Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system

Kanendra Naidu; Mohd Syukri Ali; Ab Halim Abu Bakar; Chia Kwang Tan; Hamzah Arof; Hazlie Mokhlis

doi:10.1371/journal.pone.0227494

Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system

PLoS One. 2020 Jan 30;15(1):e0227494. doi: 10.1371/journal.pone.0227494. eCollection 2020.

Authors

Kanendra Naidu¹, Mohd Syukri Ali², Ab Halim Abu Bakar², Chia Kwang Tan², Hamzah Arof³, Hazlie Mokhlis³

Affiliations

¹ Electrical Technology Section, Universiti Kuala Lumpur, British Malaysian Institute, Gombak, Selongor Darul Ehsan, Malaysia.
² Higher Institution Centre of Excellence (HICoE), UM Power Energy Dedicated Advanced Centre (UMPEDAC), Level 4, Wisma R&D, University of Malaya, Jalan Pantai Baharu, Kuala Lumpur, Malaysia.
³ Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Electric Impedance*
Electric Power Supplies*
Equipment Failure
Neural Networks, Computer*
Wavelet Analysis

Grants and funding

This work was supported by the University of Malaya, Kuala Lumpur under Fundamental Research Grant Scheme (FRGS Grant No: FP093-2018A) to AHAB. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.