Research on Doubly-Fed Wind Turbine Shaft System With Virtual Inertia for Torsional Vibration Analysis

doi:10.12096/j.2096-4528.pgt.24015

Abstract

Abstract:

Objectives Shafting mathematical model of wind turbine is the basis of torsional vibration characteristics analysis. Shafting model is very important for the reliability of analysis results. In order to study the influence of virtual inertia on the torsional vibration characteristics of transmission chain more accurately, the modeling of doubly-fed wind turbine shaft system with virtual inertia control is studied. Methods After the simulation model is determined, a damping controller is designed, which can suppress the torsional vibration of the shafting caused by virtual inertia control. Firstly, by establishing the mathematical model of various shafting, the dynamic model of fan system based on virtual inertia control is established, and the key state variables affecting the torsional vibration characteristics of the shafting are determined by modal analysis method. Secondly, the applicability of different shafting models of wind turbine is studied from two aspects : torsional vibration characteristics of wind turbine with virtual inertia control and dynamic interaction between wind turbine shafting and synchronous machine. Finally, the effectiveness of the conclusion and the damping controller is verified by simulation analysis. Results The use of virtual inertia control can effectively improve the system inertia attenuation caused by wind power grid connection, but it will also reduce the damping ratio of the system. The two-mass model has stronger observability of torsional vibration mode, and the two-mass shafting model is more sensitive to the change of virtual inertia parameters. The torsional vibration of the shafting of the three-mass model changes more than that of the two-mass model. Conclusions The two-mass shaft system model with virtual inertia control is more suitable for torsional vibration analysis of wind turbine shaft systems.

Key words: doubly-fed wind turbine, wind power grid connection, torsional vibration characteristics, virtual inertia control, damping controller, low-frequency oscillation, band-pass filter, shorted fault, wind turbine shaft system, power grid support

CLC Number:

TK 83

Zhenglong SUN, Jiqiang SAN. Research on Doubly-Fed Wind Turbine Shaft System With Virtual Inertia for Torsional Vibration Analysis[J]. Power Generation Technology, 2025, 46(2): 314-325.

Figures/Tables 24

Fig. 1 Double-fed wind turbine stucture

Fig. 2 Double-fed wind turbine shaft system three-mass model

Fig. 3 Three-mass model shaft system control process

Fig. 4 Double-fed wind turbine shaft system two-mass model

Fig. 5 Two-mass model shaft system control process

Fig. 6 Virtual inertia control chart

Fig. 7 DFIG wind turbine grid-connected system with virtual inertia control

Fig. 8 Structural diagram of double-fed fan shaft system and SG grid connection with virtual inertia

Fig. 9 Structure of grid-connected system

Fig. 10 Dynamic response curve of a system with virtual inertia after a short-circuit fault

Fig. 11 Diagram of test system

Tab. 1 Axis modalities for different axis system models

轴系系统模型	阻尼比	振荡频率/Hz
2质量块	0.3	2.103
3质量块	0.05	1.686
3质量块	0.12	2.195

Tab. 2 Participation factors of oscillation modes of different axis systems

状态变量	2质量模型轴系模态	3质量模型
状态变量	2质量模型轴系模态	轴系模态1	轴系模态2
dphi23	0	0.012	1
Speed_tur	1	1	0.905
speed_2	0.457	0.511	0.118
dphi12	0.906	0.078	0.013
speed_3	0.013	0.428	0

Fig. 12 System response curve after short circuit without virtual inertia

Fig. 13 Torsional angle variation of shafting system without virtual inertia after short-circuit fault

Fig. 14 Dynamic response curve of a system with virtual inertia after a short-circuit fault

Fig. 15 Torsional angle variation of shafting system with virtual inertia after short-circuit fault

Fig. 16 DFIG speed response curves for different shaft

Fig. 17 Change of shaft damping ratio of three-mass model different Kd

Fig. 18 Change of shaft damping ratio of two-mass model different Kd

Fig. 19 Variation of shaft system damping ratio of 3-mass model under different filter time constants

Fig. 20 Variation of shaft system damping ratio of 2-mass model under different filter time constants

Fig. 21 System frequency variation graph

Fig. 22 Variation curve of rotor speed of doubly-fed generator

References 24

1	汲国强，吴姗姗，谭显东，等．2021年我国电力供需形势分析及展望[J]．发电技术，2021，42(5)：568-575． doi:10.12096/j.2096-4528.pgt.21103
	JI G Q， WU S S， TAN X D，et al ．Analysis and prospect of China’s power supply and demand situation in 2021[J]．Power Generation Technology，2021，42(5)：568-575． doi:10.12096/j.2096-4528.pgt.21103
2	孙正龙，李浩博，刘铖，等．含虚拟惯量的双馈风电机组扭振阻尼特性分析与抑制方法研究[J]．电网技术，2021，45(12)：4671-4682．
	SUN Z L， LI H B， LIU C，et al ．Research methods forsubsynchronous oscillation induced by wind power under weak AC system：a review[J]．Power System Technology，2021，45(12)：4671-4682．
3	王泽嘉，韩民晓，范溢文．基于SVG附加电流反馈阻抗重塑的双馈风电场次/超同步振荡抑制策略[J]．中国电力，2024，57(8)：55-66．
	WANG Z J， HAN M X， FAN Y W ．Suppression strategy of sub/super-synchronous oscillations in doubly-fed wind farm based on SVG additional current feedback impedance reshaping[J]．Electric Power，2024，57(8)：55-66．
4	申洪，周勤勇，刘耀，等．碳中和背景下全球能源互联网构建的关键技术及展望[J]．发电技术，2021，42(1)：8-19 ． doi:10.12096/j.2096-4528.pgt.20113
	SHEN H， ZHOU Q Y， LIU Y ．Key technologies and prospects for the construction of global energy internet under the background of carbon neutral[J]．Power Generation Technology，2021，42(1)：8-19． doi:10.12096/j.2096-4528.pgt.20113
5	林旭，李军，杨德健，等．基于功率轨迹预设的双馈风电机组虚拟惯量控制策略[J]．智慧电力，2023，51(10)：47-53． doi:10.3969/j.issn.1673-7598.2023.10.007
	LIN X， LI J， YANG D J，et al ．Virtual inertia control strategy for doubly-fed induction generator based on preset power trajectory[J]．Smart Power，2023，51(10)：47-53． doi:10.3969/j.issn.1673-7598.2023.10.007
6	李雪萍，王自力，陈燕东，等．基于虚拟惯量模糊自适应的新能源逆变器频率主动支撑策略[J]．电力系统保护与控制，2024，52(20)：25-37．
	LI X P， WANG Z L， CHEN Y D，et al ．Active frequency support strategy for new energy inverters based on virtual inertia fuzzy adaptive control[J]．Power System Protection and Control，2024，52(20)：25-37．
7	蒋小亮，李元臣，郝元钊，等．计及新能源虚拟惯量的电力系统等效惯量评估[J]．电力科学与技术学报，2023，38(4)：169-176．
	JIANG X L， LI Y C， HAO Y Z，et al ．Evaluation of power system equivalent inertia considering new energy virtual inertia[J]．Journal of Electric Power Science and Technology，2023，38(4)：169-176．
8	张祥宇，胡剑峰，付媛，等．风储联合系统的虚拟惯量需求与协同支撑[J]．电工技术学报，2024，39(3)：672-685．
	ZHANG X Y， HU J F， FU Y，et al ．Virtual inertia demand and collaborative support of wind power and energy storage system[J]．Transactions of China Electrotechnical Society，2024，39(3)：672-685．
9	MUYEEN S M， HASANALI M D， TAKAHASHI R，et al ．Comparative study on transient stability analysis of wind turbine generator system using different drive train models[J]．IET Renew Power Generation，2007，1(2)：131-141． doi:10.1049/iet-rpg:20060030
10	VIJAY P S， NAND K， PAULAON S，et al ．Small-signal stability analysis for two-mass and three-mass shaft model of wind turbine integrated to thermal power[J]．Computers and Electrical Engineering，2019，1(2)：271-287．
11	徐筱倩，黄林彬，汪震，等．双馈风电机组虚拟惯量控制对电力系统机电振荡的影响分析[J]．电机与控制学报，2019，43(12)：11-17．
	XU X Q， HUANG L B， WANG Z，et al ．Analysis on impact of virtual inertia control of DFIG-based wind turbine on electromechanical oscillation of power system[J]．Automation of Electric Power Systems，2019，43(12)：11-17．
12	章德，刘峰，梅生伟，等．双馈风机虚拟惯量控制对传动系统扭振影响[J]．电机与控制学报，2015，19(10)：78-86．
	ZHANG D， LIU F， MEI S W，et al ．Effect of virtual inertia control on drive train torsional vibrations in dou-bly fed wind turbines[J]．Electrical Machines and Control，2015，19(10)：78-86．
13	边晓燕，刘洁，周歧斌．DFIG虚拟惯量控制对轴系振荡的影响[J]．太阳能学报，2022，41(10)：285-291．
	BIAN X Y， LIU J， ZHOU Q B ．Effect of DFIG virtual inertia control on shaft system oscillation[J]．Acta Energiae Solaris Sinica，2022，41(10)：285-291．
14	娄宇成，解大，冯俊淇，等．基于三质量块模型的失速型风机小信号建模和模态分析[J]．电力自动化设备，2015，35(8)：124-130．
	LOU Y C， XIE D， FNEG J Q，et al ．Small-signal modeling and modal analysis based on three-mass shaft model for stall wind turbine[J]．Electric Power Automation Equipment，2015，35(8)：124-130．
15	奚鑫泽，耿华，杨耕．含主动轴系扭振阻尼的并网双馈风电场惯量控制方法[J]．电工技术学报，2017，32(6)：136-144．
	XI X Z， GENG H， YANG G ．Inertia control of the grid connected doubly fed induction generator based wind farm with drive train torsion active damping[J]．Transactions of China Electromechanical Society，2017，32(6)：136-144．
16	薛安成，王清，毕天姝．双馈风机与同步机小扰动功角互作用机理分析[J]．中国电机工程学报，2016，36(2)：417-425． doi:10.13334/j.0258-8013.pcsee.2016.02.012
	XUE A C， WANG Q， BI T S ．Study on the mechanism of small signal dynamic interaction between doubly-fed induction generator and synchronous generator[J]．Proceedings of the CSEE，2016，36(2)：417-425． doi:10.13334/j.0258-8013.pcsee.2016.02.012
17	LAKS J H， PAO L Y， WRIGHT A D ．Control of wind turbines：past，present，and future[C]//American Control Conference．St. Louis，MO，USA：IEEE，2009：2096-2103． doi:10.1109/acc.2009.5160590
18	朱兴林．并网运行的直驱风力发电系统建模及仿真研究[J]．工业控制计算机，2011，24(8)：48-52． doi:10.3969/j.issn.1001-182X.2011.08.027
	ZHU X L ．Modeling and simulation of grid connected direct-driven wind power generation system[J]．Industrial Control Computer，2011，24(8)：48-52． doi:10.3969/j.issn.1001-182X.2011.08.027
19	KRPAN M， KUZLE I ．Transient stability analysis of grid connected wind turbine generator system considering multi-mass shaft modeling[J]．Electric Power Systems Research，2020，178：106005． doi:10.1016/j.epsr.2019.106005
20	邢鹏翔，侍乔明，王刚，等．风电机组虚拟惯量控制的响应特性及机理分析[J]．高电压技术，2018，44(4)：1302-1310． doi:10.13336/j.1003-6520.hve.20180329033
	XING P X， SHI Q M， WANG G，et al ．Response characteristics and mechanism analysis of virtual inertia control of wind turbine[J]．High Voltage Technology，2018，44(4)：1302-1310． doi:10.13336/j.1003-6520.hve.20180329033
21	王刚，侍乔明，付立军，等．虚拟惯量控制方式下永磁风力发电机组轴系扭振机理分析[J]．电机与控制学报，2014，18(8)：8-16． doi:10.3969/j.issn.1007-449X.2014.08.002
	WANG G， SHI Q M， FU L J，et al ．Mechanism analysis of torsional vibration for directly-driven wind turbine with permanent magnet synchronous generator shaft system with virtual inertia control[J]．Electrical Machines and Control，2014，18(8)：8-16． doi:10.3969/j.issn.1007-449X.2014.08.002
22	张祥宇，朱正振，付媛．风电并网系统的虚拟同步稳定分析与惯量优化控制[J]．高电压技术，2020，46(8)：2922-2932．
	ZHANG X Y， ZHU Z Z， FU Y，et al ．Virtual synchronous stability analysis and inertia optimization control of wind power grid-connected systems[J]．High Voltage Technology，2020，46(8)：2922-2932．
23	张祥宇，付媛，王毅，等．含虚拟惯性与阻尼控制的变速风电机组综合PSS控制器[J]．电工技术学报，2015，30(1)：159-169． doi:10.3969/j.issn.1000-6753.2015.01.021
	ZHANG X Y， FU Y， WANG Y ．Integrated PSS controller of variable speed wind turbines with virtual inertia and damping control[J]．Transactions of China Electromechanical Society，2015，30(1)：159-169． doi:10.3969/j.issn.1000-6753.2015.01.021
24	王鹏敏．并网双馈感应风电机组的稳定性与轴系扭振特性的研究[D]．太原：太原理工大学，2011．
	WANG P M ．Study of stability and shaft torsional vibration characteristics of grid-connected doubly-fed induction wind turbines[D]．Taiyuan：Taiyuan University of Technology，2011．

[1]	Beilei REN, Kunli GUO, Weizheng CAI, Bohao LI, Tinghua ZHU, Lingtao LI. Subsynchronous Oscillation Suppression of Direct-Drive Wind Turbines Based on Improved Linear Active Disturbance Rejection Control and Analysis of Impedance Stability [J]. Power Generation Technology, 2024, 45(6): 1135-1145.
[2]	Zhan LIU, Jianxun LIU, Yanyang BAO, Dazi LI. Bearing Faults Diagnosis Method Based on Stacked Auto-Encoder With Graph Regularization for Wind Turbines [J]. Power Generation Technology, 2024, 45(6): 1146-1152.
[3]	Lidong ZHANG, Hao TIE, Huiwen LIU, Qinwei LI, Wenxin TIAN, Xiuyong ZHAO, Zihan CHANG. Experimental Study on the Influence of Wind Turbine Yaw on Wake Evolution [J]. Power Generation Technology, 2024, 45(6): 1153-1162.
[4]	Ang FAN, Luping LI, Rui LIU, Minnan OUYANG, Shangnian CHEN. Research on Dynamic Characteristics of Monopile Offshore Wind Turbine Tower Under Different Wind Speed Conditions [J]. Power Generation Technology, 2024, 45(2): 312-322.
[5]	Zhipeng QIN, Gaosheng WEI, Liu CUI, Xiaoze DU. Study on Aerodynamic Performance of Wind Turbine Airfoil With Combined S-Slot and Trailing Edge Flap Control [J]. Power Generation Technology, 2024, 45(1): 24-31.
[6]	Shuai XU, Yufei YANG, Ao GANG, Yuetao XIE, Xiaoming ZHANG, Gongpeng LIU. Research on Key Technologies and Industrial Chain Cooperation Paths of Floating Offshore Wind Power Between China and Europe [J]. Power Generation Technology, 2024, 45(1): 13-23.
[7]	Xinrong YAN, Ningning ZHANG, Kuichao MA, Chao WEI, Shuai YANG, Binbin PAN. Overview of Current Situation and Trend of Offshore Wind Power Development in China [J]. Power Generation Technology, 2024, 45(1): 1-12.
[8]	Zhan LIU, Yanyang BAO, Dazi LI. Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis [J]. Power Generation Technology, 2023, 44(6): 824-832.
[9]	Caixin SUN, Bo ZHANG, Wei TANG, Yiming ZHOU, Mingzhi FU, Meng QIN, Xiaojiang GUO. Research and Practice on Localization of Offshore Wind Turbines [J]. Power Generation Technology, 2023, 44(5): 696-702.
[10]	Wenhu JIA, Qunjie XU. Research Progress of Anti-Corrosion Technology for Offshore Wind Power Facilities [J]. Power Generation Technology, 2023, 44(5): 703-711.
[11]	Ronghui WU, Dong LIU, Ye YU, Kailong MU, Lanhao ZHAO. Two-Way Fluid-Structure Interaction Numerical Simulation Method for Offshore Wind Power Based on Immersed Boundary Method [J]. Power Generation Technology, 2023, 44(1): 44-52.
[12]	Ang FAN, Luping LI, Shihai ZHANG, Minnan OUYANG, Xiankui WEN, Shangnian CHEN. A Review on Dynamic Characteristics and Life Loss of Large Wind Turbine Towers [J]. Power Generation Technology, 2022, 43(3): 421-430.
[13]	Fang FANG, Dongyang LIANG, Yajuan LIU, Yang HU, Jizhen LIU. Key Technologies for Intelligent Control and Operation and Maintenance of Offshore Wind Power [J]. Power Generation Technology, 2022, 43(2): 175-185.
[14]	Zheng LI, Xiaojiang GUO, Xuhui SHEN, Haiyan TANG. Summary of Technologies for the Development of Offshore Wind Power Industry in China [J]. Power Generation Technology, 2022, 43(2): 186-197.
[15]	Danmei HU, Li ZENG, Yunhao CHEN. Analysis of Fluid-Structure Coupling Characteristics of Semi-submersible Offshore Wind Turbines [J]. Power Generation Technology, 2022, 43(2): 218-226.