Title: Power quality disturbances classification using support vector machines with optimised time-frequency kernels

Authors: Mihir Narayan Mohanty; Aurobinda Routray; Ashok Kumar Pradhan; Prithviraj Kabisatpathy

Addresses: Department of Electronics and Instrumentation Engineering, Institute of Technical Education and Research (ITER), Siksha O Anusandhan University, Bhubaneswar, Odisha 751030, India. ' Department of Electrical Engineering, Indian Institute of Technology, Kharagour, West Bengal 721302, India. ' Department of Electrical Engineering, Indian Institute of Technology, Kharagour, West Bengal 721302, India. ' Department of Instrumentation and Electronics Engineering, College of Engineering and Technology, Techno Campus, Bhubaneswar, Odisha 751003, India

Abstract: Detection and classification of power system disturbances is necessary to ensure good power supply. The paper presents a method for accurate classification of power quality signals using support vector machines (SVM) with optimised time-frequency kernels. The Cohen's class of time-frequency-transformation has been chosen as the kernel for the SVM. A stochastic genetic algorithm (StGA) has been used to optimise the parameters of the kernels. Comparative simulation results demonstrate a significant improvement in the classification accuracy with such optimised kernels.

Keywords: power quality signals; signal classification; support vector machines; SVM; time-frequency kernels; stochastic genetic algorithms; StGAs; power disturbances; power systems.

DOI: 10.1504/IJPELEC.2012.045630

International Journal of Power Electronics, 2012 Vol.4 No.2, pp.181 - 196

Accepted: 01 Oct 2011
Published online: 28 Feb 2012 *

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