A method is proposed for operating at least one wind turbine of a wind farm including determining wind turbine individual offset information based on reactive power provided by the respective wind turbine and reactive power provided by at least one further wind turbine of the wind farm, determining wind turbine individual control information based on the wind turbine individual offset information and wind farm specific control information, operating the at least one wind turbine according to the wind turbine individual control information. Further, a controller and a device as well as a computer program product and a computer readable medium are also provided.

A method for controlling a virtual generator including at least one renewable power source, an accumulation system including a power and/or energy reserve, an inverter and a control law, the virtual generator delivering an active P/reactive Q electrical power of voltage V and of current I to a microgrid, the voltage V and current I having a frequency f, the active P/reactive Q electrical power controlling, via droop control, the frequency f and the RMS voltage V.sub.rms of the voltage V, respectively, the method including control of the virtual generator via the control law for which it carries out an adjustment of the active P/reactive Q power delivered to the microgrid, the adjustment being capable of compensating for a variation in the active/reactive power consumed by the microgrid.

Systems and methods to monitor a wind turbine azimuth drivetrain. Azimuth variation characteristics data are accumulated from wind turbines over a period of time. Clusters of values within the azimuth variation characteristics data are identified and a respective condition of the main drivetrain is associated with different clusters of values. After the associating, a measured set of azimuth variation characteristics data is received. A cluster corresponds to values in the measured set of azimuth variation characteristics data is determined and a condition associated with that cluster is determined to be a condition associated with the subject main drivetrain. That condition is then reported.

The present disclosure is directed to a system and method for controlling and/or upgrading aftermarket multivendor wind turbines. The system includes a turbine controller configured to control operations of the wind turbine, a safety device configured to provide a signal indicative of a health status of the safety device, and a secondary controller inserted between the safety device and the turbine controller. The secondary controller is configured to receive the signal from the safety device over a communication interface. As such, if the signal indicates a normal health status, the secondary controller is configured to send the signal to the turbine controller, i.e. maintain normal operation. Alternatively, if the signal indicates a poor health status, the secondary controller is configured to adjust the signal based at least in part on a signal bias to an adjusted signal and to provide the adjusted signal to the turbine controller.

Provided is a method and a system for computer-implemented determination of a drag coefficient as a control variable for controlling of a wind turbine, by receiving, as a data stream, a set of data from a number of data sources, the set of data consisting, for each data source, of a plurality of time series data values, acquired within a given time period at given points in time, and estimating, by a processing unit, the control variable based on the set of data as input of a machine learning algorithm being trained with training data of simulation time series data containing a number of operating states at different wind conditions and respective number of drag coefficients.

A method and apparatus for applying optimized yaw settings to wind turbines including receiving operating data from at least one wind turbine on a wind farm and sending the data to a supervisory control and data acquisition (SCADA) system on the at least one wind turbine to generate current SCADA data. The current SCADA data is sent a central processing center away from the wind farm. The central processing center includes an optimization system that can generate a new look up table (LUT) including at least one new wind turbine yaw setting calculated using information comprising wind direction, wind velocity, wind turbine location in the wind farm, information from a historic SCADA database, and yaw optimizing algorithms. The new LUT is then sent to a yaw setting selection engine (YSSE) where instructions regarding the use of the new LUT are generated.

Provided is a method for exchanging electric power with an electricity supply grid that has a grid frequency using a converter-controlled generation unit that may be a wind power installation or a wind farm, at a grid connection point. The method includes exchanging electric power depending on a control function. The electric power includes active and reactive power and the control function controls the power depending on at least one state variable of the electricity supply grid. It is possible to switch between a normal control function and a support control function, different from the normal control function, as the control function. The normal control function is used when it has been recognized that the electricity supply grid is operating stably and the support control function is used when a grid fault or an end of the grid fault has been recognized.

The invention relates to a method for operating a group of wind turbines in a wind power plant coupled to a utility grid, comprising the steps of determining that a wind turbine should be deactivated in response to receiving a power curtailment command from the grid, and retrieving data from each wind turbine in the group of wind turbines. Further, the method no comprises ranking all the wind turbines according to a set of ranking criteria and based on the retrieved data, and selecting a wind turbine to be deactivated based on the ranking. Further, the steps of data retrieval and ranking of all the wind turbines including any deactivated wind turbines in the group are repeated at time intervals, and the wind turbine to be deactivated is re-selected based on this updated ranking. The data may include a down time for any presently deactivated wind turbine in the group of wind turbines, reflecting for how long time the wind turbine has been presently deactivated, and the set of ranking criteria may then comprise a pause criterion taking into account the down time.

A control method and a control apparatus of a wind power generator set are provided, in which a wind speed at a location of the wind power generator set is acquired, turbulence intensity is calculated according to the wind speed, and a wind speed distribution range corresponding to the turbulence intensity is determined; a thrust variation amplitude of the thrust in the wind speed distribution range is determined based on a relationship among the thrust suffered by a wind wheel of the wind power generator set, a thrust coefficient and the wind speed; and a maximum rotating speed and a maximum torque of the wind wheel in the wind speed distribution range are adjusted according to the thrust variation amplitude. The maximum rotating speed and the maximum torque makes a fatigue load of the wind power generator set in the wind speed distribution range meet a preset standard.

The invention relates to a method for accessing SCADA data of a wind turbine in a protected manner and to an assembly designed to carry out said method. The SCADA data together with master data of the wind turbine is transmitted to a broker server in a digitally signed form, said broker server generating a metadata set on the basis of said data and transmitting the metadata set to user clients. If the user client is interested in the SCADA data, the user client transmits the metadata set back to the broker server in a digitally signed form. The broker server responds with a likewise digitally signed delivery data set comprising the metadata set signed by the user client and the SCADA and master data belonging to the metadata set.