CN104346500A - Optimum design method for wind turbine blade - Google Patents
Optimum design method for wind turbine blade Download PDFInfo
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- CN104346500A CN104346500A CN201410678250.0A CN201410678250A CN104346500A CN 104346500 A CN104346500 A CN 104346500A CN 201410678250 A CN201410678250 A CN 201410678250A CN 104346500 A CN104346500 A CN 104346500A
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The invention discloses an optimum design method for a wind turbine blade and relates to an optimum design method. The method comprises the following steps: aerodynamic performance analysis on the wind turbine blade: the design method for the wind turbine blade is based on the blade element-momentum theory, and is used for estimating an aerodynamic force generated by the section of a blade element at the axis r of a blade pitch wind wheel, and then, the relation between wing chord and basic parameters of the blade is determined initially; genetic algorithm optimization: integral optimization design is performed on a wind turbine, i.e., the optimum chord length and the optimum torsional angle of the section are determined by taking the maximum of annual energy output E of each section of the wind turbine blade as the design target. According to the optimum design method disclosed by the invention, by utilizing a developed optimum design program and aiming at a specific wind field, the optimum chord length and the optimum torsional angle of the section are designed; shown by a simulation result, the output power of the wind turbine can be obviously improved by the optimized blade; an optimum model has practicability and effectiveness.
Description
Technical field
The present invention relates to a kind of Optimization Design, particularly relate to a kind of pneumatic equipment blades Optimization Design.
Background technology
At present, energy worsening shortages in the world, problem of environmental pollution has caused people's strong interest, wind-power electricity generation has become the Main way of new century electric power development as a kind of clean energy resource, blade is one of most important parts in wind energy conversion system, and the aerodynamic configuration of blade directly determines the efficiency of wind energy conversion system.Therefore design has the key that the blade of good aerodynamic configuration is wind energy conversion system design.Method for designing in the past just considers single performance index, and have ignored overall performance, is difficult to reach desirable design effect.
Genetic algorithm originates from the research of the sixties to natural and artificial adaptive system.Genetic algorithm sets a certain target exactly, utilizes that an initial population is selected through carrying out constantly, repeatedly, crossover and mutation process, this population is drawn close gradually, the optimum solution needed for realization or satisfactory solution to this target.The blade parameter of genetic algorithm to wind energy conversion system is adopted to be optimized herein, to expect the output power improving wind energy conversion system.
Summary of the invention
The object of the present invention is to provide a kind of pneumatic equipment blades Optimization Design, propose the mathematical optimization models of pneumatic equipment blades, for best chord length and the torsional angle of specific wind field design section, blade after optimization can significantly improve the output power of wind energy conversion system, demonstrates practicality and the validity of Optimized model.
The object of the invention is to be achieved through the following technical solutions:
A kind of pneumatic equipment blades Optimization Design, described method comprises following process:
1) pneumatic equipment blades aeroperformance is analyzed: the method for designing of pneumatic equipment blades is theoretical based on momentum-foline, for estimating the aerodynamic force that foline cross section, blade pitch wind wheel axis r place produces, and then tentatively determines the relation of wing chord and blade basic parameter;
2) genetic algorithm optimization: the global optimization design of wind energy conversion system, is design object to the maximum with the annual output of energy E of pneumatic equipment blades every section, determines best chord length and the torsional angle in cross section;
The superiority-inferiority that the calculating process of genetic algorithm comprises coding, the generation of initial population, fitness value evaluation detection, fitness function show individuality or solution, selection and heredity, end condition judge.
Described a kind of pneumatic equipment blades Optimization Design, described coding, the solution data of solution space were expressed as the genotype string structure data in hereditary space by genetic algorithm before searching for, and the various combination of these string structure data just constitutes different points.
Described a kind of pneumatic equipment blades Optimization Design, the generation of described initial population, random generation original string structured data, each string structure data are called body one by one, and individuality constitutes a colony.
Described a kind of pneumatic equipment blades Optimization Design, described fitness value evaluation detects, and according to particular problem, calculates the fitness of individual in population.
Described a kind of pneumatic equipment blades Optimization Design, described selection and heredity, selection opertor, crossover operator and mutation operator act on population and obtain population of future generation.
Described a kind of pneumatic equipment blades Optimization Design, described end condition judges, method has algebraically restriction, fitness limits, stagnate algebraically limits, stagnates time limit restriction.
Advantage of the present invention and effect are:
1. the present invention proposes the mathematical optimization models of pneumatic equipment blades, this model is based on momentum-foline theory, according to the aerodynamic force acted on pneumatic equipment blades, be design object to the maximum with the annual output of energy of pneumatic equipment blades every section, use genetic algorithm to carry out optimizing search.Optimized Program of the present invention, for best chord length and the torsional angle of specific wind field design section.Simulation result shows, the blade after optimization can significantly improve the output power of wind energy conversion system, demonstrates practicality and the validity of Optimized model.
2. the blade after optimizing can significantly improve the output power of wind energy conversion system, demonstrates practicality and the validity of Optimized model.Economic benefit is obvious.
Accompanying drawing explanation
The operation result of Fig. 1 genetic algorithm in MATLAB;
Output power before and after Fig. 2 genetic algorithm optimization.
Embodiment
Below in conjunction with accompanying drawing illustrated embodiment, the invention will be further described.
The present invention is specifically implemented as follows:
1 pneumatic equipment blades aeroperformance is analyzed
The method for designing of pneumatic equipment blades is theoretical based on momentum-foline, is mainly used in the aerodynamic force estimating that foline cross section, blade pitch wind wheel axis r place produces, and then tentatively determines the relation of wing chord and blade basic parameter.
Theoretical according to momentum one foline, get
, record the Basic Design relation of wind wheel parameter and wind speed:
（1）
（2）
In formula,
---incoming flow angle;
---wind speed round;
r---the distance of foline distance blade root, m
---tip speed ratio;
---apart from the speed at wind wheel axis r place,
---flow through wind wheel place gas velocity,
m/
s
---the gas velocity of infinite point, m/s
B---blade quantity;
C
l---the lift coefficient of aerofoil profile, m
C---blade chord length, m
R---wind wheel radius, m
Can be obtained by formula (1):
（3）
Through type (3) tentatively can determine blade incoming flow angle
, and choose each foline aerofoil profile angle of attack according to design experiences
.The chord length of blade can be calculated by formula (4)
c:
（4）
Obtain propeller pitch angle simultaneously
relation:
（5）
Optimization object is:
2. genetic algorithm optimization principle
The aeroperformance of wind energy conversion system depends primarily on the aerodynamic configuration parameter of blade, in the process of design blade, after the basic parameter of design and the aerofoil profile of each design section are determined, just needs chord length and the torsional angle of determining each cross section according to design object.The global optimization design of wind energy conversion system, is design object to the maximum with the annual output of energy E of pneumatic equipment blades every section exactly, determines best chord length and the torsional angle in cross section.
Genetic algorithm simulate occur in natural selection and heredity copy, the phenomenon such as crossover and mutation, from arbitrary initial population, by Stochastic choice, crossover and mutation operation, produce the individuality that a group more conforms, make the region of becoming better and better in Swarm Evolution to search volume, constantly multiply evolution from generation to generation like this, finally converge to the individuality that a group conforms most, try to achieve the optimum solution of problem.The major calculations process of genetic algorithm is as follows:
(1) encode: the solution data Z in solution space, as the phenotype form of genetic algorithm.Coding is called to genotypic mapping from phenotype.The solution data of solution space were first expressed as the genotype string structure data in hereditary space by genetic algorithm before searching for, and the various combination of these string structure data just constitutes different points.
(2) generation of initial population: the N number of original string structured data of random generation, each string structure data are called body one by one, and individuality constitutes a colony.Genetic algorithm starts iteration using this N number of string structure as initial point.Maximum evolutionary generation T is set, life at random
Become M individuality as initial population.
(3) fitness value evaluation detects: fitness function shows the superiority-inferiority of individuality or solution.For different problems, the definition mode of fitness function is different.According to particular problem, calculating colony P (
f) in the fitness of each individuality.
(4) select and heredity: selection opertor, crossover operator and mutation operator act on population P (
t) obtain population P of future generation (
t+ 1).
(5) end condition judges.Main method has algebraically restriction, fitness limits, stagnate algebraically limits, stagnates time limit restriction etc.Use MATLAB Optimization Toolbox to programme to objective function, ask for the Constrained and Unconstrained Optimization in Constrained Nonlinear.
Embodiment:
1 design parameter
Optimizing application is designed program, the wind wheel that reference table 1 provides and genetic algorithm parameter, has carried out the optimal design of fan blade.Adopt NACA6412 series aerofoil sections herein, blade is divided into 5 cross sections, each cross section intercepts according to 0.2R distance, and calculate chord length and the torsional angle numerical value in each cross section, the pneumatic equipment blades parameter before optimization is as shown in table 2.
Table 1 design parameter value
| Parameter name | Parameter value | Parameter name | Parameter value |
| Rotor diameter (m) | 60 | Tip-speed ratio λ | 6 |
| Wind wheel cone angle (degree) | 0 | Rated power (MW) | 1.3 |
| Rated speed (r/min) | 19 | Reference altitude (m) | 60 |
| Wind rating (m/s) | 15 | Wheel hub absolute altitude (m) | 50 |
| Incision wind speed (ms -1) | 4 | Cut-out wind speed (ms -1) | 25 |
The parameter of front vane optimized by table 2
| Section radius | Chord length (m) | Torsional angle (degree) |
| 0.2R | 2.05 | 5.36 |
| 0.4R | 2.24 | 5.98 |
| 0.6R | 1.95 | 5.06 |
| 0.8R | 1.54 | 3.88 |
| R | 1.00 | 2.18 |
2 optimize the result calculated
Adopt genetic algorithm to be optimized blade, choosing chromosome length is 95, and Population Size is 100, and after 1000 genetic iteration, optimizing result as shown in Figure 1.,
As can be seen from Figure 2, when employing genetic algorithm is through general 610 iteration, the optimum solution of objective function starts convergence.
Table 3 gives chord length and the torsional angle value of the blade adopting genetic algorithm to calculate, and is that Cutting Length is on average cut into 5 sections blade, gives chord length and the torsional angle of every section of section with 0.2R.Known by the contrast of vane design of wind turbines parameter before and after optimizing, the blade chord length adopting genetic algorithm to obtain and torsional angle value and vaned chord length and torsional angle value substantially identical.Blade output power curve before and after optimizing as shown in Figure 2.
Blade parameter after table 3 optimization
| Section radius | Chord length (m) | Torsional angle (degree) |
| 0.2R | 2.15 | 5.24 |
| 0.4R | 2.26 | 5.78 |
| 0.6R | 1.98 | 5.12 |
| 0.8R | 1.56 | 3.89 |
| R | 1.02 | 2.06 |
As can be seen from Figure 2, under the prerequisite adopting the blade that goes out of genetic Algorithm Design substantially to coincide in torsional angle value, than original blade under different wind speed, power increases all to some extent.
The aeroperformance of pneumatic equipment blades of the present invention calculates, and is optimized design to aeroperformance.Simulation result shows, adopt Revised genetic algorithum under the prerequisite of substantially coincideing in torsional angle value to pneumatic equipment blades, than original blade under different wind speed, power adds about 2%.
Claims (6)
1. a pneumatic equipment blades Optimization Design, is characterized in that, described method comprises following process:
1) pneumatic equipment blades aeroperformance is analyzed: the method for designing of pneumatic equipment blades is theoretical based on momentum-foline, for estimating the aerodynamic force that foline cross section, blade pitch wind wheel axis r place produces, and then tentatively determines the relation of wing chord and blade basic parameter;
2) genetic algorithm optimization: the global optimization design of wind energy conversion system, is design object to the maximum with the annual output of energy E of pneumatic equipment blades every section, determines best chord length and the torsional angle in cross section;
The superiority-inferiority that the calculating process of genetic algorithm comprises coding, the generation of initial population, fitness value evaluation detection, fitness function show individuality or solution, selection and heredity, end condition judge.
2. a kind of pneumatic equipment blades Optimization Design according to claim 1, it is characterized in that, described coding, the solution data of solution space were expressed as the genotype string structure data in hereditary space by genetic algorithm before searching for, and the various combination of these string structure data just constitutes different points.
3. a kind of pneumatic equipment blades Optimization Design according to claim 1, is characterized in that, the generation of described initial population, and random generation original string structured data, each string structure data are called body one by one, and individuality constitutes a colony.
4. a kind of pneumatic equipment blades Optimization Design according to claim 1, is characterized in that, described fitness value evaluation detects, and according to particular problem, calculates the fitness of individual in population.
5. a kind of pneumatic equipment blades Optimization Design according to claim 1, it is characterized in that, described selection and heredity, selection opertor, crossover operator and mutation operator act on population and obtain population of future generation.
6. a kind of pneumatic equipment blades Optimization Design according to claim 1, is characterized in that, described end condition judges, method has algebraically restriction, fitness limits, stagnate algebraically limits, stagnates time limit restriction.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107194028A (en) * | 2017-04-19 | 2017-09-22 | 中国航空工业集团公司金城南京机电液压工程研究中心 | A kind of RAT blade design methods |
| CN110704989A (en) * | 2019-10-29 | 2020-01-17 | 浙江上风高科专风实业有限公司 | Modeling and production molding method based on hollow wing type axial flow fan blade |
| CN114585950A (en) * | 2019-10-28 | 2022-06-03 | 西门子歌美飒可再生能源创新与技术有限公司 | Method for computer-implemented forecasting of wind phenomena having an effect on a wind turbine |
| CN117436344A (en) * | 2023-11-10 | 2024-01-23 | 沈阳工业大学 | Wind turbine blade structure optimization design method based on parameterization description |
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| US20120051892A1 (en) * | 2010-08-31 | 2012-03-01 | Mitsubishi Heavy Industries, Ltd. | Wind turbine rotor designing method, wind turbine rotor design support device, wind turbine rotor design support program and wind turbine rotor |
| CN103514308A (en) * | 2012-06-20 | 2014-01-15 | 华锐风电科技(集团)股份有限公司 | Method and device for designing wind driven generator blades |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107194028A (en) * | 2017-04-19 | 2017-09-22 | 中国航空工业集团公司金城南京机电液压工程研究中心 | A kind of RAT blade design methods |
| CN107194028B (en) * | 2017-04-19 | 2020-10-27 | 中国航空工业集团公司金城南京机电液压工程研究中心 | Blade design method for RAT |
| CN114585950A (en) * | 2019-10-28 | 2022-06-03 | 西门子歌美飒可再生能源创新与技术有限公司 | Method for computer-implemented forecasting of wind phenomena having an effect on a wind turbine |
| CN110704989A (en) * | 2019-10-29 | 2020-01-17 | 浙江上风高科专风实业有限公司 | Modeling and production molding method based on hollow wing type axial flow fan blade |
| CN117436344A (en) * | 2023-11-10 | 2024-01-23 | 沈阳工业大学 | Wind turbine blade structure optimization design method based on parameterization description |
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