基于改进黑洞算法的DFIG控制参数分步辨识方法

曹晓庆; 娄世元; 李鹤; 马文杰; 杨荣欣; 李致

doi:10.19912/j.0254-0096.tynxb.2025-0058

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (6) : 252-260. DOI: 10.19912/j.0254-0096.tynxb.2025-0058

基于改进黑洞算法的DFIG控制参数分步辨识方法

曹晓庆¹, 娄世元², 李鹤¹, 马文杰¹, 杨荣欣¹, 李致¹

作者信息 +

STEP-BY-STEP IDENTIFICATION METHOD OF DFIG CONTROL PARAMETERS BASED ON IMPROVED BLACK HOLE ALGORITHM

Cao Xiaoqing¹, Lou Shiyuan², Li He¹, Ma Wenjie¹, Yang Rongxin¹, Li Zhi¹

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文章历史 +

摘要

为解决现有双馈感应发电机（DFIG）转子侧变流器（RSC）PI控制参数辨识速度、精度不足以及参数较多的问题,提出一种利用改进黑洞算法（IBHA）与RT-LAB半实物仿真平台来进行分步辨识的方法。首先,基于RSC控制方程,结合RT-LAB硬件在环（HIL）试验平台采集DFIG实际控制器的运行特征数据;然后,为确定不同参数的响应能力计算各参数的轨迹灵敏度,确定最佳观测量并分析其可辨识性;再次,以RT-LAB实测数据与IBHA为基础建立分步辨识策略;最后,以HIL测试数据为依据,对比分析几种算法的辨识结果,证明了所提方法的有效性。

Abstract

To address the issues of low efficiency, limited precision, and multiple parameters in existing PI controller identification methods for rotor-side converters （RSC） of doubly-fed induction generators （DFIG）, a hierarchical identification framework combining an improved black hole algorithm （IBHA） with the RT-LAB hardware-in-the-loop （HIL） co-simulation platform is proposed. Firstly, based on the RSC control dynamics, the operational characteristic data of the actual controller of DFIG is collected by the RT-LAB HIL co-simulation platform. Secondly, a trajectory sensitivity analysis is performed to quantify the dynamic response characteristics of each parameter, followed by the selection of optimal observables and a rigorous identifiability assessment based on persistent excitation criteria. Thirdly, a stepwise identification strategy is established based on the RT-LAB measured data and IBHA. Finally, based on the HIL test data, the identification results of several algorithms are compared and analyzed, which verifies the effectiveness of the proposed method.

导出引用

曹晓庆, 娄世元, 李鹤, 马文杰, 杨荣欣, 李致. 基于改进黑洞算法的DFIG控制参数分步辨识方法[J]. 太阳能学报. 2026, 47(6): 252-260 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0058

Cao Xiaoqing, Lou Shiyuan, Li He, Ma Wenjie, Yang Rongxin, Li Zhi. STEP-BY-STEP IDENTIFICATION METHOD OF DFIG CONTROL PARAMETERS BASED ON IMPROVED BLACK HOLE ALGORITHM[J]. Acta Energiae Solaris Sinica. 2026, 47(6): 252-260 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0058

中图分类号： TM614

参考文献

[1] 孙浩宁, 王德林, 李京华. 基于转速差补偿环的双馈风力机电网调频特性研究[J]. 电测与仪表, 2025, 62(8): 62-67.
SUN H N, WANG D L, LI J H.Study on characteristics of frequency regulation of DFIG based on D-value of rotor speed compensation loop in power grid[J]. Electrical measurement & instrumentation, 2025, 62(8): 62-67.
[2] 乔左江, 杜欣慧, 薛晴. 大规模风电参与系统调频研究综述[J]. 电测与仪表, 2023, 60(7): 1-12.
QIAO Z J, DU X H, XUE Q.Review of large-scale wind power participating in system frequency regulation[J]. Electrical measurement & instrumentation, 2023, 60(7): 1-12.
[3] 秦继朔, 贾科, 孔繁哲, 等. 基于寻优算法的永磁风力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 J S, JIA K, KONG F Z, 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.
[4] 赵正阳, 杜钦君, 凌辉, 等. 改进的同步磁阻电机模型预测转矩控制[J]. 太阳能学报, 2023, 44(6): 469-476.
ZHAO Z Y, DU Q J, LING H, et al.Improved model predictie torque control of synchronous reluctance motor[J]. Acta energiae solaris sinica, 2023, 44(6): 469-476.
[5] 李经纬. 双馈风电机组变流器控制系统参数辨识算法研究[D]. 天津: 河北工业大学, 2021.
LI J W.Research on parameter identification algorithms for the converter control system of doubly-fed wind turbine generators[D]. Tianjin: Hebei University of Technology, 2021.
[6] 吴碧巧, 曾实, 王天一. 面向双馈风机的分层型免疫协同进化粒子群算法多参数辨识[J]. 科学技术与工程, 2019, 19(33): 179-185.
WU B Q, ZENG S, WANG T Y.Multi-parameter identification of co-evolutionary particle swarm optimization algorithm based on hierarchical-particle immune for doubly fed induction generator[J]. Science technology and engineering, 2019, 19(33): 179-185.
[7] 董福杰, 刘颖明, 王晓东, 等. 基于寻优算法的双馈风力机变流器动态运行控制参数辨识[J]. 电力科学与工程, 2024, 40(3): 61-69.
DONG F J, LIU Y M, WANG X D, et al.Parameter identification of dynamic operation control of doubly-fed fan converter based on optimization algorithm[J]. Electric power science and engineering, 2024, 40(3): 61-69.
[8] 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 Q, WANG H, NIE Y H, 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.
[9] 吴昌友, 付熙松, 裴均珂. 基于信息共享搜索策略的自适应灰狼算法研究[J]. 电光与控制, 2022, 29(7): 22-28.
WU C Y, FU X S, PEI J K.Research on adaptive grey wolf algorithm based on information sharing search strategy[J]. Electronics optics & control, 2022, 29(7): 22-28.
[10] 杨繁杰. 双馈风电机组参数辨识及高压穿越研究[D]. 昆明: 昆明理工大学, 2023.
YANG F J.Research on parameter identification and high-voltage ride-through of doubly-fed wind turbine generators[D]. Kunming: Kunming University of Science and Technology, 2023.
[11] LI H, WU Y, LI Q H, et al.Improved identification method of doubly-fed induction generator based on trajectory sensitivity analysis[J]. International journal of electrical power & energy systems, 2021, 125: 106472.
[12] 潘学萍, 郭金鹏, 孙晓荣, 等. 双馈风电场频率响应特性的频域等值建模方法[J]. 电网技术, 2025, 49(6): 2465-2475.
PAN X P, GUO J P, SUN X R, et al.Frequency domain equivalent modelling of frequency response characteristics for DFIG-based wind farms[J]. Power system technology, 2025, 49(6): 2465-2475.
[13] 张剑, 何怡刚. 基于轨迹灵敏度分析的永磁直驱风电场等值模型参数辨识[J]. 电工技术学报, 2020, 35(15): 3303-3313.
ZHANG J, HE Y G.Parameters identification of equivalent model of permanent magnet synchronous generator wind farm based on analysis of trajectory sensitivity[J]. Transactions of China Electrotechnical Society, 2020, 35(15): 3303-3313.
[14] 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 R Q, PENG X T, QIN S Y, et al.Parameter identification of doubly-fed induction generator converter based on M-sequence[J]. Power system technology, 2022, 46(2): 578-586.
[15] CAI D S, SUN F Y, LI L L, et al.Accurate parameter identification method for coupled sub/super-synchronous oscillations for high penetration wind power systems[J]. ISA transactions, 2024, 150: 166-180.
[16] YANG F J, ZENG Y, QIAN J, et al.Parameter identification of doubly-fed induction wind turbine based on the ISIAGWO algorithm[J]. Energies, 2023, 16(3): 1355-1355.
[17] ZHENG W M, LIU N, CHAI Q W, et al.Application of improved black hole algorithm in prolonging the lifetime of wireless sensor network[J]. Complex & intelligent systems, 2023, 9(5): 5817-5829.
[18] 王育飞, 郝德扬, 薛花, 等. 计及云图和混沌特性的光伏功率组合预测方法[J]. 太阳能学报, 2023, 44(12): 74-81.
WANG Y F, HAO D Y, XUE H, et al.Combined forecasting approach of photovoltaic power based on cloud images and chaotic characteristics[J]. Acta energiae solaris sinica, 2023, 44(12): 74-81.
[19] 何伟, 陈薄, 贾清健, 等. 基于改进蛇优化算法的永磁同步电机电气参数辨识[J]. 重庆大学学报, 2024, 47(11): 81-93.
HE W, CHEN B, JIA Q J, et al.Electrical parameters identification of permanent magnet synchronous motor based on improved snake optimization algorithm[J]. Journal of Chongqing University (natural science edition), 2024, 47(11): 81-93.
[20] 徐恒山, 朱士豪, 黄永章, 等. 基于GRU-IPSO算法的双馈风机控制参数辨识[J]. 华北电力大学学报(自然科学版), 2025, 52(4): 70-80.
XU H S, ZHU S H, HUANG Y Z, et al.Control parameter identification for doubly fed induction generator based on GRU-IPSO algorithm[J]. Journal of North China Electric Power University(natural science edition), 2025, 52(4): 70-80.
[21] 方欣, 姚骏, 刘育明, 等. 双馈风电系统低压穿越期间的参数辨识方法研究[J]. 电工电能新技术, 2024, 43(2): 1-11.
FANG X, YAO J, LIU Y M, et al.Research on parameter identification method of DFIG wind power generation system during LVRT[J]. Advanced technology of electrical engineering and energy, 2024, 43(2): 1-11.