针对以往风电机组数字孪生建模受不同研究目的或单一软件的功能限制,难以建立风电机组整机模型的问题,提出一种新的风电机组孪生建模方法。该方法首先依托FAST风速性能模块,建立稳态风模型、随机湍流风模型以及风电场实时风速模型;接着采用空气动力学模块和结构动力学模块分别搭建风电机组叶片、塔架等关键部件的几何与动力学模型;最后在Simulink中搭建风电机组电气系统模型及控制策略,由此构建完整的风电机组孪生模型。将该孪生建模方法分别用于WindPACT 1.5 MW双馈风电机组与某风电场Fuhrländer 2.5 MW双馈风电机组并进行验证。结果表明：孪生模型在不同风速模型下,各重要生产参数相比设计标准及实际运行数据均具有较高的准确性。此外,通过对风电机组数字孪生系统实时仿真和现场不可测数据的孪生模拟,也进一步表明孪生模型具有可行性和有效性。
Abstract
In response to the problem that the previous digital twin modeling of the wind turbine is limited by different research purposes or the function of a single software, which makes it challenging to establish the whole model of the wind turbine, a new wind turbine twin modeling method is proposed. The method first relies on the FAST wind performance module to build the steady-state and the random turbulent wind model, the real-time wind speed model of wind farms. Then the aerodynamics module and the structural dynamics module are used to build the geometric and dynamics models of critical components such as wind turbine blade and towers respectively. Finally, the wind turbine electrical system model and control strategy were built in Simulink, which resulted in a complete twin model of the wind turbine. The twin modeling approach was validated for WindPACT 1.5 MW doubly-fed wind turbine and Fuhrländer 2.5 MW doubly-fed wind turbine at a wind farm, and the results showed that the twin model has a high accuracy of all essential production parameters compared to the design standard and actual operation data under different wind speed models. In addition, the feasibility and validity of the twin model were further demonstrated by real-time simulation of the wind turbine digital twin system and twin simulation of the unmeasurable data in the field.
关键词
风电机组 /
数字孪生 /
孪生建模 /
准确性 /
实时仿真 /
孪生模拟
Key words
wind turbines /
digital twin /
twin modeling /
accuracy /
real-time simulation /
twin simulation
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参考文献
[1] LEE J, ZHAO F, DUTTON A, et al.Global wind report 2019[C]//Global Wind Energy Council, Brussels, Belgium, 2020.
[2] DIAO J, ZHAO H, LI B, et al.Refined modeling and real-time simulation of DFIG based wind farm[C]//IEEE IAS Industrial and Commercial Power System Asia, Weihai, China, 2020.
[3] 苗风麟, 施洪生, 张小青. 风力发电机组多领域耦合模型及动态响应分析[J]. 中国电机工程学报, 2015, 35(7): 1704-1712.
MIAO F L, SHI H S, ZHANG X Q.Modelling of wind turbines coupled in multi-domain and dynamic response analysis[J]. Proceedings of the CSEE, 2015, 35(7): 1704-1712.
[4] GRIEVES M.Digital twin: manufacturing excellence through virtual factory replication[J]. White paper, 2014, 1: 1-7.
[5] TAO F, CHENG J F, QI Q L, et al.Digital twin-driven product design, manufacturing and service with big data[J]. The international journal of advanced manufacturing technology, 2018, 94(9): 3563-3576.
[6] MOGHADAM F K, REBOUÇAS G F D S, NEJAD A R. Digital twin modeling for predictive maintenance of gearboxes in floating offshore wind turbine drivetrains[J]. Forschung im ingenieurwesen, 2021, 85(2): 273-286.
[7] ZHAO H, HU W F, LIU Z Y, et al.A CapsNet-based fault diagnosis method for a digital twin of a wind turbine gearbox[C]//ASME Power Conference, online, 2021.
[8] BALDASSARRE A, CERUTI A, et al.Towards a digital twin realization of the blade system design study wind turbine blade[J]. Wind and structures, 2019, 28(5): 271-284.
[9] CHETAN M, YAO S L, GRIFFITH D T.Multi-fidelity digital twin structural model for a sub-scale downwind wind turbine rotor blade[J]. Wind energy, 2021, 24(12): 1368-1387.
[10] PARVARESH A, ABRAZEH S, MOHSENI S, et al.A novel deep learning backstepping controller-based digital twins technology for pitch angle control of variable speed wind turbine[J]. Designs, 2020, 4(2): 15.
[11] PIMENTA F, PACHECO J, BRANCO C M, et al.Development of a digital twin of an onshore wind turbine using monitoring data[J]. Journal of physics: conference series, 2020, 1618(2): 022065.
[12] SIVALINGAM K, SEPULVEDA M, SPRING M, et al.A review and methodology development for remaining useful life prediction of offshore fixed and floating wind turbine power converter with digital twin technology perspective[C]//2018 2nd International Conference on Green Energy and Applications, Singapore, 2018.
[13] GRIEVES M W.Virtually intelligent product systems: digital and physical twins[M]. USA: American Institute of Aeronautics and Astronautics, 2019: 175-200.
[14] TAO F, ZHANG M, LIU Y S, et al.Digital twin driven prognostics and health management for complex equipment[J]. CIRP annals, 2018, 67(1): 169-172.
[15] 张辰源, 陶飞. 数字孪生模型评价指标体系[J]. 计算机集成制造系统, 2021, 27(8): 2171-2186.
ZHANG C Y, TAO F.Evaluation index system for digital twin model[J]. Computer integrated manufacturing systems, 2021, 27(8): 2171-2186.
[16] DYKES K L, RINKER J.WindPACT reference wind turbines[R]. NREL/TP-5000-67667, 2018.
[17] JONKMAN J.Fast theory manual[M]. National Renewable Energy Laboratory, Golden, CO, 2013.
[18] 杨超. 风电机组动态载荷特性及主动抑制策略研究[D]. 重庆: 重庆大学, 2015.
YANG C.Active control strategies and characteristics of dynamic loads of wind turbine generator systems[D]. Chongqing: Chongqing University, 2015.
[19] LI B, ZHAO H R, DIAO J C, et al.Design of real-time co-simulation platform for wind energy conversion system[J]. Energy reports, 2020, 6(sup2): 403-409.
[20] WANG X, GAO D W, WANG J, et al.Implementations and evaluations of wind turbine inertial controls with FAST and digital real-time simulations[J]. IEEE transactions on energy conversion, 2018, 33(4): 1805-1814.
基金
国家自然科学基金（61763037）; 内蒙古科技计划（2021GG164）; 内蒙古自然科学基金（2020MS05029; 2021MS06018）
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