Preprint Article Version 1 This version is not peer-reviewed

Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier

Version 1 : Received: 12 October 2019 / Approved: 13 October 2019 / Online: 13 October 2019 (16:22:41 CEST)

How to cite: Aker, E.; Otman, M.L.; Veerasamy, V.; Aris, I.; Wahab, N.A.; Hizam, H. Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier. Preprints 2019, 2019100148 (doi: 10.20944/preprints201910.0148.v1). Aker, E.; Otman, M.L.; Veerasamy, V.; Aris, I.; Wahab, N.A.; Hizam, H. Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier. Preprints 2019, 2019100148 (doi: 10.20944/preprints201910.0148.v1).

Abstract

This paper presents the methodology to detect and identify the type of fault that occurs in shunt connected static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes classifier. To study this, the network model is designed using Mat-lab/Simulink. The different faults such as Line to Ground (LG), Line to Line (LL), Double Line to Ground (LLG) and three-phase (LLLG) fault are applied at different zones of system with and without STATCOM considering the effect of varying fault resistance. The three-phase fault current waveforms obtained are decomposed into several levels using daubechies mother wavelet of db4 to extract the features such as standard deviation and Energy values. The extracted features are used to train the classifiers such as Multi-Layer Perceptron Neural Network (MLP), Bayes and Naive Bayes (NB) classifier to classify the type of fault that occurs in the system. The results reveal that the proposed NB classifier outperforms in terms of accuracy rate, misclassification rate, kappa statistics, mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE) and root-relative square error (RRSE) than MLP and Bayes classifier.

Subject Areas

static synchronous compensator (STATCOM); discrete wavelet transform (DWT); multi-layer perceptron neural network (MLP); Bayes and Naive Bayes (NB) classifier

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