Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

• Published in: IEEE transactions on energy conversion Vol. 12; no. 4; pp. 375 - 381
• Main Authors: Djukanovic, M.B., Calovic, M.S., Vesovic, B.V., Sobajic, D.J.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-12-1997; Institute of Electrical and Electronics Engineers
• Subjects: Adaptive control systems; Applied sciences; Backpropagation; Blades; Computer simulation; Control systems; Damping; Digital simulation; Electric power plants; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Feedback control; Fuzzy control; Fuzzy systems; Hydroelectric power generation; Hydroelectric power plants; Inference engines; Multivariable control systems; Neural networks; Nonlinear control systems; Optimal control; Signal generators; Hydroelectric power plant; Transient response; Multivariable control; Control synthesis; Neural network; Low head hydrostation; Adaptive control; Fuzzy control; Vector control; Speed regulation; Excitation; Output feedback; Turbogenerator set
• ISSN: 0885-8969; 1558-0059
• DOI: 10.1109/60.638941

This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control 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 feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order nonlinear hydrogenerator model.