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access icon free Volterra bound interval type-2 fuzzy logic-based approach for multiple power quality events analysis

© The Institution of Engineering and Technology
Received 15/06/2017, Accepted 20/02/2018, Revised 06/02/2018, Published 26/02/2018

This study demonstrates detection and classification of power quality (PQ) events utilising Volterra series for feature extraction and interval type-2 fuzzy logic system (IT2FLS) for classification of PQ events. The Volterra series represented in the form of infinite power series with memory which provides a convenient and strong platform for representation of input–output relationship for non-linear systems. IT2FLS uses the concept of membership functions to perform classification of multiple PQ events. When supply power is distorted by additive noise where signal-to-noise ratio is low and uncertain, IT2FLS has shown improved performance over support vector machine, neural networks (NNs), probabilistic NN and type-1 fuzzy logic system classifiers, which makes an IT2FLS favourable for real-time applications.

Inspec keywords: probability; neural nets; power supply quality; fuzzy logic; signal classification; feature extraction; power engineering computing; nonlinear systems; support vector machines; Volterra series; signal detection

Other keywords: PQ event classification; infinite power series; PQ event detection; multiple power quality events analysis; probabilistic NN; neural networks; feature extraction; membership functions; nonlinear systems; signal-to-noise ratio; interval type-2 fuzzy logic system; type-1 fuzzy logic system classifiers; Volterra bound interval type-2 fuzzy logic-based approach; support vector machine; additive noise; IT2FLS; input-output relationship representation

Subjects: Formal logic; Power supply quality and harmonics; Neural computing techniques; Power engineering computing; Other topics in statistics; Signal detection; Digital signal processing; Knowledge engineering techniques; Other topics in statistics

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