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.

/pdf/review-of-statistical-and-analytical-degradation-models-for-1b28sbxkgr.pdf

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

Acceleration factors are calculated for crystalline silicon photovoltaic modules under system voltage stress by comparing the module power during degradation outdoors with that in accelerated testing at three temperatures and 85% relative humidity. A lognormal analysis is applied to the accelerated lifetime test data, considering failure at 80% of the initial module power. Activation energy of 0.73 eV for the rate of failure is determined for the chamber testing at constant relative humidity, and the probability of module failure at an arbitrary temperature is predicted. To obtain statistical data for multiple modules over the course of degradation in situ of the test chamber, dark I–V measurements are obtained and transformed using superposition, which is found to be well suited for rapid and quantitative evaluation of potential-induced degradation. It is determined that shunt resistance measurements alone do not represent the extent of power degradation. This is explained with a two-diode model analysis that shows an increasing second diode recombination current and ideality factor as the degradation in module power progresses. Failure modes of the modules stressed outdoors are examined and compared with those stressed in accelerated tests.

/pdf/testing-and-analysis-for-lifetime-prediction-of-crystalline-10ihdmqe38.pdf

Testing and analysis for lifetime prediction of crystalline silicon PV modules undergoing degradation by system voltage stress

Evaluation performance of photovoltaic modules after a long time operation in Saharan environment

A better understanding of the degradation modes and rates for photovoltaic (PV) modules is necessary to optimize and extend the lifetime of these modules. Lifetime and degradation science (L&DS) is used to understand degradation modes, mechanisms and rates of materials, components and systems to predict lifetime of PV modules. A PV module lifetime and degradation science (PVM L&DS) model is an essential component to predict lifetime and mitigate degradation of PV modules using reproducible open data science. Previously published accelerated testing data from Underwriter Laboratories on PV modules with fluorinated polyester backsheets, which included eight modules that were exposed up to 4000 hrs of damp heat (85% relative humidity at 85°C) and eight exposed up to 4000 hrs of ultraviolet light (80 W/m2 of 280-400 nm wavelengths at 60°C) (UV preconditioning) were used to determine statistically significant relationships between the applied stresses and measured responses. There were 15 different variables tracking aspects of system performance, degradation mechanisms, component metrics and time. Modules were analyzed for three system performance metrics (fill factor, peak power, and wet insulation). The results were statistically analyzed to identify variable transformations, statistically significant relationships (SSRs) and to develop the PVM L&DS model informed by a generalization of structural equation modeling techniques. The SSRs and significant model coefficients, combined with domain analytics, incorporating materials science, chemistry, and physics expertise, produced a pathway diagram ranking the variables' impact on the system performance, which were iteratively examined using sound statistical analysis and diagnostics. The SSRs determined from the damp heat exposure for the system response of Pmax corresponded to the degradation pathway of polyester terephthalate (PET) and ethylene vinyl acetate (EVA) hydrolysis. A linear change point for the damp heat exposure with the system response of Pmax was determined to be 1890 hrs. The UV preconditioning exposure did not induce sufficient degradation shown by the quality of the R2 values for many of the best fitting models. This exemplifies the development of a methodology to determine rank ordered lifetime and degradation pathways present in modules and their effects on module performance over lifetime.

Statistical and Domain Analytics Applied to PV Module Lifetime and Degradation Science

Failure Modes Evaluation of PV Module via Materials Degradation Approach

This paper demonstrates a multi-stress time-to-failure analysis of selected silicon PV cells due to metallic corrosion. This work indicates that the time-to-failure is a system effect and the kinetics of the reactions are different for different cells.

Damp heat versus field reliability for crystalline silicon

Thermal endurance studies were performed on the major polymeric components used in a Low concentration photovoltaic (LCPV) module using a thermal gravimetric analysis (TGA) method. Activation energy of the various polymers was obtained, and lifetimes were predicted based on the assumption of 5% weight loss for 30 years. Oven aging was used to verify the TGA thermal endurance prediction. Reasonable agreement between TGA prediction and oven baking results were reached, with the exception of ethylene-vinyl acetate (EVA) encapsulant that demonstrated a second-order reaction inconsistent with TGA assumptions. As a result, a more LCPV-relevant way to characterize the degradation of EVA was developed and demonstrated a linear relationship between PV device short-circuit current degradation and yellowness index. For the purpose of this EVA degradation analysis, a 20% reduction in PV device short circuit current was selected as the failure criteria.

Thermal endurance study of polymers used in low concentration photovoltaic modules

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Damp heat versus field reliability for crystalline silicon

Thermal endurance study of polymers used in low concentration photovoltaic modules