静止无功发生器的单神经元PID控制器设计

Design of A Single Neuron PID Controller for Static Var Generator

为了提高静止无功发生器SVG的控制性能,本文采用单神经元PID控制器取代传统PID控制器。在分析单神经元PID控制器结构和原理的基础上,采用无监督的Hebb学习规则和有监督的Delta学习规则相结合的有监督Hebb学习规则,实现单神经元PID控制器参数优化和在线自动调整。采用Matlab软件搭建单神经元PID控制器和静止无功发生器SVG的仿真模型,仿真结果表明,使用单神经元PID控制器的SVG响应速度快,具有较强的自适应性和鲁棒性。

In order to improve the control performance of static var generator SVG, a single neuron PID controller to replace the traditional PID controller is used. In the analysis of single neuron PID controller based on the structure and principles, using unsupervised Hebb learning rule and supervised learning rule combining Delta supervised Hebb learning rule, single neuron PID controller parameter optimization and online automatic adjustment are achieved. With Matlab software to build single neuron PID controller and static var generator SVG simulation model, simulation results show that the SVG with single neuron PID controller has better characteristics such as fast response, strong adaptability and robustness.