摘要
针对风力发电机组精确的数学模型难以建立的特点,采用RBF神经网络对风电机组进行了系统辨识。通过对风力发电机组转矩环和桨距环的动态过程进行分析,设计了基于RBF神经网络算法的风力发电机组转矩环与桨距环的辨识系统,采用RBF基函数构成隐含层空间,RBF参数确定后,非线性映射关系就确定了,将输入矢量直接映射到隐含层空间,对隐含层节点输出进行了线性加权求和,得到了输出层。研究结果表明,进行转矩环辨识时,辨识系统的输入信号为转矩给定,输出信号为发电机转速,辨识结果的误差率为1%;进行桨距环辨识时,辨识系统的输入信号为桨距角,输出信号为发电机转速,辨识结果的误差率为3%;采用RBF神经网络算法进行系统辨识具有较高的辨识精度和效率。
Aiming at the problems of difficult to establish the accurate mathematical model of wind power generation, identification of the wind turbine based on RBF neural network was presented. The dynamic process of the torque loop and the pitch loop was simulated, RBF neural network algorithm was adopted to identification the torque loop and the pitch loop. RBF basis function was adopted to form space. If the hidden layer RBF parameters was determined, the nonlinear mapping relation was determined. The output layer was the hidden layer nodes output linear weighted summation. The result indicate that identification of torque loop, the input is torque, the output is speed, the torque loop error rate is about 1%. Identification of pitch loop, the input is pitch angle, the output is speed, the pitch loop error rate is about 3%. The pitch loop is a very complicated nonlinear model, the model structure is influenced by many aspects, identification result error is bigger than the torque loop identification error, but the error rate is allowed. The algorithm has higher precision and efficiency.
出处
《机电工程》
CAS
2017年第6期639-642,658,共5页
Journal of Mechanical & Electrical Engineering
基金
国家科技支撑计划资助项目(2015BAA06B01)
关键词
风力发电机组
RBF神经网络
辨识
the wind turbine
radi-calbasis function（ RBF） neural network
identification
