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.

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 for quickly predicting a fatigue life of a wrinkle defect-containing main spar in a wind turbine blade is provided. The method includes: S1: testing a tensile property of a wrinkle defect-containing main spar to be tested; S2: calculating, according to surface temperature data of the specimen obtained in step S1, intrinsic dissipated energy of the main spar specimen under different loading stresses; S3: plotting a relational graph between intrinsic dissipated energy of the specimen and a corresponding ultimate tensile strength (UTS) level; S4: establishing, based on a change of the intrinsic dissipated energy in a fatigue process, a normalized residual stiffness model containing parameters to be determined, and putting fatigue test data into the model; S5: deducing a fatigue life prediction model for the wrinkle defect-containing main spar specimen according to the normalized residual stiffness model with determined parameters; and S6: obtaining a normalized failure stiffness.

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.

A method of adjusting at least one rotor blade of a wind turbine which includes the steps of receiving a command for adjustment of the rotor blade at an adjustment speed, ascertaining a current moment of inertia of the rotor blade and acceleration of the rotor blade until reaching the adjustment speed, wherein the acceleration until reaching the adjustment speed is limited in dependence on the ascertained moment of inertia. A system and a wind turbine.

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.

A method of operating at least one wind turbine including: receiving a first signal which is indicative of a level of a remuneration for electrical energy, which is fed currently and/or in the future into a grid to which the wind turbine is connected, producing a control signal dependent on the first signal, and controlling the wind turbine with the control signal for generating power of the wind turbine, that is dependent on the control signal. A wind turbine and a central control for carrying out such a method and a system comprising a central control and a plurality of wind turbines.

A method and a system for controlling a wind turbine based on sectors. Original sectors of the wind turbine are reconstructed based on a wind resource parameter and a wake-flow effect. A load is calculated and superposed for a new sector obtained from the sector reconstruction. An optimization algorithm is applied, under a condition that a constraint condition of a fatigue load is met, to find an operation parameter for maximum power generation amount of the wind turbine.