Research Of PID Excitation Control For Generators Based On Fuzzy RBF Neural Network

The generator excitation control system is the important part of the power system, it can effectively guarantee the voltage quality and improve the run stability of the power system, etc. Therefore, excitation control has the decisive significance to the running of the entire power system. A new kinds of excitation control scheme in which the fuzzy control and neural technique is integrated to the design of the g(?)nerator non-linear control is proposed in this dissertation.Based on analysis of the theory of the generator excitation control system, one machine-infinity bus power system non-linear mathematical model expressed by state equation is established. Then fuzzy RBF neural network is constructed and an effective arithmetic is developed after the neural network and fuzzy control technique are inosculated. The implementation of on-line automatic adjustment of the PID excitation regulator parameter is done according to the fuzzy RBF neural network control decision-making. Finally, a great many simulation tests are made and a comparison to the conventional PID excitation control is performed. The simulation results show that the fuzzy RBF neural network excitation control has the excellent dynamic quality and control effect, stronger robustness and adaptability, and the running characteristics and stability can be maintained well under the situations of system disturbance and fault.