Individual blade pitch control for floating wind turbine based on RBF-SMC

Abstract

In this paper, the aerodynamic model, hydrodynamic model and mooring system model are established and coupled in time domain to obtain the effective wind speed under the disturbance of wind, wave and mooring load. On this basis, the online learning ability of Radial Basis Function (RBF) neural network is used to adjust the gain of sliding mode variable structure controller in real time so that the sliding mode function tends to the switching surface, and the chattering of sliding mode variable structure controller can be effectively reduced. The RBF-SMC individual blade pitch control method which is more suitable for floating wind turbine is obtained. Based on the simulation model of floating wind turbine composed of NREL-5MW wind turbine and OC3-Hywind foundation, the traditional PI control and the control method proposed in this paper are compared and analyzed. The results show that the individual blade pitch control based on RBF-SMC can effectively reduce the sway of floating foundation, restrain the fluctuation of effective wind speed of wind turbine, and ensure the stability of output power.