Three-dimensional wind turbine wake flow calculation method considering yaw effect

Abstract

The invention discloses a three-dimensional wind turbine wake flow calculation method considering a yaw effect, which comprises the following steps of: calculating a flow direction speed distribution profile u0 (z) and a turbulence intensity distribution profile I0 (z) of inlet wind according to a basic rule of turbulence motion in a boundary layer; calculating an axial flow induction factor a of the wind turbine in a yaw state, and then determining a wake flow expansion radius rx at the downstream x position of the wind turbine; calculating the additional turbulence intensity Iadd, x at the position x of the wake flow area; the wake flow center offset yd (x) is calculated, and then the radiation radius r with the wake flow center as the circle center is determined; judging the wake flow area of the wind turbine, and calculating the average wake flow speed u * (x, z) of the wake flow area at the position where the vertical direction height is z at the downstream x distance of the wind turbine; and calculating to obtain the wind turbine airflow area speed distribution. According to the method, the wake flow center offset and wake flow speed distribution characteristics of the yaw wind turbine can be accurately predicted.

Description

Technical Field

[0001] The invention relates to the technical field of wind turbine wake calculation, and in particular to a three-dimensional wind turbine wake calculation method taking yaw effect into consideration. Background Art

[0002] At present, the research methods for wake mainly include: wind tunnel experiments, field measurement tests, computational fluid dynamics (CFD) methods and wake engineering models. Among them, wind tunnel experiments and field measurement tests can measure the physical characteristics of wind turbines and wind farm wakes, and then effectively study their wake characteristics, but their high experimental costs limit the large-scale application of this method. In addition, due to the large amount of computational evaluation work involved in the design model and local arrangement of the wind farm layout optimization process, it is not possible to fully achieve it through resource-intensive and time-consuming CFD numerical simulation under current computing conditions. The quantitative characterization method of wake effects based on engineering models has been widely used in current wind engineering projects due to its extremely high computational efficiency and acceptable accuracy. In response to this, a series of engineering prediction models have been proposed, including from one-dimensional to two-dimensional to three-dimensional, from considering single influencing factors to comprehensive multiple factors, such as Jensen, Katic, Ishihara, Frandsen, Porté-Agel, Tian, ​​Gao models, etc.

[0003] In order to effectively avoid the loss of wind farm capacity caused by the wake effect, the more traditional strategy is to guide the micro-site selection of wind farms through layout optimization methods to reduce the wake superposition between wind farm units. At present, the research on this strategy is relatively mature and has been widely used in actual engineering projects. However, this method can only be used in the planning and construction stage of wind farms and cannot be applied to completed wind farms. Therefore, in recent years, the optimization of yaw strategy has attracted attention. This method deflects the wind turbine to make the downstream units avoid the wake peak area, thereby increasing the global power generation of the wind farm, while reducing the structural load of the wind turbine and extending the service life of the wind turbine.

[0004] The CFD method can be used to accurately predict wake deflection, but the numerical calculation method is time-consuming and lengthy, which makes this method unable to meet the needs of rapid prediction to continuously avoid wake effects. It is necessary to develop a fast engineering model to predict the wake of a yawed wind turbine. The earliest model for estimating the horizontal center offset distance of the yaw wake was proposed by researchers in 2009 based on the assumptions of axial momentum conservation and top-hat wake velocity deficit. Since the model is based on a one-dimensional wake model and does not consider the influence of environmental turbulence intensity, the prediction accuracy is poor; but as the most classic yaw model, this model provides ideas for subsequent research. Later, in 2016, some researchers proposed a yaw model that takes into account both near and far wakes, but the model has the disadvantage of difficulty in obtaining relevant parameters. In 2022, some researchers simulated the evolution of the edge shape of the yaw wake based on the counter-rotating vortex pair (CVP), and established a new analytical model in combination with the Gaussian wake model. This model can effectively capture the wake deformation at different downstream positions and reveal the flow mechanism of the yaw wake. However, the form is relatively complex, which limits its large-scale promotion in actual engineering projects. In addition, some researchers have successively developed yaw wake models with their own characteristics and high accuracy, but there are also deficiencies such as not considering momentum conservation and parameters derived from experimental fitting. Considering that wake deflection has great potential to alleviate power reduction and load instability of downstream units of wind farms, it is urgent to establish an analytical model for the wake distribution under yaw conditions of wind turbines, so as to quickly realize the engineering needs of optimizing the yaw angles of each unit in the wind farm. Summary of the invention

[0005] The purpose of the present invention is to introduce the theory of conservation of mass and momentum under the yaw state on the basis of the existing three-dimensional wake model, and consider the change of the wake area morphology under the yaw condition at the same time, to establish a new wake calculation method for a yaw wind turbine, comprehensively consider the influence of various factors such as inflow wind conditions, local geographic information, aerodynamic characteristics of the unit and operating conditions on the wake, and take into account the influence of the yaw effect on the wake center offset and the wake wind speed distribution. Through AD / LES numerical simulation and UAV field measurement examples, the accuracy and versatility of the model are verified more comprehensively, thereby providing a theoretical basis and technical support for the research on yaw optimization strategy of wind farms.

[0006] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0007] A three-dimensional wind turbine wake calculation method considering yaw effect, the three-dimensional wind turbine wake method comprising the following steps:

[0008] S1, according to the basic laws of turbulent motion in the boundary layer, calculate the streamwise velocity distribution profile u0(z) and the turbulence intensity distribution profile I0(z) of the inflow wind;

[0009] S2, calculate the axial flow induction factor a of the wind turbine under yaw state, and then determine the wake expansion radius r at the downstream x position of the wind turbine x ;

[0010] S3, calculate the additional turbulence intensity I at the wake region x position add,x ;

[0011] S4, calculate the wake center offset y d (x), and then determine the radiation radius r with the center of the wake as the center;

[0012] S5: Determine the wake area of ​​the wind turbine and calculate 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);

[0013] S6, calculate the velocity distribution in the wind turbine wake area.

[0014] In step S1, the inflow wind speed profile u0(z) and turbulence intensity profile I0(z) considering the wind shear effect are approximately characterized by the logarithmic law:

[0015]

[0016] In the formula, z represents the vertical height; z0 represents the surface roughness height; z ref Indicates the reference height; u0(z ref ) and I0(z ref ) are the inflow wind speed and turbulence intensity at the reference height, respectively.

[0017] Step S2 further comprises:

[0018] Inflow wind direction wind speed u ∞ Define the thrust coefficient C′ of the wind turbine in the yaw state T , the expression is:

[0019]

[0020] In the formula, T represents the thrust acting vertically on the wind rotor, ρ represents the air density, A represents the wind rotor area, and C T represents the thrust coefficient of the wind turbine in the zero yaw state, and γ represents the yaw angle of the wind turbine;

[0021] Calculate the axial flow induction factor a of the wind turbine in the yaw state:

[0022]

[0023] Determine the wake expansion radius r at the position x downstream of the wind turbine x ;

[0024] r x =k0x+r1

[0025] In the formula, k0 represents the wake expansion coefficient, r1 represents the characteristic wake radius behind the wind rotor,

[0026] In step S3, the additional turbulence intensity I of the wind turbine in the yaw state is calculated using the following formula: add,x :

[0027]

[0028] Where s represents the dimensionless downstream position of the wind turbine wake area, s = x / D, D represents the rotor diameter, γ represents the yaw angle of the wind turbine, and C T It represents the thrust coefficient of the wind turbine in the zero yaw state.

[0029] Step S4 further comprises:

[0030] Yaw-corrected wake expansion coefficient k based on hub height x,hub * , calculate the wake center offset y d (x):

[0031]

[0032] In the formula, r1 represents the characteristic wake radius behind the wind rotor, γ represents the yaw angle of the wind turbine, and C T represents the thrust coefficient of the wind turbine in the zero yaw state, k x,hub * =k0[I wake,x,hub / I0(z)] 0.7 , k0 represents the wake expansion coefficient, I wake,x,hub represents the effective turbulence intensity at the downstream x distance of the wind turbine and the hub height;

[0033] Define the yaw angle γ as positive clockwise, considering the wake center offset y d (x), the coordinate of the center of the wake at the x position downstream of the wind turbine is (y wake +y d (x),z wake ), the radiation radius r with the wake center as the center is:

[0034]

[0035] In step S5, the average wake velocity u of the wake area at a vertical height of z at a distance x downstream of the wind turbine is calculated using the following formula: * (x,z):

[0036]

[0037] In the formula, k x,z * =k0[I wake,x,z / I0(z)] 0.7 , I wake,x,z represents the effective turbulence intensity at the x distance and height z downstream of the wind turbine; s represents the dimensionless downstream position of the wind turbine wake area.

[0038] In step S6, the inflow wind speed profile u0(z), the average wake velocity u * (x,z) and wind speed distribution function f x,r , the velocity distribution u(x,y,z) in the wind turbine wake area is calculated:

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

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] First, the three-dimensional wind turbine wake calculation method considering the yaw effect of the present invention comprehensively considers the influence of multiple factors on the wake, such as the inflow wind conditions, local geographical information of the wind farm, aerodynamic characteristics of the unit and operating conditions, while taking into account the influence of the yaw effect on the wake center offset and the wake wind speed distribution. It can accurately predict the wake center offset and wake velocity distribution characteristics of the yawed wind turbine. It is comprehensively verified by two examples, AD / LES numerical simulation and UAV field measurement, which demonstrates the excellence and robustness of the prediction of the vertical and horizontal wake velocity distribution in the wake area of ​​the yaw wake model.

[0042] Second, the three-dimensional wind turbine wake calculation method considering the yaw effect of the present invention can accurately predict the wake velocity distribution in the crosswind and vertical directions of the near wake and far wake areas under different models, different inflow wind conditions, and different yaw angles, and the prediction results are better than the control model, which comprehensively verifies the accuracy and versatility of the wake calculation method of the present invention.

[0043] Third, the three-dimensional wind turbine wake calculation method considering the yaw effect of the present invention well reflects the law that the yaw wake center offset and the wake loss decrease with the increase of yaw angle from a qualitative perspective, and at the same time shows good accuracy, efficiency and versatility from a quantitative perspective. The research results can provide a theoretical basis and technical support for the study of wind farm yaw optimization strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1A schematic diagram of a three-dimensional wind turbine wake model considering the yaw effect of the present invention;

[0045] Figure 2 This is a schematic diagram of the comparison results of the wake center offset under different yaw angles of the Clipper Liberty 2.5MW wind turbine;

[0046] Figure 3 Schematic diagram of the lateral wind speed distribution at the hub height at the downstream 3D, 5D, 7D and 9D positions of the Clipper Liberty 2.5MW wind turbine, where (a) x = 3D, (b) x = 5D, (c) x = 7D, (d) x = 9D;

[0047] Figure 4 Schematic diagram of the lateral wind speed distribution at the hub height at the downstream 2D, 4D and 6D positions of the GW131-2.2 wind turbine; where (a) x = 2D, (b) x = 4D, (c) x = 6D;

[0048] Figure 5 Schematic diagram of the vertical wind speed distribution at the center of the wind rotor at positions 2D, 4D and 6D downstream of the GW131-2.2 wind turbine. DETAILED DESCRIPTION

[0049] The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0050] The present invention considers the inflow wind conditions, local geographic information of the wind farm, aerodynamic characteristics of the unit and operating conditions, and proposes a new three-dimensional yaw wake model with multi-parameter coupling, such as Figure 1 The core construction strategy is to introduce the conservation theorem of mass and momentum under yaw state on the basis of the existing three-dimensional modified Jensen wake model (Song Yilei, Tian Linlin, Zhao Ning. Proposal and verification of three-dimensional wake model of wind turbine [J]. Acta Energiae Solaris Sinica, 2021, 42(2): 129-135.), and take into account the influence of yaw effect on the wake center offset and wake wind speed distribution.

[0051] The 2D_k Jensen model is derived on the basis of the classic Jensen model: the one-dimensional top-hat distribution is improved to a cosine velocity distribution that is more consistent with the actual wake distribution; a dynamic variable wake expansion coefficient that takes into account both the inflow atmospheric turbulence and the additional turbulence in the wake area is proposed to replace the constant wake expansion coefficient assumed by the Jensen model. Furthermore, with the development of large-scale wind turbines, the author's team further proposed a more accurate and versatile three-dimensional wake model (3D_k Jensen model) considering the wind shear effect. Its core formula and calculation steps are described as follows:

[0052] 1) Calculate the inflow wind conditions of the wind turbine.

[0053] According to the basic laws of turbulent motion in the boundary layer, the inflow wind speed profile u0(z) and turbulence intensity profile I0(z) considering the wind shear effect can be approximately characterized by the logarithmic law:

[0054]

[0055] Where: z——vertical height; z0——surface roughness height; z ref ——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 and reference turbulence intensity both use the inflow value at the hub height: u(z ref )=U hub 、I(z ref )=I hub .

[0056] 2) Determine the wake influence range.

[0057] 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:

[0058] r x =k0x+r1(2)

[0059] Where: k0——wake expansion coefficient, k0=0.5 / ln(z hub / z0); x——downstream distance of wind turbine; r1——characteristic wake radius behind the wind rotor, Among them, r d is the rotor radius, a is the axial flow induction factor, and the expression is:

[0060]

[0061] Where: C T ——Thrust coefficient of wind turbine.

[0062] 3) Prediction step for wake velocity calculation.

[0063] Determine the radiation radius r with the center of the wake as the center. This model is a zero yaw model, so r also represents the radiation radius with the center line of the wind rotor as the center, and its expression is as follows:

[0064]

[0065] Where, y is the distance from the centerline of the wind rotor in the crosswind direction; wake 、z wake ——The coordinate of the wake center at the x position downstream of the wind turbine.

[0066] Through the wake radius r x After determining the wake area of ​​the wind turbine by the radial radius r with the centerline of the wind turbine as the center, the 3D_k Jensen model calculates the average wake velocity u of the wake area at the vertical height z at the downstream of the wind turbine at a distance x * (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, and its expression is

[0067]

[0068] Where: k x,z ——characterizes the modified wake expansion coefficient at the downstream distance x and height z of the wind turbine, calculated by formula (6); s——dimensionless downstream position of the wind turbine wake area, s=x / D, where D is the rotor diameter.

[0069] k x,z =k0·I wake,x,z / I0(z) (6) Where: I wake,x,z ——The effective turbulence intensity at the downstream of the wind turbine at a distance x and height z is calculated by the following formula:

[0070]

[0071] Where: I add,x ——Additional turbulence intensity at position x in the wake region, expressed as:

[0072]

[0073] 4) Correction step for wake velocity calculation.

[0074] The model defines the wind speed distribution function f x,r , the wake velocity at the downstream x position is discretely distributed in a cosine manner:

[0075] f x,r =cos(π·r / r x +π) (9)

[0076] Finally, the inflow wind speed profile u0(z) and the average wake velocity u * (x,z) and wind speed distribution function f x,r The velocity distribution u(x,y,z) in the wind turbine wake area is obtained:

[0077] u(x,y,z)=[u0(z)-u * (x,z)]·f x,r +u *(x,z)(10)

[0078] Numerical simulation and measurement results show that the center of the wind turbine wake will shift in the direction opposite to the yaw angle when in a yaw state, and this shift directly affects the power generation of the downstream unit. Therefore, it is extremely important to accurately predict the shift of the wake center. Based on the Jiménez yaw wake center shift model, the present invention combines the 3D_k Jensen model to derive the wake center shift.

[0079] like Figure 1 As shown in the figure, the expression of the thrust T acting vertically on the wind wheel is:

[0080]

[0081] Where: T is the thrust acting vertically on the wind rotor; ρ is the air density; γ is the yaw angle of the wind turbine.

[0082] The thrust components acting in the flow direction and transverse direction on the wind rotor are:

[0083]

[0084] It should be noted that the thrust coefficient C in the yaw state T * The expression is:

[0085]

[0086] Where: U d ——The inflow wind speed perpendicular to the plane of the wind rotor. Due to the different yaw angles, the thrust T and cos 2 γ is proportional to T∝cos 2 γ, thrust coefficient C in yaw state T * It does not change with the yaw angle, and its value is the same as the thrust coefficient C of the wind turbine in the zero yaw state. T Therefore, C is used uniformly in the following text. T Represents the thrust coefficient of the wind turbine.

[0087] exist Figure 1 In the yaw wind turbine control body shown in FIG, the momentum conservation equation and mass conservation equation are applied to obtain:

[0088] m3(U ∞ -ΔU)cosθ-m1U ∞ -m2U ∞ =-T x (14)

[0089] m3(U ∞ -ΔU)(-sinθ)=-Tr (15)

[0090] Where: θ is the deflection angle, which represents the inclination angle of the velocity in the wake relative to the inflow velocity; m1 is the mass flow rate entering the control body from the inlet; m2 is the mass flow rate entering the control body from the side; m3 is the mass flow rate flowing out of the control body from the outlet. Since the deflection angle θ is very small at the downstream position, cosθ and sinθ can be approximated to 1 and θ respectively. In addition, since the downstream position U ∞ >>ΔU, it can be approximately considered that U ∞ -ΔU≈U ∞ .

[0091] According to the conservation of mass and momentum, we can get from equations (14) and (15):

[0092]

[0093] Based on this, substitute equation (16) and integrate the deflection angle θ to obtain the wake center offset y at different downstream positions x: d (x) is

[0094]

[0095] Furthermore, considering the effect of turbulence intensity on the wake center offset y d (x), similar to the 3D_k Jensen model, a modified wake expansion coefficient k suitable for yaw wake is proposed x,z * (The subscripts x and z represent the downstream distance x of the wind turbine and the height z of the wind turbine from the ground):

[0096] k x,z * =k0·[I wake,x,z / I0(z)] 0.7 (18)

[0097] Since the wake yaw is caused by the rotor being tilted to the wind direction, the wake expansion coefficient k is corrected by the yaw at the hub height. x,hub * Substituting k0 in equation (17), we get

[0098]

[0099] In the formula, k x,hub * The subscript hub represents the hub height.

[0100] In the wake model calculation process, the wind condition in the inflow direction is used as the input parameter. Therefore, the wind speed U in the inflow direction is firstly ∞Define the thrust coefficient C of the wind turbine in the yaw state T ′, the expression is

[0101]

[0102] For the calculation of yaw wake, it is necessary to calculate the axial flow induction factor a and the additional turbulence intensity I add,x C in the expression T Replace with C T ′, then equation (3) and equation (8) are modified to

[0103]

[0104] In addition, the yaw angle γ is defined as clockwise positive, considering the yaw wake center offset y d (x) (Equation (18)), the coordinates of the wake center at the x position downstream of the wind turbine are (y wake +y d (x),z wake ), the radiation radius r with the wake center as the center is corrected to:

[0105]

[0106] For the calculation of the wake wind speed under the yaw state, the yaw wake characteristics are taken into account and verified by multiple numerical simulations and measurement examples. The yaw-corrected wake expansion coefficient k x,z * (Equation (18)) replaces the correction coefficient k in the zero yaw state x,z (Formula (2)), we get

[0107]

[0108] Based on this, the calculation steps of the 3D_k-yaw Jensen model can be summarized as follows: 1) Calculate the streamwise velocity distribution profile u0(z) and the turbulence intensity distribution profile I0(z) of the inflow wind according to formula (1); 2) Calculate the axial flow induction factor a of the wind turbine under the yaw state by formula (21), and then determine the wake expansion radius r at the downstream x position of the wind turbine by formula (2) x ; 3) Calculate the additional turbulence intensity I at the wake region x using formula (22) add,x ; 4) Calculate the wake center offset y according to formula (19) d (x), and then determine the radiation radius r with the center of the wake as the center through formula (23); 5) Combine formula (2) and formula (23) to determine the wind turbine wake area (r <r x ), and then calculate the average wake velocity u of the wake area at the vertical height z at the downstream of the wind turbine at a distance x from the wind turbine according to formula (24): *(x, z); 6) Finally, the obtained parameters are substituted into formula (10) to obtain the velocity distribution in the wind turbine wake area.

[0109] In order to verify the accuracy and versatility of the calculation method of the present invention, the present invention selects high-precision AD / LES numerical simulation and unmanned aerial vehicle (UAV) field measurement examples from previous studies. The corresponding wind turbine characteristic parameters and operating conditions of each example are shown in Table 1.

[0110] Table 1 Verification examples and corresponding wind turbine characteristic parameters and operating conditions

[0111]

[0112] Example 1

[0113] In order to study the similarity of the wake of wind turbines under yaw, researchers in related fields used the actuated disk combined with large eddy simulation (AD-LES) method to conduct numerical simulation research on the evolution of the wake of the Clipper Liberty 2.5MW wind turbine at the EOLOS experimental wind farm of the University of Minnesota at different yaw angles (Li Z, YANG X. Large-eddy simulation on the similarity between wakes of wind turbines with different yawangles [J]. Journal of fluid mechanics, 2021, 921.). The characteristic parameters of the wind turbine and the inflow wind resource information are detailed in Table 1. This example uses this high-precision numerical simulation result to demonstrate the prediction accuracy and versatility of the method of the present invention.

[0114] Figure 2 and Figure 3 They are respectively the wake center offset and the wake velocity distribution in the radial direction of the hub height plane predicted by the calculation method of the present invention, and are compared with the numerical simulation results using the AD-LES method in the reference. When the yaw angles are -10°, -20° and -30° ("-" represents counterclockwise yaw), the wake velocity distribution at the 3D, 5D, 7D and 9D positions downstream of the wind turbine is verified and analyzed. The calculation results show that the shape and size of the velocity pattern predicted by the calculation method of the present invention are in good agreement with the AD-LES numerical simulation results, demonstrating the excellent prediction performance of the model of the present invention.

[0115] In terms of the wake center offset distance, Figure 2As shown in the figure, at the same downstream position, as the yaw angle increases, the wake center offset increases accordingly, and the wake evolution characteristics are consistent with the simulation results. The wake center position predicted by the model is in good agreement with the numerical simulation results, but due to the certain error in the wake center offset model derived based on simplified assumptions, the wake center offset error is slightly larger when the yaw angle is larger (γ = -30°).

[0116] In terms of wake velocity distribution, Figure 3 It can be seen that the 3D_k-yaw Jensen model shows good prediction performance for the wake velocity distribution at the hub height under different yaw conditions. Especially at the x=5D position, the model prediction results are almost completely consistent with the AD-LES calculation results. However, the error of the near-wake (3D) position is relatively large. At the x=7D and x=9D positions, the prediction results of the model proposed in the present invention also have certain errors compared with the reference data. The main reason lies in the limitations of the analytical model, but the overall accuracy is acceptable. In addition, the wake velocity distribution shows that the wake velocity loss at the same downstream position will decrease with the increase of the yaw angle of the wind turbine. This is caused by the increase in the yaw angle, which leads to a decrease in the efficiency of the wind turbine in absorbing wind energy. The AD-LES numerical simulation results also have this phenomenon. At the same time, Figure 3 It can also be clearly seen that at the same yaw angle, the offset of the maximum wake loss point (i.e., the wake center) will increase with the development of the wake; the wake loss will also recover rapidly with the development of the wake. This evolution law is the same as that of the conventional zero yaw wake.

[0117] Example 2

[0118] Recently, researchers used a UAV wind speed measurement system to study the yaw wake characteristics of a GW131-2.2 wind turbine in a wind farm in Nanle County, Puyang City, Henan Province (BAO T, LI Z, LI Y, et al. Wake measurement of wind turbine under yawed conditions using UAV anemometry system [J]. Journal of wind engineering & industrial aerodynamics, 2024, 249.). This example uses the wake velocity distribution at the downstream 2D, 4D and 6D positions of the wind turbine measured by UAV, the wake velocity distribution at the hub height, and the wake velocity distribution along the height direction of the inflow wind passing through the center of the wind rotor as the research objects to verify the calculation accuracy of the calculation method of the present invention. The characteristic parameters and operating conditions of the wind turbine are shown in Table 1.

[0119] Figure 4The prediction results of the calculation method of the present invention at different downstream positions in the horizontal plane of the hub height are compared with the wake velocity distribution of the above-mentioned UAV measurement data in the hub height plane, and the prediction results of the Bastankhah yaw wake model and the Qian yaw wake model are used as a control. Overall, the wake center offset predicted by the calculation method of the present invention can more accurately describe the wake offset phenomenon at various downstream positions. The further downstream, the more obvious the wake center offset is, and its offset y d / D continuously shifts from 0.072 at the 2D position downstream of the unit to 0.162 at the 6D position. Compared with the yaw wake model of Bastankhah and Qian, the prediction results of the newly proposed calculation method are in good agreement with the measured data, showing excellent prediction performance.

[0120] Figure 5 is the wake velocity distribution of the inflow wind along the height direction through the center of the wind wheel, and Figure 4 Similarly, the prediction results of the calculation method of the present invention and the other two yaw model are compared with the UAV measurement data. It can be seen from the figure that the maximum wake loss height is the hub height. In general, similar to the hub height wake velocity distribution, Bastankhah and Qian's model overestimates the wake recovery speed, and the calculation method proposed in the present invention has a higher prediction accuracy.

[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 present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present 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 embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. 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 capable of directing a computer or other programmable data processing device to operate 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 A 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 instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0125] Although the preferred embodiments of the present application have been described, those skilled in the art may make other 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 falling within the scope of the present application.

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

Claims

1. A three-dimensional wind turbine wake calculation method considering yaw effect, characterized in that: The three-dimensional wind turbine wake method The following steps are involved: S1, according to the basic laws of turbulent motion in the boundary layer, calculate the streamwise velocity distribution profile u0(z) and the turbulence intensity distribution profile I0(z) of the inflow wind; S2, calculate the axial flow induction factor a of the wind turbine under yaw state, and then determine the wake expansion radius r at the downstream x position of the wind turbine x ; S3, calculate the additional turbulence intensity I at the wake region x position add,x ; S4, calculate the wake center offset y d (x), and then determine the radiation radius r with the center of the wake as the center; S5: Determine the wake area of ​​the wind turbine and calculate 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); S6, calculate the velocity distribution in the wind turbine wake area.

2. The three-dimensional wind turbine wake calculation method considering the yaw effect according to claim 1 is characterized in that: In step S1, the inflow wind speed profile u0(z) and turbulence intensity profile I0(z) considering the wind shear effect are approximately characterized by the logarithmic law: In the formula, z represents the vertical height; z0 represents the surface roughness height; z ref Indicates the reference altitude; u0(z ref ) and I0(z ref ) are the inflow wind speed and turbulence intensity at the reference height, respectively.

3. The three-dimensional wind turbine wake calculation method considering the yaw effect according to claim 1 is characterized in that: Step S2 further comprises: Inflow wind direction wind speed U ∞ Define the thrust coefficient C′ of the wind turbine in the yaw state T , the expression is: In the formula, T represents the thrust acting vertically on the wind rotor, ρ represents the air density, A represents the wind rotor area, and C T represents the thrust coefficient of the wind turbine in the zero yaw state, and γ represents the yaw angle of the wind turbine; Calculate the axial flow induction factor a of the wind turbine in the yaw state: Determine the wake expansion radius r at the position x downstream of the wind turbine x ; r x =k0x+r1 In the formula, k0 represents the wake expansion coefficient, r1 represents the characteristic wake radius behind the wind rotor, 4. The three-dimensional wind turbine wake calculation method considering the yaw effect according to claim 1 is characterized in that: In step S3, the additional turbulence intensity I of the wind turbine in the yaw state is calculated using the following formula: add,x : Where s represents the dimensionless downstream position of the wind turbine wake area, s = x / D, D represents the rotor diameter, γ represents the yaw angle of the wind turbine, and C T It represents the thrust coefficient of the wind turbine in the zero yaw state.

5. The three-dimensional wind turbine wake calculation method considering yaw effect according to claim 1, characterized in that: Step S4 further comprises: Yaw-corrected wake expansion coefficient k based on hub height x,hub * , calculate the wake center offset y d (x): In the formula, r1 represents the characteristic wake radius behind the wind rotor, γ represents the yaw angle of the wind turbine, and C T represents the thrust coefficient of the wind turbine in the zero yaw state, k x,hub * =k0[I wake,x,hub / I0(z)] 0.7 , k0 represents the wake expansion coefficient, I wake,x,hub represents the effective turbulence intensity at the downstream x distance of the wind turbine and the hub height; Define the yaw angle γ as positive clockwise, considering the wake center offset y d (x), the coordinate of the center of the wake at the x position downstream of the wind turbine is (y wake +y d (x),z wake ), the radiation radius r with the wake center as the center is:

6. The three-dimensional wind turbine wake calculation method considering yaw effect according to claim 1, characterized in that: In step S5, the average wake velocity u of the wake area at a vertical height of z at a distance x downstream of the wind turbine is calculated using the following formula: * (x,z): In the formula, k x,z * =k0[I wake,x,z / I0(z)] 0.7 , I wake,x,z represents the effective turbulence intensity at the x distance and height z downstream of the wind turbine; s represents the dimensionless downstream position of the wind turbine wake area.

7. The three-dimensional wind turbine wake calculation method considering yaw effect according to claim 1, characterized in that: In step S6, the inflow wind speed profile u0(z), the average wake velocity u * (x,z) and wind speed distribution function f x,r , the velocity distribution u(x,y,z) in the wind turbine wake area is calculated: u(x,y,z)=[u0(z)-u * (x,z)]·f x,r +u * (x,z)。

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