METHOD FOR CONTROLLING A WIND TURBINE FARM

Abstract

Method for controlling a wind farm, each wind turbine having an operating point adjustable by an actuator: a) a determination by modeling of a region of space approximating the wake of each wind turbine for a misalignment angle (θ) and for a wind speed deficit threshold (δ), b) an identification, from the modeled wakes, of the wind turbines which are in the modeled wake of another wind turbine, then a storage of identification parameters linking each wind turbine to the wind turbine in the modeled wake of which it is, c) a taking into account of the identification parameters stored in step b) to group wind turbines into a plurality of sub-farms, d) a determination of at least one setpoint operating point, for the wind turbines of each sub-farm, by a method of optimizing the power generated by each sub-farm e) an application of said setpoint operating point to the wind turbines.Figure 8 to be published.

Description

Title of the invention: METHOD FOR CONTROLLING A WIND TURBINE FARM technical field

[0001] The present invention relates to the field of wind farm control to maximize power output and to reduce wind turbine fatigue.

[0002] A wind farm, also called a wind park or wind power plant, is a site comprising a number of wind turbines that produce electricity. This site can be on land or at sea. A distinction is thus made between onshore wind farms and offshore wind farms, that is to say, at sea.

[0003] The wind turbines in these farms are generally horizontal axis wind turbines equipped with a system to orient the horizontal axis of rotation in the direction of the wind, in order to maximize the energy recovered by the turbine. A wind turbine transforms the kinetic energy of the wind into electrical or mechanical energy. For the conversion of wind into electrical energy, it consists of the following elements:

[0004] - a mast allowing a rotor to be placed at a sufficient height to allow its movement (necessary for horizontal axis wind turbines) or placing this rotor at a height allowing it to be driven by a stronger and more regular wind than at ground level. The mast may house some of the electrical and electronic components (modulator, control, gearbox, generator, etc.);

[0005] - a nacelle mounted at the top of the mast, housing mechanical components, pneumatics, certain electrical and electronic components necessary for the operation of the machine (modulator, control, multiplier, generator, etc.). The nacelle can rotate to orient the rotor in the correct direction;

[0006] - a rotor, fixed to the nacelle, comprising several blades (generally three) and the nose of The wind turbine. The rotor is driven by wind energy; it is connected by a mechanical shaft, either directly or indirectly (via a gearbox and mechanical shaft system), to an electric machine (electric generator, etc.) which converts the collected energy into electrical energy. The rotor may be equipped with control systems such as variable-angle blades or aerodynamic brakes;

[0007] - possibly a transmission, composed in particular of two axes (shaft (mechanical components of the rotor and mechanical shaft of the electric machine) connected by a multiplier (gearbox).

[0008] Since the early 1990s, wind energy has experienced a resurgence of interest, particularly in the European Union where the annual growth rate is around 20 This growth is attributed to the inherent potential for carbon-free electricity production. To sustain this growth, the efficiency of wind turbines and wind farms must continue to improve. The prospect of increased wind power production necessitates the development of efficient production tools and advanced control tools to enhance machine performance. Wind turbines are designed to generate electricity at the lowest possible cost.

[0009] For this power regulation, controllers are designed for variable speed wind turbines. The objectives of the controllers are to maximize the electrical power recovered, minimize rotor speed fluctuations, and minimize fatigue and extreme loads on the structure (blades, mast, and platform). Previous technique

[0010] Wind farms are subject to a phenomenon commonly called the "wake effect," where disturbances created by turbines located upstream of the wind farm create suboptimal electricity production conditions for the other turbines. Indeed, downstream of the wind turbine, a vortex wake forms, and within this wake, the average wind speed is reduced because the turbine has captured some of the wind's kinetic energy, and the intensity of turbulence is increased. (The terms "upstream" and "downstream" refer to the positioning of a wind turbine relative to the other turbines according to the prevailing wind direction at a given time.)

[0011] A known strategy for maximizing the energy production of a wind turbine is to orient its rotor so that it faces the wind. The angle between the rotor and the wind direction, called the misalignment angle or yaw, is then 0°. Figure 1 schematically illustrates, in a non-limiting manner, the misalignment angle. Figure 1 is a top view of a wind turbine. The wind turbine comprises blades 1 and a nacelle 2, oriented in direction AA. The wind is represented by arrow U, with a direction DD. The angle 0 between direction AA and direction DD is the misalignment angle. When the turbine of the wind turbine is aligned with the wind direction, this angle 0 is zero.

[0012] In wind farms, however, applying this strategy (zero misalignment angle) to all turbines, according to a so-called "greedy" method, makes the farm subject to what is known as the wake effect: when a wind turbine extracts energy from the wind, the wind speed downstream decreases and its turbulence increases. This leads to suboptimal conditions for the energy production of the downstream turbines, with total production losses potentially reaching 40% offshore.

[0013] A number of controllable actuators can be used to reduce this effect: power capture can be influenced by controlling the blade pitch or generator torque; a turbine's wake can be deflected under downstream turbines by tilting the rotor plane, or sideways by changing the yaw, according to a technique known as wake steering. Wake steering is a wind farm-wide control strategy that generally maximizes total power output by coordinating interactions between wind turbines. Unlike standard control strategies that aim to maximize the performance of individual wind turbines, wake steering sacrifices the power output of some turbines to achieve better net performance for the entire wind farm.

[0014] In addition to maximizing production, one can also seek to limit or reduce the structural fatigue of the wind turbines. This is an additional trade-off between the production gain, the negative impact on turbine load due to misalignment, and the positive impact of redirecting the wake away from the rotors of the turbines located downstream of the misaligned turbines. A positive consequence, in the case of an advantageous trade-off, is the increase in the lifespan of the wind turbines and the reduction in maintenance costs, in addition to the production gain achieved.

[0015] One strategy therefore consists of using yaw actuators to misalign the turbines with respect to the direction of the incident wind: this allows for a redirection of the wakes to limit the impact on the downstream turbines. Finding the optimal yaw angles (which maximize the total electrical power of the wind farm while limiting or reducing turbine fatigue) is a complex problem: optimizing the annual power of a wind farm would require, in real time, significant computing power, even prohibitive when the farm is large, i.e. when it includes a large number of wind turbines (for example at least 30 or at least 50 or at least 80 wind turbines, this consideration depending on the available computing capacity).

[0016] Many studies have focused on taking into account the wakes of wind turbines to control them.

[0017] For example, a method for controlling a wind turbine farm is known from patent EP4382743, corresponding to US patent application 2024 / 0183337, in which a decentralized reinforcement learning method is implemented for each turbine, whereby the reward is calculated based on a wake propagation time (the reward is a value of a reward function of a reinforcement learning machine method). Thus, the reward is representative of the effect of the last action (for example, the control of the leading yaw).

[0018] Studies have also focused more specifically on the dynamics of wind turbines over time: For example, Bernardoni et al. (F. Bernardoni, U. Ciri, M. Rotea and S. Leonardi, “Real-time identification of clusters of turbines”, Journal of Physics: Conference Series, vol. 1618, p. 022 032, Sept. 2020. doi: 10.1088 / 1742-6596 / 1618 / 2 / 022032) consider the time series of the power supplied by each wind turbine: They first establish a numerical simulation of a wind farm in equilibrium, subjected to a reference wind. Then, they increase the wind speed in front of wind turbine i and establish the propagation delay r of this disturbance to wind turbine j. This allows them to study the correlation between the series of powers of i and the series of powers of j, shifted by a delay r. Finally, the classification of wind turbines is done according to the value of this correlation coefficient.

[0019] As for GM Starke et al. (GM Starke, P. Stanfel, C. Meneveau, DF Gayme, and J. King, "Network-based estimation of wind farm power and velocity data under changing wind direction," in 2021 American Control Conference (ACC), IEEE, 2021, pp. 1803–1810), these authors first consider a discrete dynamical system where the system state at time k is a matrix (Okjj) that summarizes the velocity deficits experienced by wind turbine i from wind turbine j. From the current value of Ok, the authors determine a coefficient kw, which is used for the geometric modeling of the wake. The wind farm is plotted in the plane, where the free-winding turbines are identified using a geometric argument (the turbines in the farm are represented as Voronoi cells). Then, the wake of i is defined as the region of the plane between the half-lines originating from wind turbine i, with a slope of kw.All wind turbines whose rotors are in this zone are considered to be in the wake of i. .

[0020] Neither of these two solutions gives complete satisfaction, particularly in terms of the computing power required, for medium and large farms.

[0021] The invention aims to improve the control of a wind farm, in particular by improving how the power it generates is optimized according to the wind configuration, more specifically by aiming for optimization that is less / more power-intensive while maintaining performance. Summary of the invention

[0022] The invention relates firstly to a method for controlling a wind farm, said wind turbines being of a single type or of different types, each wind turbine in said wind farm having at least one operating point, including its misalignment angle and / or its trim setting, said operating point(s) being adjustable by at least one actuator. The method according to the invention comprises: a) a step of determining by modeling, for different wind configurations, a region of space approximating the wake of each type of wind turbine, a region called the modeled wake, for a given range of misalignment angle 0 and for a given wind speed deficit threshold ô, b) an identification step, based on the wakes modeled in step a), of the wind turbines that are in the modeled wake of another wind turbine for each of the wind configurations, followed by the storage of identification parameters linking each wind turbine to the wind turbine in whose modeled wake it is located for each wind configuration (c) a step, for a given wind configuration, to take into account the identification parameters stored in step (b) for said wind configuration or for a wind configuration closest to the one measured, to group wind turbines into a plurality of sub-farms, each sub-farm comprising the wind turbines that are in the modeled wake of each other d) a step of determining at least one setpoint operating point, including the misalignment angle 0 and / or the curb, for the wind turbines of each sub-farm, by a method of optimizing the power (maximizing the power under structural fatigue constraints, for example) generated by each sub-farm e) a step of applying said setpoint operating point(s) determined in step d) to the wind turbines of the farm, including their misalignment angle (0) and / or their curb, by actuation of the actuator(s).

[0023] It is noted that the process of the invention, as a whole, is advantageously operated in real time, and that the updates of the process are made in real time.

[0024] The actuator(s) may be, in particular, the nacelle heading actuator, the blade pitch actuators and / or the electric generator torque control actuator(s),

[0025] In this application, the term "type" of wind turbine means that the wind farm in question may comprise only one type of wind turbine (all turbines are identical, operating in the same way and having the same dimensions), or that the farm may comprise several types of wind turbine. In the latter case, in step a), the determination is made for each type of wind turbine.

[0026] In the present application, "misalignment angle 0" means the angle between the rotor and the wind direction, also called "yaw" in English or "yaw" in French.

[0027] In the present application, "limiting" means reducing the aerodynamic efficiency of the wind turbine in operation, for example, to limit its converted electrical power to a predetermined threshold, under environmental conditions data. This can result in a change in the set speed of rotation of the wind turbine blades and / or a change in the orientation of the blades, thus changing their angle of attack and their wind exposure.

[0028] In this application, "wind configuration" means wind data, which may include wind speed, direction, turbulence intensity, and other environmental parameters such as atmospheric stability. It is referred to in English as "bin".

[0029] In the present invention, "sub-farm" means the grouping of the wind turbines of the farm into a plurality of subgroups, each containing at least one or at least two wind turbines.

[0030] In the present invention, "wake" is understood to mean its known definition for wind turbines, namely the region downstream of a wind turbine (relative to the direction of the wind), where the airflow is slowed down, corresponding to the velocity deficit as considered here, with an increase in its turbulence intensity.

[0031] In the present invention, "wind speed deficit ô" means the wind speed deficit produced by wakes from wind turbines upstream of the wind turbine in question.

[0032] The essence of the invention was thus to divide the farm into a plurality of sub-farms, each grouping together wind turbines that are in aerodynamic interaction with each other in real time, - by modeling the union envelope of all wakes (i.e., speed deficit zones) corresponding to all misalignment instructions applicable for a given wind state upstream of the wind turbine in question - by determining the real-time grouping according to the measured wind configuration, based on the modeled wakes.

[0033] The various wind configurations are acquired from step a), in particular using available database(s) updated / refreshed at a given time frequency, in particular at most 15 minutes, preferably at most or equal to 10 minutes. A real-time data acquisition and control system (known by the English acronym SCADA for "Supervisory Control And Data Acquisition") is used. The wind configuration data thus acquired can be supplemented by data acquired by real-time physical sensors.

[0034] The invention then optimizes the power that each sub-farm can generate, the sum of these powers allowing the overall optimization of the farm to be obtained, instead of performing this optimization on all the wind turbines of the farm: this sub-farm optimization is then much less demanding in terms of computing power, therefore less demanding in terms of computer resources and memory, much simpler / faster, without losing performance level. The optimization is therefore faster, because it is carried out on sub-farms rather than on all farms, resulting in faster convergence of calculations and because the calculations are parallelized, being independent for each sub-farm.

[0035] The invention thus makes it possible to take into account the aerodynamic interactions between wind turbines in order to control them efficiently, with a computing power requirement compatible on the one hand with the update frequencies of the instructions sent to the wind turbines, on the other hand with the computing capacities available on the control and supervision units of wind farms, even for parks involving a large number of wind turbines.

[0036] Preferably, at least some of steps a), b), c), d), e) are repeated, and preferably all steps b), c), d) and e) are repeated for each new wind configuration. Thus, with the invention, the way in which the wind turbines are grouped into sub-farms can be reconfigured for each wind measurement: this is a dynamic, real-time control system. The refresh rate of the entire control process can be set at intervals on the order of a minute, which can vary, in particular, between 30 seconds and 10 minutes, or even longer periods when considering large-scale flow phenomena propagating within large farms.

[0037] Step a) of determining the modeled wakes can be carried out for a misalignment angle range between -0 and +0, these values ​​corresponding to the limits defined in the wind turbine specifications, with 0 equal to 20°.

[0038] In step a), each wake modeled for the misalignment angle range 0 can be the envelope of the sum of a plurality of wakes modeled at different misalignment angle values ​​within said misalignment angle range 0, and in particular the envelope of the wakes modeled at -0, +0, and 0°.Indeed, the wakes modeled at maximum misalignment angle values ​​and at zero misalignment angle tend to partially overlap, the envelope of the grouping of the three wakes allowing to have a modeled wake particularly representative of the real wake of a wind turbine within its operating limits.

[0039] Step a) of determining the modeled wakes can be carried out for a wind speed deficit threshold θ between 0.75 m / s and 0.20 m / s, in particular between 0.70 m / s and 0.45 m / s, and especially equal to 0.65 m / s. Indeed, the very definition of a wake corresponds to a region downstream of the wind turbine (relative to the wind direction) where the wind speed is lower than that of the free wind (i.e., where the flow is not disturbed by the wind turbine in question). Choosing an appropriate wind speed deficit makes it possible, in particular, to model wakes of sufficiently small sizes, especially in width, so that the sub-farms are advantageously delimited, without interaction between modeled wakes of two wind turbines from two adjacent sub-farms.

[0040] Step a) of determining the modelled wakes is carried out for a wind speed deficit threshold ô taking into account at least one of the following parameters: the number of wind turbines in the farm, the distance between wind turbines, the configuration of the farm, the geographical positioning of the wind turbines in the farm. Thus the threshold ô can be determined according to a hyper-parameterization method taking into account several environmental, wind turbine design and operational elements.

[0041] In step a), the modelled wakes can be approximated / represented as two-dimensional representations, considering the wind configurations and the wakes at constant height, in particular a height equal to that of the axis of the rotor carrying the blades of the wind turbines: these modelled wakes then have a trapezoidal shape whose smallest base is the initial width of the wake.

[0042] In step a), the modeled wakes can alternatively be approximated / represented as three-dimensional representations. These can be representations of the type of a portion of a cone whose smallest base is the initial size of the wake. The wind configurations and wakes are then considered over a given height, in particular in an area on either side of the height of the wind turbine rotor elevation.

[0043] But we can also choose three-dimensional representations of the pseudocone type, with a section that is more oval than round, or even a section that is neither oval nor round, which may in particular be the case for floating wind farms.

[0044] The different wind configurations considered for carrying out step a) of determining wakes by modelling can be the set of possible wind configurations recorded over a given period of time, in particular over a year, in a geographical area including the wind farm.

[0045] In step b), wind configurations, in particular the distribution of wind speed and direction and / or wind speed and direction, can be measured in real time by means of at least one LiDAR sensor and / or at least one anemometric measurement system (sonic or mechanical) providing wind speed and direction and / or at least one control and data acquisition system. These different measurement means can be fixed to the wind turbine, in particular in the vicinity of the nacelles, or be remote (for example, for an offshore wind farm, mounted on floating buoys).

[0046] In step d) of determining at least one setpoint operating point, the optimization of the power generated per sub-farm can be carried out for each of the sub-farms in parallel and / or sequentially.

[0047] In both cases (parallel or sequential calculations), the optimization is much faster and much less resource-intensive than optimizing all the wind turbines in the farm at the same time, both because the simulation of production is less computationally expensive with fewer turbines and because the optimization algorithm is used in a lower dimension space (the space to be explored is smaller).

[0048] Parallel optimization will be even faster than sequential optimization, when possible.

[0049] In step d) of determining at least one setpoint operating point, the optimization of the power generated per sub-farm can be carried out by taking into account the structural fatigue constraints of the wind turbines or of each type of wind turbine in the farm. These constraints particularly concern the blades and the tower of each type of wind turbine and are generally taken into account in the farm's control. It should be noted that taking these fatigue constraints into account tends to increase the computation time for optimization; therefore, the invention is particularly interesting because it allows for this consideration while mitigating the increase in computation time. Structural fatigue, as understood in the present invention, can concern any mechanical component of the wind turbine, including the tower, blades, foundation, floating support, and mooring lines. By extension, the mechanical stress on the actuators can also be considered a constraint, just like structural fatigue.

[0050] Step a) of determining the modeled wakes can be carried out offline with possible periodic update(s) and / or possible update with the wind configuration measurement made in real time in step c).

[0051] The invention also relates to a wind farm, said wind turbines being of a single type or of different types, each wind turbine of said wind farm having at least one adjustable operating point, including its misalignment angle 0 and / or its bridle, said or said operating point(s) being adjustable by at least one actuator, such that said wind farm includes or is connected to computer means to implement the control method described above, in order to apply setpoint operating points to the wind turbines of the farm including their misalignment angle 0 and / or their bridle, by actuating their actuators (according to the setpoints determined by said computer means).

[0052] It is preferable that these computing resources / IT resources be hosted on the site of the wind farm itself. However, it is also possible to relocate them (in whole or in part) outside the site of the wind farm, as long as they can to be connected to the wind farm by a high-performance communication network (internet...).

[0053] The invention also relates to any computer means, in particular a computer, server or calculator configured to implement the wind farm control method described above.

[0054] The invention also relates to any computer program product downloadable from a communication network and / or recorded on a medium readable by a computer, a server or a calculator and / or executable by a processor, comprising program code instructions for the implementation of the wind farm control method described above, when said program is executed on a computer, a server or a calculator.

[0055] The invention also relates to any computer-readable storage medium, server or calculator, storing instructions which, when executed by a computer, server or calculator, imply that the computer, server or calculator implements the wind farm control method described above.

[0056] Other features and advantages of the process according to the invention will become apparent from the following description of non-limiting examples of embodiments, with reference to the figures attached and described below. List of figures

[0057] [Fig.1] Figure [1] illustrates the misalignment angle of a wind turbine. [Fig.2] Figure 2 represents an example of a histogram of annual wind measurements (speed and direction) over one year, the data from which can be used in the process according to the invention. [Fig.3] Figure 3 represents the wake of a wind turbine subjected to a given wind configuration, here as an example a wind speed of ws = 7 m / s and direction wd = 270°, the gradient of grey representing the speed values ​​according to the scale indicated to the right of the representation of the wake which expresses the wind speed in m / s in the area of ​​the wake considered. [Fig.4] Figure 4 represents the value of the wind speed field as well as the powers of two wind turbines, one of which is in the wake of the other, with the same speed representation conventions as in the previous figure (same conventions in all subsequent figures which represent wakes). [Fig. 5] Figure 5 represents the wake deflection due to the misalignment of the wind turbine, with the wake of a wind turbine with a zero misalignment angle in the upper part, and the wake of a wind turbine with a 20° misalignment angle in the lower part. [Figure 6] Figure 6 represents the interactions between the wakes of three wind turbines grouped into a sub-farm, with their misalignments controlled by an optimization according to the invention of the power generated by the sub-farm. [Fig.7] Fig. 7 is a graph representing the duration of power optimization calculations for simulated wind farms with an increasing number of wind turbines, with the number of wind turbines in the farm on the x-axis and the duration of the optimization in hours on the y-axis. [Fig.8] Figure 8 represents the wakes of a 9-turbine wind farm for a given wind configuration, without the grouping into sub-farms according to the invention. [Fig.9a] [Fig.9b] Figure 9a represents the wakes of a 9-turbine wind farm for a given wind configuration, and Figure 9b represents the directed graph of grouping the 9 turbines into sub-farms according to the invention corresponding to the wakes of Figure 9a. [Fig. 10a] [Fig. 10b] Figure 10a represents the wakes of a 12-turbine wind farm for a given wind configuration, and Figure 10b represents the directed graph of grouping the 12 turbines into sub-farms according to the invention corresponding to the wakes of Figure 10a. [Figure 11] Figure 11 represents the wakes of a wind turbine, for a given wind configuration (7m / s, 270°), for different values ​​of velocity deficit θ, which are respectively, from top to bottom, equal to 0.75m / s, 0.5m / s and 0.25m / s. [Fig.12] Figure 12 represents the wakes of a wind turbine, for a given wind configuration (7m / s, 270°), for misalignment angles 0 which are respectively, from top to bottom, equal to +20°, 0° and -20°, for a velocity deficit θ of 0.5 m / s. [Fig.l3a] [Fig.l3b] [Fig. 13a] represents an additional wake which corresponds to the envelope resulting from the addition of the wakes at the three misalignment angles of +20°, 0° and -20° of [Fig. 12], and [Fig. 13b] is the modeling according to the invention of the additional wake of [Fig. 13a] in the form of a trapezoid [Fig. 14a] [Fig. 14b] These two figures represent the additional wakes of a set of 12 wind turbines, depending on whether a velocity deficit threshold θ of 0.5 m / s ([Fig. 14a]) or 1.5 m / s ([Fig. 14b]) is chosen. [Fig.15] Figure 15 represents a graph, with the number of wind turbines in the farm on the x-axis and the ratio of the "classical" optimization time to the optimization time by grouping, also called "clustering" in English, according to the invention, for different values ​​of the speed deficit threshold β. The figures, particularly those of the wind turbines and their wake, remain very schematic, not necessarily to scale or in the actual spatial configuration of the wind turbines in operating position (the trends, orders of magnitude and variation of quantities of interest nevertheless remain representative of the reality of the implementation of the invention). Description of the implementation methods

[0058] The present invention relates to a method for real-time control of a wind farm. A wind farm, also called a wind park or wind power plant, is a site comprising a group of wind turbines that generate electricity. Each wind turbine (also sometimes incorrectly called a turbine) in the wind farm includes at least one actuator to modify at least one operating point of the turbine. An example of an operating point could be the turbine's yaw angle (or misalignment angle). Other operating points may include wind turbine speed limiting or modification of the wind turbine's power curve. The position of the wind turbines within the wind farm, also called the turbine layout or turbine implementation, is known in advance.

[0059] In the following description, only the control / control of the misalignment angle 0 is described and exemplified. However, other operating points can be controlled by the method according to the invention, in addition to or instead of the misalignment angle, in particular static, dynamic or helical bridling impacting the extent and advection of the wake. For more details on dynamic curtailment, see, for example, the article "Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments" by JA Frederik et al, published in August 2019 in the publication Wind Energy Science Discussions. For more details on helical bridling, see, for example, the article "The helix approach: using dynamic individual pitch control to enhance wake mixing in wind farms”, by JA Frederik et al, published in August 2020 in the publication Wind Energy, issue 08, pages 1739-1751.

[0060] In the present application, the terms upstream and downstream are defined according to the direction of the wind (an upstream wind turbine is subjected to the wind before a downstream wind turbine).

[0061] The method according to the invention comprises the following steps:

[0062] a) a step of determining, by modeling, for different wind configurations, a region of space approximating the wake of each type of wind turbine, referred to as the modeled wake region, for a given range of misalignment angle (0) and for a given wind speed deficit threshold (ô), b) a step of identifying, from the wakes modeled in step a), the wind turbines that are in the modeled wake of another wind turbine for each of the (measured) wind configurations, and then of storing identification parameters linking each wind turbine to the wind turbine in whose modeled wake it is located for each wind configuration c) a real-time step for measuring wind configuration, taking into account the identification parameters stored in step b) for said wind configuration or for a wind configuration closest to the one measured, to group wind turbines into a plurality of sub-farms, each sub-farm comprising the wind turbines that are in the modeled wake of each other d) a step of determining at least one setpoint operating point, including the misalignment angle (0) and / or the curb, for the wind turbines of each sub-farm, by a method of optimizing the power generated by each sub-farm e) a step of applying said setpoint operating point(s) determined in step d) to the wind turbines of the farm, including their misalignment angle 0 and / or their curb, by actuation of the actuator(s).

[0063] The steps of the process, or at least some of them, can be implemented by computer means, in particular at least one computer, processor(s) or calculator(s).

[0064] The steps are detailed below, with the aid of the figures. [Fig. 1] has already been described.

[0065] We first recall the definitions of certain terms used in the present request : - The power output of a wind farm:

[0066] The rotor of a wind turbine converts the mechanical energy contained in the wind into electrical energy. The relationship between the wind power Peo produced by a wind turbine and the power contained in the received wind Pvent is written

[0067] Wind turbine Cp X Pyent, OR Pyent ½ p SU3

[0068] with Cp < 0.593, the power coefficient of the wind turbine. Cp depends on the intrinsic characteristics of the wind turbine, as well as its operating conditions, and expresses the efficiency of the conversion of wind energy into electrical energy

[0069] air density

[0070] S the surface area of ​​the wind sector in contact with the blades

[0071] U the wind speed at the rotor position

[0072] A wind configuration (called a "bin" in English) is defined by the wind speed (s), its direction (d), and here also its turbulence intensity (TI). Such a wind configuration is therefore denoted by a pair of components (ws, wd) or a triplet (ws, wdTI).

[0073] In the following, W = (Ws, Wd, TI) is the random variable representing wind blowing on a wind farm. It is defined on the probability space (Q, F, P) and takes values ​​in

[0074] WQR>0 x [0, 2ir[

[0075] Its components are (ws, wd).

[0076] For a wind farm of NT wind turbines subjected to a wind yy, its power is equal to the sum of the powers P(t) of the wind turbines that compose it: F(W') = F; AlU; - Annual energy production

[0077] This is also known as AEP (which is the acronym for "average Annual Energy Production"). The AEP of a wind farm is defined as the annual energy it produces. Since P describes a quantity of energy supplied per hour, the AEP is written using the expected annual production. AEF 8760 - EFA HA

[0078] The constant 8760 corresponds to the number of hours in the year, the AEP being expressed in kWh.

[0079] For the sake of simplicity, the number of wind configurations to be studied per year is limited by fixing the possible values ​​of W. For the year in which energy production is studied, wind measurements are available, which are used to model W as a discrete variable. Based on these measurements, the probabilities of each of these possible winds are determined. Therefore: - the discrete set of possible wind speeds, such as Ws = {so, • • • J where 50 := o < 51 <... < 5 m < OO := 5 m+1

[0080]

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[0091]

[0092] - the discrete set of possible wind directions as = {dQ, . . ., d„} where d0 := 0 < d} <. . . dn <2n :=d„+i The set of possible winds is therefore w= that we reindex as {wi,...,wNw], of cardinality Nw := m • n. We can therefore write W's law: HAS ; At the We now assume that we have a set D of N measures of the values ​​of W during the year of interest. A natural choice for the {p(wSi,wdj)} is the proportion of elements of D belonging to [si, si+l[ x [dj , dj+l[. More precisely, for . , _ LU , we define ( . dj ) e FK x kVj Figure 2, for example, for a fictitious farm, represents the wind rose of wind data collected during the year 2021 at a given location. We therefore collect these different wind configurations, here over a year, to define the corresponding modeled wakes of step a) of the process according to the invention. The discretization chosen here, for the wind rose of [Fig.2], is W = {0m / s, 1m / s,..., 20m / s]x{0°, 10°, ...,350°}. This figure is actually a histogram where the radius of a "box" indicates the frequency p(Si,dj) of the wind (si, dj) possible, the shade of grey indicating the speed si of it and the orientation indicating the direction dj. By convention, the direction wd is that from which the wind is coming. It is measured clockwise, with the wind from the North at 0°. Now that the distribution of W has been constructed on the data of a year of interest, we can rewrite the AEP as follows:

[0093] The calculation of the power of each wind turbine takes into account the aerodynamic interaction between the different turbines. One of these interaction effects is the wake of a wind turbine, a simulation of which is given in [Fig. 3]. The greater the speed deficit, the lighter the shade of gray in the representation (same convention in the following relevant figures). - The wake

[0094] This is the region of disturbed flow downstream of a solid object around which a fluid flows. In the context of the present invention, it is the region downstream of a wind turbine where the airflow is slowed and often turbulent. As an illustration, [Fig.4] represents the value of the wind speed field as well as the powers of two wind turbines, one of which is in the wake of the other, according to a representation of the same type as [Fig.3].

[0095] Wind turbines which are in a wake therefore operate in a region where the wind speed is less than that of the free wind (i.e. the wind whose flow is not disturbed).

[0096] The wind power received by a wind turbine in a wake is less, therefore it will produce less energy. - The wind speed deficit

[0097] The wind speed deficit at a point x in space R3 is defined as being

[0098] AU(x) = Uœ - U(x), where Uœ is the free wind speed (upstream of the farm) and U(x) wind speed x.

[0099] In the embodiments detailed in this application, wind speed values ​​will be measured at the level of the wind turbine nacelles. Similarly, all wind turbine models and the resulting figures are drawn at a height equal to the height of the wind turbine rotor. The study is therefore carried out in a plane parallel to the ground, at a height z = z rotor, in a 2D (two-dimensional) space.

[0100] But it should be emphasized that the invention can also be advantageously applied by relying on 3D (three-dimensional) wake measurements and / or models.

[0101] In order to convert the energy present in the wind into electrical energy as efficiently as possible, the blades of a wind turbine must face the wind. Indeed, if the rotor of a wind turbine is misaligned by an angle of 0 with respect to the wind (see [Fig. 5], lower diagram, compared to the upper diagram), the power coefficient Cp of a wind turbine decreases according to the relation

[0102] Cp(0) oc cos“(0), 0 e [-jr, ir], 1 < alpha < 3

[0103] However, misaligning the nacelle of a wind turbine alters its wake. Thus, a wind turbine that was in the wake of another may find itself, after misalignment, in free wind, as can be seen in Figures 4 and 5.

[0104] Voluntary misalignments can be introduced in order to reduce the speed deficit of the wind turbines downstream of the wind and thus increase the power they produce. - The misalignment angle (or yaw) and optimization

[0105] A yaw value of 0 decreases Cp, but can increase the wind speed received by the wind turbine. This misalignment can increase the power supplied by the wind turbine.

[0106] Yaw optimization calculations for each of the wind turbines are therefore necessary to compensate for the decrease in the power coefficient Cp by the increase in the received wind speeds.

[0107] In the invention, the dependence of the power of a wind farm on the yaw values ​​0, of each wind turbine t, is taken into account. For a wind configuration w, 0,(w) [ -n, .t] is denoted by the corresponding yaw chosen, and the power of the wind farm is written

[0108] P = P(G, w\

[0109] The problem of optimizing the annual power of a wind farm as a function of the yaws is formalized below: We consider a farm which contains NT wind turbines, each represented by an index i & {1, . . ., NT}• During the year of interest, the random variable wind IV takes the values ​​Wi, . . ., w N w with the probabilities sp wï , ... ,p wNw .

[0110] It was assumed that each wind Wi corresponds to the choice of only one yaw vector 01. Indeed, the goal on the ground is to choose the misalignments of the wind turbine nacelles of the farm at each new wind measurement. [YES] We maximize the function [ A . a: j . (,..., 0*'w ) >■—> AEPi 0^..... , ) — 8 i'bO * 0*,} i=l

[0112] In practice, we want to limit the amplitude of the misalignment in order to limit the damage to the wind turbines during successive rotations in response to changes in wind: we can for example limit ourselves to yaw amplitudes between the values ​​0- = -20° and 0+ = 20°.

[0113] The problem of optimizing the average annual power of the farm can therefore be written as:

[0114] under the constraints 0i e [0-, 0+]NT , Vi = 1,..., Nw

[0115] Since the pwi are positive, we can write max p^ Pi P. w,- ; = max Pi P. ) z— / - x z—z-

[0116] We have succeeded in separating the optimization problem into Nw independent problems in 01,..., 0Nw.

[0117] For a given wind bin we W, we seek to determine a solution called "classical" OPT of W- : - argmax y"' fw)

[0118] called "classic" OPT

[0119] The calculation of P is done as follows:

[0120] Denoting 0t as the component of the yaw vector and U(t) as the wind speed received by the rotor

[0121] of the wind turbine t, we can write P(t) the power of each wind turbine in the farm as

[0122] P(t)(0,w) oc Cp(0') • U(t)(0,w)3

[0123] It is recalled that w is the measurement of the wind said to be at infinity, that is to say the ideal measured wind whose flow is not disturbed, while U(t) designates the speed of the wind actually received by the rotor of the wind turbine t.

[0124] However, it turns out that the calculation of the velocity field within the farm when the airflow is disturbed by the presence of the wind turbines is very complex and cannot be expressed analytically without making approximations (it is calculated by the numerical resolution of the Navier-Stokes equations).

[0125] For this reason, the expression of P(t) in (classical OPT) is inaccessible, so it is a so-called "black box" optimization problem.

[0126] In addition, since the calculation of a value P(t) is already costly, its numerical optimization quickly becomes prohibitive when the size of the farm increases, as can be seen from [Fig.7], which is a graph representing on the x-axis the number of wind turbines in the farm, and on the y-axis the duration of the optimization in hours.

[0127] For these reasons of computational complexity, we assume throughout this work the existence of a solution for (classical OPT) whose possible uniqueness will not be addressed. Thus, we note:

[0128] - 0(w) the maximum returned by the numerical optimization routine as soon as it converge

[0129] - Pciassic(w) := P(0,w) the power of the farm obtained by numerical resolution of (OPT »classic”)

[0130] - tciassic(w), the duration of this optimization routine.

[0131] A farm with 3 wind turbines subjected to wind w = (10m / s, 270°) was modeled in [Fig.6] and its nominal power Pnominaie = P(0 = 0,w) and PciassiqUe = P(0,w) were calculated, resulting from the resolution of (OPT »classical »).

[0132] Given the computational cost of the (classical OPT) solution, it is impossible to implement real-time optimization of the power of a large wind farm as a function of yaw angles.

[0133] The solution according to the invention is, for this power optimization (step d) of the process according to the invention) to divide the instance of (OPTciassiqUe) into several optimization instances on smaller data groups:

[0134] The invention consists of grouping the wind turbines of the farm into sub-farms, called "clusters", according to an appropriate criterion, and then independently considering the optimization of the power of each of these sub-farms. (In this application, the terms sub-farm and "cluster" are also used interchangeably, and grouping or "clustering" is used interchangeably.): this is step c) of the method of the invention.

[0135] Consequently, the optimized power of the farm by grouping into sub-farms simply becomes the sum of the optimized powers for each of the sub-farms.

[0136] Figure 7 mentioned above suggests that the optimization time for the power of the entire farm is (significantly) greater than the sum of the optimization times for each cluster of wind turbines. However, considering the power of each sub-farm independently implies that wake effects between sub-farms are preferably neglected.

[0137] It is therefore desirable that each sub-farm captures the wake effects between the wind turbines that compose it as effectively as possible. Ideally, it is desirable that two wind turbines in two different sub-farms not be in each other's wake: [Fig. 8] shows a situation where this is possible, the first sub-farm being composed of wind turbines 1 to 4 and the second sub-farm of wind turbines 6 to 9.

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146] Given a wind we W and a clustering / grouping p_ ' we define the Power of a cluster / sub-farm C and C as a function of the wind turbine loops that compose it, as follows: P^'' 7 (1?, w) = ^ 2 Note that maximizing the power of a given cluster C as a function of the loops is just a reduced-size instance of (classical OPT) where the farm considered is C. For each p, we can therefore numerically obtain 0(C)(w) a solution of max , known as OPT "clustering", whose numerical calculation will have lasted t(C)(w). We then propose the vector 0(C)(w) as an approximate solution of "classical" OPT, composed of the solution loops of the OPT(C) "clustering" problems, as follows: In other words, the vector 0(C) contains the loops resulting from the power optimization of each component cluster C. Finally, we define the optimized farm power using C clustering as P(C)clustering(w) := P(0(C),w), and the optimization time by clustering as the sum of the optimization times of each cluster, that is: . . = v ciust.ermg AC • The next step is to apply the misalignment instructions to the wind turbines by controlling their actuators appropriately: this is step d) of the process according to the invention. It is worth noting that the optimization applied to each cluster / sub-farm is an independent and distributable operation. Cluster optimization can therefore greatly benefit from the distribution or parallelization of calculations, thus significantly reducing execution time. Example of implementation We consider here a simulated farm of NT = 9 wind turbines subjected to a wind w = (10m / s, 18°) as shown in [Fig.8]. The wind turbines are numbered from 1 to 9 in the figure.

[0147] One possible cluster choice is C = {C1,C2} where

[0148] Cl = {1, 2, 3, 4, 5} and C2 = {6, 7, 8, 9}.

[0149] However, since wind turbine 7 is barely in the wake of wind turbine 6, we can ignore this interaction and choose as partition C' = {C' 1,C'2 ,C'3]

[0150] with C' 1 = {1, 2, 3, 4, 5], C'2 = {6} and C'3 = {7, 8, 9}.

[0151] After numerical resolution, the following results are obtained:

[0152] -P_le=18.87mW

[0153] - Pciassic = 20.1 ImW for an optimization time tciassic = 2.00 s

[0154] - P(C)ciustering = 20.1 ImW for a computation time t(C)ciustering = 1.12 s

[0155] - PCC^dustering = 2.10mW for a calculation time t(C')ciustering = 0.96 s

[0156] These results indicate first that the choice of partition is not unique for a given wind, and that it faithfully reflects the wake interactions between the wind turbines that make up the clusters / sub-farms.

[0157] By constructing a suitable partition for each wind data point, power calculations can be accelerated while keeping P "clustering" close to P "classical". Construction of the sub-farm grouping

[0158] Now that all the concepts have been defined when a partition of a wind farm is already available, the method described above is used to construct the grouping into sub-farms:

[0159] A wind farm is modeled as a directed graph whose nodes are its wind turbines and whose edges represent the presence of wake.

[0160] The directed graph associated with a wind farm of NT wind turbines subjected to a wind w is

[0161] G(w) = (V, E(w)), where

[0162] - V = {1,..., Nt}

[0163] - we have the binary relation ~ on V x V : i ~ j if and only if the center xj of the rotor of wind turbine j is in a region S of space corresponding to the wake downstream of wind turbine i

[0164] - £'(«£ = {(£ i) € VX y | i

[0165] The edges of the graph depend entirely on the definition of S, the region of space approximating the wake downstream of i.

[0166] An example of the construction of the relation ~ is given below.

[0167] We assume here ~ constructed.

[0168] It is recalled that the important characterization of a C cluster of wind turbines is that it captures all the wake effects of the wind turbines which compose it.

[0169] In other words, i and j belong to C if and only if there exists a path between i and j on the graph, that is: kb . . . , kn e V such that i ~ ki ~ k2 ~ . . . ~ kn ~ j.

[0170] Now, it turns out that such sets C are exactly the weakly connected components of the graph G(w).

[0171] By definition, a directed subgraph G = (V',E') <1 G = (V,E) is a weakly connected component of G if it is the maximal subgraph of G such that: Vu, ve V ', there exists an undirected path from E' between u and v.

[0172] The problem of determining the weakly connected components of a directed graph can then be solved, for example, using the BFS algorithm (acronym for the English name of this algorithm "Breadth-First Search") which determines for each node ue V the nodes of V which are reachable from u.

[0173] An implementation of the BFS algorithm is carried out.

[0174] Examples of grouping into sub-farms according to the invention are illustrated: - in Figures 9a and 9b for a grouping into sub-farms of a farm of 9 wind turbines, where four sub-farms of 2 wind turbines each have been grouped together, plus one sub-farm containing only one wind turbine - in figures 10a and 10b for a grouping into sub-farms of a farm of 12 wind turbines, where the wind turbines have been grouped into 4 sub-farms containing only one wind turbine (1,4,9,12), two sub-farms of two wind turbines (2,7) and (6,10), and one sub-farm of four wind turbines (3,5,8,11). Approximation of the wake

[0175] As mentioned previously, the wake of a wind turbine is a region of space downstream of the turbine where the airflow is slower. The velocity deficit AU is the difference between the wind speed at infinity (the speed of an ideal, undisturbed wind) and this wake speed.

[0176] The aim is, for a given wind speed we W, to determine whether wind turbine j is in the wake of wind turbine i. To do this, a simple region of space is defined which approximates the wake downstream of i. The dimensions and orientation of this region are to be chosen as a function of w.

[0177] Since we are interested in the impact of wakes on the energy production of wind turbines, we neglect power losses due to small wind speed deficits. Indeed, as the power produced by a wind turbine is proportional to the cube of the received wind speed, small speed deficits do not have a large influence on the power supplied, hence the introduction of the parameter φ, which defines the threshold value of wind speed deficit above which a wake is considered to exist.

[0178] For a wind configuration w = (ws,wd), the points in space that are in the wake S of a wind turbine are therefore those at which the measured speed is less than ws -ô: E(w,ô) = | t-(x) < -(Q = {x€ R 3 | AtUx) < 5}

[0179] The value of this parameter changes the shape of the wake, as can be seen in [Fig.11].

[0180] It is recalled that wake modeling for clustering is part of the approach to optimizing the yaws of the wind farm's turbines.

[0181] As mentioned above, and as can be seen in [Fig.12], the misalignment due to yaw modifies the shape of the wake.

[0182] The wake region modeling attempts to take these loops into account.

[0183] However, their values ​​are calculated by optimization only after clustering. Not yet having access to these values ​​at this stage, we use a region of space which will contain all the other wakes when the yaw values ​​vary between 0- = -20° and 0+ = 20°.

[0184] To do this, we study the region E = E(£ = UE(£ = 0) UE(£ =

[0185] resulting from the union of the yaw wakes 0, 0- and 0+, which we represent in [Fig.13a].

[0186] The goal now is to approximate ~ by a simple region of space.

[0187] We recall that all speeds and wakes are studied at a constant height equal to the rotor height z rotor. For the sake of notation, we retain the notation ~ for designate ~ at height z = zrotor.

[0188] We are therefore looking for a simple region of the plane that approximates

[0189] In view of Figure 13a and the other simulations carried out, a trapezoid is chosen oriented as shown in figure 13b, according to the wind direction, to approximate which requires 3 parameters to be determined,

[0190] 1. nb_diam: the wake length expressed as a multiple of the blade diameter the wind turbine

[0191] 2. init_width: the initial width of the wake, measured at the rotor

[0192] 3. h: the increase in the width of the half-wake

[0193] Once these parameters are determined for the wind configuration w, the wake downstream of a wind turbine i is approximated as the trapezoid defined by these parameters.

[0194] A wind turbine j is then in the wake of i when it belongs to the trapezoid thus defined.

[0195] We find with [Fig. 13b] the result illustrating step a) of determination of modelled wakes described above.

[0196] Final calculation to adjust the operating point of the wind turbines

[0197] This is a two-step approach:

[0198] 1. In development / offline: for each wind configuration w; = (si, di) within the discrete set of possible winds fW, the following procedure is used:

[0199] - simulation of the wake of a single wind turbine subjected to a wind speed wsi and direction wdi =270°: This is step a) of determination by modelling of wakes, notably illustrated by [Fig. 13b]

[0200] - calculation and storage in a lookup table of dimension parameters (init_width, nb_diam, h) of the smallest trapezoid containing: This is step b) identification

[0201] 2. In production / real-time: when the farm is subjected to wind wi,

[0202] - creation of a graph with nodes 1,... ,NT: This is step c) of grouping into sub-farms, notably illustrated by figures 9a and 9b for a farm of 9 wind turbines and by figures 10a and 10b for a farm of 12 wind turbines as mentioned above.

[0203] - reading the dimension parameters of the trapezoid approximating the wake in the table correspondence

[0204] - for any wind turbine i of the farm:

[0205] • calculation of the coordinates of the trapezoid downstream of i

[0206] • for any wind turbine j^i,, if j is in this trapezoid, add the edge (i, j) to the graph

[0207] representing the farm

[0208] - calculation of C, the set of weakly connected components of the graph thus built

[0209] -for any £ ' numerical calculation of the solution 0(C)(w;) of the reduced optimization problem (OPT(C) optimization)

[0210] - construction of the loop vector 0(C)(w;) of the entire farm

[0211] - calculation of the optimized power P(C)clustering(wi) = P(0(C), Wi) resulting from these misalignments: This is step d) of determining the setpoint operating points by optimizing the power of each sub-farm

[0212] - then adjustment by the actuators of the operating point(s) of the wind turbines, including the misalignment angle with the actuators that equip them, depending on this optimized power: This is step e) of applying the instructions to the wind turbines via their actuators.

[0213] Figures 14a and 14b allow visualization of the role of the hyperparameter ô which fixes the velocity deficit threshold from which the wake approximation yi is defined. Depending on the value of ô, we can have ti~jouiT°j, which illustrates the importance of adjusting this threshold, which also helps to avoid interference between the wakes of wind turbines belonging to two adjacent sub-farms.

[0214] Figure 15 represents a graph, with the number of wind turbines in the farm on the x-axis and the ratio of the "classical" optimization time to the "clustering" optimization time according to the invention on the y-axis, for different values ​​of the speed deficit threshold: - curve Cl corresponds to a velocity deficit threshold ô of 0.5 m / s, - curve C2 corresponds to a velocity deficit threshold ô of 1.0 m / s, - curve C3 corresponds to a velocity deficit threshold ô of 1.5 m / s,

[0215] It is thus verified that with the invention, the time saved to carry out the optimization is very significant, and that it is all the more important as the number of wind turbines in the farm is large.

[0216] In conclusion, it is observed that the optimization according to the invention, with grouping into sub-farms, makes it possible to optimize the energy production of the farm at least ten times faster than by the so-called classical method where power optimization is carried out on all the wind turbines of the farm, with in addition a negligible error rate compared to the classical method, and limiting computer resources and memory. The invention has thus made it possible to accelerate the execution of park management strategies, to allow their deployment on real-time computers, including for large farms, which may include several dozen or even several hundred wind turbines.

Claims

Demands

1. A method for controlling a wind farm, said wind turbines being of a single type or of different types, each wind turbine of said wind farm having at least one operating point including its misalignment angle (0) and / or its curb setting, said operating point(s) being adjustable by at least one actuator, characterized in that said method comprises: a) a step of determining by modeling, for different wind configurations, a region of space approximating the wake of each type of wind turbine, referred to as the modeled wake region, for a given range of misalignment angle (0) and for a given wind speed deficit threshold (θ), b) a step of identifying, from the wakes modeled in step a), the wind turbines that are in the modeled wake of another wind turbine for each of the wind configurations,then a step of storing identification parameters linking each wind turbine to the wind turbine in the modeled wake of which it is located for each wind configuration; c) a step, for a wind configuration, of taking into account the identification parameters stored in step b) for said wind configuration or for a wind configuration closest to the measured one, to group wind turbines into a plurality of sub-farms, each sub-farm comprising the wind turbines that are in the modeled wake of each other; d) a step of determining at least one setpoint operating point, including the misalignment angle (0) and / or the curb setting, for the wind turbines of each sub-farm, by a method of optimizing the power generated by each sub-farm; e) a step of applying said setpoint operating point(s) determined in step d) to the wind turbines of the farm, including their misalignment angle (0) and / or their curb setting.by actuation of the actuator(s).

2. A method for piloting a wind farm according to the preceding claim, characterized in that steps a), b), c), d) and e) are repeated at each new wind configuration measurement.

3. A method for controlling a wind farm according to any one of the preceding claims, characterized in that step a) of The determination of the modeled wakes is carried out for a range of misalignment angle (0) between -0 and +0, these values ​​corresponding to the limits defined in the specifications of the wind turbines, with 0 equal to 20°.

4. A method for piloting a wind farm according to the preceding claim, characterized in that, in step a), each wake modeled for the misalignment angle range (0) is the envelope of the sum of a plurality of wakes modeled at different misalignment angle values ​​in said angle range (0), and in particular the envelope of the wakes modeled at -0, +0 and 0°.

5. A method for piloting a wind farm according to any one of the preceding claims, characterized in that step a) of determining the modeled wakes is carried out for a wind speed deficit threshold (ô) between 0.75 m / s and 0.20 m / s, in particular between 0.70 m / s and 0.45 m / s, and in particular equal to 0.65 m / s.

6. A method for controlling a wind farm according to one of the preceding claims, characterized in that step a) of determining the modeled wakes is carried out for a wind speed deficit threshold (ô) taking into account at least one of the following parameters: the number of wind turbines in the farm, the distance between wind turbines, the configuration of the farm, the geographical positioning of the wind turbines in the farm.

7. A method for piloting a wind farm according to any one of the preceding claims, characterized in that, in step a), the modeled wakes are approximated - in the form of two-dimensional representations, considering the wind configurations and the wakes at constant height, in particular a height equal to that of the axis of the rotor carrying the blades of the wind turbines, of trapezoidal type whose smallest base is the initial width of the wake, - or in the form of three-dimensional representations, in particular of the type portion of a cone whose smallest base is the initial size of the wake.

8. A method for controlling a wind farm according to any one of the preceding claims, characterized in that the different wind configurations considered for carrying out step a) of wake modeling determination is the set of possible wind configurations recorded over a period of time, including over a year, in a geographical area including the wind farm.

9. A method for controlling a wind farm according to any one of the preceding claims, characterized in that, in step b), the wind configurations, in particular the wind speed and direction distribution and / or the wind speed and direction, are measured in real time by means of at least one LiDAR sensor and / or at least one anemometer and / or at least one control and data acquisition system.

10. Method for controlling a wind farm according to one of the preceding claims, characterized in that, in step d) of determining at least one setpoint operating point, the optimization of the power generated per sub-farm is carried out for each of the sub-farms in parallel and / or sequentially.

11. A method for controlling a wind farm according to any one of the preceding claims, characterized in that, in step d) of determining at least one setpoint operating point, the optimization of the power generated per sub-farm is carried out by taking into account the structural fatigue constraints of the wind turbines or of each type of wind turbine in the farm.

12. A method for piloting a wind farm according to one of the preceding claims, characterized in that step a) of determining the modeled wakes is carried out offline with possible periodic update(s) and / or possible update with the wind configuration measurement made in real time in step c).

13. Wind farm, said wind turbines being of a single type or of different types, each wind turbine of said wind farm having at least one adjustable operating point, including its misalignment angle (0) and / or its bridle, said operating point(s) being adjustable by at least one actuator, characterized in that said wind farm comprises or is connected to computer means to implement the control method according to any one of the preceding claims in order to apply setpoint operating points to the wind turbines of the farm including their misalignment angle (0) and / or their bridle, by actuating their actuators.