Hu Xiong;Jiayuan Li;Xianming Xie;Bin Xiang;Xiaoguang Jiang;Changchen Zhu;Zhixiong Liu
{"title":"基于多信息融合的配电变压器绕组材料识别","authors":"Hu Xiong;Jiayuan Li;Xianming Xie;Bin Xiang;Xiaoguang Jiang;Changchen Zhu;Zhixiong Liu","doi":"10.1109/TASC.2024.3456559","DOIUrl":null,"url":null,"abstract":"In transformer production, some manufacturers use aluminum to impersonate copper to reduce the manufacturing cost, which is difficult to be detected and can cause significant losses to the power grid. To address the problem of non-destructive identification of winding material in distribution transformers, we propose a multi- information recognition method by fusing the harmonic resistance coefficient and the appearance parameters of the transformer, namely volume and height. Then the winding material is identified by a trained support vector machine model. The test results demonstrate that our proposed method achieves 90% recognition accuracy for copper transformers and 100% accuracy for aluminum transformers with the test samples. Additionally, the proposed non-destructive method is easier to implement than other methods in engineering.","PeriodicalId":13104,"journal":{"name":"IEEE Transactions on Applied Superconductivity","volume":"34 8","pages":"1-5"},"PeriodicalIF":1.7000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification Winding Material of Distribution Transformer Based on Multi-Information Fusion\",\"authors\":\"Hu Xiong;Jiayuan Li;Xianming Xie;Bin Xiang;Xiaoguang Jiang;Changchen Zhu;Zhixiong Liu\",\"doi\":\"10.1109/TASC.2024.3456559\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In transformer production, some manufacturers use aluminum to impersonate copper to reduce the manufacturing cost, which is difficult to be detected and can cause significant losses to the power grid. To address the problem of non-destructive identification of winding material in distribution transformers, we propose a multi- information recognition method by fusing the harmonic resistance coefficient and the appearance parameters of the transformer, namely volume and height. Then the winding material is identified by a trained support vector machine model. The test results demonstrate that our proposed method achieves 90% recognition accuracy for copper transformers and 100% accuracy for aluminum transformers with the test samples. Additionally, the proposed non-destructive method is easier to implement than other methods in engineering.\",\"PeriodicalId\":13104,\"journal\":{\"name\":\"IEEE Transactions on Applied Superconductivity\",\"volume\":\"34 8\",\"pages\":\"1-5\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Applied Superconductivity\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10670453/\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Applied Superconductivity","FirstCategoryId":"101","ListUrlMain":"https://ieeexplore.ieee.org/document/10670453/","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Identification Winding Material of Distribution Transformer Based on Multi-Information Fusion
In transformer production, some manufacturers use aluminum to impersonate copper to reduce the manufacturing cost, which is difficult to be detected and can cause significant losses to the power grid. To address the problem of non-destructive identification of winding material in distribution transformers, we propose a multi- information recognition method by fusing the harmonic resistance coefficient and the appearance parameters of the transformer, namely volume and height. Then the winding material is identified by a trained support vector machine model. The test results demonstrate that our proposed method achieves 90% recognition accuracy for copper transformers and 100% accuracy for aluminum transformers with the test samples. Additionally, the proposed non-destructive method is easier to implement than other methods in engineering.
期刊介绍:
IEEE Transactions on Applied Superconductivity (TAS) contains articles on the applications of superconductivity and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Large scale applications include magnets for power applications such as motors and generators, for magnetic resonance, for accelerators, and cable applications such as power transmission.