Yuexuan Sun, Chang-Heng Li, Yunfeng Long, Zhengyong Huang and Jian Li
{"title":"用于变压器局部放电监测的柔性天线和分布式深度学习模式识别研究","authors":"Yuexuan Sun, Chang-Heng Li, Yunfeng Long, Zhengyong Huang and Jian Li","doi":"10.1088/1361-6463/ad759f","DOIUrl":null,"url":null,"abstract":"Power transformer is an important part of the power system, and continuous monitoring of partial discharges can provide a more reasonable program for fault diagnosis and operational maintenance of the transformer. However, the rigid partial discharge UHF antenna can not be installed in a conformal fit with the monitored equipment, and the partial discharge UHF signal attenuation is serious, resulting in low detection energy efficiency and gain performance can not meet the demand. The centralized deep learning local discharge pattern recognition method has low training efficiency, and distributed deep learning can improve the training efficiency, but the heterogeneous data from multiple sources will reduce the model accuracy. Due to this, this paper designs a UHF flexible composite helical antenna with miniaturization, wide bandwidth, high gain and high bending deformation stability, and investigates a federated learning pattern recognition method based on residual contraction network, which substantially improves the training efficiency while ensuring the accuracy.","PeriodicalId":16789,"journal":{"name":"Journal of Physics D: Applied Physics","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on flexible antenna and distributed deep learning pattern recognition for partial discharge monitoring of transformer\",\"authors\":\"Yuexuan Sun, Chang-Heng Li, Yunfeng Long, Zhengyong Huang and Jian Li\",\"doi\":\"10.1088/1361-6463/ad759f\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Power transformer is an important part of the power system, and continuous monitoring of partial discharges can provide a more reasonable program for fault diagnosis and operational maintenance of the transformer. However, the rigid partial discharge UHF antenna can not be installed in a conformal fit with the monitored equipment, and the partial discharge UHF signal attenuation is serious, resulting in low detection energy efficiency and gain performance can not meet the demand. The centralized deep learning local discharge pattern recognition method has low training efficiency, and distributed deep learning can improve the training efficiency, but the heterogeneous data from multiple sources will reduce the model accuracy. Due to this, this paper designs a UHF flexible composite helical antenna with miniaturization, wide bandwidth, high gain and high bending deformation stability, and investigates a federated learning pattern recognition method based on residual contraction network, which substantially improves the training efficiency while ensuring the accuracy.\",\"PeriodicalId\":16789,\"journal\":{\"name\":\"Journal of Physics D: Applied Physics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Physics D: Applied Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1088/1361-6463/ad759f\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Physics D: Applied Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1088/1361-6463/ad759f","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, APPLIED","Score":null,"Total":0}
Research on flexible antenna and distributed deep learning pattern recognition for partial discharge monitoring of transformer
Power transformer is an important part of the power system, and continuous monitoring of partial discharges can provide a more reasonable program for fault diagnosis and operational maintenance of the transformer. However, the rigid partial discharge UHF antenna can not be installed in a conformal fit with the monitored equipment, and the partial discharge UHF signal attenuation is serious, resulting in low detection energy efficiency and gain performance can not meet the demand. The centralized deep learning local discharge pattern recognition method has low training efficiency, and distributed deep learning can improve the training efficiency, but the heterogeneous data from multiple sources will reduce the model accuracy. Due to this, this paper designs a UHF flexible composite helical antenna with miniaturization, wide bandwidth, high gain and high bending deformation stability, and investigates a federated learning pattern recognition method based on residual contraction network, which substantially improves the training efficiency while ensuring the accuracy.
期刊介绍:
This journal is concerned with all aspects of applied physics research, from biophysics, magnetism, plasmas and semiconductors to the structure and properties of matter.