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http://hdl.handle.net/123456789/24880
Title: | Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system |
Authors: | Naidu, Kanendra Ali, Mohd Syukri Abu Bakar, Ab Halim Kwang Tan, Chia Arof, Hamzah Mokhlis, Hazlie UniKL BMI |
Keywords: | article artificial neural network discrete wavelet transform impedance particle swarm optimization simulation waveform device failure power supply wavelet analysis Electric Impedance Electric Power Supplies Equipment Failure Neural Networks, Computer Wavelet Analysis |
Issue Date: | 1-Jan-2020 |
Publisher: | Public Library of Science |
Citation: | Naidu K, Ali MS, Abu Bakar AH, Tan CK, Arof H, Mokhlis H (2020) Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system. PLoS ONE 15(1): e0227494. https://doi.org/10.1371/journal.pone.0227494. |
Abstract: | This paper proposes an approach to accurately estimate the impedance value of a high impedance fault (HIF) and the distance from its fault location for a distribution system. Based on the three-phase voltage and current waveforms which are monitored through a single measurement in the network, several features are extracted using discrete wavelet transform (DWT). The extracted features are then fed into the optimized artificial neural network (ANN) to estimate the HIF impedance and its distance. The particle swarm optimization (PSO) technique is employed to optimize the parameters of the ANN to enhance the performance of fault impedance and distance estimations. Based on the simulation results, the proposed method records encouraging results compared to other methods of similar complexity for both HIF impedance values and estimated distances. © 2020 Naidu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Description: | This article is indexed by Scopus |
URI: | http://hdl.handle.net/123456789/24880 |
ISSN: | 19326203 |
Appears in Collections: | Journal Articles |
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11. Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system.pdf | 1.64 MB | Adobe PDF | View/Open |
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