Design and Implementation of an AI Based Automatic Defect Recognition System for Photovoltaic Modules
Photovoltaic modules are prone to defects such as cracks,stains,and hot spots during long-term use.If these defects are not detected and treated in a timely manner,they will affect power generation efficiency and module lifespan.This article proposes a defect recognition method that combines Convolutional Neural Network(CNN)and transfer learning techniques to address this issue.By training and testing a large number of defect images of photovoltaic modules,the system can automatically identify and classify different types of defects.The experimental results show that the system has high recognition accuracy and stability in practical applications,effectively improving the automation level of defect detection in photovoltaic modules and providing reliable technical support for the intelligent operation and maintenance of photovoltaic power plants.
defects in photovoltaic modulesArtificial Intelligence(AI)Convolutional Neural Network(CNN)transfer learning
NETL
NSTL
万方数据
