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A data-driven model fusion methodology for health state evaluation of DC bus capacitor in PWM rectifier

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Abstract

In order to improve the control performance and reliability of the pulse-width modulation (PWM) rectifiers in electric vehicle (EV) charging systems, the evaluation of DC bus capacitor health status is critical. In order to accurately monitor the health status of DC bus capacitors, a data-driven model fusion method is developed. In the method, multi-layer perceptron, random forest, and XGBoost are adopted as the base learners that produce separate row predictions. The second-level learner, support vector machine (SVM) accepts the outputs of the previous learners and integrates them into the final health status prediction. Meanwhile, the feature vector is constructed by only collecting the grid voltage, the grid current and the AC component of DC bus voltage. With the feature vector as input, the proposed method is able to accurately predict the health status of DC bus capacitor. Finally, we built a three-phase PWM rectifier as an experimental platform for validation. The experimental results verify that the proposed method fully utilizes the advantages of data-driven and model fusion, and achieves a high accuracy in the health state evaluation of DC bus capacitor.

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Funding

This work was funded by National Natural Science Foundation of China to Yun Zhang with Grant number 51977145.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tian** University, Tian**, China

    **nshan Zhu, Chengqian Xu, Tianbao Song, Fei Gao & Yun Zhang

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  1. **nshan Zhu
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  2. Chengqian Xu
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  3. Tianbao Song
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Correspondence to Yun Zhang.

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Zhu, X., Xu, C., Song, T. et al. A data-driven model fusion methodology for health state evaluation of DC bus capacitor in PWM rectifier. J. Power Electron. 24, 640–651 (2024). https://doi.org/10.1007/s43236-023-00744-7

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  • DOI: https://doi.org/10.1007/s43236-023-00744-7

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