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.

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 method for operating a plurality of wind energy installations configured for supplying electric power to an electrical supply system, that each have an aerodynamic rotor with rotor blades and an electrical generator and also operating equipment, is disclosed. The wind energy installations are operated while they are not connected to the electrical supply system, where at least one of the wind energy installations produces electric power and inputs the electric power into a local DC voltage system that connects the wind energy installations if the at least one of the wind energy installations currently produces more power than needed for supplying its own operating equipment. Additionally or alternatively, the operating equipment is supplied totally or in part with power from the local DC voltage system if the at least one of the wind energy installations currently produces less power than needed for supplying its operating equipment.

A system for adjusting environmental operating conditions associated with heat generating components located within a tower of a wind turbine may include a heat generating component located within an interior of the tower, a sensor configured to monitor a heat exchange parameter associated with the wind turbine and a split heat exchange system provided relative to the tower. The split heat exchange system may include a first heat exchanger located within the interior of the tower and a second heat exchanger located exterior to the tower. The system may also include a controller communicatively coupled to the sensor and the split heat exchange system. The controller may be configured to control the operation of the split heat exchange system based at least in part on the monitored heat exchange parameter to adjust an environmental operating condition associated with the heat generating component.

A method for controlling heating of rotor blades of an aerodynamic rotor of a wind turbine, wherein, the heating of the rotor blades is initiated, if icing of the rotor blades is expected, wherein according to an icing criteria, if icing is expected is evaluated depending on a determined ambient temperature, a determined relative humidity, and a determined wind speed, each defining a determined weather parameter, and further according to the icing criteria, if icing is expected is evaluated depending on a temporal change of at least one of these weather parameters and/or of at least one other weather parameter.

A method of parameterizing a controller of a first wind energy installation wherein the controller sets a manipulated variable of the wind energy installation as a function of an input variable. An artificial intelligence determines at least one value of a parameter of the controller for at least one state/degree of being iced up of the wind energy installation based on a power curve, load, and/or downstream flow of the wind energy installation predicted with a mathematical model of the wind energy installation for at least one state/degree of being iced up, and/or determines at least one value of a parameter of the controller for at least one state/degree of being iced up of the wind energy installation, based on at least one determined state/degree of being iced up and a power, load, and/or downstream flow of the wind energy installation and/or at least one second wind energy installation.

A method to determine a dry-out period of a converter of a wind turbine is provided. A time dependent chronology of data is measured in the converter. The measured data-chronology reflects the actual humidity and the humidity-history in the converter cabinet. The measured data-chronology is used to determine a dry-out period of time, which is needed to reduce the humidity inside the converter below a given value by circulating heat inside the converter.