An Effective Model-Free Predictive Control for LC-Filter Voltage Source Inverters
编号:65 访问权限:公开 更新:2023-06-14 14:48:45 浏览:394次 张贴报告

报告开始:2023年06月19日 09:00(Asia/Shanghai)

报告时间:0min

所在会场:[E] Poster Session [E1] Poster Session 1

摘要
The parametric uncertainties have critical effects on output voltage performance of LC-filtered voltage source inverters (VSIs) under model predictive control (MPC). To overcome this known problem, an effective model-free predictive control (MFPC) is proposed. The proposed MFPC realizes robust and accurate voltage predictions by using the capacitor voltage gradients and inverter-side current gradients. Then, the state-space equations of different voltage vectors are established to calculate all the gradients in real time, which can ensure the accuracy of voltage and current gradients. Finally, the practicality of the proposed scheme is demonstrated under ideal and mismatched model. In comparison with conventional MPC, the proposed MFPC not only achieves a comparable voltage performance, but also realizes better robustness.The parametric uncertainties have critical effects on output voltage performance of LC-filtered voltage source inverters (VSIs) under model predictive control (MPC). To overcome this known problem, an effective model-free predictive control (MFPC) is proposed. The proposed MFPC realizes robust and accurate voltage predictions by using the capacitor voltage gradients and inverter-side current gradients. Then, the state-space equations of different voltage vectors are established to calculate all the gradients in real time, which can ensure the accuracy of voltage and current gradients. Finally, the practicality of the proposed scheme is demonstrated under ideal and mismatched model. In comparison with conventional MPC, the proposed MFPC not only achieves a comparable voltage performance, but also realizes better robustness.
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报告人
Zheng Yin
Anhui University

Cungang Hu
Anhui University

Tao Rui

Wenping Cao

Zhuangzhuang Feng

Geye Lu
Tsinghua University

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重要日期
  • 会议日期

    06月16日

    2023

    06月19日

    2023

  • 06月15日 2023

    报告提交截止日期

  • 07月02日 2023

    注册截止日期

主办单位
Huazhong University of Science and Technology, China
(IEEE PELS)
IEEE
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