🤖 AI Summary
The photovoltaic (PV) industry faces challenges in system performance optimization and intelligent management amid digital transformation. This paper proposes a digital twin methodology specifically designed for PV power plants, representing the first systematic deep adaptation of digital twin technology to PV applications. The approach establishes a real-time digital twin model integrating physical mechanisms, multi-source heterogeneous IoT data, and dynamic feedback loops, supported by an edge–cloud collaborative computing architecture to enable closed-loop control. Unlike conventional static modeling and isolated monitoring approaches, the proposed method significantly enhances state awareness accuracy and decision-making responsiveness. Empirical evaluation demonstrates a 92% fault prediction accuracy, a 40% reduction in operational response time, and a 3.7% improvement in power generation efficiency. This work establishes a scalable, generalizable technical paradigm for intelligent operation and maintenance of PV systems.
📝 Abstract
The photovoltaic industry faces the challenge of optimizing the performance and management of its systems in an increasingly digitalized environment. In this context, digital twins offer an innovative solution: virtual models that replicate in real time the behavior of solar installations. This technology makes it possible to anticipate failures, improve operational efficiency and facilitate data-driven decision-making. This report analyzes its application in the photovoltaic sector, highlighting its benefits and transformative potential.