Coordinated stabilizing control for the exciter and governor loops using fuzzy set theory and neural nets

Abstract This paper presents a design technique for a new hydropower plant controller using fuzzy set theory and artificial neural networks. The controller is suitable for real time operation, with the aim of improving the generating unit transients by acting through the exciter input, the guide vane and the runner blade positions. The developed fuzzy logic based controller (FLC) whose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wider range of operating conditions than conventional regulators. Digital simulations of a hydropower plant equipped with a low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, optimal state-feedback control and FLC performances are presented. The FLC, based on a set of fuzzy logic operations that are performed on controller inputs, provides a means of converting linguistic control requirements based on expert knowledge into an efficient control strategy. A fuzzy associative matrix is generated by using unsupervised learning of artificial neural networks. Results obtained on the nonlinear hydrounit mathematical model simulation demonstrate that the performance of the FLC closely agrees with that obtained if the optimal state-feedback multivariable discrete-time controller is applied.