CN106682282B - A kind of wind power plant polytypic wind-driven generator arrangement optimization method - Google Patents
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Abstract
The wind power plant polytypic wind-driven generator that the invention discloses a kind of based on genetic algorithm nesting particle swarm algorithm is arranged optimization method.Blower position, the optimal solution of type selecting when obtaining the blower position using particle swarm algorithm, the fitness as the generation blower position are chosen using genetic algorithm.The use of genetic algorithm, which ensure that, can find out feasible solution for non-linear close coupling optimization problem, particle swarm algorithm use both ensure that for Multiple Type wind-driven generator parameter it is more in the case where quickly seek obtaining type selecting solution, it can guarantee that quick two kinds of algorithm nestings use again, in the case that the number of iterations is excessive, calculating the time will not be too long.To blower position coordinates direct coding, rather than to selecting after wind power plant region division gridiron pattern gridiron pattern, can continuously be searched within the scope of wind power plant.Wind power plant need not be divided into square net by the method for the present invention, and compared with prior art, performance indicator is more preferable, and location schemes are more accurate, and practicability is stronger.
Description
Technical field
It is the present invention relates to a kind of wind power plant polytypic wind-driven generator arrangement optimization method, in particular to a kind of based on heredity
The wind power plant polytypic wind-driven generator arrangement optimization method of algorithm nesting particle swarm algorithm.
Background technique
Wind energy is a kind of pollution-free, reproducible new energy, serious in energy shortages and traditional energy environmental pollution
Modern society, Wind Power Generation Industry become one of the New Energy Industry greatly developed.Wind power plant microcosmic structure is that Wind Power Generation Industry is rationally advised
The steps necessary drawn.Wind power plant microcosmic structure before construction wind power plant can effectively improve wind energy utilization efficiency, and improving blower makes
With the service life, wind power plant O&M cost and cost of wind power generation are reduced, to realize Rational Decision and the scientific development of Wind Power Generation Industry.
Wind farm siting includes macroscopical addressing and microcosmic structure, and macroscopical addressing is intended to select wind power plant site, and microcosmic structure with emphasis on
In Fan Selection and installation site.Long-term record and analysis to local wind-resources are the major premise of wind farm siting, microcosmic choosing
Anemometer tower is installed after macroscopical addressing completion in location, to detection in wind regime progress 1 year or more and record at site, in conjunction with locality
Long-range meteorological record etc., it is comprehensive to carry out wind-resources analysis and assessment.In the base of wind-resources assessment, site topography and geomorphology comprehensive analysis
On plinth, blower quantity and model are selected, determines assembling position, to reach the expected annual output maximum of wind power plant or expected wind-force
Power generation degree electricity cost is minimum, enables the wind power plant under conditions of society, economy and environmental index meet, and reaches economic benefit maximum
Change.
The optimization of wind power plant microcosmic structure is a kind of non-linear close coupling problem, need to comprehensively consider local meteorological landform, environment
The factors such as index, land price, road distribution and construction feasibility, are related to many factors such as fluid, meteorology, electromechanics, can not
Optimal solution is obtained using traditional optimal method.Therefore, at present worldwide, the research achievement of the direction all makes mostly
Decision is optimized to particular problem with the heuritic approach based on search to calculate.The main method of optimization be genetic algorithm, with
Machine algorithm, particle swarm optimization algorithm etc..But research object is all simplified conceptual wind power plant, only selects single type mostly
Number wind-driven generator, wind power plant is divided into similar tessellated grid, " 0 " and " 1 " coding generation is carried out to each grid
Whether table installs blower, the continuous search on wind-powered electricity generation field areas two-dimensional space can not be carried out, meanwhile, the blower height of single model
Unanimously, power of fan curve is consistent, is unable to fully utilize the wind energy resources being distributed on three-dimensional space.Therefore, traditional blower row
Cloth optimization algorithm can not more efficiently improve wind field capacity efficiency and reduce wind power plant production capacity cost, need further to be changed
Into and performance boost.
In document relevant to this patent and patent, document Castro Mora, J etc. are published in 2007
In the paper " An evolutive algorithm for wind farm optimal design " of Neurocomputing,
The problem of proposing the arrangement optimization of polytypic blower simultaneously gives a kind of solution, but between not considering blower in optimizing
Wake effect.A kind of patent " wind power plant polytypic blower optimization arrangement based on genetic algorithm " (application publication number: CN
It 103793566A) proposes using genetic algorithm and solves the problems, such as that polytypic wind-driven generator is arranged, but in wind-powered electricity generation place
The searching method in domain is the artificial grid for dividing blower diameter multiple, the position relative coarseness of blower arrangement, not enough precisely.
Summary of the invention
Present invention aims to overcome that above-mentioned existing research and technology there are the problem of and defect, propose a kind of based on heredity
The square electric field polytypic wind-driven generator arrangement optimization method of algorithm nesting particle swarm algorithm.This method is to wind power plant range searching
Continuously, blower arrangement position precision can be improved, the wind-resources on vertical direction can be made full use of, have more practicability, and extend
Property it is high.
The purpose of the present invention is realized by the following technical solution: a kind of side based on genetic algorithm nesting particle swarm algorithm
Electric field polytypic wind-driven generator arrangement optimization method, method includes the following steps:
1) according to wind-resources assessment result and wind power plant landform meteorological features, initial type selecting is carried out to wind-driven generator, really
Several fixed alternative models are used for Lectotype Optimization, read in wind power plant correlation landform it is gentle as etc. parameters；
2) the initial position matrix of blower is generated at random within the scope of wind-powered electricity generation field areas transverse and longitudinal coordinate, the every row of matrix represents one
Kind blower position arrangement, i.e. a chromosome, line number representative genetic algorithm chromosome number carry out two to each row of matrix
Scale coding；
3) initial model matrix being generated in given alternative blower model and being encoded, matrix line number represents particle swarm algorithm
Population, every a line of matrix represent a particle (a kind of blower model Choice), the speed of random initializtion particle and
Position in region of search, the initial solution that the blower model as current chromosome is chosen；
4) the fitness for calculating current each particle uses the degree electricity cost of current blower position and selecting type scheme, and
Find out the individual adaptive optimal control degree of each particle and global optimum's fitness of all particles；
5) according to the particle rapidity and position evolutionary rule set in particle swarm algorithm, to the position and speed of each particle
It evolves；
6) judge whether the maximum algebra for reaching particle swarm algorithm setting, if reaching the maximum algebra of setting, stop carrying out wind
Type number optimization, chooses global optimum's fitness of particle swarm algorithm, as the fitness of current chromosome, otherwise return step
4)；
7) according to the fitness of each chromosome, global optimum's fitness of genetic algorithm, i.e. blower position type selecting are found out
Global optimum；
8) judge whether the maximum number of iterations for reaching genetic algorithm, if so, the global optimum of output genetic algorithm is suitable
The corresponding chromosome of response is as blower location schemes, and global optimum's fitness of corresponding particle swarm algorithm is as type selecting side
Case completes the arrangement optimization of polytypic wind-driven generator, otherwise carries out step 9)；
9) using all chromosomes as parent chromosome group, intersected, mutation operation, it is big according to the fitness of chromosome
Small calculating select probability carries out selection and generates child chromosome group and return step 3).
Further, genetic algorithm is to choose blower position using genetic algorithm, heredity is calculated with the nested of particle swarm algorithm
After every generation population of method generates, to work as former generation blower position as blower arrangement position, obtained currently using particle swarm algorithm
The optimal solution of type selecting when for blower position, as the fitness for working as former generation blower position.
Further, individual adaptation degree is embodied by the inverse of calculating degree electricity cost, is spent electric cost and is defined as every hair one
Spend the average unit cost of electricity.The highest individual of fitness is the maximum value for spending electric cost inverse, that is, spends of electric cost minimum
Body spends the calculation formula of electric cost CoP are as follows:
Wherein: CoE is a year cost of electricity-generating, and AEP is wind power plant average annual energy output, CiBe every Fans purchase every year at
This, CO&MIt is the annual O&M cost of wind field, ClandIt is wind power plant soil annual cost of possession, CotherIt is other expenses of wind power plant
Annual mean, PiIt is the average annual energy output of every Fans, N is the total number of units of wind electric field blower.
Further, the particle swarm algorithm chooses the blower model combination of specific position scheme, and the model of blower is special
It levies there are many parameters, to any two Fans, if any one characteristic parameter is different (airport height, blower rated power etc.)
It is considered as different model in the present invention, therefore the selection of blower model is there are many possibility, it can be according to specific when actually building
Situation is set.Particle swarm algorithm can guarantee that blower model Choice calculates quick.
Further, wind-powered electricity generation field areas is scanned for using genetic algorithm, the search density of feasible zone is calculated by heredity
The coding of position coordinates is determined in method, can continuously be searched for, search density can also require to carry out according to practical calculate
Setting is not to select again gridiron pattern after wind-powered electricity generation field areas carries out gridiron pattern division.
Compared with prior art, the invention has the following advantages that
1, careful to the feasible domain search in wind-powered electricity generation field areas blower position continuous.Because to blower position coordinates direct coding,
Rather than to selecting after wind power plant region division gridiron pattern gridiron pattern, can continuously be searched within the scope of wind power plant.
Can the selection of blower position and optimization effectively be carried out for practical wind-powered electricity generation field areas.If can pass through to improve search speed
Genetic algorithm encoding mode changes location finding density.
2, algorithm is advanced, ensure that the feasibility of solution.The use of genetic algorithm has been fully ensured that for non-linear strong coupling
Feasible solution can be found out by closing optimization problem, and the use of particle swarm algorithm both ensure that more for Multiple Type wind-driven generator parameter
In the case where can quickly seek obtaining type selecting solution, and can guarantee that quick two kinds of algorithm nestings use, in the case that the number of iterations is excessive, meter
Evaluation time will not be too long.
3, practical.The method of the present invention has been fully considered the characteristics of practical wind field region and has been utilized using polytypic blower
The characteristics of wind energy, may extend to complicated landform three-dimensional blower addressing and the case where polytypic blower loads in mixture；Coding mode is easy to
Wind-powered electricity generation field areas exists and realizes in the case where can not building blower subregion there are restrictive conditions such as road, maintenances.
4, favorable expandability, the research method and achievement, which can be promoted effectively, to be expanded into similar problem solution, and phase is solved
Answer problem.
Detailed description of the invention
Fig. 1 is wind power plant polytypic wind-driven generator arrangement optimization method flow chart of the invention.
Fig. 2 is the calculated result for being applied to embodiment by optimization method of the invention.
Specific embodiment
Implementation of the invention is made as detailed below below in conjunction with attached drawing:
Embodiment
The present embodiment carries out the blower arrangement Lectotype Optimization before generator builds field to 7 typhoon power of certain wind power plant.Alternative wind
Machine is two kinds of factory model A (rated power 1.5MW) and B (rated power 2MW), and the assembling of every kind of factory model is high
There are two types of (1.5MW have 65 meters and 80 meters of two kinds of height to degree, and 2MW has 80 meters and 90 meters of two kinds of height), i.e., blower model has 4 kinds.
Wind-powered electricity generation field areas is abscissa [0,2000] (rice), and ordinate is [0,2000] (rice) range.Do not consider in this embodiment multiple
Miscellaneous landform.Optimization aim is wind power plant degree electricity cost.Implementation steps are specific as follows:
1) according to wind-resources assessment result and wind power plant landform meteorological features, initial type selecting is carried out to wind-driven generator, really
Several fixed alternative models are used for Lectotype Optimization, read in wind power plant correlation landform it is gentle as etc. parameters；
2) the initial position matrix of blower is generated at random within the scope of wind-powered electricity generation field areas transverse and longitudinal coordinate, the every row of matrix represents one
Kind blower position arrangement, i.e. a chromosome, line number representative genetic algorithm chromosome number carry out two to each row of matrix
Scale coding；
3) initial model matrix being generated in given alternative blower model and being encoded, matrix line number represents particle swarm algorithm
Population, every a line of matrix represent a particle (a kind of blower model Choice), the speed of random initializtion particle and
Position in region of search, the initial solution that the blower model as current chromosome is chosen；
4) the fitness for calculating current each particle uses the degree electricity cost of current blower position and selecting type scheme, and
Find out the individual adaptive optimal control degree of each particle and global optimum's fitness of all particles；
5) according to the particle rapidity and position evolutionary rule set in particle swarm algorithm, to the position and speed of each particle
It evolves；
6) judge whether the maximum algebra for reaching particle swarm algorithm setting, if reaching the maximum algebra of setting, stop carrying out wind
Type number optimization, chooses global optimum's fitness of particle swarm algorithm, as the fitness of current chromosome, otherwise return step
4)；
7) according to the fitness of each chromosome, global optimum's fitness of genetic algorithm, i.e. blower position type selecting are found out
Global optimum；
8) judge whether the maximum number of iterations for reaching genetic algorithm, if so, the global optimum of output genetic algorithm is suitable
The corresponding chromosome of response is as blower location schemes, and global optimum's fitness of corresponding particle swarm algorithm is as type selecting side
Case completes the arrangement optimization of polytypic wind-driven generator, otherwise carries out step 9)；
9) using all chromosomes as parent chromosome group, intersected, mutation operation, it is big according to the fitness of chromosome
Small calculating select probability carries out selection and generates child chromosome group and return step 3).
The fitness value for working as former generation fan type Choice optimal solution is calculated, calculates and currently subrogates the individual adaptation degree set
Value, individual adaptation degree calculation formula are as follows:
Wherein: CoE is a year cost of electricity-generating, and AEP is wind power plant average annual energy output, CiBe every Fans purchase every year at
This, CO&MIt is the annual O&M cost of wind field, ClandIt is wind power plant soil annual cost of possession, CotherIt is other expenses of wind power plant
Annual mean, PiIt is the average annual energy output of every Fans, it is in the present embodiment 7 that N, which is the total number of units of wind electric field blower,；
The present invention is based on the wind power plant polytypic wind-driven generator of genetic algorithm nesting particle swarm algorithm arrangement optimization methods
It is main to be formed including initializing (including coding), calculating the links such as contemporary individual adaptation degree, filial generation generation (cross and variation).It is losing
During every generation fitness of propagation algorithm calculates, the nested optimization algorithm process to blower model, the optimization algorithm of blower model is
Particle swarm algorithm.Fig. 1 is the wind power plant polytypic wind-driven generator arrangement optimization side based on genetic algorithm nesting particle swarm algorithm
Method detailed process.Entire embodiment is to carry out the arrangement optimization of polytypic wind-driven generator according to process shown in Fig. 1 and calculate.
Fig. 2 is the wind power plant polytypic wind-driven generator arrangement optimization side using of the invention based on genetic algorithm nesting particle swarm algorithm
The result that method is arranged.Assuming that service life of fan is 20 years, spending electric cost calculation result is 0.3406 yuan/kilowatt hour, wind
Generating efficiency of the machine under wake effect is 0.9948.Use the wind power plant polytypic based on genetic algorithm nesting particle swarm algorithm
Wind-driven generator arrangement optimization method calculated result shows that blower arrangement position takes full advantage of wind power plant region, effectively
Wind energy utilization is improved, wind power plant microcosmic structure is suitable for.
Claims (5)
- The optimization method 1. a kind of wind power plant polytypic wind-driven generator based on genetic algorithm nesting particle swarm algorithm is arranged, it is special Sign is, comprising the following steps:1) according to wind-resources assessment result and wind power plant landform meteorological features, initial type selecting is carried out to wind-driven generator, if determining Dry alternative model is used for Lectotype Optimization, and it is gentle as parameter to read in wind power plant correlation landform；2) the initial position matrix of blower is generated at random within the scope of wind-powered electricity generation field areas transverse and longitudinal coordinate, the every row of matrix represents a kind of wind Arrangement, i.e. a chromosome are set in seat in the plane, and line number representative genetic algorithm chromosome number carries out binary system to each row of matrix Coding；3) initial model matrix being generated in given alternative blower model and being encoded, matrix line number represents particle swarm algorithm particle Number, every a line of matrix represents a particle, i.e., a kind of blower model Choice, the speed of random initializtion particle and is searching Position in rope domain, the initial solution that the blower model as current chromosome is chosen；4) fitness for calculating current each particle, that is, use the degree electricity cost of current blower position and selecting type scheme, and finds out The individual adaptive optimal control degree of each particle and global optimum's fitness of all particles；5) according to the particle rapidity and position evolutionary rule set in particle swarm algorithm, the position and speed of each particle is carried out It evolves；6) judge whether the maximum algebra for reaching particle swarm algorithm setting, if reaching the maximum algebra of setting, stop carrying out fan type Number optimization, chooses global optimum's fitness of particle swarm algorithm, as the fitness of current chromosome, otherwise return step 4)；7) according to the fitness of each chromosome, global optimum's fitness of genetic algorithm is found out, i.e., blower position type selecting is complete Office's optimal value；8) judge whether the maximum number of iterations for reaching genetic algorithm, if so, global optimum's fitness of output genetic algorithm Corresponding chromosome as blower location schemes, global optimum's fitness of corresponding particle swarm algorithm as selecting type scheme, The arrangement optimization of polytypic wind-driven generator is completed, step 9) is otherwise carried out；9) using all chromosomes as parent chromosome group, intersected, mutation operation, according to the big subtotal of the fitness of chromosome Select probability is calculated, selection is carried out and generates child chromosome group and return step 3).
- 2. a kind of wind power plant polytypic wind-power electricity generation based on genetic algorithm nesting particle swarm algorithm according to claim 1 Machine arrangement optimization method, which is characterized in that choose blower position using genetic algorithm, nesting uses particle swarm algorithm selection blower Model after every generation population of genetic algorithm generates, to work as former generation blower position as blower arrangement position, is calculated using population Method obtains the optimal solution of the type selecting when former generation blower position, as the fitness for working as former generation blower position.
- 3. a kind of wind power plant polytypic wind-power electricity generation based on genetic algorithm nesting particle swarm algorithm according to claim 1 Machine arrangement optimization method, which is characterized in that individual adaptation degree calculated value calculates as follows: individual adaptation degree passes through calculating degree electricity cost Inverse embody, the highest individual of fitness is the maximum value for spending electric cost inverse, i.e. the individual of degree electricity cost minimum, Spend the calculation formula of electric cost CoP are as follows:Wherein: CoE is a year cost of electricity-generating, and AEP is wind power plant average annual energy output, CiIt is the average annual cost of purchase of every Fans, CO&MIt is the annual O&M cost of wind field, ClandIt is wind power plant soil annual cost of possession, CotherIt is wind power plant other fees Annual mean, PiIt is the average annual energy output of every Fans, N is the total number of units of wind electric field blower.
- 4. a kind of wind power plant polytypic wind-power electricity generation based on genetic algorithm nesting particle swarm algorithm according to claim 1 Machine arrangement optimization method, which is characterized in that particle swarm algorithm chooses the blower model combination of specific position scheme, including same model The different cabin altitudes of fan engine room or different model blower with the different model blower of cabin altitude or different cabin altitude, If factory model blower i.e. of the same race is mounted on different cabin altitudes, it is considered as different model.
- 5. a kind of wind power plant polytypic wind-power electricity generation based on genetic algorithm nesting particle swarm algorithm according to claim 1 Machine arrangement optimization method, which is characterized in that wind-powered electricity generation field areas is scanned for using genetic algorithm, to the search density of feasible zone By being determined in genetic algorithm to the coding of position coordinates, it can continuously be searched for, be not to carry out chessboard in wind-powered electricity generation field areas Lattice again select gridiron pattern after dividing.
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| CN113962447B (en) * | 2021-10-08 | 2024-05-28 | 哈尔滨工业大学（威海） | Complex equipment batch long-term maintenance plan optimization method based on improved particle swarm algorithm |
| CN113962113B (en) * | 2021-12-22 | 2022-03-04 | 华中科技大学 | Optimized arrangement method and system for offshore wind farm fans |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103793566A (en) * | 2014-01-28 | 2014-05-14 | 同济大学 | Wind farm multi-model draught fan optimized arrangement method based on genetic algorithm |
| CN104779638A (en) * | 2015-02-06 | 2015-07-15 | 华北水利水电大学 | Dispatching method and dispatching device for optimizing units in wind power station |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160203419A1 (en) * | 2013-03-09 | 2016-07-14 | Bigwood Technology, Inc. | Metaheuristic-guided trust-tech methods for global unconstrained optimization |
-
2016
- 2016-12-08 CN CN201611125212.8A patent/CN106682282B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103793566A (en) * | 2014-01-28 | 2014-05-14 | 同济大学 | Wind farm multi-model draught fan optimized arrangement method based on genetic algorithm |
| CN104779638A (en) * | 2015-02-06 | 2015-07-15 | 华北水利水电大学 | Dispatching method and dispatching device for optimizing units in wind power station |
Non-Patent Citations (4)
| Title |
|---|
| Optimal placement of wind turbines within wind farm using binary;Sittichoke Pookpunt;《Renewable Energy 55》;20130124;第55卷;第266-276页 |
| Optimisation of offshore wind farm cable connection layout considering;Peng Hou;《IET Renewable Power Generation》;20151231;第1-9页 |
| 一种基于遗传算法改进的粒子群优化算法;潘勇;《计算机应用与软件》;20120115;第28卷(第9期);第222-224页 |
| 粒子群算法与遗传算法的结合研究;巩永光;《济宁学院学报》;20090313;第29卷(第6期);第20-22页 |
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