Artificial Neural Network and Hidden Space SVM for Fault Detection in Power System
This paper presents an artificial neural network(ANN) and hidden space support vector machines (HSSVMs) approach for locating faults in radial distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses measurements available at the substation, circuit breaker and relay statuses. The data is analyzed using the principal component analysis (PCA) technique and the faults are classified according to the reactances of their path using a combination of HSSVMs classifiers and feedforward neural networks (FFNNs). The extensive numerical experiments performed for location of different kinds of faults of the transmission line have proved very good accuracy of fault location algorithm. The average error of fault location is about 4%. The result proved its effectiveness.
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