Predictive Analytics For Fault Detection And Maintenance Optimization In Smart Microgrids: A Systematic Literature Review
Year: 2025
Type: review
Study design: Systematic Review
Abstract: 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.
DOI: 10.64252/49fpzg02
Authors: Shrunkhala Wankhede, Khushboo Jain, Nileshchandra Pikle
Institution: Indian Institute of Information Technology, Nagpur
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4413973364