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A novel two-dimensional (2D) learning framework has been proposed to addressthe feature selection problem in Power Quality (PQ) events. Unlike the existingfeature selection approaches, the proposed 2D learning explicitly incorporatesthe information about the subset cardinality (i.e., the number of features) asan additional learning dimension to effectively guide the search process. Theefficacy of this approach has been demonstrated considering fourteen distinctclasses of PQ events which conform to the IEEE Standard 1159. The searchperformance of the 2D learning approach has been compared to the other sixwell-known feature selection wrappers by considering two induction algorithms:Naive Bayes (NB) and k-Nearest Neighbors (k-NN). Further, the robustness of theselected/reduced feature subsets has been investigated considering sevendifferent levels of noise. The results of this investigation convincinglydemonstrate that the proposed 2D learning can identify significantly better androbust feature subsets for PQ events.

Efficient Feature Selection of Power Quality Events using Two Dimensional (2D) Particle Swarms