S. Reddy, P. B. Bobba, Sai Hanuman Akund, Vinay Seshu Neelam, A. Jangam, Krishna Tej Chinta, Bharath Babu Ambati
{"title":"Comparative Analysis of Artificial Intelligence Techniques used in Inverter Fault Diagnosis","authors":"S. Reddy, P. B. Bobba, Sai Hanuman Akund, Vinay Seshu Neelam, A. Jangam, Krishna Tej Chinta, Bharath Babu Ambati","doi":"10.1109/SeFeT55524.2022.9908933","DOIUrl":null,"url":null,"abstract":"Machine learning (ML) and Artificial Intelligence (AI) are evolving rapidly in our daily needs, Similarly in power electronics system (PES). There are many concepts and tools in AI and ML have been developing for the fault detection and reduction of faults. Due to poor accuracy in controlling and feedback circuit and several environmental impacts on the devices leads to improper estimation and optimisation of faults by AI and ML. In inverter fed to induction motor system we can able to face several fault problems at inverter and motor terminals. This paper presents about various concepts and tools evolved in AI and ML for Fault diagnosis and reduction in case of inverter fed induction motor system.","PeriodicalId":262863,"journal":{"name":"2022 IEEE 2nd International Conference on Sustainable Energy and Future Electric Transportation (SeFeT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 2nd International Conference on Sustainable Energy and Future Electric Transportation (SeFeT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SeFeT55524.2022.9908933","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Machine learning (ML) and Artificial Intelligence (AI) are evolving rapidly in our daily needs, Similarly in power electronics system (PES). There are many concepts and tools in AI and ML have been developing for the fault detection and reduction of faults. Due to poor accuracy in controlling and feedback circuit and several environmental impacts on the devices leads to improper estimation and optimisation of faults by AI and ML. In inverter fed to induction motor system we can able to face several fault problems at inverter and motor terminals. This paper presents about various concepts and tools evolved in AI and ML for Fault diagnosis and reduction in case of inverter fed induction motor system.