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access icon openaccess Fault detection in distribution networks in presence of distributed generations using a data mining–driven wavelet transform

This is an open access article published by the IET under the Creative Commons Attribution-NoDerivs License (http://creativecommons.org/licenses/by-nd/3.0/)
Received 01/08/2018, Accepted 11/12/2018, Revised 18/10/2018, Published 21/12/2018

Here, a data mining–driven scheme based on discrete wavelet transform (DWT) is proposed for high impedance fault (HIF) detection in active distribution networks. Correlation between the phase current signal and the related details of the current wavelet transform is presented as a new index for HIF detection. The proposed HIF detection method is implemented in two subsequent stages. In the first stage, the most important features for HIF detection are extracted using support vector machine (SVM) and decision tree (DT). The parameters of SVM are optimised using the genetic algorithm (GA) over the input scenarios. In second stage, SVM is utilised to classify the input data. The efficiency of the utilised SVM-based classifier is compared with a probabilistic neural network (PNN). A comprehensive list of scenarios including load switching, inrush current, solid short-circuit faults, HIF faults in the presence of harmonic loads is generated. The performance of the proposed algorithm is investigated for two active distribution networks including IEEE 13-Bus and IEEE 34-Bus systems.

Inspec keywords: data mining; wavelet transforms; power engineering computing; support vector machines; neural nets; fault diagnosis; genetic algorithms; discrete wavelet transforms; decision trees; power distribution faults; distributed power generation; probability

Other keywords: support vector machine; utilised SVM-based classifier; solid short-circuit faults; phase current signal; decision tree; current wavelet; HIF faults; active distribution networks; data mining–driven scheme; HIF detection method; probabilistic neural network; high impedance fault detection; IEEE 34-Bus systems; distributed generations; discrete wavelet transform; input data; IEEE 13-Bus systems

Subjects: Optimisation techniques; Other topics in statistics; Distributed power generation; Combinatorial mathematics; Integral transforms; Power engineering computing; Integral transforms; Knowledge engineering techniques; Optimisation techniques; Neural computing techniques; Combinatorial mathematics

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