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dc.contributor.authorLindig S
dc.contributor.authorKaaya I
dc.contributor.authorWeiß KA
dc.contributor.authorMoser D
dc.contributor.authorTopic M
dc.date.accessioned2019-02-01T09:00:31Z
dc.date.available2019-02-01T09:00:31Z
dc.date.issued2018
dc.identifier.issn2156-3381
dc.identifier.urihttp://dx.doi.org/10.1109/JPHOTOV.2018.2870532
dc.identifier.urihttps://ieeexplore.ieee.org/document/8472886
dc.identifier.urihttp://hdl.handle.net/10863/7947
dc.description.abstractIn 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.en_US
dc.language.isoenen_US
dc.rights
dc.titleReview of Statistical and Analytical Degradation Models for Photovoltaic Modules and Systems as Well as Related Improvementsen_US
dc.typeArticleen_US
dc.date.updated2019-02-01T08:57:09Z
dc.language.isiEN-GB
dc.journal.titleIEEE Journal of Photovoltaics
dc.description.fulltextnoneen_US


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