A detection model for dust deposition on photovoltaic (PV) panels based on light transmittance estimation

Linhong Chen, Siyuan Fan, Shengyao Sun, Shengxian Cao, Tianyi Sun, Peng Liu, Han Gao, Yanhui Zhang and Wei Ding

Energy, 2025, vol. 322, issue C

Abstract: Dust deposition on photovoltaic (PV) panels significantly reduces light transmittance and power conversion efficiency. Therefore, real-time dust detection systems are crucial for proactive cleaning and maintenance to improve light absorption and the operational efficiency of PV systems. This paper developed an end-to-end PV dust detection model, DVNET, based on light transmittance estimation. The model quantifies the dust density on PV panels using image processing to estimate light transmittance and determine optimal cleaning strategies. The DVNET architecture captures the mapping relationship between light transmittance and dust density. The model calculates the light transmittance of dusty images, enabling a quantitative assessment of dust deposition. Images are acquired and preprocessed to eliminate noise and irrelevant information and obtain optimal model training conditions. Experiments are conducted to evaluate model performance. The results indicate that the proposed model accurately estimates light transmittance using images of PV panels and generates transmittance maps to visualize the dust distribution on the panel surfaces. DVNET outperforms five benchmark models, achieving the lowest mean square error (MSE) of 0.00044. The performances of the squeeze-and-excitation block (SEBlock) module, the convolutional block attention module (CBAM), and the multiply-add-combine (MAC) attention mechanism are evaluated. The experimentally determined relative error (RE) is below 0.03 for uniform and non-uniform transmittance. The observed and predicted correlations between dust deposition and transmittance are highly similar for PV panels with different dust densities, demonstrating the model's applicability in real-world scenarios. The experiment demonstrated that the DVNET model exhibits low computational resource consumption, fast training time, as well as remarkable scalability and computational performance on large-scale datasets.

Keywords: PV panels; Dust deposition density; Transmittance estimation; Image processing (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:322:y:2025:i:c:s0360544225009260

DOI: 10.1016/j.energy.2025.135284

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