摘要
设计了一种新型的基于 BP神经网络的最优励磁控制器 ( NNOEC)。在线性最优励磁控制的基础上 ,利用 4层 BP神经网络对发电机的运行方式和系统所遭受的干扰类型进行辨识 ,通过对网络的训练 ,使得网络能够实时根据发电机的状态量来调节最优控制的反馈矩阵 ,以适应当前的运行点和所遭受的干扰。仿真结果表明 ,所设计的 NNOEC在系统运行方式较大的变化范围内都能提供很好的控制性能 。
A new type of optimal excitation controller based on BP neural network (NNOEC) is presented. In the proposedNNOEC, a four--layer BP neural network is used to identify the operation conditions of the generator and the disturbances inthe system. The trained neural network can dynamically adjust the optimal feedback matrix according to the state variables ofthe generator. Simulation results show that the designed NNOEC can provide very good damping over a wide range ofoperating conditions and good voltage performance of the generator can also be guaranteed at the same time.This is a subproject supported by National Science and Technology Key Project: Development of the Three GorgesHydroelectric Generator Excitation Systems and Equipment--Research of the Control Mode in Excitation System (97-312-01if^lb).
出处
《电力系统自动化》
EI
CSCD
北大核心
2000年第8期29-32,共4页
Automation of Electric Power Systems
基金
国家重大科技项目 (攻关 )计划!(97- 31 2 - 0 1 - 1 1 - 1 b)
关键词
BP神经网络
最优励磁控制器
电力系统
稳定性
intelligent control: optimal control: BP neural network
excitation controller
