Wind generating set blade design iterative optimization method based on tangent axis ratio index

Abstract

The invention discloses a wind generating set blade design iterative optimization method based on a tangent axis ratio index. The method comprises the following steps: firstly, calculating flapping loads, wind energy utilization coefficients and tangent axis ratios of designed blades of various versions, and then carrying out linear fitting to obtain a slope and an offset of a fitting plane; and then multi-attribute evaluation is carried out on each version blade, and the version with the optimal comprehensive performance index is found to serve as the basis of the next round of iteration. Finally, according to the slope of the fitting plane, the tangent axis ratio increment capable of keeping the waving load basically unchanged when the wind energy utilization coefficient is increased is obtained and serves as the basis of optimization and adjustment of the next iteration structure. According to the method, the direction of blade structure optimization in the design iteration process is given based on the plane linear relation between flapping loads of blades of different versions and wind energy utilization coefficients and the plane linear relation between the flapping loads of the blades of different versions and the tangent axis ratios. The method has the advantages that in the design iteration process, the blade structure is adjusted in the direction that the wind energy utilization coefficient is increased and the flapping load is not changed or reduced all the time.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation, and in particular relates to a design iterative optimization method for blades of a wind generator set. Background Art

[0002] Wind turbines are important energy production equipment in the new energy sector, playing a significant role in utilizing natural energy, providing electricity for daily life and production, and reducing carbon emissions. They are a crucial energy generation tool in today's society. Blades are the primary energy harvesting component of wind turbines. Wind energy is first converted into mechanical energy by the blades and then into electrical energy by the generator. The structural design of the blades is crucial to the wind energy harvesting capability, the turbine's power generation efficiency, and its output power. Therefore, the wind energy utilization factor (WEFC) of the blades is directly related to the profitability of the wind turbine and is a key indicator of wind turbines. Another important indicator of wind turbines is load. Loads can cause stretching, bending, and torsion of structures such as blades, towers, and tower bases, leading to fatigue and damage to the wind turbine structure. To enhance the load resistance of wind turbines, the strength of various components must be increased, which in turn increases the cost of wind turbines. Therefore, WEFC and load often present a mutually exclusive trade-off. One of the goals of iterative blade design optimization is to strike a balance between WEFC and load, gradually identifying a blade structural design solution that maximizes WEFC and minimizes load. However, it is not easy to strike a compromise between wind energy utilization coefficient and load. It is a difficult problem in the field of wind power generation to find the right approach and method for iterative design optimization so that the blade power generation performance and load performance are optimized at the same time, with low production costs and high power generation profitability.

[0003] The tangential axis ratio (TAR) metric, provided by this invention, is a blade structural indicator that links the wind energy utilization coefficient (WEC) with the blade's flapping load. When the wind turbine is operating at rated capacity, the flapping load, WEC, and TAR of different blade versions exhibit a linear correlation. This linear relationship is a plane in the three-dimensional space of "flapping load - WEC - TAR." Leveraging this plane relationship, the blade structure can be gradually optimized during the iterative design process to achieve a state with a high WEC and low load. Summary of the Invention

[0004] In order to find the optimization direction that increases the wind energy utilization coefficient and keeps the flapping load unchanged or reduces during the iterative design process of the blade, the present invention provides an iterative design optimization method for the blades of a wind turbine generator based on the above-mentioned planar linear relationship.

[0005] The technical solution to achieve this goal in the present invention consists of the following aspects: First, the flapping load, wind energy utilization coefficient, and tangential axis ratio of each designed blade version are calculated; then, these three sets of data are linearly fitted to obtain the slope and offset of the fitting plane; then, a multi-attribute evaluation is performed on each designed blade version to find the version with the best comprehensive performance indicators as the basis for the next round of iteration; finally, based on the slope of the fitting plane, the incremental tangential axis ratio that can maintain a basically unchanged flapping load while increasing the wind energy utilization coefficient is calculated, which serves as the basis for the next round of iterative optimization and adjustment of the blade structure.

[0006] The iterative design optimization method for wind turbine blades provided by the present invention is characterized by comprising the following steps:

[0007] A1. Determine the tip speed ratio λ and pitch angle β of the wind turbine at rated operating conditions;

[0008] B1. Number the n designed blades as Θ i (i=1…n);

[0009] C1. For blade Θ i (i=1…n), calculate the wind energy utilization coefficient C when the tip speed ratio and pitch angle combination is (λ, β) under rated working conditions Pi (i=1…n);

[0010] D1. For blade Θ i (i=1…n), calculate the flapping load M when the tip speed ratio and pitch angle combination is (λ, β) under rated operating conditions Yi (i=1…n);

[0011] E1. According to the blade Θ i (i=1…n) airfoil design data, when the tip speed ratio and pitch angle combination under rated working condition is (λ,β), blade Θ i (i=1…n) Lift coefficient C of the blade element at radius r from the impeller axis li (r)(i=1…n) and drag coefficient C di (r)(i=1…n);

[0012] E2. Calculate blade Θ i (i=1…n) The tangential force coefficient C of the blade element at radius r from the impeller axis yi (r), the formula is as follows:

[0013] C yi (r)=C li (r)sinφ-C di (r)cosφ(i=1…n)

[0014] Where φ is the blade inflow angle;

[0015] E3. Calculate blade Θ i (i=1…n) The axial force coefficient C of the blade element at radius r from the impeller axis xi (r), the formula is as follows:

[0016] C xi (r)=C li (r)cosφ+C di (r) sinφ(i=1…n);

[0017] E4. Calculate blade Θ i (i=1…n) The tangential axis ratio b of the blade element at the radius r from the impeller axis i (r), the formula is as follows:

[0018]

[0019] E5. Integrate along the span of the blade to calculate blade Θ i (i=1…n) Tangential axis ratio B when the tip speed ratio and pitch angle combination is (λ, β) under rated operating conditions i , the formula is as follows:

[0020]

[0021] Among them, R i is the blade Θ i Impeller radius (i=1…n);

[0022] F1. Wind energy utilization coefficient C for the three sets of data obtained Pi (i=1…n), swing load M Yi (i=1…n) and blade tangential axis ratio B i (i=1…n), with C Pi and B i As the independent variable, M Yi As the dependent variable, linear fitting is performed according to the following equation to obtain coefficients k1, k2 and k3:

[0023] M Yi =k1C Pi -k2B i +k3;

[0024] G1. Select three weight coefficients l1, l2 and l3 greater than 0 and calculate the leaf Θ according to the following formula i Comprehensive performance index J (i=1…n) i :

[0025]

[0026] H1. In the comprehensive performance index J i Based on the largest blade, the blade structure is adjusted according to the following rules to increase both the tangential axis ratio and the wind energy utilization coefficient, resulting in a new version of the blade after iteration:

[0027]

[0028] Where, ΔC P is the planned increase in wind energy utilization coefficient; ΔB is the required increase in tangential axis ratio;

[0029] H2. Evaluate whether the new blade version after iteration meets the design requirements. If so, the design iteration optimization process ends. If not, return to step B1 and treat the new blade version as one of the already designed blade versions for a new round of calculation, fitting, evaluation, and iteration.

[0030] Furthermore, the fitting method in step F1 may be the least squares method.

[0031] Furthermore, the parameters l1, l2 and l3 in step G1 may satisfy l1+l2+l3=1.

[0032] Furthermore, the parameters l1, l2 and l3 in step G1 may satisfy l1+l2=l3.

[0033] The beneficial effect of the present invention is that the iterative optimization method for wind turbine blade design provided can take into account the two conflicting indicators of wind energy utilization coefficient and flapping load. In the linear plane relationship followed by each version of the blade, the direction of blade structure optimization adjustment is given, so that when the wind energy utilization coefficient increases, the flapping load remains basically unchanged or decreases, thereby maintaining or reducing the blade cost and improving the blade's power generation profitability. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a three-dimensional schematic diagram of parameters of the blade design iterative optimization process drawn according to an embodiment of the present invention.

[0035] In the figure, My represents the flapping load; Cp represents the wind energy utilization coefficient; B represents the blade tangential axis ratio; Θ1-Θ 10 The designed blade versions are numbered; the hollow circles represent the position of the performance parameters of each version of the blade in the Cp-B-My three-dimensional space; the gray grid represents the plane corresponding to the fitting equation; the five-pointed star represents the expected position of the performance parameters of the new version of the blade in the Cp-B-My three-dimensional space after the next round of iteration; ΔC P is the planned increase in the wind energy utilization coefficient for the next iteration; ΔB is the increase in the tangential axis ratio for the next iteration; and blade Θ7 is the version with the best comprehensive performance indicators for this round. DETAILED DESCRIPTION

[0036] Example 1

[0037] Taking the iterative optimization process of blade design for a certain type of wind turbine as an example, 10 versions of the blade have been designed. After calculating according to steps B1 to E5, the wind energy utilization coefficient, flapping load, and tangential axis ratio are shown in the following table:

[0038]

[0039] After performing linear fitting according to step F1, the plane equation is obtained:

[0040] M Y =100.17C P -0.59B+6.98

[0041] Among them, k1=100.17, k2=0.59, k3=6.98.

[0042] Select l1=270, l2=0.03, l3=0.1, and calculate the comprehensive performance index J of the 10 versions of the blade according to step G1. i (i=1…10). The comprehensive performance index of blade Θ7 is the largest, which is J7=6.6611.

[0043] According to step H1, a new blade is iteratively designed based on blade Θ7, and the planned increase in wind energy utilization coefficient is selected as ΔC P =0.02, then the tangential axis ratio should be increased after adjusting the blade structure:

[0044]

[0045] The wind energy utilization coefficient of the new version of the blade is C P7 +ΔC P =0.53, the tangential axis ratio is B7+ΔB≥50.05, the expected flapping load is 30.54, which is basically unchanged compared with blade Θ7, and the expected comprehensive performance index is 7.6, which is significantly greater than J7.

[0046] Example 2

[0047] This embodiment discloses a non-transitory computer-readable medium storing instructions. When the instructions are executed by a processor, the iterative design optimization method for wind turbine blades of the present invention is executed.

[0048] The non-transitory computer-readable medium in this embodiment can be a disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), a USB flash drive, a mobile hard disk, or other media.

[0049] Example 3

[0050] This embodiment discloses a computing device including a processor and a program memory. When the processor executes the stored program, the iterative design optimization method for wind turbine blades of the present invention is implemented.

[0051] The computing device described in this embodiment can be a desktop computer, a laptop computer, a smart phone, a PDA handheld terminal, a tablet computer, an industrial control computer, an embedded system, a touch-screen integrated controller, a digital signal processor (DSP) system, a programmable array logic (FPGA) system, an ARM (Advanced RISC Machine) system, a cloud computing server, a programmable logic controller (PLC), or other terminal devices with processor functions.

[0052] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Therefore, any changes made based on the images and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A design iterative optimization method for wind turbine blades, characterized in that The following steps are involved: A1. Determine the tip speed ratio λ and pitch angle β of the wind turbine at rated operating conditions; B1. Number the n designed blades as Θ i (i=1…n); C1. For blade Θ i (i=1…n), calculate the wind energy utilization coefficient C when the tip speed ratio and pitch angle combination is (λ, β) under rated working conditions Pi (i=1…n); D1. For blade Θ i (i=1…n), calculate the flapping load M when the tip speed ratio and pitch angle combination is (λ, β) under rated operating conditions Yi (i=1…n); E1. According to the blade Θ i (i=1…n) airfoil design data, when the tip speed ratio and pitch angle combination under rated working condition is (λ, β), blade Θ i (i=1…n) Lift coefficient C of the blade element at radius r from the impeller axis li (r)(i=1…n) and drag coefficient C di (r)(i=1…n); E2. Calculate blade Θ i (i=1…n) The tangential force coefficient C of the blade element at radius r from the impeller axis yi (r), the formula is as follows: C yi (r)=C li (r)sinφ-C di (r)cosφ(i=1…n) Where φ is the blade inflow angle; E3. Calculate blade Θ i (i=1…n) The axial force coefficient C of the blade element at radius r from the impeller axis xi (r), the formula is as follows: C xi (r)=C li (r)cosφ+C di (r)sinφ(i=1…n); E4. Calculate blade Θ i (i=1…n) The tangential axis ratio b of the blade element at the radius r from the impeller axis i (r), the formula is as follows: E5. Integrate along the span of the blade to calculate blade Θ i (i=1…n) Tangential axis ratio B when the tip speed ratio and pitch angle combination is (λ, β) under rated operating conditions i , the formula is as follows: Among them, R i is the blade Θ i Impeller radius (i=1…n); F1. Wind energy utilization coefficient C for the three sets of data obtained Pi (i=1…n), swing load M Yi (i=1…n) and blade tangential axis ratio B i (i=1…n), with C Pi and B i As the independent variable, M Yi As the dependent variable, linear fitting is performed according to the following equation to obtain coefficients k1, k2 and k3: M Yi =k1C Pi -k2B i +k3; G1. Select three weight coefficients l1, l2 and l3 greater than 0 and calculate the leaf Θ according to the following formula i Comprehensive performance index J (i=1…n) i : H1. In the comprehensive performance index J i Based on the largest blade, the blade structure is adjusted according to the following rules to increase both the tangential axis ratio and the wind energy utilization coefficient, resulting in a new version of the blade after iteration: Where, ΔC P is the planned increase in wind energy utilization coefficient; ΔB is the required increase in tangential axis ratio; H2. Evaluate whether the new blade version after iteration meets the design requirements. If so, the design iteration optimization process ends. If not, return to step B1 and treat the new blade version as one of the already designed blade versions for a new round of calculation, fitting, evaluation, and iteration.

2. The iterative optimization method for designing a blade of a wind turbine generator set according to claim 1, characterized in that The fitting method in step F1 is the least squares method.

3. The iterative optimization method for designing a blade of a wind turbine generator set according to claim 1, characterized in that The parameters l1, l2 and l3 in step G1 satisfy l1+l2+l3=1.

4. The iterative optimization method for designing a wind turbine blade according to claim 1, characterized in that The parameters l1, l2 and l3 in step G1 satisfy l1+l2=l3.

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