Digital Twin-Based Predictive Maintenance of Renewable Power Generation Systems

Abstract

The increasing prevalence of renewable energy production systems, including wind turbines, solar photovoltaic (PV) plants, and hybrid energy systems, has heightened the requirement for advanced maintenance solutions to ensure high reliability, operational efficiency, and reduced downtime. Conventional maintenance strategies, including corrective and preventive maintenance, often fail to address unforeseen equipment failures resulting from dynamic operational conditions and environmental unpredictabilities. We provide a Digital Twin-based Predictive Maintenance (DT-PM) framework that amalgamates Internet of Things (IoT) sensors, edge computing, cloud-based Digital Twin models, and machine learning algorithms to facilitate real-time monitoring, issue identification, and maintenance optimization. The Digital Twin consistently updates data from physical assets to their virtual counterparts, enabling precise assessment of equipment health and identification of potential failures. The Remaining Useful Life (RUL) of critical components is assessed by advanced predictive analytics to provide data-driven maintenance scheduling. The proposed framework enhances fault detection accuracy, decreases maintenance expenses, minimizes unforeseen outages, and optimizes asset use and energy availability. Moreover, its scalable architecture supports various renewable energy sources and facilitates seamless interaction with smart grid systems. The proposed method demonstrates significant potential to enhance the dependability, sustainability, and lifecycle management of renewable energy generation systems, facilitating the shift towards intelligent and autonomous energy management in Industry 5.0 environments.