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Artificial neural network based fault diagnostic system for electric power distribution feeders
ELEOTIIO ELSEVIER
Electric Power Systems Research, 35 (1995) 1-10
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Artificial neural network based fault diagnostic system for electric power distribution feeders E.A. Mohamed
N.D. Rao
Department of Electrical and Computer Engineering, The University of Calgar.v. Calgary, Alta., Canada T2N IN4 Received 28 February 1995
Abstract
This paper describes the development of a fast, efficient, artificial neural network (ANN) based fault diagnostic system (FDS) for distribution feeders. The principal functions of this diagnostic system are: (i) detection of fault occurrence, (ii) identification of faulted sections, and (iii) classification of faults into types, e.g. HIFs (high impedance faults) or LIFs (low impedance faults). This has been achieved through a cascaded, multilayer ANN structure using the back-propagation (BP) learning algorithm. This paper shows that the FDS accurately identifies HIFs, which are relatively difficult to identify with other methods. Test results are generated using the Manitoba Hydro 24 kV distribution feeder. These results amply demonstrate the capability of the FDS in terms of accuracy and speed with respect to detection, localization, and classification of distribution feeder faults. Keywords: Neural networks; Fault analysis; Distribution systems
1. Introduction
Restoration of power supply following faults is an important responsibility of power system operators. Rapid and accurate fault diagnosis (detection, fault section identification, and type classification) plays a central role in the fulfilment of this responsibility for maintaining continuous and reliable power supply. Distribution feeder faults are divided into two major categories: low-impedance faults (LIFs) and highimpedance faults (HIFs). LIFs include conventional shunt faults: L - L - L - G , L - L - G , L - L , and L - G . HIFs include open and/or fallen conductors. Detection methods for LIFs have been successfully developed using conventional protection schemes. On the other hand, HIFs are relatively more difficult to identify and constitute a source of hazard to utility customers and personnel, because of the potential for personal injury and property damage [1--10]. Previous research for detecting HIFs includes methods based on monitoring power frequency quantities such as load and ground current levels and sequence voltages and currents on the source side of the fault location. Some researchers used the noise and harmonics produced by arcing as a fault signature [2,4-10]. The difficulty in HIF detection prompted the use of
On leave from Ain-Shams University, Cairo, Egypt. 0378-7796/95/$09.50 © 1995 Elsevier Science S.A. All rights reserved SSDI 0378-7796(95)00990-Y
artificial intelligence (AI) methodology in general, and artificial neural network (ANN) approaches [1,7,10] in particular, to distinguish HIFs from other events. Next in importance to fault detection is its localization because of its key role in the restoration process. The increasing interest in the application of A! methodology to the power engineering area has led to the suggestion of expert system development to assist dispatchers in monitoring faulted sections of a transmission system [11,12]. Ref. [13] reports the development of an expert system to locate possible fault sections, using relay and circuit breaker information. Additional information, using real-time measurements of current and voltage phasors, has been used in some expert systems [11]. The expert system based fault diagnostic systems (FDSs) reported in the research literature are fairly accurate, but the execution times are slow, with some variation from system to system. Furthermore, it is important to remember that some of the information needed by the expert systems may not be readily available for distribution networks. In the last few years, considerable progress has been made in the application of ANNs to a variety of power system problems [1,14-17]. This is because, unlike expert systems, neural networks do not rely on a knowledge base, but look for and identify patterns, given appropriate design and training. The application of ANNs to distribution system fault diagnosis is rather sparse and limited in scope. The purpose of this paper
2
E.A. Mohamed, N.D. Rao/Electric Power Systems Research 35 (1995) 1 10
is to remedy this situation by developing an FDS for distribution networks based on ANN methodology. The multilayer feedforward network with the backpropagation (BP) learning algorithm is employed in this study. The proposed FDS is characterized by the following features: (1) high computational efficiency resulting in fast response; (2) use of only local substation measurements without need for additional telemetering; (3) HIFs and LIFs are identified in a reliable fashion; (4) adaptable for use with a wide range of network topologies; (5) uncomplicated structure for easy implementation; (6) efficient overall performance. Even though the proposed FDS is intended for use with distribution networks, a similar approach can be fruitfully applied to transmission networks as well. The FDS described in this paper utilizes only the substation measurements, i.e. current and voltage waveforms. More specifically, it includes three stages consisting of four ANN blocks in cascade (Fig. 1). The first block detects the incipient fault on the distribution feeder, while the second block localizes the fault section using substation local measurements; the third and fourth blocks classify the detected fault into HIF and LIF types. The FDS has been tested using the Manitoba Hydro 24 kV distribution feeder, giving promising results for abnormal event detection of both HIF and LIF types.
2. FDS functional description The FDS functional diagram is shown in Fig. 1. The input data represent the real-time substation current and voltage measurements interfaced with a data acqui-
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3. ANN based detection methodology ANNs have emerged as a powerful pattern recognition technique. Since the need for pattern recognition arises whenever computers interact with the real world, ANNs are broadly useful in a range of applications [18,19]. ANNs can be divided into supervised and unsupervised networks, depending on the target information during the training procedure. Consider the supervised two-layer ANN shown in Fig. 2. It consists of the input nodes, a hidden layer, and an output layer. The input nodes receive the input signals, and the neurons in the output layer provide the desired output signal. The required number of hidden layers and the number of neurons for each layer are problem dependent. All neurons are linked together and to the input nodes. Activation signals in one attenuate or amplify the signals. The symbols in Fig. 2 have the following significance: P(R × Q) is a Q input pattern of R signals each. W~(SI × R) and W2($2 × S1) are the weights for the first and second layer, respectively; B1(S]) and B2($2) ~input nodelD@d~l,4uron layer# I
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sition system using the appropriate transducers. The data acquisition and preprocessing block involve an A/D converter with a sampling rate of 32 samples per cycle, an anti-aliasing filter to filter out the undesired bandwidth of noise, and a preprocessing element to normalize input quantities to the desired level. A sequence filter is also employed to generate the feeder sequence measurements. The input data are then fed simultaneously to all the FDS blocks. The fault detection block utilizes the available information to decide whether or not the prevailing state is faulty. The fault detection block triggers both the section estimation type classification blocks in the faulty state. The faulted section estimation block is used to identify the faulty section. The other two blocks are devoted to fault type classification and recognize the fault as either HIF or LIF type. The LIF classification block is again triggered by the type classification block in order to identify LIF types.
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are the first- and second-layer biases; F~(St) and F2($2) are the first- and second-layer activation functions; A~(Si × Q) and Az(S2 × Q) are the first- and secondlayer outputs; Si and $2 are the first- and second-layer neurons.
3.1. Back-propagation adaptive learning algorithm Under this scheme, the ANN is trained to emulate a complex function by presenting it with a representative set of supervised input patterns• The back-propagation (BP) learning algorithm (Fig. 3) begins by assigning a set of random values to the connection weights and node biases. Then, the BP algorithm adjusts the weights in all links and biases in all nodes so that the mean squared error (MSE) between the output and target is minimized for all given training patterns. The corresponding updates for the weights and biases are calculated using the iterative steepest descent method [20,21]. For neuron i and input P(j), the following equations encapsulate the calculations involved:
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4. S i m u l a t i o n results
To demonstrate the effectiveness of the proposed A N N based FDS, the Manitoba Hydro distribution feeder (three-phase, 24 kV, 16 km, three equal sections, 2/0 copper conductors) was used as the test system (Fig. 4). The normal full-load level is equal to 500 A, and the light-load level is 100 A. Overcurrent relaying is set at 600 A for phase relays, and 125 A for the ground relay. The main substation (115 kV Y/A 24 kV, 25 MVA) is grounded through a zigzag transformer bank. The substation and feeder data are shown in Table 1.
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4.1. Training patterns The ANNs involved in the design of the FDS were trained by a set of training patterns. Each training pattern includes the substation current and voltage phasors in addition to the unbalanced feeder current and voltage sequence phasors. During training, a multiloading level (20%-100%) as well as a triad of system events (normal, abnormal LIF, and abnormal HIF) were considered. Three ground surface types (wet soil, moderate soil, and dry soil), with each soil further subdivided into three (low, medium, and high conductivity), were employed during the training procedure. Three faulted sections on the distribution feeder were also investigated. The training subset for normal events included 160 patterns. On the other hand, the LIF and HIF training subsets consisted of 384 and 531 patterns, respectively.
4.2. Architecture of A N N I This is the first stage in the design of the FDS based on the ANN detection methodology. In this design, the network's principal function is to detect the occurrence of a distribution feeder fault subsequent to the training of the network with training patterns. Using numerical experimentation, a network architecture comprising an optimal number of hidden layers and an optimal number of hidden neurons per layer was achieved. Fig. 5 shows the mean squared error MSE as well as the learning rate r/ versus the epoch during the training procedure. The training parameters involved in this
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scheme are shown in Table 2. Fig. 6 shows the network and detector outputs during the training and testing stages. It can be observed that the network has a perfect detection (100%) accuracy for faulty conditions. However, a total ten out of 160 normal patterns during training, and three out of 50 patterns during testing, were misdetected as faults (i.e. ten and three false trips). It was found that the patterns represented a strictly normal balanced condition, which does not correspond to real-time operation. Moreover, the number of false trips can be reduced by increasing the number of training patterns. The network's overall fault identification success rate was found to be 99.1% and 98.5%, respectively, during learning and testing. A detector threshold of 0.5 was used.
4.3. Architecture of A N N 2 This block was trained offline using abnormal input patterns. The neural network is designed to identify the faulty feeder section. Therefore, the training is based on three target levels (0.1, 0.5, and 0.9). The network experienced training difficulty because of the local minimum problem. But by increasing the network size to two hidden layers with 35 neurons in each, the training procedure converged as shown in Fig. 7. Table 2 describes the network parameters involved during the training. The network and detector performance during training is shown in Fig. 8(a) and (b) based on threshold levels of 0.45 and 0.70. The architecture was then tested using 150 conditions, with results as shown in Fig. 8(c) and (d). It was found that the network's overall success rate was 93.65% and 96% for training and testing, respectively. The relative success rate for fault localization on a feeder section is dependent on the threshold levels used for the detector. The detector threshold levels of 0.45 and 0.70 were used to ensure a good overall performance.
E.A. Mohamed, N.D. Rao / Electric Power Systems Research 35 (1995) 1-10
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Table 2 Training parameters Parameter
ANN 1
ANN 2
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No. of training patterns, Q No. of input nodes, R No. of hidden layers No. of lst-hidden-layer neurons No. of 2nd-hidden-layer neurons No. of output neurons Function of 1st hidden layer, F~ a Function of 2nd hidden layer, F 2 " Function of output layer ~ Learning rate, ~/ Momentum factor, ct Learning rate increment Learning rate decrement Error goal Mean squared error, MSE Detector threshold CPU training time (hours) on a SUN computer
1075 24 1 9
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915 24 2 10 5 1 sigrnoid sigmoid linear 0.005 0.950 1.025 0.700
384 24 1 3 5 1 sigmoid sigmoid linear 0.005 0.950 1.025 0.700
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a Sigmoid and linear stand for tan-sigmoid and linear activation functions, respectively.
4.4. Architecture of A N N 3 The main function of this block is to classify/identify the HIF conditions from other abnormal feeder events. These faults are due to downed energized conductors with or without arcing. HIFs pose a difficult problem for power utilities because of personnel injuries as well as property damage. Numerous detection techniques have been suggested for these faults [21-26]. ANN based detection schemes have also been proposed [1,5]. However, none of the extant detection methods can detect all electrical conditions resulting from downed conductors, because of capacitance switching or arcwelding transients. This difficulty has been overcome in the proposed scheme by using local measurements (current and voltage phasors) as the main input data to the FDS.
The detector network was trained using abnormal patterns as input. The network was trained on patterns that simulate one, two, or three conductors open, with or without arcing. Also, arcing conditions may occur at the source or the load side, or at both sides. Moreover, different ground surfaces were considered. Fig. 9 shows the detector training results. The detector design data are shown in Table 2. The detector performance during the training as well as the testing stages is displayed in Fig. 10. The detector was able to recognize all HIF conditions perfectly, except those involving three open conductors which were downed on the source side. This type of event is very rare but could easily be detected with overcurrent protection schemes. Table 3 compares the detection accuracy of the ANN based detector with other detectors designed using pat1.2
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8
E.A. Mohamed, N.D. R a o / E l e c t r i c P o w e r S y s t e m s Research 35 (1995) 1 - 1 0
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An artificial neural network based fault diagnostic system has been developed for distribution systems. It is comprised of four cascaded A N N blocks to perform four specific functions: (i) fault detection, (ii) fault localization, (iii) HIF identification, and (iv) classification of LIF types. The multilayer feedforward network with back-propagation learning algorithm is used for this purpose. In order to test its effectiveness, the proposed FDS has been tested on the Manitoba Hydro 24 kV distribution feeder. It was found that the overall success rate of the FDS during training and testing stages was better than 96%, using only local substation measurements (current and voltage phasors). The FDS structure lends itself easily to implementation such that the online diagnosis time is a small fraction of the measurement cycle. Moreover, the FDS has successfully
E.A. Mohamed, N.D. Rao / Electric Power Systems Research 35 (1995) 1-10
9
Table 3 FDS overall performance Detector
Testing phase
Class 1 Class 2 Class 3 Class 4 Total
Class 1 Class 2 Class 3 Class 4 Total
Fault detection (ANN 1)
percent 93.75 no. of patterns 160
100 915
Section identification (ANN 2)
percent 93.04 no. of patterns 273
96.70 273
HIF detection (ANN 3)
percent 96.6 no. of patterns 384
95.5 531
100 LIF type identification percent no. of patterns 96 (ANN 4) Total
Overall performance
Training phase
99.07 1075
100 96
91.21 273
100 96
94.0 50
100 150
93.65 100 819 50
96.0 50
95.96 915
95.7 70
100 96
100 384
percent
96.3 80 100 20
92.0 50
100 20
100 20
100 20
98.5 200
98.98 1275
96,0 150
94.01 969
96,0 150
95.96 1065
100 80
96.90
100 454
97.41
96.97
The class numbers of each ANN have the following significance: ANN h class 1 is normal, class 2 is abnormal (faulty); ANN 2: each class represents the corresponding faulted section; ANN 3: class 1 represents an LIF, class 2 represents a HIF; ANN 4: each class corresponds to an LIF fault type. Input Oata
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identified HIF events, which are harder to detect with other schemes, and LIF types. Real-time testing is the next step to confirm the practical usefulness of the proposed FDS in assisting the operator to initiate restoration procedures.
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80
The authors would like to acknowledge the financial support provided by NSERC. The permission for leave
10
E.A. Mohamed, N.D. Rao /Electric Power Systems Research 35 (1995) 1-10
given by Ain-Shams University in Cairo is also acknowledged.
References [1] A.F. Sultan, G.W. Swift and D.J. Fedirchuck, Detection of high impedance arcing faults using a multi-layer perceptron, IEEE Trans. Power Delivery, 7(4) (1992) 1871 1877. [2] A.C. Westrom, A.P.S. Meliopoulos, G.J. Cokkinides and A.H. Ayoub, Open conductor detector system, 1EEE Trans. Power Delivery, 7 (1992) 1643-1651. [3] A.F. Sultan and G.W. Swift, Detecting arcing downed-wires using fault current flicker and half-cycle asymmetry, 1EEE Trans. Power Delivery, 8 (1993) 554 561. [4] W.H. Kwon, G.W. Lee, Y.M. Park, M.C. Yoon and M.H. Yoo, High impedance fault detection utilizing incremental variance of normalized even order harmonic power, IEEE Trans. Power Delivery, 6 (2) (1991) 557-564. [5] 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 Delivery, 5 (2) (1990) 905-914. [6] 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: contributions to the evaluation of the low frequency spectrum, IEEE Trans. Power Delivery, 5 (2) (1990) 676-686. [7] C.J. Kim and B.D. Russell, High-impedance fault detection system using an adaptive element model, lEE Proc. C, 140 (1993) 153-159. [8] D.I. Jeerings and J.R. Linders, A practical protective relay for down-conductor faults, 1EEE Trans. Power Delivery, 6 (2) (1991) 565-574. [9] D.I. Jeerings and J.R. Linders, Unique aspects of distribution system harmonics due to high impedance ground faults, 1EEE Trans. Power Delivery, 5 (2) (1990) 1086-1094. [10] C.J. Kim and B.D. Russell, Classification of faults and switching events by inductive reasoning and expert system methodology, IEEE Trans. Power Delivery, 4 (3) (1989) 1631 1637. [11] A.A. Girgis and M.B. Johns, A hybrid expert system for faulted section identification, fault type classification and selection of fault location algorithms, IEEE Trans. Power Delivery, 4 (2) (1989) 978-985. [12] C. Fukui and J. Kawakami, An expert system for fault section estimation using information from protective relays and circuit breakers, 1EEE Trans. Power Delivery, P W R D - I (4) (1986) 83-90.
[13] N.D. Rao, J.L. Chen and W.C. Chan, A new connectionist expert system for distribution system fault diagnosis, Prov. IEEE Can. Con.[. Electrical and Computer Engineering, Hah'[bx, Canada, 1994, Vol. 1, pp. 50-54. [14] M. Djukanovic, B. Babic, D.J. Sobajic and Y.H. Pao, Unsupervised/supervised learning concept for 24-hour load forecasting, lEE Proe. C, 140 (1993) 311-318. [15] Y. Zhang, G.P. Chen, O.P. Malik and G.S. Hope, An artificial neural network based adaptive power system stabilizer, 1EEE Trans. Energy Convers., 8 (1) (1993) 71-77. [16] Y.Y. Hsu and L.H. Jeng, Analysis of torsional oscillations using an artificial neural network, IEEE Trans. Energy Convers., 7 (1992) 684 690. [17] S. Weerasoorya and M.A. El-Sharkawi, Identification and control of a DC motor using back-propagation neural networks, IEEE Trans. Energy Convers., 6 (4) (1991) 663-669. [18] D. Hammerstrom, Neural networks at work, IEEE Spectrum, (June) (1993) 26 32. [19] D. Hammerstrom, Working with neural networks, IEEE Spectrum, (July) (1993) 46-53. [20] J.L. McClelland and D.E. Rumelhart (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vols. 1 and 2, MIT Press, Cambridge, MA, 1988. [21] J.M. Zurada, Introduction to Art~cial Neural Systems, West, St. Paul, MN, 1992. [22] J. Carr, Detection of high impedance faults on multi-grounded primary distribution systems, IEEE Trans. Power Appar. Syst., PAS-IO0 (1981) 2008-2016. [23] B.D. Russell, Detection of arcing faults on distribution feeders, EPRI Final Rep. No. EL-2757, Electric Power Research Institute, Palo Alto, CA, Dec. 1982, 146 pp. [24] R.E. Lee and M.T. Bishop, A comparison of measured high impedance fault data to digital computer model results, IEEE Trans. Power Appar. Syst., PAS-I04 (1985) 2754 2758. [25] M. Aucoin, J. Zeigler and B.D. Russell, Feeder protection and monitoring system, Part II. Staged fault test demonstration, IEEE Trans. Power Appar. Syst., PAS-I04 (1985) 14561462. [26] H. Calhoun, M.T. Bishop, C.H. Eichler and R.E. Lee, Development and testing of an electro-mechanical relay to detect fallen distribution conductors, IEEE Trans. Power Appar. Syst., PASI01 (1982) 1643-1650. [27] E.A. Mohamed, New applications of pattern recognition to power system security, Ph.D. Thesis, University of Manitoba, Canada, 1987. [28] E.A. Mohamed and G.W. Swift, Prediction of power system generator self-excitation using pattern recognition, 1EEE Trans. Power Syst., 3 (4) (1988) 1404-1410.
Electric Power Systems Research, 35 (1995) 1-10
nR
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Artificial neural network based fault diagnostic system for electric power distribution feeders E.A. Mohamed
N.D. Rao
Department of Electrical and Computer Engineering, The University of Calgar.v. Calgary, Alta., Canada T2N IN4 Received 28 February 1995
Abstract
This paper describes the development of a fast, efficient, artificial neural network (ANN) based fault diagnostic system (FDS) for distribution feeders. The principal functions of this diagnostic system are: (i) detection of fault occurrence, (ii) identification of faulted sections, and (iii) classification of faults into types, e.g. HIFs (high impedance faults) or LIFs (low impedance faults). This has been achieved through a cascaded, multilayer ANN structure using the back-propagation (BP) learning algorithm. This paper shows that the FDS accurately identifies HIFs, which are relatively difficult to identify with other methods. Test results are generated using the Manitoba Hydro 24 kV distribution feeder. These results amply demonstrate the capability of the FDS in terms of accuracy and speed with respect to detection, localization, and classification of distribution feeder faults. Keywords: Neural networks; Fault analysis; Distribution systems
1. Introduction
Restoration of power supply following faults is an important responsibility of power system operators. Rapid and accurate fault diagnosis (detection, fault section identification, and type classification) plays a central role in the fulfilment of this responsibility for maintaining continuous and reliable power supply. Distribution feeder faults are divided into two major categories: low-impedance faults (LIFs) and highimpedance faults (HIFs). LIFs include conventional shunt faults: L - L - L - G , L - L - G , L - L , and L - G . HIFs include open and/or fallen conductors. Detection methods for LIFs have been successfully developed using conventional protection schemes. On the other hand, HIFs are relatively more difficult to identify and constitute a source of hazard to utility customers and personnel, because of the potential for personal injury and property damage [1--10]. Previous research for detecting HIFs includes methods based on monitoring power frequency quantities such as load and ground current levels and sequence voltages and currents on the source side of the fault location. Some researchers used the noise and harmonics produced by arcing as a fault signature [2,4-10]. The difficulty in HIF detection prompted the use of
On leave from Ain-Shams University, Cairo, Egypt. 0378-7796/95/$09.50 © 1995 Elsevier Science S.A. All rights reserved SSDI 0378-7796(95)00990-Y
artificial intelligence (AI) methodology in general, and artificial neural network (ANN) approaches [1,7,10] in particular, to distinguish HIFs from other events. Next in importance to fault detection is its localization because of its key role in the restoration process. The increasing interest in the application of A! methodology to the power engineering area has led to the suggestion of expert system development to assist dispatchers in monitoring faulted sections of a transmission system [11,12]. Ref. [13] reports the development of an expert system to locate possible fault sections, using relay and circuit breaker information. Additional information, using real-time measurements of current and voltage phasors, has been used in some expert systems [11]. The expert system based fault diagnostic systems (FDSs) reported in the research literature are fairly accurate, but the execution times are slow, with some variation from system to system. Furthermore, it is important to remember that some of the information needed by the expert systems may not be readily available for distribution networks. In the last few years, considerable progress has been made in the application of ANNs to a variety of power system problems [1,14-17]. This is because, unlike expert systems, neural networks do not rely on a knowledge base, but look for and identify patterns, given appropriate design and training. The application of ANNs to distribution system fault diagnosis is rather sparse and limited in scope. The purpose of this paper
2
E.A. Mohamed, N.D. Rao/Electric Power Systems Research 35 (1995) 1 10
is to remedy this situation by developing an FDS for distribution networks based on ANN methodology. The multilayer feedforward network with the backpropagation (BP) learning algorithm is employed in this study. The proposed FDS is characterized by the following features: (1) high computational efficiency resulting in fast response; (2) use of only local substation measurements without need for additional telemetering; (3) HIFs and LIFs are identified in a reliable fashion; (4) adaptable for use with a wide range of network topologies; (5) uncomplicated structure for easy implementation; (6) efficient overall performance. Even though the proposed FDS is intended for use with distribution networks, a similar approach can be fruitfully applied to transmission networks as well. The FDS described in this paper utilizes only the substation measurements, i.e. current and voltage waveforms. More specifically, it includes three stages consisting of four ANN blocks in cascade (Fig. 1). The first block detects the incipient fault on the distribution feeder, while the second block localizes the fault section using substation local measurements; the third and fourth blocks classify the detected fault into HIF and LIF types. The FDS has been tested using the Manitoba Hydro 24 kV distribution feeder, giving promising results for abnormal event detection of both HIF and LIF types.
2. FDS functional description The FDS functional diagram is shown in Fig. 1. The input data represent the real-time substation current and voltage measurements interfaced with a data acqui-
Input Data I Oata acquisition & preprocessing t voltage °'-n'"
measuilments
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3. ANN based detection methodology ANNs have emerged as a powerful pattern recognition technique. Since the need for pattern recognition arises whenever computers interact with the real world, ANNs are broadly useful in a range of applications [18,19]. ANNs can be divided into supervised and unsupervised networks, depending on the target information during the training procedure. Consider the supervised two-layer ANN shown in Fig. 2. It consists of the input nodes, a hidden layer, and an output layer. The input nodes receive the input signals, and the neurons in the output layer provide the desired output signal. The required number of hidden layers and the number of neurons for each layer are problem dependent. All neurons are linked together and to the input nodes. Activation signals in one attenuate or amplify the signals. The symbols in Fig. 2 have the following significance: P(R × Q) is a Q input pattern of R signals each. W~(SI × R) and W2($2 × S1) are the weights for the first and second layer, respectively; B1(S]) and B2($2) ~input nodelD@d~l,4uron layer# I
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sition system using the appropriate transducers. The data acquisition and preprocessing block involve an A/D converter with a sampling rate of 32 samples per cycle, an anti-aliasing filter to filter out the undesired bandwidth of noise, and a preprocessing element to normalize input quantities to the desired level. A sequence filter is also employed to generate the feeder sequence measurements. The input data are then fed simultaneously to all the FDS blocks. The fault detection block utilizes the available information to decide whether or not the prevailing state is faulty. The fault detection block triggers both the section estimation type classification blocks in the faulty state. The faulted section estimation block is used to identify the faulty section. The other two blocks are devoted to fault type classification and recognize the fault as either HIF or LIF type. The LIF classification block is again triggered by the type classification block in order to identify LIF types.
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Fig. 2. Architecture of a two-layer ANN.
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E.A. Mohamed, N.D. Rao / Electric Power Systems Research 35 (1995) 1-IO
are the first- and second-layer biases; F~(St) and F2($2) are the first- and second-layer activation functions; A~(Si × Q) and Az(S2 × Q) are the first- and secondlayer outputs; Si and $2 are the first- and second-layer neurons.
3.1. Back-propagation adaptive learning algorithm Under this scheme, the ANN is trained to emulate a complex function by presenting it with a representative set of supervised input patterns• The back-propagation (BP) learning algorithm (Fig. 3) begins by assigning a set of random values to the connection weights and node biases. Then, the BP algorithm adjusts the weights in all links and biases in all nodes so that the mean squared error (MSE) between the output and target is minimized for all given training patterns. The corresponding updates for the weights and biases are calculated using the iterative steepest descent method [20,21]. For neuron i and input P(j), the following equations encapsulate the calculations involved:
W(i, J).ew = W(i, J)ol,J + A W(i, j) B(i),ew = B(i)o]d + AB(i) where
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summed over the set of all Q training patterns, goes below a preselected error goal (Eg). Here T(i) and A (i) are the target output and actual output, respectively, at neuron i, and N is the number of output neurons. Fig. 3 explains the procedure of the BP adaptive learning techniques, which may be summarized as follows. (1) First, the initial network output A and the error (MSE) are calculated. At each epoch, new weights W and biases B are determined using the current learning rate 0. The new output and error are then recalculated. (2) If the new error exceeds the old error by more than the maximum error ratio ERm,x, the new weights and biases are discarded. In addition, q is decreased by the decrement factor (LRD). (3) If the new error is less than the old error, q is increased by the increment factor (LRI). (4) This procedure is repeated to increase r/, but only to the extent that the network can learn without increase in error. Thus a near optimal q is obtained at each epoch. (5) When q is too high to guarantee a diminution in error, it gets decreased until stable learning resumes.
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Fig. 3. Learning algorithm block diagram.
4. S i m u l a t i o n results
To demonstrate the effectiveness of the proposed A N N based FDS, the Manitoba Hydro distribution feeder (three-phase, 24 kV, 16 km, three equal sections, 2/0 copper conductors) was used as the test system (Fig. 4). The normal full-load level is equal to 500 A, and the light-load level is 100 A. Overcurrent relaying is set at 600 A for phase relays, and 125 A for the ground relay. The main substation (115 kV Y/A 24 kV, 25 MVA) is grounded through a zigzag transformer bank. The substation and feeder data are shown in Table 1.
4
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0.060 0.487
0.644 4.932
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4.1. Training patterns The ANNs involved in the design of the FDS were trained by a set of training patterns. Each training pattern includes the substation current and voltage phasors in addition to the unbalanced feeder current and voltage sequence phasors. During training, a multiloading level (20%-100%) as well as a triad of system events (normal, abnormal LIF, and abnormal HIF) were considered. Three ground surface types (wet soil, moderate soil, and dry soil), with each soil further subdivided into three (low, medium, and high conductivity), were employed during the training procedure. Three faulted sections on the distribution feeder were also investigated. The training subset for normal events included 160 patterns. On the other hand, the LIF and HIF training subsets consisted of 384 and 531 patterns, respectively.
4.2. Architecture of A N N I This is the first stage in the design of the FDS based on the ANN detection methodology. In this design, the network's principal function is to detect the occurrence of a distribution feeder fault subsequent to the training of the network with training patterns. Using numerical experimentation, a network architecture comprising an optimal number of hidden layers and an optimal number of hidden neurons per layer was achieved. Fig. 5 shows the mean squared error MSE as well as the learning rate r/ versus the epoch during the training procedure. The training parameters involved in this
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scheme are shown in Table 2. Fig. 6 shows the network and detector outputs during the training and testing stages. It can be observed that the network has a perfect detection (100%) accuracy for faulty conditions. However, a total ten out of 160 normal patterns during training, and three out of 50 patterns during testing, were misdetected as faults (i.e. ten and three false trips). It was found that the patterns represented a strictly normal balanced condition, which does not correspond to real-time operation. Moreover, the number of false trips can be reduced by increasing the number of training patterns. The network's overall fault identification success rate was found to be 99.1% and 98.5%, respectively, during learning and testing. A detector threshold of 0.5 was used.
4.3. Architecture of A N N 2 This block was trained offline using abnormal input patterns. The neural network is designed to identify the faulty feeder section. Therefore, the training is based on three target levels (0.1, 0.5, and 0.9). The network experienced training difficulty because of the local minimum problem. But by increasing the network size to two hidden layers with 35 neurons in each, the training procedure converged as shown in Fig. 7. Table 2 describes the network parameters involved during the training. The network and detector performance during training is shown in Fig. 8(a) and (b) based on threshold levels of 0.45 and 0.70. The architecture was then tested using 150 conditions, with results as shown in Fig. 8(c) and (d). It was found that the network's overall success rate was 93.65% and 96% for training and testing, respectively. The relative success rate for fault localization on a feeder section is dependent on the threshold levels used for the detector. The detector threshold levels of 0.45 and 0.70 were used to ensure a good overall performance.
E.A. Mohamed, N.D. Rao / Electric Power Systems Research 35 (1995) 1-10
5
Table 2 Training parameters Parameter
ANN 1
ANN 2
ANN 3
ANN 4
No. of training patterns, Q No. of input nodes, R No. of hidden layers No. of lst-hidden-layer neurons No. of 2nd-hidden-layer neurons No. of output neurons Function of 1st hidden layer, F~ a Function of 2nd hidden layer, F 2 " Function of output layer ~ Learning rate, ~/ Momentum factor, ct Learning rate increment Learning rate decrement Error goal Mean squared error, MSE Detector threshold CPU training time (hours) on a SUN computer
1075 24 1 9
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a Sigmoid and linear stand for tan-sigmoid and linear activation functions, respectively.
4.4. Architecture of A N N 3 The main function of this block is to classify/identify the HIF conditions from other abnormal feeder events. These faults are due to downed energized conductors with or without arcing. HIFs pose a difficult problem for power utilities because of personnel injuries as well as property damage. Numerous detection techniques have been suggested for these faults [21-26]. ANN based detection schemes have also been proposed [1,5]. However, none of the extant detection methods can detect all electrical conditions resulting from downed conductors, because of capacitance switching or arcwelding transients. This difficulty has been overcome in the proposed scheme by using local measurements (current and voltage phasors) as the main input data to the FDS.
The detector network was trained using abnormal patterns as input. The network was trained on patterns that simulate one, two, or three conductors open, with or without arcing. Also, arcing conditions may occur at the source or the load side, or at both sides. Moreover, different ground surfaces were considered. Fig. 9 shows the detector training results. The detector design data are shown in Table 2. The detector performance during the training as well as the testing stages is displayed in Fig. 10. The detector was able to recognize all HIF conditions perfectly, except those involving three open conductors which were downed on the source side. This type of event is very rare but could easily be detected with overcurrent protection schemes. Table 3 compares the detection accuracy of the ANN based detector with other detectors designed using pat1.2
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8
E.A. Mohamed, N.D. R a o / E l e c t r i c P o w e r S y s t e m s Research 35 (1995) 1 - 1 0
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An artificial neural network based fault diagnostic system has been developed for distribution systems. It is comprised of four cascaded A N N blocks to perform four specific functions: (i) fault detection, (ii) fault localization, (iii) HIF identification, and (iv) classification of LIF types. The multilayer feedforward network with back-propagation learning algorithm is used for this purpose. In order to test its effectiveness, the proposed FDS has been tested on the Manitoba Hydro 24 kV distribution feeder. It was found that the overall success rate of the FDS during training and testing stages was better than 96%, using only local substation measurements (current and voltage phasors). The FDS structure lends itself easily to implementation such that the online diagnosis time is a small fraction of the measurement cycle. Moreover, the FDS has successfully
E.A. Mohamed, N.D. Rao / Electric Power Systems Research 35 (1995) 1-10
9
Table 3 FDS overall performance Detector
Testing phase
Class 1 Class 2 Class 3 Class 4 Total
Class 1 Class 2 Class 3 Class 4 Total
Fault detection (ANN 1)
percent 93.75 no. of patterns 160
100 915
Section identification (ANN 2)
percent 93.04 no. of patterns 273
96.70 273
HIF detection (ANN 3)
percent 96.6 no. of patterns 384
95.5 531
100 LIF type identification percent no. of patterns 96 (ANN 4) Total
Overall performance
Training phase
99.07 1075
100 96
91.21 273
100 96
94.0 50
100 150
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95.96 915
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100 96
100 384
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98.98 1275
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The class numbers of each ANN have the following significance: ANN h class 1 is normal, class 2 is abnormal (faulty); ANN 2: each class represents the corresponding faulted section; ANN 3: class 1 represents an LIF, class 2 represents a HIF; ANN 4: each class corresponds to an LIF fault type. Input Oata
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identified HIF events, which are harder to detect with other schemes, and LIF types. Real-time testing is the next step to confirm the practical usefulness of the proposed FDS in assisting the operator to initiate restoration procedures.
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The authors would like to acknowledge the financial support provided by NSERC. The permission for leave
10
E.A. Mohamed, N.D. Rao /Electric Power Systems Research 35 (1995) 1-10
given by Ain-Shams University in Cairo is also acknowledged.
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