A method for providing backup control for a supervisory controller of at least one wind turbine includes observing, via a learning-based backup controller of the at least one wind turbine, at least one operating parameter of the supervisory controller under normal operation. The method also includes learning, via the learning-based backup controller, one or more control actions of the at least one wind turbine based on the operating parameter(s). Further, the method includes receiving, via the learning-based backup controller, an indication that the supervisory controller is unavailable to continue the normal operation. Upon receipt of the indication, the method includes controlling, via the learning-based backup controller, the wind turbine(s) using the learned one or more control actions until the supervisory controller becomes available again. Moreover, the control action(s) defines a delta that one or more setpoints of the wind turbine(s) should be adjusted by to achieve a desired outcome.

A motion controller for a floating wind turbine including a number of rotor blades is provided. The motion controller is arranged to adjust the blade pitch of each rotor blade when the floating wind turbine is operating in winds below the rated wind speed so as to create a net force that damps a surge motion of the floating wind turbine. Also provided is a method of damping the motion of a floating wind turbine and a wind turbine having such a motion controller.

A service life evaluation method and device for a pitch bearing of a wind turbine are provided. The method includes: acquiring a probability density of a pitch driving torque in M historical periods, wherein M is a positive integer; acquiring an angle cumulative value of a pitch angle in each of the M historical periods; determining an equivalent load of the pitch bearing based on the pitch driving torque, the probability density of the pitch driving torque in the M historical periods, and angle cumulative values in the M historical periods; and determining a consumed service life of the pitch bearing based on the equivalent load of the pitch bearing

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 is a method of determining the free-flow wind speed (V.sub.?) for a wind farm, using measurements (MES), a wind farm model (MOD) and an ensemble Kalman filter (KEN).

The present disclosure is related to methods for determining a frequency of an oscillation mode of a wind turbine, comprising: determining a motion of a first mass of a first tuned mass damper in the wind turbine and deriving the frequency of the oscillation mode of the wind turbine at least partially based on the determined motion of the first mass. The present disclosure further relates to methods for operating a wind turbine, and to wind turbines, particularly offshore wind turbines, comprising tuned mass dampers.

A load control method and a load control apparatus for a wind turbine generator system are provided, and the load control method includes: obtaining feature parameters of the wind turbine generator system for load prediction; obtaining a load estimation value of the wind turbine generator system by inputting the obtained feature parameters into a virtual load sensor; adjusting a control strategy of the wind turbine generator system based on the obtained load estimation value. A controller and a computer readable storage medium storing a computer program are further included. With the load control method and apparatus for the wind turbine generator system, a trained virtual load sensor can be used to realize real-time monitoring of the load of the on-site wind turbine generator system, and a reference for adjusting the control strategy can be provided according to the load.

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.

Renewable energy provides humanity with a means of harvesting natural phenomena. However, the generating means are typically non-linear and the natural phenomenon variable such that the resulting electrical output is similarly variable and difficult to predict impacting their operators as well as consumers, regulators, planners, government bodies, etc. It would be beneficial therefore to provide engineers, infrastructure operators, regulators, planners etc. with a framework that allows for the electrical output from specific elements of infrastructure to be predicted. This framework being implantable, for example, through software processes and methods either associated with the elements of infrastructure or independent from the elements of infrastructure.

A method for tracking a gear tooth meshing angle of a gearbox of a wind turbine is disclosed. An initial reference virtual gear tooth meshing angle of the gearbox is selected, and an angular position of a high speed shaft and/or a low speed shaft of the gearbox is monitored. A virtual gear tooth meshing angle relative to the reference virtual gear tooth meshing angle is estimated, based on the monitored angular position of the high speed shaft and/or the low speed shaft and on information regarding topology of the gearbox. A number of full rotations of the high speed shaft and/or the low speed shaft which corresponds to an integer number of full periods of gear meshing of the gearbox is calculated, and the reference virtual gear tooth meshing angle is reset each time the high speed shaft and/or the low speed shaft has performed the calculated number of full rotations. The estimated virtual gear tooth meshing angle is applied to a periodic noise signal of the wind turbine.