Wind power plant wake flow velocity distribution inversion method, device and equipment and storage medium

Abstract

The invention discloses a wind power plant wake flow velocity distribution inversion method, device and equipment and a storage medium. The method comprises the steps that the actual thrust load borne by the top of a tower of each wind turbine and the rotating speed, the pitch angle, the cut-in wind speed and the cut-out wind speed of each wind turbine are obtained; based on the actual thrust load on the top of each wind turbine tower, the rotating speed, the pitch angle, the cut-in wind speed and the cut-out wind speed of each wind turbine and a wind turbine thrust load prediction model function, the inflow speed of each wind turbine is obtained; and on the basis of the inflow speed of each wind turbine, wind power plant wake flow speed distribution is obtained through a wind power plant wake flow speed model. According to the invention, the technical problem that the economic cost for measuring the wake flow velocity distribution of the wind power plant is high in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of energy technology, and in particular to a method, device, equipment and storage medium for inverting wake velocity distribution in a wind farm. Background Art

[0002] In a wind farm, each wind turbine is disturbed by the wake of its upstream and downstream turbines. By coordinating the operating status of each turbine to change its wake intensity and direction, the impact of wake loss on the total power of the wind farm can be minimized, thereby improving the power generation efficiency of the wind farm. This method of improving the power generation efficiency of wind farms requires an accurate description of the wake effect in the wind farm and a timely response to changing wind conditions.

[0003] In the prior art, the wake of a wind farm is mainly measured by using a laser radar as a wind speed measurement device. However, due to the limitations of the laser beam intensity and scanning rate, the laser radar is usually unable to complete the measurement of the wind farm wake velocity distribution. If it is widely laid in a wind farm, although it can complete the measurement of the wind farm wake velocity distribution, this method not only has low accuracy, but also has high economic costs. Summary of the invention

[0004] The present invention provides a method, device, equipment and storage medium for inverting the wake velocity distribution of a wind farm, which can solve the technical problems existing in the prior art of low accuracy and high economic cost in measuring the wake velocity distribution of a wind farm.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a method for inverting the wake velocity distribution of a wind farm, the method comprising:

[0007] Obtain the actual thrust load on the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed and cut-out wind speed of each wind turbine;

[0008] Based on the actual thrust load on the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed, cut-out wind speed of each wind turbine and the wind turbine thrust load prediction model function, the inflow velocity of each wind turbine is obtained;

[0009] Based on the inflow velocity of each wind turbine, the wind farm wake velocity distribution is obtained through a wind farm wake velocity model.

[0010] In a second aspect, an embodiment of the present invention provides a device for inverting the wake velocity distribution of a wind farm, the device comprising:

[0011] An information acquisition module is configured to acquire the actual thrust load on the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed and cut-out wind speed of each wind turbine;

[0012] A first calculation module is configured to obtain the inflow velocity of each wind turbine based on the thrust load actually received by the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed, cut-out wind speed of each wind turbine, and the wind turbine thrust load prediction model function;

[0013] The second calculation module is configured to obtain the wind farm wake velocity distribution based on the inflow velocity of each wind turbine through a wind farm wake velocity model.

[0014] Optionally, in one embodiment, the second calculation module is configured to:

[0015] Substituting the inflow velocity of each wind turbine into a second preset formula to calculate the loss distribution of the wake velocity of each wind turbine;

[0016] The wind farm wake velocity model is used to superimpose the loss distribution of each wind turbine wake velocity to obtain the wind farm wake velocity distribution.

[0017] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising: a memory, a processor; the processor is used to read and execute a computer program stored in the memory to implement the steps of the aforementioned method for inverting the wake velocity distribution of a wind farm.

[0018] In a fourth aspect, an embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed, the steps of the aforementioned method for inverting the wake velocity distribution of a wind farm are implemented.

[0019] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the aforementioned method for inverting the wake velocity distribution of a wind farm.

[0020] The beneficial effects brought by the technical solution provided by the embodiment of the present invention include:

[0021] The thrust load actually received at the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed and cut-out wind speed of each wind turbine are obtained; based on the thrust load actually received at the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed, cut-out wind speed of each wind turbine and the wind turbine thrust load prediction model function, the inflow velocity of each wind turbine is obtained, and the thrust load actually received at the top of the wind turbine tower is used as the key parameter for the safe operation of the wind turbine set. Relevant measurement devices have been widely deployed in the existing wind farm operation and maintenance. Therefore, the application of this method does not require the addition of additional sensors, which greatly reduces the economic cost;

[0022] The wind farm wake velocity distribution model is obtained by superimposing the wind turbine wake loss velocity model. For the parameters in the wind farm wake velocity distribution model, the wind turbine inflow velocity calculated by thrust back calculation is used for parameter identification instead of the empirical parameters in existing patents or literature. This can reflect the real-time velocity distribution of the wind farm wake and has higher accuracy and better applicability.

[0023] The present invention solves the technical problem of high economic cost in measuring the wind farm wake velocity distribution in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 It is a schematic diagram of a flow chart of an embodiment of a method for inverting the wake velocity distribution of a wind farm according to the present invention;

[0026] Figure 2 for Figure 1 Detailed flow chart of step S30;

[0027] Figure 3 It is a schematic diagram of the wind turbine tower bottom coordinate system of the present invention;

[0028] Figure 4 It is a schematic diagram of the wind farm coordinate system and the wind turbine tower bottom coordinate system of the present invention;

[0029] Figure 5 This is a schematic diagram of functional modules of an embodiment of a device for inverting wake velocity distribution in a wind farm according to the present invention;

[0030] Figure 6 The figure is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0033] In a first aspect, an embodiment of the present invention provides a method for inverting wake velocity distribution in a wind farm.

[0034] In one embodiment, referring to Figure 1 , Figure 1 FIG. 1 is a flow chart of an embodiment of a method for inverting the wake velocity distribution of a wind farm according to the present invention. Figure 1 As shown in Figure 2, the inversion method for the wind farm wake velocity distribution includes:

[0035] Step S10, obtaining the actual thrust load on the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed and cut-out wind speed of each wind turbine;

[0036] In this embodiment, the wind turbine tower sensor is used to measure the actual thrust load on all wind turbine towers in the wind farm, and the speed, pitch angle, cut-in wind speed and cut-out wind speed of each wind turbine are obtained. The speed, pitch angle, cut-in wind speed and cut-out wind speed of the wind turbine are basic parameters for the actual design, control and operation and maintenance of wind turbines.

[0037] Step S20, obtaining the inflow velocity of each wind turbine based on the thrust load actually received by the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed, cut-out wind speed of each wind turbine, and the wind turbine thrust load prediction model function;

[0038] In some specific embodiments, step S20 includes:

[0039] Substituting the thrust load actually received by the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed, cut-out wind speed of each wind turbine, and the wind turbine thrust load prediction model function into the first preset formula in turn, and calculating the inflow velocity of each wind turbine;

[0040] The first preset formula is as follows:

[0041]

[0042] In the formula, f obj is the objective function of the optimization problem, T model is the wind turbine thrust load prediction model function, V rotor is the inflow velocity of the wind turbine, ω is the rotation speed of the wind turbine, θ is the pitch angle of the wind turbine, T s is the actual thrust load on the top of the wind turbine tower, V cut_in is the wind turbine cut-in wind speed, V cut_out is the cut-out wind speed of the wind turbine and st is the abbreviation of subject to, which means “subject to”.

[0043] In this embodiment, the actual thrust load on the top of each wind turbine tower, the speed, pitch angle, cut-in wind speed, cut-out wind speed of each wind turbine, and the wind turbine thrust load prediction model function are sequentially substituted into the first preset formula By inversion, we can get the inflow velocity V of each wind turbine. rotor In mathematics, the symbol min is used to represent the minimum value in a set of numbers or the minimum value of a function.

[0044] The actual thrust load on the top of the wind turbine tower is used as the key parameter for the safe operation of the wind turbine. In the existing wind farm operation and maintenance, relevant measurement devices have been widely deployed. Therefore, the application of this method does not require the addition of additional sensors, which greatly reduces the economic cost. When the actual wind turbine is in operation, there is a good correspondence between the wind speed and the tower thrust load. The momentum blade element theory method or the table lookup method can quickly and accurately obtain the inflow velocity of the wind turbine.

[0045] Step S30: Based on the inflow velocity of each wind turbine, a wind farm wake velocity distribution is obtained through a wind farm wake velocity model.

[0046] In some specific embodiments, referring to Figure 2 , Figure 2 for Figure 1 Detailed flow chart of step S30 in FIG. Figure 2 As shown, step S30 includes:

[0047] Step S301, substituting the inflow velocity of each wind turbine into a second preset formula to calculate the loss distribution of the wake velocity of each wind turbine;

[0048] In this embodiment, for the loss distribution of the wake velocity of the i-th wind turbine, the inflow velocity of the i-th wind turbine is substituted into the second preset formula to calculate the loss distribution Δu of the wake velocity of the i-th wind turbine. i , the second preset formula is:

[0049]

[0050] In the formula, Δu i is the loss distribution of the wake velocity of the i-th wind turbine, i = 1, 2, ..., n, n is the number of wind turbines in the wind farm, V rotor,i is the inflow velocity of the i-th wind turbine, C T,i is the thrust coefficient of the i-th wind turbine, ρ is the air density, d i is the diameter of the i-th wind turbine, σ y,i is the wake width of the i-th wind turbine in the y direction, σ z,i is the wake width of the i-th wind turbine in the z direction, z h,iis the nacelle height of the i-th wind turbine;

[0051] σ y,i and σ z,i Determined by:

[0052]

[0053] In the formula, α i for y i The wake expansion coefficient in the direction, β i For z i The wake expansion coefficient in the direction, C y,i for y i The initial wake in the direction, C z,i For z i The initial wake of the direction, refer to Figure 3 , Figure 3 Schematic diagram of the wind turbine tower bottom coordinate system of the present invention. Figure 3 As shown, i (x i ,y i ,z i ) is the tower base coordinate system of the ith wind turbine. In mathematics, the function exp(x) represents a natural exponential function, that is, an exponential function with a real number e (e≈2.71828) as the base, and its expression is exp(x)=ex.

[0054] By analogy, by substituting the inflow velocity of each wind turbine into the second preset formula, the loss distribution of the wake velocity of each wind turbine can be obtained.

[0055] Step S302 : superimposing the loss distribution of the wake velocity of each wind turbine through the wind farm wake velocity model to obtain the wind farm wake velocity distribution.

[0056] In some specific embodiments, step S302 includes:

[0057] Substitute the loss distribution of each wind turbine wake velocity into the wind farm wake velocity model to calculate the wind farm wake velocity distribution;

[0058] The wind farm wake velocity model is as follows:

[0059]

[0060] Where U(X,Y,Z) is the wind farm wake velocity distribution, V ∞ is the inflow velocity of the wind farm, Δu i is the loss distribution of the wake velocity of the ith wind turbine, o i (x i ,y i ,z i) is the tower base coordinate system of the i-th wind turbine, O(X,Y,Z) is the wind farm coordinate system, i = 1, 2, ..., n, n is the number of wind turbines in the wind farm, (X i ,Y i ,Z i ) is the tower base coordinate of the ith wind turbine in the wind farm coordinate system.

[0061] In this embodiment, refer to Figure 4 , Figure 4 Schematic diagram of the wind farm coordinate system and wind turbine tower bottom coordinate system of the present invention. Figure 4 As shown, i (x i ,y i ,z i ) is the tower base coordinate system of the i-th wind turbine, O(X,Y,Z) is the wind farm coordinate system, (X i ,Y i ,Z i ) is the tower base coordinate of the i-th wind turbine in the wind farm coordinate system, and the loss distribution of each wind turbine wake velocity is substituted into the second preset formula By superimposing them, the wake velocity distribution of the wind farm can be calculated.

[0062] In this embodiment, the wind farm wake velocity model is a three-dimensional wake model, which is suitable for describing wakes under different atmospheric stability conditions. The wind farm wake velocity distribution model is obtained by superimposing the wind turbine wake loss velocity model. For the parameters in the wind farm wake velocity distribution model, the wind turbine inflow velocity calculated by thrust back is used for parameter identification, rather than the empirical parameters in the existing patents or literature, which can reflect the real-time velocity distribution of the wind farm wake, and has higher accuracy and better applicability.

[0063] When performing parameter identification, for the parameter α in the wind farm wake velocity distribution U i , β i , C y,i With C z,i , and is converted into the optimization problem fitting parameters. In order to meet the requirement that the number of equations in the optimization problem is greater than the number of unknown quantities, the actual thrust load on the top of each wind turbine tower measured by the sensor at m consecutive moments (m≥4) is used as the input, that is, The subscript i refers to the i-th wind turbine, and the superscript j refers to the time j. The inflow velocity of the i-th wind turbine at time j is calculated as

[0064] The unknowns that need to be solved are the parameters of the wind farm wake velocity model U, which constitute the unknown vector:

[0065] b=[α 1 C y,1 β1 C z,1 …α n C y,n β n C z,n ] T

[0066] The optimization objective function is

[0067]

[0068] The parameters in the wind farm wake velocity distribution model can be obtained by solving the above optimization problem. The parameters in the wind farm wake velocity distribution model are substituted into the second preset formula and the wind farm wake velocity model to obtain the wind farm wake velocity distribution.

[0069] In this embodiment, the thrust load actually received by the top of each wind turbine tower, the rotation speed of each wind turbine, the pitch angle, the cut-in wind speed and the cut-out wind speed are obtained; based on the thrust load actually received by the top of each wind turbine tower, the rotation speed of each wind turbine, the pitch angle, the cut-in wind speed, the cut-out wind speed and the wind turbine thrust load prediction model function, the inflow velocity of each wind turbine is obtained, and the thrust load actually received by the top of the wind turbine tower is used as the key parameter for the safe operation of the wind turbine set. In the existing wind farm operation and maintenance, relevant measurement devices have been widely deployed. Therefore, the application of this method does not require the addition of additional sensors, which greatly reduces the economic cost; the wind farm wake velocity distribution model is obtained by superimposing the wind turbine wake loss velocity model. For the parameters in the wind farm wake velocity distribution model, the wind turbine inflow velocity calculated by thrust back calculation is used for parameter identification, rather than the empirical parameters in the existing patents or literature, which can reflect the real-time velocity distribution of the wind farm wake, and has higher accuracy and better applicability. This embodiment solves the technical problem of high economic cost of measuring the wind farm wake velocity distribution in the prior art.

[0070] In a second aspect, an embodiment of the present invention further provides a device for inverting wake velocity distribution in a wind farm.

[0071] In one embodiment, referring to Figure 5 , Figure 5 Schematic diagram of the functional modules of an embodiment of the wind farm wake velocity distribution inversion device of the present invention. Figure 5 As shown, the wind farm wake velocity distribution inversion device includes:

[0072] The information acquisition module 10 is configured to acquire the actual thrust load on the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed and cut-out wind speed of each wind turbine;

[0073] The first calculation module 20 is configured to obtain the inflow velocity of each wind turbine based on the thrust load actually received by the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed, cut-out wind speed of each wind turbine and the wind turbine thrust load prediction model function;

[0074] The second calculation module 30 is configured to obtain the wind farm wake velocity distribution based on the inflow velocity of each wind turbine through a wind farm wake velocity model.

[0075] Optionally, in one embodiment, the first calculation module 20 is configured to:

[0076] Substituting the thrust load actually received by the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed, cut-out wind speed of each wind turbine, and the wind turbine thrust load prediction model function into the first preset formula in turn, and calculating the inflow velocity of each wind turbine;

[0077] The first preset formula is as follows:

[0078]

[0079] In the formula, f obj is the objective function of the optimization problem, T model is the wind turbine thrust load prediction model function, V rotor is the inflow velocity of the wind turbine, ω is the rotation speed of the wind turbine, θ is the pitch angle of the wind turbine, T s is the actual thrust load on the top of the wind turbine tower, V cut_in is the wind turbine cut-in wind speed, V cut_out is the cut-out wind speed of the wind turbine and st is the abbreviation of subject to, which means “subject to”.

[0080] Optionally, in one embodiment, the second calculation module 30 is configured to:

[0081] Substituting the inflow velocity of each wind turbine into a second preset formula to calculate the loss distribution of the wake velocity of each wind turbine;

[0082] The wind farm wake velocity model is used to superimpose the loss distribution of each wind turbine wake velocity to obtain the wind farm wake velocity distribution.

[0083] Optionally, in one embodiment, the second calculation module 30 is configured to:

[0084] For the loss distribution of the wake velocity of the i-th wind turbine, the inflow velocity of the i-th wind turbine is substituted into the second preset formula to calculate the loss distribution of the wake velocity of the i-th wind turbine. The second preset formula is:

[0085]

[0086] In the formula, Δu i is the loss distribution of the wake velocity of the i-th wind turbine, i = 1, 2, ..., n, n is the number of wind turbines in the wind farm, V rotor,i is the inflow velocity of the i-th wind turbine, C T,i is the thrust coefficient of the i-th wind turbine, ρ is the air density, d i is the diameter of the i-th wind turbine, σ y,i is the wake width of the i-th wind turbine in the y direction, σ z,i is the wake width of the i-th wind turbine in the z direction, z h,i is the nacelle height of the i-th wind turbine;

[0087] σ y,i and σ z,i Determined by:

[0088]

[0089] In the formula, α i for y i The wake expansion coefficient in the direction, β i For z i The wake expansion coefficient in the direction, C y,i for y i The initial wake in the direction, C z,i For z i The initial wake in the direction, o i (x i ,y i ,z i ) is the tower base coordinate system of the i-th wind turbine.

[0090] Optionally, in one embodiment, the second calculation module 30 is configured to:

[0091] Substitute the loss distribution of each wind turbine wake velocity into the wind farm wake velocity model to calculate the wind farm wake velocity distribution;

[0092] The wind farm wake velocity model is as follows:

[0093]

[0094] Where U(X,Y,Z) is the wind farm wake velocity distribution, V ∞ is the inflow velocity of the wind farm, Δu i is the loss distribution of the wake velocity of the ith wind turbine, o i (x i ,y i ,z i) is the tower base coordinate system of the i-th wind turbine, O(X,Y,Z) is the wind farm coordinate system, i = 1, 2, ..., n, n is the number of wind turbines in the wind farm, (X i ,Y i ,Z i ) is the tower base coordinate of the ith wind turbine in the wind farm coordinate system.

[0095] Among them, the functional implementation of each module in the above-mentioned wind farm wake velocity distribution inversion device corresponds to the various steps in the above-mentioned wind farm wake velocity distribution inversion method embodiment, and its functions and implementation processes are no longer repeated here.

[0096] In a third aspect, an embodiment of the present invention further provides an electronic device, whose structure is as follows: Figure 6 As shown, it includes: a memory and a processor, wherein the processor is used to read and execute a computer program stored in the memory to implement the aforementioned method for inverting the wake velocity distribution of a wind farm.

[0097] In a fourth aspect, an embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed, the aforementioned method for inverting the wake velocity distribution of a wind farm is implemented.

[0098] In a fifth aspect, an embodiment of the present invention provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the various processes of the above-mentioned wind farm wake velocity distribution inversion method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0099] Finally, it should be noted that: in some processes described in the embodiments of the present invention, multiple operations or steps appearing in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present invention or may be executed in parallel, and the sequence number of the operation is only used to distinguish between different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0100] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for inverting the wake velocity distribution of a wind farm, characterized in that: The method comprises: Obtain the actual thrust load on the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed and cut-out wind speed of each wind turbine; Based on the actual thrust load on the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed, cut-out wind speed of each wind turbine and the wind turbine thrust load prediction model function, the inflow velocity of each wind turbine is obtained; Based on the inflow velocity of each wind turbine, the wind farm wake velocity distribution is obtained through a wind farm wake velocity model.

2. The method for inverting the wind farm wake velocity distribution according to claim 1, characterized in that: The step of obtaining the inflow velocity of each wind turbine based on the thrust load actually received at the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed, cut-out wind speed of each wind turbine and the wind turbine thrust load prediction model function comprises: Substituting the thrust load actually received by the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed, cut-out wind speed of each wind turbine, and the wind turbine thrust load prediction model function into the first preset formula in turn, and calculating the inflow velocity of each wind turbine; The first preset formula is as follows: In the formula, f obj is the objective function of the optimization problem, T model is the wind turbine thrust load prediction model function, V rotor is the inflow velocity of the wind turbine, ω is the rotation speed of the wind turbine, θ is the pitch angle of the wind turbine, T s is the actual thrust load on the top of the wind turbine tower, V cut_in is the wind turbine cut-in wind speed, V cut_out is the cut-out wind speed of the wind turbine. st is the abbreviation of subject to, which means "subject to".

3. The method for inverting the wind farm wake velocity distribution according to claim 1, characterized in that: The step of obtaining the wind farm wake velocity distribution based on the inflow velocity of each wind turbine through a wind farm wake velocity model comprises: Substituting the inflow velocity of each wind turbine into a second preset formula to calculate the loss distribution of the wake velocity of each wind turbine; The wind farm wake velocity model is used to superimpose the loss distribution of each wind turbine wake velocity to obtain the wind farm wake velocity distribution.

4. The method for inverting the wind farm wake velocity distribution according to claim 3, characterized in that: The step of substituting the inflow velocity of each wind turbine into the second preset formula to calculate the loss distribution of the wake velocity of each wind turbine comprises: For the loss distribution of the wake velocity of the i-th wind turbine, the inflow velocity of the i-th wind turbine is substituted into the second preset formula to calculate the loss distribution of the wake velocity of the i-th wind turbine. The second preset formula is: In the formula, Δu i is the loss distribution of the wake velocity of the i-th wind turbine, i = 1, 2, ..., n, n is the number of wind turbines in the wind farm, V rotor,i is the inflow velocity of the i-th wind turbine, C T,i is the thrust coefficient of the i-th wind turbine, ρ is the air density, d i is the diameter of the i-th wind turbine, σ y,i is the wake width of the i-th wind turbine in the y direction, σ z,i is the wake width of the i-th wind turbine in the z direction, z h,i is the nacelle height of the i-th wind turbine; σ y,i and σ z,i Determined by: In the formula, α i for y i The wake expansion coefficient in the direction, β i For z i The wake expansion coefficient in the direction, C y,i for y i The initial wake in the direction, C z,i For z i The initial wake in the direction, o i (x i ,y i ,z i ) is the tower base coordinate system of the i-th wind turbine.

5. The method for inverting the wind farm wake velocity distribution according to claim 3, characterized in that: The step of superimposing the loss distribution of the wake velocity of each wind turbine through the wind farm wake velocity model to obtain the wind farm wake velocity distribution includes: Substitute the loss distribution of each wind turbine wake velocity into the wind farm wake velocity model to calculate the wind farm wake velocity distribution; The wind farm wake velocity model is as follows: Where U(X,Y,Z) is the wind farm wake velocity distribution, V ∞ is the inflow velocity of the wind farm, Δu i is the loss distribution of the wake velocity of the ith wind turbine, o i (x i ,y i ,z i ) is the tower base coordinate system of the i-th wind turbine, O(X,Y,Z) is the wind farm coordinate system, i = 1, 2, ..., n, n is the number of wind turbines in the wind farm, (X i ,Y i ,Z i ) is the tower base coordinate of the ith wind turbine in the wind farm coordinate system.

6. A wind farm wake velocity distribution inversion device, characterized in that: The device comprises: An information acquisition module is configured to acquire the actual thrust load on the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed and cut-out wind speed of each wind turbine; A first calculation module is configured to obtain the inflow velocity of each wind turbine based on the thrust load actually received by the top of each wind turbine tower, the rotation speed, pitch angle, cut-in wind speed, cut-out wind speed of each wind turbine, and the wind turbine thrust load prediction model function; The second calculation module is configured to obtain the wind farm wake velocity distribution based on the inflow velocity of each wind turbine through a wind farm wake velocity model.

7. The method for inverting the wind farm wake velocity distribution according to claim 6, characterized in that: The second computing module is configured to: Substituting the inflow velocity of each wind turbine into a second preset formula to calculate the loss distribution of the wake velocity of each wind turbine; The wind farm wake velocity model is used to superimpose the loss distribution of each wind turbine wake velocity to obtain the wind farm wake velocity distribution.

8. An electronic device, characterized in that: include: Memory and processor; The processor is used to read and execute the computer program stored in the memory to implement the steps of the method for inverting the wake velocity distribution of a wind farm as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed, the steps of the method for inverting the wake velocity distribution of a wind farm as described in any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the method for inverting the wake velocity distribution of a wind farm as claimed in any one of claims 1 to 7 are implemented.

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