Development of a hybrid fault diagnostic method for power distribution network

Fault detection in power systems with high reliability has been a subject of concern for protection engineers over the years. In this paper, we model a 132kV distribution system in Power World software package. Various types of fault cases are obtained through the study of the model. Stationery wavelet transform (SWT) is used to decompose the signal into its coefficients and extract statistical features. Subsequent to the extraction of features, support vector machine (SVM) and artificial neural network (ANN) schemes are used for fault detection and classification. Support vector regression (SVR) scheme is used for fault location. A hybrid technique for fault diagnostic comprising of SWT-(SVM, ANN) and SVR is proposed. The method showed good accuracy of classification and minimum error for fault estimation. The proposed method is tested on a machine learning platform WEKA.