Classification of disturbances in hybrid DG system using modular PNN and SVM

Abstract

This paper presents the classification of islanding and power quality (PQ) disturbances in grid-connected distributed generation (DG) based hybrid power system. The penetration of DG influences the PQ levels in the distribution networks. Islanding disturbances are separated out from the PQ disturbances based on the selection of suitable threshold value, at the initial stage of classification process. Further, the power quality disturbances are automatically classified into distinct classes based on feature extraction using S-transform followed by training of two classifiers, namely, modular probabilistic neural network (MPNN) and support vector machines (SVMs). Five different types of disturbances are considered for the classification problem. The study reveals that S-transform (ST) in association with MPNN and SVM can effectively detect and classify islanding and PQ disturbances. The proposed methodology uses features instead of real data set and thereby reduces the data size to classify disturbance signal without losing its original property. The accuracy and reliability of proposed classifier is also tested on signals contaminated with noise and PQ disturbances caused due to wind speed variation on an experimental prototype set-up.