A system and method are provided for controlling a wind turbine of a wind farm. Accordingly, a controller implements a first model to determine a modeled performance parameter for the first wind turbine. The modeled performance parameter is based, at least in part, on an operation of a designated grouping of wind turbines of the plurality of wind turbines, which is exclusive of the first wind turbine. The controller then determines a performance parameter differential for the first wind turbine at multiple sampling intervals. The performance parameter differential is indicative of a difference between the modeled performance parameter and a monitored performance parameter for the first wind turbine. A second model is implemented to determine a predicted performance parameter of the first wind turbine at each of a plurality of setpoint combinations based, at least in part, on the performance parameter differential the first wind turbine. A setpoint combination is then selected based on the predicted performance parameter and an operating state of the first wind turbine is changed based on the setpoint combination.

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.

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).

A system and method are provided for operating a wind farm. Accordingly, a wind direction affecting the wind farm is determined. Based on the wind direction, a controller identifies a turbine cluster, which is a subset of a plurality of wind turbines of the wind farm. The subset includes at least an upwind turbine and a downwind turbine that is affected by a wake emanating from the upwind turbine. With the turbine cluster identified for the given wind direction, the controller then determines a difference between a freestream maximal cluster power output and a wake-affected cluster power output for the turbine cluster. The controller then determines a mitigation setpoint combination for the subset of wind turbines. The mitigation setpoint combination is configured to establish a mitigated cluster power output. Mitigated cluster power output has a difference from the freestream maximal cluster power output that is less than the difference between the freestream maximal cluster power output in the wake-affected cluster power output for the turbine cluster. Based on the mitigation setpoint combination, an operating state of at least one wind turbine of the turbine cluster is changed.

To solve the problem of a mis-calibration of a wind turbine a computer-implemented method for re-calibrating at least one yaw-angle of a wind turbine starting from an initial yaw-angle calibration of said wind turbine, based on determining a turbulence intensity estimation value (20) related to said appropriate yaw-angle (10), wherein the turbulence intensity (TI) being a ratio of wind speed deviation to average wind speed over a pre-determined period of time. Further, to solve the problem of a mis-calibration of a wind turbine a system for re-calibrating at least one yaw-angle of a wind turbine based on above re-calibration method. Further, to solve the problem of a management of a wind park below optimum a computer-implemented method for wind park optimization based on simulation calculation including turbulence intensity estimation values (20) estimating said at least one effecting wind turbine (101,102,103) to suffer from wake from said at least one effected wind turbine (100,101,102). Further, to solve the problem of a management of a wind park below optimum a wind park, including a management system for optimizing that wind park based on above optimization method. Moreover, present invention relates to a computer-readable medium comprising such methods.

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 and method are provided for controlling a wind turbine in response to a blade liberation event. Accordingly, estimated response signatures for the wind turbine are determined. Sensor data indicative of at least two actual response signatures of components of the wind turbine to a rotor loading are collected. The actual response signatures are compared to the estimated response signatures. The two or more actual response signatures meeting or exceeding the estimated response signatures is indicative of a blade liberation event. In response to detecting the blade liberation event, a rapid shutdown control logic is initiated to decelerate the rotor at a rate which exceeds a nominal deceleration rate of the rotor.

A method for estimating a temperature of a motor of a pitch drive mechanism of a rotor blade of a wind turbine includes monitoring, via at least one sensor, an actual temperature and at least one additional operating condition of the motor during a normal operating period of the wind turbine. The method also includes storing, via a pitch controller, the monitored temperatures and the monitored additional operating conditions of the motor for the normal operating period. Further, the method includes determining a relationship between the monitored temperatures and the monitored additional operating conditions of the motor for the normal operating period. Thus, in the event that the sensor fails to operate, the method includes determining, via the pitch controller, an estimated temperature of the motor based on the relationship.

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.