Advanced Maintenance of Renewable Energy Plants

Dear Colleagues,

The rapid expansion of renewable energy generation systems—including large-scale solar and wind power plants, hybrid systems, and distributed energy resources—has introduced new technical challenges related to reliability, efficiency, and lifecycle management. As renewable installations grow in size, complexity, and geographical dispersion, traditional operation and maintenance (O&M) strategies are no longer sufficient to ensure optimal performance, availability, and cost-effectiveness. Recent advances in artificial intelligence, advanced sensing technologies, robotics, and data-driven methodologies are transforming the way renewable energy systems are monitored, diagnosed, and maintained. The integration of machine learning, digital twins, edge computing, autonomous inspection platforms, and smart sensor networks enables predictive and prescriptive maintenance strategies that significantly reduce downtime, operational costs, and failure risks, while extending asset lifetime. In parallel, the increasing penetration of hybrid renewable systems, energy storage solutions, and grid-interactive plants demands intelligent and adaptive O&M frameworks capable of managing uncertainty, degradation processes, and complex system interactions under real operating conditions.

This TOPIC aims to gather state-of-the-art research, reviews, and real-world case studies addressing innovative and emerging approaches for the advanced monitoring, operation, and maintenance of renewable energy systems, with particular emphasis on AI-based methods and next-generation sensing technologies.

Prof. Dr. Alberto Gregorio
Prof. Dr. Luis Hernández-Callejo
Topic Editors