A wind farm power generation evaluation method and device based on a three-dimensional wake model

Abstract

The present invention discloses a method and device for evaluating wind farm power generation based on a three-dimensional wake model, comprising: obtaining wind farm inflow resource information and information of each unit within the field; calculating the wake velocity distribution of a single wind turbine based on the three-dimensional wake model; calculating the mixed wake of each unit in the wind farm in combination with a wake superposition model to obtain the velocity distribution within the field; calculating the equivalent wind speed of the incoming wind rotor in front of each wind turbine based on the velocity distribution within the field; and evaluating the power generation of each wind turbine based on the equivalent wind speed of the incoming wind rotor in front of each wind turbine and its power curve. The present invention effectively applies the three-dimensional properties of the three-dimensional wake model to the evaluation of power generation, provides a scientific basis for investment decisions of wind power projects, improves the operational reliability of wind power systems, and thus solves the problem of insufficient versatility and accuracy of existing power generation evaluation methods in the face of increasingly complex flow field environments.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy wind power generation, and in particular to a method for evaluating wind farm power generation based on a three-dimensional wake model. Background Art

[0002] As the global energy crisis and climate change intensify, wind power, as a clean energy source, is rapidly developing worldwide. In China in particular, wind power has become a crucial component of the energy mix and plays a crucial role in promoting energy transition. Rapid and accurate wind farm power generation assessment technology is a key technology for ensuring the successful and efficient operation of wind power projects.

[0003] During actual wind farm operation, each wind turbine within the farm generates a wake effect characterized by a "wind speed deficit and turbulence surge" downstream. The intersecting wakes will also overlap, forming a larger mixed wake region downstream. Therefore, in-depth research on the wake effects of individual turbines within a wind farm and their mixed wakes is crucial to accurately predict wind turbine wakes and mixed wakes from multiple turbines, which is directly relevant to wind farm energy yield assessment. Current wake research methods primarily include experimental measurements (including wind tunnel experiments and field measurements), computational fluid dynamics (CFD) methods, and wake engineering models. Experimental measurements effectively investigate the wake characteristics of wind turbines and wind farms by statistically analyzing key physical parameters related to their wakes (such as velocity and turbulence intensity). However, due to the limitations of experimental measurements, CFD methods have gained considerable favor in wake research due to their flexibility and ability to obtain comprehensive flow field information through numerical solution of the governing fluid equations. However, actual wind power engineering projects often involve a lot of calculation and evaluation work such as the design model and local layout of the unit. The current calculation conditions cannot fully complete the project requirements in a short time through CFD numerical simulation methods. Therefore, the engineering model method that quantitatively characterizes the wake effect of wind turbines has been widely used in actual engineering projects due to its extremely high calculation efficiency and acceptable accuracy. The analytical modeling work of wake originated from Jensen in 1983 (JENSEN NOA note on wind generator interaction [M]. Denmark: The Park model (also known as the Jensen model), proposed by the National Laboratory in 1983, is now widely used in wind energy resource assessment and power generation calculation software. Based on this model, a series of wind turbine wake prediction models have been proposed to achieve rapid prediction of wake fields.

[0004] Beyond predicting velocity distribution within a site, further research into methods for fully utilizing wind resource data to assess turbine power generation is crucial. In the early days, only hub-height wind speed, measured by a wind tower, was used as a single input to assess the wind energy a turbine could capture. In recent years, with the rise of wind power in my country, the rapid development of larger wind turbines has made hub-height wind speed less representative. The IEC61400-12-1 standard introduces the concept of rotor equivalent wind speed (REWS) to account for the effects of wind shear, dividing the rotor area into several height layers along the height direction. However, due to the complex wakes (machine type, turbine spacing, etc.) caused by multiple turbines in an actual wind farm, as well as the uncertainty of wind direction, considering only the vertically averaged equivalent wind speed cannot comprehensively account for the complex flow field caused by the mixing of the wakes from multiple turbines.

[0005] In summary, the current major wind farm energy yield assessment methods still have the following two shortcomings: 1) Most wind farm energy yield assessments use one-dimensional or two-dimensional wake models, and fail to employ accurate three-dimensional wake models to fully account for the increasingly significant wind shear effects and detailed wake distribution caused by the increasing size of wind turbines. 2) Very few energy yield assessment methods consider the three-dimensional properties of the wake distribution, but they often use the equivalent wind speed of conventional wind turbines. While these methods account for wind shear effects to some extent, they do not truly apply the complex flow field distribution caused by the mixed wake effects of multiple turbines and uncertain wind conditions to wind farm energy yield assessments. Therefore, existing mainstream wind farm energy yield assessment methods have not considered the comprehensive impact of the complex flow field in the field, and there is still significant room for improvement. Summary of the Invention

[0006] The purpose of the present invention is to provide a wind farm power generation assessment method and device based on a three-dimensional wake model, effectively applying the three-dimensional properties of the three-dimensional wake model to the power generation assessment, providing a scientific basis for investment decisions in wind power projects, improving the operational reliability of wind power systems, and solving the problems of insufficient versatility and accuracy of existing power generation assessment methods in the face of increasingly complex flow field environments.

[0007] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0008] In a first aspect, the present invention discloses a method for evaluating wind farm power generation based on a three-dimensional wake model, the method comprising the following steps:

[0009] S1, obtain the wind farm inflow resource information and the information of each unit in the farm;

[0010] S2, the wake velocity distribution of a single wind turbine is calculated based on the three-dimensional wake model;

[0011] S3, combining the wake superposition model to calculate the mixed wake of each unit in the wind farm and obtain the velocity distribution within the farm area;

[0012] S4, based on the velocity distribution in the field, calculate the equivalent wind speed of the incoming wind rotor in front of each wind turbine;

[0013] S5, evaluating the power generation of each wind turbine according to the equivalent wind speed of the incoming wind rotor in front of each wind turbine and its power curve.

[0014] Furthermore, in step S1, the wind farm inflow wind resource information includes: the inflow wind speed U at the height of the wind turbine hub; hub and turbulence intensity I hub , vertical distribution of the incoming turbulent wind velocity.

[0015] Furthermore, in step S1, the information of each unit in the field area includes: the location information of each unit (x wake ,y wake ,z wake ) and the corresponding rotor diameter D, hub height z H , thrust coefficient curve C t and thrust coefficient curve C p Model information of each unit, including

[0016] Step S2 further comprises:

[0017] S2-1, use the logarithmic law or exponential law to calculate the inflow wind speed profile u0(z) and turbulence intensity profile I0(z) considering the wind shear effect:

[0018]

[0019] Where: z represents the vertical height; z0 represents the surface roughness height; z ref Indicates the reference altitude;

[0020] S2-2, determine the wake radius r at any cross-sectional position in the wake area x and the radiation radius r with the center line of the wind wheel as the center of the circle; among them, the wake radius r at any cross-sectional position of the wake area is x The expression is:

[0021] r x =k0x+r1;

[0022] Where: k0 represents the wake expansion coefficient, k0 = 0.5 / ln(z hub / z0);z hub represents the hub height of the wind turbine; x represents the downstream distance of the wind turbine; r1 represents the characteristic wake radius behind the wind rotor, r dis the rotor radius, a is the axial flow induction factor, and the expression is:

[0023]

[0024] Where: C T represents the wind turbine thrust coefficient;

[0025] The radiation radius r with the center of the wake as the center is expressed as follows:

[0026]

[0027] Where y represents the distance from the centerline of the wind rotor in the crosswind direction; wake 、z wake represents the coordinates of the center of the wake at the x position downstream of the wind turbine;

[0028] S2-3, through the wake radius r x After determining the wake area of ​​the wind turbine using the radial radius r with the centerline of the wind turbine as the center, calculate the average wake velocity u of the wake area at the vertical height z at the x distance downstream of the wind turbine. * (x,z), the average wake velocity u * (x,z) is used as the initial predicted velocity, and its expression is:

[0029]

[0030] Where: s represents the dimensionless downstream position of the wind turbine wake area, s = x / D, D is the rotor diameter; k x,z It represents the modified wake expansion coefficient that takes into account the influence of ambient turbulence intensity and additional turbulence intensity at the x distance and height z position downstream of the wind turbine and is calculated by the following formula:

[0031] k x,z =k0·I wake,x,z / I0(z);

[0032] Where: I wake,x,z It represents the effective turbulence intensity at the downstream position of the wind turbine at a distance x and a height z, and is calculated by the following formula:

[0033]

[0034] Where: I add,x It represents the additional turbulence intensity at position x in the wake region, and its expression is:

[0035]

[0036] S2-4, define the wind speed distribution function f x,r , and perform a cosine discrete distribution on the wake velocity at the downstream x position:

[0037] f x,r =cos(π·r / r x +π);

[0038] Combined with the inflow wind speed profile u0(z), the average wake velocity u * (x,z) and wind speed distribution function f x,r Get the wake velocity u(x,y,z) of each unit in the site:

[0039] u(x,y,z)=[u0(z)-u * (x,z)]·f x,r +u * (x,z).

[0040] Furthermore, in step S3, the layout position information of each unit in the site and the hub center height difference are comprehensively considered. Based on the wake velocity distribution of a single wind turbine obtained in step S2, the mixed wake of each unit in the wind farm is calculated in combination with the wake superposition model to obtain the velocity distribution in the site:

[0041]

[0042] Where u is the wind speed at any location in the field, u0 is the inflow wind speed at the corresponding height, and u i is the wake velocity of the i-th unit at the corresponding position.

[0043] Step S4 further comprises:

[0044] Set multiple calculation points in the wind rotor area in front of each unit in the incoming flow direction, and the spacing between the calculation points in front of each unit is within 0.1D; based on the velocity distribution in the field, obtain the wind speed of each calculation point, and average the wind speed of each calculation point in front of the wind rotor to obtain the equivalent wind speed u of the incoming flow wind rotor of each wind turbine unit. eq :

[0045]

[0046] Where u j The wind speed at the jth calculation point in the wind rotor area directly in front of the incoming flow direction of each unit is calculated.

[0047] In a second aspect, the present invention discloses a wind farm power generation evaluation device based on a three-dimensional wake model, the device comprising:

[0048] Information acquisition module, used to obtain wind farm inflow resource information and information of each unit in the farm area;

[0049] The wake velocity distribution calculation module is used to calculate the wake velocity distribution of a single wind turbine based on the three-dimensional wake model;

[0050] The velocity distribution calculation module within the field is used to calculate the mixed wake of each unit in the wind farm by combining the wake superposition model to obtain the velocity distribution within the field;

[0051] The inflow rotor equivalent wind speed calculation module is used to calculate the inflow rotor equivalent wind speed in front of each wind turbine based on the speed distribution in the field;

[0052] The power generation evaluation module is used to evaluate the power generation of each wind turbine according to the equivalent wind speed of the incoming wind rotor in front of each wind turbine and its power curve.

[0053] In a third aspect, the present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps as described above are implemented.

[0054] In a fourth aspect, the present invention discloses an electronic device, which includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method steps described above are implemented.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] First, the wind farm power generation assessment method and device based on the three-dimensional wake model of the present invention uses a wake model with three-dimensional properties, which can comprehensively predict the velocity distribution of the wind turbine wake in three-dimensional space, taking into account the diffusion of the flow direction, the self-similar distribution of the crosswind direction, and the wind shear in the vertical direction; at the same time, the equivalent wind speed calculation method used can effectively take into account the wind shear effect that becomes increasingly important as the wind rotor increases, as well as the wake effect caused by the upstream unit, into the power generation assessment of the wind turbine. In summary, the present invention can provide an accurate and efficient power generation assessment method for the early construction and later operation of wind power projects, ensuring the economy and feasibility of the project.

[0057] Second, the wind farm power generation assessment method and device based on the three-dimensional wake model of the present invention require relatively conventional input parameters that are easy to obtain, reflecting the advantages of the method of the present invention in terms of versatility and facilitating the promotion and application of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of selecting the required calculation points for the equivalent wind speed of the wind rotor of the present invention;

[0059] Figure 2 This is a flow chart of a method for evaluating wind farm power generation based on a three-dimensional wake model according to the present invention;

[0060] Figure 3A schematic diagram showing the unit numbers and layout information for the Horns Rev wind farm;

[0061] Figure 4 Schematic diagram of the power and thrust coefficient of a Vestas V80 2MW wind turbine as a function of wind speed;

[0062] Figure 5 Schematic diagram comparing the power generation of eight wind turbines arranged in series at the Horns Rev wind farm in Example 1 under a wind direction of 270° in different wind zones, where (a) represents 270°±1° and (b) represents 270°±5°.

[0063] Figure 6 Schematic diagram comparing the power generation of five wind turbines arranged in series at the Horns Rev wind farm in Example 1 under a wind direction of 222° in different wind zones, where (a) represents 222°±1° and (b) represents 222°±5°.

[0064] Figure 7 Schematic diagram comparing the power generation of five wind turbines arranged in series at the Horns Rev wind farm in Example 1 under a wind direction of 312° in different wind zones, where (a) represents 312°±1° and (b) represents 312°±5°.

[0065] Figure 8 A schematic diagram showing the numbering and layout of each unit in the Lillgrund wind farm;

[0066] Figure 9 Schematic diagram of the power and thrust coefficient of the SWT93-2.3MW wind turbine as a function of wind speed;

[0067] Figure 10 This is a schematic diagram comparing the power generation of each unit in the Lillgrund wind farm under a wind direction of 120° in Example 2;

[0068] Figure 11 This is a schematic diagram comparing the power generation of each unit in the Lillgrund wind farm under a wind direction of 222° in Example 2. DETAILED DESCRIPTION

[0069] The embodiments of the present invention are described in further detail below with reference to the accompanying drawings.

[0070] See also Figure 2 The present invention discloses a method for evaluating wind farm power generation based on a three-dimensional wake model, comprising the following steps:

[0071] S1, obtain the wind farm inflow resource information and the information of each unit in the farm;

[0072] S2, the wake velocity distribution of a single wind turbine is calculated based on the three-dimensional wake model;

[0073] S3, combining the wake superposition model to calculate the mixed wake of each unit in the wind farm and obtain the velocity distribution within the farm area;

[0074] S4, based on the velocity distribution in the field, calculate the equivalent wind speed of the incoming wind rotor in front of each wind turbine;

[0075] S5, evaluating the power generation of each wind turbine according to the equivalent wind speed of the incoming wind rotor in front of each wind turbine and its power curve.

[0076] In step S1, the wind farm inflow wind resource information mainly includes: the inflow wind speed U at the height of the wind turbine hub hub and turbulence intensity I hub , the vertical distribution of the streamwise velocity of the incoming turbulent wind (obtained by multi-point wind measuring equipment, or providing the surface roughness z0 or exponential wind profile parameter α required for its logarithmic wind profile).

[0077] The information of each unit in the site includes the location information of each unit (the latitude and longitude coordinates or relative position (x wake ,y wake ,z wake )) and the corresponding unit model information (wind wheel diameter D, hub height z H , thrust coefficient curve C t and thrust coefficient curve C p wait).

[0078] In step S2, the wake velocity distribution of a single wind turbine is calculated using a three-dimensional wake model, further comprising:

[0079] The first step is to calculate the inflow wind speed profile u0(z) and turbulence intensity profile I0(z) considering the wind shear effect using the logarithmic law or exponential law according to the basic laws of turbulent motion in the boundary layer:

[0080]

[0081] Where: z represents the vertical height; z0 represents the surface roughness height; z ref Indicates the reference height. When the logarithmic wind profile is applied to the wind power field, the reference height z ref Usually the wind turbine hub height z hub , at this time the corresponding reference wind speed u(z ref )=U hub , corresponding to the reference turbulence intensity I(z ref )=I hub .

[0082] The second step is to determine the wake radius rx And the radiation radius r with the center line of the wind wheel as the center. The wake radius r at any cross-sectional position in the wake area x The calculation of is similar to the classic Jensen model, and its expression is:

[0083] r x =k0x+r1;

[0084] Where: k0 represents the wake expansion coefficient, k0 = 0.5 / ln(z hub / z0); x represents the downstream distance of the wind turbine; r1 represents the characteristic wake radius behind the wind rotor, r d is the rotor radius, a is the axial flow induction factor, and the expression is:

[0085]

[0086] Where: C T Represents the wind turbine thrust coefficient.

[0087] The radiation radius r with the center of the wake as the center is expressed as follows:

[0088]

[0089] Where y represents the distance from the centerline of the wind rotor in the crosswind direction; wake 、z wake Indicates the x-coordinate of the wake center at the downstream of the wind turbine.

[0090] The third step is to use the wake radius r x After determining the wind turbine wake area with the radial radius r centered on the rotor centerline, the 3D_k Jensen model calculates the average wake velocity u of the wake area at a vertical height of z at a distance x downstream of the wind turbine. * (x, z), this parameter is used as the initial predicted velocity, and its expression is similar to the downstream wake velocity of the top hat distribution of the classic Jensen model, which is expressed as:

[0091]

[0092] Where: s represents the dimensionless downstream position of the wind turbine wake area, s = x / D, D is the rotor diameter; k x,z It represents the modified wake expansion coefficient that takes into account the influence of ambient turbulence intensity and additional turbulence intensity at the x distance and height z position downstream of the wind turbine and is calculated by the following formula:

[0093] k x,z =k0·I wake,x,z / I0(z);

[0094] Where: Iwake,x,z It represents the effective turbulence intensity at the downstream position of the wind turbine at a distance x and a height z, and is calculated by the following formula:

[0095]

[0096] Where: I add,x It represents the additional turbulence intensity at position x in the wake region, and its expression is:

[0097]

[0098] The fourth step is to calculate the velocity u(x,y,z) of the wake zone of each unit in the field. Define the wind speed distribution function f x,r , and perform a cosine discrete distribution on the wake velocity at the downstream x position:

[0099] f x,r =cos(π·r / r x +π);

[0100] And further combined with the inflow wind speed profile u0(z), the average wake velocity u * (x,z) and wind speed distribution function f x,r Get the wind turbine wake area velocity u(x,y,z):

[0101] u(x,y,z)=[u0(z)-u * (x,z)]·f x,r +u * (x,z)

[0102] In step S3, the influence of multiple wind turbines in the site is comprehensively considered (the layout location information of each turbine and the hub center height difference). Based on the single wake of each turbine obtained in step S2, the combined wake of each turbine in the wind farm is calculated by combining the wake superposition model to obtain the velocity distribution in the site. The root sum of squares (RSS) model is used to calculate the superposition wake effect of each turbine:

[0103]

[0104] Where u is the wind speed at any location in the field, u0 is the inflow wind speed at the corresponding height, and u i is the wake velocity of each single unit at the corresponding position (calculated in step S2, the wind speed not in the wake area of ​​the unit is consistent with the inlet wind speed), and n represents the number of wind turbines in the site.

[0105] In step S4, the existing conventional wind turbine power generation calculation method only uses the hub center single point velocity or the equivalent speed of the wind rotor considering vertical wind shear, and does not effectively take into account the three-dimensional properties of the wake distribution. In response to this, step S4 adopts a new equivalent wind speed processing method, making full use of the flow field velocity distribution of the wind rotor surface in front of each unit obtained in steps S2 and S3, and truly applying the complex flow field distribution caused by the mixed wake effect formed by multiple units in the wind farm and the uncertainty of wind conditions to the power generation assessment of the wind farm. The detailed calculation method is as follows:

[0106] Set up multiple calculation points in the wind rotor area in front of each unit in the direction of the incoming flow, and the spacing between the calculation points in front of each unit is within 0.1D; based on the velocity distribution in the field, obtain the wind speed of each calculation point, and calculate the wind speed of each calculation point in front of the wind rotor (such as Figure 1 The wind speeds shown in the figure are averaged to obtain the equivalent wind speed u of the incoming wind wheel of each wind turbine eq :

[0107]

[0108] Where u i is the area in front of the wind wheel in the direction of the incoming flow of each unit calculated in the previous step (r <r d ) The wind speed at the jth calculation point. It is worth noting that to ensure the effective equivalent wind speed of each unit’s rotor, the number of calculation points on the rotor diameter should be greater than 10 (i.e., the spacing between calculation points in front of each unit should be within 0.1D).

[0109] In step S5, the power generation of each unit in the field is calculated by combining the equivalent wind speed of each unit and its corresponding power curve obtained in step S4.

[0110] Example 1

[0111] This example selects the measurement results of the Horns Rev wind farm in Denmark in a specific wind direction (BARTHELMIE RJ, FRANDSEN ST, RATHMANN O, et al. Flow and wakes in large wind farms: Final report for UpWind WP8[J]. dtu nationallaboratoriet for The accuracy of the power generation assessment method of the present invention is verified by large eddy simulation (LES) calculation results under corresponding conditions conducted by researchers (Wu YT, Porte-Agel F. Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm [J]. Renewable Energy, 2015, 75 (mar.): 945-955.). The Horns Rev wind farm is located approximately 14 kilometers off the west coast of Denmark and is the world's first large-scale offshore wind farm. Since its construction, the wind farm has been observed many times and has a wealth of wind measurement data, making it a common research subject for wind farm wake effects and power generation assessment.

[0112] The Horns Rev wind farm consists of 80 Vestas V80 2MW wind turbines with a maximum power generation capacity of 160MW. The wind farm covers an area of ​​approximately 20 square kilometers and is arranged in a diamond shape. The vertical wind turbines are arranged at an angle of about 7 degrees to the north-south direction. The wind turbines in the wind farm are arranged in an 8×10 rectangular array with a spacing of 7D between the wind turbines (the specific layout is as follows Figure 3 shown).

[0113] The hub height of the Vestas V80 2MW wind turbine is 70m (above sea level) and the rotor diameter is 80m. The functional relationship between its power and thrust coefficient and wind speed is as follows: Figure 4 As shown. The inflow wind resource information parameter in this example is wind speed U hub =8m / s, turbulence intensity I hub =0.077 and the logarithmic wind profile parameter surface roughness z0 = 0.05m. Furthermore, to test the evaluation accuracy of the method of the present invention at different turbine spacings, inflow winds with wind directions of 270°, 222°, and 312° were selected, and measurement results were obtained for upstream and downstream wind turbine spacings of 7.0D, 9.4D, and 10.4D, respectively. Furthermore, for the three wind directions mentioned above, measurement results were selected for wind direction angles in the ranges of [-1°, 1°] and [-5°, 5°] to minimize measurement errors caused by experimental uncertainty (too few data sets).

[0114] The calculation results obtained by the wind farm power generation evaluation method proposed in this invention are compared with the reference data (measurement results and LES calculation results), and the power generation of the first unit without wake interference is dimensionless, and the result is Figure 5 、 Figure 6 and Figure 7The figures show the changes in the power generation of the tandem-arranged units at different spacings when the wind direction angles are 270°, 222° and 312° respectively. It is worth noting that the calculation results for the wind direction angle range of [-1°, 1°] and [-5°, 5°] here are the average values ​​of the calculation results for the wind direction angles (270°, 222° and 312°) -1°, 0°, +1° and -5°, -4°, -3°, -2°, -1°, 0°, +1°, +2°, +3°, +4°, +5°. Compared with 222° and 312°, the distance between the upstream and downstream units under the 270° wind direction condition is smaller, the wake effect is the greatest, and the power generation loss of the downstream wind turbine is the greatest. In addition, as Figure 7 As shown in (a), the calculation results of the present invention's method in the 312°±1° wind zone are similar to those of the LES, but significantly different from the measured results. Analysis of the measured data reveals that the measured data in this zone are relatively small, indicating a degree of randomness. In the 312°±5° wind zone, as the amount of measured data increases and the uncertainty decreases, the calculation results of the present invention's method agree well with both the measured and LES results. Overall, the power generation assessment method proposed in this embodiment demonstrates high prediction accuracy, and under many wind conditions, it compares favorably with the LES method in terms of accuracy compared to the measured and LES results.

[0115] Example 2

[0116] This example uses the Lillgrund wind farm, Sweden's largest offshore wind farm, as the research object. Located approximately 10 kilometers from the coast, the Lillgrund wind farm is a typical shallow-water offshore wind farm. When it was commissioned in December 2007, it was the third largest wind farm in the world, capable of meeting the electricity needs of approximately 60,000 households. This example uses the measured power generation results of each unit under specific wind directions and LES results (NILSSON K, IVANELL S, HANSEN KS, et al. Large-eddy simulations of the Lillgrund wind farm [J]. Wind Energy, 2014, 18(3).) as reference data to verify the calculation accuracy of the invented method.

[0117] The Lillgrund wind farm has a total rated output of 110 MW and consists of 48 Siemens SWT93-2.3 MW horizontal axis wind turbines. Figure 8 The unit numbers and layout information of Lillgrund wind farm are as follows. Figure 8 As shown, the spacing between the units in the arrangement direction is 3.3D and 4.3D respectively. Compared with the 7D unit spacing of the Hon Rev wind farm, the Lillgrund wind farm is a densely distributed offshore wind farm.

[0118] The rated output power of the SWT93-2.3MW wind turbine is 2.3MW, the hub height is 65m, and the rotor diameter is 93m. The functional relationship between its power and thrust coefficient and wind speed is as follows: Figure 9 As shown. The inflow wind resource information parameter in this example is wind speed U hub =8m / s, turbulence intensity I hub =0.057 and the logarithmic wind profile parameter surface roughness z0 = 0.01m. The accuracy of the proposed evaluation method was verified by field measurements and LES calculations of the power generation of each unit under wind direction conditions of 120° and 222° (with turbine spacing of 3.3D and 4.3D along the wind direction, respectively) within a wind direction range of [-2.5°, 2.5°].

[0119] In this example, the proposed power generation evaluation method is used to calculate the power generation of each unit in the 120°±2.5° and 222°±2.5° wind zones, and the power generation is dimensionless based on the units not affected by the wake, and compared with the measured results and LES calculation results ( Figure 10 and Figure 11 Although the proposed method exhibited slightly larger errors than the measured results for some units, the overall agreement with the measured results was even better than that of the LES method. Taking into account the uncertainty in the measurement results, the proposed method also showed good agreement using the LES calculation results as a reference.

[0120] In summary, the proposed method calculates the power generation of the Horns Rev and Lillgrund wind farms. Comparison with measured results and corresponding high-precision numerical simulations using LES reveals that the proposed method can accurately calculate wind farm power generation. This method fully accounts for the significant wind shear effects caused by large turbines and the wake effects caused by upstream turbines, achieving prediction accuracy sufficient to meet engineering requirements.

[0121] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0122] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0123] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions for executing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0125] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0126] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A wind farm power generation evaluation method based on a three-dimensional wake model, characterized in that: The method comprises the following steps: S1, obtaining the wind resource information of the large wind farm and the information of each unit in the farm area; S2, the wake velocity distribution of a single wind turbine is calculated based on the three-dimensional wake model; S3, combining the wake superposition model to calculate the mixed wake of each unit in the wind farm and obtain the velocity distribution within the farm area; S4, based on the velocity distribution in the field, calculate the equivalent wind speed of the incoming wind rotor in front of each wind turbine; S5, evaluating the power generation of each wind turbine according to the equivalent wind speed of the incoming wind rotor in front of each wind turbine and its power curve; Step S2 further comprises: S21, using the logarithmic law or exponential law to calculate the inflow wind speed profile u0(z) and turbulence intensity profile I0(z) considering the wind shear effect: Where: z represents the vertical height; z0 represents the surface roughness height; z ref Indicates the reference altitude; S22, determine the wake radius r at any cross-sectional position in the wake area x and the radiation radius r with the center line of the wind wheel as the center of the circle; among them, the wake radius r at any cross-sectional position of the wake area is x The expression is: r x =k0x+r1; Where: k0 represents the wake expansion coefficient, k0 = 0.5 / ln(z hub / z0);z hub represents the hub height of the wind turbine; x represents the downstream distance of the wind turbine; r1 represents the characteristic wake radius behind the wind rotor, r d is the rotor radius, a is the axial flow induction factor, and the expression is: Where: C T represents the wind turbine thrust coefficient; The radiation radius r with the center of the wake as the center is expressed as follows: Where y represents the distance from the centerline of the wind rotor in the crosswind direction; wake 、z wake represents the coordinates of the center of the wake at the x position downstream of the wind turbine; S23, through the wake radius r x After determining the wake area of ​​the wind turbine using the radial radius r with the centerline of the wind turbine as the center, calculate the average wake velocity u of the wake area at the vertical height z at the x distance downstream of the wind turbine. * (x,z), the average wake velocity u * (x,z) is used as the initial predicted velocity, and its expression is: Where: s represents the dimensionless downstream position of the wind turbine wake area, s = x / D, D is the rotor diameter; k x,z It represents the modified wake expansion coefficient that takes into account the influence of ambient turbulence intensity and additional turbulence intensity at the x distance and height z position downstream of the wind turbine and is calculated by the following formula: k x,z =k0·I wake,x,z / I0(z); Where: I wake,x,z It represents the effective turbulence intensity at the downstream position of the wind turbine at a distance x and a height z, and is calculated by the following formula: Where: I add,x It represents the additional turbulence intensity at position x in the wake region, and its expression is: S24, define the wind speed distribution function f x,r , and perform a cosine discrete distribution on the wake velocity at the downstream x position: f x,r =cos(πr / r x +π); Combined with the inflow wind speed profile u0(z), the average wake velocity u * (x,z) and wind speed distribution function f x,r Get the wake velocity u(x,y,z) of each unit in the site: u(x,y,z)=[u0(z)-u * (x,z)]·f x,r +u * (x,z); In step S3, the layout position information of each unit in the site and the hub center height difference are comprehensively considered. Based on the wake velocity distribution of a single wind turbine obtained in step S2, the mixed wake of each unit in the wind farm is calculated in combination with the wake superposition model to obtain the velocity distribution in the site: Where u is the wind speed at any location in the field, u0(z) is the inflow wind speed at the corresponding height, and u i (x, y, z) is the wake velocity of the i-th unit at the corresponding position; Step S4 further comprises: Set multiple calculation points in the wind rotor area in front of each unit in the direction of incoming flow, and the distance between the calculation points in front of each unit should be within 0.1D; Based on the velocity distribution in the field, the wind speed of each calculation point is obtained, and the wind speed of each calculation point in front of the wind rotor is averaged to obtain the equivalent wind speed u of the incoming wind rotor of each wind turbine. eq : Where u j The wind speed at the jth calculation point in the wind rotor area directly in front of the incoming flow direction of each unit is calculated.

2. The wind farm power generation evaluation method based on a three-dimensional wake model according to claim 1, characterized in that: In step S1, the wind farm inflow wind resource information includes: the inflow wind speed U at the height of the wind turbine hub hub and turbulence intensity I hub , vertical distribution of the incoming turbulent wind velocity.

3. The wind farm power generation evaluation method based on a three-dimensional wake model according to claim 1, characterized in that: In step S1, the information of each unit in the field includes: the location information of each unit (x wake ,y wake ,z wake ) and the corresponding rotor diameter D, hub height z H , thrust coefficient curve C t and thrust coefficient curve C p Model information of each unit, including 4. A wind farm power generation assessment device based on a three-dimensional wake model and the method according to any one of claims 1 to 3, characterized in that: The device comprises: Information acquisition module, used to obtain wind farm inflow resource information and information of each unit in the farm area; The wake velocity distribution calculation module is used to calculate the wake velocity distribution of a single wind turbine based on the three-dimensional wake model; The velocity distribution calculation module within the field is used to calculate the mixed wake of each unit in the wind farm by combining the wake superposition model to obtain the velocity distribution within the field; The inflow rotor equivalent wind speed calculation module is used to calculate the inflow rotor equivalent wind speed in front of each wind turbine based on the speed distribution in the field; The power generation evaluation module is used to evaluate the power generation of each wind turbine according to the equivalent wind speed of the incoming wind rotor in front of each wind turbine and its power curve.

5. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any one of claims 1 to 3 are implemented.

6. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method steps described in any one of claims 1 to 3 are implemented.

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