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.
Volterra bound interval type-2 fuzzy logic-based approach for multiple power quality events analysis
IET Electrical Systems in Transportation ; 8 , 3 ; 188-196
2018-03-26
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
probabilistic NN , probability , neural networks , PQ event detection , PQ event classification , interval type-2 fuzzy logic system , Volterra series , membership functions , neural nets , signal detection , IT2FLS , type-1 fuzzy logic system classifiers , feature extraction , power supply quality , support vector machine , support vector machines , fuzzy logic , input-output relationship representation , signal-to-noise ratio , multiple power quality events analysis , additive noise , nonlinear systems , signal classification , Volterra bound interval type-2 fuzzy logic-based approach , power engineering computing , infinite power series
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