Determination of Wind Turbine Configuration The present invention relates to a method and computer system for determining a configuration of a wind turbine of a given wind turbine type, the method comprising the steps of: storing in a database a plurality of combinations of physical and control parameters of the wind turbine that can be varied; determining a plurality of wind flow characteristics at a target location; applying a function which defines a relationship between a performance parameter, a fatigue life estimation, the combination of physical and control parameters and the plurality of wind flow characteristics, to at least some of the plurality of combinations in the database to determine values of the performance parameter and the fatigue life estimation for those combinations; and selecting one of the combinations of physical and control parameters as the configuration of the wind turbine for the target location on the basis of the performance parameter and fatigue life estimation values.

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).

The present disclosure is directed to a method for responding to a friction coefficient signal of a pitch bearing of a pitch drive mechanism of a wind turbine and/or for controlling the pitch drive mechanism(s) and/or a bank of ultracapacitors. The method and system include: accessing high-frequency measurement data of the at least one pitch bearing; estimating, via a torque balance model implemented by a controller, a frictional torque of the at least one pitch bearing based, at least in part, on the high-frequency measurement data; estimating, via the controller, a friction coefficient signal of the at least one pitch bearing based, at least in part, on the frictional torque; comparing the friction coefficient signal with a friction threshold; determining whether the friction coefficient signal deviates from the friction threshold based, at least in part, on the comparison; and, if so, acting.

A method for protecting a wind turbine from an extreme change in wind direction includes receiving a wind direction and/or a wind speed at the wind turbine. When a change in the wind direction or the wind speed exceeds a predetermined threshold, the method includes determining a margin to stall and/or zero lift of the at least one rotor blade of the wind turbine as a function of an angle of attack or change in the angle of attack at a blade span location of at least one rotor blade of the wind turbine. The method also includes implementing a corrective action for the wind turbine (without shutting down the wind turbine) when the margin to stall and/or zero lift exceeds a predetermined value so as to avoid stall and/or negative lift on the at least one rotor blade during operation of the wind turbine.

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 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.

A wave energy converter and method for extracting energy from water waves maximizes the energy extraction per cycle by estimating an excitation force of heave wave motion on the buoy, computing a control force from the estimated excitation force using a dynamic model, and applying the computed control force to the buoy to extract energy from the heave wave motion. Analysis and numerical simulations demonstrate that the optimal control of a heave wave energy converter is, in general, in the form of a bang-singular-bang control; in which the optimal control at a given time can be either in the singular arc mode or in the bang-bang mode. The excitation force and its derivatives at the current time can be obtained through an estimator, for example, using measurements of pressures on the surface of the buoy in addition to measurements of the buoy position. A main advantage of this approximation method is the ease of obtaining accurate measurements for pressure on the buoy surface and for buoy position, compared to wave elevation measurements.

A method for adjusting a power parameter of a wind turbine is disclosed. The method includes determining a load parameter indicative of a mechanical load of the wind turbine; estimating a turbulence of a wind speed based on the determined load parameter; and adjusting the power parameter relating to a power of the wind turbine based on the estimated turbulence. A system for adjusting a power parameter of a wind turbine is also described.

An energy audit tool for a wind turbine power system includes a data collector module configured for temporary connection to an existing turbine controller of the existing wind turbine power system. The data collector module is configured to collect operating data of the existing wind turbine power system. The energy audit tool also includes a model simulator module configured for analyzing the collected operating data, generating a model of the existing wind turbine power system based on the collected operating data, and determining an energy loss of the existing wind turbine power system from the model of the existing wind turbine power system.

A wind turbine includes a rotor, a plurality of rotor blades coupled to the rotor, and a blade pitch control system coupled to each rotor blade. A computer-implemented method for controlling the wind turbine includes determining at least one pitch position for a first blade. The method also includes determining whether there is a malfunction of the blade pitch control system associated with the first blade. The method further includes predicting a rotor imbalance using a model of at least a portion of the wind turbine. The method also includes comparing the predicted rotor imbalance with a predetermined threshold value. The method further includes one of regulating the pitch position for the second blade such that the predicted rotor imbalance is restored to a value below the predetermined threshold and regulating a pitch position for a second blade such that the predicted rotor imbalance does not exceed the predetermined threshold.