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.

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.

A method for predicting remaining useful life of a wind or water turbine or component determines in step 116 an EOH for the turbine or component and compares this in step 118 to an EOH limit obtained in step 114. This provides a simple approach to estimating remaining useful life, giving the turbine operator an indication of the condition of turbines or farms under management.

The invention relates to monitoring a wind turbine having an electrical component. An exterior temperature and power loss associated with the electrical component is obtained, and a thermal model describing the electrical component is executed, based on the exterior temperature and power loss, to determine an internal temperature of the electrical component. A further thermal model describing the electrical component is executed, based on the internal temperature and an exterior component temperature, to predict a future thermal condition of the electrical component in order to monitor operation of the wind turbine.

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.

An automatic adjustment system for a variable-speed wind turbine is provided, having a power vector generator for generating a power vector (P.sub.vec) of wind turbine output power; a mapping unit for establishing relationships (β.sub.vec, ω.sub.vec) between wind turbine output power and rotational speed and pitch angle on the basis of the power vector (P.sub.vec) and an operational trajectory of the wind turbine; a wind speed estimator for estimating a wind speed (v.sub.est); an available power calculator for calculating the available power (P.sub.est) of the wind turbine on the basis of the estimated wind speed (v.sub.est) and the established relationships (β.sub.vec, ω.sub.vec). A method of performing automatic analysis of a wind turbine configuration, a computer program product for carrying out the steps of such a method, and a wind turbine having such an automatic adjustment system are also provided.

The present invention is a method for predicting a swell-resulting characteristic for a floating system. The method updates (MAJ) a spectral model (MSH) of the swell to form a swell prediction model (MPR). This model is applied to real-time measurements (MES) to predict the swell-resulting characteristic (pred).

A method for operating a wind turbine includes determining at least one wind condition of the wind turbine for a plurality of time intervals. The method also includes determining a status of the wind turbine at the beginning of each of the plurality of time intervals. Further, the method includes determining at least one vibration parameter of the wind turbine for one or more preceding time intervals of the plurality of time intervals. Moreover, the method includes predicting whether a trip event is imminent based on the at least one wind condition, the status of the wind turbine at the beginning of each of the plurality of time intervals, and the vibration parameter. Thus, the method further includes implementing a control action for the wind turbine so as to prevent the trip event.

A method for operating a wind turbine power system connected to an electrical grid includes collecting data relating to one or more parameters of one or more electrical components of the wind turbine power system. The method may also include performing a statistical analysis of the data relating to one or more parameters of the one or more electrical components. Further, the method includes predicting future behavior of the electrical component(s) based on the statistical analysis. Moreover, the method includes determining set points for the electrical component(s) using the predicted future behavior. In addition, the method includes operating the wind turbine power system at the determined set points for the electrical component(s) so as to optimize at least one characteristic of the electrical component(s).

A method of parameterizing a controller of a first wind energy installation wherein the controller sets a manipulated variable of the wind energy installation as a function of an input variable. An artificial intelligence determines at least one value of a parameter of the controller for at least one state/degree of being iced up of the wind energy installation based on a power curve, load, and/or downstream flow of the wind energy installation predicted with a mathematical model of the wind energy installation for at least one state/degree of being iced up, and/or determines at least one value of a parameter of the controller for at least one state/degree of being iced up of the wind energy installation, based on at least one determined state/degree of being iced up and a power, load, and/or downstream flow of the wind energy installation and/or at least one second wind energy installation.