Abstract: A fault-tolerant control method and apparatus of a floating wind turbine are provided. The fault-tolerant control method and apparatus of a floating wind turbine can acquire a low-order nonlinear model of a pre-established floating wind turbine, establish a switching linear model of the floating wind turbine based on the low-order nonlinear model, acquire a modal parameter of the current floating wind turbine, and determine based on the modal parameter a sub-model that the floating wind turbine currently satisfies, so as to establish a switching sliding mode surface and a full-order state observer of the floating wind turbine based on the sub-model and the modal parameter, and further calculate a feedback output of a controller of the floating wind turbine according to the switching sliding mode surface and the full-order state observer.

Abstract: Provided are control method and device of an energy-storage coordinated floating wind turbine, relating to the technical field of wind turbines. The control method of an energy-storage coordinated floating wind turbine can construct a primary frequency regulation model of a floating wind farm based on a frequency response unit, construct a second frequency regulation model according to an energy storage system, further construct, according to the primary frequency regulation model and the second frequency regulation model, a frequency regulation model of a hybrid power system containing the floating wind farm, the energy storage system, and a pre-set thermal power unit, and design an overall frequency regulation control strategy of the hybrid power system based on the frequency regulation model of the hybrid power system.

Abstract: The present invention provides a frequency modulation dynamic modeling method and device for a wind farm, and an electronic device. The method includes: acquiring first frequency modulation data measured at a grid-connected point of the wind farm under a plurality of preset working conditions; establishing a state space model corresponding to each of the plurality of working conditions according to the first frequency modulation data; measuring the nonlinearity between the state space models corresponding to each two of the plurality of working conditions by using a gap measurement method; combining the first frequency modulation data according to the nonlinearity to obtain second frequency modulation data; and training a preset initial LSTM neural network according to the second frequency modulation data until a preset training requirement is met, and obtaining a trained frequency modulation dynamic model of the wind farm.