Future decentralized and renewable energy systems depend heavily on microgrids. However, fault detection and predictive maintenance are crucial due to their intricacy and dependence on variable sources. With an emphasis on fault detection, forecasting, and maintenance optimization, this review offers a targeted analysis of machine learning-based predictive analytics in microgrids. We suggest a methodical framework that combines neural networks, adaptive neuro-fuzzy inference systems (ANFIS), and condition-based monitoring. Along with a critical examination of these approaches' shortcomings and applicability, a comparative assessment of them is covered. In order to improve microgrid resilience, future research directions highlight the necessity of scalable, explainable AI models and real-time data integration.
Wankhede et al. (Wed,) studied this question.