AI-Based Predictive Power Quality Control in Renewable Energy Systems Using MATLAB Simulation

Abstract

The transition to renewable energy systems (RES) such as solar and wind power offers critical solutions for environmental sustainability but presents notable challenges to power quality (PQ) due to their intermittent and variable nature. PQ issues like voltage fluctuations, harmonic distortions, and frequency instability threaten the reliability and efficiency of power grids. This thesis explores emerging strategies to mitigate these issues, with a particular focus on the role of artificial intelligence (AI). The study proposes an AI-Hybrid Predictive Power Quality Control System (AI-HPQCS) that integrates real-time data acquisition, machine learning-based prediction, and optimization-based control. Through simulations and analytical modelling, the system demonstrates improved voltage regulation and reduced harmonic distortion, validating AI as a powerful tool for managing PQ in RES-integrated smart grids.