{"title":"基于CCA的双输出反激变换器故障检测方法","authors":"Cuiyu Liu, Zhiming Yang, Gang Xiang, Yang Yu","doi":"10.1109/ICSMD57530.2022.10058460","DOIUrl":null,"url":null,"abstract":"The flyback converter is highly preferred due to their cost effectiveness and electrical isolation characteristics. Because flyback converters are so crucial to the industrial world, it is crucial to assure their continuous and secure operation. A fault detection method based on CCA is suggested to efficiently identify a fault state for dual-output flyback converters. Firstly, both outputs voltage of the dual-output flyback converter are collected and then mean-centered. CCA is used to maximize the corelationship between the dual outputs. The residual matrix was constructed according to the correlation between the two outputs obtained by CCA. Then, a statistic is used to evaluate the residual matrix. Finally, calculate the corresponding threshold. The proposed method for detecting faults focuses on the correlation between the outputs, making it possible to identify faults with minimally abnormal characteristics. Fault detection in time can avoid further losses. Results from simulation experiments confirm the applicability and efficacy of the suggested method.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Fault Detection Method for Dual-output Flyback Converters Using CCA\",\"authors\":\"Cuiyu Liu, Zhiming Yang, Gang Xiang, Yang Yu\",\"doi\":\"10.1109/ICSMD57530.2022.10058460\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The flyback converter is highly preferred due to their cost effectiveness and electrical isolation characteristics. Because flyback converters are so crucial to the industrial world, it is crucial to assure their continuous and secure operation. A fault detection method based on CCA is suggested to efficiently identify a fault state for dual-output flyback converters. Firstly, both outputs voltage of the dual-output flyback converter are collected and then mean-centered. CCA is used to maximize the corelationship between the dual outputs. The residual matrix was constructed according to the correlation between the two outputs obtained by CCA. Then, a statistic is used to evaluate the residual matrix. Finally, calculate the corresponding threshold. The proposed method for detecting faults focuses on the correlation between the outputs, making it possible to identify faults with minimally abnormal characteristics. Fault detection in time can avoid further losses. Results from simulation experiments confirm the applicability and efficacy of the suggested method.\",\"PeriodicalId\":396735,\"journal\":{\"name\":\"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSMD57530.2022.10058460\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSMD57530.2022.10058460","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Fault Detection Method for Dual-output Flyback Converters Using CCA
The flyback converter is highly preferred due to their cost effectiveness and electrical isolation characteristics. Because flyback converters are so crucial to the industrial world, it is crucial to assure their continuous and secure operation. A fault detection method based on CCA is suggested to efficiently identify a fault state for dual-output flyback converters. Firstly, both outputs voltage of the dual-output flyback converter are collected and then mean-centered. CCA is used to maximize the corelationship between the dual outputs. The residual matrix was constructed according to the correlation between the two outputs obtained by CCA. Then, a statistic is used to evaluate the residual matrix. Finally, calculate the corresponding threshold. The proposed method for detecting faults focuses on the correlation between the outputs, making it possible to identify faults with minimally abnormal characteristics. Fault detection in time can avoid further losses. Results from simulation experiments confirm the applicability and efficacy of the suggested method.