基于扩张状态观测器的级联无刷双馈电机并网同步和发电鲁棒预测电流控制

doi:10.19595/j.cnki.1000-6753.tces.L10095

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摘要

级联无刷双馈电机无需电刷和滑环,具有电流谐波小、维护成本低等优点,在风力发电等领域具有广阔的应用前景。目前级联无刷双馈电机常用的控制方法有矢量控制和无差拍控制,当控制器中使用的电机参数由于温度、饱和等因素与实际值不同时,控制效果均有不同程度的恶化。为了克服这一问题,本文提出一种基于扩张状态观测器的级联无刷双馈电机鲁棒预测电流控制方法,同时适用于并网同步和发电过程。该方法基于超局部模型使用扩张状态观测器来估计系统的动态部分和总扰动,并进一步结合无差拍控制,实现控制绕组电流的快速准确控制。与传统的矢量控制和无差拍控制进行了比较,实验结果表明,该方法不仅在电机参数准确时有良好的稳态和动态性能,在电机参数变化时同样能保持较好的控制效果,具有较强的参数鲁棒性。

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关键词 ：级联无刷双馈电机, 扩张状态观测器, 参数鲁棒性, 并网同步和发电

Abstract：

The cascaded brushless doubly-fed generator (CBDFG) is composed of two coaxial wound rotor induction motors, whose rotor windings are directly connected to eliminate the need for brushes and slip rings. Therefore, it has the advantages of long service life, high reliability and low maintenance cost. CBDFG can be used for both independent generation and grid-connected generation, and has broad prospects in wind power generation and hydroelectric power generation. At present, vector control and deadbeat control are commonly used to control the CBDFG. Because the above two methods both use a large number of motor parameters, when the machine parameters used in the controllers are different from the actual values due to temperature, saturation and other reasons, the control effects have varying degrees of deterioration.
To overcome this problem, this paper proposes an extended state observer (ESO) based robust predictive current control (RPCC) method for CBDFG.
Firstly, the proposed RPCC can be applied to both grid synchronization and power generation process. The only difference between the two processes is the calculation of the reference value of the control winding current. In the process of grid synchronization, because the power side does not emit power, the virtual flux is needed to calculate the reference value of the control winding current. During power generation process, the reference value of control winding current is calculated directly according to the power reference value.
Secondly, the proposed RPCC is based on ultra-local model and uses an ESO to rapidly estimate the total disturbance caused by model uncertainty. The ESO can extend the total disturbance to a new disturbance and feed this total disturbance back to the controlled system. Based on the state space equation of ESO, the closed loop transfer function of the ESO for CBDFG is obtained. According to the closed loop transfer function obtained, the two parameters of ESO are simplified to one. Then, the control block diagram of the proposed RPCC in z domain is given, and the parameters of ESO are designed according to the characteristic equation of the proposed RPCC. In addition, the parameter stability of the proposed ESO is analyzed.
Finally, according to the designed ESO parameter and the obtained disturbance value, one-step delay compensation is carried out for the current value of the control winding, and deadbeat control is further incorporated to achieve fast and accurate control of the control winding current.
Compared with the traditional field-oriented control and deadbeat control, the experimental results show that this method not only has good steady-state and dynamic waveforms when the motor parameters are accurate, but also maintains a good control effect when the CBDFG parameters change. It shows that this method has good parameter robustness. In order to reduce the switching loss in actual wind power system, the switching frequency is usually set at several kHz. The simulation results of the proposed RPCC at low switching frequency show that although the decrease of switching frequency will increase the current THD, it is still far less than 5%, which meets the requirements of grid-connection.

Key words： Cascaded brushless doubly-fed generator Extended state observer Parameter robustness Grid synchronization and power generation

PACS:

TM346

基金资助:

国家自然科学基金（52077002）、国家重点研发计划（2021YFB2400702）和华能集团总部科技项目海上风电与智慧能源系统科技专项（一期）（HNKJ20-H88）资助项目

通讯作者: 张永昌, 男,1982年生,教授,博士生导师,研究方向为电力电子与电机控制等。E-mail：zyc@ncepu.edu.cn

作者简介: 杨长山, 男,2000年生,硕士研究生,研究方向为无刷双馈电机模型预测控制。E-mail：yangchangshanyx@163.com

引用本文:

杨长山, 张永昌, 蒋涛. 基于扩张状态观测器的级联无刷双馈电机并网同步和发电鲁棒预测电流控制[J]. 电工技术学报, 0, (): 8935-. Yang Changshan, Zhang Yongchang, Jiang Tao. Robust Predictive Current Control for Grid Synchronization and Power Generation of Cascaded Brushless Doubly-Fed Generators Based on Extended State Observer. Transactions of China Electrotechnical Society, 0, (): 8935-.

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[1] Liang S, Jin S, Shi L.Research on control strategy of grid-connected brushless doubly-fed wind power system based on virtual synchronous generator control[J]. CES Transactions on Electrical Machines and Systems, 2022, 6(4): 404-412.
[2] 阚超豪, 鲍习昌, 王雪帆, 等. 无刷双馈电机的研究现状与最新进展[J]. 中国电机工程学报, 2018, 38(13): 3939-3959+4036.
Kan Chaohao, Bao Xichang, Wang Xuefan, et al. Overview and recent developments of brushless doubly-fed machines[J]. Proceedings of the CSEE, 2018, 38(13): 3939-3959+4036.
[3] 程明, 许利通, 曹政, 等. 级联式无刷双馈电机的矢量控制系统和功率流研究[J]. 电工技术学报, 2022, 37(20): 5164-5174.
Cheng Ming, Xu Litong, Cao Zheng, et al.Research on vector control system and power flow of cascaded brushless doubly-fed induction generator[J]. Transactions of China Electrotechnical Society, 2022, 37(20): 5164-5174.
[4] 任泰安, 梁克靖, 王群京, 等. 差调制无刷双馈电机转子绕组设计及性能分析[J]. 电机与控制学报, 2022, 26(04): 57-65.
Ren Taian, Liang Kejing, Wang Qunjing, et al.Design and performance analysis of rotor winding for differential modulation brushless doubly fed machines[J]. Electric Machines and Control, 2022, 26(04): 57-65.
[5] 程明, 韩鹏, 魏新迟. 无刷双馈风力发电机的设计, 分析与控制[J]. 电工技术学报, 2016, 31(19): 37-53.
Cheng Ming, Han Peng, Wei Xinchi.Design, analysis and control of brushless doubly-fed wind turbine[J]. Transactions of China Electrotechnical Society, 2016, 31(19): 37-53.
[6] Cheng M, Luo R, Wei X.Design and analysis of current control methods for brushless doubly fed induction machines[J]. IEEE Transactions on Industrial Electronics, 2018, 66(1): 717-727.
[7] Esfandiari G, Ebrahimi M, Tabesh A, et al.Instantaneous torque control method with rated torque-sharing ratio for cascaded DFIMs[J]. IEEE Transactions on Power Electronics, 2017, 32(11): 8671-8680.
[8] Sadeghi R, Madani S, Ataei M, et al.Super-twisting sliding mode direct power control of a brushless doubly fed induction generator[J]. IEEE Transactions on Industrial Electronics, 2018, 65(11): 9147-9156.
[9] Sadeghi R, Madani S, Ataei M.A new smooth synchronization of brushless doubly-fed induction generator by applying a proposed machine model[J]. IEEE Transactions on Sustainable Energy, 2017, 9(1): 371-380.
[10] 魏新迟, 许利通, 骆仁松, 等. 考虑饱和效应的无刷双馈发电机功率模型预测控制[J]. 电工技术学报, 2021, 36(17): 3721-3729.
Wei Xinchi, Xu Litong, Luo Rensong, et al.Model predictive power control of brushless doubly-fed generator considering saturation effect[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3721-3729.
[11] 卜飞飞, 罗捷, 刘皓喆, 等. 双绕组感应发电机系统无差拍电流预测控制策略[J]. 电工技术学报, 2021, 36(24): 5213-5224.
Bu Feifei, Luo Jie, Liu Haozhe, et al.Deadbeat current predictive control strategy for dual winding induction generator system[J]. Transactions of China Electrotechnical Society, 2021, 36(24): 5213-5224.
[12] 章回炫, 范涛, 边元均, 等. 永磁同步电机高性能电流预测控制[J]. 电工技术学报, 2022, 37(17): 4335-4345.
Zhang Huixuan, Fan Tao, Bian Yuanjun, et al.High performance current predictive control for permanent magnet synchronous motor[J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4335-4345.
[13] Wei X, Cheng M, Zhu J, et al.Finite-set model predictive power control of brushless doubly fed twin stator induction generator[J]. IEEE Transactions on Power Electronics, 2019, 34(3): 2300-2311.
[14] Li X, Peng T, Dan H, et al.A modulated model predictive control scheme for the brushless doubly fed induction machine[J]. IEEE Journal of Emerging & Selected Topics in Power Electronics, 2018, 6(4): 1681-1691.
[15] Bhattarai R, Gurung N, Ghosh S, et al.Parametrically robust dynamic speed estimation based control for doubly fed induction generator[J]. IEEE Transactions on Industry Applications, 2018, 54(6): 6529-6542.
[16] Errouissi R, Al-Durra A, Muyeen S, et al.Offset-free direct power control of DFIG under continuous-time model predictive control[J]. IEEE Transactions on Power Electronics, 2017, 32(3): 2265-2277.
[17] Zhang Y, Jiang T, Jiao J.Model-free predictive current control of DFIG based on an extended state observer under unbalanced and distorted grid[J]. IEEE Transactions on Power Electronics, 2020, 35(8): 8130-8139.
[18] 徐伟, 陈俊杰, 刘毅, 等. 无刷双馈独立发电系统的改进无参数预测电流控制[J]. 电工技术学报, 2021, 36(19): 4002-4015.
Xu Wei, Chen Junjie, Liu Yi, et al.Improved nonparametric predictive current control for standalone brushless doubly-fed induction generators[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4002-4015.
[19] Liu F, Liu Z, Mei S, et al.ESO-based inertia emulation and rotor speed recovery control for DFIGs[J]. IEEE Transactions on Energy Conversion, 2017, 32(3): 1209-1219.
[20] Broekhof A, Tatlow M, McMahon R. Vector-controlled grid synchronization for the brushless doubly-fed induction generator[C]//7th IET International Conference on Power Electronics, Machines and Drives (PEMD 2014), Manchester, 2014: 1-5.
[21] Sadeghi R, Madani S M, Kashkooli M R A. Grid synchronization of brushless doubly fed induction generator using direct torque control[C]//2016 7th Power Electronics and Drive Systems Technologies Conference (PEDSTC), Tehran, 2016: 53-57.