{"title":"Analysis of the Application of Machine Vision in the Automation Control of Power Electronic Equipment","authors":"Jieping Zhang","doi":"10.62227/as/74115","DOIUrl":null,"url":null,"abstract":"This research centers on the application of machine vision in the automation control of power electronic equipment, and the purpose of the research is to improve the operation efficiency and safety of power equipment. The research combines PLC and IoT technology to build an intelligent monitoring system, which uses machine vision technology to recognize the state of power electronic equipment. The research results show that the system achieves 98\\% accuracy in switchgear image recognition, and the SIFT algorithm performs superiorly in equipment state recognition, with the shortest recognition time being 7.17 seconds and the longest not exceeding 29.98 seconds. Machine vision technology effectively improves the automation and intelligence level of power equipment, which is of great significance to the development of power industry.","PeriodicalId":55478,"journal":{"name":"Archives Des Sciences","volume":"54 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives Des Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.62227/as/74115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Multidisciplinary","Score":null,"Total":0}
引用次数: 0
Abstract
This research centers on the application of machine vision in the automation control of power electronic equipment, and the purpose of the research is to improve the operation efficiency and safety of power equipment. The research combines PLC and IoT technology to build an intelligent monitoring system, which uses machine vision technology to recognize the state of power electronic equipment. The research results show that the system achieves 98\% accuracy in switchgear image recognition, and the SIFT algorithm performs superiorly in equipment state recognition, with the shortest recognition time being 7.17 seconds and the longest not exceeding 29.98 seconds. Machine vision technology effectively improves the automation and intelligence level of power equipment, which is of great significance to the development of power industry.
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