一种基于ResPNet的光伏组件红外成像故障检测方法

Infrared Image Fault Detection of Photovoltaic Modules Based onResidual Photovoltaic Network

目前利用无人机获取光伏组件红外影像数据越来越多地应用于光伏组件故障检测中。但光伏组件红外影像数据各类别样本相似度较高,现有深度学习模型的光伏组件红外影像特征提取能力较低,导致光伏组件多故障类型分类精度偏低。针对以上问题,基于ResNet(residual network)模型构建ResPNet(residual photovoltaic network)模型进行光伏组件红外影像故障检测。ResPNet模型在ResNet模型基础上,加入了底层特征信息增强模块、多尺度特征信息增强模块、全局特征信息增强模块,用于提升模型的光伏组件红外影像特征提取能力。在公开的光伏组件红外影像数据集Infrared Solar Modules上进行实验,ResPNet模型的12类光伏组件红外影像分类精度达到84.6%,不但优于ResNet-50模型,而且优于其他的光伏组件红外影像分类模型。通过级联多个ResPNet模型,取得了该数据集目前已知最高的12类光伏组件红外影像分类检测精度(85.9%)。

At present,infrared image data of photovoltaic modules obtained by unmanned aerial vehicle is more and more used in the fault detection of photovoltaic module.However,due to the high similarity of various samples of photovoltaic module infrared image data,the existing deep learning model has a low ability to extract photovoltaic module infrared image features,resulting in low detection accuracy of photovoltaic module multi-fault types.To solve the above problems,a residual photovoltaic network(ResPNet)model is constructed based on the residual network(ResNet)model for infrared image fault detection of photovoltaic modules.On the basis of ResNet model,ResPNet adds the underlying feature information enhancement module,multi-scale feature information enhancement module and global feature information enhancement module to improve the infrared image feature extraction ability of photovoltaic modules.Experiments are conducted on Infrared Solar Modules,a disclosed infrared image dataset of photovoltaic modules.The ResPNet model achieves an infrared image classification accuracy of 84.6%for 12 types of photovoltaic modules,which is better than not only ResNet-50 model,but also other infrared image classification models.Through cascading several ResPNet models,the highest known infrared image classification detection accuracy of 12 types of photovoltaic modules in this dataset is achieved at 85.9%.