The invention relates to a system for monitoring wind turbine components including an independent data processing environment adapted to: receive a first category of data input related to operation of the wind turbine, process the received data input by one or more component specific monitoring algorithms adapted to establish an estimated component value related to a component to be monitored based on received first category data input having at least indirectly impact on the component, wherein the component specific monitoring algorithm is adapted to establish a component residual as the difference between the estimated component value and received first category of data input of the component to be monitored, and wherein the component specific monitoring algorithm furthermore is adapted to establish a component specific health value of the component to be monitored based on the established residual and put the health value at disposal for data processors outside the environment.

The present disclosure relates to a method of using a feedback loop from sensors to manipulate sea waves by applying concepts from optics interference and lensing. The method includes capturing, by one or more sensors, environmental data of an aquatic environment, wherein the environmental data relates to one or more of a wind speed, a wind direction, a wave pattern, a wave spectra, and a wave direction. The method includes analyzing, by one or more processors of a controller, the environmental data to identify a sensed environmental condition. Further, the method includes determining an optimal configuration of a wave interference device in the sensed environmental condition, wherein the controller is communicatively coupled to the wave interference device; and configuring the wave interference device to occupy the optimal configuration.

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.

A control method for increasing reactive power generation of a wind turbine having a Doubly-Fed Induction Generator (DFIG) includes obtaining, by a control device having one or more processors and one or more memory devices, wind forecast data of the wind turbine. Further, the method includes generating, by the control device, a real-time thermal model of the DFIG of the wind turbine using the wind forecast data. More specifically, the thermal model defines a thermal capacity for the DFIG that does not exceed system limits. Thus, the method also includes dynamically adjusting, by the control device, a reactive power set point of the DFIG of the wind turbine based on the real-time thermal model.

Techniques for determining a layout of a wind energy plant comprising a plurality of wind turbines at a site, where the wind turbines are configured for connection to a power grid having a power demand. Techniques include: providing an initial layout of wind turbines at initial positions within the site; obtaining site condition data for the initial layout; estimating an expected power output of the wind energy plant for a predetermined time period; forecasting the power demand within the power grid for the predetermined time period; performing an optimising process on the initial layout based on the estimated expected power output and on the forecasted power demand in order to match the expected power output to the forecasted power demand to obtain an optimised layout of the wind energy plant; and erecting the wind turbines in accordance with the optimised layout.

There is provided a method of avoiding edgewise vibrations during a non-operational period of a wind turbine. The method comprises defining a non-operational period for a wind turbine arranged at a specific site, determining expected wind conditions at the specific site during the non-operational period and defining a plurality of potential yaw orientations for the wind turbine. The method further comprises determining the relative probability of edgewise vibrations occurring during the non-operational period for each potential yaw orientation based upon the expected wind conditions during the non-operational period, determining one or more preferred yaw orientations, which are the yaw orientations in which the probability of edgewise vibrations occurring is lowest, and arranging the wind turbine in one of the preferred yaw orientations during the non-operational period.

A proactive method and related wind turbine system are provided for reducing vibrations in the rotor blades when the rotor hub is locked against rotation. The method includes determining an initial blade orientation to wind direction and wind parameters for wind impacting the rotor blades. Based on the wind parameters and blade orientation, an angle of attack is determined for the rotor blades that will at least reduce vibrations expected to be induced in the blades from the current wind conditions. With a controller, the rotor blades are pitched to achieve the angle of attack using a pitch control system. The angle of attack is determined and the rotor blades are pitched from the initial blade orientation to the new angle of attack prior to vibrations being induced in the rotor blades.

The present disclosure is directed to a system and method for assessing farm-level performance of a wind farm. The method includes operating the wind farm in a first operational mode and identifying one or more pairs of wind turbines having wake interaction. The method also includes generating a pairwise dataset for the wind turbines pairs. Further, the method includes generating a first wake model based on the pairwise dataset and predicting a first farm-level performance parameter based on the first wake model. The method also includes operating the wind farm in a second operational mode and collecting operational data during the second operational mode. Moreover, the method includes predicting a first farm-level performance parameter for the second operational mode using the first wake model and the operational data from the second operational mode. The method further includes determining a second farm-level performance parameter during the second operational mode. Thus, the method includes determining a difference in the farm-level performance of the wind farm as a function of the first and second farm-level performance parameters.

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.

The present disclosure is directed to a digital system for managing a wind farm having a plurality of wind turbines electrically coupled to a power grid. The system includes a farm-based first communication network having one or more individual wind turbine control systems communicatively coupled to the one or more wind turbines and an overall wind farm control system. The system also includes a cloud-based second communication network communicatively coupled to the first communication network via an industrial gateway. The second communication network includes a digital infrastructure having a plurality of digital models of the one or more wind turbines, wherein the plurality of digital models of the one or more wind turbines are continuously updated during operation of the wind farm via data supplied by the farm-based first communication network.