AI-Based Fault Detection and Predictive Maintenance in Wind Power Conversion Systems

¹ Mechanical Department,Vishwakarma Institute of Technology Pune India
² Assistant Professor,Department of ECE,Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127.,s.chitra_ece@psvpec.in
³ Professor, Department of Biomedical Engineering Vels Institute of Science, Technology and Advanced Studies (VISTAS) Pallavaram, Chennai, India.carunece@gmail.com
⁴ Department of Electrical Engineering, GLA University, Mathura, krishnakant.dixit@gla.ac.in
⁵ Professor,Department of IT,New Prince Shri Bhavani College of Engineering and Technology, Chennai - 600073, Tamil nadu,India hodit@newprinceshribhavai.com
⁶ Assistant Professor, Department of Electronics and Telecommunication, Dr.D.Y.Patil Institute of Technology, Pimpri, Pune
⁷ Associate professor Mohamed sathak engineering college, Kilakarai tharmar11585@gmail.com

^* dattatray.hulwan@vit.edu

Abstract

The research explores the application of Artificial Intelligence (AI) for fault detection and predictive maintenance in wind power conversion systems. Wind energy, a critical component of the global renewable energy mix, faces challenges related to system reliability and maintenance. Traditional methods for detecting faults and scheduling maintenance are often reactive and inefficient, leading to higher costs and downtime. This study proposes an AI-based approach to improve fault detection accuracy and predict potential failures before they occur. By analysing operational data from wind turbines, AI models can identify patterns indicative of faults and provide early warnings, allowing for timely maintenance. The research demonstrates that AI can significantly enhance the reliability and efficiency of wind power systems, reducing operational costs and improving energy production. The findings suggest that AI-based predictive maintenance can play a crucial role in advancing the sustainability of wind energy.