NEURAL-NET BASED COORDINATED STABILIZING CONTROL FOR THE EXCITER AND GOVERNOR LOOPS OF LOW HEAD HYDROPOWER PLANTS

This paper presents a design technique of a new adaptive optimal contr oller of the low head hydropower plant using artificial neural network s (ANN). The adaptive controller is to operate in real time to improve the generating unit transients through the exciter input, the guide v ane position and the runner blade position. The new design procedure i s based on self-organization and the predictive estimation capabilitie s of neural-nets implemented through the cluster-wise segmented associ ative memory scheme. The developed neural-net based controller (NNC) w hose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wide ra nge of operating conditions than conventional controllers. Digital sim ulations of hydropower plant equipped with low head Kaplan turbine are performed and the comparisons of conventional excitation-governor con trol, state-space optimal control and neural-net based control are pre sented. Results obtained on the non-linear mathematical model demonstr ate that the effects of the NNC closely agree with those obtained usin g the state-space multivariable discrete-time optimal controllers.