An automated system for mitigating risk from a wind turbine includes a plurality of optical imaging sensors. A controller receives and analyzes images from the optical imaging sensors to automatically send a signal to curtail operation of the wind turbine to a predetermined risk mitigating level when the controller determines from images received from the optical imaging sensors that an airborne animal is at risk from the wind turbine.

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 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.

A method for controlling a wind farm to damp low-frequency electrical oscillations, in particular subsynchronous resonances, in an electrical supply grid having a grid voltage with a nominal grid frequency is provided. The wind farm comprises at least one wind turbine connected to the electrical supply grid. The method includes sensing at least one low-frequency electrical oscillation of the electrical supply grid; determining an oscillation characteristic of each of the at least one sensed oscillation, the oscillation characteristic describing at least one property of the sensed oscillation; specifying an active-power damping signal and/or a reactive-power damping signal for damping the at least one low-frequency oscillation; feeding in an active power component in accordance with the active-power damping signal or a reactive power component in accordance with the reactive-power damping signal, the active-power damping signal and the reactive-power damping signal being specified in dependence on the determined oscillation characteristic.

Method of controlling a wind turbine comprising a rotor comprising at least a first blade, the method comprising the step of varying an induction factor of the first blade over time by dynamically changing a pitch angle of the first blade over time between a first pitch angle and a second pitch angle while the first blade is rotating, wherein the first pitch angle is different from the second pitch angle, and wherein the dynamic change of the pitch angle over time is controlled such that the respective rotational position of the first blade at which the first blade is at the first pitch angle and the respective rotational positions of the first blade at which the first blade is at the second pitch angle are displaced in time and the varying induction factor of the blade occur at different angular positions in the rotor plane over time, such that a location and/or direction of a wake formed downstream of the wind turbine is dynamically changing with respect to the rotor of the wind turbine.

Methods and apparatuses for controlling reactive power of a wind turbine, and a wind farm are provided. An exemplary method includes: obtaining operation data of single wind turbines in a wind turbine group at the current time point; determining the total maximum capacitive reactive capacity and total minimum inductive reactive capacity, satisfying a safety constraint condition at the next time point, of the wind turbine group; calculating a deviation value of a wind turbine group reactive instruction at the current time point; and updating the wind turbine group reactive instruction on the basis of the acquired, determined, and calculated data so as to perform reactive power control.

Enabling control of wind turbines is provided. The method comprises receiving power production signals from wind turbines comprising a wind farm and estimating wake travel times from upstream wind turbines to downstream turbines. Correlations of the power production signals are calculated among all pairs of wind turbines in the wind farm. Wind turbines with a power production correlation above a specified threshold at an expected time are considered to have wake interaction. A probability density function of northing directions is calculated for the wind turbine pairs with wake interaction. A determination is made whether the probability density function has a dominant direction. Responsive to the probability density function having a dominant direction, the wind turbine pairs with wake interaction are identified as turbine clusters. A control strategy is applied to each turbine cluster as an operational unit to optimize power production of the wind farm.

Systems and methods of autonomous farm-level control and optimization of wind turbines are provided. Exemplary embodiments comprise a site controller running on a site server. The site controller collects and analyzes yaw control data of a plurality of wind turbines and wind direction data relating to the plurality of wind turbines. The site server determines collective wind direction across an area occupied by the plurality of wind turbines and sends yaw control signals including desired nacelle yaw position instructions to the plurality of wind turbines. The site controller performs wake modeling analysis and determines desired nacelle positions of one or more of the plurality of wind turbines. The desired nacelle yaw position instructions systematically correct static yaw misalignment for all of the plurality of wind turbines. Embodiments of the disclosure provide means to perform whole site or partial site level controls of the yaw controllers of a utility scale wind turbine farm. The overall effect of the coordinated yaw control of wind turbines across the whole or partial site is intended to keep the wake loss of the wind turbines from the upstream wind turbines to the minimum and to maximize the production of turbines that are not waking other turbines.

Provided is a method of determining a control setting of at least one wind turbine of a wind park, the method including: determining a free-stream wind turbulence and deriving the control setting based on the free-stream wind turbulence, wherein the control setting includes a yawing offset, and wherein the yawing offset is derived to be the smaller, the higher the free-stream wind turbulence is.