Research on Real-Time Monitoring and Fault Prediction of Photovoltaic System Based on Hybrid Twin Model

In the context of global energy green transformation, the increase in installed capacity of photovoltaic systems is accompanied by efficiency losses and safety hazards caused by component failures. Traditional monitoring relies on threshold alarms and regular inspections, and the false alarm rate of distributed scenarios reaches 12–18%, making it difficult to cope with dynamic failures. This study innovatively proposes a hybrid twin model framework for real-time monitoring and fault prediction of photovoltaic systems, integrating the physical mechanism of electric thermal coupling with deep learning data-driven methods, constructing a multimodal data collaborative mapping digital image, and combining it with an edge cloud collaborative architecture. Empirical results of a 30 kW photovoltaic array show that under shadow occlusion and hotspot fault scenarios, the model detection accuracy is 95.6% and the false alarm rate is 3.2%, which shortens the response time by 47 ms compared to traditional LSTM. To address issues such as inverter harmonic anomalies, a time series attention mechanism is used to extract frequency domain features, achieving 2–4 h of early warning (accuracy rate of 89.3%). The average latency of 10,000 data points analysis in 5G environment is 82 ms, meeting real-time monitoring requirements and providing a high-precision and low-latency technical path for photovoltaic intelligent operation and maintenance.