Aspects of the present invention relate to a method for determining annual energy production of a wind farm. The method comprises: receiving a definition of the wind farm; modelling the wind field of the wind farm for a plurality of freestream wind speeds in a plurality of wind directions; defining locations for virtual measurement masts upstream of upstream turbines in each wind direction at a clearance from each upstream turbine for identifying wind farm blockage effects; determining a wind farm blockage factor for each freestream wind speed in each wind direction based on data from the virtual measurement masts; receiving measurements from a physical measurement mast located at the site of the wind farm; and determining an annual energy production for the wind farm based on adjusted wind speeds obtained by applying the blockage factors to measurements from the physical measurement mast.

A method predicting and avoiding faults that result in a shutdown of a wind turbine includes receiving operational data of the wind turbine. The method also includes predicting, via a predictive model, current or future behavior of the wind turbine using the operational data. Further, the method includes determining, via a fault detection model, whether the current or future behavior indicates an upcoming short- or long-term fault occurring in the wind turbine. Moreover, the method includes determining, via a prescriptive action model, a corrective action for the wind turbine based on whether the future behavior of the wind turbine indicates the upcoming short- or long-term fault occurring in the wind turbine. Thus, the method also includes implementing the corrective action during operation to prevent the upcoming short- or long-term fault from occurring.

A system for generating power includes an environmental engine operating on one or more computing devices that determines a wind flowing over a blade of a wind turbine, wherein the wind flowing over the blade of the wind turbine varies based on environmental conditions and operating parameters of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between at least two of a rotor speed, a blade pitch, the wind flowing over the blade, a wind speed and a turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric, and simulates execution of the selected model to determine recommended operating parameters.

A method and apparatus for inspecting a wind turbine blade. The method includes: acquiring a sound signal generated by an impingement of wind on the wind turbine blade using a sound acquisition device; generating a frequency spectrogram corresponding to the sound signal; and obtaining a damage recognition result of the wind turbine blade from the frequency spectrogram by performing image recognition on the frequency spectrogram based on a damage recognition model. With the method, a damage type of the wind turbine blade is accurately recognized based on the frequency spectrogram without manual inspection. Therefore, human resources are saved. In addition, the health state of the wind turbine blade can be monitored in real time.

An airfoil member having a root end, a tip end, a leading edge, and a trailing edge, the airfoil member including an upper skin; a lower skin; and a support network having a plurality of interconnected support members in a lattice arrangement and/or a reticulated arrangement, the support network being configured to provide tailored characteristics of the airfoil member. Also provided are methods and systems for repairing an airfoil member.

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.

A system for computing wind turbine estimated operational parameters and/or control commands, includes sensors monitoring the wind turbine, a control processor implementing a model performing a linearization evaluation to obtain a structural component dynamic behavior, a fluid component dynamic behavior, and/or a combined structural and fluid component dynamic behavior of wind turbine operation, and a module performing a calculation utilizing the linearization evaluation of the structural component dynamic behavior, the fluid component dynamic behavior, and/or the combined structural and fluid component dynamic behavior. The module being at least one of an estimation module and a multivariable control module. The estimation module generating signal estimates of turbine or fluid states. The multivariable control module determining actuator commands that include wind turbine commands that maintain operation of the wind turbine at a predetermined setting in real time. A method and a non-transitory medium are also disclosed.

An example method comprises receiving first historical data of a first time period and failure data, identifying at least some sensor data that was or potentially was generated during a first failure, removing the at least some sensor data to create filtered historical data, training a classification model using the filtered historical data, the classification model indicating at least one first classified state at a second period of time prior to the first failure indicated by the failure data, applying the classification model to second sensor data to identify a first potential failure state based on the at least one first classified state, the second sensor data being from a subsequent time period, generating an alert if the first potential failure state is identified based on at least a first subset of sensor signals generated during the subsequent time period, and providing the alert.

A method for controlling a wind turbine connected to an electrical grid includes receiving, via a controller, a state estimate of the wind turbine. The method also includes determining, via the controller, a current condition of the wind turbine using, at least, the state estimate, the current condition defining a set of condition parameters of the wind turbine. Further, the method includes receiving, via the controller, a control function from a supervisory controller, the control function defining a relationship of the set of condition parameters with at least one operational parameter of the wind turbine. Moreover, the method includes dynamically controlling, via the controller, the wind turbine based on the current condition and the control function for multiple dynamic control intervals.

Techniques for identifying a geographic location of a wind turbine using an image. An image depicting a geographic area is received. A location is identified, in the image, relating to a place where a wind turbine tower depicted in the image meets ground depicted in the image. This includes identifying two objects relating to the wind turbine tower in the image, and distinguishing a bottom end of the wind turbine tower from a top end of the wind turbine tower based on the identified two objects. A geographic location corresponding to the determined image location is determined, based on the image and the determined image location. A location of the wind turbine tower, in a database of wind turbine locations, is updated based on the geographic location.