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<p>While model predictive voltage control (MPVC) offers rapid and precise grid-forming voltage regulation in grid-forming inverters (GFIs), it is critically dependent on the accuracy of model parameters. Discrepancies between nominal and actual parameters degrade both the steady-state and dynamic performance. To overcome this limitation, this paper presents an improved model-free predictive voltage control (MFPVC) strategy. First, a Runge–Kutta representation of the GFI is formulated. Leveraging Lagrange interpolation, this model is dynamically updated, eliminating reliance on specific system parameters. Subsequently, the deadbeat control principle is applied to compute the reference output voltage vector. Accurate voltage prediction is achieved through the integration of space vector modulation within the control framework. The proposed MFPVC strategy achieves accurate predictive control that is independent of system parameter accuracy, which minimizes prediction errors. Experimental validation confirms the feasibility of the method.</p>

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Improved model-free predictive voltage control strategy for grid-Forming inverters in weak grid with renewable energy sources

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Yichun Wu,
  • Shaohua Liu,
  • Raoqi Xu,
  • Cungang Hu,
  • Tao Rui,
  • Zhuangzhuang Feng

摘要

While model predictive voltage control (MPVC) offers rapid and precise grid-forming voltage regulation in grid-forming inverters (GFIs), it is critically dependent on the accuracy of model parameters. Discrepancies between nominal and actual parameters degrade both the steady-state and dynamic performance. To overcome this limitation, this paper presents an improved model-free predictive voltage control (MFPVC) strategy. First, a Runge–Kutta representation of the GFI is formulated. Leveraging Lagrange interpolation, this model is dynamically updated, eliminating reliance on specific system parameters. Subsequently, the deadbeat control principle is applied to compute the reference output voltage vector. Accurate voltage prediction is achieved through the integration of space vector modulation within the control framework. The proposed MFPVC strategy achieves accurate predictive control that is independent of system parameter accuracy, which minimizes prediction errors. Experimental validation confirms the feasibility of the method.