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Optimal Feature Selection via NSGA-II for Power Quality Disturbances Classification | IEEE Journals & Magazine | IEEE Xplore

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

Publisher: IEEE

Abstract:

This paper presents an application of nondominated sorting genetic algorithm II (NSGA-II) for multiobjective feature selection in power quality disturbances classificatio...View more

Abstract:

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.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 14, Issue: 7, July 2018)
Page(s): 2994 - 3002
Date of Publication: 14 November 2017

ISSN Information:

Publisher: IEEE

References

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