High-impedance fault detection in medium-voltage distribution network using computational intelligence-based classifiers

This paper presents the high-impedance fault (HIF) detection and identification in medium-voltage distribution network of 13.8 kV using discrete wavelet transform (DWT) and intelligence classifiers such as adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). The three-phas...

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Veröffentlicht in:Neural computing & applications 2019-12, Vol.31 (12), p.9127-9143
Hauptverfasser: Veerasamy, Veerapandiyan, Abdul Wahab, Noor Izzri, Ramachandran, Rajeswari, Thirumeni, Mariammal, Subramanian, Chitra, Othman, Mohammad Lutfi, Hizam, Hashim
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container_end_page 9143
container_issue 12
container_start_page 9127
container_title Neural computing & applications
container_volume 31
creator Veerasamy, Veerapandiyan
Abdul Wahab, Noor Izzri
Ramachandran, Rajeswari
Thirumeni, Mariammal
Subramanian, Chitra
Othman, Mohammad Lutfi
Hizam, Hashim
description This paper presents the high-impedance fault (HIF) detection and identification in medium-voltage distribution network of 13.8 kV using discrete wavelet transform (DWT) and intelligence classifiers such as adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). The three-phase feeder network is modelled in MATLAB/Simulink to obtain the fault current signal of the feeder. The acquired fault current signal for various types of faults such as three-phase fault, line to line, line to ground, double line to ground and HIF is sampled using 1st, 2nd, 3rd, 4th and 5th level of detailed coefficients and approximated by DWT analysis to extract the feature, namely standard deviation (SD) values, considering the time-varying fault impedance. The SD values drawn by DWT technique have been used to train the computational intelligence-based classifiers such as fuzzy, Bayes, multi-layer perceptron neural network, ANFIS and SVM. The performance indices such as mean absolute error, root mean square error, kappa statistic, success rate and discrimination rate are compared for various classifiers presented. The results showed that the proffered ANFIS and SVM classifiers are more effective and their performance is substantially superior than other classifiers.
doi_str_mv 10.1007/s00521-019-04445-w
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The three-phase feeder network is modelled in MATLAB/Simulink to obtain the fault current signal of the feeder. The acquired fault current signal for various types of faults such as three-phase fault, line to line, line to ground, double line to ground and HIF is sampled using 1st, 2nd, 3rd, 4th and 5th level of detailed coefficients and approximated by DWT analysis to extract the feature, namely standard deviation (SD) values, considering the time-varying fault impedance. The SD values drawn by DWT technique have been used to train the computational intelligence-based classifiers such as fuzzy, Bayes, multi-layer perceptron neural network, ANFIS and SVM. The performance indices such as mean absolute error, root mean square error, kappa statistic, success rate and discrimination rate are compared for various classifiers presented. 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The three-phase feeder network is modelled in MATLAB/Simulink to obtain the fault current signal of the feeder. The acquired fault current signal for various types of faults such as three-phase fault, line to line, line to ground, double line to ground and HIF is sampled using 1st, 2nd, 3rd, 4th and 5th level of detailed coefficients and approximated by DWT analysis to extract the feature, namely standard deviation (SD) values, considering the time-varying fault impedance. The SD values drawn by DWT technique have been used to train the computational intelligence-based classifiers such as fuzzy, Bayes, multi-layer perceptron neural network, ANFIS and SVM. The performance indices such as mean absolute error, root mean square error, kappa statistic, success rate and discrimination rate are compared for various classifiers presented. 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subjects Adaptive systems
Artificial Intelligence
Artificial neural networks
Classifiers
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Discrete Wavelet Transform
Distribution management
Electric potential
Fault detection
Feature extraction
Fuzzy logic
Fuzzy systems
Image Processing and Computer Vision
Impedance
Multilayers
Neural networks
Original Article
Performance indices
Probability and Statistics in Computer Science
Support vector machines
Voltage
Wavelet transforms
title High-impedance fault detection in medium-voltage distribution network using computational intelligence-based classifiers
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