Real Time Intelligent Detection of PQ Disturbances With Variational Mode Energy Features and Hybrid Optimized Light GBM Classifier

Abstract

The modern era power system is constantly undergoing constructive changes and implementations both in source and load side. Certainly, the distributed generators, unconventional/nonlinear loads, charging stations etc are mostly integrated through power electronics interfaces. As a result, frequent power quality disturbances appear in the system that is to be mitigated at the earliest. Since detection is the prerequisite for mitigation, therefore the article presents a novel intelligent power quality detection scheme to detect and classify the PQ Events. At first, the energy feature of the 5 band limited modes are calculated from variational mode decomposed voltage signals. Then the mode energy features are utilized to train a novel Hybrid Arithmetic Whale Optimized light gradient boosting machine classifier. A total of 15 different PQ events have been investigated and exceptional classification results have obtained with optimum computational complexity, both under noiseless and noisy conditions. Moreover, the accuracy of the proposed PQ classification schemes found to be towering against other related pre-published works. Finally, the ability of the proposed detection scheme is validated in real time though OPAL-RT 4510 and grid simulator hardware in loop setup.