Classification of Power Quality Disturbances Using Wavelets and Support Vector Machine
AbstractIn this paper we present a new method for detection and classification of power quality disturbances. Two discrete wavelet transforms with different wavelet filters are used in the feature extraction process. In this way we eliminate the problem of the selection of the most adequate wavelets in the current methods for classification of power quality disturbances. For the classification of the power disturbances we use a support vector machine. In order to reduce the computational cost of the proposed method, binary decision tree is created and a support vector machine classifier is trained for every node of the tree. The obtained experimental results show high accuracy of the proposed method.
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