Disclosed is a wind turbine layout optimization method combining with a dispatching strategy for the wind farm. In the wind farm micro-siting stage, the installed wind turbines number and the arrangement positions are optimized. In this method, the dispatching strategy of wind turbines is considered during the layout optimization of wind turbines, and the axial induction factor of each wind turbine is introduced into the layout optimization variables. The dispatching strategy of maximizing the wind farm power generation is combined with the layout optimization of wind turbines in the construction stage of the wind farm, so that the wake effect is effectively reduced and the capacity cost is reduced, which meet the requirement of actual wind farm. A hybrid optimization algorithm is proposed in this method, with a greedy algorithm to optimize the turbine number and a particle swarm optimization (PSO) algorithm to refine the turbine layout scheme.

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.

Systems and methods enable yaw offsets on wind turbines in a wind farm. A wind turbine yaw controller receives a present wind direction signal from a local wind direction sensor and aligns the wind turbine in a substantially perpendicular direction based upon the present wind direction signal. A yaw controller is retrofitted between the wind direction sensor and the yaw controller to provide an adjusted wind direction signal to the yaw controller based upon the present wind direction signal and a yaw offset signal. An offset table relating yaw offsets with wind direction signal values may be stored locally at a wind turbine or at a site controller in communication with the wind tribune. Each wind turbine in the wind farm may be retrofitted with the yaw controller to enhance the power output of the wind farm by adjusting the wake effect between wind turbines of the wind farms.

A method for determining a yaw position offset of a wind turbine (1) is provided. A neighbouring wind turbine (2) of the wind farm is identified, the neighbouring wind turbine (2) being arranged in the vicinity of the wind turbine (1). Produced power data and/or wind speed data from the wind turbine (1) and from the neighbouring wind turbine (2), is obtained during a period of time, and a yaw position offset of the wind turbine (1) is derived, based on the obtained produced power data and/or wind speed data, and based on the geographical positions of the wind turbine (1) and the neighbouring wind turbine (2). A local maximum and a local minimum being separated by an angular difference in yaw position being substantially equal to 180°.

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.

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.

Provided is an apparatus and method for cooperative controlling wind turbines of a wind farm, wherein the wind farm includes at least one pair of turbines aligned along a common axis approximately parallel to a current wind direction and having an upstream turbine and a downstream turbine. The method includes the steps of: a) providing a data driven model trained with a machine learning method and stored in a database, b) determining a decision parameter for controlling at least one of the upstream turbine and the downstream turbine by feeding the data driven model with the current power production of the upstream turbine which returns a prediction value indicating whether the downstream turbine will be affected by wake, and/or the temporal evolvement of the current power production of the upstream turbine; c) based on the decision parameter, determining control parameters for the upstream turbine and/or the downstream turbine.

A system and method are provided for operating a wind farm. Accordingly, a wind direction affecting the wind farm is determined. Based on the wind direction, a controller identifies a turbine cluster, which is a subset of a plurality of wind turbines of the wind farm. The subset includes at least an upwind turbine and a downwind turbine that is affected by a wake emanating from the upwind turbine. With the turbine cluster identified for the given wind direction, the controller then determines a difference between a freestream maximal cluster power output and a wake-affected cluster power output for the turbine cluster. The controller then determines a mitigation setpoint combination for the subset of wind turbines. The mitigation setpoint combination is configured to establish a mitigated cluster power output. Mitigated cluster power output has a difference from the freestream maximal cluster power output that is less than the difference between the freestream maximal cluster power output in the wake-affected cluster power output for the turbine cluster. Based on the mitigation setpoint combination, an operating state of at least one wind turbine of the turbine cluster is changed.