基于模块化多电平变换器的变速驱动中循环电流的长视距MPC参考发生器

IF 7.2 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Electronics Pub Date : 2025-02-03 DOI:10.1109/TIE.2025.3528493
Sebastián Rivero;Andrés Mora;Matías Correa;Javier Pereda
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引用次数: 0

摘要

本文介绍了一种基于约束模型预测控制(MPC)的驱动用模块化多电平变换器(M2C)循环电流参考发生器。它的目的是在考虑支路电流限制的情况下,通过长期预测产生最佳循环电流来控制每个簇中的能量。将最优控制问题分解为更小的子问题,利用基于乘法器交替方向法(ADMM)的迭代算法有效地求解最优问题。因此,基于admm的参考发生器可以大大扩展预测范围,从而提高M2C性能,特别是在低速和高扭矩等具有挑战性的工作条件下平衡和减轻电容器电压的低频振荡。通过实验结果验证了所提出的控制方法,该控制方法使用每簇4个电池的M2C驱动3kw感应电机。
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Long-Horizon MPC Reference Generator for Circulating Currents in Modular Multilevel Converter-Based Variable-Speed Drives
The article introduces a circulating current reference generator based on a constrained model predictive control (MPC) in modular multilevel converters (M2C) for drive applications. It aims to control the energy in each cluster by generating optimal circulating currents over a long-horizon prediction while considering branch current limits. The optimal control problem is decomposed into smaller subproblems, which allows the utilization of an iterative algorithm based on the alternating direction method of multipliers (ADMM) to efficiently solve the optimization problem. As a result, the ADMM-based reference generator enables a considerably extended prediction horizon, which enhances M2C performance, particularly in balancing and mitigating low-frequency oscillations in capacitor voltages during challenging operating conditions like low-speed and high-torque scenarios. The proposed control is validated by experimental results using an M2C with four cells per cluster driving a 3 kW induction machine.
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来源期刊
IEEE Transactions on Industrial Electronics
IEEE Transactions on Industrial Electronics 工程技术-工程:电子与电气
CiteScore
16.80
自引率
9.10%
发文量
1396
审稿时长
6.3 months
期刊介绍: Journal Name: IEEE Transactions on Industrial Electronics Publication Frequency: Monthly Scope: The scope of IEEE Transactions on Industrial Electronics encompasses the following areas: Applications of electronics, controls, and communications in industrial and manufacturing systems and processes. Power electronics and drive control techniques. System control and signal processing. Fault detection and diagnosis. Power systems. Instrumentation, measurement, and testing. Modeling and simulation. Motion control. Robotics. Sensors and actuators. Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems. Factory automation. Communication and computer networks.
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