Identification of degree of ash accumulation on photovoltaic panels based on average grayscale value and image fusion algorithm

CHEN Jiahao; YANG Jianmeng; LI Bin; WANG Guangyi

doi:10.19666/j.rlfd.202406162

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Identification of degree of ash accumulation on photovoltaic panels based on average grayscale value and image fusion algorithm

更新时间：2025-09-28

- Identification of degree of ash accumulation on photovoltaic panels based on average grayscale value and image fusion algorithm
- Thermal Power Generation Vol. 54, Issue 3, Pages: 131-139(2025)
- 作者机构：
  
  华北电力大学能源动力与机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202406162
  CLC： TM615;TP391.41
- Published：2025
- 稿件说明：
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CHEN Jiahao, YANG Jianmeng, LI Bin, et al. Identification of degree of ash accumulation on photovoltaic panels based on average grayscale value and image fusion algorithm[J]. Thermal power generation, 2025, 54(3): 131-139.
DOI：

CHEN Jiahao, YANG Jianmeng, LI Bin, et al. Identification of degree of ash accumulation on photovoltaic panels based on average grayscale value and image fusion algorithm[J]. Thermal power generation, 2025, 54(3): 131-139. DOI： 10.19666/j.rlfd.202406162.

摘要

为了精确分析光伏板积灰情况，采用搭建的光伏积灰可视化实验台，引入平均灰度值参数，对光伏板图像进行数值化分析，验证了光伏板图像的平均灰度值与光伏板积灰密度之间存在明确的对应关系。基于此，采用5种融合方法对双光谱影像融合实验台采集的可见光图像与红外图像进行融合，将5类融合图像与可见光图像、红外图像汇总构成图像数据集，对这7类图像进行识别与分析。结果表明红外图像对光伏板积灰程度的识别效果受辐照度的影响最小，精度最高，反映出来的积灰程度变化最明显。该结论可以为积灰规律的研究提供理论基础，对光伏板积灰程度的识别具有重要意义。

Abstract

In order to accurately analyze the ash accumulation on photovoltaic panels

a photovoltaic dust visualization experimental platform was built

and the average grayscale value was introduced to numerically analyze the photovoltaic panel images. The clear correspondence between the average grayscale value of photovoltaic panel images and the dust density of photovoltaic panels was verified. On this basis

five fusion methods were used to fuse the visible light images and infrared images collected from the dual spectral image fusion experimental platform. The five types of fusion images were combined with visible light images and infrared images to form an image dataset. These seven types of images were identified and analyzed. The results showed that

the recognition effect of infrared images on the degree of ash accumulation on photovoltaic panels was the least affected by irradiance

with the highest accuracy

and the most significant change in the degree of ash accumulation was reflected. This conclusion can provide a theoretical basis for the study of ash accumulation rules and is of great significance for the recognition of the degree of ash accumulation on photovoltaic panels.

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