A method for the maintenance of a first wind energy installation from a group of wind energy installations. In the method, a future maintenance time period is identified in which a boost power of the group of wind energy installations is greater than a prescribed threshold value, wherein the boost power results from a wind speed predicted for the future maintenance time period, said wind speed being greater than a rated wind speed. The power of the first wind energy installation is reduced after the start of the maintenance time period and a boost power is drawn from a plurality of wind energy installations from the group of wind energy installations. A maintenance process is carried out at the first wind energy installation. The invention furthermore relates to a control unit suitable for carrying out the method.

A system and method for managing shadow flicker at a wind turbine site having at least one wind turbine includes determining, via a processor, a sun position of the wind turbine site. The method also includes receiving, via the processor, a spatial location of the at least one wind turbine with respect to at least one receptor. Further, the method includes determining, via the processor, whether shadow flicker is occurring at the at least one receptor based, at least in part, on the sun position and the spatial location of the at least one wind turbine. Moreover, the method includes implementing, via the processor, a corrective action for the at least one wind turbine when shadow flicker occurs at the at least one receptor to reduce the shadow flicker.

Provided is a wind turbine meteorological data collection and processing system that includes at least one wind turbine having a plurality of sensors configured to sense data including weather-related data, a controller configured to control the plurality of sensors, a processor configured to process the data, and a transmitter configured to transmit the data. The system also includes a computing platform in communication with the at least one wind turbine and to receive the data from the at least one wind turbine, and sort the data based on geographical location of the at least one wind turbine.

An energy-harvesting compute grid includes computing assemblies that cooperate with mobile energy harvesters configured to be deployed on a body of water. The plurality of energy harvesters are positioned on and move adjacent to an upper surface of a body of water, and the locations of the energy harvesters can be monitored and controlled. The wide-spread gathering by the harvesters of environmental data within that geospatial area permits the forecasting of environmental factors, the discovery of advantageous energy-harvesting opportunities, the observation and tracking of hazardous objects and conditions, the efficient distribution of data and/or tasks to and between the harvesters included in the compute grid, the efficient execution of logistical operations to support, upgrade, maintain, and repair the cluster, and the opportunity to execute data-gathering across an area much larger than that afforded by an individual harvester (e.g., radio astronomy, 3D tracking of and recording of the communication patterns of marine mammals, etc.). The computational tasks can be shared and distributed among a compute grid implemented in part by a collection of individual floating self-propelled energy harvesters thereby providing many benefits related to cost and efficiency that are unavailable to relatively isolated energy harvesters, and likewise unavailable to terrestrial compute grids of the prior art.

The present disclosure is directed to a system and method for controlling a wind turbine during adverse wind conditions. In one embodiment, the method includes monitoring one or more wind conditions near the wind turbine. Another step includes detecting one or more adverse wind conditions near the wind turbine. In response to detecting one or more adverse wind conditions, the method also includes reducing a power output of the wind turbine by a predetermined percentage. Further, the predetermined percentage is a function of a number and a type of the detected adverse wind conditions occurring during a predetermined time period.

Some embodiments are directed to a method for estimating a periodic or substantially periodic force present in a mechanical or electromechanical system, the method comprising: estimating, by a processing device, one or more harmonic frequencies of an acceleration signal representing an acceleration in the system, the substantially periodic force contributing to said acceleration; and estimating, by the processing device, the force on the basis of a dynamic model of the system, the dynamic model being defined by said one or more estimated harmonic frequencies.

In some examples, a system receives first sensor data from respective wind turbines of a plurality of wind turbines. For instance, the first sensor data may include at least a power output and a wind speed per time interval. The system trains at least one respective model for each respective wind turbine based on the first sensor data received from that respective wind turbine. Further, the system receives, for a second time period, respective second sensor data from the respective wind turbines. The system executes, using the respective second sensor data, the respective model trained using the first sensor data received from that respective wind turbine to determine, for each respective wind turbine, a predicted power output for an upcoming period. The predicted power outputs may be aggregated to determine a total predicted power output and at least one action is performed based on the total predicted power output.

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.

A method for operating a first wind turbine and a second wind turbine, the second wind turbine being located in the wake of the first wind turbine. A prediction model is fed with a current wind value of the first wind turbine, in order to predict a future time point at which the area swept by the rotor of the second wind turbine becomes partially overlapped by the wake of the first wind turbine. The second wind turbine reacts to the prediction in that a control signal is generated in order to alter the pitch angle of a rotor blade of the second wind turbine relative to the pitch angle of another rotor blade of the second wind turbine. The invention additionally relates to a control system suitable for executing the method. Implementation of the disclosed method by a control system can reduce the loading of the second wind turbine.

The present invention relates to methods, controllers, wind turbines and computer program products for controlling a wind turbine. One or more wind speed measurements upstream of a wind turbine are received 202 and a determination of an indication of a current wind speed at the wind turbine is made 204. The indication may include below rated wind speed or above rated wind speed. It is determined 205 if the wind speed is in an up transition region or a down transition region based on the received one or more wind speed measurements and the indication of said current wind speed. If determined that said wind speed is in an up transition region or a down transition region then a boost action is performed 206.