Power quality improvement in microgrids using artificial intelligence techniques: A Review

The increasing adoption of renewable energy sources (RES) is attributed to their sustainability, reduced environmental impact, and reliance on abundant natural resources. Microgrids are becoming more prevalent in the existing power systems. The intermittent nature of renewable energy-based sources, as well as the integration of power electronic converters in these microgrids, leads to various power quality issues such as voltage and frequency fluctuations, current harmonics, transients etc. To fully utilize the potential of renewable energy sources, these power quality issues must be addressed. This paper examines the use of swarm-based AI optimization techniques for improving the dynamic response and power quality in AC microgrids, along with hybrid AI approaches. The study reviewed more than 100 relevant articles to analyze AI and hybrid AI optimization methods used for power quality improvement. Swarm-based AI algorithms have inherent limitations, making hybrid models increasingly popular and promising within the AI research community. Extensive studies are going on to further improve hybrid methods to overcome their limitations and to facilitate the evolution of more methods. The review findings highlight that hybrid AI approaches, particularly ANFIS and PSO-ANN, outperform traditional methods by reducing THD and improving transient response for better power quality in AC microgrids.