Multi-layer neural networks applied to distance relaying

PII:

Electrical Power & Energy Systems, Vol. 20, No. 8, pp. 539–542, 1998 q 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain S0142-0615(98)00018-0 0142-0615/98/$ - see front matter

Multi-layer neural networks applied to distance relaying Denis V Coury and Ma´rio Oleskovicz Department of Electrical Engineering, University of Sa˜o Paulo, C. P. 359, EESC/USP Av. Dr. Carlos Botelho, 1465, 13560-250-Sa˜o Carlos (SP), Brazil possibility to solve problems related to different subjects, such as load forecasting, security assessment and economic dispatch [2]. Concerning the application in the protection field, some pattern recognition approaches to distance relaying should be mentioned [3–5]. Moreover, in Ref. [6] an approach to distance protection is also proposed. The scheme utilises the magnitudes of three-phase voltage and current phasors as inputs. An improved performance is experienced once the relay can operate correctly, keeping the reach when faced with different operation conditions. An adaptive distance protection of double circuit lines using ANNs is proposed in Ref. [7]. It was shown that the scheme improves the protection system selectivity, and can be used to estimate the actual power system condition. In addition, still in the protection field, a fault direction discriminator [8], fault location [9] and fault classification [10] for transmission lines using ANNs were investigated. This paper also presents the use of ANNs for distance relaying. Two different types of architectures concerning the input data were implemented. The main objective was to analyse the relay performance considering each of them. The backpropagation algorithm was utilised in the training process. An improvement in performance to the conventional distance relay was expected. Through the use of ANN as a pattern classifier, a reach of 96% of the transmission line length as the relay primary protection zone was implemented in this work.

This paper demonstrates the use of Artificial Neural Networks (ANNs) as pattern classifiers for a distance relay operation of transmission lines. Two different types of ANN architectures, concerning the input data, are taken into account. One approach utilises the first five post-fault samples as inputs. The other one employs the magnitudes of the three-phase voltage and current phasors (including the zero sequence) as inputs. A comparison of how well the schemes performed is carried out. An improvement concerning the use of ANNs for protection purposes is found. q 1998 Elsevier Science Ltd. All rights reserved Keywords: distance protection, relaying, artificial neural networks

I. Introduction Among the components of an electric power system, the transmission line is the most susceptible element to experience faults, especially if its physical dimension is considered. In practice, about 70–80% of faults on transmission lines are single-line-to-ground faults. A minor number of disturbances refers to three-phase faults (5%). Concerning the transmission line protection, different kinds of conventional relays are utilised, and electromechanical/solid state relays are normally used to protect them. They respond to the impedance at a fundamental frequency between the relay and the fault location, and thus determine if a fault is internal or external to a protection zone. With digital technology being ever increasingly adopted in power substations, more particularly in the protection field, distance relays have experienced some improvements related to efficient filtering methods as well as a shorter decision time [1]. The trip/no trip decision has been improved compared to electromechanical/solid state relays. However, the digital distance protection is usually designed on the basis of fixed relay settings. The uncertainty of the zone reach is typically in the order of 5% of the zone setting. Consequently, it is usual to set the first zone of the distance relay at 80–90% of the line length. The evidence presented in journals and papers denotes the scientific community belief with regards to the Artificial Neural Network (ANN) theory. Its potential has recently attracted power system researchers to look at it as a

II. The power system analysed In order to test the applicability of the proposed scheme, a simulator of the transmission line in a faulted condition was utilised. This paper makes use of a digital simulation of faulted EHV (Extra High Voltage) transmission lines known as ATP (Alternative Transient Program) [11]. The 150 km, 400 kV transmission line used to train and test the proposed ANN is shown in Figure 1. The relay is located at busbar P, and a first zone reach of 96% of the transmission line length was considered. A total number of 400 different phase-to-earth faults in various locations of the system was used in order to train the proposed ANNs. This study takes into account phase a to earth faults only. In order to train the ANN, the situations that characterised the action of the distance relay were related to response one (1). Such situations showed that a fault had occurred inside

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Figure 1. Power system analysed the relay primary protection zone and that the line should be isolated by the protection system. On the other hand, the situations that characterised non-action of the relay (faults lying outside the primary protection zone) were related to response zero (0). After the training process, a correct operation of the distance relay concerning the discrimination of the different situations described was expected. If a sufficient number of patterns is used, the relay will be able to act properly even facing situations never seen in the training process.

III. The training procedure and test results for the proposed ANNs The SNNS (Stuttgart Neural Network Simulator, version 4.1) [12] was used to create the ANN diagram, train it and obtain the weights as an output. The software provides a flexible environment for research and application of techniques that involve ANNs. The architectures utilised are described in more detail in the following topics. The supervised algorithms used were Std_Backpropagation (standard backpropagation) and BackpropMomentum (backpropagation with momentum). III.1 The first architecture analysed The first architecture refers to values sampled at 590 Hz, where the first five post-fault values of voltages and currents were utilised as inputs. Only phases a and b were used in this case. An architecture also employing phase c was tested, but it presented a similar performance with a superior number of inputs.

As an output, the ANN scheme should present values equal or near to one (1) or zero (0), showing whether the fault occurred in the relay primary protection zone or not. The chosen configuration presents 20 units in the input layer, 15 and 10 units in the first and in the hidden layers respectively, and one output layer with one unit (architecture 20-15-10-1). In order to analyse the main characteristics inherent to neural networks (ability to learn, generalisation and robustness), a total number of 1050 different faulted cases were applied to the transmission system shown in Figure 1. Faults next to the region where trip/no trip condition exchanges (144 km for the line used) had special treatment. In those cases, a lower degree of sparsity was chosen between locations used for training. This region was designed as a transition zone (94–98% of the line length) where incorrect results from the ANN distance relay were expected. Tables 1 and 2 show the results of the ANN scheme used as a distance relay. The ANN answer is shown, compared to the expected ones, for faults along the transmission lines. The correct answer column illustrates the expected trip (1.000) and no-trip (0.000) decision. The tables indicate the exact values as attained from the ANN output. For practical applications, the interval 0–0.499 can be considered as a no-trip decision and 0.5–1 as a trip decision. The values presented by the neural network that denote incorrect answers are marked with an asterisk (*) in bold-type. It should be noted that the cases utilised for the tests are different from the ones used for training. Tables 1 and 2 show the estimated output ANN distance relay for faults along the transmission line subjected to

Table 1. Estimated output of the ANN distance relay with different fault resistances and a fault inception angle of 208 Fault distance from busbar P (km)

Fault distance from busbar P (%)

R f (Q) 1

R f (Q) 10

R f (Q) 30

R f (Q) 70

R f (Q) 90

Correct answer

25 55 85 115 132 137 141 142 147 160 170 180 190 200 210

18 37 57 77 88 91 94 95 98 107 113 120 127 133 140

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 *0.591 *0.999 *0.999 0.004 0.000 0.000

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 *0.711 *0.999 *0.999 0.008 0.000 0.000

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 0.001 *0.927 *0.895 0.000 0.002 0.000

1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 0.000 0.000 0.000 0.000 0.000 0.000 0.000

1.000 1.000 1.000 1.000 1.000 1.000 0.890 0.587 0.000 0.000 0.000 0.000 0.000 0.000 0.000

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

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Table 2. Estimated output of the ANN distance relay with different fault resistances and a fault inception angle of 808 Fault distance from busbar P (km)

Fault distance from busbar P (%)

R f (Q) 1

R f (Q) 10

R f (Q) 30

R f (Q) 70

R f (Q) 90

Correct answer

25 55 85 115 132 137 141 142 147 160 170 180 190 200 210

18 37 57 77 88 91 94 95 98 107 113 120 127 133 140

1.000 1.000 *0.000 1.000 1.000 1.000 0.991 1.000 0.000 0.000 0.004 0.000 0.000 0.000 0.000

1.000 1.000 0.999 1.000 1.000 1.000 1.000 1.000 0.000 0.000 0.052 *0.954 0.000 0.000 0.000

1.000 1.000 0.999 1.000 1.000 1.000 1.000 1.000 0.000 0.000 0.002 *1.000 0.000 0.000 0.000

1.000 1.000 0.999 1.000 1.000 1.000 0.999 1.000 0.000 0.000 0.000 *0.998 0.000 0.000 0.000

1.000 1.000 0.999 1.000 1.000 0.998 0.986 0.999 0.000 0.129 0.000 *0.984 0.000 0.000 0.000

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

different fault resistances (R f ¼ 1, 10, 30, 70 and 90 Q) with a fault inception angle of 20 and 808 respectively. The results presented in the tables typify the efficiency of the scheme. It should be pointed out that about 93.5% of almost all of the 1050 cases tested estimated the expected responses for the ANN distance relay. The total number of errors observed corresponded to 6.5%. Only 1.5% of the errors were situated on the transition zone (94–98% of the line length). III.2 Second architecture analysed This approach only uses the magnitudes of the voltage and current phasors (including the zero sequence) corresponding to the post-fault fundamental frequency as inputs. The inclusion of the zero sequence component resulted in an improved performance for the chosen architecture. In order to obtain the magnitudes and phase angles of such waves, the half cycle Discrete Fourier Transform (DFT) filter was utilised. For this approach, only the fundamental frequency magnitudes of voltage and current (not the phase angles) were utilised as the inputs of the network. In this way, through three-phase voltage and current magnitudes seen at the busbar, the scheme should discriminate between faults

lying within 96% of the line length and faults outside that zone, as described earlier. In order to improve the performance of the DFT filter, a higher sample frequency (4 kHz) was utilised in this scheme. Thus, the architecture is characterised by seven units in the input layer, the first and hidden layers with seven units each, and one output layer with one unit (architecture 7-7-7-1). The performance of the ANN distance relay subjected to different fault resistances (R f ¼ 1, 10, 30, 70 and 90 Q) in the situations concerning the fault inception angles of 20 and 808 are presented in Tables 3 and 4 respectively. The faulted cases are the same ones, which were utilised in the first architecture analysed. It should also be pointed out that about 97.3% of almost all of the 1050 cases tested estimated the expected response for the ANN distance relay. The total number of errors observed corresponds to 2.7%. From this percentage, 2.6% is relative to incorrect responses that are situated on the transition zone (94–98% of the line length).

IV. Conclusion The use of an ANN as a pattern classifier to simulate a

Table 3. Estimated output of the ANN distance relay with different fault resistances and a fault inception angle of 208 Fault distance from busbar P (km)

Fault distance from busbar P (%)

R f (Q) 1

R f (Q) 10

R f (Q) 30

R f (Q) 70

R f (Q) 90

Correct answer

25 55 85 115 132 137 141 142 147 160 170 180 190 200 210

18 37 57 77 88 91 94 95 98 107 113 120 127 133 140

1.000 1.000 1.000 1.000 1.000 0.998 0.970 0.895 0.000 0.003 0.000 0.000 0.000 0.000 0.000

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

1.000 1.000 1.000 1.000 1.000 0.997 0.995 0.988 0.000 0.000 0.001 0.001 0.001 0.001 0.001

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 0.001 *0.570 0.001 0.000 0.000 0.000

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 0.000 0.322 0.000 0.000 0.000 0.001

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

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Table 4. Estimated output of the ANN distance relay with different fault resistance and a fault inception angle of 808 Fault distance from busbar P (km)

Fault distance from busbar P (%)

R f (Q) 1

R f (Q) 10

R f (Q) 30

R f (Q) 70

R f (Q) 90

Correct answer

25 55 85 115 132 137 141 142 147 160 170 180 190 200 210

18 37 57 77 88 91 94 95 98 107 113 120 127 133 140

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 0.000 0.000 0.000 0.000 0.000 0.000 0.000

1.000 1.000 1.000 1.000 1.000 0.994 *0.102 *0.042 0.000 0.000 0.001 0.000 0.001 0.001 0.001

1.000 1.000 1.000 1.000 0.999 0.997 *0.379 *0.126 0.000 0.003 0.000 0.000 0.000 0.000 0.000

1.000 1.000 1.000 1.000 1.000 0.997 *0.259 *0.138 0.000 0.000 0.000 0.000 0.000 0.000 0.000

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

distance relay was investigated in this paper. An extension of the relay primary protection zone to 96% of the line length was implemented, enhancing system security and improving the performance considering ordinary relays. Concerning the ANN architecture, two different configurations were chosen to describe the distance relay operation. For the 1050 cases which were tested, the first architecture (20-15-10-1) presented 6.48% of incorrect responses, with 1.52% in the transition zone (94–98% of the line length). The second architecture analysed (7-7-7-1) presented 2.67% of incorrect responses, with 2.57% of errors in the transition zone. It can be seen that the global performance of the second architecture is superior if compared to the first one concerning faults in the forward direction. In addition, the majority of the errors is concentrated on the transition zone where the incorrect responses can be tolerated. On the other hand, it should be mentioned that the second architecture is supplied only with the magnitudes of the three-phase voltages and currents (obtained with the use of a half cycle DFT) and the phase angle information is neglected. As a consequence of that, a misoperation for reverse faults is expected. It should also be noted that this study takes into account phase a to earth faults only. In order to extend the proposed scheme to real situations, a fault type classifier similar to the one presented in Ref. [10] should be used and a similar training process made for other types of faults. At present, this extension is under development. Concerning the time response for the implementation of the two different types of architectures, it is estimated that in both cases the relay would operate less than 11 ms after the fault occurrence. It should also be remembered that the schemes involved training processes where various faulty cases were necessary for the learning process of the relay. It must however be pointed out that this tool opens a new dimension in relay philosophy which should be widely investigated, allowing one to solve some of the many problems related to the distance protection of transmission lines.

V. Acknowledgements The authors wish to acknowledge the financial support given by FAPESP—Fundac¸a˜o de Amparo a Pesquisa do Estado de Sa˜o Paulo.

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