A correction method and a correction apparatus for a predicted wind speed of a wind farm is provided according to the present disclosure. The correction method includes: establishing wind speed deviation matrixes of a plurality of existing wind farms respectively; establishing a wind speed deviation correction model library based on the plurality of wind speed deviation matrixes of the existing wind farms; determining relevant parameters of the target wind farm, determining a matched wind speed deviation correction model in the wind speed deviation correction model library based on the relevant parameters, and correcting the predicted wind speed of the target wind farm based on the determined wind speed deviation correction model.

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.

A method for controlling a wind turbine, the wind turbine having a generator with controllable generator torque and an aerodynamic rotor with rotor blades with adjustable pitch angle, the aerodynamic rotor driving the generator with variable rotor speed depending on a wind speed, comprising the steps operating the wind turbine in a subrated mode when the wind speed is below a predetermined rated wind speed, operating the wind turbine in a rated mode when the wind speed is at or above the predetermined rated wind speed, estimating the wind speed and operating the wind turbine in subrated mode or in rated mode in dependence on the estimated wind speed.

A method of controlling a wind turbine, the method comprising: determining an initial thermal model representing thermal characteristics of a plurality of components of a first wind turbine; receiving operational data relating to thermal characteristics of components of a plurality of wind turbines; processing the initial thermal model and the operational data using an optimisation algorithm to determine a modified thermal model for the plurality of components of the first wind turbine; and controlling the first wind turbine in accordance with the modified thermal model.

A system for generating power includes an environmental engine that determines performance metrics for a plurality of wind turbines deployed at a plurality of windfarms, such that each windfarm includes a corresponding subset of the plurality of windfarms. The performance metrics for a given wind turbine of the plurality of wind turbines characterizes wind flowing over blades of the given wind turbine. The system includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a set of models for each wind turbine of the plurality of wind turbines, wherein each model of each set of models is generated with a different machine learning algorithm and selects, for each respective set of models, a model with a highest efficiency metric. The AI engine provides edge computing systems operating at the plurality of windfarms with a selected model and corresponding recommended operating parameters.

A method for adjusting a multi-dimensional operating space of a wind turbine includes receiving, via a central multi-dimensional operating space controller, a plurality of signals from a plurality of requestors of modified operating space. Each of the plurality of signals includes a data structure having requested set points for a plurality of dimensions in the operating space. The method also includes tracking, via the central multi-dimensional operating space controller, current set points for the plurality of dimensions in the operating space. Further, the method includes dynamically determining, via the central multi-dimensional operating space controller, an output signal based on the requested set points, the output signal comprising one or more changes for the current set points for the plurality of dimensions in the operating space. Moreover, the method includes controlling the wind turbine based on the output signal so as to provide a modified multi-dimensional operating space.

A data-driven wind farm frequency control method based on dynamic mode decomposition. The method enables a low-dimension nonlinear dynamic feature of a wind power system to perform global capturing in a high-dimension space through a state transition matrix given by a Koopman operator theory, thus fewer data samples are necessary while control requirements are satisfied with respect to a model fitting accuracy. Meanwhile, a pure linear feature of a control model also provides a favorable foundation for fast on-line dynamic response, thereby satisfying response accuracy and speed requirements simultaneously in an actual control step.

Systems and methods are provided for the robust, multivariable control of a power generating asset via H-infinity loop shaping using coprime factorization. Accordingly, a controller of the power generating asset computes a gain value for an H-infinity (H∞) module in real-time at predetermined sampling intervals using an actuator dynamic model. The controller then determines an acceleration factor based, at least in part, on the gain value of the H∞ module. Based, at least in part on the acceleration vector, the controller generates a control vector. An operating state of at least one component of the power generating asset is changed based on the control vector.

Systems and methods for estimating a direction of wind incident on a wind turbine, the wind turbine comprising a tower; a rotor-nacelle-assembly (RNA) carried by the tower; a deflection sensor configured to sense a position of the RNA or a deflection of the tower; and a wind direction sensor. One approach includes: obtaining deflection training data from the deflection sensor; obtaining wind direction training data from the wind direction sensor; training a machine learning model on the basis of the deflection training data and the wind direction training data in order to obtain a trained machine learning model; obtaining further deflection data from the deflection sensor; inputting the further deflection data into the trained machine learning model; and operating the machine learning model to output a wind direction estimate on the basis of the further deflection data.

The present invention relates to a method of determining the wind speed in the plane of a rotor (PR) of a wind turbine (1), by measuring (MES2) the rotational speed of the rotor, the angle of the blades and the generated power. The method according to the invention uses a wind turbine model (MOD) constructed from wind speed measurements (LID), and by use of measurement clustering (GRO) and regressions (REG).