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878 / 2022-06-30 16:43:52
New Features Derived from Dissolved Gas Analysis for Fault Diagnosis of Power Transformers Based on Membership Function
power transformers, fault diagnosis, gas ratios dissolved in oil, membership Function, IEC TC 10 database
3. High Voltage Measurement Techniques and Instrumentation
终稿
Chao Gao / Ltd.；Major equipment management office； China Nuclear Power Operations Co.
Erya Gao / Ltd.；Major equipment management office； China Nuclear Power Operations Co.
Zhongqing Yang / Department of equipment management； Suzhou Nuclear Power Research Institute
Yuhui Feng / Ltd.；Major equipment management office； China Nuclear Power Operations Co.
Bing Song / Ltd.；Major equipment management office； China Nuclear Power Operations Co.
Qian Li / Ltd.；Major equipment management office； China Nuclear Power Operations Co.
Condition-based maintenance has become an important approach to maintain long-term stable and safe operation of power equipment. With the rapid development of machine learning technology, it can effectively improve the accuracy of transformer fault diagnosis. In this paper, based on the state evaluation criterion, the membership function of DGA is established, and the membership degree of each DGA parameter is calculated as a new feature parameter. SVM and random forest algorithm are used to train and diagnose IEC TC 10 fault database respectively. The results show that the membership degree of DGA as a feature parameter is increased by 2.4 % in SVM and 33.03 % in random forest algorithm, which verifies the effectiveness of the algorithm
重要日期

  • 会议日期

    09月25日

    2022

    至

    09月29日

    2022

  • 08月15日 2022

    提前注册日期

  • 09月10日 2022

    报告提交截止日期

  • 11月10日 2022

    注册截止日期

  • 11月30日 2022

    初稿截稿日期

  • 11月30日 2022

    终稿截稿日期

主办单位
IEEE DEIS
承办单位
Chongqing University
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