Detection Method of Turn to Turn Insulation Short Circuit Fault of Dry-Type Air-Core Reactor Using Vibration Characteristics Based on Machine Learning
ID:315 View Protection:ATTENDEE Updated Time:2020-11-11 12:10:28 Hits:151 Poster Presentation

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Abstract
When the reactor produces turn-to-turn insulation fault, it will cause great damage to the long-term used reactor. Turn-to-turn short circuit, as a common and frequent case of insulation failure, will cause a great disturbance to the stability of the power system if not detected in time. In this paper, a multi-factor turn-to-turn insulation short circuit fault detection method based on machine learning is proposed. This paper chooses the factor from both the time domain and the frequency domain, including both the amplitudes and the phases of vibration signal of the measured point. The experimental platform has been established to simulate the occurrence of turn-to-turn insulation fault. The performance of the generated classification model has been evaluated and discussed. Compared with the commonly used detection method, the results show that the method has higher feasibility and accuracy.
Keywords
dry-type air-core reactor,insulation short circuit fault,machine learning,vibration characteristics,multi-factor
Speaker
Hang YANG
School of Electrical Engineering Xi’an Jiaotong University

Submission Author
Hang YANG School of Electrical Engineering Xi’an Jiaotong University
Lu Gao Xi'an Jiaotong University
Shengchang Ji Xi'an Jiaotong University
Lingyu Zhu Xi'an Jiaotong University
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Important Date
  • Conference Date

    Oct 21

    2019

    to

    Oct 24

    2019

  • Oct 13 2019

    Abstract Notification of Acceptance

  • Oct 13 2019

    Draft paper submission deadline

  • Oct 14 2019

    Draft Paper Acceptance Notification

  • Oct 29 2019

    Final Paper Deadline

Organized By
Xi'an Jiaotong University
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