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Space-vectors based hierarchical model predictive control for a modular multilevel converter

机译:模块化多电平转换器的基于空间矢量的层次模型预测控制

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The modular multilevel converter (MMC) is a competitive candidate for medium/high-power applications, specifically for high-voltage direct current transmission systems. Model predictive control (MPC) is an advanced and flexible method for power converters. The existing MPC methods for the MMC 3-phase system treat whole system as a three independent single phase system, and the computational load increases geometrically according to the increase of the level of the MMC. This paper proposes a space-vectors based hierarchical model predictive control (HMPC) strategy for a 3-phase MMC system with independent cost functions. Three hierarchical mathematical models of the MMC are derived and discretized to predict the AC-side current, circulating current and capacitor voltage, respectively. By utilizing multilevel space-vectors and hierarchical model, the considered number of states can be reduced significantly with the highest DC voltage utilization ratio and good performance. In addition, this strategy doesn't need the complex capacitor voltage sorting method and reduces the power loss by avoiding the unnecessary switching state transitions. The performance of the proposed strategy for 11-level MMC is verified through simulation results.
机译:模块化多电平转换器(MMC)是中/高功率应用(尤其是高压直流输电系统)的有竞争力的候选产品。模型预测控制(MPC)是功率转换器的一种先进且灵活的方法。 MMC三相系统的现有MPC方法将整个系统视为三个独立的单相系统,并且随着MMC级别的增加,计算负荷在几何上也会增加。提出了一种具有独立成本函数的三相MMC系统基于空间矢量的层次模型预测控制(HMPC)策略。推导并离散化了MMC的三个分层数学模型,以分别预测AC侧电流,循环电流和电容器电压。通过利用多级空间矢量和分层模型,可以以最高的直流电压利用率和良好的性能显着减少所考虑的状态数。此外,该策略不需要复杂的电容器电压分类方法,并且可以避免不必要的开关状态转换,从而降低了功率损耗。仿真结果验证了所提出的11级MMC策略的性能。

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