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中文摘要：
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。
英文摘要：
The maximum power point tracking（MPPT） control based on reducing tracking range can improve the wind energy capture efficiency of wind turbines with high rotor inertia under turbulent conditions. However this method makes optimal setting of the reduction of tracking range only according to mean wind speed and other impacting factors such as turbulence intensity and several aerodynamic and structural parameters of wind turbine, for example the optimum tip speed ratio, rotational inertia and so on, are neglected. Considering that there exist complex relationships between the setting of tracking range and various factors, which are hard to be analytically described, an MPPT control method, in which the radial basis function neural network（RBFNN） is utilized to optimize the tracking range, is proposed. In the proposed improved control method the mean wind speed and turbulence intensity are taken as the input variable of neural network, and the simulated data of specific wind turbine is taken as training samples and the compensation coefficient as the output variable of neural network, thus an optimization setting of tracking range can be realized, in which both varying wind speed and the aerodynamic and structural parameters of wind turbine can be considered simultaneously. Finally, simulation calculation and comparative analysis on generated wind speed sequence are performed and the effectiveness and the advantages of the proposed control method are validated.
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