Enhancing Power Quality in Smart Grids Through the Application of Artificial Intelligence: A Comprehensive Analysis and Case Studies

The integration of renewable energy sources and advanced monitoring systems in smart grids has introduced new challenges in maintaining high power quality. To address these challenges, the application of Artificial Intelligence (AI) techniques has gained significant attention and is emerging as a promising solution for enhancing power quality in smart grids. This paper presents a comprehensive analysis of the application of AI in enhancing power quality in smart grids. Various AI methodologies, including machine learning algorithms, expert systems, and deep learning models, are explored in the context of power quality management. The paper investigates how AI techniques can effectively analyse and predict power quality disturbances, detect anomalies, and propose optimal control strategies to mitigate power quality issues. Furthermore, a set of case studies is presented to demonstrate the practical implementation and effectiveness of AI-based solutions in real-world smart grid environments. The results highlight the potential of AI techniques in improving power quality, reducing downtime, and enhancing the reliability and efficiency of smart grid operations. The findings of this research contribute to the advancement of power quality management in smart grids and provide valuable insights for researchers, practitioners, and decision-makers in the