A HYBRID EXPERT SYSTEM BASED ON NEURAL NETWORKS

AND FUZZY LOGIC FOR FAULT IDENTIFICATION IN

ELECTRIC POWER SUBSTATIONS

Daniel da Silva Gazzana, Mario Orlando Oliveira, Arturo Suman Bretas

Federal University of Rio Grande do Sul, UFRGS, Porto Alegre, Brazil

Andre Lerm

Southern Federal Institute of Education and Technology, IFSUL, Pelotas, Brazil

Arlan Bettiol

A Vero Domino Consultoria e Pesquisa, Florianópolis, Brazil

Marcio A. Da S. Gonçalves

AES Uruguaiana, Uruguaiana, Brazil

Keywords: Artificial Neural Networks, Fuzzy Logic, Expert Systems, Electric Power Substations, Fault Detection.

Abstract: This paper presents a novel approach for on-line fault identification in an Electric Power Substation (EPS).

The proposed methodology is based on signal processing techniques allied with a Fuzzy Logic and Artificial

Neural Network. The test electric system was rigorously built in an electromagnetic transient numerical

simulator, named Alternative Transient Program (ATP), conformably to the needs presented by a

Thermoelectric Generation Plant of 711 MW - 230 kV, located in southern Brazil. Simulated test cases

demonstrate the generalization capability of the developed hybrid Expert System based on Neural Networks

and Fuzzy Logic, now utilized in a Southern Brazilian Utility.

1 INTRODUCTION

The use of a rapid and efficient method for on-line

fault detection in Electric Power Substations (EPS)

helps both in maintenance tasks and in the prompt

restoration of electrical system. The protection and

substation control have undergone dramatic changes

since the advent of powerful micro-processing and

digital communication equipment.

In the Electric Power System the monitoring and

control of substations are based generally on the

computerized Energy Management System (EMS),

Supervisory Control and Data Acquisition (SCADA)

and Oscillography Digital Register (ODR). When

faults occur in an automated substation, the faulted

devices are isolated by the operation of

corresponding protection relays and circuit breakers;

meanwhile, the SCADA system will issue alarm

messages in a short time sending them into the

operator’s consoles. In this case, the operators in the

control center are responsible for restoring the

system and must use their judgment and experiences

to determine the possible faulted elements and/or

switches as the first step in the restoration

procedures of the electric system (Chen et al., 2000).

In many cases, the fault location in EPS is

performed only with base on data assessment from

the monitoring system, as for example, the state of

switches and circuit breakers. However, this

procedure can lead to misidentification of the fault

component, especially when the substation is large.

Accordingly, it should be taken into account other

variables such as the magnitude and phase of

voltages and currents, obtained from system data

435

da Silva Gazzana D., Orlando Oliveira M., Suman Bretas A., Lerm A., Bettiol A. and A. Da S. Gonçalves M..

A HYBRID EXPERT SYSTEM BASED ON NEURAL NETWORKS AND FUZZY LOGIC FOR FAULT IDENTIFICATION IN ELECTRIC POWER

SUBSTATIONS.

DOI: 10.5220/0003085504350441

In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (ICNC-2010), pages

435-441

ISBN: 978-989-8425-32-4

 2010 SCITEPRESS (Science and Technology Publications, Lda.)

oscillography. Moreover, the evaluation of a greater

number of variables leads to the necessity of using

an Expert System (ES) to support decision making

and fault diagnostic (Fukui and J. Kawakami, 1986;

Tomsovic et al., 1987; Kezunovic et al., 1994;

Protopapas et al., 1991).

In this paper, it is presented a Fault Diagnosis

Integrated System (FDIS) used in a substation of a

Thermoelectric Generation Plant (TGP) located in

the southern Brazil. The proposed approach was

developed based on several simulations performed

under Bonneville Power Administration Alternative

Transients Program (BPA/ATP), Electromagnetic

Transients Program (EMTP) and using a hybrid

system based on Neural Networks and Fuzzy Logic.

The results obtained show that the developed

method is able to identify and locate the fault

occurrence, even when subject to operational

failures of circuit breakers.

2 FUZZY SETS AND NEURAL

NETWORK IN FAULT

DETECTION

Fuzzy Logic can systematically translate linguistic

concepts to numbers and associate elements from a

number set to concepts (Zadeh, 1965). This

capability provides a simple method that can be used

to detect and to qualify faults in Electric Power

Substations. Fuzzy based algorithms and Fuzzy

Logic are well adapted to situations where there is

no clear distinction between the concept of true and

false. Fuzzy Logic can handle situations where the

answer lies somewhere in-between. This is the

typical case of substation fault location. In general it

is difficult to estimate the fault location between the

several devices present in the electric power

substation. However, it is more adequate to classify

a fault in terms of the occurrence probability. Fuzzy

Logic permits to infer about the fault location and to

establish its certainty degree level of trust. An

important feature of Fuzzy based systems is that the

human knowledge and experience can be integrated

into the systems in a systematic way, when the

Fuzzy sets and Fuzzy rules have been defined.

In recent years, the use of Artificial Neural

Networks (ANNs) presented it self as a potential

solution to the on-line fault diagnosis in power

substations (Ebron et al., 1990; Yang et al., 1994;

Ranaweera, 1994). ANNs are computational

techniques that try to obtain a performance similar to

a human’s performance when solving problems. An

ANN can be seen as a union of simple processing

units, based on neurons that are linked to each other

through connections similar to synapses. These

connections contain the “knowledge” of the network

and the patterns of connectivity express the objects

represented in the network. The knowledge of the

network is acquired through a learning process

where the connections between processing units are

varied through weight changes. ANN is an efficient

alternative for problem solutions where it is possible

to obtain data describing the problem behavior but a

mathematical description of the process is

impossible.

The basic idea of uniting these two techniques is

to use the qualitative analysis supplied by Fuzzy

Logic, allied to the learning ability of Neural

Networks. Hybrid systems Neuro-Fuzzy can be used

to resolve this kind of problems with good accuracy

and robustness, joining the advantages of these

methodologies (Kezunovic, 2004).

3 FAULT DETECTION

METHODOLOGY

The proposed methodology is based on the three

integrated subsystems: Pre-Processing Data System,

Fault Identification System and Expert System. The

main structure of the proposed automated

disturbance analysis system can be seen in Figure 1.

Figure 1: General structure of the proposed scheme.

3.1 Pre-Processing Data System

The first procedure of the Pre-Processing Data

System is related to phasors extraction from

COMTRADE files (IEEE Standard C37.11.1.1999,

1999) and the evaluation of the state of switches and

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436

circuit breakers from SCADA system. This study

used the Discrete Fourier Transform (DFT) and

signal processing techniques to process and

evaluates the signals (Phadke and Thorp, 1999).

Figure 2 presents a basic flow chart for the signal

processing.

Figure 2: Pre-Processing Data System.

The development of fault identification algorithm is

based on the module (amplitude) and the angular

difference between voltage and current phasors

measured at the site of installation of the protective

relay.

3.2 Fault Identification System

Aiming to detect faults in respect to the TGP, it was

developed a directional relay whose main

characteristic is to determine the direction of a

failure from its installation location. Thus, when a

pre-determined threshold value is exceeded by the

current fault, a fault condition is detected and the

direction of failure is indicated by the relay (Suonan

et al., 2004). Figure 3 shows the basic installing

scheme of a directional relay.

Figure 3: Fault Identification System.

Here, the voltage at the relay location is given by:

commmm

ZIUU ⋅

−

′

111

(1)

where U

is the voltage phasor positive sequence in

the protection point; I

is the current phasor

positive sequence from the protection point to the

line; Z

com

is the impedance compensating of the

circuit.

The direction of a backward or forward fault is

determined by comparing the angle between of the

voltage and current phasors positive sequence. Thus,

the criterion for detecting a backward fault of the

directional relay is given by:

°≤

′′

≤

−

270)/(º90

IUtg

(2)

When the above condition is satisfied, a backward

fault is detected. During normal operation or

forward faults, the power flow is always toward the

load (Infinite Power System), in other words, the

source (TGP) provides power. However, when a

fault happens backward of directional relay, the

current I

changes of direction changing the value

of the angle between the voltage and current

phasors.

4 EXPERT SYSTEM (ES)

The developed ES is composed by a hybrid system

based on Neural Networks and Fuzzy Logic. In a

first stage, decision rules based on Fuzzy Logic

indicate the local in the substation where a possible

fault occurred (bus, lines, generators, transformers),

supplying a probability index associated with the

disturbance. In a second stage, an ANN classifies the

fault on a more specific manner, estimating the site

of the fault and the associated circuit breaker. The

Fuzzy Logic system runs independently of the ANN.

4.1 Fuzzy Inference System

The developed Fuzzy Inference System is composed

by six input variables (apparent power and angle of

each directional relay output) and for seven output

variables that estimate the fault location. The related

variables can be seen in Table 1.

Table 1: Fuzzy inference system variables.

Fuzzy Input Variables Fuzzy Output Variables

Angle directional relay 1 Fault on line L1

AP directional relay 1 Fault on Bus A or TR SAT-2

Angle directional relay 2 Fault on Bus B or TR SAT-1

AP directional relay 2 Fault on the CT1 generator

Angle directional relay 3 Fault on the CT2 generator

AP directional relay 3 Fault on the ST generator

Fault on line L2

In the input variables, the angle is composed for two

triangular-shaped membership functions. The first

one is related to negative angles, -360

º ≤ input ≤ 0º

and the second one related to the positive angle, 0º <

input ≤ 360º. The Apparent Power (AP) variables

were composed for one triangular-shaped

membership function corresponding to the positive

values of power, 0 ≤ input ≤ 40 MVA. The range of

the input membership functions was obtained with

base on angle and power data groups that represent

each kind of fault.

A HYBRID EXPERT SYSTEM BASED ON NEURAL NETWORKS AND FUZZY LOGIC FOR FAULT

IDENTIFICATION IN ELECTRIC POWER SUBSTATIONS

437

The output variables also are composed by one

triangular membership function for each fault, as

presented in Table 1. The range of output

membership function is in the interval 0 ≤ output ≤

2, so: output values in the mid of interval, output =

1, correspond 100% of probability of the related

fault to have occurred; values in the threshold of the

range, output = 0 or output = 2, correspond 0% of

probability of the related fault to have occurred and

output values inside of the range, 0 < output < 2

represent intermediary values of fault probability.

The base rule is composed for 37 rules that

represent faults proceedings from simulations. In the

inference process the Method of Mamdani was used

and the smallest (absolute) value of maximum was

applied in defuzzification process. In such a way,

some rules can be activated for a same group of

input data. In this case, each fault has its probability

value of occurrence. In the second step, the ANN

can classify more exactly which fault occurred.

Figure 4 illustrates the Fuzzy inference process.

Additionally, others types of membership

functions as gaussian and trapezoidal shapes were

tested presenting acceptable results, but the best

ones were obtained with triangular shape.

Figure 4: Fuzzy Inference Process.

4.2 Artificial Neural Network (ANN)

The second stage in the Expert System is composed

by a Multilayer Perceptron (MLP) Feedforward

Artificial Neural Network (Haykin, 1998). This

ANN maps input angle and power data in an

appropriate output fault location estimate. As well as

the Fuzzy System, the input variables of ANN are

the angle and apparent power of each directional

relay output. On the other hand, the ANN fault

identification is more specific than Fuzzy inference.

Beyond the fault location, the MLP structure can

identify the involved circuit breaker. So, six input

variables are mapped in 17 kinds of faults. In the

Table 2 can be seen the ANN performance, in

training stage, to classify substation faults

considering different number of neurons in hidden

layer. Table 3 shows the same faults in test stage.

Figure 5 presents the MLP Feedforward used in the

developed Expert System.

Table 2: ANN error in training process.

Type of fault

ANN Classification

Error (%)

Neurons in hidden

layer

20 50 80

Outage of line L1 for temporary

defect

8.83 0 0

Defect on bus A or transformer

SAT-2

25 0 0

Defect on bus B or transformer

SAT-1

50 0 0

Defect on CT1 generator with

fault on the circuit breaker 52-1

0 0 0

Defect on CT1 generator with

fault on the circuit breaker 52-2

25 0 0

Defect on CT2 generator with

fault on the circuit breaker 52-4

0 0 0

Defect on CT2 generator with

fault on the circuit breaker 52-5

0 0 0

Defect on ST generator with

fault on the circuit breaker 52-7

0 0 0

Defect on ST generator with

fault on the circuit breaker 52-8

0 0 0

Defect on line L1 with fault on

the circuit breaker 52-2

0 0 0

Defect on line L1 with fault on

the circuit breaker 52-3

25 0 0

Fault on CT1 generator with

circ. breaker 52-1 in

maintenance

0 16.7 0

Fault on line L1 with circuit

breaker 52-2 in maintenance

0 0 0

Fault on bus A or tr. SAT-2 with

circ. bre. 52-1 in maintenance

0 0 0

Defect on line L2 with open. of

the circ. break. 52-5 and 52-6

33.3 0 0

Defect on line L2 with fault on

the circuit breaker 52-5

16.7 0 0

Defect on line L2 with fault on

the circuit breaker 52-6

0 0 0

Global error (%) 10.8 0.98 0

The Feedforward Backpropagation Network is

composed by 6 and 17 perceptrons in the input, and

output layer respectively. For the hidden layers,

different number of neurons was tested and the

convergence results were presented in the following.

To calculate a layer's output from its net input

the hyperbolic tangent sigmoid transfer function

(hidden layer) and linear transfer function (output

layer) were used. The Levenberg-Marquardt

optimization was adopted as training function,

because it is a fast backpropagation algorithm. The

mean squared normalized error (MSE) was used as

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438

performance function.

Figure 5: MLP structure used in the Expert System.

Table 3: ANN error in test process.

Type of fault

ANN Classification

Error (%)

Neurons in hidden

layer

20 50 80

Outage of line L1 for temporary

defect

16.7 0 0

Defect on bus A or transformer

SAT-2

50 16.7 33.3

Defect on bus B or transformer

SAT-1

33.3 50 50

Defect on CT1 generator with

fault on the circuit breaker 52-1

16.7 0 16.7

Defect on CT1 generator with

fault on the circuit breaker 52-2

16.7 0 33.3

Defect on CT2 generator with

fault on the circuit breaker 52-4

16.7 16.7 50

Defect on CT2 generator with

fault on the circuit breaker 52-5

0 0 16.7

Defect on ST generator with

fault on the circuit breaker 52-7

0 0 0

Defect on ST generator with

fault on the circuit breaker 52-8

0 0 0

Defect on line L1 with fault on

the circuit breaker 52-2

16.7 0 16.7

Defect on line L1 with fault on

the circuit breaker 52-3

16.7 33.3 33.3

Fault on CT1 generator with

circ. breaker 52-1 in

maintenance

33.3 16.7 16.7

Fault on line L1 with circuit

breaker 52-2 in maintenance

16.7 0 0

Fault on bus A or tr. SAT-2 with

circ. bre. 52-1 in maintenance0

33.3 33.3 50

Defect on line L2 with open. of

the circ. break. 52-5 and 52-6

33.3 33.3 16.7

Defect on line L2 with fault on

the circuit breaker 52-5

0 0 0

Defect on line L2 with fault on

the circuit breaker 52-6

0 0 0

Global error (%)

17.6 11.8 19.6

The input data was divided in two groups, the first

one corresponding a 2/3 of total data was used in the

ANN training process and remaining data was used

in ANN tests.

Analysing Table 2 and Table 3 it can be seen that the

best ANN response is obtained with a MLP structure

composed by 50 perceptrons in hidden layer. A

major number of neurons in hidden layer not

represent significant improvements in ANN

classification process.

The Table 4 presents the number of epochs and

MSE obtained for the previously MLP structures.

The MLP structure with 6, 50 and 17 perceptrons in

the input, hidden and output layer respectively was

implemented in the developed system.

Table 4: ANN performance.

Neurons in

hidden layer

Epochs MSE

20 200 0.0195

50 150 0.0057

80 300 0.0003

5 SIMULATION AND RESULTS

To illustrate de results obtained with the Fault

Diagnosis Integrated System two cases of

disturbances in the Thermoelectric Generation Plant

substation are presented below. The simulated

system was built, rigorously, conformably to the

needs presented by a TGP of 711 MW, 230 kV,

located in southern of Brazil. Figure 6 illustrates the

electric circuit used in the simulations of the faults in

the EPS.

Figure 6: Substation model used in the simulation faults.

5.1 Defect on Line L1 with Fault on the

Circuit Breaker 52-2

The Figure 7 shows the behaviour of three-phase

voltages and currents in the occurrence of a defect in

line L1 with fault on the circuit breaker 52-2. In the

A HYBRID EXPERT SYSTEM BASED ON NEURAL NETWORKS AND FUZZY LOGIC FOR FAULT

IDENTIFICATION IN ELECTRIC POWER SUBSTATIONS

439

presented case, the defect is a three-phase short-

circuit with fault resistance (R

) of 50Ω. In this case

the angle and power used as input variable for the

Expert System are: angle 1 = 73.4º; power 1 = 10.1

MVA; angle 2 = 70.4º; power 2 = 30.7 MVA; angle

3 = 71.7º; power 3 = 33.2 MVA. The Figure 8

illustrates the software interface with the

information of the fault identification.

Figure 7: Voltage and currents on Line L1 due three-phase

short-circuit with fault resistence R

= 50Ω.

Figure 8: Software interface shows fault at Line L1.

5.2 Defect on CT2 Generator with fault

on the Circuit Breaker 52-4

In Figure 9 it can be seen the behaviour of three-

phase voltages and currents in the occurrence of a

defect in generator CT2 with fault on the circuit

breaker 52-4. In this case, the defect is a three-phase

short-circuit with low fault resistance of 0.5Ω. The

calculated angle and power used as input variable

for the Expert System are: angle 1 = -273.3º; power

1 = 30.2 MVA; angle 2 = -204.7º; power 2 = 19.3

MVA; angle 3 =-273.4 º; power 3 = 32.6 MVA. The

developed software interface with the fault location

can be seen in Figure 10.

Figure 9: Voltage and currents on out CT2 generator with

fault of the circuit breaker 52-4.

Figure 10: Software interface shows defect in generator

CT2.

6 CONCLUSIONS / DISCUSSION

This paper presented a methodology for fault

location in power electric substation with base on

Fuzzy Sets and Artificial Neural Networks. The

proposal study combine signal processing techniques

with intelligent systems to detect the local and the

related device in a fault occurrence.

This methodology starts with the extraction data

from oscillography files and with base on the state of

circuit breakers in the substation. In the development

stage, this task is replaced by simulation of the

substation operational conditions using the

Alternative Transients Program (ATP). After, the

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440

signal processing techniques are implemented

supplying input data to the Fuzzy-Neuro Expert

System that classify a possible fault.

The directional relay shows to be a robust

method to provide an indication of the fault

direction, beyond supplying angle information that is

used as input to the ES with satisfactory results.

The developed ATP/EMTP model allows the

simulation of diverse disturbances inside the

substation, which was used for compose Fuzzy Sets,

training the ANN and test the hybrid Fuzzy-Neuro

Expert System.

The Fuzzy-Neuro Expert System classifies the

fault in two levels of details. The Fuzzy System is

more generalist and identifies only the local of fault,

whereas, the ANN is qualified to indicate the related

circuit breaker. For this reason, the fact that the net

is very specialist, a level of classification error can

occur. In some ANN tests, the error is allied with the

wrong of circuit breaker and not with the local of

fault as bus, transformer, line or generators. It is

important to highlight that the ES input data are very

close and the classification process is not a trivial

task.

Several simulations of different values of epochs

was performed in network training process and the

best results were attainment with 150 epochs

converging to a MSE = 0.0057, ANN global test

error = 11.8% considering 6, 50 and 17 perceptrons

in the input, hidden and output layer respectively.

With this configuration the best results was obtained

and this structure was implemented in the fault

detection expert system.

The developed integrated system can become the

management maintenance activities more efficient.

Moreover, such system contributes for the increase

of the reliability, having as one of its benefits, the

reduction of involved time to detect and localize a

possible fault, optimizing the maintenance practices.

Currently, the system is being tested in the TGP

in the Southern Brazilian, but this methodology can

be used to detect and localize faults in similar energy

electric substations.

ACKNOWLEDGEMENTS

The authors wish to thank the American Energy

System (AES Uruguaiana) for the financial support

and Federal University of Rio Grande do Sul for the

facilities offered.

REFERENCES

Bonneville Power Administration - BPA “Alternative

Transients Program: ATP/EMTP”. Avalilable in:

http://www.emtp.org/.

Chen, W. H., Liu, C. W., Tsai M.S., 2000. “On-Line Fault

Diagnosis of Distribution Substation Using Hybrid

Cause-Effect Network and Fuzzy Rule-Based

Method,” IEEE Transaction on Power Delivery, vol.

15, no. 2.

Ebron, S., Lubkeman, D. L., White, M., 1990. “A neural

network approach to the detection of incipient faults

on power distribution feeders,” IEEE Trans. On

PWRD, vol. 5, no. 2, pp. 905–913.

Fukui, C., Kawakami, J., 1986. “An expert system for

fault section estimation using information from

protective relays and circuit breakers,” IEEE Trans.

On PWRD, vol. 1, no. 4, pp. 83–90.

Haykin, S., 1998. “Neural Networks: A Comprehensive

Foundation,” New York: Prentice Hall, 2nd ed.

IEEE Standart Common Format for Transient Data

Exchange (COMTRADE) for Power Systems, IEEE

Standard C37.11.1.1999, 1999.

Kezunovic, M., Rikalo; I., Fromen, C. W., Sevcik, D. R.,

1994. “Expert System Reasoning Streamlines

Disturbance Analysis,” IEEE Computer Application in

Power, vol. 7, pp. 15-19.

Kezunovic, M., 2004. “Intelligent applications in

substations: Disturbance analysis,” In IEEE PES

General Meeting, vol. 1, pp. 719-723, Denver, USA.

Phadke, A. G., Thorp, J.S., 1999. “Computer Relaying for

Power Systems,” England: Research Studies Press

Ltd.

Protopapas, C. A., Psaltiras, K. P., Machias, A. V., 1991.

“An expert system for substation fault diagnosis and

alarm processing,” IEEE Trans. On PWRD, vol. 6, no.

2, pp. 648–655.

Ranaweera, D.K., 1994. “Comparison of neural network

models for fault diagnosis of power systems,” Electric

Power Systems Research, pp. 99–104.

Suonan J. L., Xu Q. Q., Song G. B., Wu Y., 2004. “A

reliable directional relay based on positive sequence

compensated voltage and current components,” The

Institute of Electrical Engineers, Six Hills Way, pp.

104-107.

The Mathworks Inc. “Mathworks MatLab”. Available in:

http://www.mathworks.com/.

Tomsovic, K., Liu, C. C., Ackerman, P., Pope, S., 1987.

“An expert system as a dispatchers’ aid for the

isolation of line section faults,” IEEE Trans. On

PWRD, vol. 2, no. 3, pp. 736–742.

Yang, H. T., Chang, W. Y., Huang, C. L., 1994. “A new

neural networks approach to on-line fault section

estimation using information of protective relays and

circuit breakers,” IEEE Trans. On PWRD, vol. 9, no.

1, pp. 220–230.

Zadeh, L. A., 1965. "Fuzzy sets," Information and Control,

vol. 8, pp. 338-353.

A HYBRID EXPERT SYSTEM BASED ON NEURAL NETWORKS AND FUZZY LOGIC FOR FAULT

IDENTIFICATION IN ELECTRIC POWER SUBSTATIONS

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