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A simple and effective strategy to reduce switching losses under FS-MPC based on dynamically changing voronoi diagrams

机译:一种简单有效的策略,可基于动态变化的voronoi图来降低FS-MPC下的开关损耗

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Finite Set Model Predictive Control (FS-MPC) is a powerful and popular strategy to control power electronic converters. One important aspect of FS-MPC is to limit switching losses while tracking a given reference. This is typically achieved by including in the cost function an explicit measure of switching losses alongside the tracking error. Appropriate weighting coefficients to these two different measures are typically assigned by trial and error. The paper proposes a novel and very effective way to reduce switching losses without penalising them explicitly in the cost function. This is achieved by virtue of fictitious voltage states, which change their positions depending on the currently chosen switching state. An implementation algorithm proposed in the paper is based on principles of computational geometry, specifically, Voronoi diagrams. The algorithm is very efficient and poses almost no additional computational burden compared to standard quadratic cost function. Its application significantly reduces switching losses of FS-MPC without a major penalty in terms of its harmonic distortion. Moreover, the paper shows that the proposed algorithm can be easily combined with other advances recently developed around MPC. The paper presents extensive simulation results showing the advantages of the proposed method and comparing the performance of its various options.
机译:有限集模型预测控制(FS-MPC)是一种功能强大且流行的控制电力电子转换器的策略。 FS-MPC的一个重要方面是在跟踪给定参考的同时限制开关损耗。这通常是通过在成本函数中包括对开关损耗以及跟踪误差的明确测量来实现的。通常通过反复试验为这两种不同的度量方法分配适当的加权系数。本文提出了一种新颖且非常有效的方法来减少开关损耗,而无需在成本函数中对其进行明确的惩罚。这是通过虚拟的电压状态实现的,该电压状态会根据当前选择的开关状态更改其位置。本文提出的一种实现算法基于计算几何原理,特别是Voronoi图。与标准的二次成本函数相比,该算法非常高效,几乎没有任何额外的计算负担。它的应用可显着降低FS-MPC的开关损耗,而不会造成谐波失真方面的重大损失。此外,本文表明,所提出的算法可以很容易地与最近围绕MPC开发的其他进步相结合。本文提供了广泛的仿真结果,这些结果表明了该方法的优点,并比较了其各种选择的性能。

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