针对“热斑效应”造成光伏组件功率损耗的问题,提出一种基于超级令牌注意力机制（STA）的多模态YOLOv8n-cls模型预测功率损耗方法。在图像分类网络YOLOv8n-cls中添加STA注意力机制,以解决神经网络会捕获高冗余度的浅层的局部热斑特征问题;同时,基于YOLOv8n-cls算法搭建光伏组件红外图像与数字信息融合的多模态模型,探究辐照度参数对功率损耗预测的影响规律。构建由光伏红外图像、功率损耗及辐照度参数标签组成的光伏多源数据集,开展功率损耗预测实验。实验表明：改进后的网络模型,对于常见的树叶、沙尘、鸟粪这3种类型导致的热斑效应,Top1精度分别达到95.6%、89.0%、88.1%,相较于原模型分别提升3.3%、10.0%、7.5%,证明该算法在具有热斑效应的光伏组件红外图像预测功率损耗的优越性。
Abstract
In order to solve the problem of power loss of photovoltaic modules caused by the“hot spot effect”, a multi-modal YOLOv8n-cls model prediction method based on Super Token Attention Mechanism （STA） was proposed. The STA attention mechanism was added to the image classification network YOLOv8n-cls to solve the problem that the neural network tend to capture the local hot spot features in the shallow layer with high redundancy. At the same time, a multi-modal model of infrared image and digital information fusion of photovoltaic modules was built based on the YOLOv8n-cls algorithm to explore the influence of irradiance parameters on power loss prediction. A photovoltaic multi-source dataset composed of photovoltaic infrared images, power loss and irradiance parameter labels was constructed, and power loss prediction experiments were carried out. Experiments show that the Top1 accuracy of the improved network model for the hot spot effect caused by leaves, dust and guano reaches 95.6%, 89.0% and 88.1% respectively, which is 3.3%, 10.0% and 7.5% higher than that of the original model, which proves the superiority of the proposed algorithm in predicting the power loss of photovoltaic modules with hot spot effect.
关键词
图像分类 /
光伏 /
太阳辐照度 /
功率损耗 /
YOLOv8 /
多模态
Key words
image classification /
photovoltaics /
solar irradiance /
power loss /
YOLOv8 /
multimodal
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基金
国家自然科学基金（61502297; 12172210）
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