基于改进EfficientNet的红外图像光伏组件故障识别研究  

A study on fault recognition of photovoltaic module with infraredimages based on improved efficientnet

作　　者：吕游[1] 郑茜 齐欣宇 房方[1] 刘吉臻[1] Lyu You;Zheng Xi;Qi Xinyu;Fang Fang;Liu Jizhen(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,North China Electric Power University,Beijing 102206,China;School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China)

机构地区：[1]华北电力大学新能源电力系统国家重点实验室,北京102206 [2]华北电力大学控制与计算机工程学院,北京102206

出　　处：《仪器仪表学报》2024年第4期175-184,共10页Chinese Journal of Scientific Instrument

基　　金：中央高校基本科研业务费专项资金(2023MS029)资助。

摘　　要：光伏组件的故障会影响光伏阵列的输出性能,从而降低电站的发电效率,严重时甚至会危害电站的安全运行。传统的方法无法满足目前光伏组件故障检测快速性和正确率需求。因此,本文提出了一种基于改进EfficientNet的光伏组件故障识别方法。首先,利用采集到的光伏组件红外图像建立故障数据集,并利用图像分割和数据增强对数据集进行预处理;其次,基于EfficientNet网络构建故障识别模型,同时在模型中引入双通道注意力模块(CBAM),该模块能够抑制不必要特征的识别,增强模型对空间特征信息的提取能力,进而提高模型的识别准确率;最后,通过对比仿真实验证明模型的有效性和先进性。实验结果表明,该模型的故障识别准确率达到了90.83%,相较于原始的EfficientNet模型提高了2.83%,且模型大小仅为20.3 M,具有良好的实用性,能够满足光伏电站实际应用的需求。Failures of the photovoltaic(PV)module can affect the performance of the PV arrays,thus reducing the power generation efficiency.In serious cases,PV module failures may even jeopardize the safe operation of the power plant.Traditional methods cannot meet the current demand for fast and correct PV module fault detection.Therefore,this paper proposes a PV module fault identification method based on the improved EfficientNet algorithm.First,the collected infrared images of PV modules are utilized to establish a fault dataset,which is then preprocessed by using image segmentation and data enhancement technology.Second,a fault recognition model is constructed based on the EfficientNet network.Meanwhile,a dual-channel convolutional block attention module(CBAM)is introduced into the model,which can inhibit the recognition of unnecessary features and enhance the ability to extract spatial feature information,thus improving the recognition accuracy.Finally,comparative simulation experiments are conducted to validate the effectiveness and advancement of the proposed model.The experimental results show that the fault recognition accuracy of the model reaches 90.83%,which is 2.83%higher than that of the original EfficientNet model;in addition,the model size is only 20.3 M,which shows good practicability and can meet the requirements of practical application of PV power plants.

关 键 词：光伏组件 红外图像 故障识别 CBAM注意力机制 

分 类 号：TH17[机械工程—机械制造及自动化] TM315[电气工程—电机]