基于红外图像和改进MobileNet-V3的光伏组件故障诊断方法  

RESEARCH ON FAULT DIAGNOSIS OF PHOTOVOLTAIC MODULES BASED ON INFRARED IMAGES AND IMPROVED MOBILENET-V3

作　　者：任惠[1] 夏静 卢锦玲[1] 王允哲 辛国雨 Ren Hui;Xia Jing;Lu Jinling;Wang Yunzhe;Xin Guoyu(School of Electrical and Electronic Engineering,North China Electric Power University,Baoding 071003,China)

机构地区：[1]华北电力大学电气与电子工程学院,保定071003

出　　处：《太阳能学报》2023年第8期238-245,共8页Acta Energiae Solaris Sinica

摘　　要：为了提高光伏系统的可靠性和性能,提出一种基于红外图像和改进MobileNet-V3的光伏组件故障诊断方法。首先,分析开源光伏组件缺陷图像及其存在的问题;然后,基于存在的问题,对光伏组件红外缺陷图像进行图像增强、数据增强处理,使红外图像满足图片可用性及样本数量丰富性的要求;最后,对基本MobileNet-V3网络进行改进,实现光伏组件故障分类。实验结果显示:与传统CNN、基础MobileNet-V3相比,所提故障分类方法不仅准确率高、诊断速度快,且对各种故障类别的识别率高,具有较好的实用性和应用价值。In order to improve the reliability and performance of photovoltaic systems,a fault diagnosis method for photovoltaic modules based on infrared images and improved MobileNet-V3 is proposed.Firstly,the defect images of open-source photovoltaic modules and their existing problems are analyzed.Then,image and data enhancement are performed on the infrared defect images of photovoltaic modules aiming at the existing problems,so that the infrared images meet the requirements of image availability and sample quantity.Finally,the basic MobileNet-V3 network is improved to realize fault classification of photovoltaic modules.The experimental results show that,compared with the traditional CNN and the basic MobileNet-V3,the proposed fault classification method not only has high accuracy and fast diagnosis speed,but also has a high recognition rate for various fault categories,which has good practicability and application value.

关 键 词：光伏组件 红外成像 图像增强 故障诊断 改进MobileNet-V3算法 

分 类 号：TM615[电气工程—电力系统及自动化]