Research on Dual−Loop Model Predictive Control Based on Grid−Side Current for MMC−HVDC Systems in Wind Power

This paper proposes a dual−loop model predictive control (MPC) scheme based on grid−side current for modular multilevel converter−based high−voltage direct current (MMC−HVDC) systems. The proposed hybrid control structure combines an MPC−based inner current loop with a PI−based outer voltage loop, designed to enhance dynamic response and steady−state accuracy in HVDC transmission. With the advancement of flexible HVDC technology, modular multilevel converters (MMCs) have been widely adopted due to their excellent scalability and operational flexibility. Model predictive control (MPC), as an advanced control strategy, has demonstrated significant advantages in MMC−HVDC applications. In this study, a dual−loop control system is designed, with MPC as the inner current loop and PI control as the outer voltage loop. This structure effectively enhances control accuracy and ensures system reliability. To validate the effectiveness of the proposed control strategy, a 1000 MW wind power integration MMC−HVDC simulation model was built in Simulink. Simulation results show that the proposed dual−loop MPC strategy can significantly improve control precision and maintain the reliability of the MMC−HVDC system. The proposed strategy is validated through detailed simulations of a 1000 MW wind−integrated MMC−HVDC system, demonstrating superior performance over conventional PI control in terms of overshoot reduction and disturbance rejection.

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Publication Details

Journal
Processes
Published
2025-12-23
DOI
https://doi.org/10.3390/pr14010057
Primary Topic
HVDC Systems and Fault Protection
Type
article
Field-Weighted Citation Impact
0.00

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Controls
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article

Research on Dual−Loop Model Predictive Control Based on Grid−Side Current for MMC−HVDC Systems in Wind Power

Zhaoqing Hu, Dejun Ba, Yanjun Ma, Duanjiao Li et al.
Processes
HVDC Systems and Fault Protection
article

Research on Dual−Loop Model Predictive Control Based on Grid−Side Current for MMC−HVDC Systems in Wind Power

Zhaoqing Hu, Dejun Ba, Yanjun Ma, Duanjiao Li, Lijun Hang, Junjun Zhang, Xinxin Chen, Xiaofeng Lyu
article en

Abstract

This paper proposes a dual−loop model predictive control (MPC) scheme based on grid−side current for modular multilevel converter−based high−voltage direct current (MMC−HVDC) systems. The proposed hybrid control structure combines an MPC−based inner current loop with a PI−based outer voltage loop, designed to enhance dynamic response and steady−state accuracy in HVDC transmission. With the advancement of flexible HVDC technology, modular multilevel converters (MMCs) have been widely adopted due to their excellent scalability and operational flexibility. Model predictive control (MPC), as an advanced control strategy, has demonstrated significant advantages in MMC−HVDC applications. In this study, a dual−loop control system is designed, with MPC as the inner current loop and PI control as the outer voltage loop. This structure effectively enhances control accuracy and ensures system reliability. To validate the effectiveness of the proposed control strategy, a 1000 MW wind power integration MMC−HVDC simulation model was built in Simulink. Simulation results show that the proposed dual−loop MPC strategy can significantly improve control precision and maintain the reliability of the MMC−HVDC system. The proposed strategy is validated through detailed simulations of a 1000 MW wind−integrated MMC−HVDC system, demonstrating superior performance over conventional PI control in terms of overshoot reduction and disturbance rejection.

ProcessesVol. 14(1)
China Electric Equipment Group (China) (CN), Hangzhou Dianzi University (CN), China Southern Power Grid (China) (CN), Zhejiang University (CN)
China Southern Power Grid
Affordable and clean energy
Openalex Percentile: Top 42%
HVDC Systems and Fault Protection
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Research on Dual−Loop Model Predictive Control Based on Grid−Side Current for MMC−HVDC Systems in Wind Power — Zhaoqing Hu, Dejun Ba, et al. · Processes (2025) | TGRS Research Map | TGRS