Title: An intelligent scheme for categorising fault events in compensated power network using K-nearest neighbour technique

Authors: Sunil Kumar Singh; D.N. Vishwakarma; R.K. Saket

Addresses: Department of Electrical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi-221005, Uttar Pradesh, India ' Department of Electrical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi-221005, Uttar Pradesh, India ' Department of Electrical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi-221005, Uttar Pradesh, India

Abstract: This paper describes a machine learning-based intelligent scheme for categorisation of fault events in series compensated power network using K-nearest neighbour technique. The wavelet decomposition mechanism is applied on 3-phase post fault current signal for digging out the significant features of the shunt fault events in this proposed scheme. Afterward, K-nearest neighbour-based classifier model has been utilised for ascertaining the specific category of the fault events in the network. The category of the shunt events in the network has been ascertained on the basis of the estimated entropy of the wavelet coefficients. The feasibility and strength of proposed scheme for categorising the fault events in the compensated power network are assessed for different fault events with varying network situations in simulated compensated power network. Finally, it is observed that the proposed discrete wavelet transform and K-nearest neighbour-based scheme is very effectual in identifying the specific fault events in the compensated power network and is unaffected by changing system circumstances.

Keywords: discrete wavelet transform; machine learning; K-nearest neighbour; K-NN; fault diagnosis; protection of compensated power network.

DOI: 10.1504/IJPEC.2020.110015

International Journal of Power and Energy Conversion, 2020 Vol.11 No.4, pp.352 - 368

Received: 08 Sep 2018
Accepted: 06 Jun 2019
Published online: 01 Oct 2020 *

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