Wind farm speed loss determination method, electronic device, and storage medium

Abstract

The application relates to the technical field of wind power generation, and particularly provides a wind farm speed loss determination method, an electronic device and a storage medium, and aims to solve the problem of the influence of background wind speed change on the evaluation accuracy of wind farm speed loss under complex terrain. To this end, the method comprises the following steps: acquiring wind farm information and wind turbine information; determining a single-machine wake variable of a target wind turbine under background wind speed change based on the wind farm information and the wind turbine information, wherein the single-machine wake variable comprises a single-machine convection velocity and a single-machine wake speed loss; and 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 and the wind farm information, 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. The application can effectively improve the prediction accuracy of the wind farm speed loss 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 is no longer sufficient for construction, and many wind farms are shifting to complex terrain. Topographical features such as hills and valleys in complex terrain can cause complex flow phenomena such as streamline distortion and flow separation. These can also induce non-zero pressure gradients, leading to variations in background wind speed. These flow phenomena can significantly affect the evolution and recovery rate of wind turbine wakes.

[0003] However, existing wind farm velocity loss assessment methods primarily focus on the effects of streamline distortion and flow separation on the wake center position, often overlooking the impact of background wind speed variations caused by pressure gradients on wake recovery and wake superposition methods. Under favorable pressure gradient conditions, background wind speed increases along the flow direction, accelerating wake recovery; under adverse pressure gradient conditions, background wind speed decreases along the flow direction, slowing this process. Therefore, accounting for background wind speed variations caused by pressure gradients is crucial for improving the accuracy of wind farm velocity loss calculations in complex terrain.

[0004] Accordingly, the art requires a new solution for determining wind farm speed loss 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: obtaining wind farm information and wind turbine information; determining, based on the wind farm information and the wind turbine information, a single-machine wake variable of a target wind turbine under background wind speed changes, wherein the single-machine wake variable comprises a single-machine convective velocity and a single-machine wake speed loss; determining, 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 comprises any one of a momentum conservation superposition model, a local linear superposition model, and a wind speed product superposition model; determining, 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, comprising: step S1: determining, based on the single-machine wake variable of the target wind turbine and the wind farm information, a wind farm speed loss under background wind speed changes; The method comprises the following steps: determining an initial wind farm speed loss based on at least one of the single-machine wake variable, the wind farm information, and the initial wind farm convection speed; determining an initial wind farm speed loss based on the initial wind farm speed loss and the wind farm information, wherein the background wind speed of the next wind turbine is determined according to the flow direction coordinates of the spatial position of the wind turbine; updating the initial wind farm speed loss based on the single-machine wake variable, the background wind speed, and the wind farm information of the next wind turbine using a preset superposition model to obtain an updated initial wind farm speed loss; and repeating steps S2 to S3 until the initial wind farm speed loss is updated based on the single-machine wake variable of the last wind turbine, and determining the wind farm speed loss under the background wind speed change based on the updated initial wind farm speed loss.

[0007] In one technical solution of the above-mentioned method for determining wind farm speed loss, determining the single-unit wake variables of the target wind turbine under background wind speed changes based on the wind farm information and the wind turbine information includes: determining the flow direction change 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; and determining the single-unit convection speed and single-unit wake speed loss of the target wind turbine under background wind speed changes 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, the second flow direction turbulence intensity being the position of the wind turbine 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 turbulence intensity; determining a far wake starting point of the wind turbine generator set based on the second flow turbulence intensity, the thrust coefficient of the wind turbine generator set, and the rotor diameter; 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 rotor diameter; and determining a flow direction variation law of a dimensionless maximum velocity loss at the center of the wake of the wind turbine generator set based on the wake width, the thrust coefficient of the wind turbine generator set, and the rotor diameter.

[0009] In one technical solution of the above-mentioned method for determining 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 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 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 target wind turbine wake and the background wind speed of the wind farm, the single-unit convection velocity of the target wind turbine under the background wind speed change 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 width of the target wind turbine under the background wind speed change 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, the single-unit wake velocity loss of the target wind turbine under the background wind speed change is determined.

[0010] In one technical solution of the above-mentioned method for determining wind farm speed loss, before executing step S1, the method further includes: determining the initial wind farm convection speed based on the single-machine convection speed of the target wind turbine; after executing step S2 and before executing step S3, the method further includes: judging whether the next wind turbine is the last wind turbine in the wind farm; if not, determining the single-machine wake variable of the next wind turbine based on the background wind speed, flow direction change law and wake width at any position downstream of the next wind turbine, wherein the single-machine wake variable includes single-machine convection speed and single-machine wake speed loss.

[0011] In one technical solution of the above-mentioned method for determining wind farm speed loss, the wind farm information includes the wind farm background wind speed at the hub height plane of the wind turbine in the absence of a wind farm; the step S3: 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.

[0012] In one technical solution of the above-mentioned method for determining wind farm speed loss, step S3: based on the single-unit wake variable of the next wind turbine, the background wind speed and the wind farm information, using a local linear superposition model to update the initial wind farm speed loss to obtain an updated initial wind farm speed loss, includes: linearly superimposing the single-unit wake speed loss of the next wind turbine with the initial wind farm speed loss to obtain the updated initial wind farm speed loss.

[0013] In one technical solution of the above-mentioned method for determining 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 S3: based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, using a wind speed product superposition model to update the initial wind farm speed loss to obtain an updated initial wind farm speed loss, includes: based on the single-machine wake speed loss of the next wind turbine, the background wind speed and the wind farm background wind speed, updating the initial wind farm speed loss 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 the memory stores a computer program, 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 wind farm speed loss is implemented.

[0015] In a third aspect, a computer-readable storage medium is provided, which stores a plurality of program codes, 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 this application have at least one or more of the following beneficial effects:

[0017] The method for determining wind farm speed loss 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 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 speed 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; the method for determining the wind farm speed loss under background wind speed changes based on the single-machine wake variable of the target wind turbine group and the wind farm information using a preset superposition model includes: step S1: based on the single-machine wake variable of the target wind turbine group The method comprises the steps of: determining an initial wind farm speed loss based on at least one of the flow variable, the wind farm information and the initial wind farm convection velocity; determining the background wind speed of the next wind turbine generator set based on the initial wind farm speed loss and the wind farm 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; updating the initial wind farm speed loss based on the single-machine wake variable, the background wind speed and the wind farm information of the next wind turbine generator set using a preset superposition model to obtain an updated initial wind farm speed loss; and repeating steps S2 to S3 until the initial wind farm speed loss is updated based on the single-machine wake variable of the last wind turbine generator set, and determining the wind farm speed loss under the background wind speed change based on the updated initial wind farm speed loss. By acquiring wind farm information and wind turbine information in actual applications, the present application can determine the single-unit wake variables of the target wind turbine under background wind speed changes, and then, based on the single-unit wake variables and wind farm information of the target wind turbine under background wind speed changes, use the momentum conservation superposition model, the local linear superposition model or the wind speed product superposition model to obtain the wind farm speed loss that takes into account the background wind speed changes, which can effectively improve the prediction accuracy of the wind farm speed loss under complex terrain background wind speed changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The disclosure of this application will become more easily understood with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Among them:

[0019] Figure 1 This 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;

[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 an inverse pressure gradient calculation example according to an embodiment of the present application;

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

[0023] Figure 5 This is a schematic diagram of the flow direction variation 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 This is a schematic diagram of the flow direction variation of the wind turbine wake width according to a pressure gradient calculation example of an embodiment of the present application;

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

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

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

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

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

[0030] Figure 12 3 is a comparison diagram of velocity loss profiles of a pressure gradient calculation example according to an embodiment of the present application.

[0031] Figure 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] Figure 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 scope of protection of the present application.

[0036] In the description of this application, "module" and "processor" may include hardware, software, or a combination of both. A module may include hardware circuitry, various suitable sensors, communication ports, and memory. It may also include software components, such as program code, or a combination of software and hardware. A processor may be a central processing unit (CPU), a microprocessor, an image processor, a digital signal processor, or any other suitable processor. A processor has data and / or signal processing capabilities. A processor may be implemented in software, hardware, or a combination of both. Computer-readable storage media include any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" refers to all possible combinations of A and B, such as only A, only B, or both A and B. The terms "at least one of A or B" or "at least one of A and B" have similar meanings to "A and / or B" and may include only A, only B, or both A and B. The singular forms "a" and "the" may also include the plural forms.

[0037] The topographic 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 and recovery speed of the wind turbine wake. However, 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, ignoring the impact of background wind speed changes caused by pressure gradients 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 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, comprising: 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 speed and single-machine wake speed loss; determining the wind farm speed loss under background wind speed changes based on the single-machine wake variable of the target wind turbine group and the wind farm information, 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; determining the wind farm speed loss under background wind speed changes based on the single-machine wake variable of the target wind turbine group and the wind farm information using a preset superposition model, comprising: step S1: determining the wind farm speed loss under background wind speed changes based on the single-machine wake variable of the target wind turbine group The method comprises the steps of: determining an initial wind farm speed loss based on at least one of the single-machine wake variable, the wind farm information and the initial wind farm convection speed; determining the background wind speed of the next wind turbine group based on the initial wind farm speed loss and the wind farm information, wherein the order of the next wind turbine group is determined according to the flow direction coordinates of the spatial position of the wind turbine group; updating the initial wind farm speed loss based on the single-machine wake variable, the background wind speed and the wind farm information of the next wind turbine group using a preset superposition model to obtain an updated initial wind farm speed loss; and repeating steps S2 to S3 until the initial wind farm speed loss is updated based on the single-machine wake variable of the last wind turbine group, and determining the wind farm speed loss under the background wind speed change based on the updated initial wind farm speed loss. Based on wind farm information and wind turbine information in actual engineering applications, this application can determine the single-unit wake variables of the target wind turbine under background wind speed changes, and then, based on the single-unit wake variables and wind farm information of the target wind turbine under background wind speed changes, use momentum conservation superposition, local linear superposition model or wind speed product superposition model to obtain the wind farm speed loss under background wind speed changes, 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 showing 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 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 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 convective 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-unit wake variables of the target wind turbine under 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-unit wake variables and wind farm information of the target wind turbine under 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 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 ), hub height z h , rotor diameter D, thrust coefficient The wind turbines are arranged in the order of flow direction coordinates, i.e. x i ≤x i+1 .

[0049] The above is an explanation of step S101 , and the following further explains step S102 .

[0050] For step S102, in one embodiment, determining the single-unit wake variables of the target wind turbine under the background wind speed change based on the wind farm information and the wind turbine information includes: determining the flow direction change 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; and determining the single-unit convection velocity and single-unit 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.

[0051] Specifically, based on the wind farm information and wind turbine information, the 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 for (1≤i≤N).

[0052] Based on the determined flow direction change 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 background wind speed changes 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 a 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; and 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 direction turbulence intensity refers to the flow direction turbulence intensity at the hub height level at each wind turbine location affected by the wake. Based on the first flow direction turbulence intensity at the hub height level of the wind turbine in the absence of a wind farm and the preset additional turbulence intensity model, the second flow direction turbulence intensity at the location of the i-th wind turbine affected by the wake is determined. The additional turbulence intensity model is as follows:

[0055]

[0056] in, represents the second flow direction turbulence intensity at the position of the i-th wind turbine, I0 is the first flow direction turbulence intensity, represents the additional turbulence intensity generated by the j-th wind turbine at the location of the i-th wind turbine. When x j <x i hour, The calculation formula is as follows:

[0057]

[0058] 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 direction, x i represents the x-axis coordinate of the position of the i-th wind turbine, x j Represents the x-axis coordinate of the position of the j-th wind turbine.

[0059] And when x j ≥x i hour, That is, only the additional turbulence generated by the upstream wind turbine at the downstream position is considered.

[0060] Then, the wake expansion rate of the wind turbine is determined based on the turbulence intensity in the second direction. The calculation formula for the wake expansion rate is as follows:

[0061]

[0062] in, is the wake expansion rate of the i-th wind turbine generator set, is the second streamwise turbulence intensity of the i-th wind turbine.

[0063] The following formula is then used to determine the starting point of the far wake of the wind turbine based on the second flow direction turbulence intensity, rotor diameter, and thrust coefficient of the wind turbine:

[0064]

[0065] 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.

[0066] The following formula is used to determine the wake width at any location downstream of the wind turbine based on the turbine's wake expansion rate, the starting point of the far wake, its spatial location, and the rotor diameter. The calculation formula is:

[0067]

[0068] 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.

[0069] Finally, based on the wake width, thrust coefficient, and rotor diameter at any location downstream 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. The calculation formula is as follows:

[0070]

[0071] 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.

[0072] 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 determining of the single-machine convection velocity 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 changes in the background wind speed; the single-unit wake width of the target wind turbine under changes in the 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 changes in the 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.

[0073] Specifically, the target wind turbine is the wind turbine with the smallest flow direction 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 variation 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 action of the background wind speed is determined. The calculation formula is as follows:

[0074]

[0075] 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 hub height plane of the wind turbine in the absence of a wind farm b (x).

[0076] 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:

[0077]

[0078] 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.

[0079] 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 the wind turbines determined. And according to the tail 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:

[0080]

[0081] 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, a background wind speed at a coordinate position of a flow direction of the target wind turbine, a background wind speed of the target wind turbine.

[0082] Finally, based on the dimensionless maximum speed loss of the wake center of the wind turbine under the background wind speed, the background wind speed of the target wind turbine, the single-machine wake width of the target wind turbine, and the spatial position of the wind turbine, the single-machine wake speed loss of the target wind turbine under the background wind speed variation is determined, and the calculation formula is as follows:

[0083]

[0084] wherein, the single-machine wake speed loss of the target wind turbine, the background wind speed of the target wind turbine, C i (x) is the dimensionless maximum speed loss of 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 the spatial spanwise coordinate of the position of the target wind turbine, z is the spatial vertical coordinate of any position downstream of the target wind turbine, z i the spatial vertical coordinate of the position of the target wind turbine, σ i (x) is the single-machine wake width of the target wind turbine under the background wind speed variation.

[0085] The above is a description of step S102, and the following further describes step S103.

[0086] For step S103, in an embodiment, 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 variation, including:

[0087] Step S1: 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, the initial wind farm speed loss is determined.

[0088] Step S2: 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 coordinate of the spatial position of the wind turbine.

[0089] Step S3: 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.

[0090] Step S4: Repeat steps S2 to S3 until the initial wind farm speed loss is updated based on the wake variable of the last wind turbine, and determine the wind farm speed loss under background wind speed changes based on the updated initial wind farm speed loss.

[0091] The above steps S1 to S4 are further explained below.

[0092] In one embodiment, before executing step S1 , the method further includes: determining the initial wind farm convection velocity based on the single-unit convection velocity of the target wind turbine.

[0093] Specifically, the single-unit convection velocity of the target wind turbine is used as the initial wind farm convection velocity.

[0094] In one embodiment, step S1: 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.

[0095] Specifically, suppose that the speed loss of a wind farm with 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

[0096] Based on at least one of the target wind turbine wake variables, wind farm information, and initial wind farm convection velocity, any one of the preset superposition models is used to determine the initial wind farm velocity loss.

[0097] In one embodiment, step S2: based on the initial wind farm speed loss and the wind farm information, determining the background wind speed of the next wind turbine, the order of the next wind turbine is determined according to the flow direction coordinates of the spatial position of the wind turbine.

[0098] 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.

[0099] Based on the determined initial wind farm speed loss and the wind farm background wind speed at the wind turbine hub height plane in the absence of a wind farm, the background wind speed of the next wind turbine is determined. The calculation formula is as follows:

[0100]

[0101] 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 in the absence of a wind farm, N q is the total number of velocity sampling points, N q =16,y n+1,q is 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.

[0102] y n+1,q The calculation formula is:

[0103]

[0104] Among them, y n+1 is the spanwise coordinate of the next wind turbine.

[0105] z n+1,q The calculation formula is:

[0106]

[0107] Among them, z n+1 is the vertical coordinate of the next wind turbine.

[0108] Among them, r q The calculation formula is:

[0109]

[0110] In one embodiment, after executing step S2 and before executing step S3, the method further includes: determining whether the next wind turbine is the last wind turbine in the wind farm; if not, determining the single-machine wake variable of the next wind turbine based on the background wind speed and flow direction change law of the next wind turbine and the wake width at any position downstream of the next wind turbine, wherein the single-machine wake variable includes the single-machine convection velocity and the single-machine wake velocity loss.

[0111] Specifically, it is determined whether the flow direction coordinate of the spatial position of the next wind turbine is greater than the flow direction coordinate of the spatial position of other wind turbines. If not, the next wind turbine is not the last wind turbine in the wind farm. Based on the background wind speed of the next wind turbine, Flow direction change pattern and the wake width at any position downstream of the next wind turbine The calculation steps of the single-unit wake variables of the target wind turbine are used above, and the parameters corresponding to the target wind turbine are replaced with the parameters corresponding to the next wind turbine to determine the single-unit convection speed and single-unit wake speed loss of the next wind turbine.

[0112] In one embodiment, step S3: based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, a preset superposition model is used to update the initial wind farm speed loss to obtain an updated initial wind farm speed loss.

[0113] Specifically, based on the single-machine convection velocity, single-machine wake velocity loss and background wind speed of the next wind turbine, as well as 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.

[0114] In one embodiment, step S4: repeating steps S2 to S3 until the initial wind farm speed loss is updated based on the single-machine wake variable of the last wind turbine, and determining the wind farm speed loss under background wind speed changes based on the updated initial wind farm speed loss.

[0115] 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, flow direction change law and wake width of any downstream position of the next wind turbine, the single-machine wake variable of the next wind turbine is determined. Based on the single-machine wake variable, background wind speed and wind farm information of the next wind turbine, the initial wind farm speed loss is updated to obtain the updated initial wind farm speed loss. The cycle 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.

[0116] In one embodiment, the wind farm information includes the wind farm background wind speed at the hub height plane of the wind turbine in the absence of a wind farm; the step S3: 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.

[0117] Specifically, the calculation formula for the initial wind farm convection velocity is as follows:

[0118]

[0119] in, is the initial wind farm convection velocity, is the single-unit convection velocity of the wind turbine.

[0120] When i=1, the initial wind farm convection velocity is equal to the single-unit convection velocity of the target wind turbine.

[0121] The calculation formula of the momentum conservation superposition model is as follows:

[0122]

[0123] 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.

[0124] 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.

[0125] 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:

[0126]

[0127] 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.

[0128] The calculation formula of wind farm speed is as follows:

[0129]

[0130] Among them, U b (x) is the background wind speed of the wind farm, is the initial wind farm speed loss.

[0131] Determine whether the updated initial wind farm convection velocity meets the preset conditions. The preset conditions are:

[0132]

[0133] in, is the initial wind farm convection velocity, is the updated initial wind farm convection velocity.

[0134] 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.

[0135] 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 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.

[0136] In one technical solution of the above-mentioned method for determining wind farm speed loss, step S3: based on the single-unit wake variable of the next wind turbine, the background wind speed and the wind farm information, using a local linear superposition model to update the initial wind farm speed loss to obtain an updated initial wind farm speed loss, includes: linearly superimposing the single-unit wake speed loss of the next wind turbine with the initial wind farm speed loss to obtain the updated initial wind farm speed loss.

[0137] Specifically, the wake velocity loss of a single 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:

[0138]

[0139] in, is the initial wind farm speed loss, is the wake velocity loss of a single wind turbine.

[0140] 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 S3: 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.

[0141] 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:

[0142]

[0143] 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.

[0144] Based on the above steps, this application uses wind farm information and wind turbine information in actual applications to determine the single-unit wake variables of target wind turbines under varying background wind speeds. Based on this, it uses momentum conservation superposition, a local linear superposition model, or a wind speed product superposition model to determine the wind farm speed loss under varying background wind speeds. Compared to existing wind farm speed loss calculation methods, this application fully considers the variations in background wind speed within the wind farm, is applicable to conditions with varying background wind speeds, has strong universality, and can effectively improve the accuracy of wind farm speed loss prediction under varying background wind speeds.

[0145] 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 will be described below with reference to a specific implementation example.

[0146] In this embodiment, the wind farm velocity loss determination method of the present application provides three wind farm velocity loss superposition models that consider background wind speed variations, which can be used to calculate wind farm flow in complex terrain.

[0147] The high-precision large eddy simulation (LES) method was used to verify the proposed method under the conditions of favorable pressure gradient (FPG, background wind speed increases along the stream direction) and adverse pressure gradient (APG, background wind speed decreases along the stream direction). The slope case was set to simulate different background wind speed variation patterns. The slope case 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:

[0148]

[0149] Where x0 is the starting position of the windward slope, H is the height of the slope platform, L1 is the projected length of the windward slope in the flow direction, L3 is the projected length of the leeward slope in the flow direction, and L2 is the length of the platform. Specific parameter settings are shown in Table 1.

[0150]

[0151] Table 1 Example settings (FPG and APG represent forward pressure gradient conditions and adverse pressure gradient conditions, respectively)

[0152] The computational domain size is 80D × 12D × 10D. The example with the suffix WT includes a wind turbine, while the other two examples do not. They are used to provide wind farm wind environment parameters and calculate wind farm speed losses.

[0153] In this embodiment, the wind turbine group information includes the total number N of wind turbine groups, spatial location (x i ,y i ,z i ), 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 .

[0154] 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 stream direction turbulence intensity I0.

[0155] The wind farm parameters of the verification example in this embodiment are shown in Table 2, where FPG-WT is a pressure gradient calculation example, and APG-WT is an adverse pressure gradient calculation example. The calculation example settings of the pressure gradient calculation example FPG-WT are as follows: Figure 2 As shown, the calculation settings of the adverse pressure gradient example APG-WT are as follows Figure 3 shown.

[0156]

[0157] Table 2 Wind farm parameters

[0158] The flow direction variation law of the background wind speed in the wind farm of 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 of the wind farm in the adverse pressure gradient calculation example APG-WT is as follows Figure 5 shown.

[0159] 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.

[0160] Specifically, the second directional turbulence intensity at the position of the i-th wind turbine affected by the wake is determined based on the first directional 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. In this embodiment, the second directional turbulence intensity of the directional turbulence intensities of the wind turbines in the forward pressure gradient example FPG-WT and the adverse pressure gradient example APG-WT are shown in Table 3.

[0161]

[0162] Table 3. Second direction turbulence intensity of wind turbine in verification example

[0163] The second stream turbulence intensity 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.

[0164]

[0165] Table 4 Wake expansion rate of wind turbines in verification example

[0166] The starting point of the far wake of the wind turbine is determined based on the turbulence intensity in the second flow direction, the thrust coefficient of the wind turbine, and the rotor diameter. In this embodiment, the calculated values ​​of the starting points of the far wake of different wind turbines in the forward pressure gradient calculation example FPG-WT and the adverse pressure gradient calculation example APG-WT are shown in Table 5.

[0167]

[0168] Table 5. Table of starting points of far wake of wind turbines in verification example

[0169] 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 rotor diameter, 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 adverse pressure gradient calculation example APG-WT, the flow direction change law of the wake width of different wind turbines is as follows Figure 7 shown.

[0170] 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 calculation 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: Figure 9 shown.

[0171] Then, 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. 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.

[0172] In this embodiment, the comparison between 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 and the calculation results of the three wind farm velocity loss superposition models of this application is as follows: Figure 10 As shown in the figure, 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 the figure. Figure 11 shown.

[0173] In this embodiment, the normalized speed loss profiles in the hub height plane at 5D positions downstream of different wind turbines in the pressure gradient calculation example FPG-WT and the three wind farm speed loss superposition models of this application are as follows: Figure 12 As shown, the normalized speed loss profiles in the hub height plane at 5D positions downstream of different wind turbines in the adverse pressure gradient calculation example APG-WT and the three wind farm speed loss superposition models of this application are as follows: Figure 13 Show.

[0174] By comparing with the large eddy simulation results, it can be seen that the three wind farm speed loss superposition models of this application show high prediction accuracy in predicting the wind farm speed loss under background wind speed changes, and can accurately predict the size and spatial distribution of wind farm speed loss, thus filling the gap in this technical field.

[0175] 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. 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.

[0176] It will be understood by those skilled in the art that all or part of the processes in the method for implementing the above 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. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which 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, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0177] Another aspect of the present application provides an electronic device.

[0178] See attached Figure 14 , Figure 14 It is a schematic diagram of the main structure of an electronic device according to an embodiment of the present application.

[0179] like Figure 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 processor 12 are communicatively connected via a bus. The memory 11 can be configured to store a program for executing the wind farm speed loss determination method of the above-mentioned method embodiment, and the processor 12 can be configured to execute the program in the memory 11, which includes but is not limited to a program for executing the wind farm speed loss determination method of the above-mentioned 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 section of the embodiment of the present application. The electronic device can be an electronic device formed by various electronic devices.

[0180] Another aspect of the present application provides a computer-readable storage medium.

[0181] In an embodiment of a computer-readable storage medium according to the present application, the computer-readable storage medium can be configured to store a program for executing the wind farm speed loss determination method of the above-described method embodiment. This program can be loaded and executed by a processor to implement the above-described wind farm speed loss determination method. For ease of illustration, only the portions relevant to the embodiments of the present application are shown. For specific technical details not disclosed, please refer to the method section of the embodiments of the present application. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, in the embodiments of the present application, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0182] Thus far, the technical solution of the present application has been described in conjunction with an embodiment shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection 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 may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection 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 velocity and a single-machine wake velocity 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 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 method of determining the wind farm speed loss under background wind speed changes using a preset superposition model based on the single-machine wake variable of the target wind turbine and the wind farm information includes: Step S1: determining an initial wind farm speed loss based on at least one of a single-machine wake variable of a target wind turbine, the wind farm information, and an initial wind farm convection speed; Step S2: 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 wind turbine generator set's spatial position; Step S3: Based on the single-machine wake variable of the next wind turbine, the background wind speed and the wind farm information, a preset superposition model is used to update the initial wind farm speed loss to obtain an updated initial wind farm speed loss; Step S4: Repeat steps S2 to S3 until the initial wind farm speed loss is updated based on the wake variable of the last wind turbine, and determine the wind farm speed loss under background wind speed changes based on the updated initial wind farm speed loss.

2. The method for determining wind farm speed loss according to claim 1, characterized in that: The determining, based on the wind farm information and the wind turbine information, of a single-unit wake variable of a target wind turbine under a background wind speed change includes: Determining a flow direction variation law of a dimensionless maximum velocity loss at a center of a wind turbine wake based on the wind farm information and the wind turbine information; Based on the flow direction variation law and the wind farm information, the single-unit convection velocity and single-unit wake velocity loss of the target wind turbine group under 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, a flow direction variation law of the dimensionless maximum velocity loss at the center of the wind turbine wake includes: Determining a second flow direction turbulence intensity based on the first flow direction turbulence intensity, where the second flow direction turbulence intensity is the flow direction turbulence intensity at a location of the wind turbine affected by the wake; determining a wake expansion rate of the wind turbine generator set based on the second flow direction turbulence intensity; Determining a starting point of a far wake of the wind turbine generator set based on the turbulence intensity of the second flow direction, the thrust coefficient of the wind turbine generator set, and the rotor diameter; Determining the wake width at any position downstream of the wind turbine based on the wake expansion rate, the far wake starting point, the spatial position of the wind turbine and the rotor diameter; Based on the wake width, the thrust coefficient of the wind turbine generator set and the 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-unit convection velocity and the single-unit wake velocity loss of the target wind turbine 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 under the background wind speed change; Determining the single-unit convective 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; Determining 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 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, the single-unit wake velocity loss of the target wind turbine under the background wind speed change is determined.

5. The method for determining wind farm speed loss according to claim 3, characterized in that: Before executing step S1, the method further includes: Determine the initial wind farm convection velocity based on the single-unit convection velocity of the target wind turbine; After executing step S2 and before executing step S3, the method further includes: Determining whether the next wind turbine is the last wind turbine in the wind farm; If not, the single-machine wake variable of the next wind turbine is determined 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, wherein the single-machine wake variable includes the single-machine convection speed and the single-machine wake speed loss.

6. The method for determining wind farm speed loss according to claim 1, characterized in that: The wind farm information includes the wind farm background wind speed at the hub height plane of the wind turbine in the absence of a wind farm; Step S3: 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 velocity based on the initial wind farm velocity loss and the wind farm background wind speed; Determine whether the updated initial wind farm convection velocity meets the preset conditions; If so, the initial wind farm speed loss is updated based on the single-machine convection speed and the single-machine wake speed loss of the next wind turbine generator set to obtain an updated initial wind farm speed loss.

7. The method for determining wind farm speed loss according to claim 1, characterized in that: The step S3: 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 1, characterized in that: The wind farm information includes the wind farm background wind speed at the hub height plane of the wind turbine in the absence of a wind farm; Step S3: 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: The initial wind farm speed loss is updated based on the wake speed loss of the next wind turbine, the background wind speed, and the wind farm background wind speed to obtain an updated initial wind farm speed loss.

9. An electronic device comprising at least one processor and at least one memory, wherein the memory is adapted to store 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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