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Transmission Line using Discrete Wavelet Transform and Back- propagation Neural Network based on Clarke’s Transformation

Saini, Makmur and Moh.Zin, Abdullah and Mustafa, M.Wazir and Sultan, Ahmad Rizal (2016) Transmission Line using Discrete Wavelet Transform and Back- propagation Neural Network based on Clarke’s Transformation. Applied Mechanics and Materials, 818. pp. 156-165. ISSN 1662-7482

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Abstract

This paper proposes a new technique of using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation for fault classification and detection on a single circuit transmission line. Simulation and training process for the neural network are done by using PSCAD / EMTDC and MATLAB. Daubechies4 mother wavelet (DB4) is used to decompose the high frequency components of these signals. The wavelet transform coefficients (WTC) and wavelet energy coefficients (WEC) for classification fault and detect
patterns used as input for neural network training back-propagation (BPNN). This information is then fed into a neural network to classify the fault condition. A DWT with quasi optimal performance for preprocessing stage are presented. This study also includes a comparison of the
results of training BPPN and DWT with and without Clarke’s transformation, where the results show that using Clarke transformation in training will give in a smaller mean square error (MSE) and mean absolute error (MAE). The simulation also shows that the new algorithm is more reliable and accurate.

Item Type: Article
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Jurusan Teknik Elektro > D4 Teknik Listrik
Depositing User: Mr Ahmad Rizal Sultan
Date Deposited: 30 Apr 2023 14:38
Last Modified: 14 May 2023 23:24
URI: https://repository.poliupg.ac.id/id/eprint/1026

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