Hierarchical progressive identification strategy for DFIG parameters based on multi-timescale fault process partitioning

WANG Yu; WANG Tong; WANG Xiaotong

doi:10.11930/j.issn.1004-9649.202507006

您当前的位置：

首页 >

文章列表页 >

Hierarchical progressive identification strategy for DFIG parameters based on multi-timescale fault process partitioning

更新时间：2026-03-17

- Hierarchical progressive identification strategy for DFIG parameters based on multi-timescale fault process partitioning
- Vol. 59, Issue 3, Pages: 142-155(2026)
- 作者机构：
  
  新能源电力系统全国重点实验室（华北电力大学）,北京,102206
- 作者简介：
- 基金信息：
- DOI：10.11930/j.issn.1004-9649.202507006
  CLC：
- Online First：16 March 2026，
  
  Published：16 March 2026
- 稿件说明：
移动端阅览
王宇, 王彤, 王潇桐. 基于多时间尺度故障过程分区的DFIG参数分层递进式辨识策略[J]. 中国电力, 2026, 59(3): 142-155.

WANG Yu, WANG Tong, WANG Xiaotong. Hierarchical progressive identification strategy for DFIG parameters based on multi-timescale fault process partitioning[J]. 2026, 59(3): 142-155.
王宇, 王彤, 王潇桐. 基于多时间尺度故障过程分区的DFIG参数分层递进式辨识策略[J]. 中国电力, 2026, 59(3): 142-155. DOI： 10.11930/j.issn.1004-9649.202507006.

WANG Yu, WANG Tong, WANG Xiaotong. Hierarchical progressive identification strategy for DFIG parameters based on multi-timescale fault process partitioning[J]. 2026, 59(3): 142-155. DOI： 10.11930/j.issn.1004-9649.202507006.

摘要

针对多工况下双馈风电机组黑盒模型参数辨识问题，提出基于多时间尺度故障过程分区的分层递进式参数辨识策略。首先，基于黑盒模型动态响应特性确定了模型结构及待辨识参数。其次，通过摄动理论量化分析了不同时间尺度参数灵敏度，依据不同阶段主导参数响应特性建立了层次化递进式辨识方法。然后，通过不同层级参数响应差异化特性，利用差分进化方法实现了多参数自适应辨识。最后，建立了适用不同厂家不同型号的白盒模型参数辨识方法，结果表明：所提出的分层递进式辨识策略对不同工况和型号的具有适用性和鲁棒性，与传统参数辨识方法相比，所提辨识方法具有更好的快速性和准确性。

Abstract

To address the parameter identification problem of black-box models for doubly-fed induction generator (DFIG) wind turbines under multiple operating conditions

this paper proposes a hierarchical progressive parameter identification strategy based on the partitioning of multi-timescale fault process. Firstly

the model structure and parameters to be identified are determined according to the dynamic response characteristics of the black-box model. Subsequently

the sensitivity of parameters across different time scales is quantitatively analyzed using perturbation theory

and a hierarchical progressive identification method is established according to the dominant parameter response characteristics in different operational stages. Furthermore

by leveraging the differential response characteristics of parameters across hierarchical levels

the differential evolution method is adopted to realize adaptive identification of multiple parameters. Finally

a white-box model parameter identification method applicable to various manufacturers and models is developed. Comparative results show that the proposed hierarchical progressive identification strategy has good applicability and robustness under different operating conditions and for different models. Additionally

comparisons results with traditional parameter identification methods also demonstrate that the proposed approach exhibits superior rapidity and accuracy.

关键词

Keywords

references

1李丹, 秦世耀, 李少林, 等. 基于混沌粒子群的双馈风电机组LVRT实测建模及暂态参数辨识[J]. 中国电力, 2024, 57 (8): 75- 84.

LI Dan, QIN Shiyao, LI Shaolin, et al. LVRT measurement model and transient parameter identification of wind turbine based on chaotic particle swarm[J]. Electric Power, 2024, 57 (8): 75- 84.

2张兴, 孙艳霞, 李丽娜, 等. 风电机组电磁暂态建模及验证[J]. 中国电力, 2020, 53 (7): 106- 112.

ZHANG Xing, SUN Yanxia, LI Lina, et al. Electromagnetic transient modelling and verifying of wind turbine generator[J]. Electric Power, 2020, 53 (7): 106- 112.

3田芳, 黄彦浩, 史东宇, 等. 电力系统仿真分析技术的发展趋势[J]. 中国电机工程学报, 2014, 34 (13): 2151- 2163.

TIAN Fang, HUANG Yanhao, SHI Dongyu, et al. Developing trend of power system simulation and analysis technology[J]. Proceedings of the CSEE, 2014, 34 (13): 2151- 2163.

4TANG W, HU J B, CHANG Y Z, et al. Modeling of DFIG-based wind turbine for power system transient response analysis in rotor speed control timescale[J]. IEEE Transactions on Power Systems, 2018, 33 (6): 6795- 6805.

5DU K J, MA X P, ZHENG Z X, et al. LVRT capability improvement of DFIG-based wind turbines with a modified bridge-resistive-type SFCL[J]. IEEE Transactions on Applied Superconductivity, 2021, 31 (8): 5603005.

6徐恒山, 曾宪金, 张旭军, 等. 基于RT-LAB的DFIG网侧变流器控制参数多目标分步辨识方法[J]. 电网技术, 2025, 49 (2): 771- 780.

XU Hengshan, ZENG Xianjin, ZHANG Xujun, et al. Multi-objective step-by-step identification method of control parameters for DFIG grid side converter based on RT-LAB[J]. Power System Technology, 2025, 49 (2): 771- 780.

7郭强, 王鹤, 聂永辉, 等. 考虑恢复暂态过程的直驱发电系统低电压穿越模型参数解耦辨识方法[J]. 高电压技术, 2021, 47 (10): 3430- 3440.

GUO Qiang, WANG He, NIE Yonghui, et al. Decoupling identification method of low-voltage ride-through model parameters of direct drive power generation system considering recovery transient process[J]. High Voltage Engineering, 2021, 47 (10): 3430- 3440.

8JIN Y Q, LU C J, JU P, et al. Probabilistic preassessment method of parameter identification accuracy with an application to identify the drive train parameters of DFIG[J]. IEEE Transactions on Power Systems, 2020, 35 (3): 1769- 1782.

9刘金海, 陈为. 表贴式永磁同步电机准稳态多参数在线辨识[J]. 电工技术学报, 2016, 31 (17): 154- 160.

LIU Jinhai, CHEN Wei. Online multi-parameter identification for surface-mounted permanent magnet synchronous motors under quasi-steady-state[J]. Transactions of China Electrotechnical Society, 2016, 31 (17): 154- 160.

10潘学萍, 高远, 金宇清, 等. 风电机组驱动系统参数辨识[J]. 电网技术, 2013, 37 (7): 1990- 1994.

PAN Xueping, GAO Yuan, JIN Yuqing, et al. Parameter identification of drive system for fixed-speed wind power generation units[J]. Power System Technology, 2013, 37 (7): 1990- 1994.

11WANG Y F, LU C, ZHU L P, et al. Comprehensive modeling and parameter identification of wind farms based on wide-area measurement systems[J]. Journal of Modern Power Systems and Clean Energy, 2016, 4 (3): 383- 393.

12潘学萍, 殷紫吟, 鞠平, 等. 基于短路电流辨识双馈感应发电机的模型参数[J]. 电力自动化设备, 2017, 37 (11): 27- 31.

PAN Xueping, YIN Ziyin, JU Ping, et al. Model parameter identification of DFIG based on short circuit current[J]. Electric Power Automation Equipment, 2017, 37 (11): 27- 31.

13李辉, 吴优, 谢翔杰, 等. 基于改进PSO的双馈风电机组传动链参数辨识[J]. 太阳能学报, 2021, 42 (12): 134- 142.

LI Hui, WU You, XIE Xiangjie, et al. Parameter identification of transmission chain for doubly-fed wind turbine based on improved particle swarm optimization[J]. Acta Energiae Solaris Sinica, 2021, 42 (12): 134- 142.

14金宇清, 鞠平, 刘伟航, 等. 基于量测信号扰动的DFIG变流器控制参数辨识方法[J]. 电力系统自动化, 2016, 40 (8): 36- 42.

JIN Yuqing, JU Ping, LIU Weihang, et al. Parameter identification method for converter controller of DFIG based on measurement signal disturbance[J]. Automation of Electric Power Systems, 2016, 40 (8): 36- 42.

15张仰飞, 袁越, 郝思鹏, 等. 数字PI控制器的参数辨识及实验验证[J]. 电力自动化设备, 2010, 30 (11): 40- 43.

ZHANG Yangfei, YUAN Yue, HAO Sipeng, et al. Parameter identification of digital PI controller and experiment validation[J]. Electric Power Automation Equipment, 2010, 30 (11): 40- 43.

16许饶琪, 彭晓涛, 秦世耀, 等. 基于M序列的双馈风机变流器参数辨识方法研究[J]. 电网技术, 2022, 46 (2): 578- 586.

XU Raoqi, PENG Xiaotao, QIN Shiyao, et al. Parameter identification of doubly-fed induction generator converter based on M-sequence[J]. Power System Technology, 2022, 46 (2): 578- 586.

17MA Y X, ZHAO H R, WANG P, et al. Parameter identification and stability analysis of DFIG[C]//2022 International Conference on Power Energy Systems and Applications (ICoPESA). Singapore, Singapore. IEEE, 2022: 390–395.

18李立, 郑天悦, 黄世楼, 等. 基于扩展卡尔曼滤波的DFIG变流器控制系统参数辨识方法[J]. 电网与清洁能源, 2022, 38 (12): 50- 60.

LI Li, ZHENG Tianyue, HUANG Shilou, et al. A parameters identification method of DFIG converter control system based on extended Kalman filter[J]. Power System and Clean Energy, 2022, 38 (12): 50- 60.

19孔祥平, 袁宇波, 阮思烨, 等. 面向故障暂态建模的光伏并网逆变器控制器参数辨识[J]. 电力系统保护与控制, 2017, 45 (11): 65- 72.

KONG Xiangping, YUAN Yubo, RUAN Siye, et al. Controller parameter identification of the grid connected PV inverter for fault transient modeling[J]. Power System Protection and Control, 2017, 45 (11): 65- 72.

20潘学萍, 温荣超, 鞠平, 等. 双馈风电机组网侧控制器参数辨识的频域方法[J]. 电网技术, 2015, 39 (3): 634- 638.

PAN Xueping, WEN Rongchao, JU Ping, et al. A frequency-domain based method to identify parameters of grid side converter controller for doubly fed induction generators[J]. Power System Technology, 2015, 39 (3): 634- 638.

21潘学萍, 鞠平, 温荣超, 等. 解耦辨识双馈风电机组转子侧控制器参数的频域方法[J]. 电力系统自动化, 2015, 39 (20): 19- 25, 108.

PAN Xueping, JU Ping, WEN Rongchao, et al. Decoupling estimation of parameters in rotor side controller of DFIG-based wind turbine by frequency domain method[J]. Automation of Electric Power Systems, 2015, 39 (20): 19- 25, 108.

22薛飞, 李宏强, 李旭涛, 等. 基于LSTM神经网络的双馈风机控制参数辨识方法[J]. 中国电力, 2023, 56 (6): 31- 39.

XUE Fei, LI Hongqiang, LI Xutao, et al. Identification method for control parameters of doubly-fed induction generator based on LSTM neural network[J]. Electric Power, 2023, 56 (6): 31- 39.

23王浩远, 贾科, 毕天姝, 等. 基于故障及其恢复特征的光伏逆变器电流环控制参数辨识方法[J]. 电网技术, 2025, 49 (6): 2265- 2273.

WANG Haoyuan, JIA Ke, BI Tianshu, et al. Identification method of current loop control parameters of PV inverter based on fault and its recovery characteristic[J]. Power System Technology, 2025, 49 (6): 2265- 2273.

24沈欣炜, 郑竞宏, 朱守真, 等. 光伏并网逆变器控制参数的dq 轴解耦辨识策略[J]. 电力系统自动化, 2014, 38 (4): 38- 43.

SHEN Xinwei, ZHENG Jinghong, ZHU Shouzhen, et al. A dq axis decoupling parameter identification strategy for grid-connected inverter controller of photovoltaic generation system[J]. Automation of Electric Power Systems, 2014, 38 (4): 38- 43.

25秦继朔, 贾科, 孔繁哲, 等. 基于寻优算法的永磁风机并网逆变器故障穿越控制参数分步辨识[J]. 中国电机工程学报, 2021, 41 (S1): 59- 69.

QIN Jishuo, JIA Ke, KONG Fanzhe, et al. Stepwise parameter identification of fault ride-through control parameters of PMSG grid-connected inverter based on optimization algorithm[J]. Proceedings of the CSEE, 2021, 41 (S1): 59- 69.

26朱益华, 李卫星, 李成翔, 等. 基于频域通用建模的双馈风电机组参数的一体化辨识方法[J]. 电力系统自动化, 2024, 48 (15): 92- 101.

ZHU Yihua, LI Weixing, LI Chengxiang, et al. Integrated identification method for parameters of doubly-fed wind turbines based on general modeling in frequency domain[J]. Automation of Electric Power Systems, 2024, 48 (15): 92- 101.

27乔腾, 张益铭, 曹一家, 等. 基于概率可靠性评估的永磁直驱风机低电压穿越控制模型参数辨识[J]. 中国电力, 2021, 54 (12): 102- 111.

QIAO Teng, ZHANG Yiming, CAO Yijia, et al. Parameter identification of low voltage ride-through control model for permanent magnet direct-drive wind turbine based on probabilistic reliability assessment[J]. Electric Power, 2021, 54 (12): 102- 111.

28张晓英, 何蓉, 史冬雪, 等. 基于组合模型的风电高渗透电力系统区域惯量辨识[J]. 电力系统保护与控制, 2025, 53 (22): 100- 110.

ZHANG Xiaoying, HE Rong, SHI Dongxue, et al. Regional inertia identification of high wind power penetration power systems based on a combination model[J]. Power System Protection and Control, 2025, 53 (22): 100- 110.

29陈波, 斯琪, 谌艳红, 等. 基于广义奈奎斯特判据的新能源多场站系统小信号稳定薄弱并网单元辨识[J]. 电力科学与技术学报, 2024, 39 (5): 129- 140.

CHEN Bo, SI Qi, CHEN Yanhong, et al. Identification of grid-connected units with the weakest small-signal stability in multiple renewable energy stations based on generalized Nyquist criterion[J]. Journal of Electric Power Science and Technology, 2024, 39 (5): 129- 140.

30肖琦, 陆俊, 孙霄羽, 等. 基于插值优化的联邦学习异常用电辨识研究[J]. 电力信息与通信技术, 2025, 23 (1): 1- 9.

XIAO Qi, LU Jun, SUN Xiaoyu, et al. Research on abnormal power consumption identification of federal learning based on interpolation optimization[J]. Electric Power Information and Communication Technology, 2025, 23 (1): 1- 9.

31邓俊, 崔浩瀚, 晁璞璞, 等. 基于主导特性解析的双馈风机控制器参数高效并行辨识方法[J]. 电力自动化设备, 2025, 45 (4): 60- 66.

DENG Jun, CUI Haohan, CHAO Pupu, et al. Efficient parallel identification method of DFIG controller parameters based ondominant response characteristic analysis[J]. Electric Power Automation Equipment, 2025, 45 (4): 60- 66.

32NB/T 31053—2021 风电机组电气仿真模型验证规程[S].

NB/T 31053—2021 Code of practice for electrical simulation model validation of wind turbines[S].

33GB/T 19963.1—2021 风电场接入电力系统技术规定第1部分: 陆上风电[S].

GB/T 19963.1—2021 Technical specification for connecting wind farm to power system: Part 1: On shore wind power[S].

34王潇桐, 王彤, 邓俊, 等. 光伏逆变器机电暂态模型的控制模式及参数一体化辨识策略[J]. 电网技术, 2023, 47 (9): 3547- 3558.

WANG Xiaotong, WANG Tong, DENG Jun, et al. Control mode and parameter integration identification of photovoltaic inverter electromechanical transient model[J]. Power System Technology, 2023, 47 (9): 3547- 3558.

35张伟骏, 陈文龙, 黄霆, 等. 基于轨迹灵敏度的同步发电机参数辨识[J]. 中国电力, 2019, 52 (9): 102- 109.

ZHANG Weijun, CHEN Wenlong, HUANG Ting, et al. Parameter identification method of synchronous generator based on trajectory sensitivity[J]. Electric Power, 2019, 52 (9): 102- 109.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Electromagnetic Transient Modelling and Verifying of Wind Turbine Generator

Parameter Identification Method of Synchronous Generator Based on Trajectory Sensitivity

Related Author

Yanxia SUN

Lina LI

Caiyun GAO

Junhua MA

Liping GAO

Keke LIU

Guoqing HE

Guanghui LI

Related Institution

State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems (China Electric Power Research Institute)

State Grid Fujian Electric Power Research Institute

College of Electronic and Information Engineering, Tongji University

Dispatching and Control Center of State Grid

Beijing Fengtai Power Supply Company

AI问答

⁰