Nonlinear identification and control of turbogenerators using local model networks

Local model (LM) networks are applied to the identification of the global nonlinear dynamics of a turbogenerator excitation loop. A hybrid algorithm is used in conjunction with prior plant information to optimise the learning process. The resulting model was then used to devise a nonlinear generalised minimum variance (GMV) controller. This controller was found to outperform linear GMV controllers tuned at each generator operating point