A Health Monitoring Framework for Thermal Degradation Mitigation in Solar Power Plants

Abstract

Prognostics and Health Management (PHM) of photovoltaic (PV) systems requires integrated approaches that link temperature forecasting with physical degradation modeling under thermal stress. This study addresses key limitations of existing PHM frameworks, such as the lack of high-resolution climate projections and limited coupling with degradation models, by proposing a unified PHM methodology tailored for high-temperature scenarios. The framework consists of three main components: (1) a formal problem definition of PV performance loss during extreme temperature and high cooling demand periods; (2) high-resolution spatiotemporal forecasting of temperatures; (3) probabilistic modeling of PV thermal degradation. The proposed approach integrates two
innovations, including a Gaussian copula–based risk assessment for capturing joint distributions of environmental stressors (e.g., air temperature, solar irradiance, and wind speed) and a Spatiotemporal Graph Neural Network (ST-GNN) architecture for accurate prediction of extreme temperature
events. Accelerated aging tests and ERA5 reanalysis data (1974–2023) have been used to parameterize the probabilistic aging models. Preliminary results from forecasting experiments achieved root-mean-square errors of 5.1–5.5°C across three representative Spanish climate zones. Future work will focus on enhancing the expressiveness of spatial dependencies through dynamic graph structures with learnable edge weights, as well as propagating predictive uncertainty from temperature forecasts into degradation models using uncertainty quantification techniques.