Year: 2025
Type: article
Abstract: This study examines the role of Artificial Intelligence (AI) in Predictive Maintenance (PdM) for Renewable Energy (RE) infrastructure, with a focus on wind turbines and solar Photovoltaic (PV) systems. The aim is to illustrate how AI-based algorithms can predict failures to maximize operational expenditure by reducing unscheduled equipment downtime. The approach taken focuses on remotely supervised and controlled SCADA systems, along with sensor data, implementing machine learning (ML) and deep learning (DL) for anomaly detection and remaining useful life (RUL) prediction. Findings demonstrate sustained enhancements in asset dependability, raised energy yield, and significant cost reductions. This work highlights the importance of AI-assisted Predictive Maintenance (PdM) for the robust growth and optimal functionality of Renewable Energy (RE) assets globally.
DOI: 10.64252/qqdv1613
Authors: Ashu Nayak, Dr. D Kalidoss, Rajender Sharma
Institution: Kalinga University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412021960