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 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 presents a comprehensive review of swarm-based and hybrid AI optimization techniques for improving the dynamic response and power quality in AC microgrid, analyzing over 100 relevant articles. The comparison of different algorithms is done in a systematic manner based on the speed of convergence, complexity of computation, capability of harmonic mitigation, and transient response. The major contribution of this paper is in understanding the limitations of individual swarm intelligence approaches and establishing the effectiveness of hybrid AI solutions. The results indicate that hybrid methods, especially ANFIS and PSO-ANN models, have better convergence speed, lower total harmonic distortion, and enhanced transient stability, which makes them more appropriate for power quality improvement in microgrids with renewable energy integration.