Implementation of Machine Learning for Power Quality Improvement in DG Systems

The integration of distributed generation (DG) systems, like solar and wind power, presents significant challenges for power quality, including issues with voltage stability, harmonic distortion, and transient disturbances. Traditional power quality solutions often lack flexibility and adaptability, making machine learning (ML) a promising alternative. This review examines how ML models including neural networks, support vector machines, and deep learning architectures can improve power quality by detecting and mitigating disturbances in real time. Key topics covered include step by step real-time implementation strategies, application of ML and artificial for power quality improvement and its advantages. By highlighting recent advances and identifying research gaps, this review offers insights into the future role of ML in maintaining power quality within DG-integrated smart grids.