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 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.

The present disclosure relates to controlling an operation of a wind turbine. A first plurality of extreme load measures indicative of extreme loads experienced by at least part of the wind turbine during the first period of time are determined and a load probability characteristic is then determined based on a statistical analysis of the distribution of the first plurality of extreme load measures. A control strategy for controlling the operation of the wind turbine is then modified based at least in part on a comparison of the load probability characteristic and a design load limit and the wind turbine is then subsequently controlled in accordance with the modified control strategy for a second period of time.

The present disclosure provides a method and an apparatus for self-adaption of a cut-out strategy. The method may include: predicting, using a wind speed prediction model, a wind resource parameter of a wind turbine at each machine location; predicting, using a load prediction model, a fatigue load and a limit load of the wind turbine based on the predicted wind resource parameter and an air density; comparing the predicted fatigue load and limit load with a reference load; and determining the cut-out strategy based on a result of the comparison, wherein determining the cut-out strategy includes determining a cut-out wind speed and an output power.

According to the disclosure, an artificial intelligence (AI) model receives a power production amount, a power production efficiency, a control variable and the like states as input information through information exchange between a wind turbine and the AI model, and therefore it is possible to provide a control method using the AI model with regard to even the wind turbine given no power coefficient.

Embodiments are generally directed to techniques for operating a wind turbine of a wind power plant. An associated method comprises determining, using one or more sensors of the wind turbine, a first power production level of the wind turbine; determining, during an unconstrained operation of the wind turbine, one or more available power correction factors using the first power production level; determining, using one or more wind power parameters applied to a predefined model for estimating an available power of the wind turbine, an estimated available power value; adjusting the estimated available power value using the one or more available power correction factors to produce the available power value; and controlling, using the available power value, the wind turbine to produce a second power production level.

Disclosed are a system and a method for reinforcement learning-based real time robust variable pitch control of a wind turbine system. The system includes: a wind speed collecting module to collect wind speed values of a wind farm; a wind turbine information collecting module to collect a rotor angular speed; a reinforcement signal generating module to generate a reinforcement signal based on the collected rotor angular speed and the rated rotor angular speed; a variable pitch robust control module including an action network and a critic network, wherein the action network is configured to generate an action value based on the wind speed of the wind farm and the rotor angular speed and output the action value to the critic network; the critic network is configured to perform learning training based on the reinforcement signal and the action value, generate a cumulative return value and output the cumulative return value to the action network; and the action network performs learning training based on the cumulative return value to update the action value and output the updated action value; and a control signal generating module connected to the action network, configured to generate a corresponding control signal based on the received action value. The wind power generator adjusts the pitch angle based on the control signal, which realizes adjustment of the rotor angle speed and guarantees smooth and stable power output of the wind turbine.

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.