Quantification of Environmental Effects on PV Module Degradation: A Physics-Based Data-Driven Modeling Method

This paper explains the fusion of the physics-based material degradation mechanism with the statistics-based data modeling approach for predicting the degradation rate of photovoltaic (PV) modules. The degradation of PV module is mainly associated with the module construction type and climatic condition at its use location. The aim of this paper is to quantify the effect of dynamic environmental stresses (dynamic covariates) on the power degradation of the module over its lifetime. There are various physics-based models, such as Arrhenius model, for understanding the physical or chemical reaction-related root causes of PV degradation. But, to estimate the underlying material properties, such as activation energy (Ea), statistical modeling plays a key role. In addition, instead of being continuously monitored, the performance characteristics of PV modules are often measured only at intervals like quarterly or annually, which makes it difficult to model the complete degradation path of the module. On the other hand, the information on dynamic covariates is recorded more frequently with the development of sophisticated sensors and data acquisition systems. This information can be integrated through physics-based models to study the effects of environmental variables in degradation processes. Hence, in this paper, a cumulative exposure model is used to link the module degradation path and the environmental variables, including module temperature (both static and cyclic), ultraviolet radiation, and relative humidity, which are recorded as multivariate time series.