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.

A system and method are provided for controlling a wind turbine. Accordingly, a controller of the wind turbine detects a transient grid event and generates a torque command via a drive-train-damper control module. The torque command is configured to establish a default damping level of a torsional vibration resulting from the transient grid event. The controller also determines at least one oscillation parameter relating to the torsional vibration and determines a target generator torque level based thereon. The target generator torque level corresponds to an increased level of damping the torsional vibration relative to the default damping level.

Disclosed is a method of condition monitoring a WTG (Wind Turbine Generator) comprising acts of collecting and storage of at least the following data sets together with their time stamps. Collection of generator power production measurements. Collection of mechanical status measurements. Collection of generator torque measurements. Collection of nacelle direction measurements. Collection of meteorological conditions measurements. The method compromises a further act of synchronizing the data sets. The invention also relates to a system for condition monitoring a WTG. The invention further relates to a system for visually inspecting a WTG.

Disclosed is a method of condition monitoring a WTG (Wind Turbine Generator) comprising acts of collecting and storage of at least the following data sets together with their time stamps. Collection of generator power production measurements. Collection of mechanical status measurements. Collection of generator torque measurements. Collection of nacelle direction measurements. Collection of meteorological conditions measurements. The method compromises a further act of synchronizing the data sets. The invention also relates to a system for condition monitoring a WTG. The invention further relates to a system for visually inspecting a WTG.

The invention relates to a system for monitoring wind turbine components including an independent data processing environment adapted to: receive a first category of data input related to operation of the wind turbine, process the received data input by one or more component specific monitoring algorithms adapted to establish an estimated component value related to a component to be monitored based on received first category data input having at least indirectly impact on the component, wherein the component specific monitoring algorithm is adapted to establish a component residual as the difference between the estimated component value and received first category of data input of the component to be monitored, and wherein the component specific monitoring algorithm furthermore is adapted to establish a component specific health value of the component to be monitored based on the established residual and put the health value at disposal for data processors outside the environment.

The invention relates to a system for monitoring wind turbine components including an independent data processing environment adapted to: receive a first category of data input related to operation of the wind turbine, process the received data input by one or more component specific monitoring algorithms adapted to establish an estimated component value related to a component to be monitored based on received first category data input having at least indirectly impact on the component, wherein the component specific monitoring algorithm is adapted to establish a component residual as the difference between the estimated component value and received first category of data input of the component to be monitored, and wherein the component specific monitoring algorithm furthermore is adapted to establish a component specific health value of the component to be monitored based on the established residual and put the health value at disposal for data processors outside the environment.

A method for operating a wind farm having a plurality of wind turbines, each of which is assigned a minimum power limit (CMPLj), wherein a power setpoint value (SPP) for a power which is to be fed in is specified for the wind farm, in dependence on which power setpoint value (SPP), individual wind turbines are activated or shut down, wherein the activation or shut-down of one or more wind turbines in the wind farm takes place in dependence on the respectively assigned minimum power limits (CMPLj), wherein each wind turbine of the wind farm is assigned a current value for its minimum power limit, and wherein the value takes into account variable operating conditions and/or ambient conditions of the respective wind turbine.

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.