基于RBF神经网络PID控制的SSSC控制器研究

Research on the Controller of Static Synchronous Series Compensator Based on RBF Neural Network PID Control

在介绍静止同步串联补偿器（SSSC）的数学模型和控制结构的基础上,提出了一种基于RBF神经网络PID控制策略的SSSC新型潮流控制器,设计了控制器的结构。选择含SSSC的电力系统工作在有功功率控制模式下,利用PID控制器对系统进行闭环控制,而采用RBF神经网络的自学习能力可以改善传统PID控制器中参数固定不变的缺点,对PID控制器的3个参数进行实时在线调整,确保输电线路的有功功率能快速达到给定的参考值。最后,在MATLAB/Simulink仿真环境中对所设计的控制器进行了仿真验证。并同传统PID控制器和BP神经网络PID控制器分别进行比较,结果表明所提出控制策略具有较好的适应性和鲁棒性。

In order to introduce the mathematical model and the control structure of the static synchronous series compensator （SSSC）, a novel power flow controller based on RBF neural network PID control strategy is proposed in this paper. And the structure of the controller is designed. The power system with SSSC is selected to work in active power control mode, and adopt closed loop control by using PID controller. The self-learning ability of RBF neural network can be used to improve the fixed parameters of traditional PID controller, The three parameters of the PID controller are adjusted on-line in real time, so that the active power of the transmission line can be quickly reached a given reference value. Finally, the controller is simulated and verified in the MATLAB/Simulink simulation environment. Compared with the traditional PID controller and BP neural network PID controller, the results show that the proposed control strategy has better adaptability and robustness.