Generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time. Generating the control schedule includes determining a value indicative of the current remaining fatigue lifetime of the turbine, or one or more turbine components, based on measured wind turbine site and/or operating data; applying an optimisation function that varies an initial control schedule to determine an optimised control schedule by varying the trade off between energy capture and fatigue life consumed by the turbine or the one or more turbine components until an optimised control schedule is determined.

Methods, systems, and devices for controlling a yaw offset of an upstream wind turbine based on reinforcement learning are provided. The method includes receiving data indicative of a current state of the first wind turbine and of a current state of a second wind turbine adjacent to the first wind turbine downstream along a wind direction, determining one or more controlling actions associated with the yaw offset of the first wind turbine based on the current state of the first wind turbine, the current state of the second wind turbine, and a reinforcement learning algorithm, and applying the determined one or more controlling actions to the first wind turbine.

A wind turbine comprising a tower, a rotor including a plurality of blades, an electrical generator operatively coupled to the rotor, and a control system including an active damping module configured to monitor oscillatory motion of the wind turbine and to output a damping demand signal to damp the oscillatory motion. The control system is configured to perform a rotor imbalance determination process including: controlling the rotating frequency of the rotor so that it substantially coincides with the natural frequency of the tower, determining rotor imbalance data based on the damping demand signal and evaluating said rotor imbalance data to determine the presence of a rotor imbalance condition, and correcting the rotor imbalance condition by applying pitch control inputs to one or more of the plurality of blades so as to reduce the severity of the rotor imbalance. The invention may also be expressed as a method.

Provided is a method and a system for computer-implemented determination of a drag coefficient as a control variable for controlling of a wind turbine, by receiving, as a data stream, a set of data from a number of data sources, the set of data consisting, for each data source, of a plurality of time series data values, acquired within a given time period at given points in time, and estimating, by a processing unit, the control variable based on the set of data as input of a machine learning algorithm being trained with training data of simulation time series data containing a number of operating states at different wind conditions and respective number of drag coefficients.

The invention regards an apparatus or method for controlling the profile of a blade on a wind turbine having at least a first blade and a second blade, the first blade comprise at least one first sensor system adapted to determine a first blade state and the second blade comprise at least one second sensor system adapted to determine a second blade state, wherein the profile of the second blade is controlled based on the determined first blade state and the determined second blade state.

The conventional wind farm control system has a problem in that it is difficult to obtain information with high spatial resolution and information sufficient for improving machine learning cannot be obtained. An artificial intelligence (AI) system according to the present invention includes: a learning device to perform machine learning on a wind vector, to predict a power generation amount of a wind turbine, and compare the predicted amount with a measured power generation amount, the learning device choosing, when the power difference therebetween is a predetermined threshold value or larger, a laser radar system for measuring the wind vector and then deriving measurement parameters; and a control device to send the measurement parameters derived by the learning device to the laser radar system.

A method is provided for monitoring an operational parameter of a wind turbine. The method comprising defining a peer limit, measuring the operational parameter during operation of the wind turbine; and comparing the measured operational parameter to the peer limit. The wind turbine is a member of a peer group of wind turbines, each wind turbine of the peer group comprising a common characteristic. The peer limit is defined using measurements of the operational parameter measured on the wind turbines of the peer group of wind turbines.

A method for operating a wind turbine includes determining one or more loading and travel metrics or functions thereof for one or more components of the wind turbine during operation of the wind turbine. The method also includes generating, at least in part, at least one distribution of cumulative loading data for the one or more components using the one or more loading and travel metrics during operation of the wind turbine. Further, the method includes applying a life model of the one or more components to the at least one distribution of cumulative loading data to determine an actual damage accumulation for the one or more components of the wind turbine to date. Moreover, the method includes implementing a corrective action for the wind turbine based on the damage accumulation.

A method for controlling a wind turbine connected to an electrical grid includes receiving, via a controller, a state estimate of the wind turbine. The method also includes determining, via the controller, a current condition of the wind turbine using, at least, the state estimate, the current condition defining a set of condition parameters of the wind turbine. Further, the method includes receiving, via the controller, a control function from a supervisory controller, the control function defining a relationship of the set of condition parameters with at least one operational parameter of the wind turbine. Moreover, the method includes dynamically controlling, via the controller, the wind turbine based on the current condition and the control function for multiple dynamic control intervals.

Provided are a method and an apparatus for controlling a pitch of a wind turbine under an extreme turbulence wind condition. The method includes: obtaining a first pitch parameter for a current time; obtaining a historical second pitch parameter in a predetermined time period before the current time; determining an updated threshold based on the obtained historical second pitch parameter; comparing the first pitch parameter for the current time with the determined updated threshold; and updating the first pitch parameter for the current time based on a result of the comparison.