Fully developed wind farm equivalent roughness calculation method and device, storage medium

Abstract

The present application relates to the technical field of wind power generation, and particularly provides a full development wind farm equivalent roughness calculation method and device and storage medium, aiming to solve the problem that the current calculation of the equivalent roughness of the wind farm does not consider the influence of the arrangement of the wind farm, and is only applicable to the tandem arrangement, but not applicable to the irregular arrangement. For this purpose, the full development wind farm equivalent roughness calculation method includes: S100: inputting the parameters of a target wind farm; S200: determining the planar thrust coefficient of the target wind farm through a coupling model according to the parameters of the target wind farm; S300: calculating the equivalent roughness of the target wind farm according to the parameters of the target wind farm and the planar thrust coefficient of the target wind farm. The method can consider the influence of the specific arrangement of the wind turbine generator set, has high universality, and greatly improves the prediction accuracy of the equivalent roughness.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, specifically providing a method and apparatus for calculating the equivalent roughness of a wind farm, a control device, and a storage medium. Background Technology

[0002] Large-scale wind power bases represent the primary form of wind power development and utilization in my country. During operation, these bases undergo complex interactions with the atmospheric boundary layer, resulting in the downstream wind farms being in a fully developed state. Studying the wind farm flow mechanisms under these conditions helps to gain a deeper understanding of the energy sources of wind farms. A fully developed wind farm is typically considered to have equivalent roughness. Calculating this equivalent roughness allows for the further calculation of physical quantities such as wind speed profiles, thereby quantifying the wind farm's output power and providing a reference for base planning. Therefore, accurately calculating the equivalent roughness of a fully developed wind farm is crucial for the performance evaluation of large-scale wind power bases.

[0003] Currently, the calculation of equivalent roughness does not take into account the influence of wind farm layout and is only applicable to tandem layouts, not irregular layouts. This limits the applicability of existing calculation methods.

[0004] Accordingly, the field needs a new scheme for calculating the equivalent roughness of wind farms that fully considers the layout of wind farms to solve the above problems. Summary of the Invention

[0005] The present invention aims to solve the above-mentioned technical problem, namely that the current calculation of the equivalent roughness of wind farms does not take into account the influence of wind farm layout, and is only applicable to tandem layouts, but not to irregular layouts. The present invention provides a method and apparatus, control device and storage medium for fully developing the calculation of the equivalent roughness of wind farms.

[0006] In a first aspect, the present invention provides a method for fully developing the equivalent roughness calculation of a wind farm, the method comprising:

[0007] Step S100: Input the parameters of the target wind farm;

[0008] Step S200: Determine the planar thrust coefficient of the target wind farm using a coupled model based on the parameters of the target wind farm;

[0009] Step S300: Calculate the equivalent roughness of the target wind farm based on the parameters of the target wind farm and the planar thrust coefficient of the target wind farm.

[0010] In one of the above-mentioned technical solutions for fully developing the equivalent roughness calculation method of wind farms, step S300 specifically includes:

[0011]

[0012] in, Let D be the equivalent roughness of the target wind farm, D be the rotor diameter of the wind turbine, and zh be the hub height of the wind turbine. Z represents the planar thrust coefficient of the target wind farm, k is the von Kármán constant, and Z... 0，lo For surface roughness, The dimensionless wake of the wind farm is given an additional eddy viscosity coefficient.

[0013] In one of the above-mentioned technical solutions for fully developing the equivalent roughness calculation method of wind farms, step S100 specifically includes:

[0014] Step S1000: Input the WT of each wind turbine in the target wind farm. i coordinates (x) i y i The area of ​​the wind farm is S, and the hub height of the wind turbine is z. h Rotor diameter D, thrust coefficient C T Surface roughness Z of wind farm site 0，lo Surface friction speed u *，lo Free flow wind speed U ∞ and atmospheric boundary layer height δ;

[0015] Step S1002: Based on the parameters of the input target wind farm, construct a model of a fully developed wind farm by spatially translating the target wind farm.

[0016] In one of the above-mentioned technical solutions for fully developing the equivalent roughness calculation method of wind farms, step S200 specifically includes:

[0017] Step S2000: Based on the parameters of the target wind farm, specify the initial wake model expansion rate (WTi) for each wind turbine. Where K is the von Kármán constant and α is the wake expansion rate proportionality coefficient;

[0018] Step S2002: WT of each wind turbine i Initial wake model expansion rate Substitute into the wind farm flow field model to calculate the WT of each wind turbine in the target area. i Hub height local plane average velocity Wind turbine plane average wind speed and local planar thrust coefficient

[0019] Step S2004: WT of each wind turbine i Local planar thrust coefficient Substitute into the boundary layer model to calculate the spatiotemporal average velocity at the local hub height.

[0020] Step S2006: Update the WT of each wind turbine based on the parameters of the target wind farm and the wake expansion ratio proportionality coefficient α. i The wake model expansion rate;

[0021] Step S2008: Correct the wake expansion ratio scaling factor α. Calculate the corrected wake model expansion ratio based on the corrected wake expansion ratio scaling factor α. According to the modified wake model expansion rate Recalculate the WT per wind turbine in the target area in step S2022 i Hub height local plane average velocity Wind turbine plane average wind speed and local planar thrust coefficient And the local hub height space-time average velocity in step S2004 Until relative error If the value is less than the first threshold, output the WT of each wind turbine. i Wind turbine plane average wind speed Average wind speed at hub height plane

[0022] Step S2010: WT per wind turbine i Wind turbine plane average wind speed Average wind speed at hub height plane Substitution Calculate the planar thrust coefficient of a wind farm

[0023] In one of the above-mentioned technical solutions for fully developing the equivalent roughness calculation method of wind farms, step S2002 specifically includes:

[0024] Step S20020: Calculate the maximum wake velocity loss in the hub height plane of wind turbine i using the super-Gaussian wake model, which is:

[0025]

[0026] Where x′ is the wind turbine WT i The relative flow direction coordinates, Δ = 0.5D is the characteristic width of the Gaussian function, and erf is the error function. It is the wake width. It is the initial velocity loss at the wind turbine, u ∞，i It is the upstream velocity of wind turbine i;

[0027] Among them, u ∞，i for:

[0028]

[0029] Where, δu m (x i ) refers to the WT of wind turbine units m In wind turbine WT i Speed ​​loss at the wind turbine

[0030] Step S20022: Utilize the wake shape function W i Calculate the wake velocity loss distribution in the plane at the hub height of wind turbine i, for

[0031] δu i (x′, y′, z′)=δu i，max (x′)W(x′,r′)

[0032] Where (y′, z′) is the WT of the wind turbine unit. i The relative spanwise and vertical coordinates, Wake shape function W i for:

[0033]

[0034] in,

[0035] Step S20024: Determine the wind farm flow field based on the linear wake superposition model, for

[0036]

[0037] Step S20026: Calculate the local planar average velocity at the hub height based on the wind farm flow field. Wind turbine plane average wind speed and local planar thrust coefficient

[0038] Local plane average velocity at hub height for

[0039]

[0040] Where VC is the WT of the wind turbine. i The corresponding von Lonnoy region, This corresponds to the area of ​​the von Lund-Noël region;

[0041] Wind turbine plane average wind speed for

[0042]

[0043] Among them, A d It is a wind turbine WTi In the corresponding wind turbine region, A = πD 2 / 4 is the area of ​​the wind turbine;

[0044] Local plane thrust coefficient for

[0045]

[0046] Where L refers to the influence of the wind turbine's WT i The collection of von Lonoy regions.

[0047] In one of the above-mentioned technical solutions for fully developing the equivalent roughness calculation method of wind farms, step S2004 specifically includes:

[0048] Step S20040: Calculate the local equivalent roughness of the wind farm for

[0049]

[0050] in,

[0051] Step S20042: Calculate the spatiotemporal average velocity of the local hub height using the boundary layer. for

[0052]

[0053] Where, δ ibl (x i ) is the WT of the wind turbine unit i The height of the inner boundary layer at that location is:

[0054]

[0055] Where Δx is the wind turbine WT i Distance from the origin of the wind farm in the direction of flow.

[0056] In one of the above-mentioned technical solutions for fully developing the equivalent roughness calculation method of wind farms, step S2006 specifically includes:

[0057] Step S20060: Establish sets F and W for free-flowing and non-free-flowing wind turbines in the wind farm, with the following criteria:

[0058]

[0059] Step S20062: The wake expansion coefficient of each wind turbine in the wind farm is:

[0060]

[0061] in, WT per wind turbine i The local friction speed.

[0062] In a second aspect, the present invention provides a device for fully developing the equivalent roughness calculation apparatus for wind farms, the apparatus comprising:

[0063] The input module is used to input the parameters of the target wind farm;

[0064] The determination module is used to determine the planar thrust coefficient of the target wind farm based on the parameters of the target wind farm through a coupled model;

[0065] The calculation module is used to calculate the equivalent roughness of the target wind farm based on the parameters of the target wind farm and the planar thrust coefficient of the target wind farm.

[0066] In a third aspect, the present invention provides a control device comprising a processor and a storage device, the storage device being adapted to store a plurality of program codes, the program codes being adapted to be loaded and run by the processor to perform the method described in any of the above-described technical solutions for fully developing wind farm equivalent roughness calculation methods.

[0067] In a fourth aspect, the present invention provides a computer-readable storage medium storing a plurality of program codes adapted to be loaded and run by a processor to perform the method described in any of the above-described technical solutions of the fully developed wind farm equivalent roughness calculation method.

[0068] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects:

[0069] In implementing the technical solution of this invention, a method for calculating the equivalent roughness of a wind farm is provided. This method determines the planar thrust coefficient of the wind farm by inputting the parameters of the target wind farm and through a coupled model. Will Substitute into the formula to determine the equivalent roughness of the target wind farm Based on the above steps, this invention, by inputting target wind farm parameters and coupling a super-Gaussian wake model and a boundary layer model, determines the scaling factor of the wake expansion rate, thereby obtaining the average wind speed in the rotor plane and the average wind speed in the hub height plane, thus obtaining the wind farm planar thrust coefficient, and finally calculating the equivalent roughness of the target wind farm. Compared with existing equivalent roughness calculation models, this method can consider the influence of the specific arrangement of wind turbine units, has high universality, and greatly improves the prediction accuracy of equivalent roughness. Attached Figure Description

[0070] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. Furthermore, similar numbers in the drawings are used to denote similar components, wherein:

[0071] Figure 1 This is a schematic diagram of the main steps of a fully developed wind farm equivalent roughness calculation method according to an embodiment of the present invention;

[0072] Figure 2 This is a schematic diagram of the layout of the target wind farm in a verification example according to an embodiment of the present invention;

[0073] Figure 3 This is a schematic diagram of a large wind power base with a target wind farm layout structure according to an embodiment of the present invention;

[0074] Figure 4 This is a schematic diagram of the planar flow field at the hub height of a wind farm, calculated based on the super-Gaussian wake model in an embodiment of the present invention.

[0075] Figure 5 This is a schematic diagram of the von Lonoy region corresponding to each wind turbine in this embodiment of the invention;

[0076] Figure 6 This is a schematic diagram of the spatiotemporal average velocity of the local hub height calculated using the wake model in an embodiment of the present invention;

[0077] Figure 7 This is a schematic diagram of the spatiotemporal average velocity of the local hub height calculated using the boundary layer model in an embodiment of the present invention;

[0078] Figure 8 This is a schematic diagram of the main structure of a fully developed wind farm equivalent roughness calculation method apparatus according to an embodiment of the present invention. Detailed Implementation

[0079] Some embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0080] In the description of this invention, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and may also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes 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" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0081] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a fully developed wind farm equivalent roughness calculation method according to an embodiment of the present invention. Figure 1 As shown, the method for calculating the equivalent roughness of a fully developed wind farm in this embodiment of the invention mainly includes the following steps S100-S300.

[0082] Step S101: Input the parameters of the target wind farm;

[0083] Step S200: Determine the planar thrust coefficient of the target wind farm using a coupled model based on the parameters of the target wind farm;

[0084] Step S300: Calculate the equivalent roughness of the target wind farm based on the parameters of the target wind farm and the planar thrust coefficient of the target wind farm.

[0085] Based on the above steps S100-S300, the present invention determines the planar thrust coefficient of the wind farm by inputting the parameters of the target wind farm and through a coupling model. Will Substitute into the formula to determine the equivalent roughness of the target wind farm Based on the above steps, this invention, by inputting target wind farm parameters and coupling a super-Gaussian wake model and a boundary layer model, determines the scaling factor of the wake expansion rate, thereby obtaining the average wind speed in the rotor plane and the average wind speed in the hub height plane, thus obtaining the wind farm planar thrust coefficient, and finally calculating the equivalent roughness of the target wind farm. Compared with existing equivalent roughness calculation models, this method can consider the influence of the specific arrangement of wind turbine units, has high universality, and greatly improves the prediction accuracy of equivalent roughness.

[0086] Steps S100 to S300 will be further explained below.

[0087] In a specific example, the present invention uses the high-precision large eddy simulation (LES) method to verify the proposed method for calculating the equivalent roughness of a fully developed wind farm. The LES example settings are shown in Table 1.

[0088] Table 1

[0089]

[0090] In one embodiment of the present invention, step S100 may further include:

[0091] Step S1000: Input the WT of each wind turbine in the target wind farm. i coordinates (x) i y i The area of ​​the wind farm is S, and the hub height of the wind turbine is z. h Rotor diameter D, thrust coefficient C T Surface roughness Z of wind farm site 0，lo Surface friction speed u *，lo Free flow wind speed U ∞ And atmospheric boundary layer height δ.

[0092] In a specific example, the parameters of the target wind farm are shown in Table 2.

[0093] Table 2

[0094]

[0095]

[0096]

[0097] The target wind farm layout obtained using the data from the MI example in Table 1 is as follows: Figure 2 As shown.

[0098] Step S1002: Based on the parameters of the input target wind farm, construct a model of a fully developed wind farm by spatially translating the target wind farm.

[0099] In a specific example, two wind farms with identical turbine configurations are set up upstream of the calculated wind farm. Wind farms of similar size are then set up on either side of this row of wind farms, forming a large wind power base to ensure the target wind farm is in a near-fully developed state. In a specific example, the large wind power base is arranged as follows: Figure 3 As shown.

[0100] In one embodiment of the present invention, step S200 may further include:

[0101] Step S2000: Based on the parameters of the target wind farm, specify the initial wake model expansion rate (WTi) for each wind turbine. Where k is the von Kármán constant, k = 0.4, and α is the wake expansion rate proportionality coefficient; in this embodiment, α is set to 1.7, then the corresponding...

[0102] Step S2002: WT of each wind turbine i Initial wake model expansion rate Substitute into the wind farm flow field model to calculate the WT of each wind turbine in the target area. i Hub height local plane average velocity Wind turbine plane average wind speed and local planar thrust coefficient

[0103] In one embodiment of the present invention, step S2002 may further include:

[0104] Step S20020: Calculate the maximum wake velocity loss in the hub height plane of wind turbine i using the super-Gaussian wake model, which is:

[0105]

[0106] Where x′ is the wind turbine WT i The relative flow direction coordinates, Δ = 0.5D is the characteristic width of the Gaussian function, and erf is the error function. It is the wake width. It is the initial velocity loss at the wind turbine, u ∞，i It is the upstream velocity of wind turbine i;

[0107] Among them, u ∞，i for:

[0108]

[0109] Where, δu mm (x i ) refers to the WT of wind turbine units m In wind turbine WT i Speed ​​loss at the wind turbine

[0110] Step S20022: Utilize the wake shape function W i Calculate the wake velocity loss distribution in the plane at the hub height of wind turbine i, for

[0111] δu i (x′, y′, z′)=δu i，max (x′)W(x′,r′)

[0112] Where (y′, z′) is the WT of the wind turbine unit. i The relative spanwise and vertical coordinates, Wake shape function W i for:

[0113]

[0114] in,

[0115] Step S20024: Determine the wind farm flow field based on the linear wake superposition model, for

[0116]

[0117] In a specific example, the planar flow field u at the hub height of the wind farm calculated based on the super-Gaussian wake model in this embodiment of the invention. wm (x, y, z) h )like Figure 4 As shown.

[0118] Step S20026: Calculate the local planar average velocity at the hub height based on the wind farm flow field. Wind turbine plane average wind speed and local planar thrust coefficient

[0119] Local plane average velocity at hub height for

[0120]

[0121] Where VC is the WT of the wind turbine. i The corresponding von Lonnoy region, This corresponds to the area of ​​the von Lund-Noël region;

[0122] Wind turbine plane average wind speed for

[0123]

[0124] Among them, A d It is a wind turbine WT i In the corresponding wind turbine region, A = πD 2 / 4 is the area of ​​the wind turbine;

[0125] Local plane thrust coefficient for

[0126]

[0127] Where L refers to the influence of the wind turbine's WT i The collection of von Lonoy regions.

[0128] In a specific example, the von Lonoi region corresponding to each wind turbine is as follows: Figure 5 In this embodiment, the local hub height spatiotemporal average velocity is calculated using the wake model. like Figure 6 As shown.

[0129] Step S2004: WT of each wind turbine i Local planar thrust coefficient Substitute into the boundary layer model to calculate the spatiotemporal average velocity at the local hub height.

[0130] In one embodiment of the present invention, step S2004 may further include:

[0131] Step S20040: Calculate the local equivalent roughness of the wind farm for

[0132]

[0133] in,

[0134] Step S20042: Calculate the spatiotemporal average velocity of the local hub height using the boundary layer. for

[0135]

[0136] Where, δ ibl (x i ) is the WT of the wind turbine unit i The height of the inner boundary layer at that location is:

[0137]

[0138] Where Δx is the wind turbine WT i Distance from the origin of the wind farm in the direction of flow.

[0139] In a specific example, this embodiment uses the local hub height spatiotemporal average velocity calculated by the boundary layer model. like Figure 7 As shown.

[0140] Step S2006: Update the WT of each wind turbine based on the parameters of the target wind farm and the wake expansion ratio proportionality coefficient α. i The wake model expansion rate.

[0141] In one embodiment of the present invention, step S2006 may further include:

[0142] Step S20060: Establish sets F and W for free-flowing (unaffected by wake) and non-free-flowing (affected by wake) wind turbines in the wind farm, with the following criteria:

[0143]

[0144] Step S20062: The wake expansion coefficient of each wind turbine in the wind farm is:

[0145]

[0146] in, WT per wind turbine i The local friction speed.

[0147] Step S2008: Correct the wake expansion ratio scaling factor α. Calculate the corrected wake model expansion ratio based on the corrected wake expansion ratio scaling factor α. According to the modified wake model expansion rate Recalculate the WT per wind turbine in the target area in step S2022 i Hub height local plane average velocity Wind turbine plane average wind speed and local planar thrust coefficient And the local hub height space-time average velocity in step S2004 Until relative error If the value is less than the first threshold, output the WT of each wind turbine. i Wind turbine plane average wind speed Average wind speed at hub height plane

[0148] In a specific example, the wake model expansion rate is an iterative process. First, an initial wake expansion rate scaling factor α is set. Based on α, an initial wake model expansion rate is calculated. Then, based on this initial wake model expansion rate, the target area WT for each wind turbine in step S2022 is calculated. i Hub height local plane average velocity Wind turbine plane average wind speed and local planar thrust coefficient And the local hub height space-time average velocity in step S2004 If relative error Then, the wake expansion ratio coefficient α is adjusted, and the WT of each wind turbine in the target area in step S2022 is calculated iteratively. i Hub height local plane average velocity Wind turbine plane average wind speed and local planar thrust coefficient And the local hub height space-time average velocity in step S2004 Until relative error Output WT per wind turbine i Wind turbine plane average wind speed Average wind speed at hub height plane

[0149] In a specific example, the relative error calculated by the iterative process of determining the optimal wake expansion ratio coefficient α using the iterative method is shown in Table 3.

[0150] Table 3

[0151] α error 1.7 0.79 1.8 0.48 1.9 0.44 2.0 0.41 2.1 0.37 2.2 0.34 2.3 0.31 2.4 0.28

[0152] As can be seen from Table 3, the optimal wake expansion ratio proportionality coefficient α = 2.4.

[0153] Step S2010: WT per wind turbine i Wind turbine plane average wind speed Average wind speed at hub height plane Substitution Calculate the planar thrust coefficient of a wind farm

[0154] In a specific example, the calculation obtained in this embodiment

[0155] In one embodiment of the present invention, step S300 may further include:

[0156]

[0157] in, Let D be the equivalent roughness of the target wind farm, D be the rotor diameter of the wind turbine, and zh be the hub height of the wind turbine. Z represents the planar thrust coefficient of the target wind farm, k is the von Kármán constant, and Z... 0，lo For surface roughness, The dimensionless wake of the wind farm is given an additional eddy viscosity coefficient.

[0158] In a specific example, the calculation obtained in this embodiment The equivalent roughness of the wind farm obtained through LES relative error The roughness is only 1%, which shows that the method for calculating the equivalent roughness of a fully developed wind farm proposed in this invention can accurately calculate the equivalent roughness of an irregularly arranged fully developed wind farm with high precision, filling a gap in this technical field.

[0159] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effects of the present invention, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of the present invention.

[0160] Furthermore, the present invention also provides a device for fully developing the equivalent roughness calculation of wind farms.

[0161] See appendix Figure 8 , Figure 8 This is a main structural block diagram of a fully developed wind farm equivalent roughness calculation device according to an embodiment of the present invention. Figure 8 As shown, the equivalent roughness calculation device for a fully developed wind farm in this embodiment of the invention mainly includes an input module 11, a determination module 12, and a calculation module 13. In some embodiments, the input module 11 can be configured to input parameters of the target wind farm. The determination module 12 can be configured to determine the planar thrust coefficient of the target wind farm through a coupling model based on the parameters of the target wind farm. The calculation module 13 can be configured to calculate the equivalent roughness of the target wind farm based on the parameters of the target wind farm and the planar thrust coefficient of the target wind farm. In one embodiment, a description of the specific functions can be found in steps S100-S300.

[0162] The above-described wind farm equivalent roughness calculation device is used to execute the wind farm equivalent roughness calculation method embodiment shown in Figure 8. The technical principles, technical problems solved, and technical effects of the two are similar. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the wind farm equivalent roughness calculation device can be found in the description of the embodiment of the wind farm equivalent roughness calculation method, and will not be repeated here.

[0163] Those skilled in the art will understand that all or part of the processes in the method of the above embodiment of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0164] Furthermore, the present invention also provides a control device. In one embodiment of the control device according to the present invention, the control device includes a processor and a storage device. The storage device can be configured to store a program for executing the fully developed wind farm equivalent roughness calculation method of the above-described method embodiments. The processor can be configured to execute the program in the storage device, which includes, but is not limited to, the program for executing the fully developed wind farm equivalent roughness calculation method of the above-described method embodiments. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. This control device can be a control device device comprising various electronic devices.

[0165] Furthermore, the present invention also provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to the present invention, the computer-readable storage medium can be configured to store a program for performing the fully developed wind farm equivalent roughness calculation method of the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described fully developed wind farm equivalent roughness calculation method. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The computer-readable storage medium can be a storage device comprising various electronic devices. Optionally, in the embodiments of the present invention, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0166] Furthermore, it should be understood that since the various modules are only provided to illustrate the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor itself, or a part of the processor's software, hardware, or a combination of software and hardware. Therefore, the number of modules shown in the figures is merely illustrative.

[0167] Those skilled in the art will understand that the various modules in the device can be adaptively split or combined. Such splitting or combining of specific modules will not cause the technical solution to deviate from the principles of the present invention; therefore, the technical solutions after splitting or combining will fall within the protection scope of the present invention.

[0168] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for fully developing the equivalent roughness calculation of a wind farm, characterized in that, include: Step S100: Input the parameters of the target wind farm; Step S200: Determine the planar thrust coefficient of the target wind farm using a coupled model based on the parameters of the target wind farm; Step S300: Calculate the equivalent roughness of the target wind farm based on the parameters of the target wind farm and the planar thrust coefficient of the target wind farm; The step of determining the planar thrust coefficient of the target wind farm using a coupled model based on the parameters of the target wind farm includes: Step S2000: Based on the parameters of the target wind farm, specify the WT for each wind turbine. i Initial wake model expansion rate , where κ is the von Kármán constant and α is the wake expansion rate proportionality coefficient; Step S2002: WT of each wind turbine i Initial wake model expansion rate Substitute into the wind farm flow field model to calculate the WT of each wind turbine in the target area. i Hub height local plane average velocity Average wind speed in the plane of the wind turbine and local planar thrust coefficient ; Step S2004: WT of each wind turbine i Local planar thrust coefficient Substitute into the boundary layer model to calculate the spatiotemporal average velocity at the local hub height. ; Step S2006: Update the WT of each wind turbine based on the parameters of the target wind farm and the wake expansion ratio proportionality coefficient α. i The wake model expansion rate; Step S2008: Correct the wake expansion ratio scaling factor α. Calculate the corrected wake model expansion ratio based on the corrected wake expansion ratio scaling factor α. According to the modified wake model expansion rate Recalculate the WT of each wind turbine in the target area in step S2002. i Hub height local plane average velocity Average wind speed in the plane of the wind turbine and local planar thrust coefficient And the local hub height space-time average velocity in step S2004 until relative error If the value is less than the first threshold, output the WT of each wind turbine. i Wind turbine plane average wind speed Average wind speed at hub height plane ; Step S2010: WT for each wind turbine i Wind turbine plane average wind speed Average wind speed at hub height plane Substitution Calculate the planar thrust coefficient of the wind farm. ; The step of calculating the equivalent roughness of the target wind farm based on its parameters and the planar thrust coefficient includes: in, Let z be the equivalent roughness of the target wind farm, D be the rotor diameter of the wind turbine, and z be the sludge density. h The hub height of the wind turbine. Let be the plane thrust coefficient of the target wind farm, and k be the von Kármán constant. For surface roughness, Add eddy viscosity coefficient to the dimensionless wake of the wind farm; Among them, the WT of each wind turbine i Initial wake model expansion rate Substitute into the wind farm flow field model to calculate the WT of each wind turbine in the target area. i Hub height local plane average velocity Average wind speed in the plane of the wind turbine and local planar thrust coefficient ,include: Step S20020: Calculate the maximum wake velocity loss in the hub height plane of wind turbine i using the super-Gaussian wake model, which is: in, It is a wind turbine WT i The relative flow direction coordinates, ∆=0.5D is the characteristic width of the Gaussian function, and erf is the error function. It is the wake width. It is the initial velocity loss at the wind turbine. It is the upstream velocity of wind turbine i; in, for: in, This refers to the WT of wind turbine units. m In wind turbine WT i Speed ​​loss at the wind turbine ; Step S20022: Utilize the wake shape function Calculate the wake velocity loss distribution in the plane at the hub height of wind turbine i, for in, It is a wind turbine WT i The relative spanwise and vertical coordinates, Wake shape function W i for: in, Step S20024: Determine the wind farm flow field based on the linear wake superposition model, for Step S20026: Calculate the local planar average velocity at the hub height based on the wind farm flow field. Average wind speed in the plane of the wind turbine and local planar thrust coefficient , Local plane average velocity at hub height ,for Where VC is the WT of the wind turbine. i The corresponding von Lonnoy region, This corresponds to the area of ​​the von Lund-Noël region; Wind turbine plane average wind speed ,for in, It is a wind turbine WT i The corresponding wind turbine area, It is the area of ​​the wind turbine; Local plane thrust coefficient ,for Where L refers to the influence of the wind turbine's WT i The collection of von Lonoy regions.

2. The method for calculating the equivalent roughness of a fully developed wind farm according to claim 1, characterized in that, Step S100 specifically includes: Step S1000: Input the WT of each wind turbine in the target wind farm. i coordinates (x) i y i The area S of the wind farm and the hub height z of the wind turbine are shown. h Rotor diameter D, thrust coefficient C T Surface roughness of wind farm sites Surface friction speed Free flow wind speed and atmospheric boundary layer height δ; Step S1002: Based on the parameters of the input target wind farm, construct a model of a fully developed wind farm by spatially translating the target wind farm.

3. The method for calculating the equivalent roughness of a fully developed wind farm according to claim 1, characterized in that, Step S2004 specifically includes: Step S20040: Calculate the local equivalent roughness of the wind farm ,for in, ; Step S20042: Calculate the spatiotemporal average velocity of the local hub height using the boundary layer. ,for in, It is a wind turbine WT i The height of the inner boundary layer at that location is: Where ∆x is the wind turbine WT i Distance from the origin of the wind farm in the direction of flow.

4. The method for calculating the equivalent roughness of a fully developed wind farm according to claim 1, characterized in that, Step S2006 specifically includes: Step S20060: Establish sets F and W for free-flowing and non-free-flowing wind turbines in the wind farm, with the following criteria: Step S20062: The wake expansion coefficient of each wind turbine in the wind farm is: in, WT per wind turbine i The local friction speed.

5. A device for fully developing the equivalent roughness calculation of wind farms, characterized in that, include: The input module is used to input the parameters of the target wind farm; The determination module is used to determine the planar thrust coefficient of the target wind farm based on the parameters of the target wind farm through a coupled model; The calculation module is used to calculate the equivalent roughness of the target wind farm based on the parameters of the target wind farm and the planar thrust coefficient of the target wind farm. The step of determining the planar thrust coefficient of the target wind farm using a coupled model based on the parameters of the target wind farm includes: Step S2000: Based on the parameters of the target wind farm, specify the WT for each wind turbine. i Initial wake model expansion rate , where κ is the von Kármán constant and α is the wake expansion rate proportionality coefficient; Step S2002: WT of each wind turbine i Initial wake model expansion rate Substitute into the wind farm flow field model to calculate the WT of each wind turbine in the target area. i Hub height local plane average velocity Average wind speed in the plane of the wind turbine and local planar thrust coefficient ; Step S2004: WT of each wind turbine i Local planar thrust coefficient Substitute into the boundary layer model to calculate the spatiotemporal average velocity at the local hub height. ; Step S2006: Update the WT of each wind turbine based on the parameters of the target wind farm and the wake expansion ratio proportionality coefficient α. i The wake model expansion rate; Step S2008: Correct the wake expansion ratio scaling factor α. Calculate the corrected wake model expansion ratio based on the corrected wake expansion ratio scaling factor α. According to the modified wake model expansion rate Recalculate the WT of each wind turbine in the target area in step S2002. i Hub height local plane average velocity Average wind speed in the plane of the wind turbine and local planar thrust coefficient And the local hub height space-time average velocity in step S2004 until relative error If the value is less than the first threshold, output the WT of each wind turbine. i Wind turbine plane average wind speed Average wind speed at hub height plane ; Step S2010: WT for each wind turbine i Wind turbine plane average wind speed Average wind speed at hub height plane Substitution Calculate the planar thrust coefficient of the wind farm. ; The step of calculating the equivalent roughness of the target wind farm based on its parameters and the planar thrust coefficient includes: in, Let z be the equivalent roughness of the target wind farm, D be the rotor diameter of the wind turbine, and z be the sludge density. h The hub height of the wind turbine. Let be the plane thrust coefficient of the target wind farm, and k be the von Kármán constant. For surface roughness, Add eddy viscosity coefficient to the dimensionless wake of the wind farm; Among them, the WT of each wind turbine i Initial wake model expansion rate Substitute into the wind farm flow field model to calculate the WT of each wind turbine in the target area. i Hub height local plane average velocity Average wind speed in the plane of the wind turbine and local planar thrust coefficient ,include: Step S20020: Calculate the maximum wake velocity loss in the hub height plane of wind turbine i using the super-Gaussian wake model, which is: in, It is a wind turbine WT i The relative flow direction coordinates, ∆=0.5D is the characteristic width of the Gaussian function, and erf is the error function. It is the wake width. It is the initial velocity loss at the wind turbine. It is the upstream velocity of wind turbine i; in, for: in, This refers to the WT of wind turbine units. m In wind turbine WT i Speed ​​loss at the wind turbine ; Step S20022: Utilize the wake shape function Calculate the wake velocity loss distribution in the plane at the hub height of wind turbine i, for in, It is a wind turbine WT i The relative spanwise and vertical coordinates, Wake shape function W i for: in, Step S20024: Determine the wind farm flow field based on the linear wake superposition model, for Step S20026: Calculate the local planar average velocity at the hub height based on the wind farm flow field. Average wind speed in the plane of the wind turbine and local planar thrust coefficient , Local plane average velocity at hub height ,for Where VC is the WT of the wind turbine. i The corresponding von Lonnoy region, This corresponds to the area of ​​the von Lund-Noël region; Wind turbine plane average wind speed ,for in, It is a wind turbine WT i The corresponding wind turbine area, It is the area of ​​the wind turbine; Local plane thrust coefficient ,for Where L refers to the influence of the wind turbine's WT i The collection of von Lonoy regions.

6. A control device, comprising a processor and a storage device, said storage device being adapted to store a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by the processor to perform the method for calculating the equivalent roughness of a fully developed wind farm as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform the method for calculating the equivalent roughness of a fully developed wind farm as described in any one of claims 1 to 4.

Images