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  • 标题:Fault Diagnosis in Transformer Based on Weighted Degree of Grey Slope Incidence of Optimized Entropy
  • 本地全文:下载
  • 作者:Anping Zhang ; Anping Zhang ; Sujie Geng
  • 期刊名称:MATEC Web of Conferences
  • 电子版ISSN:2261-236X
  • 出版年度:2016
  • 卷号:77
  • 页码:1-5
  • DOI:10.1051/matecconf/20167701004
  • 语种:English
  • 出版社:EDP Sciences
  • 摘要:Dissolved gas analysis (DGA) is an important method to find the hidden or incipient insulation faults of oil-immersed power transformer. However, code deficiency exists in the gas ratio methods specified by the IEC standard and complexity of fault diagnosis for power transformer. Hence a new model based on optimized weighted degree of grey slope incidence was put forward. Firstly, the entropy weight is used to determine objective weight of indices; then the model fault types are obtained by weighted degree of grey slope incidence. The combination of entropy weight with grey slope incidence analysis can fully utilize over all information of DGA and give full play to the superiority of grey slope incidence, which overcomes shortcomings of original grey slope incidence analysis. The experimental results also demonstrate that the improved method has higher accuracy compared with three-ratio method and general grey slope incidence analysis method. The diagnosis accuracy is 92.8%.
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