This paper describes a new method for high impedance fault (HIF) detection based on s-transform (ST) and pattern recognition technique. Conventional distance, over current and ground fault relays are difficult to apply for High Impedance Fault (HIF) detection in distribution line because of sensitivity, diversity, selectivity issues in case of low value of fault current. Recently, s-transform has been successfully applied for different power system protection problem. It is a very useful tool to analyze transient fault signal to provide both time and frequency information unlike Fourier transform and the same has been considered for high impedance fault detection in this work. The features extracted using s-transform to train and test the two different intelligent classifier like artificial neural network (ANN) and support vector machine (SVM) separately, to discriminate the HIF with other transient phenomenon (Load switching, capacitor Switching) and also normal fault condition. A comparative study of these two classifiers has been reported based on their detection accuracy. It has been found that the proposed techniques are highly effective for high impedance fault detection under a wide range of operating conditions and noisy environment in a high voltage distribution
A new approach for classification has been presented in this paper. The proposed technique, Modified Radial Basis Functional Neural Network (MRBFNN) consists of assigning weights between the input layer and the hidden layer of Radial Basis functional Neural Network (RBFNN). The centers of MRBFNN are initialized using Particle swarm Optimization (PSO) and variance and centers are updated using back propagation and both the sets of weights are updated using Recursive Least Square (RLS). Our simulation result is carried out on Wisconsin Breast Cancer (WBC) data set. The results are compared with RBFNN, where the variance and centers are updated using back propagation and weights are updated using Recursive Least Square (RLS) and Kalman Filter. It is found the proposed method provides more accurate result and better classification.
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