Inderscience Publishers

Power quality disturbances classification using support vector machines with optimised time–frequency kernels

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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

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