AI-Assisted Predictive Maintenance of Renewable Energy Infrastructure

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.