Control method and device for a wind farm

Abstract

Method and arrangement for controlling a wind farm. A method of controlling a plurality of wind turbines (3a, 3b, 3c) of a wind farm (1) is described. The method comprises determining an axial induction zone (48) of at least one wind turbine (3a, 3b, 3c) of the wind farm (1), and modifying the axial induction zone (48) by adjusting at least one of the following operational variables in order to control wind farm congestion: a yaw angle (γ) of a blade rotor (11a, 11b, 11c) of the wind turbine (3a, 3b, 3c), a pitch offset angle of at least one blade (11a, 11b, 11c) of the blade rotor (13a, 13b, 13c), a rotor speed of the blade rotor (13a, 13b, 13c).

Description

TECHNICAL FIELD

[0001] The present invention relates to a control method and a control device for controlling power production and structural loads in a wind farm, wherein in particular wind farm blockage will be taken into account. Furthermore, the present invention relates to a wind farm comprising the above-mentioned control device. BACKGROUND

[0002] A wind farm comprises a plurality of wind turbines which extract energy from the wind and convert the energy into electrical energy. Due to the extraction of energy by rotating rotor blades, the wind flow is influenced and altered upstream and downstream of the considered wind turbine. In particular, downstream of a wind turbine in operation, there is a wake region which is generated due to the interaction with the rotor blades of an upstream wind turbine, in which the wind speed is reduced and the wind conditions are generally altered compared to the wind conditions upstream of the considered wind turbine. Typically, a downstream wind turbine can experience a lower wind speed than an upstream wind turbine. Furthermore, upstream of a wind turbine in operation there is an axial induction region, i.e. an area of slowed flow in front of the wind turbine rotor. The combination of the axial induction regions of each turbine can lead to wind farm blockage, which is a slowing of the wind flow in front of the wind farm. This can reduce the electrical power production of the wind farm itself relative to the available power in a free inflow (far upstream). By the combination of several induction regions, the blockage can be increased, which leads to the fact that the wind turbines in the wind farm can produce less yield than they would produce independently. This can be combined with the wake effect, but it can also exist in the case that the wind turbines are arranged in a front row such that they are not affected by the wake from other turbines.

[0003] Methods and control devices for controlling power production and structural loads in a wind farm taking into account axial induction factors are shown in CN108708825A, EP3047143A1, US2013 / 300115A1 and EP3438448A1. The axial induction factors can not provide a complete description of the axial induction region and the phenomena associated therewith.

[0004] Therefore, there can be a need for a method and a control device for controlling power production and structural loads in a wind farm, wherein performance targets can be achieved in a reliable manner, in particular taking into account the axial induction region between different wind turbines appropriately in order to optimize the wind farm power output and / or to avoid wind farm blockage, in particular. SUMMARY

[0005] This need can be met by the subject-matter according to the independent claims. Advantageous embodiments of the present invention are described by the dependent claims.

[0006] According to an embodiment of the present invention, a method for controlling multiple wind turbines in a wind farm is provided, the method comprising:

[0007] Determine the axial sensing region of at least one wind turbine in the wind farm, wherein the axial sensing region is determined based on at least one of the following input parameters:

[0008] Inflow wind direction,

[0009] Inflow wind speed, and

[0010] Inflow turbulence; and

[0011] The axial sensing zone can be modified to control wind farm blockage by adjusting at least one of the following operands:

[0012] The yaw angle of the blade rotor of the wind turbine.

[0013] The pitch offset angle of at least one blade of the blade rotor,

[0014] The rotor speed of the blade rotor.

[0015] The method may be performed by a separate wind turbine control unit and / or by a wind farm control unit (e.g., a field controller). The control settings may be characterized in particular by the setting of one or more values ​​of one or more of one or more operating parameters.

[0016] The control settings of at least one wind turbine or all control settings of all wind turbines may be specifically configured to achieve performance objectives, such as optimizing or maximizing power output and / or keeping the structural loads experienced by individual wind turbines within acceptable limits.

[0017] According to embodiments of the invention, manipulated variables are derived to optimize wind farm performance in terms of power generation and / or structural load on at least one, and particularly all, wind turbines. Combined congestion effects distribute load unevenly across different turbines in a wind farm. For example, due to congestion, turbines towards the front edge of the row may bear a heavier load than those in the middle of the row. Therefore, manipulating the manipulated variables to reduce structural load on certain turbines (e.g., to redistribute load more evenly) by controlling congestion can be beneficial.

[0018] Free-flowing (or inflowing) wind is considered to include wind entering a wind turbine but not affected by any other wind turbine (e.g., not altered by any other wind turbine), particularly not affected by any wind turbine rotor forces. Free-flowing wind can be understood as wind unaffected by any wake from any upstream turbine. Therefore, free-flowing wind can be considered as the wind that would impinge on the wind turbine under consideration when no other wind turbine (e.g., upstream) influences, modifies, or alters its characteristics (e.g., characterized by wind speed, wind direction, and / or wind turbulence). Free-flowing wind turbulence can be considered as turbulence of free-flowing wind, and therefore as turbulence of wind impinging on the wind turbine under consideration, which is not altered by any other wind turbine potentially upstream of the wind turbine under consideration.

[0019] This invention takes into account free-flow wind direction and / or free-flow wind speed and / or free-flow turbulence in order to determine control settings.

[0020] Free-flow wind (wind speed, wind direction, and / or wind turbulence) can be measured and / or determined (e.g., involving calculations) from operating parameters, for example, of a forward turbine in a wind farm (i.e., a turbine facing winds unaffected by other wind turbines, e.g., without any other wind turbines located upstream). Free-flow wind turbulence can be measured (e.g., indirectly), or can be based on measured quantities and / or calculations, for example, considering at least the operating characteristics of the wind turbine under consideration or one or more other wind turbines in the wind farm. Free-flow wind turbulence can be based, for example, on the variance of free-flow wind speed. Free-flow wind turbulence can also take into account the variance or variation of free-flow wind direction and / or free-flow wind speed.

[0021] Exporting control settings (or all control settings for all wind turbines in a wind farm) may involve calculations, particularly applying physical / mathematical models to model or simulate all wind turbines in a wind farm, including wake interactions between different wind turbines. Control settings may include the definition of a set of values ​​for one or more operating parameters of the wind turbines under consideration.

[0022] The inflow turbulence (i.e., free-flow wind turbulence) can be relevant because it determines the extent to which the axially inductive zone (upstream of a particular wind turbine) will mix with the surrounding flow and revert to free-flow wind conditions. Free-flow wind turbulence can be a complex phenomenon that may otherwise be difficult to characterize. Therefore, for simplification, inflow wind turbulence can be approximated or taken as the variance of the free-flow wind velocity.

[0023] The axial sensing zone can be the area upstream of the wind turbine under consideration, in which the airflow can be affected by rotor forces or by the operation of the upstream wind turbine.

[0024] Determining or deriving control settings can involve optimization based on offline models. Specifically, a set of optimized control settings can be pre-generated for each wind condition using a model describing the system behavior under each (free-flow) condition, and then field operations can be performed using these optimized settings. The model can be used to test different control settings and predict system behavior. For example, through iteration and other types of optimization algorithms, optimized settings that give optimal system behavior (e.g., optimal power output) can be found. For example, optimized settings for each condition can be stored in tables in electronic memory or any other data structure. In field operations, the data structure containing pre-generated sets of optimized control settings associated with different wind conditions can be searched to obtain the control setting associated with the current wind condition. Possibly, interpolation between settings or conditions stored in tables can be performed to derive the control settings for the current (wind) condition.

[0025] Embodiments of the present invention can optimize the arrangement of control signals to improve wind farm performance (e.g., increase, optimize, or maximize power production) by controlling the axial sensing region, particularly its orientation. The optimal arrangement can be determined using a parameterized axial sensing region model that describes or accounts for the interactions of different wind turbines.

[0026] Axial sensing zone models can be adapted (e.g., using one or more model parameters) to current wind conditions. To adjust the axial sensing zone model (especially by defining the model parameters), characterization of the inflow (i.e., free-flowing wind) may be necessary. Specifically, in addition to free-flowing wind speed and / or direction, turbulence in the free-flowing wind can be considered. In particular, increased inflow / ambient turbulence can enhance mixing between the axial sensing zone and the surrounding free-flowing flow.

[0027] According to an embodiment of the invention, wind turbulence is estimated using the mean over a specific time period and the standard deviation of the estimated free-flow wind speed. For example, the estimated turbine strength is calculated as follows:

[0028] TurbEst = ((Par1·σ) / MeanWindSpeed) + Par2, where σ is the standard deviation of the free-flow wind speed, defined as the square root of the variance, and Par1 and Par2 are two tuning parameters; MeanWindSpeed ​​is the mean of the free-flow wind speed.

[0029] According to embodiments of the invention, free-flow wind speed is determined based on the operating conditions and / or wind measurements of at least one forward wind turbine facing winds substantially unaffected by any other wind turbines, particularly using 3D data tables. Measuring free-flow wind speed can be difficult to perform directly at the wind turbine because measurements at the wind turbine can be affected by rotor operation. Therefore, deriving the free-flow wind speed based on the operating conditions of the wind turbine can be advantageous.

[0030] The effective wind speed in the inflow (i.e., free-flow wind speed) can be estimated based on a turbine-specific three-dimensional rotor aerodynamic data table (or any other data structure), where electrical power and thrust entries are functions of blade pitch angle and / or rotor rotational speed and / or inflow wind speed. For example, the current power level (e.g., power production) and blade pitch angle and rotor speed can be used to derive the effective wind speed (i.e., free-flow wind speed) from the table or data structure.

[0031] By using turbine operating conditions as a sensor instead of local anemometer wind speed measurements, wind speed determination can be less sensitive to small-scale turbulence and flow blockage effects in parts of the wind turbine. Specifically, the resulting effective wind speed can be low-pass filtered using a large filter time constant (e.g., 600 s). In other embodiments, free-flow wind speeds can be measured by upstream or forward wind turbines. Furthermore, free-flow wind speed values ​​derived from several forward wind turbines can be combined, for example, averaged.

[0032] According to embodiments of the invention, operating conditions include: the current power level; and / or the current blade pitch angle of at least one rotor blade; and / or the rotational speed of the rotor of the preceding wind turbine. Thus, for example, the free-flow wind speed can be derived using conventionally available equations or models. The current power level may be related to or equal to the current power production or current power output of the wind turbine.

[0033] The free-flow wind direction can be determined by measuring the wind direction at the cabin. In particular, the wind direction measurement can be low-pass filtered using a low filter time constant (e.g., 600 s).

[0034] It should be understood that features disclosed, described, or explained individually or in any combination in the context of a method for controlling power production in multiple wind turbines can also be applied individually or in any combination to a control apparatus for controlling power production in multiple wind turbines according to embodiments of the present invention, and vice versa.

[0035] According to an embodiment of the present invention, a control device is provided for controlling the power production of multiple wind turbines in a wind farm. The control device includes a processor adapted to determine an axial sensing zone of at least one wind turbine in the wind farm, the axial sensing zone being determined based on inflow wind direction, inflow wind speed, or inflow wind turbulence, and the processor is adapted to generate control settings for at least one wind turbine in the wind farm to modify the axial sensing zone by adjusting at least one of the following operational variables in order to control wind farm congestion:

[0036] The yaw angle of the blade rotor of the wind turbine.

[0037] The pitch offset angle of at least one blade of the blade rotor,

[0038] The rotor speed of the blade rotor.

[0039] In addition, a wind farm is provided, including a plurality of wind turbines and a control device described above, the control device being communicatively connected to the wind turbines to supply appropriate control settings to each wind turbine.

[0040] It should be noted that embodiments of the invention have been described with reference to different subjects. In particular, some embodiments have been described with reference to method-type claims, while others have been described with reference to device-type claims. However, those skilled in the art will understand from the above and below description that, unless otherwise stated, any combination of features related to different subjects, in particular any combination of features between the features of method-type claims and the features of device-type claims, is also considered to be disclosed herein, except for any combination of features belonging to one type of subject matter.

[0041] The above and other aspects of the invention will become apparent from the examples of the embodiments described below, and will be explained with reference to these examples. The invention will be described in more detail below with reference to examples of embodiments, but the invention is not limited to these examples. Attached Figure Description

[0042] Embodiments of the present invention will now be described with reference to the accompanying drawings. The present invention is not limited to the embodiments described or illustrated.

[0043] Figure 1 A wind farm according to an embodiment of the present invention is illustrated schematically;

[0044] Figure 2 The schematic diagram illustrates the wind flow characteristics upstream and downstream of a wind turbine, thus creating an axial sensing zone;

[0045] Figure 3The diagram schematically illustrates a control device for controlling power generation in multiple wind turbines according to an embodiment of the present invention, which may include... Figure 1 In the wind farm shown in the figure; and

[0046] Figures 4 to 6 The execution effect of the steps of the method according to an embodiment of the present invention is illustrated schematically. Detailed Implementation

[0047] The illustrations in the attached diagrams are schematic.

[0048] Figure 1 The wind farm 1 schematically shown includes multiple wind turbines 3a, 3b, 3c and a control device 5 according to an embodiment of the invention, which is used to determine the control settings of at least one wind turbine of the wind farm 1. Each wind turbine 3a, 3b, 3c includes a corresponding wind turbine tower 7a, 7b, 7c having a corresponding nacelle 9a, 9b, 9c mounted on top, the nacelle housing a generator having a rotating shaft connected to a wind turbine rotor 11a, 11b, 11c. At the wind turbine rotor 11a, 11b, 11c, multiple corresponding rotor blades 13a, 13b, 13c are connected, the rotor blades driving the generator to generate electrical energy. The wind turbines 3a, 3b, 3c may also each include a converter, particularly an AC-DC-AC converter, for converting the variable frequency power stream output by the generator into a fixed frequency power stream having a frequency of, for example, 50 Hz or 60 Hz. Each wind turbine may also include a wind turbine transformer for converting the output voltage to a higher medium voltage.

[0049] A wind farm may include more than three wind turbines, such as 20 to 100 wind turbines or even more. The methods and apparatus of the present invention may be applied to only a portion of the wind turbines in a wind farm. The power output terminals of the wind turbines may typically be connected to a common coupling point, which supplies electrical energy (optionally via a wind farm transformer) to a public power grid (not shown). Each wind turbine 3a, 3b, 3c may include a wind turbine controller.

[0050] Control device 5, used to determine control settings for at least one wind turbine 3a, 3b, 3c, receives measurement signals and / or operating parameters 15a, 15b, 15c from the respective wind turbines 3a, 3b, and 3c, and supplies control signals 17a, 17b, 17c to the respective wind turbines 3a, 3b, 3c to control their operation. Specifically, control signals 17a, 17b, 17c include one or more control settings or are encoded with one or more control settings that modify the axial sensing area of ​​the wind turbines 3a, 3b, 3c (as better defined below). Control signals 17a, 17b, 17c allow adjustment of at least one of the following operating variables of the wind turbines 3a, 3b, 3c:

[0051] The yaw angle γ of blade rotors 11a, 11b, and 11c

[0052] The pitch offset angles of blades 11a, 11B, and 11c

[0053] Rotor speeds of blade rotors 13a, 13b, and 13c.

[0054] Modifying the axial sensing zone allows for control of wind farm blockages.

[0055] Yaw angle γ is the angle between the direction of the free-flowing wind and the direction of the rotation axis of the wind turbines 3a, 3b, 3c under consideration. Pitch offset angle is the angle of the blades 11a, 11b, 11c about their respective longitudinal axes, which are radially oriented relative to the rotation axis of the blade rotors 13a, 13b, 13c.

[0056] Control device 5 derives corresponding control settings based on the inflow wind direction and / or inflow wind speed and / or inflow wind turbulence (which are supplied via control signals 17a, 17b, 17c). Control device 5 can determine the inflow wind turbulence, for example, based on the variance of the inflow wind speed. The inflow wind speed can be determined, for example, based on the operating conditions and / or wind measurements of at least one forward wind turbine, such as wind turbine 3a, which faces the wind indicated by reference numeral 19 (e.g., having a specific inflow wind speed, direction, and turbulence).

[0057] Operating conditions and / or wind measurements of the forward turbine 3a can be supplied to the control unit 5, for example, via signal 15a. Operating conditions may specifically include the current power level, current blade pitch angle, and current rotor speed of the forward turbine 3a. Based on these values, the control unit 5 can then (e.g., using one or more tables, relationships, or program modules) derive the inflow wind speed for multiple successive time points or time intervals. The control unit 5 can then calculate the variance of the inflow wind speed to obtain an estimate of the inflow turbulence.

[0058] Control device 5 also provides an implementation of an axial sensing zone model, which has model parameters that can be defined based on, for example, measured inflow wind direction, free-flow wind speed, and inflow wind turbulence. The axial sensing zone model can be used to derive wind turbine control signals 17a, 17b, 17c (individually for the respective wind turbines) to derive and supply wind turbine control signals 17a, 17b, 17c, for example, to meet performance objectives, such as optimizing power production across the entire wind farm 1.

[0059] Figure 2 The diagram schematically illustrates the wind flow upstream and downstream of a wind turbine having rotor blades rotating in a rotor disk 43. Upstream of the rotor disk 43, wind 19 has a free-flow wind speed U, which, in a coordinate system, varies with lateral position x according to a first curve 45 for first turbulence and with lateral position x according to a second curve 46 for second turbulence. The coordinate system has wind speed as the ordinate 20 and lateral range x as the abscissa.

[0060] An axial sensing zone 48 is defined immediately upstream of the rotor disk 43. The axial sensing zone 48 is described as a region where the inflow velocity is reduced due to the extraction of kinetic energy from the free flow.

[0061] The rotor disk 43, including the rotating rotor blades, applies a force 47 to the wind 19, thereby causing a decrease in the wind speed 45 downstream of the rotor disk 43. The wind speeds 45 and 46 reach a minimum U downstream of the rotor disk 43. min And then, within the wake region 49, the wind speed U increases significantly, essentially upstream. Region 51 defines the shape of the wake 49. Within the mixing region 53, on the radially outer side of the rotor tube 55, the free-flowing wind mixes with the wind affected by the rotor disk 43.

[0062] For different free-flow turbulence conditions of wind 19, the shapes of the wind speeds 45 and 46 downstream of the wind turbine are different. Specifically, compared to the lower (first) turbulence wind speed (first curve 45), the higher (second) free-flow turbulence wind speed (second curve 46) can recover to the upstream value U closer to the rotor disk 43. The higher second turbulence wind speed is indicated by reference numeral 46. The lower second turbulence wind speed is indicated by reference numeral 45. Therefore, taking into account the free-flow turbulence, it becomes possible to derive optimized control settings for all wind turbines in a wind farm.

[0063] Figure 3 An embodiment of the control device 5, as an exemplary implementation, is illustrated schematically. As input, the control device 5 includes a free-flow wind speed 21, which may be based, for example, on the operating conditions of the upstream wind turbine, such as... Figure 1The upstream wind turbine 3a is shown. Control unit 5 includes a variance determination module 23, which determines the variance of the free-flow wind speed 21 and performs scaling to output a free-flow turbulence intensity 25, which is supplied to a turbulence pile-up module 27. A turbulence pile-up range definition module 29 supplies the turbulence pile-up range to the turbulence pile-up module 27, which outputs a turbulence pile-up index 29, which is supplied to a table selection module 31. In electronic memory, control unit 5 includes a control setting lookup table (or other data structure) 33, which associates control settings with specific tree-flow wind conditions. The control setting lookup table 33 may have been determined using an axial sensing zone algorithm or determination model 35, which may perform optimization based on the axial sensing zone model for each wind condition (especially offline). The table selection module 31 selects the table corresponding to the current free-flow turbulence intensity 25 from multiple control setting lookup tables 33, and provides the corresponding control settings from it (specifically for each wind turbine 3a, 3b, 3c in wind farm 1). The output of the table selection module 31 can be a lookup table with optimized control settings for each wind direction and wind speed of the free-flow wind.

[0064] The control unit 5 also receives, for example, free-flow wind direction 36 and free-flow wind speed 37 measured or determined from the front turbine 3a as input. Optimized control settings can be obtained from a lookup table 39 output by the table selection model 31, or can be interpolated between two or more tables providing control settings close to the current free-flow wind direction and speed 37 (using the interpolation module 40). Finally, the control unit 5 outputs optimized wind turbine control settings 41, which can then be individually supplied to all wind turbines 3a, 3b, 3c via control signals 17a, 17b, 17c.

[0065] As an alternative to having an associated data table with control settings for each turbulent stack, a fully parameterized model can be defined using free-flow turbulence, free-flow wind speed, and free-flow wind direction as inputs. Therefore, a single 3D lookup table can be predefined for optimized control settings for each wind turbine, using the three input parameters mentioned above as options.

[0066] In addition to using pre-computed offline models, models and lookup tables can also be adaptively configured using learning controllers (e.g., models and control settings that are updated online using online parameter fitting based on measurements and online optimization).

[0067] Figures 4 to 6The results obtained by means of the method and control device of the present invention are schematically illustrated. Figures 61, 62, and 63 show the corresponding shapes of the axial sensing zone 48 in front of the blade rotor 13a. The incoming airflow comes from the left side of each figure. In the first figure 61, the axial sensing zone 48 is manipulated to the right (as seen from upstream of the turbine) relative to a yaw angle γ = 0° (second figure 62) with a negative yaw angle γ = -25° relative to the direction of the incoming airflow. The axial sensing zone 48 is manipulated to the left with a positive yaw angle γ = 25° relative to a yaw angle γ = 0° (second figure 62), as seen in the third curve 63. The shape of the axial sensing zone 48 can be further modified by controlling insufficient wind speed in the axial sensing zone 48 via the blade pitch and rotor speed.

[0068] By optimizing the deflection of the axial sensing zone 48 of each wind turbine via the yaw offset angle, and / or by adjusting (reducing or increasing) the insufficient wind speed in the axial sensing zone via pitch and rotor speed, congestion of the flow toward the turbine itself or downstream turbines can be reduced, and the available power flowing into the free flow can be utilized more optimally to increase overall wind farm production.

[0069] It should be noted that the term "comprising" does not exclude other elements or steps, and "a" or "an" does not exclude a plurality. Furthermore, elements described in association with different embodiments may be combined. It should also be noted that reference numerals in the claims should not be construed as limiting the scope of the claims.

Claims

1. A method for controlling multiple wind turbines (3a, 3b, 3c) of a wind farm (1), the method comprising: An axial sensing region (48) of at least one wind turbine (3a, 3b, 3c) of the wind farm (1) is determined, wherein the axial sensing region (48) is a region upstream of the wind turbine (3a, 3b, 3c), wherein the axial sensing region (48) is described as a region where the inflow wind speed is reduced due to the extraction of kinetic energy from the free flow, wherein the axial sensing region (48) is determined based on at least one of the following input parameters: Inflow wind direction, Inflow wind speed, and Inflow turbulence; and The axial sensing zone (48) can be modified by adjusting at least one of the following operating variables in order to control wind farm blockage: The yaw angle (γ) of the blade rotors (11a, 11b, 11c) of the wind turbines (3a, 3b, 3c), The pitch offset angle of at least one blade (13a, 13b, 13c) of the blade rotor (11a, 11b, 11c), The rotor speed of the blade rotors (11a, 11b, 11c).

2. The method according to claim 1, wherein, At least one table or function is generated to associate the input parameters with the operation variables.

3. The method according to claim 2, wherein, The at least one table or function optimizes the power production of the wind farm (1) by determining a set of operational variables for each set of input parameters.

4. The method according to any one of claims 1 to 3, wherein, The operational variables are derived to optimize the power production of the wind farm (1).

5. The method according to any one of claims 1 to 3, wherein, The operational variables are derived to optimize the structural load on at least one wind turbine (3a, 3b, 3c) of the wind farm (1).

6. The method according to any one of claims 1 to 3, wherein, The operational variables are derived to optimize the structural load on all wind turbines (3a, 3b, 3c) in the wind farm (1).

7. A control device (5) for controlling a plurality of wind turbines (3a, 3b, 3c) of a wind farm (1), the control device (5) comprising a processor adapted to determine an axial sensing zone (48) of at least one wind turbine (3a, 3b, 3c) of the wind farm (1), the axial sensing zone (48) being an area upstream of the wind turbine (3a, 3b, 3c), the axial sensing zone (48) being described as an area where the inflow wind speed is decelerated due to the extraction of kinetic energy from the free flow wind, the axial sensing zone (48) being determined based on the inflow wind direction or inflow wind speed or inflow wind turbulence, and the processor being adapted to generate control settings for at least one wind turbine (3) of the wind farm (1) to modify the axial sensing zone (48) by adjusting at least one of the following operational variables in order to control wind farm congestion: The yaw angle (γ) of the blade rotor (11a, 11b, 11c) of the wind turbine (3a, 3b, 3c), and the pitch offset angle of at least one blade (13a, 13b, 13c) of the blade rotor (11a, 11b, 11c). The rotor speed of the blade rotors (11a, 11b, 11c).

8. Wind farm (1), including: Multiple wind turbines (3a, 3b, 3c); and The control device (5) according to claim 7 is connected to the wind turbine to supply corresponding control settings to each wind turbine.

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