Research on the intelligent control strategy based on FNNC and GAs for hydraulic turbine generating units

It is difficult to gain better control performance using general control strategy to control hydraulic turbine generating units system because it is a complicated non-linear MIMO system. In this study, a new control technique, which efficiently get optimal control parameters for fuzzy neural network controller through the training of neural network and genetic algorithms, was proposed and then applied to control turbine generating unit system. In the designed control system, fuzzy reasoning rules, member function and parameters can be given through genetic algorithms when error is bigger and can be trained on-line through neural network when error is less. The improved genetic algorithms, which overcomes general genetic algorithmspsila disadvantage, has quick training speed and gives whole optimized parameters for fuzzy neural network controller. RBF neural network is employed to identify and predict the relation between input and output of hydroelectric generating units system. Simulation experiment results show that the designed controller can control hydraulic turbine generating units efficaciously and has quick controlling speed and less controlling max-error. So it provides a good control strategy for hydraulic turbine generating units system.