Classification of Multiple Power Quality Disturbances Using Support Vector Machine and One-versus-One Approach

Abstract

This paper presents a classifier for recognizing power quality disturbances (PQD) problem. The so called support vector machine (SVM) is an effective classification tool, but it can only process binary classification problems. This paper integrated SVM and the one-versus-one (OVO) approach to form the OVO-based SVM (OSVM) which can process the multiple classification problem such as PQD. Using the proposed methodology can reduce a great quantity of the training data, less memory space and computing time are required. With IEEE 14-bus power system, seven power quality disturbing events were tested and compared with artificial neural network (ANN). The simulation results were conducted to show the shortened processing time and effectiveness of the proposed approach.