摘要
为了提高双馈风电机组的转速控制性能,采用了基于BP神经网络的PID控制方案和基于RBF神经网络辨识的PID控制方案,在推导出双馈风电机组暂态电势恒定情况下随风速变化的二阶转速调节模型基础上,分别编制仿真程序,对风电机组转速控制进行了跟踪仿真分析。针对上述两种方案的缺陷,提出了基于RBF网络辨识的单神经元网络PID控制和基于RBF网络辨识的BP神经网络PID控制两种改进控制方案,达到了优化风电机组转速控制性能的目的。
For improving the quality of rotating speed control of doubly-fed generator, BPNN（back propagation neural network） PID controller and RBFNN （radial basis function neural network） PID control schemes are investigated in the paper. On the basis of building the second-order rotating speed control model of doubly-fed generator with variable wind speed and constant transient voltage, the simulation programs are programmed respectively and applied into the model to simulate rotating speed control of the generators. To avoid defects of these two schemes above, neuro element PID controller based on RBFNN identification and BPNN PID controller based on RBFNN identification are brought forward which has obtained satisfied control effects.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2009年第19期14-18,共5页
Power System Protection and Control
基金
国家自然科学基金项目(50667002)
新疆教育厅重点项目(XJEDU2008I62)~~
关键词
人工神经网络
PID控制
双馈风电机组转速控制
BP网络
RBF网络
ANN
PID control
doubly-fed generator rotating speed control
back propagation neural network （BPNN）
radial basis function neural network（RBFNN ）
