Self-tuning PID Control of Hydro-turbine Governor Based on Genetic Neural Networks

摘要

A genetic neural networks (GNN) control strategy for hydro-turbine governor is proposed in this paper. Considering the complex dynamic characteristic and uncertainty of the hydro-turbine governor model and taking the static and dynamic performance of the governing system as the ultimate goal, the novel controller combined the conventional PID control theory with genetic algorithm (GA) and neural networks (NN) is designed. The controller consists of three parts: GA, NN and classical PID controller. The controller is a variable structure type; therefore, its parameters can be adaptively adjusted according to the signal of the control error. The results of simulation show that the presented control strategy has enhanced response speed and robustness and achieves good performance when applied to the hydro-turbine governing system.