This paper proposes a digital-model-based approach for near-real-time assessment of the technical condition of photovoltaic (PV) modules, considering the influence of external operational and climatic factors and enabling early-stage fault detection. Solar irradiance, module temperature, ambient temperature, relative humidity, and soiling level are considered as input variables, while current, voltage, and power are treated as the key observable electrical outputs. The proposed method estimates the expected current, voltage, and power of a normally operating PV module by means of a physics-based digital model and compares these values with measured data. Normalized residuals are then used to construct an adaptive diagnostic threshold and a composite anomaly score. In addition, an integral Health Index (HI) is introduced for interpretable assessment of module condition. The approach is intended to distinguish natural power fluctuations caused by weather from genuine technical degradation and faults such as soiling, partial shading, hot spots, connection resistance growth, string faults, and long-term degradation. A hybrid architecture combining a physics-based model, digital twin principles, residual analysis, adaptive thresholds, and machine-learning classification is proposed as the basis for predictive maintenance of photovoltaic power plants.
Development of Digital Models and Algorithms for Assessing the Technical Condition, Early Fault Detection, And Efficiency Optimization of Photovoltaic Modules
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Abstract
References
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