Power Quality Monitoring and Feedback Control for Hybrid Renewal Energy System using Meta Heuristic Optimization Technique

Solar and wind energy are becoming more popular. Hybrid Renewable Energy System (HRES) is a fast-growing power industry segment. Power quality (PQ) issues have increased as renewable energy has increased in the electricity supply. This study will help electrical engineers identify power quality (PQ) disruptions from hybrid renewable power sources like solar and wind. The investigation will predict powergeneration-induced PQ problems. A self-contained Hybrid Renewable Energy System (HRES) is the project’s main goal. We will collect and analyses energy system data using Grey Wolf Optimization to improve mitigation and reduce PQ interruptions. The suggested technique analyses live data from several sources and historical data in many formats. To prevent problems, the hardware’s controllers will receive the data. Algorithms and designers will improve system efficiency and design ability. The monitoring system will predict PQ disturbances caused by electricity generation from different energy sources. The suggested device labels PQ disturbances by impact.