In the mechanical fault detection method based on the traditional Shannon-Nyquist theory, the signal sampling rate is required to be high and the fault detection rate is low. Compressed sensing collects signals at a sampling rate far lower than Nyquist’s sampling theorem by using the signal sparse characteristics, so as to reduce the hardware pressure of data acquisition, transmission and storage. In this paper, a fault detection method based on compressed sensing for breaking and closing coils of HV circuit breakers is proposed. Firstly, the current model of circuit breaker opening and closing coil is built in MATLAB and Simulink to generate the simulation model signals, which can replace the actual signals for fault diagnosis and analysis. Then, the discrete cosine transform (DCT) was selected as the sparse basis matrix using the compressed sensing theory, and the orthogonal matching pursuit algorithm was used to reconstruct the signals. By compressing and reducing the dimensions of the fault signals, the time-domain signals in the compression faults were extracted for fault detection. The experimental results show that this method can reduce the sampling rate and improve the efficiency and accuracy of fault diagnosis.


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    Titel :

    Fault Detection of High Voltage Circuit Breaker Opening and Closing Coil Based on Compressed Sensing


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Qin, Yong (Herausgeber:in) / Jia, Limin (Herausgeber:in) / Liang, Jianying (Herausgeber:in) / Liu, Zhigang (Herausgeber:in) / Diao, Lijun (Herausgeber:in) / An, Min (Herausgeber:in) / Qi, Hong (Autor:in) / Dong, Weiguang (Autor:in) / Zhang, Zhouwei (Autor:in)

    Kongress:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021



    Erscheinungsdatum :

    2022-02-23


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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