Design and Validation of an Institutionally Embedded Smart Supervision Mechanism for Renewable Energy Stations

The operational governance of renewable energy stations faces complex challenges due to rapid capacity expansion and evolving regulatory demands. Traditional supervision methods, which rely on manual inspections and fragmented decision-making, are insufficient for ensuring stable operations in high-penetration renewable energy systems. This study proposes a closed-loop smart supervision mechanism organized into four core stages: perception, aggregation, analysis, and decision-making. The mechanism incorporates multi-source sensing, edge-cloud data fusion, deep learning-based forecasting, graph-structured fault diagnosis, and rule-based dispatch execution, embedding institutional logic throughout the supervisory workflow. A simulated case study of a photovoltaic station demonstrates the mechanism's effectiveness in improving response times, enhancing closed-loop execution, and strengthening institutional accountability during voltage fluctuations induced by abnormal irradiance events. This framework offers a transferable supervisory architecture for digitalized governance in renewable energy and provides both technical and institutional insights for future implementation.