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Investigation on C and ESR Estimation of DC-Link Capacitor in Maglev Choppers Using Artificial Neural Network

Author

Listed:
  • Xiaoyu Chen

    (College of Information and Communication, National University of Defense Technology, Wuhan 430000, China)

  • Xin Yang

    (College of Electrical and Information Engineering, Huan University, Changsha 410082, China)

  • Yue Zhang

    (College of Electrical and Information Engineering, Huan University, Changsha 410082, China)

Abstract

The reliability of capacitors is one of the most important issues in power electronics. The health status of capacitors can be evaluated through the comparison of estimated C/ESR values with their original values. In this paper, a two-input artificial neural network (ANN) is proposed for C and ESR estimation of DC-link capacitors in Maglev choppers; combined with the existing voltage and current sensors that are used for protection and control, it provides a promising solution for the health condition monitoring of the Maglev chopper in the Maglev train. Compared with prior-art research, the actual capacitor degradation progress where both C and ESR degrade is considered in training. Moreover, ANN’s advantage of fitting nonlinear and complex relationships is explored by building an aggressive mapping between the voltage ripple at 5 kHz to C/ESR at 120 Hz, which cannot be described or analyzed by linear circuit equations. Thus, cross validation must be implemented to avoid an occasional poor fitting, and ensure the stability of ANN. Experimental results show that the proposed ANN outperforms the support vector regression in both ESR and C estimation. While C estimation suffered from over-fitting and instability, ESR estimation by the ANN is accurate and stable with a low average prediction error within 1%, showing great potential for condition monitoring of DC-link capacitors in Maglev choppers.

Suggested Citation

  • Xiaoyu Chen & Xin Yang & Yue Zhang, 2022. "Investigation on C and ESR Estimation of DC-Link Capacitor in Maglev Choppers Using Artificial Neural Network," Energies, MDPI, vol. 15(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8564-:d:974204
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    References listed on IDEAS

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    1. Gao, Lei & Liu, Tianyuan & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2021. "Comparing deep learning models for multi energy vectors prediction on multiple types of building," Applied Energy, Elsevier, vol. 301(C).
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