A method of controlling pitch of individual blades in a wind turbine is described, together with a suitable controller. Wind speed is determined as a function of azimuthal angle. Wind speed is then predicted for individual blades over a prediction horizon using this determination of wind speed as a function of azimuthal angle. The predicted wind speed for each individual blade is used in a performance function, which is optimized to control individual blade pitches.

In one embodiment, a method includes determining, by a controller, a first wind pressure associated with a first port of a first probe, determining, by the controller, a second wind pressure associated with a second port of the first probe, and determining, by the controller, a reference wind pressure associated with an end portion of the first probe. The method also includes calculating, by the controller, a first reference differential using the first wind pressure and the reference wind pressure, calculating, by the controller, a first rotational differential using the first wind pressure and the second wind pressure, and calculating, by the controller, an angular coefficient using the first reference differential and the first rotational differential. The method further includes calculating, by the controller, a wind velocity using the first reference differential and the angular coefficient. The wind velocity represents a wind velocity relative to a vehicle.

A power control method and apparatus for a wind power generator. The power control method comprises: predicting, according to historical wind resource data, wind resource data within a predetermined future time period (S10); estimating, according to the remaining design lifetime of a wind power generator, the maximum design lifetime allowed to be consumed within the predetermined future time period (S20); determining, according to the predicted wind resource data and the estimated maximum design lifetime, optimal output powers of the wind power generator in respective wind velocity ranges within the predetermined future time period (S30); and controlling operation of the wind power generator according to the determined optimal output powers of the wind power generator in the respective wind velocity ranges within the predetermined future time period (S40).

Aspects of the present invention relate to controlling a renewable energy power plant comprising a plurality of wind turbine generators (WTG)s and an energy storage system (ESS). A method includes: controlling the plurality of WTGs to stop generating power, and thereby to enter a non-exporting mode of operation of the renewable energy power plant, during which one or more auxiliary systems of the renewable energy power plant are powered to maintain at least one of the plurality of WTGs in a standby state, operable to start generating power upon demand; wherein the one or more auxiliary systems are powered during the non-exporting mode of operation.

A computing system is configured to detect irregular yawing at wind turbines. To this end, the computing system (i) for each respective turbine of an identified cluster of wind turbines: (a) obtains yaw-activity data indicative of the respective turbine's yaw activity during a window of time, and (b) based on obtained yaw-activity data, derives a yaw-activity-measure dataset having measures of the respective turbine's yaw activity during time intervals within the window of time, (ii) based on the respective yaw-activity-measure datasets for the turbines in the cluster, derives a cluster-level yaw-activity-measure dataset, (iii) evaluates the respective yaw-activity-measure dataset for one or more turbines in the cluster as compared to the cluster-level yaw-activity-measure dataset, (iv) based on the evaluation, identifies at least one turbine of the cluster that exhibited irregular yaw activity, and (v) transmits, to an output device, a notification of the irregular yaw activity at the at least one turbine.

Provided is an apparatus and method for cooperative controlling wind turbines of a wind farm, wherein the wind farm includes at least one pair of turbines aligned along a common axis approximately parallel to a current wind direction and having an upstream turbine and a downstream turbine. The method includes the steps of: a) providing a data driven model trained with a machine learning method and stored in a database, b) determining a decision parameter for controlling at least one of the upstream turbine and the downstream turbine by feeding the data driven model with the current power production of the upstream turbine which returns a prediction value indicating whether the downstream turbine will be affected by wake, and/or the temporal evolvement of the current power production of the upstream turbine; c) based on the decision parameter, determining control parameters for the upstream turbine and/or the downstream turbine.

A bearing protection arrangement of a journal bearing arranged between a housing and a rotary component of a wind turbine. The bearing protection arrangement includes a wind speed monitor arranged to monitor wind speed in the vicinity of the wind turbine and to generate a wake-up signal when the wind speed exceeds a pre-defined minimum; a mode switch module of a backup battery arranged to provide restart power to an auxiliary of the wind turbine, which mode switch module is adapted to switch the backup battery from a normal-power mode into a low-power mode to conserve sufficient restart power after low wind-speed conditions, and to switch the backup battery from the low-power mode into a normal-power mode in response to the wake-up signal. A wind turbine and a method of protecting a journal bearing of a wind turbine during standstill.

A wind turbine control apparatus, method and non-transitory computer-readable medium are disclosed. The wind turbine control apparatus comprises a generator connected to a wind turbine with a drive train. The drive train comprises a rotor, a low speed shaft, a gear box, a high speed shaft, and a controller module. The controller module is configured to obtain a maximum power within a large range of varying wind velocities by operating the rotor at a neural network determined optimal angular speed for the current wind velocity.

A nacelle assembly for a wind turbine is connected to a wind turbine tower through a yaw system and includes a front region coupled to a rotor having a rotor hub and at least one rotor blade. The nacelle assembly includes a nacelle having a cover structure to house wind turbine components and an add-on wind flow deflector system that is coupled to an outside of the cover structure. The wind flow deflector system guides wind flowing to the nacelle for reducing a drag of the nacelle when a wind direction is misaligned with respect to the longitudinal wind direction in a yaw system failure event.

The present application discloses a power generation device so as to solve the generator set overspeed problem. The power generation device comprises: a stand column; and at least one generator set located on the stand column. The generator set comprises a support, blades connected to the support, and a power generator generating power by means of rotation of the blades, and an adjustment device located on the support and used for adjusting a windage area of the blades by moving or rotating the blade according to a wind speed. The wind power generation device may reduce an effective windage area to zero when the wind force is too high, thereby improving stability and applicability in a changeable environment and prolonging the service life of the apparatus.