Fault Detection and Classification in Double-Circuit Transmission Line in Presence of TCSC Using Hybrid Intelligent Method

In this paper, an effective method for fault detection and classification in a double-circuit transmission line compensated with TCSC is proposed. The mutual coupling of parallel transmission lines and presence of TCSC affect the frequency content of the input signal of a distance relay and hence fault detection and fault classification face some challenges. One of the most effective methods for fault detection and classification in a compensated line is pattern-recognition methods. Prerequisites for the optimal using of these methods are the extraction and selection of appropriate features to feed the classifier. In this paper, wavelet transform as a signal processing tool to extract features is used. Due to variety of mother wavelets, firstly the best mother wavelet is identified by using a new method and the feature vector is made by the coefficients obtained from the best mother wavelet. After this stage, decision tree, support vector machines, and k-nearest neighbor as the classifies are trained by feeding the feature vector. Then, their accuracies are evaluated against different simulation scenarios to select the best classifier which has the best performance among others. In this paper, the sample system and the proposed method is implemented in MATLAB environment.