses of higher harmonic components in zero-sequence voltage and current calculated from field test data, the
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Fig. 7. Voltage V applied to the arc and arc current i
waveforms.
Fig. 5. Magnetizing characteristic of the nonlinear load used in simulations. TABLE II UNWANTED AND FAILURE ALARMS OF ALGORITHM I
Fig. 6. Nonlinear HIF arc model.
nonlinear load represented as a Y circuit has been connected to the busbar. The inductive load of magnetizing characteristics depicted in Fig. 5 was a source of harmonics of magnitudes in good accordance with harmonics observed in prefault conditions in the field experiments data. As the basic theory on which the arc model is based was described in detail in the previous paper [13] (approach based on theory from [14]), a short reminder is presented here only. The nonlinear high-impedance model, implemented in ATP models as is shown in Fig. 6, was switched into the network at selected points of fault inception. The relation between the feeder voltage applied to arc and the arc current is shown in Fig. 7. As a result of dynamic arc resistance representation, the arc current lags behind the applied voltage so the arcing mechanism representation is in good accordance with the fundamental theory of high-impedance ground faults. IV. TRAINING ANNS The training of ANNs for both algorithms has been carried out with the use of simulation data only. The faults were staged on unloaded test feeder and on the feeders 2 and 5 at three locations (at the relaying point, approximately in the middle, and at the end of the line) with fault currents ranging from 16 to 34 A [6], [15]. The unbalance of load as well as the level of loads was different for each fault case. Moreover, the faults in feeder 5 were modeled with and without the presence of the nonlinear
load connected to the busbars. For testing purposes, the faults were incepted in feeders 3 and 4 with variable loads in all feeders and with some feeders unloaded. The ANNs were fed with samples of zero-sequence voltage that were common for all relays and with samples of zero-sequence currents associated with particular feeders. The relaying and current ) were signals (i.e., zero-sequence voltage calculated from phase voltages and currents as (3) (4) and stored in the data file with the sampling rate of 10 kHz. Prior to sampling, voltages and currents were filtered with antialiasing analog second-order Butterworth filters with a cutoff frequency of 1 kHz. The data obtained from the simulations were processed in a MATLAB programming environment, where the tested HIF detecting algorithms were implemented. V. ALGORITHM PERFORMANCE TESTS ON DATA OBTAINED FROM SIMULATIONS The performance tests of both algorithms based on the ANN and in the sliding data window fed with samples of of length have been carried out with the use of training and testing data. The results obtained are shown in Tables II and III. The training data related to 27 cases in faulty and 108 cases in healthy feeders were classified whereas the testing data are related to 18 cases in faulty and 72 cases in healthy feeders. Thorough investigations of input and output signals as well as intermediate signals of the algorithms in time and frequency
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TABLE III UNWANTED AND FAILURE ALARMS OF ALGORITHM II
Fig. 9. Performance of algorithm I (k = 60) for HIF inception in the middle of the feeder 3. (a) Zero-sequence current and voltage measured at the relaying point of the healthy feeder 4. (b) Outputs of voltage and current filters. (c) Output of the ANN. (d) Algorithm I output.
Fig. 8. Performance of Algorithm I (k = 60) for HIF inception in the middle of the feeder 3. (a) Zero-sequence current and voltage measured at the relaying point of the feeder 3. (b) Outputs of voltage and current filters. (c) Output of the ANN. (d) Algorithm I output.
domains revealed that both algorithms operate and make decisions mainly based on the third harmonic present in the relaying signals. The response of Algorithm I for the case of the fault staged on feeder 3 is shown in Figs. 8 and 9 for the ANN being fed with data from instrument transformers from the faulty feeder and from a healthy one (no. 4), respectively. The output signals of the ANNs reflect phase displacement between periodic signals with third harmonics as the main component in input voltage and current signals. In case of signals associated with the healthy feeder, the displacement is from some 45 to 45 , whereas in cases of faulty feeder classification, it is between some 135 up to 225 . These situations correspond to the estimation of the positive and negative active power of periodic signals extracted from the zero-sequence voltage and currents at relaying points with dominating third harmonic components. However,
the analysis is very simplified since the signals are not purely sinusoidal and contain some fraction of higher harmonics (6th and 9th). The responses of the Algorithm II for the same relaying signals are shown in Figs. 10 and 11. It is readily noticeable that , , , are composed the intermediate signals mainly of third harmonics, what has been confirmed by means of fast Fourier transform (FFT) analysis. As depicted in the scheme in Fig. 2 and expressed by (2), the output of the ANN, that is the core of the whole algorithm, is constructed as the sum of multiplied intermediate signals transformed by the sigmoid tangent function associated with the output neuron of the net. The resulting output signal contains less peaks, so that its averaging over 166-sample (full-cycle) window gives a clearer response. The observations of Algorithm II performance in the time domain have shown that the algorithm output signals for faulty and healthy feeders are better separated when compared with output signals of Algorithm I. The testing of the Algorithm II has also been carried out for transients resulting from switching capacitor banks as well as for feeders’ energization and de-energization. The performance of the Algorithm II in case of the change of supplied vars at the point of installation from 998 kVAR to 3MVAR is presented in Fig. 12. As a result of the switching, the ANN generates a transient signal in the form of peaks. However, the density of the peaks is so low in this case that the averaging of the ANN output signal makes Algorithm II fully reliable. The same results have been observed for other capacitor switching operations.
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MICHALIK et al.: NEW ANN-BASED ALGORITHMS FOR DETECTING HIFs
Fig. 10. Performance of Algorithm II (k = 80) for HIF inception at the middle of the feeder 3. (a) Zero-sequence current and voltage measured at the relaying point of feeder 3. (b) Outputs of voltage and current filters. (c) Output of the ANN. (d) Algorithm II output.
The example of Algorithm II performance for the energization of feeder 2 when the algorithm is fed with relaying signals from instrument transformers associated with feeder 3 (healthy one) is presented in Fig. 13. No alarms have been generated for all investigated feeder energization and de-energization actions. VI. ALGORITHM PERFORMANCE TESTS ON DATA OBTAINED FROM HIFS STAGED ON THE LIVE MV NETWORK A. Conditions of the Staged HIF Tests The algorithm was tested on data recorded during high-impedance ground faults staged on the live MV line in a fragment of a Korean power distribution system. The data used for testing were obtained by courtesy of the Next-Generation Power Technology Center and KEPCo, Korea. The HIF faults were staged at the end of an 11-km-long test line. The ground faults were incepted to different types of earth surface and the grounding conditions for each experiment are summarized in Table IV.
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Fig. 11. Performance of Algorithm II (k = 80) for HIF inception at the middle of the feeder 3. (a) Zero-sequence current and voltage measured at the relaying point of the healthy feeder 4. (b) Outputs of the voltage and current filters. (c) Output of the ANN. (d) Algorithm II output.
B. Test Summary The results of the Algorithm II tests with a selected 80-sample data window for all recorded HIF cases available are shown in Table V. The tests were carried out with the use of recorded data, yet with prior filtration with a second-order Butterworth digital filter with a cutoff frequency of 1 kHz. The tested algorithm was able to detect firmly and selectively (no alarm trigger in prefault conditions) the HIF cases D931200 to D951200. Analyzing the missing alarms of the algorithm (the last four cases of staged HIFs from Table V), it must be noted that the cases could not be recognized correctly because of nonarcing ground faults via good insulation (dry wood and organic material). Thus, the time span of only 0.8 s for which the data were available was too short for the arc to develop. The notes made during experiments indicate that it was not until a few tens of seconds passed that attributes of the arcing fault appeared. The frequency analysis of fault currents confirmed that there was no change in the frequency spectrum of the relevant signals after fault inception for the cases considered. So, in fact, very low nonarcing leakage
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TABLE IV CONDITIONS OF STAGED HIFS
TABLE V RESULTS OF THE ALGORITHM II TESTS (k = 80) Fig. 12. Performance of Algorithm II (k = 80) for capacitor banks switching. (a) Zero-sequence current and voltage measured at the relaying point of the test feeder. (b) Outputs of voltage and current filters. (c) Output of the ANN. (d) Algorithm II output.
Some unstable response of ANN is noticeable in the prefault conditions. This disadvantage can result from some inevitable discrepancies between the data-acquisition model simulated in EMTP and the real one used in the MV network. The performance of Algorithm II on recorded data is satisfactory, although no single case of the real fault in the MV network was shown to the ANN during training. VII. CONCLUSION Fig. 13. Performance of Algorithm II (k = 80) in case of feeder 2 energization. (a) Zero-sequence current and voltage measured at the relaying point of the feeder 3. (b) Output of the ANN. (c) Algorithm II output.
ground fault current was flowing in the network at the beginning of the fault and such a fault could not be detected by the method proposed. The performance of Algorithm II for recorded fault case D932200 is shown in Fig. 14. It is well visible that ANN makes instantaneous decisions on the basis of orthogonal components and [Fig. 14(b) and (c)]. of third harmonics in
The results of performance tests of the proposed HIF detection algorithms show that the minimum required width of the input data window is 40 samples (4 ms). Algorithm I and II perform reliably for all faults modeled in EMTP regardless of the fault location and the level of fault current. The reliability has been confirmed for investigated switching operations on capacitor banks and feeders. Applied herein methodology that is based on training ANN can be included into optimization techniques. Actually, the training stage was applied to finding optimal (i.e., best with regard to the adopted error criterion) filters used for processing harmonic content in zero-sequence voltage and current signals.
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where the error is expressed as the difference between actual and target outputs of the net (A.2) The output signal is expressed by the general equation (A.3) where in this paper is the sigmoid tangent activation funcare the multition of the output neuron. The arguments of plied outputs of linear neurons shifted by bias, that is (A.4)
(A.5) If the steepest-descent gradient training method for tuning weights of ANN is used, as expressed by following equation: (A.6) then the change of the previous weight values is proportional to learning rate and the resulting gradient is calculated as follows:
(A.7) The correction of the bias is made according to
Fig. 14. Performance of Algorithm II (k = 80) for the HIF staged in a real network on the test feeder. (a) Zero-sequence current and voltage measured at the relaying point of the feeder 1. (b) and (c) Outputs of voltage and current filters. (d) Output of the ANN. (e) Algorithm II output signal.
(A.8) The equations used for the correction of
are analogous.
REFERENCES The test results also indicate that the network configuration is not of essential importance for HIF detection. The most important is the ratio of the higher harmonic arc signature level to the level of background noise coming from network nonlinearity. So if the ANNs are trained on simulation signals obtained for the worst background noise level (for instance, maximum values allowed by the relevant standards), then generic use of the proposed algorithms seems quite feasible. However, separate training of the ANNs be carried out for networks operating with different types of neutral point earthing. In this respect, a specific approach is necessary, so, in practical implementations, three basic versions of the relaying algorithms should be available. APPENDIX The following squared error criterion in training of the ANN applied in Algorithm I is used: (A.1)
[1] B. D. Russel and R. P. Chinchali, “A digital signal processing algorithm for detecting arcing faults on power distribution feeders,” IEEE Trans. Power Del., vol. 4, no. 1, pp. 132–140, Jan. 1989. [2] A. A. Girgis, W. Chang, and E. B. Makram, “Analysis of hifh impedance fault generated signals using a Kalman filtering approach,” IEEE Trans. Power Del., vol. 5, no. 4, pp. 1714–1724, Nov. 1990. [3] A. E. Emanuel, D. Cyganski, J. A. Orr, S. Shiller, and E. M. Gulachenski, “High impedance fault arcing on sandy soil in 15 kV distribution feeders: Contribution to the evaluation of the low frequency spectrum,” IEEE Trans. Power Del., vol. 5, no. 2, pp. 676–686, Apr. 1990. [4] D. C. Yu and S. H. Khan, “An adaptive high and low impedance fault detection method,” IEEE Trans. Power Del., vol. 9, no. 4, pp. 1812–1821, Oct. 1994. [5] B. M. Aucoin and B. D. Russel, “Distribution high impedance fault detection using high frequency current components,” IEEE Trans. Power App. Syst., vol. PAS-101, no. 6, pp. 1596–1606, Jun. 1982. [6] T. M. Lai, A. Snider, E. Lo, and D. Sutanto, “High impedance fault detection using discrete wavelet transform and frequency range and RMS conversion,” IEEE Trans. Power Del., vol. 20, no. 1, pp. 397–407, Jan. 2005. [7] S. J. Huang and C. T. Hsieh, “High impedance fault detection utilizing a morlet wavelet transform approach,” IEEE Trans. Power Del., vol. 14, no. 4, pp. 1401–1410, Oct. 1999. [8] R. Keyhani, M. Deriche, and E. Palmer, “A high impedance fault detector using a neural network and subband decomposition,” in Proc. Int. Symp. Signal Processing and Its Applications, Kuala Lumpur, Malaysia, Aug. 13–16, 2001, pp. 458–461.
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[9] A. F. Sultan, G. W. Swift, and D. J. Fedirchuk, “Detection of high impedance arcing faults using a multi-layer perceptron,” IEEE Trans. Power Del., vol. 7, no. 4, pp. 1871–1877, Oct. 1992. [10] S. Ebron, D. L. Lubkeman, and M. White, “A neural network approach to the detection of incipient faults on power distribution feeders,” IEEE Trans. Power Del., vol. 5, no. 2, pp. 905–914, Apr. 1990. [11] M. T. Yang, J. C. Gu, C. Y. Jeng, and W. S. Kao, “Detection high impedance fault in distribution feeder using wavelet transform and artificial neural networks,” in Proc. Int. Conf. Power System Technology, Singapore, Nov. 21–24, 2004, pp. 652–657. [12] J. A. Momoh, L. G. Dias, and D. N. Laird, “An implementation of a hybrid intelligent tool for distribution system fault diagnosis,” IEEE Trans. Power Del., vol. 12, no. 2, pp. 1035–1040, Apr. 1997. [13] M. Michalik, M. Lukowicz, W. Rebizant, S.-J. Lee, and S.-H. Kang, “High impedance fault detection in distribution networks with use of wavelet-based algorithm,” IEEE Trans. Power Del., vol. 21, no. 4, pp. 1793–1802, Oct. 2006. [14] M. Kizilcay and T. Pniok, “Digital system simulation of fault arcs in power systems,” ETEP, vol. 1, no. 1, pp. 55–60, Jan./Feb. 1991. [15] S. R. Nam, J. K. Park, Y. C. Kang, and T. H. Kim, “A modeling method of a high impedance fault in a distribution system using two series timevarying resistances in EMTP,” in Proc. IEEE Power Eng. Soc. Summer Meeting, Vancouver, BC, Canada, Jul. 2001, vol. 2, pp. 1175–1180. Marek Michalik received the M.Sc. degree in electronics and the Ph.D. degree from the Electrical Engineering Department of the Wroclaw University of Technology (WUT), Wroclaw, Poland, in 1968 and 1975, respectively. In 1969, he joined WUT, where he is currently Assistant Professor. His research interests are digital signal processing for power system relaying and microprocessor system applications to power system control.
Mirosław Łukowicz was born in Poland in 1969. He received the M.Sc. and Ph.D. degrees from the Wroclaw University of Technology, Wroclaw, Poland in 1993 and 1998, respectively. Currently, he is an Assistant Professor at the Institute of Electrical Power Engineering at the Wroclaw University of Technology. His research activities include modeling and analyzing transient phenomena in power systems, application of AI techniques to signal processing, and decision-making in protective relaying.
Waldemar Rebizant (M’00–SM’05) was born in Wroclaw, Poland, in 1966. He received the M.Sc., Ph.D., and D.Sc. degrees from the Wroclaw University of Technology (WUT), Wroclaw, in 1991, 1995, and 2004, respectively. Since 1991, he has been a faculty member with the Electrical Engineering Faculty at the WUT and is currently Assistant Professor and Vice Dean for Faculty Development and International Cooperation. His research interests are digital signal processing and AI techniques for power system protection purposes.
Seung-Jae Lee (S’78–M’88) was born in Seoul, Korea, in 1955. He received the B.S. and M.S. degrees from Seoul National University, Seoul, Korea, in 1979 and 1981, respectively, and the Ph.D. degree from the University of Washington, Seattle, in 1988. Currently, he is a Professor with the Department of Electrical Engineering, Myongji University, Myongji, Korea, where he has been since 1988. He is also a Director of the Next Generation Power Technology Center. His main research areas are protective relaying, distribution automation, and AI applications to power systems.
Sang-Hee Kang (S’90–M’93) is an Associate Professor at Myongji University, Myongji, Korea. He received the B.S., M.S., and Ph.D. degrees from Seoul National University, Seoul, Korea, in 1985, 1987, and 1993, respectively. He was a Visiting Fellow and a Visiting Scholar at the University of Bath, Bath, U.K., in 1991 and 1999, respectively. His research interest is to develop digital protection systems for power systems using digital-signal-processing techniques.
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