基于集成优化的感应电机无权重系数预测转矩控制

doi:10.19595/j.cnki.1000-6753.tces.201587

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摘要预测转矩控制作为一种新兴的控制策略,通过计算目标函数中的转矩与磁链误差,选取最优开关状态,近年来被广泛应用于交流调速电机系统。与传统的矢量控制相比,预测转矩控制具有动态响应快、开关频率低等优势。为统一目标函数中不同误差项的量纲,预测转矩控制需要设计系数用于调节转矩与磁链误差的权重。然而,依据经验设计的权重系数难以适应不同工况条件。针对以上问题,该文提出一种基于集成优化结构的无权重系数预测转矩控制策略。该控制策略同时优化转矩与磁链误差项,分别得到3个转矩最优与磁链最优的开关状态序列,通过集成优化算法求解最优开关状态。实验结果表明,基于集成优化的无权重系数预测转矩控制在多种工况下具备较好的稳态与动态性能。

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	谢昊天
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	Kennel Ralph

关键词 ：感应电机, 预测转矩控制, 无权重系数, 集成优化

Abstract：As an emerging control strategy, predictive torque control (PTC) selects the optimal switching state by calculating the torque and flux tracking errors in the objective function, which is widely spread in the AC electrical drive system. Compared with traditional field orient control (FOC), PTC shows its superiority of fast dynamic response and low switching frequency. In order to unify the different terms in the objective function, parameter design is required to modify the weighting of torque and flux. However, the empirical weighting parameter is not easy to be fine-tuned according to different operating conditions. To solve the aforementioned issue, an ensemble optimization based weighting factor-less PTC is proposed for induction machines. The proposed method optimizes the tracking errors of torque and flux simultaneously. Thus, three switching states for optimal torque and flux are obtained, respectively. The optimal solution is subsequently achieved by the ensemble optimization mechanism. The effectiveness of the proposed method is verified by experimental results. Compared with the conventional PTC, the proposed method performs better at both steady state and transient state.

Key words： Induction machines predictive torque control weighting factor-less ensemble optimization

收稿日期: 2020-12-04

PACS:

TM346

基金资助:国家自然科学基金资助项目（51877207）

通讯作者: 汪凤翔男,1982年生,研究员,博士生导师,研究方向为电机驱动系统及电力电子变换器的模型预测控制、无位置传感器控制技术。E-mail: fengxiang.wang@fjirsm.ac.cn

作者简介: 谢昊天男,1990年生,博士研究生,研究方向为电机驱动系统的模型预测控制、无位置传感器控制技术。E-mail: haotian.xie@tum.de

引用本文:

谢昊天, 汪凤翔, 柯栋梁, 唐莹, Kennel Ralph. 基于集成优化的感应电机无权重系数预测转矩控制[J]. 电工技术学报, 2022, 37(12): 2992-3003. Xie Haotian, Wang Fengxiang, Ke Dongliang, Tang Ying, Kennel Ralph. Ensemble Optimization Based Weighting Factor-Less Predictive Torque Control for Induction Machines. Transactions of China Electrotechnical Society, 2022, 37(12): 2992-3003.

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