Evolutionary and Swarm Based Optimization of Fit k-Nearest Neighbor Classifier for Classification of Power Quality Disturbances

Abstract

In the electric network, integration of renewable energy sources and switching in large industrial loads and non-linear loads result in Power Quality Disturbances (PQDs). PQDs arise malfunctioning the sensitive equipment integrated with power grid; therefore, it is necessary to overcome the effects of poor power quality (PQ) in electric power supply. It is possible to monitor PQ with the advancement of signal processing and artificial intelligence automatically. In this paper, techniques of automatic detection and classification of PQDs are proposed using discrete wavelet transform–multi-resolution analysis (DWT–MRA) and evolutionary and swarm intelligence algorithms in renewable integrated power grid. Fit k-nearest neighbor (KNN) classifier along with DWT–MRA technique is used to classify the PQDs and it is optimized using evolutionary and swarm intelligence algorithms like, genetic algorithms, particle swarm optimization, and grey wolf optimization. Performance of algorithm is compared using accuracy of classification and confusion matrix. Further robustness of classifier is tested with Simulink model of IEEE bus test system that indicated the renewable integrated power grid environment.