A method for operating a cluster of a plurality of wind turbines is disclosed. For each of the wind turbines, one or more parameter values of a parameter being indicative for a condition occurring at the wind turbine are derived, based the measurements obtained by the wind turbine. In the case that a derived parameter value for a specific wind turbine exceeds a trigger value, measures for mitigating an effect of the condition at the specific wind turbine are initiated. The derived parameter values for the wind turbines of the cluster of wind turbines are compared to an expected distribution of the parameter values. In the case that a distribution of the derived parameter values differs from the expected distribution of the parameter values, the trigger value is adjusted, and the adjusted trigger value is subsequently applied when comparing the derived parameter values to the trigger value.

A method for operating a cluster of a plurality of wind turbines is disclosed. For each of the wind turbines, one or more parameter values of a parameter being indicative for a condition occurring at the wind turbine are derived, based the measurements obtained by the wind turbine. In the case that a derived parameter value for a specific wind turbine exceeds a trigger value, measures for mitigating an effect of the condition at the specific wind turbine are initiated. The derived parameter values for the wind turbines of the cluster of wind turbines are compared to an expected distribution of the parameter values. In the case that a distribution of the derived parameter values differs from the expected distribution of the parameter values, the trigger value is adjusted, and the adjusted trigger value is subsequently applied when comparing the derived parameter values to the trigger value.

A method of estimating a second power curve of a wind turbine regarding a second operational configuration includes: receiving power output data pertaining to a first operational configuration, in particular reference operational configuration; receiving power data relating to power output pertaining to the second operational configuration; deriving the second power curve using the power output data pertaining to the first operational configuration, the power output data pertaining to the second operational configuration and a first power curve of the first operational configuration.

Calculating energy loss during an outage, including: determining that windspeed data indicating device windspeeds measured at an energy generating device are unavailable within a particular time duration; receiving meteorological data associated with a site location of the energy generating device, the meteorological data including meteorological windspeed data collected within the particular time duration; and predicting one or more estimated device windspeeds at the energy generating device during the particular time duration based on the meteorological data using a trained model for the energy generating device, the trained model being trained using a machine learning algorithm that utilizes historical meteorological windspeed data associated with the site location collected during a previous time duration and corresponding historical device windspeed data measured at the energy generating device during the previous time duration.

The present invention relates to method for estimating energy production (107) from a wind turbine (101) with plurality of blades (102). The method comprises obtaining one or more infrared images (103) of each blade (102) of the wind turbine (101). Further, identifying one or more cross-sectional regions (302) of each of the blade (102) using the one or more infrared images (103) based on a boundary region (301), wherein the boundary region (301) is indicating a transition from a laminar air flow to a turbulent air flow. Furthermore, determining plurality of polar values indicative of an aerodynamic profile for each of the one or more cross-sectional regions (302) based on one or more panel method based techniques and the boundary region (301). Finally, estimating the energy production (107) for the wind turbine (101) based on one or more blade (102)-element momentum (BEM) based techniques using the plurality of polar values.

A control arrangement for a variable-speed wind turbine includes a loading analysis module configured to analyse a number of environment values to establish whether the momentary wind turbine loading is lower than a loading threshold when the rotational speed of the aerodynamic rotor has reached its rated value; and a speed boost module configured to determine a speed increment for the rotational speed of the aerodynamic rotor if the wind turbine loading is lower than the loading threshold.

Evaluating localized atmospheric conditions for selected cloud seeding to enhance localized electrical power generation from wind turbines by receiving, at a computer, wind farm data related to a plurality of wind turbines for generating electrical power at a location. The wind farm data collected from sensors at the location. An atmospheric condition in the atmosphere at the location is assessed by the computer, using the wind farm data and the data of the atmospheric conditions. The computer generates a prediction of an impact of the atmospheric condition on the atmospheric wind speed resulting in a wind turbine power output reduction. A determination is made when to initiate cloud seeding to generate rain at the location and reduce the atmospheric condition. Generating a communication to a control system which includes a recommendation to initiate the cloud seeding based on the prediction.

The present invention concerns a method for determining the production availability of an offshore wind farm comprising at least one floating wind turbine, the method comprising: obtaining wind farm data, obtaining strategy data relative to operation and maintenance resources to carry out an action on the floating wind turbine(s), obtaining meteorological data relative to an offshore environment for the offshore wind farm over a given period of time, determining motion parameters as a function of the wind farm data and of the meteorological data, and determining the production availability of the offshore wind farm in the offshore environment over the given period of time on the basis of the wind farm data, of the strategy data, of the meteorological data, and of the determined motion parameters.