A method for controlling pitching of at least one rotor blade of a wind turbine includes receiving, via one or more position localization sensors, position data relating to the at least one rotor blade of the wind turbine. Further, the method includes determining, via a controller, a blade deflection signal of the at least one rotor blade based on the position data. Moreover, the method includes determining, via a computer-implemented model stored in the controller, a pitch command for the at least one rotor blade as a function of the blade deflection signal and an azimuth angle of the at least one rotor blade.

A deep learning-based cooling system temperature prediction apparatus has an artificial neural network modeled by connecting a plurality of artificial neural network submodels each including an input layer, a hidden layer, and an output layer is used. A pump flow speed, a cooling water flow rate, a battery inlet cooling water temperature, a motor inlet cooling water temperature, a radiator outlet cooling water temperature, a battery temperature, and a motor temperature are predicted by inputting at least one of a predetermined control variable, an environment variable, or a time variable to the plurality of artificial neural network submodels in accordance with a physical causality. A number of the plurality of artificial neural network submodels and the control variables or environment variables that are sequentially input to each submodel depend on divisional control and integral control of the cooling system.

A method for controlling pitching of at least one rotor blade of a wind turbine includes receiving, via one or more position localization sensors, position data relating to the at least one rotor blade of the wind turbine. Further, the method includes determining, via a controller, a blade deflection signal of the at least one rotor blade based on the position data. Moreover, the method includes determining, via a computer-implemented model stored in the controller, a pitch command for the at least one rotor blade as a function of the blade deflection signal and an azimuth angle of the at least one rotor blade.

A deep learning-based cooling system temperature prediction apparatus has an artificial neural network modeled by connecting a plurality of artificial neural network submodels each including an input layer, a hidden layer, and an output layer is used. A pump flow speed, a cooling water flow rate, a battery inlet cooling water temperature, a motor inlet cooling water temperature, a radiator outlet cooling water temperature, a battery temperature, and a motor temperature are predicted by inputting at least one of a predetermined control variable, an environment variable, or a time variable to the plurality of artificial neural network submodels in accordance with a physical causality. A number of the plurality of artificial neural network submodels and the control variables or environment variables that are sequentially input to each submodel depend on divisional control and integral control of the cooling system.

A kite system includes a kite and a ground station. The ground station includes a sensor that can be utilized to determine an angular position and velocity of the kite relative to the ground station. A controller utilizes a fuzzy logic control system to autonomously fly the kite. The system may include a ground station having powered winding units that generate power as the lines to the kite are unreeled. The control system may be configured to fly the kite in a crosswind trajectory to increase line tension for power generation. The sensors for determining the position of the kite are preferably ground-based.