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

¹ Professor,Department of CSE,New Prince Shri Bhavani College of Engineering and Technology, Chennai - 600073, Tamil nadu, India
² Assistant Professor,Department of S&H,Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127.,s.rajarajeswari_maths@psvpec.in
³ Department of Computer Engineering,Vishwakarma Institute of Technology Pune India sonali.antad@vit.edu
⁴ Assistant Professor, School of Business and Management, CHRIST University, Bangaluru Email -sbjeshurun@gmail.com
⁵ Department of Electrical Engineering, GLA University, Mathura, arti.badhoutiya@gla.ac.in
⁶ Wagle, Department of Mechanical Engineering, Dr.D.Y.Patil Institute of Technology, Pimrpi,Pune.
⁷ Department of EEE, School of Engineering, Mohan Babu University, Tirupati, Andhra Pradesh, India.

^* hodcse@newprinceshribhavani.com

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.