Wind power plant speed loss determination method, electronic equipment and storage medium

Abstract

The invention relates to the technical field of wind power generation, particularly provides a wind power plant speed loss determination method, electronic equipment and a storage medium, and aims to solve the problem of influence of background wind speed change on wind power plant speed loss evaluation precision under a complex terrain. Therefore, the method provided by the invention comprises the following steps: acquiring wind power plant information and wind turbine generator information; based on the wind power plant information and the wind turbine generator information, single-machine wake flow variables of the target wind turbine generator under the background wind speed change are determined, and the single-machine wake flow variables comprise single-machine convection speed and single-machine wake flow speed loss; based on the single-machine wake flow variable of the target wind turbine generator and the wind power plant information, a preset superposition model is adopted to determine the wind power plant speed loss under the background wind speed change, and the preset superposition model comprises any one of a momentum conservation superposition model, a local linear superposition model and a wind speed product superposition model. The method can effectively improve the prediction accuracy of the speed loss of the wind power plant under the condition of background wind speed change.

Description

Technical Field

[0001] The present application relates to the technical field of wind power generation, and in particular to a method for determining speed loss in a wind farm, an electronic device and a storage medium. Background Art

[0002] As the scale and number of wind farms continue to grow, flat terrain can no longer meet construction needs, and a large number of wind farms have begun to turn to complex terrain construction. In complex terrain, terrain features such as hills and valleys can cause complex flow phenomena such as streamline distortion and flow separation in the airflow, and may induce non-zero pressure gradients, causing changes in background wind speed. These flow phenomena will significantly affect the evolution and recovery speed of the wind turbine wake.

[0003] However, existing wind farm speed loss assessment methods mainly focus on the impact of streamline distortion and flow separation on the wake center position, and often ignore the impact of background wind speed changes caused by pressure gradient on wake recovery and wake superposition methods. Under favorable pressure gradient conditions, the background wind speed will increase along the flow direction, which can accelerate wake recovery; under adverse pressure gradient conditions, the background wind speed will decrease along the flow direction, which will slow down this process. Therefore, considering the background wind speed changes caused by pressure gradient is crucial to improving the calculation accuracy of wind farm speed loss in complex terrain.

[0004] Accordingly, the art needs a new wind farm speed loss determination solution to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects, the present application is proposed to solve or at least partially solve the technical problem of the influence of background wind speed changes on the calculation accuracy of wind farm speed loss under complex terrain.

[0006] In a first aspect, a method for determining a wind farm speed loss is provided, the method comprising: acquiring wind farm information and wind turbine group information; determining a single-machine wake variable of a target wind turbine group under a background wind speed change based on the wind farm information and the wind turbine group information, wherein the single-machine wake variable comprises a single-machine convective velocity and a single-machine wake speed loss; based on the single-machine wake variable of the target wind turbine group and the wind farm information, determining the wind farm speed loss under a background wind speed change using a preset superposition model, wherein the preset superposition model comprises any one of a momentum conservation superposition model, a local linear superposition model and a wind speed product superposition model.

[0007] In a technical solution of the above-mentioned method for determining wind farm speed loss, the single-machine wake variable of the target wind turbine group under the background wind speed change is determined based on the wind farm information and the wind turbine group information, including: determining the flow direction change law of the dimensionless maximum speed loss at the center of the wind turbine group wake based on the wind farm information and the wind turbine group information; determining the single-machine convection speed and single-machine wake speed loss of the target wind turbine group under the background wind speed change based on the flow direction change law and the wind farm information.

[0008] In a technical solution of the above-mentioned method for determining the speed loss of a wind farm, the wind farm information includes a first flow direction turbulence intensity at a hub height plane of a wind turbine in the absence of a wind farm; the wind turbine information includes a rotor diameter, a thrust coefficient, and a spatial position of the wind turbine; and determining the flow direction variation law of the dimensionless maximum speed loss at the center of the wind turbine wake based on the wind farm information and the wind turbine information includes: determining a second flow direction turbulence intensity based on the first flow direction turbulence intensity, wherein the second flow direction turbulence intensity is a flow direction turbulence intensity of the wind turbine position affected by the wake. The invention relates to a method for determining a wake expansion rate of a wind turbine generator set based on the second flow direction turbulence intensity; determining a far wake starting point of the wind turbine generator set based on the second flow direction turbulence intensity and the thrust coefficient and the wind rotor diameter of the wind turbine generator set; determining a wake width at any position downstream of the wind turbine generator set based on the wake expansion rate, the far wake starting point, the spatial position of the wind turbine generator set and the wind rotor diameter; and determining a flow direction variation law of a dimensionless maximum velocity loss at a wake center of the wind turbine generator set based on the wake width, the thrust coefficient and the wind rotor diameter of the wind turbine generator set.

[0009] In a technical solution of the above-mentioned method for determining the speed loss of a wind farm, the wind farm information includes the wind farm background wind speed at the plane of the wind turbine hub height in the absence of a wind farm; the target wind turbine is the wind turbine with the smallest flow direction coordinate in the spatial position within the wind farm; the determination of the single-machine convection speed and the single-machine wake speed loss of the target wind turbine under the background wind speed change based on the flow direction change law and the wind farm information includes: determining the dimensionless maximum speed loss of the wake center of the target wind turbine under the background wind speed change based on the flow direction change law and the wind farm background wind speed ; Based on the dimensionless maximum velocity loss at the center of the wake of the target wind turbine and the background wind speed of the wind farm, determine the single-unit convection velocity of the target wind turbine under the change of background wind speed; based on the wake width at any position downstream of the wind turbine and the background wind speed of the wind farm, determine the single-unit wake width of the target wind turbine under the change of background wind speed; based on the dimensionless maximum velocity loss at the center of the wake of the wind turbine, the background wind speed of the wind farm, the single-unit wake width of the target wind turbine and the spatial position of the wind turbine, determine the single-unit wake velocity loss of the target wind turbine under the change of background wind speed.

[0010] In a technical solution of the above-mentioned method for determining the speed loss of a wind farm, the wind farm speed loss under the background wind speed change is determined by using a preset superposition model based on the single-machine wake variable of the target wind turbine and the wind farm information, including: step S1: determining the initial wind farm convection speed based on the single-machine convection speed of the target wind turbine; step S2: determining the initial wind farm speed loss based on at least one of the single-machine wake variable of the target wind turbine, the wind farm information and the initial wind farm convection speed; step S3: determining the background wind speed of the next wind turbine based on the initial wind farm speed loss and the wind farm information, and the order of the next wind turbine is determined according to the flow direction coordinates of the spatial position of the wind turbine; step S4: judging whether the next wind turbine is the last one in the wind farm Wind turbine; step S5: if not, then based on the background wind speed of the next wind turbine, the flow direction change law and the wake width at any position downstream of the next wind turbine, determine the single-machine wake variable of the next wind turbine, wherein the single-machine wake variable includes the single-machine convection speed and the single-machine wake speed loss; step S6: based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, use a preset superposition model to update the initial wind farm speed loss to obtain an updated initial wind farm speed loss; step S7: repeat steps S3 to S6 until the initial wind farm speed loss is updated based on the single-machine wake variable of the last wind turbine, and then determine the wind farm speed loss under the background wind speed change based on the updated initial wind farm speed loss.

[0011] In a technical solution of the above-mentioned method for determining the speed loss of a wind farm, the wind farm information includes the background wind speed of the wind farm at the hub height plane of the wind turbine in the absence of a wind farm; the step S6: based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, the initial wind farm speed loss is updated using a momentum conservation superposition model to obtain an updated initial wind farm speed loss, including: updating the initial wind farm convection speed based on the initial wind farm speed loss and the wind farm background wind speed; judging whether the updated initial wind farm convection speed meets a preset condition; if so, updating the initial wind farm speed loss based on the single-machine convection speed and the single-machine wake speed loss of the next wind turbine to obtain an updated initial wind farm speed loss.

[0012] In a technical solution of the above-mentioned method for determining the wind farm speed loss, the step S6: based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, the initial wind farm speed loss is updated using a local linear superposition model to obtain an updated initial wind farm speed loss, including: linearly superimposing the single-machine wake speed loss of the next wind turbine with the initial wind farm speed loss to obtain an updated initial wind farm speed loss.

[0013] In a technical solution of the above-mentioned method for determining the wind farm speed loss, the wind farm information includes the wind farm background wind speed at the wind turbine hub height plane in the absence of a wind farm; the step S6: based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, the initial wind farm speed loss is updated using a wind speed product superposition model to obtain an updated initial wind farm speed loss, including: based on the single-machine wake speed loss of the next wind turbine, the background wind speed and the wind farm background wind speed, the initial wind farm speed loss is updated to obtain an updated initial wind farm speed loss.

[0014] In a second aspect, an electronic device is provided, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any one of the technical solutions of the above-mentioned method for determining the speed loss of a wind farm is implemented.

[0015] In a third aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, wherein the program codes are suitable for being loaded and run by a processor to execute the method described in any one of the technical solutions of the above-mentioned method for determining wind farm speed loss.

[0016] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects:

[0017] The wind farm speed loss determination method provided in the present application includes: obtaining wind farm information and wind turbine group information; determining the single-machine wake variable of the target wind turbine group under the background wind speed change based on the wind farm information and the wind turbine group information, wherein the single-machine wake variable includes the single-machine convection velocity and the single-machine wake speed loss; based on the single-machine wake variable of the target wind turbine group and the wind farm information, using a preset superposition model to determine the wind farm speed loss under the background wind speed change, wherein the preset superposition model includes any one of a momentum conservation superposition model, a local linear superposition model, and a wind speed product superposition model. The present application can determine the single-machine wake variable of the target wind turbine group under the background wind speed change by obtaining wind farm information and wind turbine group information in actual applications, and then can obtain the wind farm speed loss considering the background wind speed change based on the single-machine wake variable and wind farm information of the target wind turbine group under the background wind speed change by using the momentum conservation superposition model, the local linear superposition model, or the wind speed product superposition model, which can effectively improve the prediction accuracy of the wind farm speed loss under the background wind speed change in complex terrain. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The disclosure of the present application will become easier to understand with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present application. Among them:

[0019] Figure 1 is a schematic flow chart of main steps of a method for determining wind farm speed loss according to an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of setting up a pressure gradient calculation example according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of the reverse pressure gradient calculation example setting according to an embodiment of the present application;

[0022] Figure 4 It is a schematic diagram of the flow direction variation law of the background wind speed in a wind farm according to a pressure gradient calculation example of an embodiment of the present application;

[0023] Figure 5 It is a schematic diagram of the flow direction variation law of the background wind speed in a wind farm according to an adverse pressure gradient calculation example of an embodiment of the present application;

[0024] Figure 6 It is a schematic diagram of the flow direction variation law of the wake width of a wind turbine according to a pressure gradient calculation example of an embodiment of the present application;

[0025] Figure 7 is a schematic diagram of the flow direction variation law of the wake width of a wind turbine according to an adverse pressure gradient calculation example of an embodiment of the present application;

[0026] Figure 8 It is a schematic diagram of the flow direction change law of the maximum velocity loss at the center of the wake of a wind turbine according to a pressure gradient calculation example of an embodiment of the present application;

[0027] Fig. 9 It is a schematic diagram of the flow direction change law of the maximum velocity loss at the center of the wake of a wind turbine according to an adverse pressure gradient calculation example of an embodiment of the present application;

[0028] Fig.10 is a schematic diagram comparing the results of wind farm velocity loss in a pressure gradient calculation example according to an embodiment of the present application;

[0029] Fig.11 is a schematic diagram comparing the results of wind farm velocity loss in an adverse pressure gradient calculation example according to an embodiment of the present application;

[0030] Fig.12 It is a comparison diagram of velocity loss profiles of a pressure gradient calculation example according to an embodiment of the present application.

[0031] Fig.13 is a comparison diagram of velocity loss profiles of an adverse pressure gradient calculation example according to an embodiment of the present application;

[0032] Fig.14 It is a schematic diagram of the main structure of an electronic device according to an embodiment of the present application.

[0033] Reference numerals:

[0034] 11: memory; 12: processor. DETAILED DESCRIPTION

[0035] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0036] In the description of the present application, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, memory, and may also include software parts, such as program code, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware or a combination of the two. Computer-readable storage media include any suitable medium that can store program code, such as a disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B" and may include only A, only B or A and B. The singular terms "one" and "the" may also include plural forms.

[0037] The terrain features in complex terrain can cause complex flow phenomena such as streamline distortion and flow separation in the airflow, and may induce non-zero pressure gradients, causing changes in background wind speed. These flow phenomena will affect the evolution law and recovery speed of the wind turbine wake. However, the existing wind farm speed loss assessment methods mainly focus on the impact of streamline distortion and flow separation on the center position of the wake, and ignore the impact of background wind speed changes caused by pressure gradients on wake recovery and wake superposition methods. Under the condition of favorable pressure gradient, the background wind speed will increase along the flow direction, which can accelerate the wake recovery; under the condition of adverse pressure gradient, the background wind speed will decrease along the flow direction, which will slow down this process. Therefore, considering the background wind speed changes caused by pressure gradients is crucial to improving the calculation accuracy of wind farm speed losses in complex terrain.

[0038] To this end, the present application provides a method for determining wind farm speed loss, including: obtaining wind farm information and wind turbine group information; determining the single-machine wake variable of the target wind turbine group under background wind speed changes based on the wind farm information and the wind turbine group information, wherein the single-machine wake variable includes single-machine convection velocity and single-machine wake speed loss; based on the single-machine wake variable of the target wind turbine group and the wind farm information, using a preset superposition model to determine the wind farm speed loss under background wind speed changes, wherein the preset superposition model includes any one of a momentum conservation superposition model, a local linear superposition model, and a wind speed product superposition model. Based on the wind farm information and wind turbine group information in actual engineering applications, the present application can determine the single-machine wake variable of the target wind turbine group under background wind speed changes, and then based on the single-machine wake variable of the target wind turbine group under background wind speed changes and the wind farm information, the wind farm speed loss under background wind speed changes can be obtained by using momentum conservation superposition, a local linear superposition model, or a wind speed product superposition model, which can effectively improve the prediction accuracy of wind farm speed loss under complex terrain background wind speed changes.

[0039] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a method for determining wind farm speed loss according to an embodiment of the present application. Figure 1 As shown, the method for determining the wind farm speed loss in the embodiment of the present application mainly includes the following steps S101 to S103.

[0040] Step S101: Obtain wind farm information and wind turbine group information.

[0041] In this embodiment, the wind farm information may be the arrangement information and wind environment information of the wind farm, and the wind turbine group information may be the geometric parameter information and location information of the wind turbine group.

[0042] Step S102: determining a single-machine wake variable of a target wind turbine under a background wind speed change based on the wind farm information and the wind turbine information, wherein the single-machine wake variable includes a single-machine convection velocity and a single-machine wake velocity loss;

[0043] Step S103: Based on the single-machine wake variable of the target wind turbine and the wind farm information, a preset superposition model is used to determine the wind farm speed loss under the background wind speed change, wherein the preset superposition model includes any one of a momentum conservation superposition model, a local linear superposition model and a wind speed product superposition model.

[0044] Based on the methods described in steps S101 to S103 above, the present application can obtain the single-machine wake variables of the target wind turbine under the background wind speed changes based on the wind farm information and wind turbine information in actual engineering applications, and then can determine the wind farm speed loss considering the background wind speed changes based on the single-machine wake variables and wind farm information of the target wind turbine under the background wind speed changes, using the momentum conservation superposition model, the local linear superposition model or the wind speed product superposition model, which can effectively improve the prediction accuracy of the wind farm speed loss under the background wind speed changes in complex terrain.

[0045] The above steps S101 to S103 are further explained below.

[0046] With respect to step S101, wind farm information and wind turbine group information are acquired.

[0047] Specifically, the wind farm information includes the first stream turbulence intensity I0 at the hub height plane of the wind turbine in the absence of a wind farm and the wind farm background wind speed U b (x), and the number of wind turbines N.

[0048] Wind turbine information includes the spatial location of the wind turbine (x i ,y i ,z i ), wheel hub height z h , wind wheel diameter D, thrust coefficient The wind turbines are arranged in the order of flow direction coordinates, that is, x i ≤x i+1 .

[0049] The above is the description of step S101 , and the following is a further description of step S102 .

[0050] For step S102, in one embodiment, determining the single-machine wake variable of the target wind turbine group under the background wind speed change based on the wind farm information and the wind turbine group information includes: determining the flow direction change law of the dimensionless maximum velocity loss at the center of the wind turbine group wake based on the wind farm information and the wind turbine group information; determining the single-machine convection velocity and single-machine wake velocity loss of the target wind turbine group under the background wind speed change based on the flow direction change law and the wind farm information.

[0051] Specifically, based on the wind farm information and wind turbine information, an empirical formula is used to calculate the WT of a single wind turbine under zero pressure gradient. i The flow direction variation law of the dimensionless maximum velocity loss at the wake center of (1≤i≤N).

[0052] Based on the determined flow direction variation law of the dimensionless maximum velocity loss at the wake center of a single wind turbine and the wind farm information, the single-unit convection velocity and single-unit wake velocity loss of the target wind turbine under the background wind speed change are determined.

[0053] In one embodiment, the wind farm information includes a first flow turbulence intensity at a hub height plane of the wind turbine in the absence of a wind farm; the wind turbine information includes a rotor diameter, a thrust coefficient, and a spatial position of the wind turbine; determining the flow direction variation law of the dimensionless maximum velocity loss at the center of the wind turbine wake based on the wind farm information and the wind turbine information includes: determining a second flow direction turbulence intensity based on the first flow direction turbulence intensity, the second flow direction turbulence intensity being the flow direction turbulence intensity at the position of the wind turbine affected by the wake; determining a wake expansion rate of the wind turbine based on the second flow direction turbulence intensity; determining a far wake starting point of the wind turbine based on the second flow direction turbulence intensity and the thrust coefficient and rotor diameter of the wind turbine; determining a wake width at any position downstream of the wind turbine based on the wake expansion rate, the far wake starting point, the spatial position and rotor diameter of the wind turbine; determining a flow direction variation law of the dimensionless maximum velocity loss at the center of the wind turbine wake based on the wake width, the thrust coefficient and rotor diameter of the wind turbine.

[0054] Specifically, the second flow turbulence intensity refers to the flow turbulence intensity at the hub height level at each wind turbine unit affected by the wake. According to the first flow turbulence intensity at the hub height level of the wind turbine unit without a wind farm and the preset additional turbulence intensity model, the second flow turbulence intensity at the i-th wind turbine unit affected by the wake is determined. The additional turbulence intensity model is as follows:

[0055] in, represents the second flow direction turbulence intensity at the position of the i-th wind turbine unit, I0 is the first flow direction turbulence intensity, represents the additional turbulence intensity generated by the j-th wind turbine at the i-th wind turbine. j <x i hour, The calculation formula is as follows:

[0056] in, is the thrust coefficient of the i-th wind turbine, D is the rotor diameter of the i-th wind turbine, I0 is the turbulence intensity in the first flow direction, x i represents the x-axis coordinate of the position of the i-th wind turbine unit, x j Represents the x-axis coordinate of the position of the j-th wind turbine.

[0057] When x j ≥x i hour, That is, only the additional turbulence generated by the upstream wind turbine at the downstream position is considered.

[0058] Then, the wake expansion rate of the wind turbine is determined according to the turbulence intensity in the second flow direction. The calculation formula of the wake expansion rate is as follows:

[0059] in, is the wake expansion rate of the i-th wind turbine, is the second streamwise turbulence intensity of the i-th wind turbine.

[0060] The following formula is then used to determine the starting point of the far wake of the wind turbine based on the second flow turbulence intensity, rotor diameter and thrust coefficient of the wind turbine: in, is the starting point of the far wake of the i-th wind turbine, is the thrust coefficient of the i-th wind turbine, D is the rotor diameter of the i-th wind turbine, is the second streamwise turbulence intensity of the i-th wind turbine.

[0061] The following formula is used to determine the wake width at any position downstream of the wind turbine based on the wake expansion rate of the wind turbine, the starting point of the far wake, the spatial position and the diameter of the wind rotor. The calculation formula is:

[0062] in, is the wake width at any position downstream of the i-th wind turbine, D is the rotor diameter of the i-th wind turbine, is the wake expansion rate of the i-th wind turbine, x is any position downstream of the i-th wind turbine, x i is the flow direction coordinate of the i-th wind turbine, is the starting point of the far wake of the i-th wind turbine.

[0063] Finally, according to the wake width, thrust coefficient and rotor diameter at any position downstream of the wind turbine, the flow direction change law of the dimensionless maximum velocity loss at the center of the wind turbine wake is determined. The calculation formula is as follows:

[0064] in, is the flow direction variation law of the dimensionless maximum velocity loss at the wake center of the i-th wind turbine, is the thrust coefficient of the i-th wind turbine, is the wake width at any position downstream of the i-th wind turbine, and D is the rotor diameter of the i-th wind turbine.

[0065] In one embodiment, the wind farm information includes the wind farm background wind speed at the wind turbine hub height plane in the absence of a wind farm; the target wind turbine is the wind turbine with the smallest flow direction coordinate in the spatial position within the wind farm; the determination of the single-machine convection speed and single-machine wake velocity loss of the target wind turbine under the background wind speed change based on the flow direction change law and the wind farm information includes: determining the dimensionless maximum velocity loss of the wake center of the target wind turbine under the background wind speed change based on the flow direction change law and the wind farm background wind speed; determining the dimensionless maximum velocity loss of the wake center of the target wind turbine under the background wind speed change based on the flow direction change law and the wind farm background wind speed; determining the dimensionless maximum velocity loss of the wake center of the target wind turbine under the background wind speed change based on the target The dimensionless maximum velocity loss at the center of the wind turbine wake and the background wind speed of the wind farm are used to determine the single-unit convection velocity of the target wind turbine under the change of background wind speed; the single-unit wake width of the target wind turbine under the change of background wind speed is determined based on the wake width at any position downstream of the wind turbine and the background wind speed of the wind farm; the single-unit wake velocity loss of the target wind turbine under the change of background wind speed is determined based on the dimensionless maximum velocity loss at the center of the wind turbine wake, the background wind speed of the wind farm, the single-unit wake width of the target wind turbine and the spatial position of the wind turbine.

[0066] Specifically, the target wind turbine is the wind turbine with the smallest flow coordinate among all wind turbines in the wind farm. The background wind speed of the target wind turbine is equal to the background wind speed of the wind farm. Based on the flow direction change law of the dimensionless maximum velocity loss at the wake center of the target wind turbine and the background wind speed of the wind farm, the dimensionless maximum velocity loss at the wake center of the target wind turbine under the background wind speed is determined. The calculation formula is as follows:

[0067] Among them, C i (x) is the dimensionless maximum velocity loss at the wake center of the target wind turbine under the background wind speed, is the flow direction variation law of the dimensionless maximum velocity loss at the wake center of the target wind turbine, is the background wind speed at the target wind turbine flow direction coordinate position, is the background wind speed of the target wind turbine, Equal to the wind farm background wind speed U at the plane of wind turbine hub height without wind farm b (x).

[0068] Based on the dimensionless maximum velocity loss of the wake center of the target wind turbine under the background wind speed and the background wind speed of the target wind turbine, the single-unit convection velocity of the target wind turbine under the background wind speed change is determined. The calculation formula is as follows:

[0069] in, is the single-unit convection velocity of the target wind turbine, is the background wind speed of the target wind turbine, C i (x) is the dimensionless maximum velocity loss at the wake center of the target wind turbine under the background wind speed.

[0070] Then, the wake width at any position downstream of the target wind turbine is obtained based on the wake width at any position downstream of all wind turbines. And according to the wake width at any position downstream of the target wind turbine and the background wind speed of the target wind turbine, determine the single-unit wake width σ of the target wind turbine under the background wind speed change i (x), the calculation formula is as follows:

[0071] Among them, σ i (x) is the single-unit wake width of the target wind turbine under the background wind speed change, is the wake width at any position downstream of the target wind turbine, is the background wind speed at the target wind turbine flow direction coordinate position, is the background wind speed of the target wind turbine.

[0072] Finally, based on the dimensionless maximum velocity loss of the center of the wind turbine wake under the background wind speed, the background wind speed of the target wind turbine, the single-unit wake width of the target wind turbine and the spatial position of the wind turbine, the single-unit wake velocity loss of the target wind turbine under the background wind speed change is determined. The calculation formula is as follows:

[0073] in, is the single-unit wake velocity loss of the target wind turbine, is the background wind speed of the target wind turbine, C i (x) is the dimensionless maximum velocity loss at the wake center of the target wind turbine under the background wind speed, y is the spatial spanwise coordinate of any position downstream of the target wind turbine, y i is the spatial spanwise coordinate of the target wind turbine location, z is the spatial vertical coordinate of any position downstream of the target wind turbine, and z i is the spatial vertical coordinate of the target wind turbine location, σ i (x) is the single-unit wake width of the target wind turbine under the background wind speed change.

[0074] The above is the description of step S102 , and the following is a further description of step S103 .

[0075] With respect to step S103, in one embodiment, the determining the wind farm speed loss under the background wind speed change using a preset superposition model based on the single-machine wake variable of the target wind turbine and the wind farm information includes:

[0076] Step S1: determining the initial wind farm convection velocity based on the single-machine convection velocity of the target wind turbine group.

[0077] Specifically, the single-unit convection velocity of the target wind turbine is used as the initial wind farm convection velocity.

[0078] Step S2: determining the initial wind farm speed loss based on at least one of the single-machine wake variable of the target wind turbine group, the wind farm information and the initial wind farm convection speed.

[0079] Specifically, suppose that the wind farm speed loss of n wind turbines is calculated The initial value of n is 1, the wind turbine is i, 1≤i≤n, the target wind turbine is i=1, and the initial wind farm speed loss is

[0080] Based on at least one of the single-machine wake variables of the target wind turbine, wind farm information and the initial wind farm convection velocity, any one of the preset superposition models is used to determine the initial wind farm velocity loss

[0081] Step S3: Based on the initial wind farm speed loss and the wind farm information, the background wind speed of the next wind turbine is determined, and the order of the next wind turbine is determined according to the flow direction coordinates of the spatial position of the wind turbine.

[0082] Specifically, the arrangement order of all wind turbines is determined according to the flow coordinates of the spatial positions of the wind turbines, that is, the flow coordinate of the target wind turbine is the smallest, the flow coordinate of the second wind turbine is greater than the flow coordinate of the target wind turbine, and so on.

[0083] Based on the determined initial wind farm speed loss and the wind farm background wind speed at the wind turbine hub height plane without a wind farm, the background wind speed of the next wind turbine is determined. The calculation formula is as follows:

[0084] in, is the background wind speed of the next wind turbine, U b (x) Wind farm background wind speed at the hub height plane of the wind turbine without wind farm, N q is the total number of velocity sampling points, N q =16,y n+1,qis the spanwise coordinate of the sampling point on the rotor of the next wind turbine, z n+1,q is the vertical coordinate of the sampling point on the rotor of the next wind turbine, is the initial wind farm speed loss.

[0085] y n+1,q The calculation formula is:

[0086] Among them, y n+1 is the spanwise coordinate of the next wind turbine.

[0087] z n+1,q The calculation formula is:

[0088] Among them, z n+1 is the vertical coordinate of the next wind turbine.

[0089] Among them, r q The calculation formula is:

[0090] Step S4: determining whether the next wind turbine generator set is the last wind turbine generator set in the wind farm.

[0091] Specifically, it is determined whether the flow direction coordinate of the spatial position of the next wind turbine generator set is greater than the flow direction coordinates of the spatial positions of other wind turbine generator sets.

[0092] Step S5: If not, determining the single-machine wake variable of the next wind turbine generator set based on the background wind speed of the next wind turbine generator set, the flow direction change law and the wake width at any position downstream of the next wind turbine generator set, wherein the single-machine wake variable includes the single-machine convection velocity and the single-machine wake velocity loss.

[0093] Specifically, if the next wind turbine is not the last wind turbine in the wind farm, the background wind speed of the next wind turbine is Flow direction change rules and the wake width at any position downstream of the next wind turbine The calculation steps of the single-machine wake variables of the target wind turbine set are adopted, and the parameters corresponding to the target wind turbine set are replaced with the parameters corresponding to the next wind turbine set to determine the single-machine convection speed and single-machine wake speed loss of the next wind turbine set.

[0094] Step S6: Based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, the initial wind farm speed loss is updated using a preset superposition model to obtain an updated initial wind farm speed loss.

[0095] Specifically, based on the single-machine convection velocity, single-machine wake velocity loss and background wind speed of the next wind turbine and the background wind speed of the wind farm, the momentum conservation superposition model, the wind speed product superposition model or the local linear superposition model is used to update the initial wind farm velocity loss to obtain the updated initial wind farm velocity loss.

[0096] Step S7: Repeat steps S3 to S6 until the initial wind farm speed loss is updated based on the wake variable of the last wind turbine, and then determine the wind farm speed loss under background wind speed changes based on the updated initial wind farm speed loss.

[0097] Specifically, based on the updated initial wind farm speed loss and the wind farm background wind speed, the background wind speed of the next wind turbine is determined, that is, n=n+1, based on the background wind speed of the next wind turbine, the flow direction change law and the wake width of any downstream position, the single-machine wake variable of the next wind turbine is determined, based on the single-machine wake variable of the next wind turbine, the background wind speed and wind farm information, the initial wind farm speed loss is updated to obtain the updated initial wind farm speed loss, and the cycle process is repeated until the initial wind farm speed loss is updated based on the single-machine wake variable of the last wind turbine, that is, n=N, the recursion is terminated, and the initial wind farm speed loss after the last update is used as the wind farm speed loss under the background wind speed change.

[0098] In one embodiment, the wind farm information includes the wind farm background wind speed at the wind turbine hub height plane in the absence of a wind farm; the step S6: based on the single-machine wake variable, background wind speed and the wind farm information of the next wind turbine, the initial wind farm speed loss is updated using a momentum conservation superposition model to obtain an updated initial wind farm speed loss, including: updating the initial wind farm convection speed based on the initial wind farm speed loss and the wind farm background wind speed; judging whether the updated initial wind farm convection speed meets a preset condition; if so, updating the initial wind farm speed loss based on the single-machine convection speed and single-machine wake speed loss of the next wind turbine to obtain an updated initial wind farm speed loss.

[0099] Specifically, the calculation formula for the initial wind farm convection velocity is as follows:

[0100] in, is the initial wind farm convection velocity, is the single-unit convection velocity of the wind turbine.

[0101] When i=1, the initial wind farm convection velocity is equal to the single-unit convection velocity of the target wind turbine.

[0102] The calculation formula of the momentum conservation superposition model is as follows:

[0103] in, is the initial wind farm speed loss, is the single-unit convection velocity of the wind turbine, is the initial wind farm convection velocity, is the wake velocity loss of a single wind turbine.

[0104] When i=1, is the single-unit convection velocity of the target wind turbine, is the single-unit wake velocity loss of the target wind turbine, Equal to the single-unit convection velocity of the target wind turbine.

[0105] The following formula is used to update the initial wind farm convection velocity based on the determined initial wind farm velocity loss and wind farm background wind speed:

[0106] in, is the updated initial wind farm convection velocity, U w (x,y,z) is the wind farm speed, is the initial wind farm speed loss.

[0107] The calculation formula of wind farm speed is as follows:

[0108] Among them, U b (x) is the background wind speed of the wind farm, is the initial wind farm speed loss.

[0109] Determine whether the updated initial wind farm convection velocity meets the preset conditions, which are:

[0110] in, is the initial wind farm convection velocity, is the updated initial wind farm convection velocity.

[0111] When the updated initial wind farm convection speed meets the preset conditions, the initial wind farm speed loss is updated based on the single-machine convection speed and single-machine wake speed loss of the next wind turbine, the calculation formula of the initial wind farm convection speed, and the calculation formula of the initial wind farm speed loss to obtain an updated initial wind farm speed loss.

[0112] If the updated initial wind farm convection velocity does not meet the preset conditions, then Will Substitute into the calculation formula of initial wind farm speed loss and iteratively calculate the initial wind farm speed loss And based on the initial wind farm speed loss The initial wind farm convection velocity is updated until the initial wind farm convection velocity meets a preset condition.

[0113] In a technical solution of the above-mentioned method for determining the wind farm speed loss, the step S6: based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, the initial wind farm speed loss is updated using a local linear superposition model to obtain an updated initial wind farm speed loss, including: linearly superimposing the single-machine wake speed loss of the next wind turbine with the initial wind farm speed loss to obtain an updated initial wind farm speed loss.

[0114] Specifically, the single-machine wake velocity loss of the wind turbine is linearly superimposed with the initial wind farm velocity loss to obtain the updated initial wind farm velocity loss. The calculation formula of the local linear superposition model is as follows:

[0115] in, is the initial wind farm speed loss, is the wake velocity loss of a single wind turbine.

[0116] In one embodiment, the wind farm information includes the wind farm background wind speed at the wind turbine hub height plane in the absence of a wind farm; the step S6: based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, the initial wind farm speed loss is updated using a wind speed product superposition model to obtain an updated initial wind farm speed loss, including: based on the single-machine wake speed loss of the next wind turbine, the background wind speed and the wind farm background wind speed, the initial wind farm speed loss is updated to obtain an updated initial wind farm speed loss.

[0117] Specifically, based on the wake velocity loss of the next wind turbine, the background wind speed and the wind farm background wind speed, the wind speed product superposition model is used to update the initial wind farm velocity loss. The calculation formula of the wind speed product superposition model is as follows:

[0118] in, is the initial wind farm speed loss, U b (x) is the background wind speed of the wind farm, is the single-unit wake velocity loss of the wind turbine, is the background wind speed of the wind turbine.

[0119] Based on the above steps, the present application can obtain the single-machine wake variable of the target wind turbine under the background wind speed change through the wind farm information and wind turbine information in actual application, and on this basis, use the momentum conservation superposition, local linear superposition model or wind speed product superposition model to obtain the wind farm speed loss under the background wind speed change. Compared with the existing wind farm speed loss calculation method, the present application fully considers the background wind speed change in the wind farm, is applicable to different background wind speed change conditions, has strong universality, and can effectively improve the prediction accuracy of wind farm speed loss under background wind speed change.

[0120] The above is a description of steps S101 to S103 of the method for determining the speed loss of a wind farm of the present application. The method for determining the speed loss of a wind farm of the present application is described below in conjunction with a specific implementation example.

[0121] In this embodiment, the wind farm speed loss determination method of the present application provides three wind farm speed loss superposition models that take into account the background wind speed change, which can be used to calculate the wind farm flow under complex terrain.

[0122] The high-precision large eddy simulation (LES) method was used to verify the method of the present application under the conditions of favorable pressure gradient (FPG, background wind speed increases along the flow direction) and adverse pressure gradient (APG, background wind speed decreases along the flow direction). The slope example was set to simulate different background wind speed variation patterns. The slope example consists of a windward slope, a platform and a leeward slope. The slope height Z r The analytical expression of (x,y) is as follows:

[0123] Among them, x0 is the starting position of the windward slope, H is the height of the slope platform, L1 is the projection length of the windward slope in the flow direction, L3 is the projection length of the leeward slope in the flow direction, and L2 is the length of the platform. The specific parameter settings are shown in Table 1. Table 1 Example setting table (FPG and APG represent forward pressure gradient conditions and reverse pressure gradient conditions respectively)

[0124] The computational domain size is 80D × 12D × 10D. The calculation example with the suffix WT includes wind turbines, while the other two calculation examples do not. They are used to provide wind environment parameters of wind farms and calculate the speed loss of wind farms.

[0125] In this embodiment, the wind turbine group information includes the total number N of wind turbine groups, the spatial position (x i,y i ,z i ), wheel hub height z h , rotor diameter D and thrust coefficient Among them, the wind turbines are arranged in order of wind direction, that is, to ensure that x i ≤x i+1 .

[0126] The wind farm information includes the wind farm background wind speed U at the hub height plane in the absence of a wind farm. b (x) and parameters such as the first flow direction turbulence intensity I0.

[0127] The wind farm parameters of the verification example in this embodiment are shown in Table 2, where FPG-WT is a pressure gradient example, APG-WT is an adverse pressure gradient example, and the example settings of the pressure gradient example FPG-WT are as follows: Figure 2 As shown in Figure 2, the example settings for the adverse pressure gradient example APG-WT are as follows: Figure 3 shown. Table 2 Wind farm parameters

[0128] The flow direction variation law of the wind farm background wind speed in the pressure gradient calculation example FPG-WT in this embodiment is as follows: Figure 4 As shown in the figure, the flow direction variation law of the background wind speed in the wind farm of the adverse pressure gradient calculation example APG-WT is as follows Figure 5 shown.

[0129] Firstly, based on the wind farm information and wind turbine information, the flow direction variation law of the dimensionless maximum velocity loss at the center of the wind turbine wake is determined.

[0130] Specifically, according to the first flow turbulence intensity at the hub height plane of the wind turbine in the absence of a wind farm and the preset additional turbulence intensity model, the second flow turbulence intensity at the position of the i-th wind turbine affected by the wake is determined. In this embodiment, the second flow turbulence intensity of different wind turbine flow turbulence intensities in the forward pressure gradient example FPG-WT and the adverse pressure gradient example APG-WT is shown in Table 3. Table 3. Second direction turbulence intensity of wind turbines in verification example

[0131] The turbulence intensity in the second stream direction obtained by calculation The wake expansion rate of each wind turbine is calculated. In this embodiment, the wake expansion rates of different wind turbine flow direction turbulence intensities in the forward pressure gradient calculation example FPG-WT and the adverse pressure gradient calculation example APG-WT are shown in Table 4. Table 4 Wake expansion rate of wind turbines in verification example

[0132] According to the turbulence intensity in the second flow direction, the thrust coefficient and the rotor diameter of the wind turbine, the starting point of the far wake of the wind turbine is determined. In this embodiment, the calculated values ​​of the starting points of the far wakes of different wind turbines in the forward pressure gradient example FPG-WT and the adverse pressure gradient example APG-WT are shown in Table 5. Table 5. Table of starting points of far wake of wind turbines in verification example

[0133] Based on the wake expansion rate of the wind turbine, the starting point of the far wake, the spatial position of the wind turbine and the diameter of the wind rotor, the wake width at any position downstream of the wind turbine is determined. In this embodiment, the flow direction variation law of the wake width of different wind turbines in the pressure gradient calculation example FPG-WT is as follows: Figure 6 As shown in the figure, the flow direction variation law of the wake width of different wind turbines in the adverse pressure gradient calculation example APG-WT is as follows Figure 7 shown.

[0134] Based on the wake width, thrust coefficient and rotor diameter of the wind turbine, the flow direction variation law of the dimensionless maximum velocity loss at the center of the wind turbine wake is determined. In this embodiment, the flow direction variation law of the dimensionless maximum velocity loss at the center of the wake of different wind turbines in the pressure gradient calculation example FPG-WT is as follows: Figure 8 As shown in the adverse pressure gradient example APG-WT, the flow direction variation law of the dimensionless maximum velocity loss at the wake center of different wind turbines is as follows: Fig. 9 shown.

[0135] Furthermore, based on the flow direction change law of the dimensionless maximum velocity loss in the wake center of different wind turbines and the wind farm information, the single-unit convection velocity and single-unit wake velocity loss of the wind turbine under the background wind speed change are determined; and according to the single-unit convection velocity and single-unit wake velocity loss of the wind turbine and the wind farm information, the momentum conservation superposition, local linear superposition model or wind speed product superposition model is used to determine the wind farm speed loss under the background wind speed change.

[0136] In this embodiment, the large eddy simulation results of the normalized velocity loss at the center of the hub height plane wake in the pressure gradient calculation example FPG-WT are compared with the calculation results of the three wind farm velocity loss superposition models of this application. Fig.10 As shown in FIG. 1 , the comparison between the large eddy simulation results of the normalized velocity loss at the center of the hub height plane wake in the adverse pressure gradient calculation example APG-WT and the calculation results of the three wind farm velocity loss superposition models of this application is shown in FIG. Fig.11 shown.

[0137] In this embodiment, the normalized velocity loss profiles in the hub height plane spanwise direction calculated by the large eddy simulation at the 5D position downstream of different wind turbines in the pressure gradient calculation example FPG-WT and the three wind farm velocity loss superposition models of this application are as follows: Fig.12 As shown, the normalized velocity loss profiles in the hub height plane spanwise direction calculated by the large eddy simulation at the 5D position downstream of different wind turbines in the adverse pressure gradient calculation example APG-WT and the three wind farm velocity loss superposition models of this application are shown in Fig.13 Show.

[0138] By comparing with the large eddy simulation results, it can be seen that the three wind farm speed loss superposition models of the present application show high prediction accuracy in predicting the wind farm speed loss under the background wind speed change, and can accurately predict the size and spatial distribution of the wind farm speed loss, thus filling the gap in this technical field.

[0139] It should be pointed out that the above embodiments are only preferred specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims. In addition, although the various steps are described in a specific order in the above embodiments, it can be understood by those skilled in the art that in order to achieve the effect of the present application, different steps do not have to be executed in such an order, and they can be executed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent to the technical solutions described in the present application, and therefore will also fall within the protection scope of the present application.

[0140] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0141] Another aspect of the present application provides an electronic device.

[0142] See attached Fig.14 , Fig.14It is a schematic diagram of the main structure of an electronic device according to an embodiment of the present application.

[0143] like Fig.14 As shown, in an electronic device embodiment according to the present application, the electronic device includes at least one memory 11 and at least one processor 12, and the memory 11 and the processor 12 are connected by bus communication. The memory 11 can be configured to store a program for executing the wind farm speed loss determination method of the above method embodiment, and the processor 12 can be configured to execute the program in the memory 11, which includes but is not limited to the program for executing the wind farm speed loss determination method of the above method embodiment. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The electronic device can be an electronic device formed by various electronic devices.

[0144] Another aspect of the present application also provides a computer-readable storage medium.

[0145] In an embodiment of a computer-readable storage medium according to the present application, the computer-readable storage medium may be configured to store a program for executing the wind farm speed loss determination method of the above method embodiment, and the program may be loaded and run by a processor to implement the above wind farm speed loss determination method. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The computer-readable storage medium may be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.

[0146] So far, the technical solution of the present application has been described in conjunction with an embodiment shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present application.

Claims

1. A method for determining wind farm speed loss, characterized in that: The method comprises: Obtain wind farm information and wind turbine information; Based on the wind farm information and the wind turbine information, determining a single-machine wake variable of the target wind turbine under background wind speed changes, wherein the single-machine wake variable includes a single-machine convection speed and a single-machine wake speed loss; Based on the single-machine wake variable of the target wind turbine and the wind farm information, a preset superposition model is used to determine the wind farm speed loss under background wind speed changes, wherein the preset superposition model includes any one of a momentum conservation superposition model, a local linear superposition model and a wind speed product superposition model.

2. The method for determining wind farm speed loss according to claim 1, characterized in that: The step of determining the single-machine wake variable of the target wind turbine under the background wind speed change based on the wind farm information and the wind turbine information includes: Based on the wind farm information and the wind turbine information, determining the flow direction change law of the dimensionless maximum velocity loss at the center of the wind turbine wake; Based on the flow direction variation law and the wind farm information, the single-machine convection velocity and single-machine wake velocity loss of the target wind turbine group under the background wind speed variation are determined.

3. The method for determining wind farm speed loss according to claim 2, characterized in that: The wind farm information includes the first flow direction turbulence intensity at the hub height plane of the wind turbine in the absence of a wind farm; the wind turbine information includes the rotor diameter, thrust coefficient, and spatial position of the wind turbine; The determining, based on the wind farm information and the wind turbine information, the flow direction change law of the dimensionless maximum velocity loss at the center of the wind turbine wake comprises: Determining a second flow direction turbulence intensity based on the first flow direction turbulence intensity, wherein the second flow direction turbulence intensity is the flow direction turbulence intensity at a position of the wind turbine generator set affected by the wake; determining a wake expansion rate of the wind turbine generator set based on the second flow direction turbulence intensity; Determining the starting point of the far wake of the wind turbine generator set based on the turbulence intensity of the second flow direction and the thrust coefficient and the wind rotor diameter of the wind turbine generator set; Determine the wake width at any position downstream of the wind turbine generator set based on the wake expansion rate, the far wake starting point, the spatial position of the wind turbine generator set and the wind rotor diameter; Based on the wake width, the thrust coefficient of the wind turbine generator set and the wind rotor diameter, a flow direction variation law of the dimensionless maximum velocity loss at the center of the wind turbine generator set wake is determined.

4. The method for determining wind farm speed loss according to claim 3, characterized in that: The wind farm information includes the wind farm background wind speed at the wind turbine hub height plane in the absence of a wind farm; the target wind turbine is the wind turbine with the smallest flow direction coordinate in the spatial position within the wind farm; The determining, based on the flow direction change law and the wind farm information, of the single-machine convection velocity and the single-machine wake velocity loss of the target wind turbine group under the background wind speed change includes: Based on the flow direction change law and the background wind speed of the wind farm, determining the dimensionless maximum velocity loss of the wake center of the target wind turbine group under the background wind speed change; Determine the single-unit convection velocity of the target wind turbine under the background wind speed change based on the dimensionless maximum velocity loss of the wake center of the target wind turbine and the background wind speed of the wind farm; Determine the single-unit wake width of the target wind turbine under the background wind speed change based on the wake width at any position downstream of the wind turbine and the background wind speed of the wind farm; Based on the dimensionless maximum speed loss at the center of the wind turbine wake, the background wind speed of the wind farm, the single-unit wake width of the target wind turbine and the spatial position of the wind turbine, the single-unit wake speed loss of the target wind turbine under the change of the background wind speed is determined.

5. The method for determining wind farm speed loss according to claim 3, characterized in that: The method of determining the wind farm speed loss under the background wind speed change by using a preset superposition model based on the single-machine wake variable of the target wind turbine group and the wind farm information includes: Step S1: determining the initial wind farm convection velocity based on the single-machine convection velocity of the target wind turbine group; Step S2: determining the initial wind farm speed loss based on at least one of the single-machine wake variable of the target wind turbine group, the wind farm information and the initial wind farm convection speed; Step S3: determining the background wind speed of the next wind turbine generator set based on the initial wind turbine generator set speed loss and the wind turbine generator set information, wherein the order of the next wind turbine generator set is determined according to the flow direction coordinates of the spatial position of the wind turbine generator set; Step S4: determining whether the next wind turbine is the last wind turbine in the wind farm; Step S5: If not, determining the single-machine wake variable of the next wind turbine generator set based on the background wind speed of the next wind turbine generator set, the flow direction change law and the wake width at any position downstream of the next wind turbine generator set, wherein the single-machine wake variable includes the single-machine convection speed and the single-machine wake speed loss; Step S6: Based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, the initial wind farm speed loss is updated using a preset superposition model to obtain an updated initial wind farm speed loss; Step S7: Repeat steps S3 to S6 until the initial wind farm speed loss is updated based on the wake variable of the last wind turbine, and then determine the wind farm speed loss under background wind speed changes based on the updated initial wind farm speed loss.

6. The method for determining wind farm speed loss according to claim 5, characterized in that: The wind farm information includes the wind farm background wind speed at the wind turbine hub height plane in the absence of a wind farm; The step S6: based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, the initial wind farm speed loss is updated using a momentum conservation superposition model to obtain an updated initial wind farm speed loss, including: Updating the initial wind farm convection speed based on the initial wind farm speed loss and the wind farm background wind speed; Determine whether the updated initial wind farm convection velocity meets the preset conditions; If so, based on the single-machine convection speed and single-machine wake speed loss of the next wind turbine generator set, the initial wind farm speed loss is updated to obtain an updated initial wind farm speed loss.

7. The method for determining wind farm speed loss according to claim 5, characterized in that: The step S6: based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, the initial wind farm speed loss is updated using a local linear superposition model to obtain an updated initial wind farm speed loss, including: The single-machine wake velocity loss of the next wind turbine generator set is linearly superimposed on the initial wind farm velocity loss to obtain an updated initial wind farm velocity loss.

8. The method for determining wind farm speed loss according to claim 5, characterized in that: The wind farm information includes the wind farm background wind speed at the wind turbine hub height plane in the absence of a wind farm; The step S6: based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, the initial wind farm speed loss is updated using a wind speed product superposition model to obtain an updated initial wind farm speed loss, including: Based on the wake velocity loss of the next wind turbine, the background wind speed and the background wind speed of the wind farm, the initial wind farm velocity loss is updated to obtain an updated initial wind farm velocity loss.

9. An electronic device comprising at least one processor and at least one memory, wherein the memory is suitable for storing a plurality of program codes, wherein: The program code is suitable for being loaded and run by the processor to execute the wind farm speed loss determination method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the wind farm speed loss determination method according to any one of claims 1 to 8.

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