An Efficient Method for Feature Extraction and Selection in Power Quality Recognition

Abstract

—Since decade, many time-frequency analysis methods with combination of classifiers have been studied in literature for recognition of power quality (PQ) events. In these studies, feature extraction and selection have a vital role to enhance PQ classification accuracy and to reduce computational complexity for PQ recognition. This paper presents a new method to detect and classify PQ disturbances based on modified Stockwell Transform (ST) for extraction of features and Hybrid Grey Wolf Optimization (HGWO) for feature selection along with K Nearest Neighbor (KNN) classifier. Simulation of the proposed method using MATLAB is carried out through a wide range of eighteen synthetic PQ events to validate the effectiveness of the selected features. Further, an experiment is extended for six classes of real PQ events acquired from self excited induction generator (SEIG) system in a laboratory experimental setup. Proposed method is also employed on those real time data to study the classification accuracy performance. In these experiments, an impressive accuracy of 99.94% and 99.3% for synthetic and real time PQ event data, respectively are reported. Hence, it is observed from result analysis, this proposed method can be utilized for recognition of PQ events in real time power system.