Wavelet Analysis and Machine Learning Approach for Improved Protection of PV-Wind-SVC Integrated Smart Power System

In order to assure uninterrupted electrical power transmission throughout a nation’s network, it is necessary to integrate reactive power compensation devices and renewable energy sources in addition to other systematic maintenance procedures to maintain balance and regulate voltage changes. A responsive security system is essential to guard against disturbances caused by both balanced and unbalanced electrical problems. By employing wavelet and machine learning analysis to analyze transitory signals using mathematical and electrical concepts, this work sheds light on power network problems. The growing use of reactive power devices and renewable energy sources, however, comes with difficulties because of demand fluctuations, uncertainty around renewable supply, and grid complexity that is exacerbated by unidentified grid models. The study uses supervised and unsupervised machine learning methods to address these issues and advance power system analysis. By using a wavelet-based machine learning approach, the suggested algorithm has been tested for the detection and discrimination of fault behavior in PV-Wind integrated power system networks in the presence and absence of Static Var compensator. It has been shown that the algorithm is more effective than traditional techniques. © The Institution of Engineers (India) 2024.