A method for operating a wind power installation for the purpose of generating electrical power from wind, wherein the wind power installation has an aerodynamic rotor having rotor blades that can be adjusted in their blade angle, and the rotor is operated at a settable rated rotor speed, wherein a turbulence class at a site of the wind power installation is determined, and the rated rotor speed is defined in dependence on the determined turbulence class.

An operation control system for a wind farm having wind turbines includes: a remaining-lifetime estimation unit for estimating remaining lifetime of a component of each wind turbine; a sales-income estimation unit for estimating, for each wind turbine, an income from sales of electric power under a plurality of output control conditions; a maintenance-cost estimation unit for estimating, for each wind turbine, maintenance cost based on the remaining lifetime of the component under each of the plurality of power limit conditions; a power-limit-condition selection unit for selecting, for each wind turbine, a power limit condition that maximizes income obtained from the wind farm, based on the income from sales of electric power and the maintenance cost estimated for each wind turbine under each of the power limit conditions; and an operation command unit for sending an operation command to each wind turbine based on the selected power limit condition.

A method for operating a wind turbine includes determining one or more loading and travel metrics or functions thereof for one or more components of the wind turbine during operation of the wind turbine. The method also includes generating, at least in part, at least one distribution of cumulative loading data for the one or more components using the one or more loading and travel metrics during operation of the wind turbine. Further, the method includes applying a life model of the one or more components to the at least one distribution of cumulative loading data to determine an actual damage accumulation for the one or more components of the wind turbine to date. Moreover, the method includes implementing a corrective action for the wind turbine based on the damage accumulation.

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.

A method of controlling a turbine of a floating wind turbine structure to reduce fatigue of moorings of the floating wind turbine structure includes curtailing the turbine based on a pitching motion of the floating wind turbine structure and on a wind direction at the floating wind turbine structure relative to an orientation of the moorings of the floating wind turbine structure. Optionally, the curtailment may be further based on a degree of displacement of the floating wind turbine structure from a reference location.

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.

A method and a system for estimating remaining components life of an operational wind turbine from actual wind turbine operation conditions after it was commissioned, using a data acquisition module configured to measure real historical data of said operational wind turbine, and an additional state detection unit configured to identify historical states of operation. The method comprises extracting historical data from the data acquisition module at time intervals, identify operational states of the wind turbine, validate the identified operational states and identify uncertain data that do not match. Next, simulate a turbine model for each operational state identified and wind condition thereof at each time interval, and calculate a fatigue equivalent load for each operational state and wind condition.

The present disclosure relates to methods and systems for determining fatigue loads in a wind turbine. Such a method may comprise obtaining operational metadata representative for an operational situation, and determining whether the operational metadata corresponds to operational metadata from one of a plurality of previously defined operational situations for which fatigue loads are known, the plurality of previously defined operational situations being stored in an operational situations database. If the operational metadata representative for the operational situation substantially corresponds to operational metadata from the stored previous operational situation, then the fatigue loads for the stored previously define operational situation are summed to historically accumulated fatigue loads to determine total accumulated fatigue loads. Methods for registering an operational situation in a wind turbine are also provided.

The present invention relates to a method of controlling a wind turbine by automatic online selection of a controller that minimizes the wind turbine fatigue. The method therefore relies on an (offline constructed) database (BDD) of simulations of a list (LIST) of controllers, and on an online machine learning step for determining the optimal controller in terms of wind turbine (EOL) fatigue. Thus, the method allows automatic selection of controllers online, based on a fatigue criterion, and switching between the controllers according to the measured evolution of wind condition.

A method for protecting a wind turbine from extreme and fatigue loads associated with high wind speed events includes receiving, via a wind turbine condition estimator programmed in a turbine controller of the wind turbine, operating data indicative of current wind turbine operation. Further, the method includes determining, via the wind turbine condition estimator, a plurality of estimated wind turbine conditions at the wind turbine by solving a control algorithm having one or more equations using the operating data. The estimated wind turbine conditions include, at least, an estimated wind speed at the wind turbine and a loading proxy of the wind turbine. As such, the method includes implementing, via the turbine controller, a corrective action only when each of the estimated wind turbine conditions indicates that one or more loading conditions of the wind turbine exceeds a predetermined limit.