The invention relates to a method for determining the energy production to be expected for a wind power installation for a forecast time period, which may be an expected annual energy production. The installation has installation components. In the method, at least one of the installation components is selected as a thermally relevant component and chronologically distributed wind speed values are specified for the forecast time period. An expected power output level of the installation is determined for one of the wind speed values. In the power output level determining step, a component temperature which is assigned to this wind speed value is taken into account by the at least one thermally relevant component. The expected power output level of the installation, which is determined for the wind speed value, is used to determine the energy production of the installation which is to be expected for the forecast time period.

A method includes training a prediction model to forecast a likelihood of curtailment for at least one wind turbine. The prediction model is trained, by a processor system, using historical information and historical instances of curtailment. The method also includes forecasting the likelihood of curtailment for the at least one wind turbine using the trained prediction model. The method also includes outputting the forecasted likelihood.

Temperature Control Based on Weather Forecasting Examples are generally directed to techniques for controlling a temperature of a blade, such as a blade in a wind turbine system. One example of the present disclosure is a method of controlling a temperature of a blade. The method includes inputting current weather conditions and future weather conditions into a processor, generating a first power production, generating a second power production curve based on future weather conditions, comparing the first power production curve to the second power production curve, determining which power production curve minimizes a new power production loss of the blade, and adjusting a heating cycle of the blade based on the power production curve that minimizes the net power productions loss of the blade.

A method of controlling a wind turbine having a nacelle, a rotor, a rotating hub, a first rotor blade and at least a second rotor blade, both rotor blades being mounted to the hub. The method includes measuring the strain in the first rotor blade by a strain measurement device attached to the first rotor blade; and choosing the operational parameters of the wind turbine based on the measured strain such that fatigue damage of the second rotor blade is reduced. A wind turbine is controlled by such a method.

The present disclosure relates to a method and a system for repairing a wind generator based on weather information. According to an exemplary embodiment of the present disclosure, it is possible to effectively calculate a repairing time of the wind generator based on weather information.

A method of estimating the energy production of a wind turbine or group of wind turbines is described. The method comprises obtaining, from a climate library, climate data in respect of a selected geographical location, the climate data comprising wind speed and direction at the selected geographical location, generating a plurality of power curves, each power curve defining a power output of a wind turbine as a function of wind speed for a particular climatic condition or range of climatic conditions, and estimating an energy production for the wind turbine or group of wind turbines using the generated power curves and wind speed data.

The present invention provides a turbine control system and method. The turbine control system includes at least one control means accommodated on a turbine rotor wherein the control means is actuated by a controller in a first or second direction on a plane of the rotor turbine to control a rate of change of moment of inertia of the turbine and thereby controlling the operation of the turbine. The invention also provides a turbine farm including a plurality of turbines operating to provide a maximum and stable power output. The farm includes a master controller configured for controlling the operation of each of the plurality of turbines and individual controller of the turbines efficiently.

The invention provides a method for controlling a wind turbine, including predicting behaviour of one or more wind turbine components such as a wind turbine tower over a prediction horizon using a wind turbine model that describes dynamics of the one or more wind turbine components or states. The method includes determining behavioural constraints associated with operation of the wind turbine, wherein the behavioural constraints are based on operational parameters of the wind turbine such as operating conditions, e.g. wind speed. The method includes using the predicted behaviour of the one or more wind turbine components in a cost function, and optimising the cost function subject to the determined behavioural constraints to determine at least one control output, such as blade pitch control or generator speed control, for controlling operation of the wind turbine.

A method for operating a plurality of wind turbines, in which a first current estimated wind value is derived from operating parameters of a first wind turbine, and in which a second current estimated wind value is derived from operating parameters of a second wind turbine. A prediction model is applied to derive, from the first current estimated wind value and the second current estimated wind value, a wind prediction, applicable to a future time point, for a third wind turbine. The wind prediction is processed in a controller, in order to generate a control signal for the third wind turbine that is effective before the future time point. The invention additionally relates to an associated control system. The loading for particular wind turbines can be reduced in that the wind conditions are predicted for a future time point.

The invention relates to a method for creating a prediction model for ice build-up on wind turbines. The method comprises: detecting ice build-up data at at least one wind turbine; using meteorological data for the location of the at least one wind turbine; feeding in the ice build-up data of the at least one wind turbine and the meteorological data in a machine learning method; and creating a prediction model based on the machine learning method. The invention also relates to a method for predicting ice build-up on wind turbines. The method comprises: detecting ice build-up data at at least one wind turbine; using meteorological data for the location of the at least one wind turbine; and predicting an ice build-up based on a machine-learned model.