Aerodynamic Optimization Method, Equipment and Medium for Wind Turbine Blades

Abstract

This application relates to the technical field of wind power generation, and specifically provides a method, device, and medium for aerodynamic optimization of wind turbine blades, aiming to solve the technical problem of poor power generation efficiency corresponding to the existing blade aerodynamic optimization method. For this purpose, the method for aerodynamic optimization of wind turbine blades in this application includes: obtaining blade attribute information and inflow wind speed; determining the chord length distribution and twist angle distribution based on the blade attribute information and inflow wind speed; determining the annual power generation of the wind turbine based on the chord length distribution and twist angle distribution; optimizing the chord length distribution and twist angle distribution based on the annual power generation to obtain the optimal solutions of the chord length distribution and twist angle distribution. In this way, the chord length distribution and twist angle distribution of the blade can be determined more accurately, which helps to maximize the aerodynamic performance of the blade at different wind speeds, thereby improving the overall power generation efficiency of the wind turbine.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and specifically provides a method, device and medium for aerodynamic optimization of wind turbine blades. Background Art

[0002] As a form of clean energy generation, wind power generation has been widely used globally. In recent years, the wind power industry in China has developed rapidly, and large capacity and high efficiency have gradually become the design requirements for current wind turbines. As one of the most important components of a wind turbine, the blade undertakes the important link of converting wind energy into mechanical energy, and its aerodynamic performance plays a decisive role in the efficiency of the wind turbine.

[0003] The existing aerodynamic shape of the blade is only designed according to different airfoils, without considering the differences between smooth airfoils and rough airfoils, resulting in poor power generation efficiency of the wind turbine. Summary of the Invention

[0004] To overcome the above defects, this application is proposed to provide a solution to solve or at least partially solve the technical problem of poor power generation efficiency corresponding to the existing blade aerodynamic optimization method. This application provides a method, device and medium for aerodynamic optimization of wind turbine blades.

[0005] In a first aspect, this application provides a method for aerodynamic optimization of wind turbine blades, the method comprising:

[0006] Obtain blade attribute information and incoming flow wind speed;

[0007] Determine chord length distribution and twist angle distribution based on the blade attribute information and the incoming flow wind speed;

[0008] Determine the annual power generation of the wind turbine based on the chord length distribution and the twist angle distribution;

[0009] Optimize the chord length distribution and the twist angle distribution based on the annual power generation to obtain the optimal solutions of the chord length distribution and the twist angle distribution.

[0010] In an embodiment of this application, determining the chord length distribution and the twist angle distribution based on the blade attribute information and the incoming flow wind speed includes:

[0011] Determine blade aerodynamic information based on the blade attribute information;

[0012] Respectively determine a first mapping relationship between chord length and radial distance, and a second mapping relationship between twist angle and radial distance based on the blade attribute information, the blade aerodynamic information and the incoming flow wind speed, where the radial distance is the distance between each section of the blade and the blade root;

[0013] Parameterize the first mapping relationship and the second mapping relationship to obtain the chord length distribution and the twist angle distribution.

[0014] In an embodiment of the present application, determining the blade aerodynamic information based on the blade attribute information includes:

[0015] Respectively determine the first aerodynamic parameters corresponding to the smooth airfoil and the second aerodynamic parameters corresponding to the rough airfoil based on the blade attribute information;

[0016] Obtain the first weighting factor;

[0017] Perform weighted summation on the first aerodynamic parameters and the second aerodynamic parameters based on the first weighting factor to obtain the blade aerodynamic information.

[0018] In an embodiment of the present application, parameterizing the first mapping relationship and the second mapping relationship includes:

[0019] Obtain the first type of control points based on the first mapping relationship and the second mapping relationship;

[0020] Establish a B-spline curve based on the first type of control points;

[0021] Interpolate the B-spline curve to obtain the second type of control points.

[0022] In an embodiment of the present application, determining the annual power generation of the wind turbine based on the chord length distribution and the twist angle distribution includes:

[0023] Determine the third mapping relationship between the wind energy utilization coefficient and the tip speed ratio based on the chord length distribution and the twist angle distribution;

[0024] Determine the predicted power of the wind turbine based on the third mapping relationship;

[0025] Determine the annual power generation of the wind turbine based on the predicted power.

[0026] In an embodiment of the present application, optimizing the chord length distribution and the twist angle distribution based on the annual power generation includes:

[0027] Determine the optimization objective based on the annual power generation;

[0028] Establish an initial population, where the initial population includes multiple individuals;

[0029] Starting from the initial population, use the genetic algorithm to perform target optimization iteration to maximize the optimization objective;

[0030] When the optimization objective converges, obtain the optimal solutions of the chord length distribution and the twist angle distribution.

[0031] In one embodiment of the present application, the method further includes:

[0032] Obtaining a constraint condition based on the blade attribute information;

[0033] Performing a first correction on the optimal solution based on the constraint condition.

[0034] In one embodiment of the present application, the optimal solution includes a first optimization result corresponding to a smooth airfoil and a second optimization result corresponding to a rough airfoil; the method further includes:

[0035] Obtaining a second weighting factor;

[0036] Performing a weighted sum on the first optimization result and the second optimization result based on the second weighting factor to perform a second correction on the optimal solution.

[0037] In a second aspect, there is provided an electronic device, including:

[0038] At least one processor;

[0039] And a memory communicatively connected to the at least one processor;

[0040] Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the foregoing aerodynamic optimization method for a wind turbine blade is implemented.

[0041] In a third aspect, there is provided a computer-readable storage medium, which stores multiple program codes, and the program codes are adapted to be loaded and run by a processor to execute the foregoing aerodynamic optimization method for a wind turbine blade according to any one of the foregoing.

[0042] One or more of the above technical solutions of the present application have at least one or more of the following

[0043] Beneficial effects:

[0044] The aerodynamic optimization method for a wind turbine blade in the present application includes: obtaining blade attribute information and inflow wind speed; determining chord length distribution and twist angle distribution based on the blade attribute information and the inflow wind speed; determining the annual power generation of the wind turbine based on the chord length distribution and the twist angle distribution; optimizing the chord length distribution and the twist angle distribution based on the annual power generation to obtain an optimal solution for the chord length distribution and the twist angle distribution. In this way, the chord length distribution and the twist angle distribution of the blade can be determined more accurately, which helps to maximize the aerodynamic performance of the blade at different wind speeds, thereby improving the overall power generation efficiency of the wind turbine. At the same time, calculating the annual power generation of the wind turbine based on the determined chord length distribution and twist angle distribution and performing iterative optimization with this as the optimization target is beneficial to improving the power generation efficiency of the wind turbine. Description of the Drawings

[0045] With reference to the accompanying drawings, the disclosure of the present application will become more understandable. It is easily understood by those skilled in the art that these drawings are only for illustrative purposes and are not intended to limit the protection scope of the present application. In addition, similar numbers in the drawings are used to represent similar components, where:

[0046] Figure 1 is a schematic diagram of the main process of the blade aerodynamic optimization method of a wind turbine in an embodiment of the present application;

[0047] Figure 2 is a schematic diagram of the complete process of the blade aerodynamic optimization method of a wind turbine in an embodiment of the present application;

[0048] Figure 3 is a schematic diagram of the complete process of the blade aerodynamic optimization method of a wind turbine in another embodiment of the present application;

[0049] Figure 4 is a schematic diagram of the main structural block diagram of the blade aerodynamic optimization device of a wind turbine in an embodiment of the present application;

[0050] Figure 5 is a schematic diagram of the structure of an electronic device in an embodiment of the present application. Detailed Embodiments

[0051] The following describes some embodiments of the present application with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principle of the present application and are not intended to limit the protection scope of the present application.

[0052] In the description of the present application, "module" and "processor" may include hardware, software, or a combination of both. A module may include a hardware circuit, various suitable sensors, communication ports, memory, and may also include a software part, such as program code, and may also be a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, in hardware, or in a combination of both. The non-transitory computer-readable storage medium includes any suitable medium for storing program code, such as magnetic disks, hard disks, optical discs, flash memories, read-only memories, random access memories, and the like. The term "A and / or B" represents all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one A or B" or "at least one of A and B" has a meaning similar to "A and / or B" and may include only A, only B, or A and B. The singular terms "a" and "this" may also include the plural form.

[0053] Currently, the traditional aerodynamic shape of the blade is only obtained according to different airfoil designs, without considering the differences between smooth airfoils and rough airfoils, resulting in poor power generation efficiency of wind turbines.

[0054] Therefore, this application proposes an aerodynamic optimization method, device, and medium for wind turbine blades.

[0055] Refer to the appendix Figure 1 , Figure 1 which is a schematic diagram of the main steps of the aerodynamic optimization method for wind turbine blades according to an embodiment of this application.

[0056] As Figure 1 shown, the aerodynamic optimization method for wind turbine blades in the embodiment of this application mainly includes the following steps S10 - step S40.

[0057] Step S10: Obtain blade attribute information and the inflow wind speed.

[0058] Step S20: Determine the chord length distribution and the twist angle distribution based on the blade attribute information and the inflow wind speed.

[0059] Step S30: Determine the annual power generation of the wind turbine based on the chord length distribution and the twist angle distribution.

[0060] Step S40: Optimize the chord length distribution and the twist angle distribution based on the annual power generation to obtain the optimal solutions of the chord length distribution and the twist angle distribution.

[0061] Based on the above steps S10 - step S40, first obtain the blade attribute information and the inflow wind speed; determine the chord length distribution and the twist angle distribution based on the blade attribute information and the inflow wind speed; determine the annual power generation of the wind turbine based on the chord length distribution and the twist angle distribution; optimize the chord length distribution and the twist angle distribution based on the annual power generation to obtain the optimal solutions of the chord length distribution and the twist angle distribution. In this way, the chord length distribution and the twist angle distribution of the blade can be determined more accurately, which helps to maximize the aerodynamic performance of the blade at different wind speeds, thereby improving the overall power generation efficiency of the wind turbine. At the same time, calculating the annual power generation of the wind turbine based on the determined chord length distribution and twist angle distribution and performing iterative optimization with this as the optimization goal is beneficial to improving the power generation efficiency of the wind turbine.

[0062] The above steps S10 to S40 will be further described below.

[0063] Regarding step S10, the blade attribute information may include the main design parameters of the blade, the blade roughness, the blade airfoil, etc. Specifically, the blade attribute information can be obtained according to the external conditions related to the operation of the wind turbine, such as the main design target (e.g., the rated power of the wind turbine) and the reference proposed site.

[0064] The main design parameters of the blade can include blade length, root cut-off circle diameter, maximum chord length of the blade, rotor hub diameter, rotor diameter, average operating wind speed of the rotor, rotational speed range (including minimum rotational speed and maximum rotational speed), etc.

[0065] The incoming flow wind speed refers to the wind speed at the cross-section of the wind turbine blade.

[0066] The above is a further description of step S10. Next, a further description of step S20 will be continued.

[0067] Specifically, the above step S20 can be implemented through the following steps S201 to S203.

[0068] Step S201: Determine the blade aerodynamic information based on the blade attribute information.

[0069] The blade aerodynamic information refers to the aerodynamic performance indicators of the blade in the airflow, mainly including lift coefficient Cl, drag coefficient Cd, lift-drag ratio Cl / Cd, and pitching moment coefficient Cm.

[0070] In an embodiment of the present application, according to the blade attribute information, the blade aerodynamic data under smooth airfoil and rough airfoil can be obtained by using wind tunnel tests or high-precision numerical simulation methods. It should be noted that the wind tunnel test or high-precision numerical simulation method belongs to the conventional methods in this field and will not be elaborated here.

[0071] In another embodiment of the present application, determining the blade aerodynamic information based on the blade attribute information includes: respectively determining the first aerodynamic parameters corresponding to the smooth airfoil and the second aerodynamic parameters corresponding to the rough airfoil based on the blade attribute information; obtaining the first weighting factor; and performing weighted summation on the first aerodynamic parameters and the second aerodynamic parameters based on the first weighting factor to obtain the blade aerodynamic information.

[0072] The first aerodynamic parameters and the second aerodynamic parameters refer to the blade aerodynamic data corresponding to the smooth airfoil and the rough airfoil respectively, mainly including lift coefficient Cl, drag coefficient Cd, lift-drag ratio Cl / Cd, and pitching moment coefficient Cm. The first aerodynamic parameters and the second aerodynamic parameters can be specifically obtained according to the blade attribute information by using wind tunnel tests or high-precision numerical simulation methods.

[0073] The first weighting factor is used to balance the aerodynamic performance of the smooth blade and the rough blade to improve the adaptability of the blade to external environmental conditions. Specifically, the first weighting factor can be an empirical value obtained through experiments. In different application scenarios, the first weighting factor can be adaptively adjusted according to the actual operating conditions of the wind turbine, considering the smooth operating time and the rough operating time. No specific limitation is made here.

[0074] Specifically, after obtaining the first aerodynamic parameters corresponding to the smooth airfoil and the second aerodynamic parameters corresponding to the rough airfoil, the first aerodynamic parameters and the second aerodynamic parameters are further weighted and summed according to the first weighting factor to obtain the final blade aerodynamic information.

[0075] Exemplarily, assume that the lift coefficients and drag coefficients of the smooth airfoil and the rough airfoil at the angle of attack α are C l1 , C d1 and C l2 , C d2 respectively. Then, the lift coefficient and drag coefficient of the airfoil required by the user (which can be a smooth airfoil or a rough airfoil) at the angle of attack α are:

[0076] C l0 = δC l1 + (1 - δ)C l2

[0077] C d0 = δC d1 + (1 - δ)C d2

[0078] where C l0 , C d0 are the final lift coefficient and drag coefficient respectively, and δ is the first weighting factor.

[0079] Step S202: Based on the blade attribute information, the blade aerodynamic information, and the inflow wind speed, determine the first mapping relationship between the chord length and the radial distance, and the second mapping relationship between the twist angle and the radial distance, where the radial distance is the distance between each section of the blade and the blade root.

[0080] The first mapping relationship is the mapping relationship between the chord length of the blade and the distance from each section of the blade to the blade root.

[0081] The second mapping relationship is the mapping relationship between the twist angle of the blade and the distance from each section of the blade to the blade root.

[0082] Exemplarily, a curve can be used as an example of the mapping relationship, and no specific limitation is made thereto.

[0083] Specifically, first determine the tip speed ratio λ0 according to the rotational speed of the wind turbine:

[0084]

[0085] In the above formula, λ0 is the tip speed ratio, ω is the angular velocity of the blade rotation, R is the radius of the wind wheel, and V is the inflow wind speed.

[0086] The tip speed ratio λ r of the blade section at a distance r from the blade root is

[0087]

[0088] Among them, R is the radius of the wind turbine rotor, and r is the distance from the blade cross-section to the root of the blade.

[0089] Inflow angle and the tip speed ratio λ r The relationship between them is

[0090]

[0091] The twist angle of each cross-section of the blade (the first mapping relationship) is

[0092]

[0093] Among them, α is the angle of attack.

[0094] The chord length C(r) of each cross-section of the blade and the tip speed ratio λ r The relationship (the second mapping relationship) is

[0095]

[0096] Among them, B is the number of blades, and C l is the lift coefficient.

[0097] Step S203: Parameterize the first mapping relationship and the second mapping relationship to obtain the chord length distribution and the twist angle distribution.

[0098] In a specific embodiment of the present application, parameterizing the first mapping relationship and the second mapping relationship includes: obtaining a first type of control points based on the first mapping relationship and the second mapping relationship; establishing a B-spline curve based on the first type of control points; interpolating the B-spline curve to obtain a second type of control points.

[0099] The first type of control points are the key control points of the chord length distribution and the twist angle distribution, and the second type of control points are the control points obtained by interpolation.

[0100] In order to ensure the continuity of the geometric aerodynamic shape, the B-spline curve is used to parameterize the first mapping relationship and the second mapping relationship.

[0101] Assume that the coordinates of n control points of the chord length and the twist angle distributed along the radial direction are [X C , Y C and [X β , Y C :

[0102] X C = [x c1 , x c2 , x c3 , …, x cn ; YC = [y c1 , y c2 , y c3 , …, y cn ;

[0103] X β = [x β1 , x β2 , x β3 , …, x βn ; Y β = [y β1 , y β2 , y β3 , …, y βn ;

[0104] Among them, X C , Y C , X β , Y β respectively represent the distance of the chord length control point from the blade root, the chord length value of the chord length control point, the distance of the twist angle control point from the blade root, and the twist angle value of the twist angle control point.

[0105] The Bezier Curve is used to represent the parametric scheme. The connection line of the control points is the boundary of the obtained curve. The key control points (the first type of control points) of the distribution curve can be:

[0106] For the chord length curve: The starting control point is [0, D root , the maximum chord length control point is [X Cmax , C max , and the tip control point is [1, 0];

[0107] For the twist angle curve: The starting control point is [0, β root , the maximum twist angle control point is [X βmax , β max , and the tip control point is [1, β tip .

[0108] Among them, D root is the root diameter, X Cmax is the coordinate when obtaining the maximum chord length C max ; β root is the twist angle at the root, X βmax is the coordinate when obtaining the maximum twist angle β max , and β tip is the twist angle at the tip.

[0109] Specifically, according to the above control points, the relationship between the Bezier curve and the key control points can be established as B = g(X, Y), and then the Bezier curve is interpolated to obtain the second type of control points, that is, the corresponding chord length and twist angle values C = B(XC ), β = B(X β ).

[0110] That is to say, two segments of curves can be adopted to ensure that the positions of the starting point and the maximum point of the curve remain unchanged. The first segment of the Bezier curve is from the blade root to the maximum value of the chord length or the twist angle, and the second segment of the Bezier curve is from the maximum value to the blade tip. Exemplarily, the control points of the first segment of the curve can be 4, and the second segment can be freely selected. Since the description range is relatively large, it can be 5 or more. Exemplarily, the following curves can be used as examples of the two-segment Bezier curve:

[0111] The first segment:

[0112] X: 0 → X Cmax or X βmax

[0113] X C = [0, x c2 , x c3 , 0.2]; Y C = [D root , y c2 , y c3 , C max ;

[0114] X β = [0, x β2 , x β3 , 0.08]; Y β = [y β1 , y β2 , y β3 , β max ;

[0115] The second segment:

[0116] X: X Cmax or X βmax → 1

[0117] X C = [0.2, x c6 , x c7 , …, 1]; Y C = [C max , y c6 , y c7 , …, 0];

[0118] X β = [0.08, x β6 , x β7 , …, 1]; Y β = [β max , y β6 , y β7 , …, y βn ;

[0119] The chord length distribution curve and the twist angle distribution curve can be obtained through the above steps.

[0120] The above is a further description of step S20. Next, a further description of step S30 will be continued.

[0121] Specifically, the above step S30 can be implemented through the following steps S301 to S303.

[0122] Step S301: Determine the third mapping relationship between the wind energy utilization coefficient and the tip speed ratio based on the chord length distribution and the twist angle distribution.

[0123] Specifically, according to the chord length distribution and the twist angle distribution curves, the third mapping relationship between the utilization coefficient and the tip speed ratio can be obtained by using the blade element momentum theory (BEM). The blade element momentum theory (BEM) not only includes the Betz limit derived from the momentum theory, but also includes the blade element theory and its combination with the momentum theory.

[0124] In an embodiment of the present application, the expression of the wind energy utilization coefficient can be:

[0125]

[0126]

[0127]

[0128]

[0129] Among them, C P is the wind energy utilization coefficient, ω is the angular velocity of the blade rotation, V is the inflow wind speed, R is the wind turbine radius, a is the axial induction factor, b is the tangential induction factor, r is the distance of the blade section from the root of the blade, σ is the local solidity, C n is the axial force coefficient, C t is the tangential force coefficient, B is the number of blades, c is the chord length of the blade element profile, is the inflow angle at the blade element (related to β(r)).

[0130] Step S302: Determine the predicted power of the wind turbine based on the third mapping relationship.

[0131] The predicted power of the wind turbine refers to the power of the wind turbine corresponding to the current blade parameters.

[0132] Specifically, based on the control logic of modern wind turbines, the power of the wind turbine blade rotor can be divided into four stages with the change of wind speed:

[0133] I: Starting from the cut-in wind speed, at the minimum rotational speed n minRunning, as the wind speed increases, the tip speed ratio reaches λ Cpmax , and the corresponding power coefficient is Cpmax.

[0134] II: As the wind speed increases, the wind turbine speed increases, and the tip speed ratio remains at λ Cpmax , tracking the maximum power coefficient until the maximum speed n max .

[0135] III: Keeping the maximum speed n max , as the wind speed increases, the power continues to increase until the rated power P rated .

[0136] IV: As the wind speed continues to increase, through pitch control, the power is maintained near the rated power.

[0137] Therefore, the power calculation methods for the above four stages are as follows:

[0138]

[0139] Among them, ρ is the air density, D tip is the wind turbine diameter, C p is the wind energy utilization coefficient, C pmax is the maximum wind energy utilization coefficient.

[0140] Based on the above formula, the power of the wind turbine can be obtained.

[0141] Step S303: Determine the annual power generation of the wind turbine based on the predicted power.

[0142] The calculation formula for calculating the annual power generation AEP based on the predicted power is:

[0143]

[0144]

[0145]

[0146] Among them, N is the number of intervals of the power curve; f(V i < V < V i+1 ) is the cumulative probability distribution function of the wind speed; V i is the standardized average wind speed of the i-th interval, V i+1 is the standardized average wind speed of the (i + 1)-th interval, P(V i ) is the average output power of the i-th interval, P(V i+1 ) is the average output power of the (i + 1)-th interval, v i is the instantaneous wind speed. V aveis the average wind speed. k and c are parameters related to the Weibull wind speed distribution and are related to the average wind speed V ave Specifically, k is selected according to the wind speed distribution (e.g., k = 2.5). When k < 0.2, it indicates that the wind speed distribution varies greatly from the average value; denotes the Γ function with as the independent variable and can be directly called in MATLAB.

[0147] The above is a further description of step S30. Next, we will continue to further describe step S40.

[0148] Specifically, step S40 can be implemented through the following steps S401 to S404.

[0149] Step S401: Determine the optimization goal based on the annual power generation.

[0150] Specifically, the maximization of the annual energy production AEP max can be used as the optimization goal, and let -AEP max be the return value of the objective function.

[0151] Step S402: Establish an initial population, where the initial population includes multiple individuals, and each individual is the coordinate values of the control points in the first and second Bessel curves, specifically including the chord length and twist angle of the control points distributed along the radial direction.

[0152] Specifically, establish an initial population and set the population size and the maximum number of genetic generations. Exemplarily, the population size can be set to 250, and the maximum number of genetic generations can be set to 300.

[0153] Before performing the objective optimization, the variation ranges of each variable can also be constrained. First, estimate the variation ranges of each variable, then compare the ranges with the optimization results, and adjust the constraints according to the position of the results in the ranges in order to obtain a more appropriate optimization result.

[0154] Step S403: Starting from the initial population, use the genetic algorithm to perform objective optimization iterations to maximize the optimization goal.

[0155] Specifically, the genetic algorithm function "ga" in MATLAB can be used for single-objective optimization, which means to find the minimum value of the objective function through the genetic algorithm. At the initial stage of the optimization, the numerical stability of the function will be tested. If an error is reported, the objective function needs to be modified according to the prompt. During the optimization process, there may be situations where the BEM calculation returns null values, infinite values, complex numbers, etc., and the calculation program needs to be optimized to enhance the robustness of the optimization process.

[0156] Step S404: When the optimization goal converges, obtain the optimal solutions of the chord length distribution and the twist angle.

[0157] Specifically, when the optimization objective converges, the optimal curve representations of the chord length and twist angle are obtained.

[0158] In a specific embodiment of the present application, the method further includes: obtaining constraint conditions based on blade attribute information; and performing a first correction on the optimal solution based on the constraint conditions.

[0159] For the obtained optimization result (the optimal curve representations of the chord length and twist angle) after optimization, first, the relationship between the optimization result and the constraint conditions should be judged. If the optimization result is close to the constraint boundary, the constraints should be adjusted in a timely manner and re-optimized. Exemplarily, the constraint conditions can first consider whether key constraint points such as the root cylinder, maximum chord length, and maximum twist angle are satisfied; secondly, the continuity of the shape and structural stability can be considered, such as the chord length around the maximum chord length should not change too fast, and the points near the maximum chord length and maximum twist angle should not be too close, etc. Finally, the optimized results of the chord length and twist angle for smooth-running and rough-running blades are obtained.

[0160] By using the blade attribute information to clarify and optimize the constraint conditions in the design process, it can ensure that the optimization result is more in line with the actual characteristics and operating environment of the blade. The correction process based on the actual constraints not only improves the accuracy of the optimization result but also significantly enhances its adaptability and feasibility in practical applications, thus avoiding performance degradation or operation problems caused by ignoring key constraints.

[0161] In a specific embodiment of the present application, the optimal solution includes a first optimization result corresponding to a smooth airfoil and a second optimization result corresponding to a rough airfoil; the method further includes: obtaining a second weighting factor; and performing a weighted sum on the first optimization result and the second optimization result based on the second weighting factor to perform a second correction on the optimal solution.

[0162] The first optimization result is the distribution of the optimal chord length and twist angle corresponding to the smooth airfoil, and the second optimization result is the distribution of the optimal chord length and twist angle corresponding to the rough airfoil.

[0163] The second weighting factor is used to balance the aerodynamic performance of smooth and rough blades to improve the adaptability of the blade to external environmental conditions. Specifically, the second weighting factor can be an empirical value obtained through experiments. In different application scenarios, according to the actual operating conditions of the wind turbine, the second weighting factor can be adaptively adjusted considering the smooth-running and rough-running times, and no specific limitation is made here. In one embodiment, the second weighting factor can be the first weighting factor.

[0164] Exemplarily, assume that at the r0 section, the chord length and twist angle distributions of the smooth airfoil are C r0-1 and β r0-1 , and the chord length and twist angle of the rough airfoil are C r0-2 and β r0-2, the chord length C of the highly environmentally adaptable blade required r0 and the twist angle β r0 are as follows:

[0165] C r0 = δC r0-1 + (1 - δ)C r0-2

[0166] β r0 = δβ r0-1 + (1 - δ)β r0-2

[0167] Thus, an aerodynamic shape of the blade with high adaptability to the operating environment after roughness correction is obtained.

[0168] By optimizing the chord length distribution and the twist angle distribution, the efficiency of wind energy conversion is improved. By introducing a second weighting factor, the optimization results can be weighted and summed under different operating conditions, so as to obtain an aerodynamic shape of the blade that is more adaptable to different environmental conditions, and the performance of the wind turbine can be significantly improved.

[0169] Next, as Figure 2 and Figure 3 shown, two specific implementation manners of the present application are described in detail.

[0170] In one embodiment, as Figure 2 shown, after obtaining the first aerodynamic parameter of the smooth airfoil and the second aerodynamic parameter of the rough airfoil according to the wind tunnel test or the simulation experiment respectively, the first aerodynamic parameter and the second aerodynamic parameter can be weighted and summed according to the first weighting factor to obtain the final blade aerodynamic information. Then, the optimal solutions of the chord length and the twist angle distribution can be calculated according to the foregoing steps S20 to S40.

[0171] In another embodiment, as Figure 3 shown, according to the foregoing steps S10 to S40, the optimal solutions (the first optimization results) of the chord length and the twist angle distribution corresponding to the smooth airfoil, and the optimal solutions (the second optimization results) of the chord length and the twist angle distribution corresponding to the rough airfoil can be obtained respectively. The optimal solutions of the final chord length and the twist angle distribution are obtained by weighting and summing the first optimization result and the second optimization result according to the second weighting factor.

[0172] Although the above embodiments describe each step in a specific order, those skilled in the art can understand that in order to achieve the effects of the present application, the different steps do not necessarily have to be executed in such an order, and they can be executed simultaneously (in parallel) or in other orders, and these variations are within the protection scope of the present application.

[0173] Furthermore, the present application also provides an aerodynamic optimization device for a wind turbine blade.

[0174] Refer to the appendix Figure 4 , Figure 4 which is the main structural block diagram of the aerodynamic optimization device for the wind turbine blade according to an embodiment of the present application.

[0175] As Figure 4 shown, the aerodynamic optimization device for the wind turbine blade in the embodiment of the present application mainly includes an acquisition module 11, a first determination module 12, a second determination module 13, and an optimization module 14. In some embodiments, one or more of the acquisition module 11, the first determination module 12, the second determination module 13, and the optimization module 14 may be combined together into one module.

[0176] In some embodiments, the acquisition module 11 may be configured to acquire blade attribute information and the inflow wind speed.

[0177] The first determination module 12 may be configured to determine the chord length distribution and the twist angle distribution based on the blade attribute information and the inflow wind speed.

[0178] The second determination module 13 may be configured to determine the annual power generation of the wind turbine based on the chord length distribution and the twist angle distribution.

[0179] The optimization module 14 may be configured to optimize the chord length distribution and the twist angle distribution based on the annual power generation to obtain the optimal solutions of the chord length distribution and the twist angle distribution.

[0180] In one implementation manner, the description of the specific implementation functions can be referred to the steps S10 to S40.

[0181] The above-mentioned aerodynamic optimization device for the wind turbine blade is used to execute Figure 1 the embodiment of the aerodynamic optimization method for the wind turbine blade shown. The technical principles, the technical problems solved, and the technical effects produced by both are similar. Those skilled in the art of the present technology can clearly understand that for the convenience and conciseness of description, the specific working process and related descriptions of the aerodynamic optimization device for the wind turbine blade can refer to the content described in the embodiment of the aerodynamic optimization method for the wind turbine blade, which will not be elaborated here.

[0182] Furthermore, it should be understood that since the setting of each module is only for explaining the functional units of the device of the present application, the physical devices corresponding to these modules can be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of software and hardware. Therefore, the number of each module in the figure is only illustrative.

[0183] Those skilled in the art can understand that the various modules in the device can be adaptively split or combined. Such splitting or combination of specific modules will not cause the technical solution to deviate from the principle of the present application. Therefore, the technical solutions after splitting or combination will all fall within the protection scope of the present application.

[0184] Those skilled in the art can understand that all or part of the processes in the method of the above-mentioned embodiment of the present application can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium can include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc., that can carry the computer program code.

[0185] Furthermore, the present application also provides an electronic device. The electronic device may include at least one processor; and a memory communicatively connected to the at least one processor; wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the wind turbine blade aerodynamic optimization method described in any of the above embodiments is implemented. Refer to Figure 5 as shown Figure 5 exemplarily shows the structure of the electronic device, which includes a processor 100 and a memory 200.

[0186] Furthermore, the present application also provides a computer-readable storage medium. In an embodiment of the computer-readable storage medium according to the present application, the computer-readable storage medium can be configured to store a program for executing the wind turbine blade aerodynamic optimization method of the above-mentioned method embodiment. The program can be loaded and run by a processor to implement the above-mentioned wind turbine blade aerodynamic optimization method. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown. For the specific technical details not disclosed, please refer to the method part of the embodiments of the present application. The computer-readable storage medium can be a memory device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiments of the present application is a non-transitory computer-readable storage medium.

[0187] So far, the technical solutions of the present application have been described in conjunction with the specific embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principle of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present application.

Claims

1. A method for aerodynamic optimization of a wind turbine blade, characterized in that, The method includes: Obtaining blade attribute information and the inflow wind speed; Determining blade aerodynamic information based on the blade attribute information, including: respectively determining a first aerodynamic parameter corresponding to a smooth airfoil and a second aerodynamic parameter corresponding to a rough airfoil based on the blade attribute information; obtaining a first weighting factor; performing weighted summation on the first aerodynamic parameter and the second aerodynamic parameter based on the first weighting factor to obtain the blade aerodynamic information; Determining the chord length distribution and the twist angle distribution based on the blade attribute information, the blade aerodynamic information, and the inflow wind speed; Determining the annual power generation of the wind turbine based on the chord length distribution and the twist angle distribution; Optimizing the chord length distribution and the twist angle distribution based on the annual power generation to obtain the optimal solutions of the chord length distribution and the twist angle distribution.

2. The aerodynamic optimization method for a wind turbine blade according to claim 1, wherein Determining the chord length distribution and the twist angle distribution based on the blade attribute information, the blade aerodynamic information, and the inflow wind speed, including: Respectively determining a first mapping relationship between the chord length and the radial distance, and a second mapping relationship between the twist angle and the radial distance based on the blade attribute information, the blade aerodynamic information, and the inflow wind speed, where the radial distance is the distance between each section of the blade and the blade root; Parametrizing the first mapping relationship and the second mapping relationship to obtain the chord length distribution and the twist angle distribution.

3. The aerodynamic optimization method for a wind turbine blade according to claim 2, wherein Parametrizing the first mapping relationship and the second mapping relationship includes: Obtaining a first type of control points based on the first mapping relationship and the second mapping relationship; Establishing a B-spline curve based on the first type of control points; Interpolating the B-spline curve to obtain a second type of control points.

4. The aerodynamic optimization method for a wind turbine blade according to claim 1, characterized in that, Determining the annual power generation of the wind turbine based on the chord length distribution and the twist angle distribution, including: Determining a third mapping relationship between the power coefficient and the tip speed ratio based on the chord length distribution and the twist angle distribution; Determining the predicted power of the wind turbine based on the third mapping relationship; Determining the annual power generation of the wind turbine based on the predicted power.

5. The aerodynamic optimization method for a wind turbine blade according to claim 1, wherein Optimizing the chord length distribution and the twist angle distribution based on the annual power generation, including: Determining an optimization objective based on the annual power generation; Establishing an initial population, where the initial population includes multiple individuals; Starting from the initial population, using a genetic algorithm to perform target optimization iteration to maximize the optimization objective; When the optimization objective converges, obtaining the optimal solutions of the chord length distribution and the twist angle distribution.

6. The aerodynamic optimization method for a wind turbine blade according to claim 1, wherein, The method further includes: Obtaining constraint conditions based on the blade attribute information; Performing a first correction on the optimal solutions based on the constraint conditions.

7. The aerodynamic optimization method for the wind turbine blade according to claim 1, wherein The optimal solutions include a first optimization result corresponding to a smooth airfoil and a second optimization result corresponding to a rough airfoil; The method further includes: Obtaining a second weighting factor; Performing weighted summation on the first optimization result and the second optimization result based on the second weighting factor to perform a second correction on the optimal solutions.

8. An electronic device, characterized in that, Includes: At least one processor; And a memory communicatively connected to the at least one processor; Among them, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, it implements the wind turbine blade aerodynamic optimization method according to any one of claims 1 to 7.

9. A computer-readable storage medium storing multiple program codes, characterized in that, The program code is adapted to be loaded and run by a processor to execute the wind turbine blade aerodynamic optimization method according to any one of claims 1 to 7.

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