A system and method are provided for controlling a wind turbine of a wind farm. Accordingly, a controller prepares a yaw bias correction function based, at least in part, on a yaw offset function, and on wind speed measurement data and wind direction reference data of a wind event acting on at least a portion of the wind farm. The controller also applies the yaw bias correction function based at least in part on position data of a nacelle of the wind turbine, to yaw the nacelle of the wind turbine.

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.

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 for iteratively estimating a wind speed at a wind turbine is disclosed, said wind turbine comprising a rotor carrying a set of wind turbine blades, each wind turbine blade having a variable pitch angle. A minimum tip speed ratio, λ.sub.min, is derived, based on an obtained pitch angle, θ. The minimum tip speed ratio, λ.sub.min, defines a limit between a stable and an unstable control region. An initial tip speed ratio, λ.sub.init>λ.sub.min, is selected, and an initial estimated wind speed, v.sub.init, is derived based on the initial tip speed ratio, λ.sub.int, and an obtained rotational speed, ω, of the rotor. An estimated wind speed, v.sub.est, is iteratively derived, based on the obtained rotational speed, ω, and the obtained pitch angle, ω, and using the derived initial estimated wind speed, v.sub.init, as a starting point. The iterative process converges fast and reliably.

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.

This disclosure related to systems and methods that facilitate the secure collection and management of operational data relating to a power generation system that includes one or more wind turbines. Embodiments disclosed herein may also be used to provide various insights on wind farm operation and management using collected operational data. Further embodiments facilitate policy-managed access to operational data, including policy-managed access implementing differential privacy, in a manner allowed and/or otherwise controlled by parties having ownership rights or interests in the data.

An Equipment Health Monitoring method for an engine and an Equipment Health Monitoring system for performing the method are provided. At least some of the following units are used: an Engine Simulation Unit, a Possibilistic Drift Computation Unit, a Fuzzy String Generator Unit, an Experience-based String Matching Unit and an Information Fusion and Prognosis Unit.

The present disclosure is directed to a system and method for forecasting a farm-level power output of a wind farm having a plurality of wind turbines. The method includes collecting actual operational data and/or site information for the wind farm. The method also includes predicting operational data for the wind farm for a future time period. Further, the method includes generating a model-based power output forecast based on the actual operational data, the predicted operational data, and/or the site information. In addition, the method includes measuring real-time operational data from the wind farm and adjusting the power output forecast based on the measured real-time operational data. Thus, the method also includes forecasting the farm-level power output of the wind farm based on the adjusted power output forecast.

A system and method are provided for controlling a wind turbine based on a collective pitch-offset. Accordingly, a wind condition acting on a rotor of the wind turbine is determined, and a first collective pitch angle for the plurality of rotor blades is set, and the wind turbine is operated. A thrust of the rotor based, at least in part, on the wind condition is determined, and an actual collective pitch angle is calculated. A collective pitch offset is determined based on the difference between the first collective pitch angle and the actual collective pitch angle. The collective pitch offset is integrated with at least one pitch setpoint command. The at least one pitch setpoint command is transmitted to a pitch control mechanism of the wind turbine.