Wind power blade design method, device, equipment, medium and product

Abstract

The invention discloses a wind power blade design method and device, equipment, a medium and a product, and belongs to the field of wind power blade design. The wind power blade design method comprises the steps that input configuration data and value ranges corresponding to multiple design variables are obtained; variable values are selected from the value ranges corresponding to the design variables; blade appearance analysis is carried out based on the configuration data and the variable values, and candidate appearance data and performance data of the blade are obtained; evaluating whether the performance data meets a preset condition or not according to the target function and the constraint condition; if not, the variable value of the design variable is selected again, blade appearance analysis is conducted again, candidate appearance data and performance data are obtained, and the candidate appearance data obtained through last analysis are determined as final appearance data of the blade until the performance data meet the preset condition or the number of iterations reaches the preset number of times. The problem of multiple working conditions can be solved, and an optimal solution in a global design space can be obtained.

Description

Technical Field

[0001] The present application belongs to the field of wind turbine blade design, and in particular, relates to a wind turbine blade design method, device, equipment, medium and product. Background Art

[0002] At present, in the engineering design of wind turbine blades, the blade design can be carried out based on the analytical design method of the classical blade element momentum theory (BEM). The analytical design method is an inverse design method, which mainly includes: according to the optimal wind energy conversion efficiency (optimal power coefficient Cp) assumption of BEM, under the specific airfoil selection and its spanwise distribution (relative thickness distribution, lift and drag coefficient distribution), the blade chord length and twist angle distribution under a certain design tip speed ratio are calculated, thereby obtaining the aerodynamic layout characteristics of the blade.

[0003] However, the analytical design method has the following disadvantages: 1) Single operating condition design (tip speed ratio), which makes it difficult to handle multi-operating condition problems; 2) Empirical design: The selection and adjustment of design variables such as airfoil thickness layout, lift distribution, and tip speed ratio are heavily dependent on the designer's experience, and often require repeated trial and error, making it difficult to obtain the optimal solution in the global design space. Summary of the invention

[0004] The embodiments of the present application provide a wind turbine blade design method, device, equipment, medium and product that can handle multiple working condition problems and obtain the optimal solution in the global design space.

[0005] In a first aspect, an embodiment of the present application provides a wind turbine blade design method, comprising:

[0006] Obtain input configuration data and value ranges corresponding to multiple design variables, the configuration data including wind condition data, wind turbine unit data adapted to the blade, blade data, model-related data, and airfoil data;

[0007] Selecting variable values ​​of the design variables from the value ranges corresponding to the multiple design variables;

[0008] Perform blade shape analysis based on the configuration data and the selected variable values ​​to obtain candidate blade shape data and performance data;

[0009] Use the objective function and constraints to evaluate whether the performance data meets the preset conditions;

[0010] If the conditions are not met, the variable values ​​of the design variables are selected again from the value ranges corresponding to the multiple design variables, and the blade shape analysis is returned based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade, until the performance data meets the preset conditions or the number of iterations reaches the preset number, and the candidate shape data obtained from the last analysis is determined as the final shape data of the blade.

[0011] In some embodiments of the present application, multiple design variables include a design tip speed ratio and a design lift coefficient, an induced clipping factor, and a relative thickness at multiple different blade span positions, and the candidate shape data of the blade include the twist angle distribution and the chord length distribution of each blade element segment, wherein the induced clipping factor is the percentage deviation between the actual design application induction factor of the blade element segment and the induction factor under the theoretical optimal power coefficient assumption.

[0012] In some embodiments of the present application, the plurality of different blade spanwise positions include positions where the blade spanwise is at 0%, 15%, 25%, 35%, 55%, 85%, 95%, and 100%, respectively.

[0013] In some embodiments of the present application, the value ranges of the design lift coefficients at a plurality of different blade spanwise positions are determined in the following manner:

[0014] According to the designed lift coefficient distribution of the entire blade and the number of blade element units required to be discrete, the lift coefficient value at the corresponding blade element position is generated according to a predetermined blade element discrete criterion.

[0015] In some embodiments of the present application, blade shape analysis is performed based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade, including:

[0016] Generate an aerodynamic data table of each blade element airfoil of the blade according to the configuration data and the selected variable values;

[0017] Based on the aerodynamic data table, the twist angle distribution and chord length distribution of each blade element are obtained;

[0018] Integrate the torsion angle distribution and chord length distribution of all blade element segments to obtain candidate shape data of the blade;

[0019] The performance of the blade is analyzed according to the candidate shape data of the blade to obtain the performance data of the blade.

[0020] In some embodiments of the present application, before integrating the twist angle distribution and chord length distribution of all blade element segments to obtain candidate shape data of the blade, the method further includes:

[0021] The twist angle distribution and chord length distribution of the blade root section and the blade tip section in each blade element section are simplified to obtain the simplified twist angle distribution and chord length distribution of the blade root section and the blade tip section. The simplified processing includes linear processing and nonlinear clipping.

[0022] In some embodiments of the present application, the performance data includes aerodynamic performance data and geometric characteristic data, and the objective function and constraint conditions are used to evaluate whether the performance data meets the preset conditions, including:

[0023] evaluating whether the aerodynamic performance data and the geometric characteristic data meet the first preset condition;

[0024] When it is determined that the aerodynamic performance data and the geometric characteristic data satisfy the first preset condition, it is evaluated whether the aerodynamic performance data and the geometric characteristic data satisfy the second preset condition.

[0025] In some embodiments of the present application, the aerodynamic performance data includes at least one of power coefficient, annual power generation, blade root limit load and aerodynamic characteristics, and the geometric characteristic data includes at least one of absolute thickness distribution, blade surface area, blade aspect ratio and wind rotor solidity;

[0026] The first preset condition includes that at least one of the absolute thickness, stall margin, power coefficient and blade root load satisfies the preset constraint value, the stall margin includes the stall margin of the design lift coefficient or the stall margin of the design angle of attack, and the second preset condition includes that the function value of a multi-objective function with at least one of the power coefficient, annual power generation, blade root limit load and aerodynamic characteristics and at least one of the absolute thickness distribution, blade surface area, blade aspect ratio and wind rotor solidity as targets is maximized.

[0027] In a second aspect, an embodiment of the present application provides a wind turbine blade design device, comprising:

[0028] An acquisition module is used to acquire input configuration data and value ranges corresponding to multiple design variables, wherein the configuration data includes wind condition data, wind turbine unit data adapted to the blade, blade data, model-related data, and airfoil data;

[0029] A selection module, used for selecting variable values ​​of design variables from value ranges corresponding to multiple design variables;

[0030] An analysis module is used to analyze the blade shape based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade;

[0031] An evaluation module is used to evaluate whether the performance data meets the preset conditions based on the objective function and constraint conditions;

[0032] The optimization module is used to select the variable values ​​of the design variables from the value ranges corresponding to the multiple design variables again when the conditions are not met, and return to perform blade shape analysis based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade, until the performance data meets the preset conditions or the number of iterations reaches the preset number, and the candidate shape data obtained from the last analysis is determined as the final shape data of the blade.

[0033] In a third aspect, an embodiment of the present application provides a wind turbine blade design device, the device comprising: a processor and a memory storing computer program instructions;

[0034] When the processor executes the computer program instructions, the wind turbine blade design method of any of the above embodiments is implemented.

[0035] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the wind turbine blade design method of any of the above embodiments is implemented.

[0036] In a fifth aspect, an embodiment of the present application provides a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the wind turbine blade design method of any of the above embodiments.

[0037] According to the wind turbine blade design method, device, equipment, medium and product provided in the embodiments of the present application, the blade shape can be analyzed based on the configuration data and multiple design variables to obtain candidate shape data and performance data of the blade. Compared with the single working condition design of the prior art, the solution of the present application can handle multi-working condition problems, and each design variable has its own corresponding value range. Through continuous iteration, the variable value that makes the blade performance meet the optimal performance can be found. Therefore, the optimal solution in the global design space can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0039] Figure 1 A schematic diagram of a wind turbine blade design method provided in an embodiment of the present application;

[0040] Figure 2 It is a schematic diagram of the relative thickness layout and lift coefficient layout of the airfoil;

[0041] Figure 3 The blade chord length and twist angle distribution obtained by conventional analytical design;

[0042] Figure 4 A framework diagram for wind turbine blade design optimization method;

[0043] Figure 5 The chord length comparison chart of the case design results;

[0044] Figure 6 Load distribution comparison diagram for case design results;

[0045] Figure 7 A schematic diagram of the structure of a wind turbine blade design device provided in an embodiment of the present application;

[0046] Figure 8 A schematic diagram of the structure of a wind turbine blade design device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0048] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0049] At present, in the engineering design of wind turbine blades, the blade design can be carried out based on the analytical design method of the classical blade element momentum theory (BEM). The analytical design method is an inverse design method, which mainly includes: according to the optimal wind energy conversion efficiency (optimal power coefficient Cp) assumption of BEM, under the specific airfoil selection and its spanwise distribution (relative thickness distribution, lift and drag coefficient distribution), the blade chord length and twist angle distribution under a certain design tip speed ratio are calculated, thereby obtaining the aerodynamic layout characteristics of the blade.

[0050] However, the analytical design method has the following disadvantages: 1) Single-operating-condition design (tip speed ratio), which makes it difficult to handle multiple-operating-condition problems; 2) Empirical design: The selection and adjustment of design variables such as airfoil thickness layout, lift distribution, and tip speed ratio are heavily dependent on the designer's experience, and often require repeated trial and error, making it difficult to obtain the optimal solution in the global design space. In addition, the analytical results of the analytical design method belong to the optimal Cp design under the ideal induction factor distribution, but may not meet the low load requirements, and cannot further control the efficiency-load.

[0051] In this field, direct numerical optimization methods can also be used for blade design. The direct numerical optimization method is a forward design method, which mainly includes: parametric characterization of the aerodynamic shape characteristics of the blade (such as chord length, twist angle, thickness distribution, etc.), driving the change of design parameters through a mathematical optimization algorithm, generating a new blade shape, evaluating aerodynamic efficiency, load characteristics, and geometric structure characteristics, and repeatedly iterating until the performance of the new shape meets the design goals. The direct numerical optimization method has the following disadvantages: This method generally optimizes the macroscopic aerodynamic characteristics of the wind turbine blade (such as power coefficient Cp, power generation, limit load, etc.), and it is difficult to control the operating load characteristics and aerodynamic risks of different blade element positions in the blade span (such as load distribution balance, local stall risk, etc.).

[0052] In response to the above problems, the embodiments of the present application provide a wind turbine blade design method, device, equipment, medium and product that can handle multiple working conditions and obtain the optimal solution in the global design space. Furthermore, it can also achieve dual design control of macro blade performance indicators and local aerodynamic loads.

[0053] Figure 1 A schematic diagram of a wind turbine blade design method provided in an embodiment of the present application;

[0054] Next, combine Figure 1 The wind turbine blade design method of the embodiment of the present application is introduced, and the wind turbine blade design method includes the following steps:

[0055] S110, obtaining input configuration data and value ranges corresponding to a plurality of design variables, wherein the configuration data includes wind condition data, wind turbine unit data adapted to the blade, blade data, model-related data, and airfoil data;

[0056] S120, selecting a variable value of a design variable from value ranges corresponding to each of the plurality of design variables;

[0057] S130, performing blade shape analysis based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade;

[0058] S140, evaluating whether the performance data meets preset conditions based on the objective function and the constraint conditions;

[0059] S150, if the conditions are not met, select the variable values ​​of the design variables from the value ranges corresponding to the multiple design variables again, and return to perform blade shape analysis based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade, until the performance data meets the preset conditions or the number of iterations reaches the preset number, and the candidate shape data obtained from the last analysis is determined as the final shape data of the blade.

[0060] According to the wind turbine blade design method provided in the embodiment of the present application, the blade shape can be analyzed based on the configuration data and multiple design variables to obtain candidate shape data and performance data of the blade. Compared with the single working condition design of the prior art, the solution of the present application can handle multi-working condition problems, and each design variable has its own corresponding value range. Through continuous iteration, the variable value that makes the blade performance meet the optimal performance can be found. Therefore, the optimal solution in the global design space can be obtained.

[0061] Regarding the above S110, the configuration data includes: a) wind condition data, such as air density, annual average wind speed, turbulence intensity, wind shear index, wind speed probability distribution, etc.; b) wind turbine unit data adapted to the blades, such as the number of blades, rated power, rotor diameter, hub radius, tower height, rotor inclination and elevation, rotor speed range, cut-in and cut-out wind speeds, control type, etc.; c) blade data, such as blade root pitch circle diameter, maximum chord length; d) model-related data, such as the number of blade elements in the BEM model, the maximum number of iterations of the induction factor, the iteration tolerance of the induction factor, etc.; e) airfoil data, including airfoil geometry data and aerodynamic data, wherein the airfoil geometry data is usually a two-dimensional coordinate with a certain format, and the aerodynamic data needs to provide aerodynamic coefficients (lift coefficient, drag coefficient, moment coefficient, etc.) in polar coordinates within a complete 360-degree attack angle range.

[0062] Selecting airfoil data is also called selecting "basic airfoil". The aerodynamic layout of the blade can be regarded as the accumulation of infinite blade airfoils with different thicknesses, chord lengths, and twist angles along a specific axis. The so-called "basic airfoil" is a family of airfoils with a finite number of different relative thicknesses, and its relative thickness range covers the relative thickness distribution of the airfoils contained in the target blade; the infinite blade airfoil shapes and aerodynamic data constituting the blade are generated by the basic airfoil using a specific algorithm, such as a linearized or nonlinear interpolation algorithm. There are certain principles for the selection of basic airfoils. Under the condition that there are sufficient alternative airfoil families, the appropriate airfoil family should be selected according to different blade design problems. For example, for the design of wind turbine blades operating in low wind speed, high turbulence, and high wind shear areas, an airfoil family with a relatively gentle stall should be selected; for wind turbines whose operating environment is prone to causing dirt and roughening of the blade surface, an airfoil family with a low sensitivity to leading edge roughness should be selected.

[0063] In some embodiments of the present application, multiple design variables include a design tip speed ratio and a design lift coefficient, an induced clipping factor, and a relative thickness at multiple different blade span positions, and the candidate shape data of the blade include the twist angle distribution and the chord length distribution of each blade element segment, wherein the induced clipping factor is the percentage deviation between the actual design application induction factor of the blade element segment and the induction factor under the theoretical optimal power coefficient assumption.

[0064] Specifically, the analytical design method relied on by the present application is based on the BEM model, which divides a complete blade into a limited number of blade segments (blade elements), and solves the shape parameters (chord length, twist angle, etc.) of a blade element segment according to the given input parameters of the blade element segment. After obtaining the shape parameter points of all blade element segments, the shape distribution of the entire blade is obtained. The design variables include the design tip speed ratio, the induction factor distribution (the induction factor of the blade element segment at different positions in the blade span, including the axial induction factor and the tangential induction factor), the relative thickness distribution of the blade element airfoil, and the aerodynamic characteristics of the blade element airfoil (including the lift coefficient, the drag coefficient and its corresponding angle of attack).

[0065] Among the above-mentioned design variables, except for the design tip speed ratio variable, the remaining design variables can be regarded as numerical distributions along the span direction of the blade. The actual number of variables of each design variable (such as the axial induction factor) depends on the number of blade element segment units obtained by discretizing the blade; the more blade element segments the blade is divided into, the more actual design variables the design variable has. A larger number of blade segment units can improve the accuracy of blade aerodynamic analysis, but it increases the calculation cost. In general, for blades of a length of 100 meters, the number of blade element segments that need to be divided is more than 50. If the design variables of all blade segments are used as design variables, the design variable matrix of the optimization problem will undoubtedly be too large and the design space will be too complex. In order to simplify the problem, the present application adopts a method of defining its distribution curve through a limited number of design variable points at specific blade span positions to realize the parameterization of each design variable. The specific blade element position points selected in the present application are 8 relative positions of the blade span direction, namely 0%, 15%, 25%, 35%, 55%, 85%, 95%, and 100%. Figure 2 This is a schematic diagram of the relative thickness layout and lift coefficient layout of the airfoil. Figure 2, the definition points of the design lift coefficient and relative thickness and the distribution curves they control are given, where the spline curve defined by the lift coefficient values ​​at 8 spanwise positions gives the design lift coefficient distribution of the entire blade, and further, the lift coefficient values ​​at the corresponding blade element positions can be generated according to the required number of discrete blade element units and a certain blade element discreteness criterion. This method can effectively reduce the number of design variables in the optimization problem (in the example given in the figure, it is controlled by 8 blade spanwise position definition points, but the blade root is a cylindrical segment and is usually set to a fixed value. This application uses a fixed blade spanwise position (the horizontal axis coordinate of the definition point) and only uses the design variable value (the vertical axis coordinate of the definition point) as a variable, so the number of lift coefficient-related variables is 7). The same method is used to define other design variables in a parameterized manner.

[0066] Among them, the design lift coefficient, drag coefficient and design angle of attack are one-to-one corresponding to a specific airfoil. After the design lift coefficient distribution is defined, the corresponding drag coefficient distribution and design angle of attack distribution can be obtained according to the aerodynamic curve characteristics of the airfoil without redundant definition. Therefore, this application uses the design lift coefficient of the airfoil as the main design variable for parameterization, and does not parameterize the drag coefficient and angle of attack distribution to reduce the number of design variables.

[0067] Among them, the parametric distribution of the induction factor is parameterized by means of additional factors. The classic analytical design of blades is based on the optimal Cp assumption, that is, the axial induction factor of the entire blade span to each blade element segment meets the ideal conditions of momentum theory (a simplified solution is that the ideal axial induction factor of the entire blade is 1 / 3). In order to improve the conditional extremes of the classic analytical design method, the design method proposed in this application uses the induction factor as a design variable, rather than limiting it to a single ideal value, so as to achieve a trade-off between aerodynamic efficiency and load in multiple working conditions. Furthermore, this application does not directly control

[0068] The axial induction factor is defined on the basis of retaining the optimal Cp induction assumption. The tailoring factor AF of the induction factor is defined as the actual design application induction factor a of the blade segment and the induction factor a under the theoretical optimal Cp assumption. ideal The deviation percentage of AF is as follows: when AF=0, it means that the induction factor of the blade element design is the ideal induction factor value. Similarly, the control point at a specific blade span position is used to define the entire induction tailoring factor AF distribution, and the control point group is the design variable related to the induction factor.

[0069] AF=(aa ideal ) / a ideal

[0070] After parameterizing the above design variables such as the design tip speed ratio, the induction factor distribution, the airfoil relative thickness distribution, the airfoil lift coefficient distribution, etc., the design variable matrix of the blade design method of the present application is obtained, as shown in Table 1 below:

[0071] Table 1 Design variables table

[0072]

[0073]

[0074] It should be noted that the distribution curve may be defined by the curve distribution in the example or by a linear distribution; the blade span position points may increase, decrease or change, but this does not affect the validity of the method proposed in this application.

[0075] Regarding the above S120 and S130, variable values ​​of design variables are selected from the value ranges corresponding to the multiple design variables; the blade shape is analyzed based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade.

[0076] In some embodiments of the present application, blade shape analysis is performed based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade, including:

[0077] Generate an aerodynamic data table of each blade element airfoil of the blade according to the configuration data and the selected variable values;

[0078] Based on the aerodynamic data table, the twist angle distribution and chord length distribution of each blade element are obtained;

[0079] Integrate the torsion angle distribution and chord length distribution of all blade element segments to obtain candidate shape data of the blade;

[0080] The performance of the blade is analyzed according to the candidate shape data of the blade to obtain the performance data of the blade.

[0081] Furthermore, before integrating the torsion angle distribution and chord length distribution of all blade element segments to obtain candidate shape data of the blade, the design method of the wind turbine blade may further include:

[0082] The twist angle distribution and chord length distribution of the blade root section and the blade tip section in each blade element section are simplified to obtain the simplified twist angle distribution and chord length distribution of the blade root section and the blade tip section. The simplified processing includes linear processing and nonlinear clipping.

[0083] Regarding the blade shape analysis, the blade shape analysis can be performed based on the BEM model to obtain the candidate shape data of the blade and the performance data of the blade.

[0084] Among them, the performance data of the blade may include aerodynamic performance data and geometric feature data. The aerodynamic performance analysis of the blade can be realized based on the aerodynamic calculation tool of the BEM model to obtain the aerodynamic performance data of the blade, and the geometric characteristics of the blade can be analyzed based on the geometric analysis tool to obtain the geometric feature data of the blade.

[0085] Figure 3 The blade chord length and twist angle distribution obtained by conventional analytical design;

[0086] Regarding the simplified processing, in general, the distribution of chord length and twist angle obtained by analytical design is as shown in the attached figure. Figure 3 As shown, the chord length and twist angle of the blade root area are too large, which is not conducive to production and manufacturing. In actual blades, the blade root is usually designed as a cylindrical section, and the blade tip is designed with a tapered chord length distribution. Since the aerodynamic load of the blade root accounts for a small proportion, the blade tip post-processing area is only a few meters, and the geometric post-processing method required for blade engineering design does not affect the validity of this application.

[0087] Regarding the above S140 and S150, the objective function and constraints are used to evaluate whether the performance data meets the preset conditions; if not, the variable values ​​of the design variables are selected again from the value ranges corresponding to the multiple design variables, and the blade shape analysis based on the configuration data and the selected variable values ​​is returned to obtain candidate shape data and performance data of the blade, until the performance data meets the preset conditions or the number of iterations reaches the preset number, the candidate shape data obtained from the last analysis is determined as the final shape data of the blade.

[0088] In some embodiments of the present application, the performance data includes aerodynamic performance data and geometric characteristic data, and the objective function and constraint conditions are used to evaluate whether the performance data meets the preset conditions, including:

[0089] evaluating whether the aerodynamic performance data and the geometric characteristic data meet the first preset condition;

[0090] When it is determined that the aerodynamic performance data and the geometric characteristic data meet the first preset condition, it is evaluated whether the aerodynamic performance data and the geometric characteristic data meet the second preset condition.

[0091] The aerodynamic performance data include at least one of the power coefficient, annual power generation, blade root limit load and aerodynamic characteristics, the aerodynamic characteristics may include the design lift coefficient and the design angle of attack, and the geometric characteristic data include at least one of the absolute thickness distribution, blade surface area, blade aspect ratio and wind rotor solidity;

[0092] The first preset condition includes that at least one of the absolute thickness, stall margin, power coefficient and blade root load satisfies the preset constraint value, the stall margin includes the stall margin of the design lift coefficient or the stall margin of the design angle of attack, and the second preset condition includes that the function value of a multi-objective function with at least one of the power coefficient, annual power generation, blade root limit load and aerodynamic characteristics and at least one of the absolute thickness distribution, blade surface area, blade aspect ratio and wind rotor solidity as targets is maximized.

[0093] Specifically, after parameterizing the design variables and obtaining the design variable matrix, it is necessary to further define the objective function and constraint matrix of the blade aerodynamic layout optimization to form a complete mathematical model of the optimization problem.

[0094] For example, the objective function of blade aerodynamic layout optimization proposed in this application includes at least the annual power generation of the blade and the blade root load, so as to improve the defect that the original analytical design method cannot perform multi-objective design, and can further include blade power coefficient, axial thrust, geometric structure parameters (such as blade surface area, absolute thickness, blade solidity, aspect ratio) and other characteristics. The objective function expression can be expressed as:

[0095] F obj =s 1 .w 1 .f aerody +s 2 .w 2 .f geo

[0096] Where s is the normalization factor of the sub-goal parameter, w is the weight coefficient; f aerody f is the target parameter related to blade aerodynamics, which can be composed of one or more of the following aerodynamic parameter indicators: such as blade power coefficient, annual power generation, blade root load, axial thrust, etc.; geo The target parameter is related to the blade geometry and may be composed of one or more of the following geometry parameters: absolute thickness, surface area, solidity, aspect ratio, etc.

[0097] In the present application, multiple target parameters (power generation, load, geometric structure parameters, etc.) can be controlled through the objective function to achieve a balance of multiple target parameters.

[0098] In terms of constraints, firstly, the method proposed in this application can impose constraints on all the above-mentioned target parameters (especially core target parameters such as power generation, load, Cp, etc.) to form a constraint matrix; further, in order to improve the problem that it is difficult to directly control the local aerodynamic risk of blade elements in traditional direct numerical optimization methods of blades, the proposed constraint matrix also includes aerodynamic characteristics related to the local load and stall characteristics of blades, such as stall margin, etc.; the stall margin proposed in this application is limited from two aspects: the design lift coefficient or the design angle of attack, requiring the design lift coefficient to be lower than the maximum lift coefficient by more than 0.4, and the design angle of attack to be lower than the critical stall angle of attack by more than 4 degrees. In addition, in order to improve the feasibility of the structural stiffness of the aerodynamic design results, the absolute thickness of a specific cross-section is further selected as a constraint condition. An example of the main constraints proposed in this application is summarized in the following table (the specific values ​​in the table are only for illustrative purposes).

[0099] Table 2 Constraints

[0100] Constraints unit Constraint Value Power coefficient Cp [.] >0.45 Annual Electricity Generation AEP MWh >44000 Blade root limit load My_max kNm <45000 Lift coefficient stall margin (Stall_cl) [.] >0.4 Absolute thickness at spanwise position 1 AbsT1 [m] 2.5 Absolute thickness at spanwise position 2 AbsT1 [m] 1.3 Absolute thickness at spanwise position 3 AbsT1 [m] 0.9 Absolute thickness at spanwise position 4 AbsT1 [m] 0.5

[0101] Based on the wind turbine blade design method of the present application, whether the new design meets the target is determined by defining design variables, generating blade shapes, analyzing blade performance, and judging whether design goals and constraints are met; if not, the design variables are driven to change through the optimization algorithm, a new blade shape is generated, and the next round of iteration is entered, until the target is achieved or the set number of iterations is terminated. The present application combines the numerical optimization design of wind turbine blades with the analytical design method, which can achieve the balance of macroscopic multi-objective parameters of wind turbine blades under multiple working conditions (power generation, load, geometric structure parameters, etc.), while controlling the local load conditions and aerodynamic risks at different spanwise positions, thereby improving the aerodynamic adaptability of the design results.

[0102] Figure 4 Schematic diagram of the optimization method for wind turbine blade design;

[0103] Figure 4 This is a simplified schematic diagram of the entire optimization process for wind turbine blades, which mainly includes the following steps:

[0104] S401, design input configuration, including wind condition parameters, platform parameters, blade parameters, model parameters and airfoil configuration, the airfoil configuration includes geometric coordinate data and aerodynamic Polar data;

[0105] S402, design variables, including lift distribution control points, induced shear control points, relative thickness distribution control points, and design tip speed ratio;

[0106] S403, analytical design pre-processing;

[0107] S404, analytical design shape, analytical torsion angle distribution and analytical chord length distribution;

[0108] S405, geometry processing, deals with the chord length and twist angle of the blade root and blade tip;

[0109] S406, blade model, obtain the outer blade element matrix and perform BEM data modeling;

[0110] S407, performance analysis, performing performance analysis on the obtained blade model to obtain performance data, S4071, obtaining aerodynamic performance data, S4072, obtaining geometric characteristic data;

[0111] S408, constraint analysis, analyzing whether the performance data of the blade meets the constraint conditions. If the constraint conditions are met, the next step of target judgment is performed. If the constraint conditions are not met, the optimization algorithm is used to select the value of the design variable again for the next round of iteration. Constraint conditions can be set for absolute thickness, stall margin, power coefficient and blade root load;

[0112] S409, target judgment, using the target function to judge the performance data of the blade, and when it meets the design target, obtaining the optimized version of the blade shape, and when it does not meet the design target, using the optimization algorithm to select the value of the design variable again for the next round of iteration;

[0113] S410, optimization algorithm, using the optimization algorithm to select the value of the design variable again;

[0114] S411, optimized version, that is, the optimized blade shape is obtained.

[0115] Figure 5 The chord length comparison chart of the case design results;

[0116] Figure 6 Load distribution comparison diagram for case design results;

[0117] Next, combine Figure 5 and Figure 6 , the wind turbine blade design method of the embodiment of the present application is explained through a specific example.

[0118] A case design of a blade length of 110m+ is carried out for a 10MW wind turbine platform. This case uses the general DU airfoil family (the relative thickness of the basic airfoil is 40%, 35%, 30%, 35%, 21%, plus a 100% thick circular cross-section airfoil, a total of six airfoils) as the basic airfoil for design description. Table 3 gives some key input parameters. The platform adapted to the target blade is a 10MW host platform, and the blade length is limited to around 110m.

[0119] Table 3 Main input parameters of blade case design

[0120]

[0121]

[0122] Table 4 gives the definition of the upper and lower boundaries of the main design variables. The design tip speed ratio range is 10.5-12.5. The thickness distribution range of the airfoil and the distribution of its lift characteristics are set considering the aerodynamic characteristics of the DU airfoil family. In general, the relative thickness distribution of the blade decreases monotonically from the root to the shoulder of the blade. Since the thick airfoil (40% thick airfoil) in the DU airfoil series is highly sensitive to the roughness of the leading edge, in order to ensure the low roughness sensitivity of the blade, it is necessary to limit the spanwise range of the thick airfoil. In this case, the relative thickness of the airfoil near 25% of the blade is limited to 37.5%-41%. The setting of the lift coefficient design variable is based on two criteria. On the one hand, in order to make the design space cover the optimal Cp point, the lift coefficient at the definition point must cover the lift coefficient corresponding to the maximum lift-to-drag ratio of the corresponding basic airfoil; on the other hand, in order to actively control the stall risk of the local blade element, the upper limit of the airfoil lift coefficient at the definition point is required to be kept at a distance of more than 0.4 from the corresponding maximum lift coefficient value of the airfoil. The definition of the induced trimming factor is set to 0 for the definition points within 85% (following the ideal induced distribution), and a certain low induced trimming space is set for the three definition points of the blade tip to perform low load design optimization.

[0123] It should be noted that in another case optimization, all induced trimming factors were set to 0, and the design was performed without considering induced trimming for comparative analysis.

[0124] Table 4 Variable matrix of case design

[0125]

[0126]

[0127] Table 5 gives the main constraint parameters of the case optimization design, where the lower boundary of the optimal Cp is set to 0.45, and the maximum value of the blade root load My is limited to no more than 35000 kNm. In addition, constraints are imposed on the absolute thickness of the key spanwise positions of the blade structure to ensure that the optimized shape can provide sufficient structural stiffness.

[0128] Table 5 Case design constraints

[0129]

[0130]

[0131] The specific objective function of the case optimization is a multi-objective function of annual power generation, blade root limit load and absolute thickness, as shown below; it should be noted that the specific objective function can be expanded with the complexity of the problem, and the following formula is only an example.

[0132] F obj =s 11 .w 11 .AEP+s 12 .w 12 .My_Max+s 21 w 21 AbsThick

[0133] As mentioned above, s 11 ,s 12 is the aerodynamic normalization factor, w 11 ,w 12 is the aerodynamic-related weight coefficient; s 21 is the geometry-related normalization factor, w 21 are weights and coefficients related to the geometric structure. The normalization factor is calculated according to the magnitude of each parameter, and the weight coefficient is allocated in a ratio of 1, 2, and 1. The specific expression of the objective function is:

[0134] F obj =0.000025AEP-0.00005My_Max+AbsThick

[0135] Attached Figure 5 Two design results based on this application are given, namely Optimization Case 1 and Optimization Case 2. These case design results are iterated with a design result (initial case) using a traditional forward direct numerical optimization method as the initial solution. The result of Optimization Case 1 is to set all induced trimming variables to 0, that is, the induced factors all use the ideal induced factor distribution during the iteration process; the result of Optimization Case 2 is to use all the design variables in Table 4 for iteration. The comparison of the core blade performance parameters of the three design results is shown in Table 6. The blade element load characteristic distribution (circulation distribution) of the three design schemes is shown in the attached figure. Figure 6 shown.

[0136] Table 6 Comparison of main performance parameters of case design results

[0137]

[0138] The results given in Table 6 are the aerodynamic characteristic parameters under the respective optimal conditions. The three schemes compared do not indicate that one result is the best, but are only used to illustrate the advantages of the scheme proposed in this application and the potential for further improving the performance of the blade. It can be seen from the table that, based on the initial case, the optimized case 1 obtained by the present application further improves the power generation of the initial blade while significantly reducing the blade root limit bending moment load My_Max, better balancing the aerodynamic efficiency-load ratio of the blade, and the ratio of power generation to load increases from 0.986 to 1.057. Note that, as mentioned above, the result of the optimized case 1 is the case where the induced trimming factors all adopt the ideal value (all the induced trimming factor variables are set to 0). When the induced trimming factor variables are further activated, that is, the settings in Table 4 are adopted, the design result obtained is the optimized case 2. It can be seen that the trimming optimization of the induction factor at the tip of the blade can achieve a reduction of about 1% in the power generation relative to the initial reference blade, and a 10% reduction in the blade root bending moment load. At this time, the aerodynamic load efficiency ratio is further optimized, and the power generation to load ratio increases from 0.986 to 1.098.

[0139] On the other hand, the load comparison of the three blades is shown in the following figure. Figure 6 It can be seen that the two results obtained by the aerodynamic layout hybrid design optimization method proposed in this application are more balanced and smoother than the load of the initial reference blade, and improve the aerodynamic risk caused by the local load mutation of the initial reference blade.

[0140] The above case results are only used as a verification of the blade optimization method proposed in this application.

[0141] The wind turbine blade design method proposed in this application, on the one hand, utilizes the design features of load balancing and controllable aerodynamic risk of local blade elements of traditional analytical design methods, avoiding the problems of sudden load changes and difficult to control aerodynamic risks of local blade elements in traditional direct design methods of numerical optimization of blade shape. On the other hand, the globalization and automated design advantages of numerical optimization design technology are applied to the analytical design method of transmission to improve it; in particular, through the parametric control of its design elements, the single-operating-condition and experience-dependent characteristics of the original analytical design method are eliminated; more importantly, through the parametric control of the distribution of induced factors, the limit condition restrictions (optimal Cp conditions) of the original design method are improved, and the design space of the analytical design method is fully expanded, which can realize the dual design control of macro blade performance indicators and local aerodynamic loads.

[0142] In addition, the wind turbine blade design method of the present application can also be improved in the following ways:

[0143] (1) Slightly change the spanwise position and number of design variables;

[0144] (2) Simply expand the composition or constraints of the optimization objective function;

[0145] (3) Simply change the weight coefficient, normalization factor, etc. of the sub-objective function;

[0146] (4) Slightly change the geometric post-processing method of the non-main wind energy capture area of ​​the blade root or blade tip;

[0147] Figure 7 A schematic diagram of the structure of a wind turbine blade design device provided in an embodiment of the present application;

[0148] Combination Figure 7 , introduces a wind turbine blade design device provided in an embodiment of the present application, the wind turbine blade design device comprising:

[0149] The acquisition module 701 is used to acquire input configuration data and value ranges corresponding to multiple design variables, wherein the configuration data includes wind condition data, wind turbine data adapted to the blade, blade data, model-related data, and airfoil data;

[0150] A selection module 702 is used to select a variable value of a design variable from a value range corresponding to each of the multiple design variables;

[0151] The analysis module 703 is used to analyze the blade shape based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade;

[0152] An evaluation module 704 is used to evaluate whether the performance data meets the preset conditions based on the objective function and the constraint conditions;

[0153] The optimization module 705 is used to select the variable values ​​of the design variables from the value ranges corresponding to the multiple design variables again when the conditions are not met, and return to perform blade shape analysis based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade, until the performance data meets the preset conditions or the number of iterations reaches the preset number, and the candidate shape data obtained from the last analysis is determined as the final shape data of the blade.

[0154] In some embodiments of the present application, multiple design variables include a design tip speed ratio and a design lift coefficient, an induced clipping factor, and a relative thickness at multiple different blade span positions, and the candidate shape data of the blade include the twist angle distribution and the chord length distribution of each blade element segment, wherein the induced clipping factor is the percentage deviation between the actual design application induction factor of the blade element segment and the induction factor under the theoretical optimal power coefficient assumption.

[0155] In some embodiments of the present application, the plurality of different blade spanwise positions include positions where the blade spanwise is at 0%, 15%, 25%, 35%, 55%, 85%, 95%, and 100%, respectively.

[0156] In some embodiments of the present application, the value ranges of the design lift coefficients at a plurality of different blade spanwise positions are determined in the following manner:

[0157] According to the designed lift coefficient distribution of the entire blade and the number of blade element units required to be discrete, the lift coefficient value at the corresponding blade element position is generated according to a predetermined blade element discrete criterion.

[0158] In some embodiments of the present application, the parsing module 703 includes:

[0159] A generating unit, used for generating an aerodynamic data table of each blade element airfoil of the blade according to the configuration data and the selected variable values;

[0160] An analysis unit, used for performing analysis based on an aerodynamic data table to obtain a twist angle distribution and a chord length distribution of each blade element segment;

[0161] An integration unit is used to integrate the torsion angle distribution and chord length distribution of all blade element segments to obtain candidate shape data of the blade;

[0162] The analysis unit is used to perform performance analysis on the blade according to the candidate shape data of the blade to obtain performance data of the blade.

[0163] In some embodiments of the present application, the parsing module 703 further includes:

[0164] The processing unit is used to simplify the torsion angle distribution and chord length distribution of the root segment and the tip segment in each blade element segment before integrating the torsion angle distribution and chord length distribution of all blade element segments to obtain the candidate shape data of the blade, so as to obtain the simplified torsion angle distribution and chord length distribution of the root segment and the tip segment, wherein the simplification processing includes linear processing and nonlinear clipping.

[0165] In some embodiments of the present application, the performance data includes aerodynamic performance data and geometric characteristic data, and the evaluation module 704 includes:

[0166] A first evaluation unit, used to evaluate whether the aerodynamic performance data and the geometric characteristic data meet a first preset condition;

[0167] The second evaluation unit is used to evaluate whether the aerodynamic performance data and the geometric characteristic data meet a second preset condition when it is determined that the aerodynamic performance data and the geometric characteristic data meet the first preset condition.

[0168] In some embodiments of the present application, the aerodynamic performance data includes at least one of power coefficient, annual power generation, blade root limit load and aerodynamic characteristics, and the geometric characteristic data includes at least one of absolute thickness distribution, blade surface area, blade aspect ratio and wind rotor solidity;

[0169] The first preset condition includes that at least one of the absolute thickness, stall margin, power coefficient and blade root load satisfies the preset constraint value, the stall margin includes the stall margin of the design lift coefficient or the stall margin of the design angle of attack, and the second preset condition includes that the function value of a multi-objective function with at least one of the power coefficient, annual power generation, blade root limit load and aerodynamic characteristics and at least one of the absolute thickness distribution, blade surface area, blade aspect ratio and wind rotor solidity as targets is maximized.

[0170] According to the wind turbine blade design device provided in the embodiment of the present application, the blade shape can be analyzed based on configuration data and multiple design variables to obtain candidate shape data and performance data of the blade. Compared with the single working condition design of the prior art, the solution of the present application can handle multi-working condition problems, and each design variable has its own corresponding value range. Through continuous iteration, the variable value that makes the blade performance meet the optimal performance can be found. Therefore, the optimal solution in the global design space can be obtained.

[0171] Figure 8 A schematic diagram of the structure of a wind turbine blade design device provided in an embodiment of the present application;

[0172] The wind turbine blade design device may include a processor 801 and a memory 802 storing computer program instructions.

[0173] Specifically, the processor 801 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0174] The memory 802 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 802 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 802 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 802 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 802 is a non-volatile solid-state memory.

[0175] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0176] The processor 801 implements the wind turbine blade design method in the above embodiment by reading and executing the computer program instructions stored in the memory 802 .

[0177] In one example, the wind turbine blade design device may further include a communication interface 803 and a bus 810. Figure 8 As shown, the processor 801, the memory 802, and the communication interface 803 are connected via a bus 810 and communicate with each other.

[0178] The communication interface 803 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0179] Bus 810 includes hardware, software or both, and the parts of the planning equipment of the wireless network base station are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 810 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0180] The wind turbine blade design device executes the wind turbine blade design method in the embodiment of the present application, thereby achieving Figure 1 Wind turbine blade design method.

[0181] In addition, in combination with the wind turbine blade design method in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the wind turbine blade design methods in the above embodiment is implemented.

[0182] In combination with the wind turbine blade design method in the above embodiments, an embodiment of the present application further provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the wind turbine blade design method in any of the above embodiments.

[0183] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0184] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0185] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0186] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box 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, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0187] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A wind turbine blade design method, characterized in that: The method comprises: Acquire input configuration data and value ranges corresponding to a plurality of design variables, wherein the configuration data includes wind condition data, wind turbine unit data adapted to the blade, blade data, model-related data, and airfoil data; Selecting a variable value of a design variable from the value ranges corresponding to each of the plurality of design variables; Performing blade shape analysis based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade; Using the objective function and the constraint conditions, evaluating whether the performance data meets the preset conditions; If the conditions are not met, the variable values ​​of the design variables are selected again from the value ranges corresponding to the multiple design variables, and the blade shape analysis is returned based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade, until the performance data meets the preset conditions or the number of iterations reaches the preset number, and the candidate shape data obtained from the last analysis is determined as the final shape data of the blade.

2. The wind turbine blade design method according to claim 1, characterized in that: The multiple design variables include a design tip speed ratio and a design lift coefficient, an induced clipping factor, and a relative thickness at multiple different blade span positions; the candidate shape data of the blade include a torsion angle distribution and a chord length distribution of each blade element segment; wherein the induced clipping factor is a deviation percentage between an actual design application induction factor of a blade element segment and an induction factor under a theoretical optimal power coefficient assumption.

3. The wind turbine blade design method according to claim 2, characterized in that: The plurality of different blade spanwise positions include positions where the blade spanwise is at 0%, 15%, 25%, 35%, 55%, 85%, 95%, and 100%, respectively.

4. The wind turbine blade design method according to claim 3, characterized in that: The value ranges of the design lift coefficients at the plurality of different blade spanwise positions are determined by: According to the designed lift coefficient distribution of the entire blade and the number of blade element units required to be discrete, the lift coefficient value at the corresponding blade element position is generated according to a predetermined blade element discrete criterion.

5. The wind turbine blade design method according to claim 2, characterized in that: The blade shape analysis is performed based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade, including: Generating an aerodynamic data table of each blade element airfoil of the blade according to the configuration data and the selected variable values; Analyze based on the aerodynamic data table to obtain the twist angle distribution and chord length distribution of each blade element segment; Integrate the torsion angle distribution and chord length distribution of all blade element segments to obtain candidate shape data of the blade; The performance of the blade is analyzed according to the candidate shape data of the blade to obtain the performance data of the blade.

6. The wind turbine blade design method according to claim 5, characterized in that: Before integrating the torsion angle distribution and chord length distribution of all blade element segments to obtain candidate shape data of the blade, the method further includes: The twist angle distribution and chord length distribution of the blade root segment and the blade tip segment in each blade element segment are simplified to obtain the simplified twist angle distribution and chord length distribution of the blade root segment and the blade tip segment, wherein the simplified processing includes a linear processing method and a nonlinear clipping.

7. The method for designing a wind turbine blade according to any one of claims 1 to 6, characterized in that: The performance data includes aerodynamic performance data and geometric characteristic data. The objective function and constraint conditions are used to evaluate whether the performance data meets the preset conditions, including: evaluating whether the aerodynamic performance data and the geometric characteristic data meet a first preset condition; When it is determined that the aerodynamic performance data and the geometric characteristic data satisfy the first preset condition, it is evaluated whether the aerodynamic performance data and the geometric characteristic data satisfy a second preset condition.

8. The wind turbine blade design method according to claim 7, characterized in that: The aerodynamic performance data includes at least one of power coefficient, annual power generation, blade root limit load and aerodynamic characteristics, and the geometric characteristic data includes at least one of absolute thickness distribution, blade surface area, blade aspect ratio and wind rotor solidity; The first preset condition includes that at least one of the absolute thickness, the stall margin, the power coefficient and the blade root load satisfies the preset constraint value, the stall margin includes the stall margin of the design lift coefficient or the stall margin of the design angle of attack, and the second preset condition includes that the function value of a multi-objective function with at least one of the power coefficient, the annual power generation, the blade root limit load and the aerodynamic characteristics and at least one of the absolute thickness distribution, the blade surface area, the blade aspect ratio and the wind wheel solidity as targets is maximized.

9. A wind turbine blade design device, characterized in that: The device comprises: An acquisition module, used to acquire input configuration data and value ranges corresponding to a plurality of design variables, wherein the configuration data includes wind condition data, wind turbine data adapted to the blade, blade data, model-related data, and airfoil data; A selection module, used for selecting a variable value of a design variable from a value range corresponding to each of the plurality of design variables; An analysis module, used for analyzing the blade shape based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade; An evaluation module, used to evaluate whether the performance data meets preset conditions based on an objective function and constraint conditions; The optimization module is used to select the variable value of the design variable from the value range corresponding to each of the multiple design variables again when the conditions are not met, and return to perform blade shape analysis based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade, until the performance data meets the preset conditions or the number of iterations reaches the preset number, and the candidate shape data obtained from the last analysis is determined as the final shape data of the blade.

10. A wind turbine blade design device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the wind turbine blade design method according to any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for designing a wind turbine blade according to any one of claims 1 to 8 is implemented.

12. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the wind turbine blade design method according to any one of claims 1 to 8.

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