跟踪区间优化的风力机最大功率点跟踪控制

发布时间：2018-04-25 21:26

本文选题：风力发电 + 最大功率点跟踪　；参考：《电网技术》2014年08期

【摘要】：基于收缩跟踪区间的最大功率点跟踪控制能够改善湍流风速条件下大转动惯量风力机的风能捕获效率。但是,该方法仅依据平均风速优化设定收缩跟踪区间,忽略了湍流强度、风力机的某些气动、结构参数(如最佳叶尖速比、转动惯量等)等其它影响因素。考虑到跟踪区间优化设定与多种因素存在难以解析描述的复杂关系,提出了一种运用径向基函数神经网络优化跟踪区间的最大功率点跟踪控制。该改进方法以平均风速和湍流强度作为神经网络的输入变量,以具体风力机仿真数据作为训练样本,以补偿系数作为神经网络的输出变量。从而使得跟踪区间的优化设定不仅能够考虑变化的风速条件,而且能同时反映具体风力机的气动、结构设计。最后,对模拟风速序列进行了仿真计算与比较分析,验证了该方法的有效性和优越性。
[Abstract]:The maximum power point tracking control based on shrinkage tracking region can improve the wind energy capture efficiency of large inertia wind turbine under turbulent wind speed. However, the method only optimizes the shrinkage tracking range based on the average wind speed, neglecting the turbulence intensity, some aerodynamic factors of the wind turbine, structural parameters (such as the optimum blade tip speed ratio, moment of inertia, etc.). Considering the complex relationship between the optimal setting of tracking interval and many factors, a maximum power point tracking control based on radial basis function (RBF) neural network is proposed to optimize the tracking interval. The improved method takes the mean wind speed and turbulence intensity as the input variables of the neural network, takes the simulation data of the wind turbine as the training sample, and takes the compensation coefficient as the output variable of the neural network. Therefore, the optimal setting of tracking interval can not only consider the variable wind speed condition, but also reflect the aerodynamic and structural design of the wind turbine at the same time. Finally, the simulation calculation and comparative analysis of the simulated wind speed series are carried out to verify the effectiveness and superiority of the method.
【作者单位】：南京理工大学自动化学院;
【基金】：国家自然科学基金项目(61203129,61174038,61104064) 中国博士后科学基金资助项目(2013M541674)~~
【分类号】：TM315

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