ARTIFICIAL NEURAL NETWORK APPLICATIONS
FOR POWER SYSTEM PROTECTION
Gaganpreet Chawla
Mohinder S. Sachdev
G. Ramakrishna
Student Member, IEEE
Life Fellow, IEEE
Member, IEEE
Power System Research Group, University of Saskatchewan
57 Campus Drive, Saskatoon, SK S7N 5A9 Canada
Abstract
The most commonly used systems for protecting transmission and
sub transmission lines belong to the family of distance relays. Over
the past eighty years, successful designs based on electromechanical, solid-state and digital electronics technologies have been produced
and marketed. These relays implement various characteristics, such
as impedance, offset-impedance, admittance, reactance and blinders.
The Artificial Neural Network based designs of distance relays proposed so far work well for ideal fault conditions but are not able to
maintain the integrity of the boundaries of the relay characteristics
of generic designs. This paper reviews ANN models that have been
proposed in the past for protecting components of power systems and
presents a methodology that fully exploits the potential of ANNs in designing generic distance relays that retain the integrity of the boundaries of their characteristics.
Keywords — Artificial Neural Networks; Distance Relays.
1
Introduction
Artificial neural network based technology, which is inspired by biological neural networks, has developed rapidly
in the previous decade and has been applied in power system protection applications. Specific applications include
direction discrimination for protecting transmission lines
[1-2], fault classification for faults on double circuit lines
[3], ANN based distance relays [4], differential protection of
three phase power transformers [7] and faults on generator
windings [8]. The ANN based designs of generic protection
systems proposed so far work well only for ideal fault conditions but do not maintain the integrity of the boundaries of
the relay characteristics. This deficiency exists even if the
networks are trained to identify the operating states in the
neighborhood of the boundaries of the characteristics. This
is because of the fact that none of the designs fully exploit
the potential of ANNs in implementing generic relay characteristics for maintaining the integrity of their boundaries.
Investigations of the fundamental drawbacks of ANNs and
development of new strategies for designing ANNs, which
would work as generic distance relays with clearly defined
operating boundaries, are needed.
This paper reviews a few ANN models that have been
proposed for protecting different components of power systems, such as transmission lines, transformers and generators. A methodology for the development of ANNs by
analyzing and utilizing the relationships between the input
data and the outputs expected from the ANN is then presented. The proposed methodology helps in fully utilizing
the potential of ANNs in implementing generic distance re-
0-7803-8886-0/05/$20.00 ©2005 IEEE
CCECE/CCGEI, Saskatoon, May 2005
lay characteristics in such a manner that the integrity of
the boundaries of the relay characteristics is maintained.
2
2.1
Protecting Power System Components
Transmission Line Protection
One of the initial developments in application of ANNs for
protecting transmission lines was the design and implementation of a fault direction discriminator [1]. A multi-layer
feed forward network with a 12-4-1 configuration was used
in this design. This ANN based directional relay used sampled values of voltages and currents, processed them to determine if a fault is on the line side of the relay or is on
the bus side of the relay. Patterns from all three phases
(consisting of Va, Vb, Vc, and Ia, Ib, Ic) were used to train
the network. The performance of the designed protection
system was tested by using data obtained from simulations
performed on the EMTDC/PSCAD software package. This
was a significant development because it showed that it
is possible to use ANNs in the designs of protection systems. However, concerns were expressed about the use of
ANN based systems in practical applications because the
integrity of their design for protecting parallel lines had
not been checked. Also the design was not checked for differentiating between faults and major load changes.
A few other ANN Models for protecting transmission
lines have been presented since then. A recent design uses
a finite impulse response ANN (FIRANN) for detecting the
onset of faults and determining the direction of the fault
on high-voltage transmission lines [2]. The configuration of
the proposed network is 45-35-5. Three of the five outputs
of the network identified faults of the three phases (one
for each phase); the fourth output determined the direction of the fault and the fifth output identified undercurrent/undervoltage conditions. A total of 100,000 patterns
(that comprised of voltages and currents of all three phases
and their sums) from different relays locations in a modeled
system were used to train this network. This ANN design
is rather complex as compared to the previously proposed
designs. In this paper, one network provides five outputs
but does not clearly define the operations that take place
inside the network. Also, the reasons for using such a large
network and for using such a large number of patterns for
training the network are not discussed. These essential but
unaddressed issues lead to the uncertainty about the integrity of the ANN when applied on a power system.
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2.2
Transformer Protection
Artificial Neural Networks have found their use in the protection of power transformers as well. A time delay artificial neural network processes the normalized values of
samples of currents [5]. The Discrete Fourier transform filters the fundamental frequency and harmonic components
of the currents. The fundamental and harmonics of second
to fifth order are applied to a multi-layer feed forward network for implementing differential protection [6].
A recent paper describes the use of a FIRANN as a differential relay for protecting three phase power transformers
[7]. Two FIRANNs with delay units form the two networks.
The first FIRANN with two hidden layers and a configuration of 6-6-4-1 detects the existence of a system fault.
The second network, which has a configuration of 6-8-8-2,
provides two outputs. One output indicates a fault in the
transformer protection zone and the second output indicates that that fault is outside the protection zone. Even
though these two networks have different structures, same
input patterns, which comprised of voltages and currents
from all the phases, were used to train the networks. The
choice of the configurations of the networks and the number
of the patterns used for training the networks is not discussed. The paper does not provide much insight in to the
internal processes of the used ANN. Moreover, the paper
does not include enough evidence to show that the designed
network works with adequate integrity in the neighborhood
of the boundary of the relay characteristics.
2.3
Generator Protection
The use of ANN based systems for protecting generators
has not received much attention so far. A recent paper
presents the implementation of an ANN-based fault diagnosis scheme for generator windings [8]. According to this
paper, the proposed network has the ability for detecting
and classifying generator winding faults with higher sensitivity and stability boundaries as compared to conventional
differential relays in addition to the ability for identifying
the faulted phases.
This paper states that there is no way to determine the
best configuration for an ANN, therefore three networks
are tried and the network that provides the best results
is chosen. The first network has a configuration of 6-3-7,
uses six samples of currents as inputs and provides seven
outputs identifying phase to ground, two phase and three
phase faults. The second design uses three networks with
a configuration of 2-2-1; each network (one for each phase)
uses two sets of inputs and provides three outputs. In the
third design, seven networks are used; a set of six inputs is
applied to each network that has a configuration of 6-3-1.
Each network detects one type of fault.
2.4
question of maintaining the integrity of the boundaries of
the relay characteristics, however, is not addressed in them.
If a trained ANN does not perform well, especially near the
boundary of the desired characteristic, during the testing
phase, then appropriate inputs have to be given to train it
again to improve its performance. At this point, it becomes
extremely essential to understand the impact of the different types of inputs on the training of the ANN for obtaining
the desired results from them. Therefore, comprehension of
the internal structure of an ANN is very important.
3
A methodology that fully exploits the potential of ANNs
and makes the whole process simple is presented in this
paper. In this methodology, the processes assigned to the
different layers of an ANN are segregated by dividing the
network into sub-networks; each sub-network is responsible
for performing an assigned protection function. This process helps in better understanding the internal structure of
the ANN and makes the process of modifying the network
simpler whenever required.
As discussed previously in this paper, ANNs are associated with some acceptability issues. Analysis of an artificial
neural network based fault direction discriminator [10] was
an attempt to address some of those issues. The acceptability issues arise because of undefined relationships between
the inputs and the outputs provided by an ANN.
The proposed networks use the normal structure of an
ANN as shown in Figure 1. The inputs (Va, Vb, Vc and Ia,
Ib, Ic in almost all the cases) are given to the input layer
and the outputs are obtained from output layer.
Figure 1: Normal Structure of an ANN.
3.1
Overview and Comments
The papers reviewed in Sections 2.1 to 2.3 use ANNs for
protection of different components of a power system. The
Proposed Methodology
The Proposed Design
In this section, an ANN based methodology for protecting
transmission lines is presented. The proposed methodology
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can be used for designing ANN based systems for protecting other components of power systems. To develop a system that maintains the integrity of operation around the
boundaries of the relay characteristics, modifications must
be made to the inputs that are provided to the network. Instead of providing the conventional inputs (Va, Vb, Vc and
Ia, Ib, Ic), inputs that assist in achieving the desired relay
characteristics were used to train the network. This created
a direct relationship between the inputs and the outputs expected from the networks while maintaining the integrity of
operation around the boundary of the relay characteristics.
Figure 2 shows the characteristics of a mho (admittance)
relay. An ANN was designed and was trained to give +1
output for faults which are in the protected zone of the relay (class P1) and -1 output for patterns of normal system
operation (class P2). The network acted like a pattern classifier and differentiated between two classes of patterns.
The characteristic of an admittance relay, shown as an
example, was achieved by the developed design. A similar
procedure could be used to develop networks that would
implement other characteristics, such as offset mho, reactance and blinders. Figure 3 shows a modified model of
required results. Making the following three modifications
Figure 3. Representation for the proposed Model of the
ANN.
to this network, it became possible to identify all kinds of
faults.
a. One output was obtained from each sub-network.The
output from a sub-network identified faults for the assigned
type such as phase A, B or C fault. If during the testing
stage, an ANN did not work well for faults of the assigned
type, that sub-network was modified or its training data
and procedure were evaluated and modified.
b. A combination of these outputs detected phase to
phase faults. By using logic comparators in the next layer,
phase to phase faults were detected. Outputs from the three
AND logic comparators were combined to detect A-B, B-C
and A-C faults.
c. Combining the outputs from all three networks detected three-phase faults. Instead of using neural layers as
the next stage, an AND neuron was used by fixing specific
weights of the neuron.
Figure 2: Characteristics of a mho (admittance) relay.
3.2
the ANN Structure used in the proposed development. For
each phase, voltages and currents obtained from power system simulations were applied to the network as illustrated
in figure 3. This structure segregated the whole network
into 3 sub-networks, one sub-network for each phase. This
approach is similar to that used in a computer program
which has a number of sub-routines instead of a single
large program. This methodology helped in assigning specific functions to each sub-network by changing the internal
structure of the conventional ANN. Each sub-network was
responsible for one protection function and it was not necessary to change the whole structure of the ANN for making changes to a protection function. Outputs from these
sub-networks were given to an output layer to obtain the
Training the Proposed Network
The adopted structure of the network allows keeping a
check on all the operations taking place in an ANN. Also,
simply by looking at the output of each sub-network, it is
possible to modify the inputs to be given to the ANN for
its proper training.
The training was conducted by back-propagating the errors in such a manner that the ANN maintained the integrity of operation around the boundary of the relay characteristics. The outputs obtained from all the layers of the
ANN were examined. This ensured that appropriate errors
are back-propagated for updating the weights of the ANN.
The inputs used to train the ANN were suitable for detecting faults in zone 1 only i.e. 80 % of the transmission
line. This approach will be adapted in the future work
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for designing ANN based systems for protecting zone-2 and
zone-3 of the transmission system.
4
Sample Study
so that the integrity of the generic relay characteristics is
maintained. The proposed methodology is a general purpose approach that can be applied to design networks for
protecting other components of power system as well.
References
[1]
Figure 4: Sample waveforms for point A
Figure 5: Sample waveforms for point B
Figure 4 and 5 show sample waveforms of voltage and current for points A and B respectively in the relay characteristics shown in Figure 2. These inputs were used to test the
trained ANN. It can be seen from the relay characteristics,
point A is just within the boundary region being protected
by the relay and point B is right outside the protection zone
of the relay; both the points having the same magnitude of
impedance. In spite of a marginal distinction between the
two set of waveforms, the trained ANN was able to differentiate between the faults within the protected zone from the
patterns of normal system operation. The output obtained
from the trained ANN when subjected to the first input
was +1 (point A) and when subjected to second input was
-1(point B). These results confirm that the adopted structure of the ANN maintains the integrity of generic relay
characteristics.
5
Conclusions
ANN based designs of relays proposed previously have been
examined in this paper. A new methodology that fully exploits the potential of an artificial neural network for its application to protect transmission lines has been presented.
The proposed design provides a better understanding of the
internal structure of an ANN and makes it convenient to
modify the ANN during training. The conventional inputs
that are generally used to train ANNs have been modified
H.Singh, M.S. Sachdev, T.S. Sidhu "Design, Implementation and Testing of an Artificial Neural Network Based Fault Direction Discriminator for protecting Transmission Lines," IEEE Transactions on
Power Delivery , Vol. 10, No. 2, 1995, pp 697-706.
[2] A.L.O Fernandez, N.K.I Ghonaim, "A Novel Approach using a FIRANN for Fault Detection and
Direction Estimation for High Voltage Transmission Lines," IEEE Transactions on Power Delivery,
Vol. 17, No. 4, Oct 2002, pp 894-900.
[3] A. Bennett, A. T. Johns, Q. Y. Xuann , R.K Aggarwal,
R. W. Dunn, "A Novel Classification Technique for
Double-circuit lines Based on Combined Unsupervised/Supervised Neural Network," IEEE Transactions on Power Delivery , Vol. 14, No. 4, 1999, pp
1250-1255.
[4] B Balamurugan, R Venkatesan, "A Real-Time Hardware Fault Detector Using an Artificial Neural
Network for Distance Protection," IEEE Transactions on Power Delivery, Vol.16, No. 1, Jan 2001, pp
75-82.
[5] A.J.Flechsig, J.L.Meador, L.G.Perez, Z.Obradovic,
"Training an Artificial Neural Network to Discriminate between Magnetizing Inrush and Internal Faults," IEEE Transactions on Power Delivery,
Vol.9, No. 1, Jan 1994, pp 434-441.
[6] K. Ning, L.M.Wedephol, M. Nagpal, M.S. Sachdev,
"Using a Neural Network for Transformer Protection," Proceedings of the International Conference of
Energy, Management and Power Delivery, Vol. 2, Singapore, Nov 21-23, 1995, pp 674-679.
[7] A.L.Orille-Fernandez, Jaime A. Valencia , N.K.I
Ghonaim,"A FIRANN as a Differential Relay
for Three Phase Power Transformer Protection,"
IEEE Transactions on Power Delivery, Vol.16, No. 2,
April 2001, pp 215-218.
[8] Abdel-Maxoud I. Talaab, Hatem A. Darwish, Tamer
A. Kawady,"Development and Implementation of
an ANN-Based Fault Diagnosis Scheme for Generator Winding Protection ," IEEE Transactions on
Power Delivery, Vol.16, No. 2, April 2001, pp 208-214.
[9] M.S. Sachdev, Co-ordinator, ,Microprocessor Relays
and Protection Systems Tutorial Text Publication
no.88 EH-O269-1-PWR, IEEE, New York, 1987.
[10] T.S. Sidhu, L. Mital and M.S. Sachdev,"A Comprehensive Analysis of an Artificial Neural Network Based Fault Direction Discriminator," IEEE
Transactions on Power Delivery, Vol.19, No. 3, July
2004, pp 1042-1048.
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