A Data-Driven Control for Modular Multilevel Converters Based on Model-Free Adaptive Control with an Event-Triggered Scheme

Ying Fang, Yanhua Liu, Aolong Fu, S. Shi, Zhenbin Zhang
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Abstract

Modular multilevel converters (MMCs) have gained widespread adoption in high-voltage direct current (HVDC) transmission due to their high voltage levels, low harmonic content, and high scalability. However, conventional control methods such as finite control set model predictive control (FCS-MPC) suffer from a heavy computational burden and sensitivity to system parameter variations, limiting the performance of MMCs. This paper proposes a data-driven approach based on model-free adaptive control with an event-triggered mechanism that demonstrates superior robustness against parameter mismatches and enhanced dynamic performance in response to sudden output changes. Moreover, the introduction of the event-triggered mechanism effectively reduces redundant operations, decreasing the computational burden and switching losses. Finally, the proposed strategy is validated through a MATLAB/Simulink simulation model.
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基于无模型自适应控制与事件触发方案的模块化多电平转换器数据驱动控制
模块化多电平转换器(MMC)具有电压等级高、谐波含量低和可扩展性强等优点,因此在高压直流(HVDC)输电领域得到了广泛应用。然而,有限控制集模型预测控制(FCS-MPC)等传统控制方法存在计算负担重、对系统参数变化敏感等问题,限制了 MMC 的性能。本文提出了一种基于无模型自适应控制的数据驱动方法,该方法具有事件触发机制,对参数失配具有卓越的鲁棒性,在响应输出突变时具有更强的动态性能。此外,事件触发机制的引入有效减少了冗余操作,降低了计算负担和开关损耗。最后,通过 MATLAB/Simulink 仿真模型对所提出的策略进行了验证。
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