PREDICTION MODEL OF PHOTOVOLTAIC POWER LOSS UNDER PARTIAL OCCLUSION

Liu Linjun; Li Qiong; Feng Li; Rao Huiqing; Lu Yong

doi:10.19912/j.0254-0096.tynxb.2024-1397

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 243-253. DOI: 10.19912/j.0254-0096.tynxb.2024-1397

PREDICTION MODEL OF PHOTOVOLTAIC POWER LOSS UNDER PARTIAL OCCLUSION

Liu Linjun¹, Li Qiong¹, Feng Li¹, Rao Huiqing¹, Lu Yong²

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Abstract

The phenomenon of foreign body occlusion often occurs in photovoltaic modules during long-term outdoor operation, which affects photovoltaic power generation. Studying the prediction of power generation efficiency loss under occlusion conditions is very useful for monitoring the severity of photovoltaic module occlusion defects and guiding module cleaning scientifically. In this paper, a forecast model of PV module output power loss under partial occlusion based on Tent-SSA-BP is proposed. Firstly, the output characteristics of PV modules under partial occlusion are analyzed through simulation experiments. On the basis of a large number of simulation data, the Tent-SSA-BP prediction model is established. Because BP neural network model is easy to fall into local optimal solution and the prediction accuracy is not high, the Tent-SSA-BP prediction model established in this paper solves the local optimal problem easily caused by the original network, improves the global search ability, and quickly finds a better parameter combination. Through comparative analysis of experiments, the determination coefficient of Tent-SSA-BP prediction model is improved by 0.04 compared with the original network, which has a better degree of model fitting and improves the prediction accuracy of the model.

Key words

photovoltaic power generation / power forecasting / back propagation network / sparrow search algorithm （SSA） / Tent map

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Liu Linjun, Li Qiong, Feng Li, Rao Huiqing, Lu Yong. PREDICTION MODEL OF PHOTOVOLTAIC POWER LOSS UNDER PARTIAL OCCLUSION[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 243-253 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1397

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