漂浮式风机气-水动力耦合下的独立变桨控制方法

doi:10.19595/j.cnki.1000-6753.tces.180919

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Abstract In this paper, an individual blade pitch control strategy based on radical-basis function neural network (RBFNN) control was proposed for the floating offshore wind turbines (FOWT) bearing air-hydrodynamic coupling loads. Considering the effects of wind shear and tower shadow firstly, a more accurate aerodynamic model was established. The aerodynamic model was coupled with the hydrodynamic model to obtain the relative wind speed under the disturbance of wind and wave. On this basis, an individual pitch control method based on RBFNN was proposed. The RBFNN is used to approximate the unknown nonlinear function of the pitch control system. The adaptive rate was derived by Lyapunov method, and the weights of the neural network were adjusted online to improve the dynamic performance of the proposed pitch control system. The proposed control strategy and the traditional PI controller were compared in the combination of FAST-Matlab/Simulink. The results showed that the proposed method can effectively stabilize the output power of FOWT and reduce the load fluctuation and pitch oscillation of floating base to a certain extent.

Key words： Floating offshore wind turbines pitch control coupling of hydrodynamic and aerodynamic forces multi-degree of freedom

Received: 30 May 2018 Published: 20 September 2019

PACS:	TM315
	P752

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	Zhou Lawu
	Yang Binyou
	Han Bing
	Li Feilong
	Hu Zhou

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Zhou Lawu,Yang Binyou,Han Bing等. The Individual Blade Pitch Control for the Floating Offshore Wind Turbines Bearing the Air-Hydrodynamic Coupling Loads[J]. Transactions of China Electrotechnical Society, 2019, 34(17): 3607-3614.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.180919 OR https://dgjsxb.ces-transaction.com/EN/Y2019/V34/I17/3607

[1] Global Wind Energy Council. Global wind report 2016[R]. Global Wind Energy Council, 2017.
[2] 徐涛. 我国海上风电现状[J]. 风能产业, 2013(7): 8-12.
Xu Tao.The current situation of sea wind power in China[J]. Wind Energy Industry, 2013(7): 8-12.
[3] Hand Brian, Andrew Cashman, Ger Kelly.A low-order model for offshore floating vertical axis wind turbine aerodynamics[J]. IEEE Transactions on Industry Applications, 2017, 53(1): 512-520.
[4] 杨悦, 李国庆. 基于VSC-HVDC的海上风电小干扰稳定控制[J]. 电工技术学报, 2016, 31(13): 101-110.
Yang Yue, Li Guoqing.The small signal stability control of offshore wind farm based on VSC-HVDC[J]. Transactions of China Electrotechnical Society, 2016, 31(13): 101-110.
[5] 黄玲玲, 曹家麟, 张开华, 等. 海上风电机组运行维护现状研究与展望[J]. 中国电机工程学报, 2016, 36(3): 729-738.
Huang Lingling, Cao Jialin, Zhang Kaihua, et al.Status and prospects on operation and maintenance of offshore wind turbines[J]. Proceedings of the CSEE, 2016, 36(3): 729-738.
[6] 万德成, 程萍, 黄扬, 等. 海上浮式风机气动力-水动力耦合分析研究进展[J]. 力学季刊, 2017, 38(3): 385-407.
Wan Decheng, Cheng Ping, Huang Yang, et al.Overview of study on aero- and hydro-dynamic interaction for floating offshore wind turbines[J]. Chinese Quarterly of Mechanics, 2017, 38(3): 385-407.
[7] 周国龙, 叶舟, 丁勤卫, 等. 漂浮式风力机Spar平台垂荡板强迫振荡的优化探讨[J]. 太阳能学报, 2017, 38(3): 684-690.
Zhou Guolong, Ye Zhou, Ding Qinwei, et al.Research on improvement of forced oscillation for heave plates of floating wind turbine spar platform[J]. Acta Energiae Solaris Sinica, 2017, 38(3): 684-690.
[8] Iijima K, Srinivasamurthy S, Fujikubo M.Structural modeling for flexible floating offshore wind turbine: flexible model vs rigid model[C]//IEEE/OES International Convention-Return to the Oceans Techno-Ocean 2016, Kobe, Japan, 2016, DOI:10.1109/ Techno-Ocean.2016.7890662.
[9] Homer J R, Naganmune R.Physics-based 3-D control-oriented modeling of floating wind turbines[J]. IEEE Transactions on Control Systems Technology, 2017, 26(1): 14-26.
[10] Lemmer F, Raach S, Schlipf D, et al.Parametric wave excitation model for floating wind turbines[J]. Energy Procedia, 2016, 94: 290-305.
[11] Bagherieh O, Nagamune R.Utilization of blade pitch control in low wind speed for floating offshore wind turbines[C]//2014 American Control Conference, Portland, OR, USA, 2014, DOI:10.1109/ACC.2014. 6859448.
[12] Li Xianwei, Gao Huijun.Load mitigation for a floating wind turbine via generalized H_∞ structural control[J]. IEEE Transactions on Industrial Electronics, 2016, 63(1): 332-342.
[13] Hussain J, Mishra M K.Adaptive maximum power point tracking control algorithm for wind energy conversion systems[J]. IEEE Transactions on Energy Conversion, 2016, 31(2): 697-705.
[14] Han Bing, Zhou Lawu.Research on individual pitch control below rated wind speed[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2017, 12(1): 38-44.
[15] 中国船级社. 海上移动平台入级与建造规范[M]. 北京: 人民交通出版社, 2005.
[16] 方学智. 船舶与海洋工程概论[M]. 北京: 清华大学出版社, 2013.
[17] 高月文, 李春, 叶舟, 等. 风波流多环境海上漂浮式风力机张力腿平台动态特性[J]. 水资源与水工程学报, 2014, 25(2): 91-97.
Gao Yuewen, Li Chun, Ye Zhou, et al.Dynamic characteristics of wind turbine tension leg platform for multienvironment of wind wave current above sea[J]. Journal of Water Resources and Water Engineering, 2014, 25(2): 91-97.
[18] 唐友刚, 李嘉文, 曹菡, 等. 新型海上风机浮式平台运动的频域分析[J]. 天津大学学报:自然科学与工程技术版, 2013, 46(10): 879-884.
Tang Yougang, Li Jiawen, Cao Han, et al.Frequency domain analysis of motion of floating platform for offshore wind turbine[J]. Journal of Tianjin University: Science and Technology, 2013, 46(10): 879-884.
[19] Nasiri M, Milimonfared J, Fathi S H.Modeling, analysis and comparison of TSR and OTC methods for MPPT and power smoothing in permanent magnet synchronouss generator-based wind turbines[J]. Energy Conversion and Management, 2014, 86(5): 892-900.
[20] 娄尧林, 蔡旭, 叶杭冶, 等. 基于转矩随动控制的风电机组最优发电研究[J]. 电工技术学报, 2018, 33(8): 1884-1893.
Lou Yaolin, Cai Xu,Ye Hangye, et al.Optimal generator study based on torque follow-up control for wind turbine[J]. Transactions of China Electrotechnical Society, 2018, 33(8): 1884-1893.
[21] 王晓兰, 唐慧敏, 包广清, 等. 抑制载荷的风力机扰动前馈与预测反馈复合控制[J].电工技术学报, 2016, 31(2): 230-235, 251.
Wang Xiaolan, Tang Huimin, Bao Guangqing, et al.Control strategy combining disturbance feed-forward with predictive feedback for wind turbines to reduce loads[J]. Transactions of China Electrotechnical Society, 2016, 31(2): 230-235, 251.
[22] Lian J M, Lee Y G, Scott D S.Self-organizing radial basis function network for real-time approximation of continuous-time dynamical systems[J]. IEEE Transactions on Neural Networks, 2008, 19(3): 460-474.
[23] 靖永志, 何飞, 张昆仑. 基于RBF神经网络和LS-SVM组合模型的磁浮车间隙传感器温度补偿[J]. 电工技术学报, 2016, 31(15): 73-80.
Jing Yongzhi, He Fei, Zhang Kunlun.Temperature compensation of maglev train gap sensor based on RBF neural network and LS-SVM combined model[J]. Transactions of China Electrotechnical Society, 2016, 31(15): 73-80.
[24] Jonkman J M, Butterfield S, Musial W, et al.Definition of a 5-MW reference wind turbine for offshore system development[C]//National Renewable Energy Laboratory, Golden, CO, USA, 2009: 1-75.
[25] Jonkman J.Definition of the floating system for phase IV of OC3[C]//National Renewable Energy Laboratory, Golden, CO, USA, 2010: 509-513.
[26] 徐应瑜, 胡志强, 刘格梁. 10MW级海上FOWT运动特性研究[J]. 海洋工程, 2017, 35(3): 44-51.
Xu Yingyu, Hu Zhiqiang, Liu Geliang.Kinetic characteristics research of the 10 MW-level offshore floating wind turbine[J]. The Ocean Engineering, 2017, 35(3): 44-51.