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Optimal Feature Selection via NSGA-II for Power Quality Disturbances Classification

This paper presents an application of nondominated sorting genetic algorithm II (NSGA-II) for multiobjective feature selection in power quality disturbances classification. Classification error and number of features are collectively minimized to ensure good accuracy and feasible computation time. NSGA-II gives different Pareto-optimal solutions based on the combination of objectives. Considering equal priority for both the objectives, a fitness function is provided to retrieve the best solution set from the first Pareto-front. S-transform and time–time transform are employed for detection and feature extraction. Decision tree is used for classification. The proposed technique is tested on disturbances simulated as per IEEE-1159 standards and real disturbances acquired from an experimental setup. The results show quick convergence, admirable accuracy, and reduced computational time.