基于神经网络风力发电机组载荷优化控制策略研究
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摘要
为减轻大型风力机叶片在工作过程中受到的空气动力载荷均值,提出一种基于神经网络的风力机载荷优化控制策略。基于神经网络在线建立了风力机载荷数学模型;在功率控制的基础上,以减小载荷为目标,利用梯度法计算出控制量的修正量,从而实现功率与载荷综合优化控制的目的。将该控制策略应用于2 MW风机的非线性模型,用Matlab/Simulink进行了仿真,仿真实验结果表明,对比传统PI控制器,该控制策略能有效降低风机叶片的空气动力载荷均值。
A load optimizing control strategy based on neural network model to reduce mean aerodynamic fatigue load of blade for large-scale wind turbine was proposed. The mathematic model of wind turbine was set up by neural network on-line. And load reduction was adopted as another control on the basis of power control. The negative gradient of load is calculated as the modifying signal. Then the negative gradient signal is added to the output of PI controller to accomplish the goal of power and load optimization. The control strategy was applied to nonlinear model of 2 MW wind turbine in Matlab/Simulink. The results show that the controller is effective in reducing mean value of aerodynamic load of blade,comparing to traditional PI control.
A load optimizing control strategy based on neural network model to reduce mean aerodynamic fatigue load of blade for large-scale wind turbine was proposed. The mathematic model of wind turbine was set up by neural network on-line. And load reduction was adopted as another control on the basis of power control. The negative gradient of load is calculated as the modifying signal. Then the negative gradient signal is added to the output of PI controller to accomplish the goal of power and load optimization. The control strategy was applied to nonlinear model of 2 MW wind turbine in Matlab/Simulink. The results show that the controller is effective in reducing mean value of aerodynamic load of blade,comparing to traditional PI control.
引文
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[2]Nourdine S,Camblong H,Vechiu I,et al.Comparison of Wind Turbine LQG Controllers Using Individual Pitch Control to Alleviate Fatigue Loads[C]//18th Mediterranean Conference on Control&Automation.Congress Palace Hotel,Marrakech,Morocco.2010:1591-1596.
[3]SvenCreutzThomsen,HenrikNiemann,NielsKj?lstadPoulsen.Stochastic Wind Turbine Control in Multiblade Coordinates[C]//2010 American Control Conference Marriott Waterfront,Baltimore,MD,USA.2010:2772-2777.
[4]Yang Zhongzhou,Li Yaoyu,John E Seem.Individual Pitch Control for Wind Turbine Load Reduction Including Wake Interaction[C]//2011 American Control Conference on O’Farrell Street,San Francisco,CA,USA.2011:5207-5212.
[5]Johannes friis,Nielsen Ebbe,Bonding Jesper,et al.Repetitive Model Predictive Approach to Individual Pitch Control of Wind Turbines[C]//2011 50th IEEE Conference on Decision and Control and European Control Conference(CDC-ECC)Orlando,FL,USA.2011:3664-3670.
[6]Trudnowski Daniel,LeMieux David.Independent Pitch Control Using Rotor Position Feedback for Wind-shear and Gravity Fatigue Reduction in a Wind Turbine[C]//Proceedings of the American Control Conference.Anchorage,2002:4335-4340.
[7]王晓东.大型双馈风电机组动态载荷优化控制策略研究[D].沈阳:沈阳工业大学,2011.
[8]Liu Wenzhi,Wu Jianxin.3D Modeling Methods of Aerodynamic Shape for Large-scale Wind Turbine Blades[C]//2009International Conference on Information Technology and Computer Science,2009:7-10.
[9]孔屹刚,顾浩,王杰,等.基于风剪切和塔影效应的大型风力机载荷分析与功率控制[J].东南大学学报:自然科学版,2010,40(1):228-233.
[10]戴巨川,胡燕平,刘德顺,等.MW级变桨距风电机组叶片转矩计算与特性分析[J].太阳能学报,2010,31(8):1030-1036.
[11]宋显成.大型水平轴风力机风轮气动性能计算与优化设计[D].兰州:兰州理工大学,2010.
[12]叶杭冶.风力发电机组的控制技术[M].第2版.北京:机械工业出版社,2005.
[13]李士勇.模糊控制·神经控制和智能控制论[M].哈尔滨:哈尔滨工业大学出版社,1996.