Machine Learning Algorithms for Predictive Maintenance in Hybrid Renewable Energy Microgrid Systems

Abstract

The rapid expansion of hybrid renewable energy microgrid systems presents new challenges in maintaining system reliability and performance. This paper explores the application of machine learning algorithms for predictive maintenance in such systems, focusing on the early detection of potential failures to optimize operational efficiency and reduce downtime. By integrating real-time data from solar, wind, and storage components, the proposed models predict the remaining useful life (RUL) of critical components. The results demonstrate significant improvements in predictive accuracy, offering a robust solution for enhancing the reliability and longevity of renewable energy microgrids.

Keywords

• predictive maintenance
• hybrid renewable energy systems
• machine learning
• microgrid
• remaining useful life (rul)
• energy management