Fault Detection and Classification on Transmission Line using Cascade and Feed Forward Back Propagation Neural Based on Clarke’s Transformation

A. M. Shiddiq, Yunus and Makmur, Saini and Ahmad, Rizal Sultan and Rahimuddin, Rahimuddin (2019) Fault Detection and Classification on Transmission Line using Cascade and Feed Forward Back Propagation Neural Based on Clarke’s Transformation. In: 2019 IEEE International Conference on Applied System Innovation, April, 11-15, 2019, Fukuoka, Jepang.

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Official URL: https://2019.icasi-conf.net/

Abstract

This paper proposed a comparative study for detecting and classifying fault on parallel transmission line using cascade forward and feed forward back propagation. Both calculations were based on discrete wavelet transform (DWT) and Clarke’s transformation. Daubechies4 mother wavelet (Db4) was used to decompose coefficients of wavelet transforms coefficients (WTC) and wavelet energy coefficients (WEC) of high frequency signals. The coefficients were inputs for training of neural network back-propagation (BPNN). The results showed that the feed forward back propagation approach based on Artificial Neural Network (ANN) models achieved better results than Cascade forward back propagation approach models, particularly in detecting and classifying fault on a parallel transmission line. The results showed that the introduced approach for fault analysis is capable to classify rapidly and correctly all the faults on the parallel transmission line.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Department of Mechanical Engineering > Teknik Pembangkit Energi
Depositing User: admin admin pnup
Date Deposited: 16 Jun 2020 03:57
Last Modified: 16 Jun 2020 03:57
URI: http://repository.poliupg.ac.id/id/eprint/1429

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