Multiclass power quality events classification using variational mode decomposition with fast reduced kernel extreme learning machine-based feature selection

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.

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