Detection and Classification of Power Quality Abnormality Using S-Transform and KNN Classifier

Abstract

In this work power quality abnormality present in power supply was detected and classified using S Transform and k-nearest neighbors Classifier (KNN). The S-transform is used in this paper is to analysis of Power Quality abnormalities under the noisy condition of stationary signals and also it has the ability to sense the various types of disturbance accurately. From S-Transform signal ten types of features values like entropy, range, SD is extracted. The K-NN classifier is trained with 500 different types of sample data taken by varying the voltage, frequency etc. The K-NN classifier is tested with 100 different types of sample data. The KNN classifier has high classification accuracy, less calculation time and learning capability and reduction in complexity are improved. The simulation result of S-transform and KNN Classifier are more efficient in both detection and classification power quality abnormalities when compare to the existing techniques.