RESEARCH ON LIFETIME DEGRADATION MODELING AND GAUSSIAN STOCHASTIC PROCESS REGRESSION METHOD FOR LIFE PREDICTION OF PHOTOVOLTAIC MODULES

Tang Shengxue; Song Xiao; Li Ming; Yao Shuailiang; Chen Li; Sun Zhiguo

doi:10.19912/j.0254-0096.tynxb.2021-0687

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (12) : 41-49. DOI: 10.19912/j.0254-0096.tynxb.2021-0687

RESEARCH ON LIFETIME DEGRADATION MODELING AND GAUSSIAN STOCHASTIC PROCESS REGRESSION METHOD FOR LIFE PREDICTION OF PHOTOVOLTAIC MODULES

Tang Shengxue^1,2, Song Xiao^1,2, Li Ming³, Yao Shuailiang³, Chen Li^1,2, Sun Zhiguo^1,2

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Abstract

The degradation process of photovoltaic（PV） lifetime with service time is highly nonlinear and random due to internal aging and external environment. Aiming at the degradation mechanism and process of PV modules, this paper analyzes the degradation process of PV modules, the degradation factors and their effects on the characteristics of PV modules, and proposes a degradation circuit model of PV cell based on exponential degradation. The degradation model is used to quantitatively analyze the influence of degradation factors on the output power. Then, the equivalent series resistance and output power are selected as the life prediction indicators, and a joint Gaussian process life prediction method based on output power and equivalent series resistance is proposed, and the influence of kernel function on life prediction is analyzed. Finally, Simulation and verifications show that the proposed life prediction model has the advantages of high accuracy and strong robustness.

Key words

photovoltaic modules / degradation / model / Gaussian process regression（GPR） / life prediction

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Tang Shengxue, Song Xiao, Li Ming, Yao Shuailiang, Chen Li, Sun Zhiguo. RESEARCH ON LIFETIME DEGRADATION MODELING AND GAUSSIAN STOCHASTIC PROCESS REGRESSION METHOD FOR LIFE PREDICTION OF PHOTOVOLTAIC MODULES[J]. Acta Energiae Solaris Sinica. 2022, 43(12): 41-49 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0687

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