Classification of power system disturbances using support vector machines

Authors:
Sami Ekici
Affiliations:
Department of Electrical Education, Firat University, 23119 Elazig, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

This paper presents an effective method based on support vector machines (SVM) for identification of power system disturbances. Because of its advantages in signal processing applications, the wavelet transform (WT) is used to extract the distinctive features of the voltage signals. After the wavelet decomposition, the characteristic features of each disturbance waveforms are obtained. The wavelet energy criterion is also applied to wavelet detail coefficients to reduce the sizes of data set. After feature extraction stage SVM is used to classify the power system disturbance waveforms and the performance of SVM is compared with the artificial neural networks (ANN).