46 / 2024-03-30 12:45:34
Research on Online Monitoring Methods for the Closing Point of 12kV Air Load Switch
Air load switch,Online monitoring,Closing point,Wavelet decomposition reconstruction method
Final Paper
Huiqin Fu / Jieyang Power Supply Bureau of Guangdong Power Grid Co., Ltd
Jiamian Huang / Jieyang Power Supply Bureau of Guangdong Power Grid Co., Ltd
Yuanping Luo / Jieyang Power Supply Bureau of Guangdong Power Grid Co., Ltd
Rong Li / Jieyang Power Supply Bureau of Guangdong Power Grid Co., Ltd
Jingxuan Liang / Xiamen Huadian Switchgear Co., Ltd.
As one of the electrical equipment with the most frequent switching actions, the closing point of the air load switch is undoubtedly a critical feature among many mechanical characteristics. Online monitoring of the closing point has always been a key research direction in the field of intelligent switchgear technology, as well as a challenging technical issue. This paper proposes an online monitoring method for the closing point based on the travel curve of the air load switch. By deeply analyzing the force situation of the moving contact before and after the closing point, and using the closing speed curve as the key information, the wavelet decomposition-reconstruction method is employed to extract feature quantities from the curve, achieving accurate calculation of the closing point. To verify the effectiveness of the method, 50 sets of no-load closing tests were conducted and compared with actual closing point samples. The results show that the method has good stability, with calculation errors controlled within ±10%. This provides a theoretical basis for the online monitoring research of the mechanical characteristics of intelligent air load switches and is of significant importance for the development of intelligent switchgear technology.
Important Date
  • Conference Date

    Nov 10

    2024

    to

    Nov 13

    2024

  • Nov 11 2024

    Draft paper submission deadline

  • Nov 19 2024

    Registration deadline

Sponsored By
Xi’an Jiaotong Universit