Francis turbine electrohydraulic inlet guide vane control by artificial neural network 2 degree-of-freedom PID controller with actuator fault

Abstract

Hydropower system has great attention due to the cheapest and simplest renewable power generation, available potential and environmental concern. Francis turbine has wide operating range compare to other hydro turbine runner. Electrohydraulic Francis turbine inlet guide vane system has advantages like, high power density, self-lubrication property, very good controllability and rugged. Francis turbine inlet guide vane system consists of inlet guide vane actuation system, ring inlet guide blade arrangement, and controller. The main challenges with the electrohydraulically actuated inlet guide vane system are nonlinear characteristic, parametric uncertainty, and external disturbances. Further with respect to the real-life application of the electrohydraulic actuator piston seal damage may occur due to fitting problem of the seal, impurities in the hydraulic oil, high temperature of the hydraulic oil and so on. Recently some researchers are reported on modelling accuracy, accurate controller design and stability analysis. In the present study mathematical model of the Francis turbine has been developed based on velocity diagram to capture effect of water flow dynamics on turbine power generation with the consideration of Euler’s equation of turbo machinery. Detailed mathematical model of the inlet guide vane actuating system has been integrated with Francis turbine model. Artificial neural network 2 degree-of-freedom proportional integral derivative controller has been developed for Francis turbine power/speed control. The controller performance has been studied with the consideration of actuator piston seal damage. The controller performance has been studied with 30%, 60%, and 90% step increase of power demand. The controller performance also has been studied with 0.005 and 0.05 Hz frequency sinusoidal power demand. The proposed controller performance has been compared with conventional proportional integral derivative controller through various performance indexes. The proposed controller performances also have been compared with recently reported work due to step increase of torque and step decreases of power demand.

Keywords

Francis turbine artificial neural network electrohydraulic inlet guide vane control

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