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

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

doi:10.1371/journal.pone.0227494

Kanendra Naidu, Hamzah Arof + Show 5 more

Open Access

https://doi.org/10.1371/journal.pone.0227494

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Journal: PloS one	Publication Date: Jan 30, 2020
Citations: 16	License type: CC BY 4.0

Affiliation: University of Kuala Lumpur, University of Malaya

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.

Highlights

Underground distribution systems are widely implemented due to a higher level of security against environmental hazards
The results show that the particle swarm optimization (PSO)-optimized artificial neural network (ANN) delivers better performance compared to standard ANN in estimating the fault impedance and distance values as depicted in Figs 7 and 8 respectively
The estimation of fault impedance values and distance based on PSO-optimized ANN is proposed

Summary

Introduction

Underground distribution systems are widely implemented due to a higher level of security against environmental hazards. Identifying the HIF location in an underground system is difficult due to the low fault current and non-visibility [1]. Discrete wavelet transforms (DWT) is a mathematical function that transforms the original signal into time and frequency domain components. Both filters decompose the original signal into highfrequency and low-frequency components. The highfrequency component, yhigh and the low-frequency component, ylow for the original signal, x(k) passes through the high-pass filter, h(2n-k) and low-pass filter, l(2n-k) with downsampling by a value of two (2) are shown by the following equations: X1

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Results

Conclusion