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88 / 2021-10-22 09:16:24
Fault Diagnosis Method of Three-phase Inverter Based on Time Convolutional Neural Network
Inverter fault,TCN,Fault diagnosis
Power electronics technology and application > Power converter topologies and design
Draft Rejected
Gang Li / Jilin University
Jing Li / Jilin University
Jinru Song / Jilin University
Yizhuo He / Jilin University
Qun Guo / Jilin University
Power electronics converter is widely used in various applications. When the converter failure occurs, the system will be damaged, so it is of great significance to timely detect the fault and apply effective protection. In this paper, the open-circuit fault of the three-phase inverter is taken as the main object. The time convolutional neural network (TCN) model is designed to realize the fault diagnosis strategy of the inverter based on feature extraction. The simulated experimental results show that the neural network model can realize high accuracy fault diagnosis for different inverter circuits. The overall classification performance is good, and the loss is small. It is shown that the time convolutional neural network method can diagnose different IGBT fault intelligently in the inverter. It is proved that the neural network model is effective and feasible for inverter circuit fault diagnosis.
Important Date

  • Conference Date

    Jul 11

    2023

    to

    Aug 18

    2023

  • Nov 10 2021

    Draft paper submission deadline

  • Dec 10 2021

    Registration deadline

  • Dec 11 2021

    Contribution Submission Deadline

Sponsored By
IEEE IAS
Organized By
IEEE IAS Student Chapter of Southwest Jiaotong University (SWJTU)
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
IEEE PELS (Power Electronics Society) Student Chapter of HUST
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