基于神经网络的风力发电并网控制研究

Study on Control of Wind Power Generation Grid Connection Based on Neural Network

针对风力发电并网时所产生的电流冲击与波动过大的问题,文中基于其运行特性与控制原理,提出了一种基于神经网络的风力发电并网控制技术。该技术结合了BP神经网络与PID控制,使得控制器能够对转子电流进行控制,具有独立于被控对象的优点。加之双馈发电机,因而可以实现空载数学模型的并网控制。通过与传统控制技术比较可知,文中所提出的技术算法简单、响应速度快且精度高,能较好地控制电网电压波动,具有一定的有效性。

In view of the problem that wind power generation grid connection can produce too large the current impact and fluctuation,this paper studies the operation characteristics and control principle,and proposes a wind power generation grid connection control technology based on neural network. The technique combines BP neural network and PID control,to make the controller can control the rotor current,and has the advantages of independent of the controlled object,coupled with doubly fed generator,thus can realize the grid connection control with no-load mathematical model. By comparing the traditional control technology,the results show that the proposed algorithm is simple,and has fast response speed and high accuracy,good control of the voltage fluctuation,which is effective.