Review of Statistical and Analytical Degradation Models for Photovoltaic Modules and Systems as Well as Related Improvements

Abstract

In this work, we investigate practical approaches of available degradation models and their usage in photovoltaic (PV) modules and systems. On the one hand, degradation prediction of models is 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, seasonal-and trend-decomposition using Loess (STL), Holt-Winters exponential smoothing 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, if monitoring data at PV module level are available in controlled 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. Finally, a model proposed for potential induced degradation 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.

International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

“…Currently, there are numerous statistical PLR determination methods in the literature to choose from. A comparative study of methods found in the literature has been performed by Lindig et al 1 …”

“…Several statistical methods have been proposed and compared 1 ; however, there is no industry‐wide consensus, and thus, no standardized approach to PLR calculations. At the IEC level, there was an early effort to introduce a standard or a Technical Reference for the calculation of degradation with the IEC 61724‐Part 4; however, the initial activity was discontinued 2 …”

Global Climate Data Processing and Mapping of Degradation Mechanisms and Degradation Rates of PV Modules

“…[10], three degradation processes are defined and empirically expressed due to the combination of climate degradation factors: Hydrolysis-degradation (k H ) related to the effect of temperature and humidity, photo-degradation (k P ) depends on temperature, humidity and UV irradiance, and thermo-mechanical-degradation (k Tm ) due to high temperature and temperature differences. The models are presented in Equations (11) to (14):…”

“…For example, while in deserts the risk of sand-storms is high, in highlands wind gusts and snow loads can damage the PV installations. Important findings and calculation methods about the climatic triggers of degradation modes under different climate zones have been presented in [3][4][5][6][7][8][9][10][11]. Irradiation, temperature, and humidity have been identified as the main degradation precursors, leading to different degradation mechanisms depending on the stress level in each climate zone.…”

Solar photovoltaic systems in Indonesia's grid: experiences of end-users and evaluations of photovoltaic systems performance

“…STL uses locally weighted regression and thereby provides robust estimates of the trend and seasonal components that are not distorted by outliers and missing values compared to other methods [243,244]. STL handles any type of seasonality, not only monthly or quarterly data.…”