SR-STM: Simulation–Reality Spatial–Temporal Model for Early Warning of Power Tilt in Nuclear Power Plants

Balanced in-core power levels in nuclear power plants (NPPs) are critical for safety, whereas power tilt disrupts this balance, reducing safety margins and posing risks. Early warning for power tilt offers an effective way of optimizing monitoring. Due to abnormal-sample scarcity and security concerns, common data-driven models train on the simulated data generated by simulators. However, achieving a satisfactory effect in practices is difficult because simulators imperfectly emulate reality. Thus, we propose a power tilt-oriented early warning method called simulation–reality spatial–temporal model (SR-STM). Motivated by the physical model in NPPs, a knowledge-guided hierarchical graph is designed to characterize spatial correlations among local power levels for SR-STM’s input. The SR-STM uses a lightweight spatial–temporal network (LST-Net) as a feature extractor, balancing precision, and efficiency. To bridge sim-real interdomain discrepancies, SR-STM utilizes node-alignment adversarial learning (NAAL) for fine weight tuning in subdomain, and eigenvalue-based scale alignment (ESA) for sim-real feature proximity. Forecasting local power levels using the SR-STM, dynamic metrics and alarm limits are calculated and compared to perform the early warning task. The online experimental prototype verifies that SR-STM surpasses various state-of-the-art methods in terms of early warning and sim-real cross-domain tasks.