Open AccessMulticlass power quality events classification using variational mode decomposition with fast reduced kernel extreme learning machine‐based feature selection
Author(s) -
Chakravorti Tatiana,
Dash Pradipta Kishore
Publication year - 2018
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2017.0123
Subject(s) - extreme learning machine , pattern recognition (psychology) , kernel (algebra) , feature selection , artificial intelligence , linear discriminant analysis , computer science , feature (linguistics) , feature extraction , signal processing , selection (genetic algorithm) , machine learning , artificial neural network , mathematics , digital signal processing , combinatorics , linguistics , philosophy , computer hardware
In this study, a modern adaptive signal processing technique called variational mode decomposition (VMD) has been used for power quality (PQ) events detection. Numerous single, as well as multiple PQ events, are simulated according to IEEE std. 1159‐2009 and laboratory experimental signals are collected and passed through the VMD algorithm. VMD decomposes the signal into different modes and from these modes, different features have been extracted. To reduce the dimension of the feature set Fischer linear discriminant analysis (FDA) has been used. As a new contribution to the literature, VMD aided FDA‐based feature selection with reduced kernel extreme learning machine technique has been used for detection and classification of multiple PQ disturbances. The performance of the proposed combined technique shows higher classification accuracy while classifying multiple PQ disturbances and the results are comparable with many existing methods.