Classifications of disturbances using wavelet transform and support vector machine

Authors: NEDA HAJIBANDEH, FARAMARZ FAGHIHI, HOSSEIN RANJBAR, HESAM KAZARI

Abstract: This paper proposes a new method to detect and classify all kinds of faults, capacitor switching, and load switching in a power system network based on wavelet transform and support vector machines (SVMs). In this regard, a sample of a power system is simulated via MATLAB/Simulink, and by reading the voltage of the point of common coupling and using the wavelet transform, the differences of the outputs of the wavelet transform are investigated. The SVM approach is employed to distinguish the type of the transient (capacitor switching, fault, and/or load switching) in use for the high level outputs of the wavelet transform. Similar to neural networks, this method, which is based on learning, is considered as a proper tool for data classification. The results of simulations demonstrate that the combination of wavelet transform and SVM recognizes the type of the transient correctly and effectively as well as distinguishes capacitor switching and load switching events from all kinds of faults such as three-phase-to-earth fault, phase-to-phase fault, two-phase-to-earth fault, and single-phase-to-earth fault. In the end, the accuracy of the presented approach is evaluated and the simulation results are proposed for different attributes of transients in the power system network.

Keywords: Disturbance detection, support vector machine, wavelet transform

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