Investigate the Effect of Artificial Neural Network Parameters to Improve Fault Distance and Impedance Accuracy

Abstract This paper investigates the effect of artificial neural network (ANN) parameters against the ANN accuracy on cable fault location. The investigation is conducted through the fault impedance and distance estimations during the occurrence of high impedance fault (HIF) in the distribution syst...

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Bibliographic Details
Published inIOP conference series. Materials Science and Engineering Vol. 1127; no. 1; p. 12037
Main Authors Ali, Mohd Syukri, Bakar, Ab Halim Abu, Nasrudin, Abd Rahim
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.03.2021
Subjects
Accuracy
Artificial neural networks
Fault location
High impedance
Investigations
Neural networks
Parameters
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Summary:Abstract This paper investigates the effect of artificial neural network (ANN) parameters against the ANN accuracy on cable fault location. The investigation is conducted through the fault impedance and distance estimations during the occurrence of high impedance fault (HIF) in the distribution system. The measured three-phase voltage and current signals are utilized and fed into the ANN to estimate the fault impedance and distance. The accuracy of the estimated fault impedance and distance is evaluated with respect to the variation of ANN parameters. Based on the analysis, it shows that more accurate results can be obtained by utilizing the optimal value of ANN parameters.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/1127/1/012037