Title :
Resource allocation network based neural PID adaptive control for generator excitation system
Author :
Tengfei Zhang ; Jing Yang ; Fumin Ma
Author_Institution :
Coll. of Autom., Nanjing Univ. of Posts & Telecommun., Nanjing, China
Abstract :
In this paper, a neural PID adaptive controller based on the identification of resource allocation network (RAN) was proposed for the generator excitation system, which not only has the ability of neural network such as powerful nonlinear mapping and self-learning, but also can adjust the PID controller performance. The controller dynamically increases the number of hidden nodes through the learning samples, building the model online and dynamically adjusting the PID parameters to achieve the system output tracking input. Simulation results show that this method is better to stabilize the static and dynamic terminal voltages of the generator compared to the conventional PID control.
Keywords :
adaptive control; learning systems; machine control; neurocontrollers; nonlinear control systems; resource allocation; self-adjusting systems; synchronous generators; three-term control; tracking; PID controller performance; PID parameters; RAN; conventional PID control; dynamic terminal voltage; generator excitation system; learning samples; neural PID adaptive control; neural network; nonlinear mapping; resource allocation network identification; self-learning; static terminal voltage; system output tracking input; Adaptive systems; Generators; Neural networks; PD control; Power system stability; Radio access networks; Synchronous generator; excitation system; neural PID control; resource allocation network;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561136