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A Distributed Model Predictive Control Strategy for Back-to-Back Converters

机译:背靠背转换器的分布式模型预测控制策略

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摘要

In recent years, model predictive control (MPC) has been successfully used for the control of power electronics converters with different topologies and for different applications. MPC offers many advantages over more traditional control techniques such as the ability to avoid cascaded control loops, easy inclusion of constraint, and fast transient response. On the other hand, the controller computational burden increases exponentially with the system complexity and may result in an unfeasible realization on modern digital control boards. This paper proposes a novel distributed MPC (DMPC), which is able to achieve the same performance of the classical MPC while reducing the computational requirements of its implementation. The proposed control approach is tested on a ac/ac converter in a back-to-back configuration used for power flow management. Simulation results are provided and validated through experimental testing in several operating conditions.
机译:近年来,模型预测控制(MPC)已成功用于控制具有不同拓扑和不同应用的电力电子转换器。与更传统的控制技术相比,MPC具有许多优势,例如避免级联控制回路,易于包含约束和快速瞬态响应的能力。另一方面,随着系统复杂度的增加,控制器的计算负担成倍增加,并且可能导致在现代数字控制板上无法实现。本文提出了一种新颖的分布式MPC(DMPC),它能够实现与传统MPC相同的性能,同时降低了其实现的计算要求。所提出的控制方法在背对背配置中用于功率流管理的交流/交流转换器上进行了测试。通过在几种操作条件下的实验测试提供并验证了仿真结果。

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