Review of statistical and analytical degradation models for photovoltaic modules and systems as well as related improvements
In this work, we investigate the practical approaches of available degradation models and their usage in PV modules and systems degradation prediction. On one hand models are described for the calculation of degradation at system level where the degradation mode is unknown and hence the physics cannot be included by the use of analytical models. Several statistical models are thus described and applied for the calculation of the performance loss using as case study two PV systems, installed in Bolzano/Italy. Namely, simple linear regression (SLR), classical seasonal decomposition (CSD), Seasonal and Trend decomposition using Loess (STL), Holt-Winters exponential smoothing (HW) and autoregressive integrated moving average (ARIMA) are discussed. The performance loss results show that SLR produces results with highest uncertainties. In comparison, STL and ARIMA perform with the highest accuracy, whereby STL is favored because of its easier implementation. On the other hand, when monitoring data at PV module level is available in controlled and test conditions, analytical models can be applied. Several analytical models depending on different degradations modes are thus discussed. A comparison study is carried out for models proposed for corrosion. Although the results of the models in question agree in explanation of experimental observations, a big difference in degradation prediction was observed. Lastly, a model proposed for Potential Induced Degradation (PID) was applied to simulate the degradation of PV systems maximum power in three climatic zones; alpine (Zugspitze, Germany), maritime (Gran Canaria, Spain) and arid (Negev, Israel). As expected, a more severe degradation is predicted for arid climates.