NEURAL NETWORK APPROACH TO FAULT DETECTION EN ELECTRIC POWER SYSTEMS

Ernesto VBzquez, Hector J. Altuve, Oscar L. Chacbn

Universidad Autdnoma de Nuevo Leon Facultad de Ingenieria Mechica y Elkctrica

Doctorado en Ingenieria ElCctrica Apartado Postal 89-F

San Nicolks de 10s Garza, 66451 Nuevo Leon, MEXICO

e-mail: evazquez@gama.fime.uanl.mx

Abstract. This paper describes a fault detector that uses artificial neural networks (ANN). It represents the first step to the development of a neural distance relay for protecting transmission lines. We envisage the fault detection problem as a pattern classification process. Our suggested approach is based on the fact that when a fault occurs, a change in the system impedance takes place and, as a consequence, the current phase and amplitude change. The ANN-based fault detector is trained to detect this changes as indicators of the instant of fault inception. Results showing the performance of the fault detector are presented in the paper, indicating that it is fast, robust and accurate.

Keywords: Power Systems, Artificial Neural Networks, Fault Detection

I. INTRODUCTION

Protective relaying of power systems is aimed to safeguard the expensive equipment and also to maintain system integrity that is necessary for continuous and economical supply of electric power customers [l]. Protective relays for transmission lines use the input voltage and current signals to detect faults in the protected line and to send a tripping signal to a circuit breaker to disconnect the line.

Fig. 1 represents the functional blocks of a protective relay for transmission lines. The fault is detected in the first module, classified in the second one, and then the fault place is estimated. A tripping signal is issued if the fault is within the zone protected by the relay (protection zone). Neural network approaches have been reported for fault classification [4] and for a part of the fault place estimation process, which is directional discrimination [5,6].

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

TfiPPing V

i signal

Fig. 1. Functional blocks of a transmission line relay

Different algorithmic approaches to fault detection have been proposed; the most sensitive method is the so-called transient monitor [2], which is based on a comparison of the real signal samples with samples calculated by a least-squares estimation. The inherent pattern-recognition nature of the fault detection problem suggests the application of feed-forward artificial neural networks (FFNN) for this task. In this paper a novel neural-network-based fault detector is proposed. The detector is simulated using the Neural Network Toolbox of MATLAB. Results showing the performance of the ANN-based detector are presented, indicating that it is fast, robust and accurate.

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11. FAULT DETECTION USING ANN

A fault detector must detect the fault inception and to issue an output signal indicating this conditiion. During normal operating conditions the currents and voltages of the power system are sinusoidal signals presenting very low levels of harmonic contamination. Load variation with time may produce slow amplitude changes in current signals and, in a lesser extent, in voltage signals.

The inception of the fault introduces abrupt changes of amplitude and phase in voltage and current sign,als. Fault signals can be contaminated with different transient components [1,2], such as exponentially-decay ng dc-offset (mainly in current signals) and high-frequency damped oscillai ions (mainly in voltage signals), among other components. These changes of amplitude and phase, and the appearance of transient components, can be used to detect the inception of a fault.

The neural network approach to fault detection can be posed as a pattern-recognition problem: the ANN is trained to recognize pure sinusoidal signals as indicators of a normal system condition; abrupt changes of amplitude or phase, or the presence of transient components are used ais indicators of fault inception. Current and voltage signals can be used for fault detection, but current signal is in general more sensitive than voltiige to fault inception. Amplitude and phase changes are greater for current signals than for voltage signals:, in addition, the dc-offset of fault curirent is a clear indicator of many faiult conditions. Therefore, current was selected as input signal to the detector in this work.

The structure of the ANN-based fault detector is depicted in Fig. 2. Input current is low-pass filtered before the A/Ll conversion to avoid aliasing. Low-pass Butterworth filters having different orders and cut-off frequencies were evaluated for this task. A set of consecutive samples of current signal form the: input to the neural network. Using a 960 Hz sampling rate (16 samples per 60 Hz cycle), data windows with different numbers of samples were evaluated. The results are presented for datal windows containing 5 and 10 samples (5 and 10 inputs to the ANN respectively). Only one hidden layer (20 neurons) was found to be necessary for this application. The output layer requires only one neuron, with a two-state output.

Current samples I

-4 Anti-aliasing 1 filter + l - 0-

hput layer Hidden layer output layer (5 or 10) (20 neurons) (1 neuron)

Fig. 2. ANN-based fault detector structure

The activation function for each neuron is given by:

1 f(C) = - 1 -e-=

Fault No fault

where Z represents the sum of all the weighted inputs to the neuron. The fault detector was simulated using the Neural Network Toolbox of MATLAB; the moment factor and the adaptive learning rate were used for training the network [3].

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111. ANN-BASED DETECTOR TRAINING

The simplified power system model used to generate current signals for training patterns is depicted in Fig. 3. In this model the current I in the detector during the fault condition is formed by the superposition of the load or pre-fault component I, and the fault component IF [2]:

r = r, 4- LF which is equivalent to:

i = i, + i, (3)

Faulted transmission line

/ Fault Fault detector location

Fig. 3. Simplified power system model

The pre-fault current can be written as:

i, = I, sin(ot+cu-9,) (4)

where I, is the maximum value of z,, o is the angular frequency, pL is the phase angle with reference to the voltage (load power-factor angle), and a is a fixed angle, introduced to adjust the time reference.

Considering dc-offset as the only transient component contained in the fault current, the fault component is 121:

where IF is the maximum value of i , pF is the phase angle with reference to the voltage, and 7 is the system time constant. For a= pF the fault component is purely sinusoidal, whereas for a+ pF the exponentially decaying dc-offset term is also present. A typical current signal obtained for this model is shown in Fig. 4, for a fault occurring at t=O, and for a+qPF.

-0.05 0 005 0.1 0.15 0.2 Time

Fig. 4. Typical current transition from pre-fault to fault condition

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During normal system conditions relay input current is not greater than its rated current. It is convenient to use the relay rated current to normalize the input current, which will be no greater than unity in normal conditions. Normalized fault current can be greater than unity, as can be seen in Fig. 4.

The process for generating input patterns to the ANN is depicted in Fig. 5. Current signal is calculated as a string of samples corresponding to a 28.8 kHz sampling frequency. This "analog" signal is alpplied to the digital model of the anti-aliasing filter and then decimated, to simulate the 960 Hz sampling process. Finadly, the samples are normalized to relay rated current.

Calculated current

Anti-aliasing Sampling sdhg +- filter * at 960 Hz +-

Fig. 5. Process for generating input patterns to the ANN

Different neural networks were trained to identify very slight transitions from pre-fault to the fault state. Training patterns were formed in each case by a set of overlapping data windows which cover two cycles of pre-fault current and two cycles of fault current. For each network different conditions were simulated, composed by different combinations of pre-fault (IL) and fault (IF) current values. Normalized values of 1 , O 95, 0.9 and 0.85 were selected for IL in all cases; the minimum values for I, (minimum fault levels) were 0.1, 0.2 and 0.5 in different training sets, in order to evaluate its influence on the fault detector sensitivity. Fault current patterns for training were generated without dc-offset (a= 4pF in (5 ) ) , in order to take advantage of the presence of offset in the real fault current as an additional indicator of the fault condition for the ANN. Table I summarizes the training conditions for some of the fault detectors simulated in this work. The number of training cycles required to reach a summed-squares error of 0.01 is included in the table.

Table I Training conditions for different ANN-based lEault detectors

IV. TEST RESULTS

ANN-based detectors were extensively tested using current signals obtained from the simplified power system model (Fig. 3), and also signals generated by simulations using the Electromagnetic Transient Program (EMTP). A section of the Northeastern Mexican Power System was simulated for this purpose.

Fig. 6 depicts the results of two tests including a sinusoidal fault current (Fig. 6a) and an EMTP-generated fault current containing dc-offset (Fig. 6b). Fault detector output is depicted as a function of time for each case. A 5-sample data window and a second-order low-pass Butterworth filter with a cut-off frequency of 1360 Hz were used in these fault detectors. The outputs of three detectors are shown, trained for minimum fault levels (MFL) of 0.5, 0.2 and 0.1 respectively.

All the detectors performed well in the test. An output signal is issued as soon as the first sample of the fault condition enters the neural network. It represents a 1-millisecond operating time for a 960 Hz sampling rate. For a sinusoidal fault current (Fig. 6a) the detector resets as soon as the data window crosses the discontinuity originated by the fault in the current signal. The dc-offset (Fig. 6b) introduces a delay in the reset of the fault detector, but this is not relevant for the application.

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Current (normalized) Current (normalized)

1 -

n. hh ,,L o.;/ , 1, , 1 0 .5-

5 0 -5

1 - 0.ki , 1, , 1 0.5-

i n L

1 o.;l . 1, , 1 0.5:

0 20 40 60 0 50 100 150

MFL=O 5 MFL=O 5

Y

"0 20 40 60 -0 50 100 150

MFL=O. 2 M FL=0. 2 I I I I

MFL=O.I MFL=O.I I I I I

Time (ms) Time (ms)

Fig. 6. Test results for different fault detectors

An important requirement for a fault detector is to remain unoperated under normal load changing conditions or in presence of harmonic contamination in load current. The detectors were tested under these conditions also. Fig. 7 depicts the results of two such tests, for the detector with MFL=0.5 used in the previous test. The detector was shown to remain stable in presence of rapidly changing load current, as can be seen in Fig. 7a. On the other hand, for a 10% total harmonic distortion in load current (Fig. 7b) false output signals are issued.

This problem can be solved by rejecting the harmonic components in a previous filtering stage, or by increasing the length of the data window. The effect of both solutions is illustrated in Fig. 8. Fig. 8a depicts the result of using a fourth-order low-pass Buttenvorth filter with a cut-off frequency of 90 Hz. The harmonic rejection capability of this filter enhances the fault detector response to load current having harmonic contamination. The price for this is an increase in the time delay introduced by a sharper filter.

A longer data window was found to be an alternative solution to this problem. Fig. 8b shows the response of the detector having a 10-cycle data window and a second-order Butterworth filter with cut-off frequency of 360 Hz. The detector is immune to harmonics in this case and retains its capability to detect fault inception when the first fault-current sample is processed.

V. CONCLUSIONS

1. This paper presents a novel neural network approach to fault detection for transmission line protection. 2. It was shown the capability of a neural network to detect even slight amplitude and phase changes in

current signal as a consequence of the inception of a fault. An adequate neural network structure for this task was found to have five or ten inputs, a hidden layer with 20 neurons, an one output neuron; a sigmoidal activation function is used for the neurons.

3. A fault detector with a 5-sample data window and a second order Butterworth anti-aliasing filter (with 360 Hz cut-off frequency) exhibits a high sensitivity for fault detection. On the other hand, this detector may misoperate for load currents contaminated with harmonics.

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Current (normalized) Current (normalized) I 1

Current (normalized) 5 7 7

Current (normalized) 1

5

0 0 0

-5

0 200 400 200 300 400

1 . 5 ~ 7 MFL=0.5 4 . 5 ~ -

300 400 Time (ms) Time (ms)

(a> (b) Fig. 7. Test results for the fault detector; a) time- varying load current; b) harmonic contamination

in load current

200 300 400 200 300 400

Time (ms)

(4 Fig. 8. Test result for the fault detector; a) 5-

sample data window, fourth-order B;utterworth filter; b) 10'-sample window, second-order filter

4. A fourth-order Butterworth anti-aliasing filter with a cut-off frequency of 90 Hz rejects well the harmonics and avoids detector misoperations. The price for this is an additional delay in the detector response as a consequence of the increased filter delay.

5. A fault detector with a 10-sample data window exhibits an excellent harmonic rejection capability. It can be used with a second-order anti-aliasing Butterworth filter with a. cut-off frequency of 3601 Hz.

VI. REFERENCES

[l] S. H. Horowitz and A. G. Phadke, Power System Relaying, Great Britain: Research Studies Press LTD., 1992.

[2] A. G. Phadke and J. S. Thorp, Computer Relaying for Power Systems, Great Britain: Research Studies Press LTD., 1988.

[3] J. M. Zurada, Artificial Neural Systems, U.S.A.: West Publishing Co., 1992. [4] T. Dalstein and B. Kulicke, "Neural network approach to fault classification for high speed protective

relaying," paper 94SM 488-7 PWRD, A paper recommended and approved forpresentation at /he IEEEIPES 1994 Summer Meeting, San Francisco, CA, .July 24-28, 1994.

[5] T. Dalstein, D. J. Sobajic, B. Kulicke and Y. H. Pao, "Neural network approach to fault direction identification in electric power systems," Proceedings of the Twenty-Five Annual North American Power Symposium, Howard University, Washington, DC, October 1993, pp. 290-299.

[6] T. S. Sidhu, H. Singh and M. S. Sachdev, "Design, implementation and testing of an artificial neural network based fault direction discriminator for protecting transmission lines," IEEE Trans. on Power Delivery, vol. 10, no. 2, April 1995, pp. 697-706.

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