The present disclosure relates to a wind turbine comprising a wind turbine rotor with a plurality of blades, a generator operatively coupled to the wind turbine rotor for generating electrical power and a power electronic converter for converting electrical power generated by the generator to a converted AC power of predetermined frequency and voltage. The wind turbine further comprises a wind turbine controller configured to receive values of one or more operational parameters of the wind turbine from one or more sensors and further configured to temporarily increase a speed of the generator to above a nominal generator speed if the values of the operational parameters satisfy a potential trip criterion. The present disclosure also relates to methods for controlling wind turbines.

A wind turbine is disclosed which comprises a control system configured to execute at least one ice removal routine which comprises a heating stage of at least one of the blades (3), and a mechanical removal ice stage. A wind turbine removing ice method is also disclosed which comprises a stage wherein the presence of ice is detected on at least one of the blades and, once said presence of ice is detected, comprises a stage wherein at least one ice removal routine is activated which comprises, in turn, a heating stage of at least one of the blades and a mechanical removing ice stage on at least said blade.

An apparatus for monitoring an ambient environment of a wind turbine is described. The apparatus comprises a cooling system comprising first and second heat exchangers, and a fluid circuit arranged to enable coolant to flow between the first and second heat exchangers. The apparatus further comprises a processor configured to: monitor one or more operational parameters of the cooling system; determine an efficiency of the cooling system based on the monitored one or more operational parameters; and calculate a liquid water content of the ambient environment based on the measured efficiency of the cooling system.

According to an embodiment, a method of controlling a temperature of a blade includes generating a first power production curve based on current weather conditions and generating a second power production curve based on future weather conditions. The method also includes, in response to determining that the second power production curve reduces a net power production loss of the blade more than the first power production curve, adjusting a heating cycle of the blade based on the second power production curve rather than the first power production curve.

A system and method are provided for controlling a wind turbine of a wind farm. Accordingly, a controller implements a first model to determine a modeled performance parameter for the first wind turbine. The modeled performance parameter is based, at least in part, on an operation of a designated grouping of wind turbines of the plurality of wind turbines, which is exclusive of the first wind turbine. The controller then determines a performance parameter differential for the first wind turbine at multiple sampling intervals. The performance parameter differential is indicative of a difference between the modeled performance parameter and a monitored performance parameter for the first wind turbine. A second model is implemented to determine a predicted performance parameter of the first wind turbine at each of a plurality of setpoint combinations based, at least in part, on the performance parameter differential the first wind turbine. A setpoint combination is then selected based on the predicted performance parameter and an operating state of the first wind turbine is changed based on the setpoint combination.

Provided is an apparatus and method for cooperative controlling wind turbines of a wind farm, wherein the wind farm includes at least one pair of turbines aligned along a common axis approximately parallel to a current wind direction and having an upstream turbine and a downstream turbine. The method includes the steps of: a) providing a data driven model trained with a machine learning method and stored in a database, b) determining a decision parameter for controlling at least one of the upstream turbine and the downstream turbine by feeding the data driven model with the current power production of the upstream turbine which returns a prediction value indicating whether the downstream turbine will be affected by wake, and/or the temporal evolvement of the current power production of the upstream turbine; c) based on the decision parameter, determining control parameters for the upstream turbine and/or the downstream turbine.

The present disclosure relates to methods (300) to determine a duration of a period for warming up of a power converter (20) of a wind turbine (1). The method (300) comprises determining (301) a first indicator that is indicative of a time that the power converter (20) has been inactive. Further, the method (300) comprises determining (302) the period for warming up at least partially based on the first indicator. A power converter assembly is also disclosed.

A system for generating power includes an environmental engine operating on one or more computing devices that determines a wind flowing over a blade of a wind turbine, wherein the wind flowing over the blade of the wind turbine varies based on environmental conditions and operating parameters of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between at least two of a rotor speed, a blade pitch, the wind flowing over the blade, a wind speed and a turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric, and simulates execution of the selected model to determine recommended operating parameters.

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.

A system and method are provided for controlling a wind turbine of a wind farm. Accordingly, a controller implements a first model to determine a modeled performance parameter for the first wind turbine. The modeled performance parameter is based, at least in part, on an operation of a designated grouping of wind turbines of the plurality of wind turbines, which is exclusive of the first wind turbine. The controller then determines a performance parameter differential for the first wind turbine at multiple sampling intervals. The performance parameter differential is indicative of a difference between the modeled performance parameter and a monitored performance parameter for the first wind turbine. A second model is implemented to determine a predicted performance parameter of the first wind turbine at each of a plurality of setpoint combinations based, at least in part, on the performance parameter differential the first wind turbine. A setpoint combination is then selected based on the predicted performance parameter and an operating state of the first wind turbine is changed based on the setpoint combination.