Dynamic Digital Twin Modelling and Semi-Physical Simulation of Wind Turbine Operation

doi:10.16182/j.issn1004731x.joss.22-1307

Abstract

Abstract:

For the accurate mapping and real-time simulation requirements proposed by digital twin technology, a multi-input multi-output (MIMO) finite difference domain-hybrid semi-mechanical (FDD-HSM) digital twin modeling method is proposed, and a semi-physical simulation system of wind turbine digital twin with physical controller is established for the complex nonlinear operation characteristics of large wind turbines. The integrated dynamic MIMO-FDD-HSM model structure is constructed. Finite difference regression vectors are defined to characterize the operating conditions of the wind turbine, and finite difference space tight convex partitioning, parametric model identification, and non-parametric model training are completed under full operating conditions. The wind turbine digital twin simulation system is built in conjunction with the physical controller to carry out real-time hardware-in-the-loop simulation. The results show that the simulation system has high accuracy and real-time approximation capability.

Key words: wind power generation, digital twin, hybrid semi-mechanical, finite difference domain, semi-physical simulation

CLC Number:

TP391.9

Hu Yang, Wang Weiran, Fang Fang, Song Ziqiu, Xu Yuhan, Liu Jizhen. Dynamic Digital Twin Modelling and Semi-Physical Simulation of Wind Turbine Operation[J]. Journal of System Simulation, 2024, 36(3): 636-648.

Figures/Tables 18

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 1

Fig. 5

Table 2

Table 3

Table 4

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

Table 5

References 26

1	国家能源局. 2021年全国电力工业统计数据发布[N]. 国家电网报, 2022-01-27(001).
2	蒲天骄, 陈盛, 赵琦, 等. 能源互联网数字孪生系统框架设计及应用展望[J]. 中国电机工程学报, 2021, 41(6): 2012-2029.
	Pu Tianjiao, Chen Sheng, Zhao Qi, et al. Framework Design and Application Prospect for Digital Twins System of Energy Internet[J]. Proceedings of the CSEE, 2021, 41(6): 2012-2029.
3	Zhu Yinzhu, Mi Yang. The Study of Variable Speed Variable Pitch Controller for Wind Power Generation Systems Based on Sliding Mode Control[C]//2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA). Piscataway, NJ, USA: IEEE, 2016: 415-420.
4	Prajapat Ganesh P, Senroy Nilanjan, Narayan Kar Indra. Wind Turbine Structural Modeling Consideration for Dynamic Studies of DFIG Based System[J]. IEEE Transactions on Sustainable Energy, 2017, 8(4): 1463-1472.
5	Wang Long, Zhang Zijun, Long Huan, et al. Wind Turbine Gearbox Failure Identification with Deep Neural Networks[J]. IEEE Transactions on Industrial Informatics, 2017, 13(3): 1360-1368.
6	魏乐, 胡晓东, 尹诗. 基于优化XGBoost的风电机组发电机前轴承故障预警[J]. 系统仿真学报, 2021, 33(10): 2335-2343.
	Wei Le, Hu Xiaodong, Yin Shi. Optimized-XGBoost Early Warning of Wind Turbine Generator Front Bearing Fault[J]. Journal of System Simulation, 2021, 33(10): 2335-2343.
7	Hsu M C, Akkerman I, Bazilevs Y. Finite Element Simulation of Wind Turbine Aerodynamics: Validation Study Using NREL Phase VI Experiment[J]. Wind Energy, 2014, 17(3): 461-481.
8	Serret J, Rodriguez C, Tezdogan T, et al. Code Comparison of a NREL-FAST Model of the Levenmouth Wind Turbine with the GH Bladed Commissioning Results[C]//ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. New York, NY, USA: ASME, 2018: V010T09A054.
9	Eduardo José Novaes Menezes, Alex Maurício Araújo, Janardan Singh Rohatgi, et al. Active Load Control of Large Wind Turbines Using State-Space Methods and Disturbance Accommodating Control[J]. Energy, 2018, 150: 310-319.
10	Ebadollahi Saeed, Saki Saman. Wind Turbine Torque Oscillation Reduction Using Soft Switching Multiple Model Predictive Control Based on the Gap Metric and Kalman Filter Estimator[J]. IEEE Transactions on Industrial Electronics, 2018, 65(5): 3890-3898.
11	Pan Xueping, Ju Ping, Wu Feng, et al. Hierarchical Parameter Estimation of DFIG and Drive Train System in a Wind Turbine Generator[J]. Frontiers of Mechanical Engineering, 2017, 12(3): 367-376.
12	潘晨阳, 胡阳, 奚芸华. 大型风机主导机械动态的智能灰箱建模及其线性状态空间表征[J]. 控制理论与应用, 2020, 37(6): 1260-1269.
	Pan Chenyang, Hu Yang, Xi Yunhua. Intelligent Grey-box Modeling and Linear State-Space Representation of Dominating Mechanical Dynamics for Large-scale Wind Turbine[J]. Control Theory & Applications, 2020, 37(6): 1260-1269.
13	Zhou Mike, Yan Jianfeng, Feng Donghao. Digital Twin Framework and Its Application to Power Grid Online Analysis[J]. CSEE Journal of Power and Energy Systems, 2019, 5(3): 391-398.
14	房方, 姚贵山, 胡阳, 等. 风力发电机组数字孪生系统[J]. 中国科学(技术科学), 2022, 52(10): 1582-1594.
	Fang Fang, Yao Guishan, Hu Yang, et al. Digital Twin System of a Wind Turbine[J]. Scientia Sinica(Technologica), 2022, 52(10): 1582-1594.
15	Fahim M, Sharma V, Cao T V, et al. Machine Learning-based Digital Twin for Predictive Modeling in Wind Turbines[J]. IEEE Access, 2022, 10: 14184-14194.
16	徐樾, 贾立, 付轩熠. 基于Wiener模型的风力发电系统变桨距控制[J]. 系统仿真学报, 2022, 34(8): 1741-1749.
	Xu Yue, Jia Li, Fu Xuanyi. Variable Pitch Control of Wind Power Generation System Based on Wiener Model[J]. Journal of System Simulation, 2022, 34(8): 1741-1749.
17	Dong Cheol Shin, Dong Myung Lee. Development of Real-Time Implementation of a Wind Power Generation System with Modular Multilevel Converters for Hardware in the Loop Simulation Using MATLAB/Simulink[J]. Electronics, 2020, 9(4): 606.
18	Lakshmi Narayanan V, Ramakrishnan R. Design and Implementation of an Intelligent Digital Pitch Controller for Digital Hydraulic Pitch System Hardware-in-the-loop Simulator of Wind Turbine[J]. International Journal of Green Energy, 2021, 18(1): 17-36.
19	Chu Jingchun, Yuan Ling, Hu Yang, et al. Comparative Analysis of Identification Methods for Mechanical Dynamics of Large-scale Wind Turbine[J]. Energies, 2019, 12(18): 3429.
20	姚琦. 风电场有功调度与频率支撑优化控制研究[D]. 北京: 华北电力大学, 2020.
	Yao Qi. Research on Active Power Dispatching and Frequency Optimization Control of Wind Farm[D]. Beijing: North China Electric Power University, 2020.
21	候文昌. 风电机组机械侧运行动态建模研究[D]. 北京: 华北电力大学, 2021.
	Hou Wenchang. Research on Dynamic Modeling of Mechanical Operation of Wind Turbine[D]. Beijing: North China Electric Power University, 2021.
22	王会盼, 刘吉臻, 胡阳, 等. 基于预测控制的大惯量风机全工况功率调度跟踪[J]. 电网技术, 2020, 44(7): 2520-2528.
	Wang Huipan, Liu Jizhen, Hu Yang, et al. Power Dispatching Tracking of Large-inertia Wind Turbine Under Full Operating Conditions Based on Predictive Control[J]. Power System Technology, 2020, 44(7): 2520-2528.
23	Hure Nikola, Vašak Mario. Clustering-based Identification of MIMO Piecewise Affine Systems[C]//2017 21st International Conference on Process Control (PC). Piscataway, NJ, USA: IEEE, 2017: 404-409.
24	史运涛, 杨震安, 李志军, 等. 基于数据驱动的混杂系统建模与优化控制研究[J]. 系统仿真学报, 2013, 25(11): 2709-2716.
	Shi Yuntao, Yang Zhenan, Li Zhijun, et al. Method of Hybrid System Modeling and Optimizing Control Based on Data-driven[J]. Journal of System Simulation, 2013, 25(11): 2709-2716.
25	胡阳, 简睿妮, 房方. 基于FDD–HSM方法的复杂拓扑供热管道动态等值建模[J]. 控制理论与应用, 2022, 39(3): 509-518.
	Hu Yang, Jian Ruini, Fang Fang. Dynamic Equivalent Modelling of Complex Topological Heating Pipeline Based on Finite Difference Domain-hybrid Semi-mechanism Method[J]. Control Theory & Applications, 2022, 39(3): 509-518.
26	Merker Jochen. On Sparsity of Soft Margin Support Vector Machines[J]. Journal of Advances in Applied Mathematics, 2017, 2(3): 109-114.

参数	量值
额定功率/MW	5
切入风速/(m/s)	3
额定风速/(m/s)	11.4
切出风速/(m/s)	25
切入转子转速/(r/min)	6.9
额定转子转速/(r/min)	12.1
齿轮箱传动比N_g	97
低速轴转动惯量J_r/(kN·m²)	38 677 060
高速轴转动惯量J_g/(kN·m²)	534.116
传动系统刚度系数A_stif/(kN·m/rad)	867 637
传动系统阻尼系数B_damp/(kN·m/(rad/s))	6 215
风轮半径R/m	63
发电机效率η/%	94.4
空气密度ρ/(kg/m³)	1.225

系数	χ₃-χ₂	χ₂-χ₁
b₁	24.479 2	909.366 0
b₂	9 277.357 0	0.354 6
b₃	7 278.462 5	13 914.124 6
b₄	24.455 4	909.119 7
b₅	9 267.522 0	0.352 0
b₆	7 289.170 3	13 912.810 6
b₇	-753.087 1	66 140.236 8
b₈	-317 229.361 4	6 703 903.171 6
b₉	-324 452.375 8	6 702 204.424 9
b₁₀	-330 375.563 8	6 700 569.554 1
b₁₁	-334 280.769 2	6 698 982.689 2

系数	χ₁	χ₂	χ₃
J_r	42 645 023.35	31 829 148.45	32 674 067.85
J_g	592.48	554.35	533.94
A_stif	867 613	867 675	867 637
B_damp	6 270.20	6 212.43	6 215.25
a₁	-21 322.51	-15 550.09	-15 962.88
a₂	-21 322.51	-15 549.39	-15 962.16
b₁	1 749 998.36	2 399 348.05	2 463 039.85
b₂	-1 590 591.38	-2 585 319.39	-2 653 947.89
c₁	-848.17	-6 217.85	-6 382.91
c₂	-519.48	-6 216.67	-6 381.70
d₁	-6.26	-6.80	-6.98
d₂	28.93	5.49	5.64
d₃	-16.64	-18.58	-19.08
d₄	29.88	23.57	24.20

子模型域	J_r	J_g	A_stif	B_damp
χ₁	-10.26	-10.93	3.19e-9	-1.43e-4
χ₂	17.71	-3.79	-5.05e-9	6.65e-6
χ₃	15.52	6.17e-5	0	-6.47e-6

工作域		RMSE	MAE	MAPE/%
场景1	ω_g	0.016 1	0.008 9	0.011 2
场景1	T_shaft	23.881 1	17.715 8	2.317 2
场景2	ω_g	0.225 5	0.159 8	0.130 0
场景2	T_shaft	63.366 6	45.298 5	1.091 3
场景3	ω_g	0.378 4	0.176 8	0.1466
场景3	T_shaft	64.534 4	38.493 3	1.154 6
场景4	ω_g	0.194 4	0.132 9	0.108 6
场景4	T_shaft	45.903 5	33.646 9	0.861 2