A field-function methodology predicting the service lifetime of photovoltaic modules

Weidong Liu, Ben Xu, Yan Liu, Shaoshuai Li and Weian Yan

Renewable and Sustainable Energy Reviews, 2024, vol. 192, issue C

Abstract: The service lifetime of photovoltaic (PV) modules is an essential basis for the business investment and operation in PV power generation systems, with continuous distribution in specific geographical areas. To accurately predict the service lifetime of PV modules operated at a specific location, a continuous quantitative field-function methodology based on geographical clustering of influencing factors is proposed in this research. Firstly, failure mode and effects analysis technology is applied to systematically analyze the relevant factors affecting the performance degradation of PV modules, where eight key factors are extracted and quantitatively described. Secondly, the fuzzy c-means algorithm clusters the geographical regions to identify the areas with continuously distributed service lifetimes of PV modules. Finally, the continuous quantitative field-function model with latitude and longitude as variables is designed to describe each clustering category's service lifetime distribution characteristics. The model uses Pearson correlation coefficient to quantitatively describe the differences in performance degradation influencing factors at different latitude and longitude positions, and the model parameters are determined by fitting analysis of the known service life data of PV modules at specific latitude and longitude positions. Based on this model, the PV module field degradation rates in Guangzhou, Shenzhen, and Zhuhai in mainland China are estimated with relative errors to be 3.7149 %, 8.1525 %, and 6.6753 %, respectively. Compared to the existing approach, not only are the relative errors reduced by 1.4783 %, 5.1455 %, and 4.485 %, but the variations of service lifetime with geographic latitude and longitude are also visually and clearly described.

Keywords: Photovoltaic modules; Performance degradation; Service lifetime prediction; FMEA; Regional clustering (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.rser.2023.114266

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