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Computationally efficient direct model predictive control with long prediction horizons for medium voltage drives.

计算高效的直接模型预测控制，具有针对中压驱动器的长预测范围。

基本信息

批准号：
273592357
负责人：
Professor Dr.-Ing. Ralph Kennel
金额：
--
依托单位：
Lehrstuhl für Elektrische Antriebssysteme und Leistungselektronik (EAL)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/273592357?language=en
关键词：
Computationally efficient direct model predictive

项目摘要

For medium-voltage (MV) ac drives the inverter's switching losses should be kept low. To achieve this, the switching frequency could be decreased, but this would lead to an unacceptable increase in the total harmonic distortion (THD) of the motor's current.An alternative is to implement model predictive control (MPC) algorithms to resolve the aforementioned contradiction, since MPC is capable of handling multiple control objectives that may be conflicting. Moreover, MPC is very often implemented as a direct controller, i.e. the control and modulation problems are tackled in one stage, thus the flexibility of such schemes is great. Therefore, MPC has been proven to be effective forcontrolling the currents (or the electromagnetic torque and the stator flux) of the motor, and, at the same time, for keeping the switching frequency and the currents' THD low.Nonetheless, in many MPC problems (as the one above), a long prediction horizon is required for adequate closed-loop performance. Hence, the computational requirements are difficult to meet, even with modern microprocessors that are powerful. Therefore, the challenge to develop MPC schemes with long prediction horizons and a modest computational complexity remains.The goal of this project is to develop a direct MPC strategy with long prediction horizons for MV drives (consisting of a multilevel inverter and an induction motor - IM). The control objectives are multiple and of equivalent importance: the stator current should closely track its reference, its THD should be low, and the switching frequency (or equivalently the switching losses) are to be kept small. However, in order to implement the devised strategy in real time the required computational effort should be small.To achieve all the control objectives, the goal is to formulate the problem as an integer least squares optimization problem (or a similar one). In this way a discrete search approach could be adapted (such as the sphere decoding algorithm) to solve the problem in an efficient manner. However, issues such as the initial radius selection of the hypersphere, or the factorization of the optimization problem matrices should be takeninto account and effectively tackled.A very important advantage of the proposed method is that the complexity of the formulated optimization problem does not depend on the voltage levels of the inverter that drives the motor. This is because the sphere decoding algorithm searches for the optimal solution in a hypersphere, the structure of which is independent of the levels of the inverter. This implies that the MPC algorithm can be easily adapted for different (multilevel)inverter topologies without additional computational overhead.A final, important task of this project is to implement the algorithm in a scaled-down prototype (low voltage drive system) in order to validate its effectiveness. The drive will consist of a three level neutral-point clamped inverter and an IM.

对于中压（MV）交流驱动器，逆变器的开关损耗应保持较低。为了实现这一点，可以降低开关频率，但这将导致电机电流的总谐波失真（THD）的不可接受的增加。一种替代方案是实施模型预测控制（MPC）算法来解决上述矛盾，因为MPC能够处理可能相互冲突的多个控制目标。此外，MPC经常被实现为直接控制器，即在一个阶段中处理控制和调制问题，因此这种方案的灵活性很大。因此，MPC已被证明是有效的控制电流（或电磁转矩和定子磁链）的电动机，并在同一时间，保持开关频率和电流的THD低。然而，在许多MPC问题（如上一个），需要一个长的预测地平线足够的闭环性能。因此，即使使用功能强大的现代微处理器，也难以满足计算要求。因此，开发具有长预测范围和适度计算复杂性的MPC方案的挑战仍然存在。本项目的目标是为MV驱动器（由多电平逆变器和感应电动机- IM组成）开发具有长预测范围的直接MPC策略。控制目标是多重的，并且具有同等的重要性：定子电流应紧密地跟踪其参考，其THD应低，并且开关频率（或等效的开关损耗）应保持小。然而，为了在真实的时间内实施所设计的策略，所需的计算工作量应该是小的。为了实现所有的控制目标，目标是将问题公式化为整数最小二乘优化问题（或类似的问题）。以这种方式，可以采用离散搜索方法（例如球形解码算法）来以有效的方式解决问题。该方法的一个重要优点是优化问题的复杂性不依赖于驱动电机的逆变器的电压水平.这是因为球解码算法在超球中搜索最优解，超球的结构与反相器的级别无关。这意味着MPC算法可以很容易地适应不同的（多电平）逆变器拓扑结构，而不需要额外的计算开销。本项目的最后一个重要任务是在缩小的原型（低压驱动系统）中实现该算法，以验证其有效性。该驱动器将包括一个三电平中点箝位逆变器和IM。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

UltraZohm - a Powerful Real-Time Computation Platform for MPC and Multi-Level Inverters

UltraZohm - 适用于 MPC 和多电平逆变器的强大实时计算平台

DOI：
10.1109/precede.2019.8753306
发表时间：
2019
期刊：
2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE)
影响因子：
0
作者：
S. Wendel;A. Geiger;E. Liegmann
通讯作者：
E. Liegmann

A Modified Sphere Decoder for Online Adjustment of the Switching Frequency

一种用于在线调节开关频率的改进球形解码器

DOI：
10.1109/iecon.2019.8927249
发表时间：
2019
期刊：
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
影响因子：
0
作者：
Q. Yang;E. Liegmann;P. Karamanakos;R. Kennel
通讯作者：
R. Kennel

Constrained long-horizon direct model predictive control for power electronics

电力电子设备的约束长视野直接模型预测控制

DOI：
10.1109/ecce.2016.7854957
发表时间：
2016
期刊：
2016 IEEE Energy Conversion Congress and Exposition (ECCE)
影响因子：
0
作者：
P. Karamanakos;T. Geyer;R. Kennel
通讯作者：
R. Kennel

Computationally efficient sphere decoding for long-horizon direct model predictive control

用于长视野直接模型预测控制的计算高效球体解码

DOI：
10.1109/ecce.2016.7854955
发表时间：
2016
期刊：
2016 IEEE Energy Conversion Congress and Exposition (ECCE)
影响因子：
0
作者：
P. Karamanakos;T. Geyer;R. Kennel
通讯作者：
R. Kennel

Long-horizon direct model predictive control with active balancing of the neutral point potential

具有中性点电位主动平衡的长视野直接模型预测控制

DOI：
10.1109/precede.2017.8071274
发表时间：
2017
期刊：
2017 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE)
影响因子：
0
作者：
E. Liegmann;P. Karamanakos;T. Geyer;T. Mouton;R. Kennel
通讯作者：
R. Kennel