A Comprehensive Review of Artificial Intelligence Techniques for Power Quality Improvement in Renewable Energy-Based Microgrids: Recent Trends, Challenges, and Future Directions

The rapid integration of Renewable Energy Sources (RES), particularly solar photovoltaic (PV) and wind energy systems, has significantly transformed the operational dynamics of modern microgrids. Despite their environmental and economic benefits, the intermittent nature of RES and the widespread use of power electronic converters introduce major Power Quality (PQ) challenges, including voltage sags and swells, harmonic and suprachiasmatic distortion, frequency instability, voltage imbalance, and reactive power fluctuations. Conventional PQ mitigation approaches based on fixed-gain controllers and passive filtering techniques often exhibit limited adaptability under the nonlinear, stochastic, and time-varying conditions of renewable-energy-based microgrids. This paper presents a comprehensive and systematic review of recent Artificial Intelligence (AI)-based techniques developed for PQ improvement in renewable energy microgrids. The review methodology is based on a structured literature analysis of publications from 2021 to early 2026 collected from major scientific databases, including IEEE Xplore, Scopus, Web of Science, and ScienceDirect. The selected studies are critically analyzed according to AI methodology, application domain, computational complexity, real-time feasibility, and PQ mitigation performance. The review covers a broad range of AI approaches, including Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), Graph Neural Networks (GNNs), Federated Learning, and hybrid intelligent optimization techniques. Furthermore, the paper examines recent advances in AI-enabled harmonic mitigation, reactive power optimization, voltage stability enhancement, edge intelligence, lightweight AI deployment, and autonomous microgrid control. Comparative analyses are also provided to evaluate the strengths and limitations of different AI architectures in terms of accuracy, adaptability, scalability, inference latency, and embedded implementation capability. The findings indicate that AI-based methods provide substantial improvements in adaptive PQ monitoring, disturbance classification, predictive control, and real-time mitigation compared with conventional techniques. However, several critical challenges remain unresolved, including data scarcity, model interpretability, cybersecurity vulnerability, computational burden, and reliable deployment under real-world operating conditions. Finally, future research directions are discussed, highlighting the growing importance of lightweight AI, Transformer-based architectures, hybrid cloud-edge intelligence, and physics-informed AI frameworks for the development of next-generation self-healing and autonomous smart microgrids.