NEURO-FUZZY CONTROLLER OF LOW HEAD HYDROPOWER PLANTS USING ADAPTIVE-NETWORK BASED FUZZY INFERENCE SYSTEM

This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy infe rence system (ANFIS). The new design technique enhances fuzzy controll ers with self-learning capability for achieving prescribed control obj ectives in a near optimal manner. The controller has flexibility for a ccepting more sensory information, with the main goal to improve the g enerator unit transients, by adjusting the exciter input, the wicket g ate and runner blade positions. The developed ANFIS controller whose c ontrol signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedba ck control are presented. To demonstrate the effectiveness of the prop osed control scheme and the robustness of the acquired neuro-fuzzy con troller, the controller has been implemented on a complex high-order n on-linear hydrogenerator model.