CN106886833A - A kind of wind-driven generator addressing Lectotype Optimization method suitable for Complex Constraints condition - Google Patents
A kind of wind-driven generator addressing Lectotype Optimization method suitable for Complex Constraints condition Download PDFInfo
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Abstract
The invention discloses a kind of wind-driven generator addressing Lectotype Optimization method suitable for Complex Constraints condition.For the optimization problem of wind-driven generator addressing type selecting, when there are the Nonlinear Constraints such as beeline between whole audience minimum annual electricity generating capacity and blower fan, blower fan position, the optimal case of type selecting when obtaining the blower fan position using particle cluster algorithm are chosen using genetic algorithm.The inventive method effectively sets the object function of particle cluster algorithm, adds penalty function to be added thereto minimum generated energy restrictive condition, nested with genetic algorithm to use as wind-driven generator Lectotype Optimization algorithm.In genetic algorithm, the effective object function of setting genetic algorithm, add penalty function will most short blower fan distance, i.e. safe distance restrictive condition is added thereto, and carries out the position model optimization that wind power plant's generator polytypic is loaded in mixture.The method can solve the problem that the optimization problem that there is complex nonlinear constraints, and more preferably, selecting type scheme is more accurate, and practicality is stronger for performance indications.
Description
Technical field
It is more particularly to a kind of to be applied to again the present invention relates to a kind of wind power plant polytypic wind-driven generator arrangement optimization method
The wind-driven generator addressing Lectotype Optimization method of miscellaneous constraints.
Background technology
Wind energy is a kind of pollution-free, reproducible new energy, serious in energy scarcity and traditional energy environmental pollution
Modern society, Wind Power Generation Industry is as one of New Energy Industry greatly developed.Wind power plant microcosmic structure is that Wind Power Generation Industry is rationally advised
The steps necessary drawn.The wind power plant microcosmic structure built before wind power plant can effectively improve wind energy utilization efficiency, and improving blower fan makes
With the life-span, wind power plant O＆M cost and cost of wind power generation are reduced, so as 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 selection wind power plant site, and microcosmic structure emphasis exists
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 in location after macroscopical addressing completion, detection and the record of more than a year is carried out to wind regime at site, with reference to locality
Long-range meteorological record etc., comprehensively carries out wind-resources analysis and assessment.In wind-resources assessment, the base of site topography and geomorphology comprehensive analysis
On plinth, blower fan quantity and model are selected, determine assembling position, annual production maximum or expected wind-force are expected to reach wind power plant
Generating degree electricity cost is minimum, makes 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 consider local meteorology landform, environment
The factors such as index, land price, road distribution and construction feasibility, are related to many factors such as fluid, meteorology, electromechanics, it is impossible to
Optimal solution is drawn using traditional optimal method.Therefore, at present worldwide, the achievement in research of the direction all makes mostly
Decision-making 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..Increase because wind speed profile increases with height above sea level, each model blower fan is in different wind
Each advantageous and inferior position under energy distribution situation.In wind power plant microcosmic structure, Multiple Type, height assembling same
Individual wind power plant, can effectively improve wind energy utilization and whole field generating efficiency, and then reduce the cost of wind power generation.Meanwhile,
During wind power plant microcosmic structure, various restrictive conditions are there is also, such as need to meet safe distance and wind power plant normal year most
Low generated energy etc..
In the document and patent related to this patent, document Castro Mora, J etc. are published in 2007 years
In the paper " An evolutive algorithm for wind farm optimal design " of Neurocomputing,
Propose the problem of polytypic blower fan arrangement optimization and give a kind of solution, but between not considering blower fan in optimization
Wake effect.Patent《A kind of wind power plant polytypic blower fan optimization arrangement based on genetic algorithm》(application publication number：CN
103793566A) propose using genetic algorithm to solve the problems, such as that polytypic wind-driven generator is arranged, but the blower fan for using
Model chooses optimized algorithm and does not consider the of overall importance of optimized algorithm, relative coarseness, not enough precisely.These researchs are to complicated pact
Beam condition does not all consider or assumes to obtain very simple, is not appropriate for being used in microcosmic structure is put into practice.
The content of the invention
Present invention aim to overcome that problem and defect that above-mentioned existing research and technology are present, propose that one kind is applied to multiple
The wind-driven generator addressing Lectotype Optimization method of miscellaneous constraints.The method is continuous to wind power plant range searching and meet safety
Distance, improves on the basis of position arrangement precision, takes into full account Lectotype Optimization algorithm and can make full use of the wind on vertical direction
Resource, considers the restrictive condition of the minimum generated energy of wind power plant year in addition.The inventive method is actual to have more practicality, and extension
Property is high.
The purpose of the present invention is realized by following technical scheme：A kind of wind-driven generator suitable for Complex Constraints condition
Addressing Lectotype Optimization method, the method is comprised 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
Fixed several alternative models are used for Lectotype Optimization, read in wind power plant correlation landform it is gentle as etc. parameter；
2) according to addressing requirements of type selecting, the Complex Constraints condition of optimization problem is taken out, is target by constraints slacking
Penalty function in function, makes optimum results meet the constraint of the safe distance between blower fan and wind field year minimum generated energy about
Beam；
3) the random initial position matrix for generating blower fan in the range of wind-powered electricity generation field areas transverse and longitudinal coordinate, often row represents one to matrix
Blower fan position arrangement is planted, i.e., one chromosome, line number represents genetic algorithm chromosome number, and each row to matrix carries out two
Scale coding；
4) generate initial model matrix in given alternative blower fan model and encode, matrix line number represents particle cluster algorithm
Population, every a line of matrix represents a particle (a kind of blower fan model Choice), the speed of random initializtion particle and
Position in region of search, as the initial solution that the blower fan model of current chromosome is chosen；
5) calculate the fitness of current each particle, i.e., using the degree electricity cost of current blower fan position and selecting type scheme, and
Obtain the individual adaptive optimal control degree of each particle and global optimum's fitness of all particles；
6) according to the particle rapidity and position evolutionary rule set in particle cluster algorithm, position and speed to each particle
Evolved；
7) judge whether to reach the maximum algebraically that particle cluster algorithm sets, if reaching the maximum algebraically of setting, stop into sector-style
Type number optimizes, and global optimum's fitness of particle cluster algorithm is chosen, as the fitness of current chromosome, otherwise return to step
5)；
8) according to the fitness of each chromosome, global optimum's fitness of genetic algorithm, i.e. blower fan position type selecting are obtained
Global optimum；
9) judge whether to reach the maximum iteration of genetic algorithm, if so, then the global optimum of output genetic algorithm fits
, used as blower fan location schemes, global optimum's fitness of its corresponding particle cluster algorithm is used as type selecting side for the corresponding chromosome of response
Case, completes the arrangement optimization of polytypic wind-driven generator, otherwise carries out step 10)；
10) using all chromosomes as parent chromosome group, intersected, mutation operation, according to the fitness of chromosome
Size calculates select probability, carries out selection generation child chromosome group and return to step 4).
Further, blower fan position is chosen using genetic algorithm, nesting carries out blower fan model optimization using particle cluster algorithm,
I.e. every time after selection blower fan position, the optimal solution of type selecting when drawing the blower fan position using particle cluster algorithm, as the generation wind
The fitness that seat in the plane is put.There is nonlinear restriction in optimization problem, nonlinear restriction includes the minimum range and wind-powered electricity generation between blower fan
The annual minimum generated energy in field.Nonlinear Constraints cause Fan Selection and addressing optimization problem extremely complex and be difficult to obtain most
Excellent solution.
Further, individual adaptation degree calculated value is calculated as follows：Individual adaptation degree by degree of calculating electricity cost inverse come
Embody, individual being of fitness highest spends electric cost maximum reciprocal, that is, spend the individuality of electric cost minimum, the electric cost of degree
The computing formula of CoP is：
Wherein：CoE is a year cost of electricity-generating, and AEP is wind power plant average annual energy output, CiIt is the purchase year of every Fans
Equal cost, CO&MIt is the annual O＆M cost of wind field, ClandIt is wind power plant soil annual cost of possession, CotherIt is
Wind
The annual mean of electric field other fees, PiIt is the average annual energy output of every Fans, N is wind electric field blower head station
Number.
Further, it is optimization problem P2 optimization problem P1 to be relaxed, nested optimization problem P3 in P2；
P1 is expressed as follows：
min CoP
Wherein：E0It is year minimum generated energy, DsIt is safe distance between blower fan, di,jIt is distance between blower fan, xi, yiIt is blower fan
Position two-dimensional coordinate,WithIt is respectively the bound of blower fan two-dimensional coordinate.
P2 is expressed as follows：
The Nonlinear Constraints that safe distance will must be fulfilled between blower fan add genetic algorithm as penalty function
Object function, then the object function of genetic algorithm be expressed as：
Wherein：C1It is penalty factor.
P3 is expressed as follows：
The constraints of wind field year minimum generated energy is added the object function of particle cluster algorithm as penalty function, then
The object function of particle cluster algorithm is expressed as：
min(CoP+C2e)
Wherein：C2It is penalty factor, E0It is the whole minimum generated energy of wind field year.
Further, the blower fan model for choosing ad-hoc location scheme using particle cluster algorithm is combined, including same model blower fan
The different model blower fan of the different cabin altitudes of cabin or the different model blower fan with cabin altitude or different cabin altitudes, i.e., together
If planting the model blower fan that dispatches from the factory installed in different cabin altitudes, different model is considered as.
Further, the requirement for actual wind power plant adds constraints and solves, and microcosmic structure result is to wind energy profit
With in hgher efficiency, practicality is more preferable.
Compared with prior art, the present invention has advantages below：
1st, it is careful to the feasible domain search in wind-powered electricity generation field areas blower fan position continuous.Because to blower fan position coordinates direct coding,
Rather than to being selected gridiron pattern after wind power plant region division gridiron pattern, can continuously be searched in the range of wind power plant.
Effectively the selection of blower fan position and optimization can be carried out for actual wind-powered electricity generation field areas.If in order to improve search speed, can pass through
Genetic algorithm encoding mode changes location finding density.
2nd, algorithm is advanced, it is ensured that the feasibility of solution.The use of genetic algorithm has been fully ensured that for non-linear strong coupling
Closing optimization problem can obtain feasible solution, and the use of point group's formula particle cluster algorithm both ensure that for Multiple Type wind-driven generator ginseng
Type selecting solution can be quickly sought obtaining in the case that number is more, can guarantee that quick two kinds of algorithm nestings are used again, the excessive feelings of iterations
Under condition, the time of calculating will not be long, while having more preferable Global Optimality.
3rd, it is practical.The characteristics of the inventive method has taken into full account actual wind field region and utilized using polytypic blower fan
The characteristics of wind energy, may extend to the situation that complicated landform three-dimensional blower fan addressing and polytypic blower fan are loaded in mixture；Coded system is easy to
There is the restrictive conditions such as road, maintenance in wind-powered electricity generation field areas, exist and realized in the case of can not building blower fan subregion.
4th, using adding the method for penalty function by safe distance addition object function between blower fan, punished by appropriately configured
Penalty factor is met the solution of Nonlinear Constraints；Both ensure that the continuity of addressing region of search, meet again safety away from
From restrictive condition.
5th, the year of whole wind field minimum generated energy is added the target of Lectotype Optimization algorithm using the method for addition penalty function
In function, the solution of Nonlinear Constraints is met by appropriately configured penalty factor；Ensure that wind power plant annual electricity generating capacity is big
In setting minimum value.
6th, favorable expandability, the research method and achievement can be promoted effectively in expansion to similar problem solving, solve phase
Answer problem.
Brief description of the drawings
Fig. 1 is wind power plant polytypic wind-driven generator arrangement optimization method flow chart of the invention.
Fig. 2 is the result of calculation that embodiment is applied to by optimization method of the invention.
Specific embodiment
Implement to make as detailed below to of the invention below in conjunction with accompanying drawing：
Embodiment
The present embodiment carries out the blower fan arrangement Lectotype Optimization that generator is built before field to 8 typhoon power of certain wind power plant.Alternative wind
Machine is two kinds of model A that dispatch from the factory (rated power is 1.5MW) and B (rated power is 2MW), and the assembling of every kind of model of dispatching from the factory is high
Degree has two kinds (1.5MW has 65 meters and 80 meters of two kinds of height, and 2MW has 80 meters and 90 meters of two kinds of height), i.e., blower fan model has 4 kinds.
Wind-powered electricity generation field areas is abscissa [0,2000] (rice), and ordinate is [0,2000] (rice) scope.Do not consider multiple in this embodiment
Miscellaneous landform.Blower fan number of units is 7, and safe distance is 5 times of rotor diameters, i.e., 550 meters, the minimum generated energy of wind power plant year between blower fan
It is 8MW.Optimization aim is that wind power plant degree electricity cost is minimum.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
Fixed several alternative models are used for Lectotype Optimization, read in wind power plant correlation landform it is gentle as etc. parameter；
2) according to addressing requirements of type selecting, the Complex Constraints condition of optimization problem is taken out, is target by constraints slacking
Penalty function in function, makes optimum results meet the constraint of the safe distance between blower fan and wind field year minimum generated energy about
Beam；
3) the random initial position matrix for generating blower fan in the range of wind-powered electricity generation field areas transverse and longitudinal coordinate, often row represents one to matrix
Blower fan position arrangement is planted, i.e., one chromosome, line number represents genetic algorithm chromosome number, and each row to matrix carries out two
Scale coding；
4) according to given alternative blower fan model, determine the search space of particle cluster algorithm, all particles are pressed into the field of search
Domain is divided into M independent population subspace, and the population in every sub-spaces is more than 3；
5) generate initial model matrix and encode, matrix line number represents particle cluster algorithm population, every a line generation of matrix
One particle of table (a kind of blower fan model Choice)；
6) in every sub-spaces, the speed of all particles of random initializtion and the position in region of search calculate current
The fitness of each particle, i.e., using the degree electricity cost of current blower fan position and selecting type scheme, and obtain the individuality of each particle
Global optimum's fitness of adaptive optimal control degree and all particles, using global optimum position in subspace as the optimal position of current group
Put.
7) in every sub-spaces, according to the particle rapidity and position evolutionary rule that are set in particle cluster algorithm, to each
The position of particle and speed are evolved；
8) judge whether to reach a point maximum algebraically for group's formula particle cluster algorithm setting, if reaching the maximum algebraically of setting, stop
Blower fan model optimization is carried out, the optimal location of population, selects the optimal location in whole search space in relatively more every sub-spaces,
And using its fitness as global optimum's fitness of particle cluster algorithm, as the fitness of current chromosome, otherwise return to step
It is rapid 6)；
9) according to the fitness of each chromosome, global optimum's fitness of genetic algorithm, i.e. blower fan position type selecting are obtained
Global optimum；
10) judge whether to reach the maximum iteration of genetic algorithm, if so, then the global optimum of output genetic algorithm fits
, used as blower fan location schemes, global optimum's fitness of its corresponding particle cluster algorithm is used as type selecting side for the corresponding chromosome of response
Case, completes the arrangement optimization of polytypic wind-driven generator, otherwise carries out step 11)；
11) using all chromosomes as parent chromosome group, intersected, mutation operation, according to the fitness of chromosome
Size calculates select probability, carries out selection generation child chromosome group and return to step 5).
The fitness value when former generation fan type Choice optimal solution is calculated, calculating currently subrogates the individual adaptation degree put
It is worth, individual adaptation degree computing formula is：
Wherein：CoE is a year cost of electricity-generating, and AEP is wind power plant average annual energy output, CiBe every Fans purchase it is average annual into
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, is in the present embodiment 7.
Further, a kind of wind-driven generator addressing Lectotype Optimization method suitable for Complex Constraints condition, solution it is excellent
Changing problem representation is：
min CoP
Wherein：E0It is year minimum generated energy, DsIt is safe distance between blower fan, di,jIt is distance between blower fan, xi, yiIt is blower fan
Position two-dimensional coordinate,WithIt is respectively the bound of blower fan two-dimensional coordinate.
The mesh that safe distance adds genetic algorithm as penalty function will must be fulfilled between Nonlinear Constraints blower fan
Scalar functions, then the object function of genetic algorithm be expressed as：
Wherein：DsIt is in the present embodiment 550 meters.
Nonlinear Constraints wind field year, minimum generated energy added the object function of particle cluster algorithm as penalty function,
Then the object function of particle cluster algorithm is expressed as：
min(CoP+C2e)
Wherein：E0It is the whole minimum generated energy of wind field year, in the present embodiment
In be 8MW.
The present invention mainly includes initialization suitable for the wind-driven generator addressing Lectotype Optimization method of Complex Constraints condition
(including coding), calculate the link composition such as contemporary individual adaptation degree, filial generation generation (cross and variation).In every generation of genetic algorithm
During fitness is calculated, the nested optimized algorithm process to blower fan model, the optimized algorithm of blower fan model is point group's formula population calculation
Method.Fig. 1 is the idiographic flow of the wind-driven generator addressing Lectotype Optimization method suitable for Complex Constraints condition.Entirely embodiment is
According to the flow shown in Fig. 1, carry out the arrangement optimization of polytypic wind-driven generator and calculate.Fig. 2 is applied to using of the invention
The result that the wind-driven generator addressing Lectotype Optimization method of Complex Constraints condition is arranged.Assuming that service life of fan is 20
Year, the electric cost calculation result of degree is 0.5519 yuan/kilowatt hour, and generating efficiency of the blower fan under wake effect is 0.9940, wind-powered electricity generation
It is 8.49MW to be expected generated energy field year.Calculated using the wind-driven generator addressing Lectotype Optimization method suitable for Complex Constraints condition
Result shows that blower fan arrangement position takes full advantage of wind power plant region, effectively increases wind energy utilization, it is adaptable to wind-powered electricity generation
Field microcosmic structure.
Claims (6)
1. a kind of wind-driven generator addressing Lectotype Optimization method suitable for Complex Constraints condition, it is characterised in that including following
Step：
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 it is determined that
A dry alternative model is used for Lectotype Optimization, read in wind power plant correlation landform it is gentle as etc. parameter；
2) according to addressing requirements of type selecting, the Complex Constraints condition of optimization problem is taken out, is object function by constraints slacking
In penalty function, make that optimum results meet the constraint of the safe distance between blower fan and wind field year minimum generated energy is constrained；
3) the random initial position matrix for generating blower fan in the range of wind-powered electricity generation field areas transverse and longitudinal coordinate, often row represents a kind of wind to matrix
The chromosome of arrangement, i.e., one is put in seat in the plane, and line number represents genetic algorithm chromosome number, and each row to matrix carries out binary system
Coding；
4) generate initial model matrix in given alternative blower fan model and encode, matrix line number represents particle cluster algorithm particle
Number, every a line of matrix represents a particle (a kind of blower fan model Choice), the speed of random initializtion particle and is searching
Position in rope domain, as the initial solution that the blower fan model of current chromosome is chosen；
5) fitness of current each particle is calculated, i.e., using the degree electricity cost of current blower fan position and selecting type scheme, and is obtained
The individual adaptive optimal control degree and global optimum's fitness of all particles of each particle；
6) according to the particle rapidity and position evolutionary rule set in particle cluster algorithm, position and speed to each particle are carried out
Evolve；
7) judge whether to reach the maximum algebraically that particle cluster algorithm sets, if reaching the maximum algebraically of setting, stopping carries out fan type
Number optimization, choose particle cluster algorithm global optimum's fitness, as the fitness of current chromosome, otherwise return to step 5)；
8) according to the fitness of each chromosome, global optimum's fitness of genetic algorithm is obtained, i.e., blower fan position type selecting is complete
Office's optimal value；
9) judge whether to reach the maximum iteration of genetic algorithm, if so, then exporting global optimum's fitness of genetic algorithm
Corresponding chromosome as blower fan location schemes, global optimum's fitness of its corresponding particle cluster algorithm as selecting type scheme,
The arrangement optimization of polytypic wind-driven generator is completed, step 10 is otherwise carried out)；
10) using all chromosomes as parent chromosome group, intersected, mutation operation, the fitness size according to chromosome
Select probability is calculated, selection generation child chromosome group and return to step 4 is carried out).
2. a kind of wind-driven generator addressing Lectotype Optimization method suitable for Complex Constraints condition according to claim 1,
Characterized in that, choosing blower fan position using genetic algorithm, nesting carries out blower fan model optimization using particle cluster algorithm, i.e., every time
After selection blower fan position, the optimal solution of type selecting when drawing the blower fan position using particle cluster algorithm, as the generation blower fan position
Fitness.There is nonlinear restriction in optimization problem, nonlinear restriction includes minimum range and the wind power plant year between blower fan
Minimum generated energy.Nonlinear Constraints cause Fan Selection and addressing optimization problem extremely complex and be difficult to obtain optimal solution.
3. a kind of wind-driven generator addressing Lectotype Optimization method suitable for Complex Constraints condition according to claim 1,
Characterized in that, individual adaptation degree calculated value is calculated as follows：Individual adaptation degree is embodied by the inverse of degree of calculating electricity cost, is fitted
Individual being of response highest spends electric cost maximum reciprocal, that is, spend the individuality of electric cost minimum, the meter of the electric cost CoP of degree
Calculating formula is：
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-driven generator addressing choosing suitable for Complex Constraints condition according to claim 1,2 or 3 any one
Type optimization method, it is characterised in that it is optimization problem P2 that optimization problem P1 relaxes, nested optimization problem P3 in P2；
P1 is expressed as follows：
min CoP
Wherein：E0It is year minimum generated energy, DsIt is safe distance between blower fan, di,jIt is distance between blower fan, xi, yiIt is the position of blower fan
Put two-dimensional coordinate,WithIt is respectively the bound of blower fan two-dimensional coordinate.
P2 is expressed as follows：
The Nonlinear Constraints that safe distance will must be fulfilled between blower fan add the target of genetic algorithm as penalty function
Function, then the object function of genetic algorithm be expressed as：
Wherein：C1It is penalty factor.
P3 is expressed as follows：
The constraints of wind field year minimum generated energy is added the object function of particle cluster algorithm as penalty function, then particle
The object function of group's algorithm is expressed as：
min(CoP+C2e)
Wherein：C2It is penalty factor, E0It is the whole minimum generated energy of wind field year.
5. a kind of wind-driven generator addressing Lectotype Optimization method suitable for Complex Constraints condition according to claim 1,
Characterized in that, combined using the blower fan model that particle cluster algorithm chooses ad-hoc location scheme, including same model fan engine room
The different model blower fan of different cabin altitudes or the different model blower fan with cabin altitude or different cabin altitudes, i.e., it is of the same race to dispatch from the factory
If model blower fan is arranged on different cabin altitudes, different model is considered as.
6. a kind of wind-driven generator addressing Lectotype Optimization method suitable for Complex Constraints condition according to claim 1,
Characterized in that, the requirement for actual wind power plant adds constraints and solves, microcosmic structure result is to wind energy utilization efficiency
Higher, practicality is more preferable.
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| CN111985691A (en) * | 2020-07-20 | 2020-11-24 | 明阳智慧能源集团股份公司 | Site selection method for wind power plant booster station |
| CN114611789A (en) * | 2022-03-11 | 2022-06-10 | 长江勘测规划设计研究有限责任公司 | Wind power plant position dynamic optimization method with unit electric energy static investment optimization as target |
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