Power quality disturbances classification using support vector machines with optimised time-frequency kernels
by Mihir Narayan Mohanty; Aurobinda Routray; Ashok Kumar Pradhan; Prithviraj Kabisatpathy
International Journal of Power Electronics (IJPELEC), Vol. 4, No. 2, 2012

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.

Online publication date: Sat, 23-Aug-2014

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