Method, device, equipment and medium for determining blade shape parameters of fan

Abstract

The embodiment of the present application provides a method, device, equipment and medium for determining the blade shape parameters of a wind turbine. The method comprises: obtaining multiple groups of candidate blade shape parameters of a wind turbine; for each group of candidate blade shape parameters, using an iterative algorithm to determine the axial induction factor and the tangential induction factor until a preset iteration stop condition is met; based on the axial induction factor and the tangential induction factor of each of the multiple groups of candidate blade shape parameters, determining the target blade shape parameters of the wind turbine from the multiple groups of candidate blade shape parameters. According to the embodiment of the present application, the design efficiency of blade shape parameters can be improved.

Description

Technical Field

[0001] The present application relates to the field of wind power generation, and in particular to a method, device, equipment and medium for determining blade shape parameters. Background Art

[0002] With the continuous development of wind power generation technology, wind turbines have now been widely used.

[0003] Before a wind turbine is put into use, it is necessary to reasonably design the appearance of each component of the wind turbine to determine its appearance parameters. In particular, it is necessary to determine reasonable appearance parameters of the blades of the wind turbine, because the blade appearance parameters will directly affect the performance and benefits of the wind turbine.

[0004] At present, blade shape parameters are usually designed manually to determine reasonable blade shape parameters. However, this method of determining blade shape parameters can only be designed based on manual experience and cannot guarantee the performance of the fan. Summary of the invention

[0005] The embodiments of the present application provide a method, device, equipment and medium for determining blade profile parameters of a wind turbine, which can optimize the performance of the wind turbine.

[0006] In a first aspect, an embodiment of the present application provides a method for determining blade profile parameters of a wind turbine, the method comprising:

[0007] Obtain multiple groups of candidate blade shape parameters of a wind turbine generator set;

[0008] For each group of candidate blade shape parameters, an iterative algorithm is used to determine the axial induction factor and the tangential induction factor until a preset iteration stop condition is met; when the preset iteration stop condition is not met and the target thrust coefficient in the Nth iteration process is less than a first preset threshold, the axial induction factor and the tangential induction factor in the N+1th iteration process are determined according to the axial induction factor in the Nth iteration process, the target thrust coefficient in the Nth iteration process, and the blade loss factor, wherein N is a positive integer;

[0009] Based on the axial induction factor and the tangential induction factor of each group of candidate blade shape parameters, determine the fan performance parameters corresponding to each group of candidate blade shape parameters;

[0010] Determine the maximum value among the wind turbine performance parameters corresponding to each of the multiple groups of candidate blade shape parameters, and determine the target blade shape parameters of the wind turbine generator set from the candidate blade shape parameters corresponding to the maximum value;

[0011] Among them, the preset iteration stop condition is that the change of the axial induction factor between the Nth iteration process and the N-1th iteration process is less than the second preset threshold and the change of the tangential induction factor between the Nth iteration process and the N-1th iteration process is less than the third preset threshold.

[0012] In a second aspect, an embodiment of the present application provides a device for determining blade profile parameters of a wind turbine, the device comprising:

[0013] A parameter acquisition module, used to acquire multiple groups of candidate blade shape parameters of a wind turbine generator set;

[0014] The processing module is used to perform the following steps for each set of candidate blade shape parameters:

[0015] An iterative algorithm is used to determine the axial induction factor and the tangential induction factor until a preset iteration stop condition is met; when the preset iteration stop condition is not met and the target thrust coefficient in the Nth iteration process is less than a first preset threshold, the axial induction factor and the tangential induction factor in the N+1th iteration process are determined according to the axial induction factor in the Nth iteration process, the target thrust coefficient in the Nth iteration process, and the blade loss factor, wherein N is a positive integer;

[0016] A parameter determination module is used to determine the wind turbine performance parameters corresponding to each group of candidate blade shape parameters based on the axial induction factor and the tangential induction factor of each group of candidate blade shape parameters; determine the maximum value of the wind turbine performance parameters corresponding to each of the multiple groups of candidate blade shape parameters, and determine the target blade shape parameters of the wind turbine generator set from the candidate blade shape parameters corresponding to the maximum value;

[0017] Among them, the preset iteration stop condition is that the change of the axial induction factor between the Nth iteration process and the N-1th iteration process is less than the second preset threshold and the change of the tangential induction factor between the Nth iteration process and the N-1th iteration process is less than the third preset threshold.

[0018] In a third aspect, an embodiment of the present application provides a device for determining blade profile parameters of a wind turbine, the device comprising:

[0019] a memory for storing computer program instructions;

[0020] The processor is used to read and run the computer program instructions stored in the memory to execute the method for determining the blade shape parameters of the wind turbine provided by the first aspect or any optional implementation manner of the first aspect.

[0021] In a fourth aspect, an embodiment of the present application provides a computer storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the method for determining the blade shape parameters of a wind turbine provided by the first aspect or any optional implementation scheme of the first aspect is implemented.

[0022] The method, device, equipment and medium for determining the blade shape parameters of the wind turbine of the embodiment of the present application can first obtain multiple groups of candidate blade shape parameters, and then accurately obtain the axial induction factor and tangential induction factor of each group of candidate blade shape parameters by continuously adjusting the axial induction factor and tangential induction factor until the iteration stop condition is met, and then accurately determine the wind turbine performance parameters of each group of candidate blade shape parameters according to the axial induction factor and tangential induction factor of each group of candidate blade shape parameters. Then determine the maximum value of the wind turbine performance parameters corresponding to each of the multiple groups of candidate blade shape parameters, and determine the target blade shape parameters of the wind turbine generator set from the candidate blade shape parameters corresponding to the maximum value. Therefore, in the design process of the wind turbine blades, compared with the manual setting method, the blade shape parameters with the best wind turbine performance can be selected, and then the blade shape parameters with the best wind turbine performance parameters are used to design the wind turbine blades, thereby improving the performance of the wind turbine. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

[0024] Figure 1 It is a flow chart of a method for determining the blade shape parameters of a wind turbine provided in an embodiment of the present application;

[0025] Figure 2 It is a flow chart of another method for determining the blade shape parameters of a wind turbine provided in an embodiment of the present application;

[0026] Figure 3 It is a structural schematic diagram of a device for determining blade shape parameters of a wind turbine provided in an embodiment of the present application;

[0027] Figure 4 It is a schematic diagram of the hardware structure of a device for determining the blade shape parameters of a wind turbine provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] 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.

[0029] 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 existence of other identical elements in the process, method, article or device including the elements.

[0030] Since the blade shape parameters of wind turbines are related to the safety of wind turbines and power generation efficiency, it is necessary to design the shape parameters of wind turbines. At present, the blade shape parameters are often designed based on manual experience. This manual design method can only be designed based on manual experience and cannot guarantee the performance of wind turbines.

[0031] Therefore, a technical solution for blade design that can improve wind turbine performance is needed.

[0032] In order to improve the performance of a wind turbine, in one case, a target shape parameter may be determined from a plurality of groups of candidate blade shape parameters in the following manner. Figure 1 FIG. 1 is a flow chart of a method for determining the blade shape parameters of a wind turbine provided in an embodiment of the present application. Figure 1 As shown, the method for determining blade shape parameters includes steps S101 to S110.

[0033] S101, obtaining the jth group of candidate blade shape parameters from multiple groups of candidate blade shape parameters. The jth group of candidate blade shape parameters includes the local solidity σ j and the local speed ratio λ rj . Wherein, j is any positive integer, and j is less than or equal to the number of groups of candidate blade shape parameters.

[0034] S102, in the i-th iteration, using the axial induction factor a in the N-th iteration N , local solidity σ j and the local speed ratio λ rj , calculate the target thrust coefficient C T . Among them, N is 1, 2, ..., until the iteration stops.

[0035] S103, determine the target thrust coefficient C T Is it greater than or equal to 0.64F, where F is the Prandtl wingtip loss factor.

[0036] S104, if the target thrust coefficient C T Greater than or equal to 0.64F, use formula (1) to calculate the axial induction factor a during the N+1th iteration N+1 .

[0037] Among them, formula (1) can be expressed as:

[0038]

[0039] In formula (1), a c It represents the critical value of the axial induction factor corrected by the high-load thrust model, which can be set according to the actual scenario and specific needs and will not be described in detail here.

[0040] S105, if the target thrust coefficient C T Less than 0.64F, use formula (2) to calculate the axial induction factor a during the N+1th iteration N+1 .

[0041] Among them, formula (2) can be expressed as:

[0042]

[0043] In formula (2), φ represents the relative flow angle of attack, C n represents the normal force coefficient.

[0044] Where, the normal force coefficient C n It is expressed as formula (3):

[0045] C n =C l cosφ+C d sinφ (3)

[0046] In formula (3), C l is the lift coefficient, C d Represents the drag coefficient.

[0047] S106, using formula (4) to calculate the tangential induction factor a' in the N+1th iteration process N+1 .

[0048] Among them, formula (4) can be expressed as:

[0049]

[0050] S107, determining the change Δa of the axial induction factor in the Nth iteration process N Is it greater than the second preset threshold ε1, and the change amount Δa′ of the tangential induction factor in the Nth iteration process N Is it greater than the second preset threshold ε2?

[0051] S108, if Δa N >ε1 or Δa′ N >ε2, then set N equal to N+1 and return to execute steps S102 to S107.

[0052] S109, if Δa N ≤ε1 and Δa′ N ≤ε2, the iteration is stopped and the axial induction factor a in the Nth iteration is N and tangential induction factor a′ N , as the axial induction factor and tangential induction factor of the jth group of candidate blade shape parameters.

[0053] S110, determining target blade shape parameters of the wind turbine generator set from the multiple groups of candidate blade shape parameters based on the axial induction factors and tangential induction factors of the multiple groups of candidate blade shape parameters.

[0054] Compared with the manual method, the method shown in S101 to S110 can improve the design efficiency of blade shape parameters.

[0055] However, this solution does not take fan performance into consideration during blade design and cannot guarantee fan performance during operation.

[0056] In addition, this process has the following possibilities that may lead to solution failure:

[0057] (1) A trivial solution that makes the axial induction factor and / or the circumferential induction factor equal to 1 through iteration results in the inability to obtain the candidate blade shape parameters;

[0058] (2) It falls into infinite iteration, which makes it impossible to obtain the candidate blade shape parameters;

[0059] (3) The axial induction factor and / or the circumferential induction factor are invalid (including the case where they are complex or infinite), resulting in the inability to obtain the candidate blade shape parameters. The invalid axial induction factor includes: the case where the axial induction factor is complex or infinite.

[0060] In order to improve the performance of the wind turbine, the embodiments of the present application also provide a method, device, equipment and medium for determining the blade shape parameters of the wind turbine, which can be applied to the application scenario of designing the shape parameters of wind turbine generator sets. Compared with the above-mentioned manual method, the axial induction factor and the tangential induction factor can be continuously adjusted during the iteration process until the iteration stop condition is met, and the axial induction factor and tangential induction factor of each group of candidate blade shape parameters can be accurately and quickly output. And the axial induction factor and tangential induction factor of each group of candidate blade shape parameters can be used to accurately determine the wind turbine performance parameters of each group of candidate blade shape parameters. Thereby, it is possible to select appropriate blade shape parameters according to the wind turbine performance parameters, and then use the blade shape parameters with the best wind turbine performance parameters to design the blades of the wind turbine, thereby improving the performance of the wind turbine.

[0061] In order to better understand the present application, the following will describe in detail the method, device, equipment and medium for determining the blade shape parameters of the wind turbine according to the embodiments of the present application in conjunction with the accompanying drawings. It should be noted that these embodiments are not intended to limit the scope of the present application.

[0062] Figure 2 It is a flow chart of another method for determining the blade shape parameters of a wind turbine provided in an embodiment of the present application.

[0063] like Figure 2 As shown, the method for determining blade profile parameters includes steps S201 to S204. Optionally, the execution subject of each step S201 to S204 may be a server with computing functions.

[0064] S201, obtaining multiple groups of candidate blade shape parameters of a wind turbine generator set.

[0065] In some embodiments, the blade shape parameters include: impeller rotation angular velocity, number of blades, blade chord length and blade spanwise position. In one embodiment, the above parameters can be set according to specific scenarios and actual needs. For example, the impeller rotation angular velocity can be set according to at least one factor of the type of motor used, the transmission system, the load limit and the power generation efficiency. For another example, the number of blades can be selected as 3 or 4, etc., and the specific number is not limited. For another example, the chord length of the blade can be set according to commonly used airfoil parameters. For another example, the spanwise position of the blade can be an empirical value.

[0066] In some embodiments, dimensionless blade shape parameters may be used to facilitate calculation. For example, each set of candidate blade shape parameters may include a local speed ratio λr and the local solidity σ.

[0067] Among them, the local speed ratio λ r It can be expressed as formula (5):

[0068] λ r =wr / υ0 (5)

[0069] In formula (5), w is the impeller rotation angular velocity, r is the spanwise position of the blade, and v0 is the incoming wind speed.

[0070] Among them, the local solidity σ can be expressed as formula (6):

[0071] σ=Bc / 2πr (6)

[0072] In formula (6), B is the number of blades and c is the chord length.

[0073] In this embodiment, compared with using parameters such as angular velocity, number of blades, chord length, etc., using local speed ratio and local solidity has the following advantages:

[0074] 1. Since the local speed ratio and local solidity are dimensionless parameters, only a small number of dimensionless parameters can represent a large number of blade shape parameters. For example, there may be multiple sets of different angular velocities, numbers of blades, and chord lengths corresponding to the same local speed ratio and local solidity. At this time, only one set of local speed ratio and local solidity is required to obtain the thrust coefficients corresponding to multiple sets of different angular velocities, numbers of blades, and chord lengths, thereby improving the design efficiency of blade shape parameters.

[0075] 2. Using dimensionless numbers can avoid having too many variables, thus avoiding the problem of the conclusions being too complicated and unrepresentative.

[0076] In a specific embodiment, if the candidate blade profile parameters include local solidity and local speed ratio, the specific implementation of S201 may include the following steps a1 to a4.

[0077] Step a1, obtaining the value range of the local solidity and the value range of the local speed ratio.

[0078] The value range of the local solidity and the value range of the local speed ratio can be set according to the actual situation and specific needs, and there is no limitation on this. For example, the value range of the local solidity can be [0.005, 1], and the local speed ratio can be [0.5, 12].

[0079] Step a2, within the local solidity value range, take M1 solidity parameters, where the interval between every two adjacent solidity parameters is the same. For example, they can be 0.005, 0.01, 0.015, etc. The value of M1 can be set according to the specific scenario and actual needs. For example, it can be determined according to the accuracy of the design. The larger the value of M1, the higher the accuracy of the design, for example, it can be 20.

[0080] Step a3, within the value range of the local speed ratio, select M2 speed ratio parameters, wherein the interval between every two adjacent speed ratio parameters is the same. Specifically, the specific value selection method of the speed ratio parameter is similar to the specific method of the solidity parameter in step a2, and will not be repeated here.

[0081] Step a4, select any one solidity parameter from the M1 solidity parameters and any one speed ratio parameter from the M2 speed ratio parameters to obtain a set of candidate blade shape parameters, and obtain a total of M1×M2 sets of candidate blade shape parameters.

[0082] S202: for each group of candidate blade shape parameters, an iterative algorithm is used to determine the axial induction factor a and the tangential induction factor a′ until a preset iteration stop condition is met.

[0083] First, set the preset iteration stop condition.

[0084] The preset iteration stop conditions in the Nth iteration process include: the change in the axial induction factor Δa in the Nth iteration process N is less than the second preset threshold ε1 and the change Δa′ of the tangential induction factor in the Nth iteration process N Less than the third preset threshold ε2.

[0085] Specifically, for the Nth iteration process, the change in the axial induction factor Δa is N , which is equal to the axial induction factor a of the Nth iteration process N and the axial induction factor a during the N-1th iteration N-1 That is, Δa N =a N -a N-1 .

[0086] The change of the tangential induction factor Δa′ for the Nth iteration process N , which is equal to the tangential induction factor a′ of the Nth iteration process N and the tangential induction factor a′ in the N-1th iteration N-1 That is, Δa′ N =a′ N -a′ N-1 .

[0087] For the second preset threshold ε1 and the third preset threshold ε2, the second preset threshold ε1 represents the allowable error limit of the axial induction factor. The third preset threshold ε2 represents the allowable error limit of the tangential induction factor. The second preset threshold ε1 and the third preset threshold ε2 can be the same or different, and there is no limitation on this. The second preset threshold ε1 and the third preset threshold ε2 can be set according to specific scenarios and actual needs, and no further description is given. For example, ε1 and ε2 can be equal to 1×10 -8 .

[0088] Secondly, in S202, it also includes C in the Nth iteration process T The determination steps are as follows: T Specific implementations include:

[0089] Step b1: for each j group of candidate blade shape parameters, use the axial induction factor a in the Nth iteration process N and the tangential induction factor a′ during the Nth iteration N , determine the relative incoming flow angle φ. Where j is an arbitrary integer less than the number of candidate blade shape parameters.

[0090] Specifically, the relative incoming flow angle φ satisfies formula (7):

[0091]

[0092] It should be noted that λ rj is the local speed ratio of the jth group of candidate blade shape parameters.

[0093] Step b2, calculate the normal force coefficient C according to the relative incoming flow attack angle φ calculated in step b1 n .

[0094] Among them, C n The calculation formula of can be found in the above formula (3), which will not be repeated here.

[0095] Step b3, the normal force coefficient C calculated in step b2 n , the local solidity σ of the jth group of candidate blade shape parameters j , relative incoming flow angle φ and axial induction factor a N , calculate the target thrust coefficient C in the Nth iteration process T .

[0096] Specifically, the target thrust coefficient C in the Nth iteration process is T Satisfies formula (8):

[0097]

[0098] In addition, S202 illustrates a situation where a preset iteration condition is met. However, in each iteration process, there is a possibility that the preset iteration stop condition is not met.

[0099] Specifically, in the Nth iteration process, if the iteration process does not meet the preset iteration stop condition and the target thrust coefficient C in the iteration process is T If the value is less than the first preset threshold, the axial induction factor a in the Nth iteration process can be used. N , the target thrust coefficient C in the Nth iteration process T and blade loss factor F a , determine the axial induction factor a during the N+1th iteration N+1 and tangential induction factor a′ N+1 . Wherein, N is a positive integer.

[0100] For the first preset threshold, in some embodiments, the first preset threshold is used to characterize the critical state of the thrust load. Specifically, the first preset threshold can be expressed as a ratio of a preset empirical value and a blade loss factor F a The product of. The preset empirical value can be set according to the actual situation and specific needs. For example, the preset empirical value can be in the interval of [0.6,1]. In one example, different preset empirical coefficients can be selected according to the type of high-load thrust model. For example, if the spera thrust model is selected, the preset empirical coefficient can be 0.64. In addition, the Glauert thrust model, Buhl thrust model and GH-Bladed thrust model can also be selected, and there is no limitation on this.

[0101] To determine the axial induction factor a N+1 and tangential induction factor a′ N+1 In some embodiments, the axial induction factor a is determined during the N+1th iteration. N+1 and tangential induction factor a′ N+1 The specific implementation method may include the following steps c1 and c2.

[0102] Step c1: According to the target thrust coefficient C in the Nth iteration process T Blade loss factor F multiple of target a The ratio of determines the preset proportional coefficient.

[0103] Among them, the blade loss factor F a It can be set according to specific scenarios and specific needs, for example, it can be the Prandtl tip loss factor, the Prandtl root loss factor, the product of the Prandtl tip loss factor and the Prandtl root loss factor, or other blade loss factors, and there is no specific limitation on this.

[0104] In one example, the target multiple may be 4. Accordingly, the preset proportionality factor may be expressed as C T / 4F a .

[0105] Step c2: Based on the first coefficient, the axial induction factor a in the Nth iteration process N , and the preset proportional coefficient, determine the axial induction factor a in the N+1th iteration process N+1 .

[0106] In one example, the first coefficient can be taken as 1. If the preset proportional coefficient is expressed as C T / 4F a , then the axial induction factor a in the N+1th iteration process is N+1 It is expressed as the following formula (9):

[0107] a N+1 =C T / (4F a (1-a N )) (9)

[0108] It should be noted that, according to formula (8), formula (9) can also be transformed into the following formula (10):

[0109]

[0110] In this embodiment, when C T Less than 0.64F a When , formula (9) can be transformed into formula (11):

[0111] a N+1 =C T / (4F a (1-a N ))<0.64F a / (4F a (1-a N ))=0.16 / (1-a N ) (11)

[0112] If a N+1 If a is less than 1, N Less than 0.84, and Figure 1 Compared with the determination method shown, the above three solutions can be avoided. For example, compared with the original formula (2), formula (11) can effectively avoid a from easily falling into the vicinity of a=1 during the solution process, thereby ensuring the solution success rate and improving the calculation success rate of the fan performance parameters, and correspondingly improving the design efficiency of the blade shape parameters. In addition, using Figure 1When the determination method shown is used to design the fan blade shape parameters, if the solution of some candidate blade shape parameters fails, only the fan performance parameters of the remaining one or more sets of candidate blade shape parameters can be selected from the remaining one or more sets of candidate blade shape parameters. There is a possibility that the blade shape parameters corresponding to the optimal fan performance parameters cannot be selected due to the failure to solve the candidate blade shape parameters corresponding to the optimal performance parameters. The determination method provided in the embodiment of the present application can accurately obtain the fan performance parameters of each set of candidate blade shape parameters, thereby further improving the performance parameters of the fan.

[0113] Accordingly, the robustness of the design method of blade shape parameters is improved.

[0114] In some embodiments, the values ​​of the axial induction factor a1 and the tangential induction factor a′1 in the first iteration process can be set according to actual conditions, and the two can be the same or different, such as being equal to 0. Exemplarily, the value of the axial induction factor a1 can be in the interval of [0, 1 / 3], and the value of the tangential induction factor a′1 can be in the interval of [-0.01, 0.01].

[0115] S203: Determine the fan performance parameters corresponding to each group of candidate blade shape parameters based on the axial induction factors and tangential induction factors of each group of candidate blade shape parameters.

[0116] In some embodiments, the wind turbine performance parameters may be parameters that can reflect the working performance of the wind turbine under operating conditions, such as wind energy utilization coefficient and blade unit efficiency of wind turbine power generation, and are not limited to this.

[0117] S204, determining the maximum values ​​among the wind turbine performance parameters corresponding to each of the multiple groups of candidate blade shape parameters, and determining the target blade shape parameters of the wind turbine generator set from the candidate blade shape parameters corresponding to the maximum values.

[0118] For example, if the wind energy utilization efficiencies corresponding to the respective candidate blade shape parameters are obtained, the maximum wind energy utilization efficiency can be selected from them. The candidate blade shape parameters corresponding to the maximum wind energy utilization efficiency can be determined as the target blade shape parameters of the wind turbine generator set. The wind turbine blades are designed based on the target blade shape parameters.

[0119] The method for determining the blade shape parameters of a wind turbine in the embodiment of the present application can first obtain multiple groups of candidate blade shape parameters, and then accurately obtain the axial induction factor and tangential induction factor of each group of candidate blade shape parameters by continuously adjusting the axial induction factor and tangential induction factor until the iteration stop condition is met, and then accurately determine the wind turbine performance parameters of each group of candidate blade shape parameters according to the axial induction factor and tangential induction factor of each group of candidate blade shape parameters. Then determine the maximum value of the wind turbine performance parameters corresponding to each of the multiple groups of candidate blade shape parameters, and determine the target blade shape parameters of the wind turbine generator set from the candidate blade shape parameters corresponding to the maximum value. Therefore, in the design process of the wind turbine blades, compared with the manual setting method, the blade shape parameters with the best wind turbine performance can be selected, and then the blade shape parameters with the best wind turbine performance parameters are used to design the wind turbine blades, thereby improving the performance of the wind turbine.

[0120] In addition, through the embodiments of the present application, the fan blades can be designed from the perspective of the fan operating performance, thereby improving the design reliability.

[0121] In some embodiments, at the target thrust coefficient C T ≥ the first preset threshold, the specific implementation of calculating the axial induction factor in the N+1th iteration process can refer to the relevant description of S104, which will not be repeated here.

[0122] In some embodiments, the specific implementation of calculating the tangential induction factor in the N+1th iteration process can refer to the relevant description of S106, which will not be repeated here.

[0123] In some embodiments, in order to further improve the design speed of blade shape parameters, during the Nth iteration process, step d1 and step d2 are also included.

[0124] Step d1, using the relaxation factor rf, the axial induction factor a during the Nth iteration N and the axial induction factor a during the N+1th iteration N+1 , determine the first adjustment amount of the axial induction factor.

[0125] In one embodiment, the first adjustment amount can be expressed as: rf×a N+1 +(1-rf)×a N .

[0126] Accordingly, step d1 can be implemented as the following steps d11 to d13:

[0127] Step d11, based on the relaxation factor rf and the axial induction factor a during the N+1th iteration N+1 The product of rf×a determines the first adjustment component N+1 .

[0128] Step d12, determining a second adjustment component according to the product of the second coefficient, the relaxation factor and the axial induction factor in the Nth iteration process.

[0129] For example, the second coefficient can be 1, and accordingly, the second adjustment component can be expressed as (1-rf)×a N .

[0130] Step d13: convert the first adjustment component rf×a N+1 and the first adjustment component (1-rf)×a N The sum of the values ​​is determined as the first adjustment amount.

[0131] Step d2, using the first adjustment amount, updating the axial induction factor in the N+1th iteration process to obtain the updated axial induction factor a in the N+1th iteration process N+1 .

[0132] In one embodiment, the updated a N+1 It can be expressed as formula (12):

[0133] a N+1 =rf×a N+1 +(1-rf)×a N (12)

[0134] Among them, a on the left side of the formula N+1 Represents the updated a N+1 , the a on the right side of the formula N+1 Indicates a before update N+1 .

[0135] Through this embodiment, since the axial induction factor is constantly converging during the iteration process, by setting the relaxation factor, the convergence rate of the axial induction factor can be accelerated, and the iteration speed of the axial induction factor and the tangential induction factor is correspondingly improved, thereby improving the design speed of the blade shape parameters.

[0136] The relaxation factor in the above steps has a value less than 1. The initial value of the relaxation factor can be set to 1, or to any value less than 1, such as 1 / 2.

[0137] In one embodiment, in order to further improve the design speed of blade profile parameters, the relaxation factor decreases as the number of iterations increases. That is, the relaxation factor in the Nth iteration process is less than the relaxation factor in the N-1th iteration process.

[0138] In another example, an iteration number threshold may be set, and if the number of iterations is greater than the iteration number threshold, the relaxation factor is reduced by a preset ratio, wherein the preset ratio may be 1 / 2, or may be changed to other ratios, such as 1 / 3, which is not limited.

[0139] In another example, if the axial induction factor is equal to 1, falls into an infinite loop, is a complex number, or is infinite, the relaxation factor is reduced by a preset ratio. The preset ratio can refer to the relevant description of the previous example, which will not be repeated here.

[0140] In some embodiments, in order to further improve the design speed of blade shape parameters, the method further includes: steps e1 to e3.

[0141] Step e1, obtain the axial induction factor a during the N+2th iteration N+2 .

[0142] Among them, the axial induction factor a N+2 Please refer to the relevant instructions in the calculation process of the N+1th iteration, which will not be repeated here.

[0143] Step e2, according to the axial induction factor a in the Nth iteration process N , the axial induction factor a during the N+1th iteration N+1 and the axial induction factor a during the N+2th iteration N+2 , determine the fourth adjustment amount.

[0144] In one embodiment, the fourth adjustment amount A can be expressed as formula (13):

[0145] A=a N -(a N+1 -a N ) 2 / (a N+2 -2a N+1 +a N ) (13)

[0146] Accordingly, step e2 can be specifically implemented as steps e21 to e26.

[0147] Step e21, determining the first change of the axial induction factor between the N+1th iteration process and the Nth iteration process, i.e., a N+1 -a N .

[0148] Step e22, determining the second change of the axial induction factor in the second iteration process after the N+2th iteration and the N+1th iteration process, that is, a N+2 -a N+1 .

[0149] Step e23, determining a first difference between the second change amount and the first change amount, i.e., a N+2 -2a N+1 +a N .

[0150] Step e24, determining the ratio of the square of the first variation to the first difference, that is, (a N+1 -a N ) 2 / (a N+2 -2a N+1 +a N )

[0151] Step e25, determining the ratio as the third variation.

[0152] Step e26, determining the difference between the axial induction factor in the Nth iteration process and the third change amount as the fourth adjustment amount.

[0153] Step e3: using the fourth adjustment amount, updating the axial induction factor in the N+1th iteration process to obtain an updated axial induction factor in the N+1th iteration process.

[0154] That is, the updated a N+1 =A=a N -(a N+1 -a N ) 2 / (a N+2 -2a N+1 +a N ).

[0155] Through this embodiment, the axial induction factor in the current iteration process can be continuously corrected according to the previous iteration result and the next iteration structure during the iteration process, thereby improving the convergence rate and further improving the design speed of the blade shape parameters.

[0156] In some embodiments, since the above step b2 involves the normal force coefficient C n , and the normal force coefficient C n and lift coefficient C l and the drag coefficient C d Therefore, the method for determining the blade shape parameters of the wind turbine provided in the embodiment of the present application may also include determining the lift coefficient C l and the drag coefficient C d steps.

[0157] Step g1, obtaining multiple angles of attack and a lift coefficient and a drag coefficient corresponding to each of the multiple angles of attack by using a difference method at the same Reynolds number.

[0158] Step g2, calculating the lift-to-drag ratio corresponding to each angle of attack, wherein the lift-to-drag ratio corresponding to each angle of attack is equal to the ratio of the lift coefficient to the drag coefficient corresponding to the angle of attack.

[0159] Step g3, determining the best angle of attack among multiple angles of attack based on the lift-to-drag ratio of each angle of attack.

[0160] Step g4: Determine the lift coefficient and drag coefficient corresponding to the optimal angle of attack to calculate the normal force coefficient C n The lift coefficient C used l and the drag coefficient C d .

[0161] In order to verify the calculation speed and calculation success rate of the method for determining the blade shape parameters of the wind turbine shown in steps S101 to S110, hereinafter referred to as method one, and the method for determining the blade shape parameters of the wind turbine shown in steps S201 to S204, steps d11 to d13 and steps e1 to e3, hereinafter referred to as method two, the applicant conducted the following experiments.

[0162] The applicant defines twist angle (-5° to 25°), local speed ratio (0.5 to 12) and local solidity (0.005 to 0.1) as given parameter variation intervals. Dividing the above three parameter intervals into 20 equal parts, 20×20×20 different parameter combinations can be obtained. Then, for these 8000 different combinations and the 14 airfoils shown in Table 1, method one and method two are used for blade design respectively, and the design efficiency is compared by the average failure rate and the average number of iterations required for each successful solution. Among them, the second preset threshold and the third preset threshold are set to 1×10 -8 At the same time, for the convenience of calculation, the blade loss factor is temporarily assumed to be 1.

[0163] Among them, for the sake of fairness, the author has added steps d11 to d13 to method one, thereby avoiding the impact of its absence on method one.

[0164] Table 1

[0165]

[0166]

[0167] As shown in Table 1, the Reynolds number of the S809 airfoil in Example 1 is 0.75E6; the Reynolds number of the S815 airfoil in Example 2 is 1E6; the Reynolds number of the S815 (rough) airfoil in Example 3 is 1E6; the Reynolds number of the FFA-W3-241 airfoil in Example 4 is 12E6; the Reynolds number of the FFA-W3-301 airfoil in Example 5 is 10E6; the Reynolds number of the FFA-W3-360 airfoil in Example 6 is 10E6; the Reynolds number of the FFA-W3-480 airfoil in Example 7 is 10E6; the Reynolds number of the FFA-W3-80 in Example 8 is 10E6. The A-W3-600 airfoil has a Reynolds number of 6E6; the NACA64618 airfoil in Example 9 has a Reynolds number of 7E6; the DU93-W-210LM airfoil in Example 10 has a Reynolds number of 7E6; the DU91-W2-250LM airfoil in Example 11 has a Reynolds number of 7E6; the DU97-W-300LM airfoil in Example 12 has a Reynolds number of 7E6; the DU99-W-350LM airfoil in Example 13 has a Reynolds number of 7E6; and the DU99-W-405LM airfoil in Example 14 has a Reynolds number of 7E6.

[0168] It should be noted that the "rough" in Example 3 is a special case in the test of wind turbine-specific airfoils, with specific test standards. It is mainly used to simulate the state of the airfoil after running in the actual environment for a period of time (it may be affected by sand, dust, raindrops, insects, etc., resulting in an uneven surface).

[0169] Table 2 shows the average failure rate of blade design and the average number of iterations required for each successful solution using method 1 and method 2 for the 8000 different combinations and the 14 airfoils shown in Table 1.

[0170] Table 2

[0171] Method 2 Method 1 Average failure rate (%) 0 5.52％ Average number of iterations 6.74 44.76

[0172] As can be seen from Table 2, the solution success rate of method 2 is 100%, which is much better than that of method 1. The average number of iterations of method 2 is only 1 / 7 of that of method 1. If the total number of calculations is 112,000 times, the 5% failure probability of method 1 means more than 5,600 solution failures, which is much higher than that of method 2.

[0173] For further verification, the applicant also selected different blade loss factors for testing.

[0174] Table 3 shows the average failure rate and the average number of iterations required for each successful solution for different blade loss factors under the same twist angle, local speed ratio and local solidity.

[0175] Table 3

[0176]

[0177] It can be seen from Table 3 that under different blade loss factors, the average failure rate and average number of iterations of Method 2 are much smaller than those of Method 1. Therefore, compared with Method 1, Method 2 improves the design efficiency of blade shape parameters, the robustness of the design method, and the design speed.

[0178] Based on the same application concept, in addition to providing a method for determining the blade shape parameters of a wind turbine, the embodiment of the present application also provides a corresponding device for determining the blade shape parameters of a wind turbine.

[0179] The following is a detailed description of a device for determining blade shape parameters of a wind turbine according to an embodiment of the present application, in conjunction with the accompanying drawings.

[0180] Based on the same application concept, an embodiment of the present application provides a device for determining blade shape parameters of a wind turbine. Figure 3 Schematic diagram of a device for determining the blade shape parameters of a wind turbine provided in an embodiment of the present application. Figure 3 As shown, the fan blade profile parameter determination device 300 includes:

[0181] A parameter acquisition module 310 is used to acquire multiple groups of candidate blade shape parameters of a wind turbine generator set;

[0182] The processing module 320 is used to perform the following steps for each set of candidate blade shape parameters:

[0183] An iterative algorithm is used to determine the axial induction factor and the tangential induction factor until a preset iteration stop condition is met; when the preset iteration stop condition is not met and the target thrust coefficient in the Nth iteration process is less than a first preset threshold, the axial induction factor and the tangential induction factor in the N+1th iteration process are determined according to the axial induction factor in the Nth iteration process, the target thrust coefficient in the Nth iteration process, and the blade loss factor, wherein N is a positive integer;

[0184] The parameter determination module 330 is used to determine the wind turbine performance parameters corresponding to each group of candidate blade shape parameters based on the axial induction factor and the tangential induction factor of each group of candidate blade shape parameters; and determine the maximum value of the wind turbine performance parameters corresponding to each of the multiple groups of candidate blade shape parameters, and determine the target blade shape parameters of the wind turbine generator set from the candidate blade shape parameters corresponding to the maximum value;

[0185] Among them, the preset iteration stop condition is that the change of the axial induction factor between the Nth iteration process and the N-1th iteration process is less than the second preset threshold and the change of the tangential induction factor between the Nth iteration process and the N-1th iteration process is less than the third preset threshold.

[0186] In some embodiments of the present application, the processing module 320 specifically includes:

[0187] A first determining unit is used to determine a preset proportional coefficient according to a ratio of a target thrust coefficient to a target multiple of a blade loss factor in an Nth iteration process;

[0188] The second determining unit is used to determine the axial induction factor in the N+1th iteration process according to the first coefficient, the axial induction factor in the Nth iteration process, and the preset proportional coefficient.

[0189] In some embodiments of the present application, the processing module 320 specifically includes:

[0190] A third determining unit is used to determine a first adjustment amount of the axial induction factor by using the relaxation factor, the axial induction factor in the Nth iteration process, and the axial induction factor in the N+1th iteration process;

[0191] The first updating unit is used to update the axial induction factor in the N+1th iteration process by using the first adjustment amount to obtain the updated axial induction factor in the N+1th iteration process.

[0192] In some embodiments of the present application, the relaxation factor decreases as the number of iterations increases.

[0193] In some embodiments of the present application, the processing module 320 specifically includes:

[0194] An acquisition unit, used for acquiring the axial induction factor in the N+2th iteration process;

[0195] a fourth determining unit, configured to determine a fourth adjustment amount according to the axial induction factor in the Nth iteration process, the axial induction factor in the N+1th iteration process, and the axial induction factor in the N+2th iteration process;

[0196] The second updating unit is used to update the axial induction factor in the N+1th iteration process by using the first adjustment amount to obtain an updated axial induction factor in the N+1th iteration process.

[0197] In some embodiments of the present application, the blade shape parameters include:

[0198] Impeller rotational angular velocity, number of blades, blade chord length and blade spanwise position;

[0199] In some embodiments of the present application, the blade shape parameters include: local solidity and local speed ratio.

[0200] The blade shape parameter determination device of the wind turbine of the embodiment of the present application can first obtain multiple groups of candidate blade shape parameters, and then obtain the target thrust coefficient of each group of candidate blade shape parameters by continuously adjusting the axial induction factor, the tangential induction factor and the target thrust coefficient until the iteration stop condition is met, and then determine the target blade shape parameters of the wind turbine generator set from the multiple groups of candidate blade shape parameters according to the target thrust coefficient of each group of candidate blade shape parameters. Since the target thrust coefficient of each candidate blade shape parameter obtained by continuously adjusting the axial induction factor can reflect the thrust load state of each candidate blade shape parameter, the shape parameters of the target shape blade can be automatically determined according to the thrust load state of each candidate blade shape parameter, which improves the design efficiency of the blade shape parameters compared to the manual setting method.

[0201] According to other details of the device for determining the blade profile parameters of a fan according to an embodiment of the present application, in combination with the above Figure 2 The method for determining the blade shape parameters of the wind turbine described in the example shown is similar and can achieve the corresponding technical effect. For the sake of simplicity, it will not be repeated here.

[0202] Figure 4 It is a schematic diagram of the hardware structure of a device for determining the blade shape parameters of a wind turbine provided in an embodiment of the present application.

[0203] The device for determining the profile parameters of a wind turbine blade may include a processor 401 and a memory 402 storing computer program instructions.

[0204] Specifically, the processor 401 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.

[0205] The memory 402 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 402 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 402 may be inside or outside of a blade profile parameter determination device for a wind turbine. In a particular embodiment, the memory 402 is a non-volatile solid-state memory.

[0206] 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 one aspect of the present application.

[0207] The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any one of the methods for determining the blade profile parameters of the wind turbine in the above embodiments.

[0208] In one example, the device for determining the blade profile parameters of a wind turbine may further include a communication interface 403 and a bus 410. Figure 4 As shown, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 410 and communicate with each other.

[0209] The communication interface 403 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0210] Bus 410 includes hardware, software or both, and the parts of online data flow billing equipment 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 410 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0211] The fan blade shape parameter determination device can execute the fan blade shape parameter determination method in the embodiment of the present application, thereby realizing the combination of Figures 2 to 3 The invention describes a method and a device for determining the profile parameters of a fan blade.

[0212] In addition, in combination with the method for determining the blade shape parameters of a fan 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 the processor, any one of the methods for determining the blade shape parameters of a fan in the above embodiment is implemented.

[0213] 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.

[0214] The functional blocks shown in the above 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.

[0215] 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.

[0216] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device, equipment 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.

[0217] The above are only specific implementation methods 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 method for determining the blade shape parameters of a fan, characterized in that: include: Obtain multiple sets of candidate blade shape parameters of a wind turbine generator set; For each set of candidate blade shape parameters, an axial induction factor and a tangential induction factor are determined using an iterative algorithm until a preset iteration stop condition is satisfied; if the preset iteration stop condition is not satisfied and the target thrust coefficient in the Nth iteration is less than a first preset threshold, the axial induction factor and the tangential induction factor in the N+1th iteration are determined based on the axial induction factor in the Nth iteration, the target thrust coefficient in the Nth iteration, and the blade loss factor, where N is a positive integer; Determining, based on the axial induction factor and the tangential induction factor of each group of candidate blade shape parameters, wind turbine performance parameters corresponding to each group of candidate blade shape parameters; Determine the maximum value among the wind turbine performance parameters corresponding to each of the multiple groups of candidate blade shape parameters, and determine the target blade shape parameters of the wind turbine generator set from the candidate blade shape parameters corresponding to the maximum value; Among them, the preset iteration stopping condition is that the change in the axial induction factor between the Nth iteration process and the N-1th iteration process is less than a second preset threshold and the change in the tangential induction factor between the Nth iteration process and the N-1th iteration process is less than a third preset threshold.

2. The method according to claim 1, characterized in that Determining the axial induction factor in the N+1th iteration process according to the axial induction factor in the Nth iteration process, the target thrust coefficient in the Nth iteration process, and the blade loss factor specifically includes: determining a preset proportional coefficient according to a ratio of a target thrust coefficient to a target multiple of the blade loss factor during the Nth iteration; The axial induction factor in the N+1th iteration process is determined according to the first coefficient, the axial induction factor in the Nth iteration process, and the preset proportional coefficient.

3. The method according to claim 1, characterized in that After determining the axial induction factor in the N+1th iteration process based on the axial induction factor in the Nth iteration process, the target thrust coefficient in the Nth iteration process, and the blade loss factor, the method further includes: Determining a first adjustment amount of the axial induction factor by using the relaxation factor, the axial induction factor in the Nth iteration process, and the axial induction factor in the N+1th iteration process; The first adjustment amount is used to update the axial induction factor in the N+1th iteration process to obtain an updated axial induction factor in the N+1th iteration process.

4. The method according to claim 3, characterized in that The relaxation factor decreases as the number of iterations increases.

5. The method according to claim 1, characterized in that After determining the axial induction factor in the N+1th iteration process based on the axial induction factor in the Nth iteration process, the target thrust coefficient in the Nth iteration process, and the blade loss factor, the method further includes: Obtain the axial induction factor during the N+2th iteration; determining a fourth adjustment amount according to the axial induction factor during the Nth iteration, the axial induction factor during the N+1th iteration, and the axial induction factor during the N+2th iteration; The fourth adjustment amount is used to update the axial induction factor in the N+1th iteration process to obtain an updated axial induction factor in the N+1th iteration process.

6. The method according to claim 1, characterized in that The blade shape parameters include: Impeller rotational angular velocity, number of blades, chord length of the blades, and spanwise position of the blades; or, Local solidity and local speed ratio.

7. A device for determining the blade shape parameters of a fan, characterized in that: The device comprises: A parameter acquisition module is used to obtain multiple sets of candidate blade shape parameters of the wind turbine generator set; The processing module is configured to execute the following steps for each set of candidate blade shape parameters: An iterative algorithm is used to determine the axial induction factor and the tangential induction factor until a preset iteration stop condition is satisfied; if the preset iteration stop condition is not satisfied and the target thrust coefficient in the Nth iteration is less than a first preset threshold, the axial induction factor and the tangential induction factor in the N+1th iteration are determined based on the axial induction factor in the Nth iteration, the target thrust coefficient in the Nth iteration, and the blade loss factor, where N is a positive integer; a parameter determination module for determining, based on the axial induction factor and the tangential induction factor of each set of candidate blade shape parameters, wind turbine performance parameters corresponding to each set of candidate blade shape parameters; determining a maximum value among the wind turbine performance parameters corresponding to each of the multiple sets of candidate blade shape parameters, and determining target blade shape parameters of the wind turbine generator set from the candidate blade shape parameters corresponding to the maximum value; Among them, the preset iteration stopping condition is that the change in the axial induction factor between the Nth iteration process and the N-1th iteration process is less than a second preset threshold and the change in the tangential induction factor between the Nth iteration process and the N-1th iteration process is less than a third preset threshold.

8. The device according to claim 7, characterized in that The processing module specifically includes: a first determining unit, configured to determine a preset proportional coefficient according to a ratio of a target thrust coefficient to a target multiple of the blade loss factor in the Nth iteration process; The second determining unit is configured to determine the axial induction factor in the N+1th iteration process according to the first coefficient, the axial induction factor in the Nth iteration process, and the preset proportional coefficient.

9. A device for determining the blade shape parameters of a wind turbine, characterized in that: The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the method for determining the blade shape parameters of a wind turbine according to any one of claims 1 to 6 is implemented.

10. A computer storage medium, characterized in that The computer storage medium stores computer program instructions, which, when executed by a processor, implement the method for determining the blade profile parameters of a wind turbine according to any one of claims 1 to 6.

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