基于模糊粗糙集理论的电力变压器早期故障诊断系统

Xiong Hao, Lin Weiguo, Chang Guanghui, Gu Huimin
{"title":"基于模糊粗糙集理论的电力变压器早期故障诊断系统","authors":"Xiong Hao, Lin Weiguo, Chang Guanghui, Gu Huimin","doi":"10.1109/ICPST.2006.321559","DOIUrl":null,"url":null,"abstract":"Based on fuzzy rough set theory (FRS), the paper is meant to present a new diagnosis system with gas ratios method for transformer incipient fault diagnosis, which not only has the capability of coping with incomplete information inputting and rules reduction just as in conventional rough set theory (RS), but also has the capability of fuzzification of thresholds of continuous values of attributes. Since strict thresholds setting is said to undergo the diagnosis effectiveness, A method of fuzzy subsets extraction from database based on KDD technology is employed to the setting attributes, which improve the power of prevailing IEC/IEEE criteria in fields of strict thresholds setting. Finally, results of testing the proposed diagnosis system on actual dissolved gas records are addressed, which confirmed that rules represented by fuzzy terms and extracted based on FRS allow diagnosis results to be satisfied, and diagnosis system proposed can provide a satisfactory accuracy.","PeriodicalId":181574,"journal":{"name":"2006 International Conference on Power System Technology","volume":"138 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Improved Power Transformer Diagnosis System for Incipient Fault Based on Fuzzy Rough Set Theory\",\"authors\":\"Xiong Hao, Lin Weiguo, Chang Guanghui, Gu Huimin\",\"doi\":\"10.1109/ICPST.2006.321559\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on fuzzy rough set theory (FRS), the paper is meant to present a new diagnosis system with gas ratios method for transformer incipient fault diagnosis, which not only has the capability of coping with incomplete information inputting and rules reduction just as in conventional rough set theory (RS), but also has the capability of fuzzification of thresholds of continuous values of attributes. Since strict thresholds setting is said to undergo the diagnosis effectiveness, A method of fuzzy subsets extraction from database based on KDD technology is employed to the setting attributes, which improve the power of prevailing IEC/IEEE criteria in fields of strict thresholds setting. Finally, results of testing the proposed diagnosis system on actual dissolved gas records are addressed, which confirmed that rules represented by fuzzy terms and extracted based on FRS allow diagnosis results to be satisfied, and diagnosis system proposed can provide a satisfactory accuracy.\",\"PeriodicalId\":181574,\"journal\":{\"name\":\"2006 International Conference on Power System Technology\",\"volume\":\"138 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 International Conference on Power System Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPST.2006.321559\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 International Conference on Power System Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPST.2006.321559","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

基于模糊粗糙集理论,提出了一种新的变压器早期故障诊断系统,该系统不仅具有传统粗糙集理论的不完全信息输入和规则约简能力,而且具有属性连续值阈值的模糊化能力。由于严格阈值设置具有诊断有效性,因此采用基于KDD技术的数据库模糊子集提取方法对设置属性进行提取,提高了现行IEC/IEEE标准在严格阈值设置领域的效力。最后,通过对实际溶解气体记录的测试,验证了模糊项表示规则和基于FRS提取规则的诊断结果令人满意,诊断系统能够提供满意的诊断精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
An Improved Power Transformer Diagnosis System for Incipient Fault Based on Fuzzy Rough Set Theory
Based on fuzzy rough set theory (FRS), the paper is meant to present a new diagnosis system with gas ratios method for transformer incipient fault diagnosis, which not only has the capability of coping with incomplete information inputting and rules reduction just as in conventional rough set theory (RS), but also has the capability of fuzzification of thresholds of continuous values of attributes. Since strict thresholds setting is said to undergo the diagnosis effectiveness, A method of fuzzy subsets extraction from database based on KDD technology is employed to the setting attributes, which improve the power of prevailing IEC/IEEE criteria in fields of strict thresholds setting. Finally, results of testing the proposed diagnosis system on actual dissolved gas records are addressed, which confirmed that rules represented by fuzzy terms and extracted based on FRS allow diagnosis results to be satisfied, and diagnosis system proposed can provide a satisfactory accuracy.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A New Research for Internal Fault Simulation Model of Transformer Studies on the Variable Speed Wind Turbine Control System Based on PSCAD/EMTDC Research on Dynamic Load Modeling Using Back Propagation Neural Network for Electric Power System Voltage Sensitivity Analysis in Voltage Support of the China Southern Power Grid Study on Line Detection and Fault Location with Automatic Track Arc Suppression Coil Device
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1