Year: 2024
Type: article
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.
Authors: D.B. Hulwan, Chitra S, Arun Chokkalingan, Krishna Kant Dixit, S. Subburam, Vandana Katarwar, S. Tharmar
Institution: GLA University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6903324694
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