Provided is a method for computer-implemented monitoring of wind turbines in a wind farm each wind turbine including, an upper section being pivotable around a vertical yaw axis wherein the following steps are performed: i) obtaining a digital image of the upper section of the first wind turbine, the image being a current image taken by a camera installed on the upper section of the second wind turbine; ii) determining a yaw misalignment angle between the first and second wind turbines by processing the image by a trained data driven model, where the image is fed as a digital input to the trained data driven model and the trained data driven model provides the yaw misalignment angle as a digital output, the yaw misalignment angle being the obtuse angle between the rotor axis of the first wind turbine and the rotor axis of the second wind turbine.

A method of providing safety configuration parameters for a wind turbine is provided. The method comprises receiving a safety configuration file at a location of the wind turbine, and comparing a turbine ID associated with the safety configuration file to a turbine ID of the wind turbine stored at the location of the wind turbine. A tamper check is performed on the safety configuration file to determine if data in the safety configuration file has been modified. If the turbine ID associated with the safety configuration file matches the turbine ID of the wind turbine, and if the tamper check determines that the data has not been modified, a safety configuration parameter associated with a safety system of the wind turbine is extracted from the file and stored.

The present disclosure relates to methods for determining a maximum power setpoint for a wind turbine comprising determining a temperature of one or more wind turbine components, and determining one or more component temperature errors by determining a difference between the temperatures of the wind turbine components and a corresponding threshold temperature for the components. The methods further comprise determining a present power output of the wind turbine and determining the maximum power setpoint at least partially based at least on the component temperature errors, and on a present power output of the wind turbine. The present disclosure further relates to methods for determining a setpoint reduction and to wind turbine control systems and wind turbines configured for such methods.

The present disclosure relates to methods for determining a maximum power setpoint for a wind turbine comprising: determining an ambient temperature, determining a temperature of one or more wind turbine components and determining a current power output of the wind turbine. The methods further comprise determining the maximum power setpoint based at least partially on a thermodynamic model of the wind turbine components, the ambient temperature, the temperature of the components of the wind turbine and on the present power output of the wind turbine. The present disclosure further relates to methods for determining a setpoint reduction and to wind turbine control systems and wind turbines configured for such methods.

Systems and methods are provided for the robust, multivariable control of a power generating asset via H-infinity loop shaping using coprime factorization. Accordingly, a controller of the power generating asset computes a gain value for an H-infinity (H∞) module in real-time at predetermined sampling intervals using an actuator dynamic model. The controller then determines an acceleration factor based, at least in part, on the gain value of the H∞ module. Based, at least in part on the acceleration vector, the controller generates a control vector. An operating state of at least one component of the power generating asset is changed based on the control vector.

A method for controlling a wind turbine using an estimated wind speed is provided. The wind turbine has a rotor rotatable with a variable rotor speed and having rotor blades with adjustable pitch angles and a generator adapted to control a generator torque or a generator output power. The method comprises measuring the rotor speed, determining a common pitch angle representative of at least one or all pitch angles of the rotor blades, determining the generator torque or an output power of the generator and estimating the wind speed by means of an observer. The observer uses a model of the wind turbine as an observer model, uses as input variables, the measured rotor speed, the determined common pitch angle and the determined generator torque or the determined output power, and incorporates expected variations of the wind as at least one stochastic variable.

A wind turbine including yaw control comprising a controller receiving an input signal, and providing an output control signal to a yaw actuator. The input signal to the controller is based on: a first feedback signal that is indicative of the relative wind direction determined with respect to the wind turbine, wherein the first feedback signal is filtered with a first low pass filter; and a second feedback signal that is indicative of the activity of the yaw actuator. The control technique of the invention significantly improves the ability of a yaw system to maintain a zero degree yaw error during steady state wind conditions, or in other words to maintain an accurate heading of the nacelle pointing into the wind, as well as reducing the maximum yaw error experienced during yaw system activation.

The present invention provides a method and controller for controlling noise emissions from individual blades (18) of a wind turbine (10), the method (60) comprising defining (720) a wind turbine model (321) describing dynamics of the wind turbine (10), the wind turbine model (321) including a description of intensity and direction of noise emissions from each individual blade (18) as a function of azimuthal angle (312); and applying (730) a model-based control algorithm (32) using the wind turbine model (321) to determine at least one control output (331), and using (740) the at least one control output (331) to control noise emissions from each individual blade (18).

A method is disclosed for controlling a wind turbine, where the wind turbine includes with a tower and a rotor and comprises having at least one rotor blade with an adjustable blade pitch angle, and where a change in a power value takes place in a time interval (TE) and by the control of one or more operating parameters which determine power to be fed in by the wind turbine. The method comprises determining a parameterized time function of a tower deflection for the time interval (TE). A series of boundary conditions are defined for the parameterized time function of the tower deflection and a thrust of the rotor of the wind turbine is determined for the parameterized time function of the tower deflection. A function is then calculated for controlling the one or more operating parameters from the thrust of the rotor.

A method for controlling a multirotor wind turbine is disclosed. A first operational state of each of the energy generating units of the wind turbine is obtained. A difference in thrust acting on at least two of the energy generating units is detected. At least one constraint parameter of the set of operational constraints is adjusted in accordance with prevailing operating conditions and in accordance with the detected difference in thrust, and a new operational state for at least one of the energy generating units is derived, based on the at least one adjusted constraint parameter, the new operational state(s) counteracting the detected difference in thrust. Finally, the wind turbine is controlled in accordance with the new operational states for the energy generating units.