- Email: [email protected]
Neural network based classifier for power system protection
Electric
ELSEVIER
Neural network W.W.L. School
of Electrical
Power
Systems
Research
42 (1997)
109- 114
based classifier for power system protection Keerthipala
and Electron
*, Chan Tat Wai, Wang
Engineering,
Nanyang
Technological
Huisheng
Unit~ersiiJ~. Singapore
639798,
Singapore
Received 20 February 1996
Abstract This paper presents a novel technique for classifying power system faults and hence performing the function of a protective relay. An artificial neural network model is developed and trained, using a data-base consisting of the results of transient and steady state performance of the power system, to capture all of the salient features and form a single relay module. The trained module is tested on a prototype power system, and found to give reasonably accurate results for classification of faults. 0 1997 Elsevier Science .%A. Keywords;
System protection; Artificial neural network applications; EMTDC
1. Introduction
In order to generate electric power and transmit it over long distances to load centers a large amount of capital investment is made. It is therefore important to run the power system at peak efficiency and protect it from accidents. However, some accidents are inevitable as insulation deteriorates or unforeseen things occur, such as strokes of lightning. Insulation aging and lightning strokes together can cause a tremendous amount of damage to highly expensive power equipment. Protective relays minimize this damage, and hence the expenses, by locating and classifying the fault immediately and opening the correct circuit breakers (CBS) to isolate the faulted section. However, modern protective relays are set to take advantage of digital technology, and new relay algorithms are also being introduced and implemented using microprocessors and digital signal processors for faster and better performance [1 11. This paper describes a relatively new technique in fault classification using artificial neural networks (ANNs). 1.1. Protective relays Protective relays are extremely important in the reliable operation of a power system, and in particular for a large scale power system network [l]. The simplest * Corresponding author. Tel.: + 65 799 1306; fax: + 65 791 2687. 0378.7796/97/$17.00 0 1997 Elsevier Science S.A. All rights reserved Pi1 SO378-7796(96)01185-6
simulation
form of a protective scheme consists of a sensing device, a relay, and a circuit breaker. For example, a current transformer (CT) is a sensing device which keeps track of the line current of a particular section of the system and sends a secondary level ‘true’ replica of the current waveform to the protective relay. The relay in turn makes a ‘sensible’ judgement, based on the information arriving from the sensing device, as to whether there exists a system fault or an abnormality in the particular section concerned. If the relay ‘sees’that there is a fault, it will instruct the circuit breaker to interrupt and isolate the affected section of the power system. 1.2. Classcjkation of systemfaults In practice the most common type of fault is the single line to ground fault. Other types which occur less frequently are three-phase faults, phase-to-phase faults, and two-phase-to-ground faults. To detect these faults there are a number of variables such as current (I), voltage (v), and frequency (f) that can be sensed through current transformers, voltage transformers, and frequency sensorsrespectively. In order to maintain the power system ‘fault free’ and without overloading, relays can use one or more of these sensedvariables in making a judgement. Hence, different types of relay can exist with each being dedicated to an assignedfunction: for example, over-current, over-voltage, impedance, dis-
110
W. W.L. Keerthipala et al. /Electric Power Systems Research 42 (1997) 109-l 14
tance, directional, and frequency relays. Their functions are self explanatory. However, all these different types of relays use the same basic sensing parameters (voltage, current, and frequency) in different possible combinations made out of positive, negative, and zero sequence components. These relays are hardware devices and often bulky and expensive. If most of these different functions can be integrated into a single module, it would be more efficient and less expensive. The present study aims at achieving this goal through ANN-based fault classification.
trained ANN module (Fig. 3). The ANN module has been pre-trained such that its outputs represent the corresponding fault type. In the present study the faulted phase or phases are determined by the ANN module. These outputs are binary and if the ANN module ‘sees’the ‘A’ phase current is too high the ‘A’ unit (Neuron) will have an output of 1; otherwise the output will be 0. Similarly, if it ‘sees’the Neutral wire is involved the ‘N’ unit (Neuron) will output 1; otherwise 0. This ANN module was trained with waveforms acquired from the prototype power system of which a complete circuit diagram with parameters is given in Fig. 6.
2. Data-base of fault waveforms Forming a data-base of system waveforms under fault and normal conditions can be achieved by two different methods: experimentation and computer simulation. Comprehensive electromagnetic transient simulation programs such as EMTP and EMTDC [2] can be used to simulate various types of faults at different locations of a large power system network [1,3]. For a known power system, depending on the fault type, it is pre-determined as to which type of relay should respond. Also the degree of response is generally accounted by block average integration of comparator outputs [4]. Thus it is possible to establish a look-up table (or a data-base) which presents the relay output against a given fault condition. Such a data-base is used to train an ANN to extract all the salient features of the existing relay scheme. The present study initially usesan EMTDC (Electromagnetic Transients including D.C.) type simulation (see Fig. 1) of the power system (Fig. 6) in order to build up a data-base on fault type against the waveforms of the fault current and voltage and its location. The ANN model is then successfully trained to extract all the salient features of the system including those of the relay. Once the neural network is trained its responsefor a given system condition (fault, overload, or normal) is observed. The model presented in this paper can differentiate faults or overload from normal loading with a reasonable degree of accuracy.
2.2. Prototype power system A simple three phase power system was built in order to test the ANN protective schemeon an experimental basis. This simple system consists of a three-phase power supply, a short transmission line represented by lumped elements, and a three-phase resistive load. The current transformers (CT, 6015 A) and potential transformers (PT, 24015 V) are connected to a PC 586 (Pentium) via an analog to digital (DAS-8 card) interface. Thus the ANN module residing in the PC 586 can monitor three-phase voltages and three line currents at
2.1. Fault condition The fault condition is represented by a set of three phase voltages and currents sensedby the transducers attached to the relay. One cycle length of waveform is sufficient to represent a current or voltage signal at a given time. This one cycle of waveform is sent through the level detectors and block average comparators [4] for a ‘decision making process’ in the case of a conventional relay unit. However, in the present investigation, the six signals (three phase voltages and three line currents) of one sampled point each are sent through a
Fig. 1. Current and voltage waveforms for a double phase-to-ground fault, at the load end, of the power system (Fig. 6).
W. W.L.
Keerthipala
CT
et al. /Electric
Power
ZL
Systems
Research
Fig. 2. The ANN-based
software
relay.
the transmitting end of the power system. Various faults (e.g. three phase to neutral, two phase to neutral, and one phase to neutral etc.) were administered at the load end, and corresponding line currents and phase voltages were acquired by the computer for the ANN module to process and produce the relay-equivalentoutput to be sent to the molded case circuit breaker (MCCB). Fig. 2 shows a single line diagram of one end of the power system and the function of the ANN module as a software relay. The maximum fault current that is tested in this prototype power system is 10 A, and the normal line current is at 2 A while 5 A is considered an overload.
3. Neural network
model
ANNs are computational structures, and they loosely mimic their biological counterparts. The basic unit is a Neuron, and amongst neurons there exist ‘connections’. Each neuron can influence the other through these connections, and the degree of influence is proportional to the weight (strength) associated with the connection. Among the different connectionist architectures most widely used in power system applications [5,6] is the Back Propagation type model. Each unit or neuron processes all the inputs and send the output to the next unit connected. 3.1. Buck propagation
type ANN
109-I
14
111
set of input and output (target) pattern pairs. This organized procedure based on error correction via feed back is popularly known as a ‘back-propagation’ algorithm.
3.2. Training
1
42 (1997)
of the ANN
model
The ANN model used is executed by a structured computer software package [lo] that can update neurons almost simultaneously. This model resides in the Pentium and requires a large amount of random access memory during operation. The proposed ANN configuration (see Fig. 3) has six input neurons (input layer l), receiving three phase currents and three phase voltages of secondary level, and four output neutrons indicating the faulty line. The hidden layer directly connected to the output layer has 16 neurons to bridge input layer 2 with the output layer. The input layer 2 has 12 neurons with the first six receiving three phase currents and voltages and the other six receiving their respective sequence components (through the ANN-based sequence net). For a set of inputs there is a corresponding set of output ‘target’ values already stored in a data array as explained in Section 2. These inputs and target outputs were ‘shown’ to the ANN in a sequential manner so that the weights were updated step by step. the error between the actual output and the target was evaluated after every update. The back-propagation learning algorithm employed works towards reduction of the r.m.s error, and the training ceases as the total sum of squares (tss) error reaches just below the error criteria (say 1.0%) initially set. The weights are then supposed to have converged enough that they should represent the non-linear transfer functions between in-
Fault
classifier
model
Some neurons are directly connected to known inputs (see Fig. 3) and such neurons are called input layer neurons, Their output activations are initially unknown. Some other neutrons are directly connected to outputs and they are called output layer neurons whose inputs are initially unknown. The hidden layer neurons act as a bridge between input layer and output layer neurons. As initial activations of some of the neurons are unknown an iterative type of computation scheme [7,8] is used to evaluate the best possible connection weights between neurons of different layers for a given
Input
‘a
Fig.
3. ANN
‘b
‘c
%
configuration.
h
v,
1
W. W.L.
112 RMS
Error
(Nahvork
Keerthipala
et al. /Electric
C)
0.08
a 0.0 0.04
H
Research
42 (1997)
109-I
14
As the training progressed the r.m.s. error [square root of the tss of error) (target - actual output)] reduces to a mean value of about 0.07 as can be seen from Fig. 4.
0.1
i
Systems
3.3. Training results (tss error)
0.12 e
Power
O.CC?
3.4. Flobv chart of ANN fault cluss$er Fig. 4. Plot
of square
root
of tss vs. epoch
number.
puts and outputs of the ANN model accurately. However, the ANN-based sequence net was trained separately before the start of training of the ANN-based fault classifier (see Fig. 3).
Once the ANN is trained with a tss error of nearly 0.01 the training is stopped, and the weight matrices are stored for future use in the actual implementation. Fig. 5 shows a schematic flow chart of the fault classification.
4. Results and discussion Having trained the ANN model with several hundreds of input/output pattern pairs over a large number of epochs (more than 200 000) it was possible to achieve convergence in the update of weights. At this point training was stopped and the updated weights were stored in matrix form for testing of the ANN model with new inputs which were never shown to the model before. The results of such a testing with simulated waveforms as input are presented in Table 1. Having seen the success in fault classification with simulated waveforms as input to the ANN model, it was further tested with on-line experimental waveforms. Table 2 summarises the results of the response of the ANN when implemented on the experimental power system shown in Fig. 6. Each category of faults was tested at least 10 times for consistency. The ANN model responded to different types of faults with a reasonable degree of accuracy. It only failed to respond accurately for marginal overload cases. Although it gives almost 100% accuracy for Table 1 Response set)
of the ANN
(by simulation)
(“XI correct
classification
of test
NO
Number Eneqise
relay to trip
ABC AB BC CA AN BN CN ABN BCN CAN No fault Fig.
5. Flow
chart
of the ANN-based
fault
classification.
of presentations
( x I 000 000)
0.2
0.8
1.4
2.0
2.6
3.2
90.74 94.94 93.33 94.07 97.04 95.80 93.70 93.83 93.09 93.21 100.0
90.37 95.80 94.20 93.83 96.91 95.31 94.32 94.94 93.95 94.23 100.0
90.62 95.80 94.32 93.70 95.68 95.43 93.46 94.69 93.58 94.07 100.0
90.74 95.06 94.81 94.32 95.06 94.94 92.72 93.21 91.60 92.59 100.0
91.4s 5.68 94.94 94.07 95.31 95.68 93.09 93.58 91.73 92.96 100.0
91.48 95.68 94.81 94.07 95.43 95.80 93.21 93.09 92.22 92.84 100.0
W. W.L. Table 2 Response Fault
Keerthipala
et al. ,/Electric
Power
Systems
Research
42 (1997)
113
109-114
5. Conclusions of the ANN
pattern
A-B-C A-B B-C C-A A-N B-N BBN C-N A-B-N B-C-N C-A-N No fault
(on real-time Response Tripped Tripped Tripped Tripped Tripped Tripped Tripped Tripped Tripped Tripped Tripped Tripped
testing)
Classification of power system faults using artificial neural networks has been achieved in real time. This means that the ANN model with further refinement can be implemented in an actual power system to monitor the occurrence of faults and to interrupt the circuit breakers through a computer link. Alternatively, this ANN model can be built into a solid state VLSI device [9].
of the MCCB 10 09 05 09 08 07 07 07 09 09 09 00
times times times times times times times times times times times times
out out out out out out out out out out out out
of of of of of of of of of of of of
10 10 10 10 10 10 10 10 10 10 IO 10
tests tests tests tests tests tests tests tests tests tests tests tests
Acknowledgements The authors wish to thank the School of EEE, Nanyang Technological University, for providing research facilities and grants (RP31/93) to conduct this research project, and the final year project students involved in the FYP 1027, 1994/95, for their contribution in forming the prototype power system. Further we thank the International Relations Office, NTU, for arranging a visiting student (Miss Wang Huisheng) from Xian Jiao Tong University, China to undertake simulation aspects of the project.
waveforms of EMTDC simulation, its accuracy for real-time system waveforms is limited, as can be seen from Table 2. One reason for this is the fact that the modelling of the prototype power system on simulation cannot be 100% accurate.
resitance load bank 40A ELCB 30mA trip
f -
\ DAS-8
board
>
power system 10%; nominal
Ll EC :< ,-,
< < <
&
24O:W Fault
actuator
L-J4
‘-TEE-J Fig. 6. Prototype leakage reactance,
c
(experimental set-up). load resistance/phase,
Isolation transformer rating, 3 kVA; transmission line inductance, 48 mH; approximate 44 R: current transformer burden, 1.0 Q maximum input to the DAS-8 board, 5.0 V.
Fig. 7. Power
system
ready
for EMTDC
simulation.
114
Appendix A. EMTDC
W. W.L.
simulation
Keerthipala
et al. /Electric
Power
of the power system
The draft package of the PSCADiEMTDC software [2] can be used to construct the on-line diagram of the power system for simulation. After assigning the faults this software generates on its own a fortran 77 code and an appropriate data file for transient simulation. The user-friendly graphical user interface of the PSCAD further allows the user to plot waveforms as the simulation is progressed. Fig. 1 shows a typical output of a simulation of the system shown in Fig. 7.
References 1] P.G. McLaren, R. Kuffel, J. Giesbrecht, W.W.L. Keerthipala, A. Castro, D. Fedirchuk, S. Innes, K. Mustaphi, K. Sletten, On site relay transient testing for a series compensation upgrade, IEEE Trans. Power Deliv. 9 (3) (1994) 130X- 1315. 21 PSCAD/EMTDC Power System Simulation Software User’s Manual, Manitoba HVDC Research Centre, Winnipeg, Canada, 1994 release. 31 W.W.L. Keerthipala, T.W. Chan, Neural network based integration of over-current and impedance relays. Paper presented at
Systems
Research
42 (1997)
109-l
14
the IPEC ‘95, Singapore. Feb. 27-March 1. 1995. on the 500 kV series com[41 Hong M. Yang, Relay performance pensated Dorsey Forbes Chisago transmission line, M.Sc. thesis. University of Manitoba, Canada, October 1991. Keerthipala, Chan Tat Wai, Kee Chin Siang, Hu Leong [51 W.W.L. Peng, DSP implementation of a neural network based fault classifier for power system protection, Proc. IEEE Int. Conf. on Systems, Man and Cybernetics, Vancouver, Canada, October 22-25, 1995. Low Tah Chong, Tham Chong Leong, An 161W.W.L. Keerthipala. application of a back-propagation type neural network for harmonic distortion analysis, paper presented at the IPEC ‘95, Feb 27-March 1, Conference Proceedings. pp. 501-506. Neural Computing, Theory and Practice. Van 171 P.D. Wasserman, Nostrand Reinhold Press, New York, 1989. D.E. Rumelhart, Explorations in Parallel Dis[81 J.L. McClelland, tributed Processing, MIT Press, Massachusetts, 1988. VLSI devices and circuits for neural [91 H.C. Card. W.R. Moore. networks, lnt. J. Neural Syst. 1 (2) (1989) 149-165. Reference guide for NeuralWorks Professional II/ PO1 NeuralWare, plus and NeuralWorks Explorer, 1991. u 11 A.J. Neufeld, E.N. Dirks. P.G. McLaren, G.W. Swift and R.W. Hayw-ood, A microprocessor platform for a generic protection system, paper presented at the 33rd Midwest Symp. Circuits and Systems, Calgary, Canada, August I2- 15. 1990, Conference Proceedings, pp. 377-380.
ELSEVIER
Neural network W.W.L. School
of Electrical
Power
Systems
Research
42 (1997)
109- 114
based classifier for power system protection Keerthipala
and Electron
*, Chan Tat Wai, Wang
Engineering,
Nanyang
Technological
Huisheng
Unit~ersiiJ~. Singapore
639798,
Singapore
Received 20 February 1996
Abstract This paper presents a novel technique for classifying power system faults and hence performing the function of a protective relay. An artificial neural network model is developed and trained, using a data-base consisting of the results of transient and steady state performance of the power system, to capture all of the salient features and form a single relay module. The trained module is tested on a prototype power system, and found to give reasonably accurate results for classification of faults. 0 1997 Elsevier Science .%A. Keywords;
System protection; Artificial neural network applications; EMTDC
1. Introduction
In order to generate electric power and transmit it over long distances to load centers a large amount of capital investment is made. It is therefore important to run the power system at peak efficiency and protect it from accidents. However, some accidents are inevitable as insulation deteriorates or unforeseen things occur, such as strokes of lightning. Insulation aging and lightning strokes together can cause a tremendous amount of damage to highly expensive power equipment. Protective relays minimize this damage, and hence the expenses, by locating and classifying the fault immediately and opening the correct circuit breakers (CBS) to isolate the faulted section. However, modern protective relays are set to take advantage of digital technology, and new relay algorithms are also being introduced and implemented using microprocessors and digital signal processors for faster and better performance [1 11. This paper describes a relatively new technique in fault classification using artificial neural networks (ANNs). 1.1. Protective relays Protective relays are extremely important in the reliable operation of a power system, and in particular for a large scale power system network [l]. The simplest * Corresponding author. Tel.: + 65 799 1306; fax: + 65 791 2687. 0378.7796/97/$17.00 0 1997 Elsevier Science S.A. All rights reserved Pi1 SO378-7796(96)01185-6
simulation
form of a protective scheme consists of a sensing device, a relay, and a circuit breaker. For example, a current transformer (CT) is a sensing device which keeps track of the line current of a particular section of the system and sends a secondary level ‘true’ replica of the current waveform to the protective relay. The relay in turn makes a ‘sensible’ judgement, based on the information arriving from the sensing device, as to whether there exists a system fault or an abnormality in the particular section concerned. If the relay ‘sees’that there is a fault, it will instruct the circuit breaker to interrupt and isolate the affected section of the power system. 1.2. Classcjkation of systemfaults In practice the most common type of fault is the single line to ground fault. Other types which occur less frequently are three-phase faults, phase-to-phase faults, and two-phase-to-ground faults. To detect these faults there are a number of variables such as current (I), voltage (v), and frequency (f) that can be sensed through current transformers, voltage transformers, and frequency sensorsrespectively. In order to maintain the power system ‘fault free’ and without overloading, relays can use one or more of these sensedvariables in making a judgement. Hence, different types of relay can exist with each being dedicated to an assignedfunction: for example, over-current, over-voltage, impedance, dis-
110
W. W.L. Keerthipala et al. /Electric Power Systems Research 42 (1997) 109-l 14
tance, directional, and frequency relays. Their functions are self explanatory. However, all these different types of relays use the same basic sensing parameters (voltage, current, and frequency) in different possible combinations made out of positive, negative, and zero sequence components. These relays are hardware devices and often bulky and expensive. If most of these different functions can be integrated into a single module, it would be more efficient and less expensive. The present study aims at achieving this goal through ANN-based fault classification.
trained ANN module (Fig. 3). The ANN module has been pre-trained such that its outputs represent the corresponding fault type. In the present study the faulted phase or phases are determined by the ANN module. These outputs are binary and if the ANN module ‘sees’the ‘A’ phase current is too high the ‘A’ unit (Neuron) will have an output of 1; otherwise the output will be 0. Similarly, if it ‘sees’the Neutral wire is involved the ‘N’ unit (Neuron) will output 1; otherwise 0. This ANN module was trained with waveforms acquired from the prototype power system of which a complete circuit diagram with parameters is given in Fig. 6.
2. Data-base of fault waveforms Forming a data-base of system waveforms under fault and normal conditions can be achieved by two different methods: experimentation and computer simulation. Comprehensive electromagnetic transient simulation programs such as EMTP and EMTDC [2] can be used to simulate various types of faults at different locations of a large power system network [1,3]. For a known power system, depending on the fault type, it is pre-determined as to which type of relay should respond. Also the degree of response is generally accounted by block average integration of comparator outputs [4]. Thus it is possible to establish a look-up table (or a data-base) which presents the relay output against a given fault condition. Such a data-base is used to train an ANN to extract all the salient features of the existing relay scheme. The present study initially usesan EMTDC (Electromagnetic Transients including D.C.) type simulation (see Fig. 1) of the power system (Fig. 6) in order to build up a data-base on fault type against the waveforms of the fault current and voltage and its location. The ANN model is then successfully trained to extract all the salient features of the system including those of the relay. Once the neural network is trained its responsefor a given system condition (fault, overload, or normal) is observed. The model presented in this paper can differentiate faults or overload from normal loading with a reasonable degree of accuracy.
2.2. Prototype power system A simple three phase power system was built in order to test the ANN protective schemeon an experimental basis. This simple system consists of a three-phase power supply, a short transmission line represented by lumped elements, and a three-phase resistive load. The current transformers (CT, 6015 A) and potential transformers (PT, 24015 V) are connected to a PC 586 (Pentium) via an analog to digital (DAS-8 card) interface. Thus the ANN module residing in the PC 586 can monitor three-phase voltages and three line currents at
2.1. Fault condition The fault condition is represented by a set of three phase voltages and currents sensedby the transducers attached to the relay. One cycle length of waveform is sufficient to represent a current or voltage signal at a given time. This one cycle of waveform is sent through the level detectors and block average comparators [4] for a ‘decision making process’ in the case of a conventional relay unit. However, in the present investigation, the six signals (three phase voltages and three line currents) of one sampled point each are sent through a
Fig. 1. Current and voltage waveforms for a double phase-to-ground fault, at the load end, of the power system (Fig. 6).
W. W.L.
Keerthipala
CT
et al. /Electric
Power
ZL
Systems
Research
Fig. 2. The ANN-based
software
relay.
the transmitting end of the power system. Various faults (e.g. three phase to neutral, two phase to neutral, and one phase to neutral etc.) were administered at the load end, and corresponding line currents and phase voltages were acquired by the computer for the ANN module to process and produce the relay-equivalentoutput to be sent to the molded case circuit breaker (MCCB). Fig. 2 shows a single line diagram of one end of the power system and the function of the ANN module as a software relay. The maximum fault current that is tested in this prototype power system is 10 A, and the normal line current is at 2 A while 5 A is considered an overload.
3. Neural network
model
ANNs are computational structures, and they loosely mimic their biological counterparts. The basic unit is a Neuron, and amongst neurons there exist ‘connections’. Each neuron can influence the other through these connections, and the degree of influence is proportional to the weight (strength) associated with the connection. Among the different connectionist architectures most widely used in power system applications [5,6] is the Back Propagation type model. Each unit or neuron processes all the inputs and send the output to the next unit connected. 3.1. Buck propagation
type ANN
109-I
14
111
set of input and output (target) pattern pairs. This organized procedure based on error correction via feed back is popularly known as a ‘back-propagation’ algorithm.
3.2. Training
1
42 (1997)
of the ANN
model
The ANN model used is executed by a structured computer software package [lo] that can update neurons almost simultaneously. This model resides in the Pentium and requires a large amount of random access memory during operation. The proposed ANN configuration (see Fig. 3) has six input neurons (input layer l), receiving three phase currents and three phase voltages of secondary level, and four output neutrons indicating the faulty line. The hidden layer directly connected to the output layer has 16 neurons to bridge input layer 2 with the output layer. The input layer 2 has 12 neurons with the first six receiving three phase currents and voltages and the other six receiving their respective sequence components (through the ANN-based sequence net). For a set of inputs there is a corresponding set of output ‘target’ values already stored in a data array as explained in Section 2. These inputs and target outputs were ‘shown’ to the ANN in a sequential manner so that the weights were updated step by step. the error between the actual output and the target was evaluated after every update. The back-propagation learning algorithm employed works towards reduction of the r.m.s error, and the training ceases as the total sum of squares (tss) error reaches just below the error criteria (say 1.0%) initially set. The weights are then supposed to have converged enough that they should represent the non-linear transfer functions between in-
Fault
classifier
model
Some neurons are directly connected to known inputs (see Fig. 3) and such neurons are called input layer neurons, Their output activations are initially unknown. Some other neutrons are directly connected to outputs and they are called output layer neurons whose inputs are initially unknown. The hidden layer neurons act as a bridge between input layer and output layer neurons. As initial activations of some of the neurons are unknown an iterative type of computation scheme [7,8] is used to evaluate the best possible connection weights between neurons of different layers for a given
Input
‘a
Fig.
3. ANN
‘b
‘c
%
configuration.
h
v,
1
W. W.L.
112 RMS
Error
(Nahvork
Keerthipala
et al. /Electric
C)
0.08
a 0.0 0.04
H
Research
42 (1997)
109-I
14
As the training progressed the r.m.s. error [square root of the tss of error) (target - actual output)] reduces to a mean value of about 0.07 as can be seen from Fig. 4.
0.1
i
Systems
3.3. Training results (tss error)
0.12 e
Power
O.CC?
3.4. Flobv chart of ANN fault cluss$er Fig. 4. Plot
of square
root
of tss vs. epoch
number.
puts and outputs of the ANN model accurately. However, the ANN-based sequence net was trained separately before the start of training of the ANN-based fault classifier (see Fig. 3).
Once the ANN is trained with a tss error of nearly 0.01 the training is stopped, and the weight matrices are stored for future use in the actual implementation. Fig. 5 shows a schematic flow chart of the fault classification.
4. Results and discussion Having trained the ANN model with several hundreds of input/output pattern pairs over a large number of epochs (more than 200 000) it was possible to achieve convergence in the update of weights. At this point training was stopped and the updated weights were stored in matrix form for testing of the ANN model with new inputs which were never shown to the model before. The results of such a testing with simulated waveforms as input are presented in Table 1. Having seen the success in fault classification with simulated waveforms as input to the ANN model, it was further tested with on-line experimental waveforms. Table 2 summarises the results of the response of the ANN when implemented on the experimental power system shown in Fig. 6. Each category of faults was tested at least 10 times for consistency. The ANN model responded to different types of faults with a reasonable degree of accuracy. It only failed to respond accurately for marginal overload cases. Although it gives almost 100% accuracy for Table 1 Response set)
of the ANN
(by simulation)
(“XI correct
classification
of test
NO
Number Eneqise
relay to trip
ABC AB BC CA AN BN CN ABN BCN CAN No fault Fig.
5. Flow
chart
of the ANN-based
fault
classification.
of presentations
( x I 000 000)
0.2
0.8
1.4
2.0
2.6
3.2
90.74 94.94 93.33 94.07 97.04 95.80 93.70 93.83 93.09 93.21 100.0
90.37 95.80 94.20 93.83 96.91 95.31 94.32 94.94 93.95 94.23 100.0
90.62 95.80 94.32 93.70 95.68 95.43 93.46 94.69 93.58 94.07 100.0
90.74 95.06 94.81 94.32 95.06 94.94 92.72 93.21 91.60 92.59 100.0
91.4s 5.68 94.94 94.07 95.31 95.68 93.09 93.58 91.73 92.96 100.0
91.48 95.68 94.81 94.07 95.43 95.80 93.21 93.09 92.22 92.84 100.0
W. W.L. Table 2 Response Fault
Keerthipala
et al. ,/Electric
Power
Systems
Research
42 (1997)
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5. Conclusions of the ANN
pattern
A-B-C A-B B-C C-A A-N B-N BBN C-N A-B-N B-C-N C-A-N No fault
(on real-time Response Tripped Tripped Tripped Tripped Tripped Tripped Tripped Tripped Tripped Tripped Tripped Tripped
testing)
Classification of power system faults using artificial neural networks has been achieved in real time. This means that the ANN model with further refinement can be implemented in an actual power system to monitor the occurrence of faults and to interrupt the circuit breakers through a computer link. Alternatively, this ANN model can be built into a solid state VLSI device [9].
of the MCCB 10 09 05 09 08 07 07 07 09 09 09 00
times times times times times times times times times times times times
out out out out out out out out out out out out
of of of of of of of of of of of of
10 10 10 10 10 10 10 10 10 10 IO 10
tests tests tests tests tests tests tests tests tests tests tests tests
Acknowledgements The authors wish to thank the School of EEE, Nanyang Technological University, for providing research facilities and grants (RP31/93) to conduct this research project, and the final year project students involved in the FYP 1027, 1994/95, for their contribution in forming the prototype power system. Further we thank the International Relations Office, NTU, for arranging a visiting student (Miss Wang Huisheng) from Xian Jiao Tong University, China to undertake simulation aspects of the project.
waveforms of EMTDC simulation, its accuracy for real-time system waveforms is limited, as can be seen from Table 2. One reason for this is the fact that the modelling of the prototype power system on simulation cannot be 100% accurate.
resitance load bank 40A ELCB 30mA trip
f -
\ DAS-8
board
>
power system 10%; nominal
Ll EC :< ,-,
< < <
&
24O:W Fault
actuator
L-J4
‘-TEE-J Fig. 6. Prototype leakage reactance,
c
(experimental set-up). load resistance/phase,
Isolation transformer rating, 3 kVA; transmission line inductance, 48 mH; approximate 44 R: current transformer burden, 1.0 Q maximum input to the DAS-8 board, 5.0 V.
Fig. 7. Power
system
ready
for EMTDC
simulation.
114
Appendix A. EMTDC
W. W.L.
simulation
Keerthipala
et al. /Electric
Power
of the power system
The draft package of the PSCADiEMTDC software [2] can be used to construct the on-line diagram of the power system for simulation. After assigning the faults this software generates on its own a fortran 77 code and an appropriate data file for transient simulation. The user-friendly graphical user interface of the PSCAD further allows the user to plot waveforms as the simulation is progressed. Fig. 1 shows a typical output of a simulation of the system shown in Fig. 7.
References 1] P.G. McLaren, R. Kuffel, J. Giesbrecht, W.W.L. Keerthipala, A. Castro, D. Fedirchuk, S. Innes, K. Mustaphi, K. Sletten, On site relay transient testing for a series compensation upgrade, IEEE Trans. Power Deliv. 9 (3) (1994) 130X- 1315. 21 PSCAD/EMTDC Power System Simulation Software User’s Manual, Manitoba HVDC Research Centre, Winnipeg, Canada, 1994 release. 31 W.W.L. Keerthipala, T.W. Chan, Neural network based integration of over-current and impedance relays. Paper presented at
Systems
Research
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14
the IPEC ‘95, Singapore. Feb. 27-March 1. 1995. on the 500 kV series com[41 Hong M. Yang, Relay performance pensated Dorsey Forbes Chisago transmission line, M.Sc. thesis. University of Manitoba, Canada, October 1991. Keerthipala, Chan Tat Wai, Kee Chin Siang, Hu Leong [51 W.W.L. Peng, DSP implementation of a neural network based fault classifier for power system protection, Proc. IEEE Int. Conf. on Systems, Man and Cybernetics, Vancouver, Canada, October 22-25, 1995. Low Tah Chong, Tham Chong Leong, An 161W.W.L. Keerthipala. application of a back-propagation type neural network for harmonic distortion analysis, paper presented at the IPEC ‘95, Feb 27-March 1, Conference Proceedings. pp. 501-506. Neural Computing, Theory and Practice. Van 171 P.D. Wasserman, Nostrand Reinhold Press, New York, 1989. D.E. Rumelhart, Explorations in Parallel Dis[81 J.L. McClelland, tributed Processing, MIT Press, Massachusetts, 1988. VLSI devices and circuits for neural [91 H.C. Card. W.R. Moore. networks, lnt. J. Neural Syst. 1 (2) (1989) 149-165. Reference guide for NeuralWorks Professional II/ PO1 NeuralWare, plus and NeuralWorks Explorer, 1991. u 11 A.J. Neufeld, E.N. Dirks. P.G. McLaren, G.W. Swift and R.W. Hayw-ood, A microprocessor platform for a generic protection system, paper presented at the 33rd Midwest Symp. Circuits and Systems, Calgary, Canada, August I2- 15. 1990, Conference Proceedings, pp. 377-380.