Techniques for controlling rotor speed of a wind turbine. One technique includes defining a system model describing resonance dynamics of a wind turbine component, such as a wind turbine tower, where the system model has a nonlinear input term, e.g. a periodic forcing term. A transform is applied to the system model to obtain a transformed model for response oscillation amplitude of the wind turbine component, where the transformed model has a linear input term. A wind turbine model describing dynamics of the wind turbine is then defined, and includes the transformed model. A model-based control algorithm, e.g. model predictive control, is applied using the wind turbine model to determine at least one control output, e.g. generator torque, and the control output is used to control rotor speed of the wind turbine.

A method for determining an available power of a wind farm, wherein the wind farm comprises a plurality of wind power installations with a rotor having rotor blades, the blade angle of which can be adjusted is provided. A wind farm which is set up to carry out the method for determining an available power is provided. The method comprises providing a shading matrix which determines at least one effective wind speed of each of the wind power installations in the wind farm as a function of at least one wind speed and wind direction and wind farm throttling using a park wake model. The method makes it possible to accurately determine an available power of a wind farm even when the wind farm is operated with throttled power.

A method for constrained control of a wind turbine includes receiving a plurality of operating parameters corresponding to the wind turbine. The plurality of operating parameters includes a wind preview parameter and a plurality of constraint parameters. The method further includes generating a constraint parameter estimate corresponding to a future time instant for at least one constraint parameter of the plurality of constraint parameters based on the plurality of operating parameters and a wind preview model. The method also includes predicting an extreme event corresponding to the at least one constraint parameter based on the constraint parameter estimate. The method includes determining a control parameter value corresponding to a wind turbine control parameter among a plurality of wind turbine control parameters. The method also includes operating the wind turbine using a feedforward control technique based on the control parameter value to circumvent the extreme event.

Disclosed are a method and apparatus for detecting a fault, and a method and apparatus for training a model. The method includes: acquiring characteristic data and actual temperature of a first wind turbine among n wind turbines, wherein the characteristic data of the first wind turbine is intended to characterize a working state of the first wind turbine, and n is an integer greater than 1; acquiring a prediction temperature set by inputting the characteristic data of the first wind turbine into a temperature prediction model corresponding to each of the n wind turbines; and detecting, based on the predicted temperature set and the actual temperature of the first wind turbine, whether the first wind turbine encounters a fault. Compared with the related art which depends on the working experience of the staff, the technical solution according to the embodiments of the present disclosure can more accurately detect whether a wind turbine encounters a fault, and provide early warning in time, so as to reduce the failure rate of the wind turbine.

A method for monitoring of a wind turbine including: a) acquiring respective values of one or more operation variables of the wind turbine at the corresponding operation time point; b) determining the value of a tower clearance resulting from the acquired values of the operation variables with the aid of a trained data driven model, where the value of an output variable which is the tower clearance or a variable correlated with the tower clearance is predicted by feeding the acquired value of each operation variable as a digital input to the trained data driven model which outputs the predicted value of the output variable as a digital output, the tower clearance being shortest the distance between the tower and the tip of a specific rotor blade from the rotor blades when the specific rotor blade is in its lowermost position in which it points downward in the vertical direction.

An optimal dispatching method and system for a wind power generation and energy storage combined system are provided. Uncertainty of a wind turbine output is characterized based on spatio-temporal coupling of the wind turbine output and an interval uncertainty set. Compared with a traditional symmetric interval uncertainty set, the uncertainty set that considers spatio-temporal effects effectively excludes some extreme scenarios with a very small probability of occurrence and reduces conservativeness of a model. A two-stage robust optimal dispatching model for the wind power generation and energy storage combined system is constructed, and a linearization technology and a nested column-and-constraint generation (C&CG) strategy are used to efficiently solve the model.

An optimal dispatching method and system for a wind power generation and energy storage combined system are provided. Uncertainty of a wind turbine output is characterized based on spatio-temporal coupling of the wind turbine output and an interval uncertainty set. Compared with a traditional symmetric interval uncertainty set, the uncertainty set that considers spatio-temporal effects effectively excludes some extreme scenarios with a very small probability of occurrence and reduces conservativeness of a model. A two-stage robust optimal dispatching model for the wind power generation and energy storage combined system is constructed, and a linearization technology and a nested column-and-constraint generation (C&CG) strategy are used to efficiently solve the model.

A method for controlling pitching of at least one rotor blade of a wind turbine includes receiving, via one or more position localization sensors, position data relating to the at least one rotor blade of the wind turbine. Further, the method includes determining, via a controller, a blade deflection signal of the at least one rotor blade based on the position data. Moreover, the method includes determining, via a computer-implemented model stored in the controller, a pitch command for the at least one rotor blade as a function of the blade deflection signal and an azimuth angle of the at least one rotor blade.

The disclosure relates to a method for operating a wind turbine wherein the method includes: operating the wind turbine over an operating period in accordance with a control strategy, providing one or more input values representing a load acting on at least one component of the wind turbine and providing uncertainties of the input values, determining, based on the input values, an aggregated load value representing an aggregated load acting on the at least one component of the wind turbine over an aggregation period, determining, based on the uncertainties of the input values, an uncertainty of the aggregated load value, determining a statistical load aggregate from the aggregated load value and the uncertainty of the aggregated load value, adjusting the control strategy based on the statistical load aggregate. The disclosure further relates to a wind turbine and a wind farm configured to perform the above method.

The invention provides a method for controlling a wind turbine, including predicting behaviour of one or more wind turbine components such as a wind turbine tower over a prediction horizon using a wind turbine model that describes dynamics of the one or more wind turbine components or states. The method includes determining behavioural constraints associated with operation of the wind turbine, wherein the behavioural constraints are based on operational parameters of the wind turbine such as operating conditions, e.g. wind speed. The method includes using the predicted behaviour of the one or more wind turbine components in a cost function, and optimising the cost function subject to the determined behavioural constraints to determine at least one control output, such as blade pitch control or generator speed control, for controlling operation of the wind turbine.