CN102142103A - Real-coded genetic algorithm-based optimizing method for micrositing of wind power station - Google Patents

Abstract

The invention discloses a real-coded genetic algorithm-based optimizing method for the micrositing of a wind power station. In the method, the measured wind speed in the wind farm is corrected by an index model in the direction of relative height; a power characteristic curve of a wind machine is discretized by a linearized method; for the wake flow of the wind machine, a linearized wake flow model is adopted; the wind speed of the wind machines at the wake flow of a plurality of wind machines is solved by a method of the summation of squared differences, when part of the wind machines are positioned in the wake flow, the wind speed is revised by a method of area coefficients; based on an optimizing target function of the micrositing in the design of the wind power station, when the total number of the wind machines in the wind power station is determined, the total generated energy is used as the target function, and when the total number of the wind machines in the wind power station is not determined, the kilowatt-hour cost is used as the target function; and the microcosmic arrangement site of each wind machine in the wind power station is obtained by the real-coded genetic algorithm-based optimizing method. By the method, the reliability of forecast is high, the optimizing efficiency is high and results are accurate.

Description

A kind of wind energy turbine set microcosmic addressing optimization method based on Real Coding Genetic Algorithm

Technical field

The present invention relates in given wind energy turbine set zone, utilize the wake model of wind energy conversion system and based on the genetic algorithm of real coding, the Optimization Design of every wind energy conversion system of optimized choice particular location in wind energy turbine set belongs to the problem in Energy Project and electrical engineering field.

Background technology

Wind-power electricity generation in recent years, is greatly developed in the countries in the world that comprising China as being hopeful most one of energy that substitutes fossil energy, wherein, China in the past in 5 years a year installation amount rate of growth surpassed 100%.

In the practical application of wind energy, what at first should give consideration is exactly the location problem of wind power plant, and the quality that the site is selected has played important effect to the economy of wind-power electricity generation.The wind energy turbine set addressing is divided into macroscopical addressing and microcosmic addressing, the principle that the macroscopic view addressing is followed is according to the result of wind energy resources investigation with subregion, select best site,, improve economy, stability and the reliability of power supply in the hope of increasing the output of wind power generating set; The microcosmic addressing then is in the zonule of selecting in macroscopical addressing, consideration reaches wind disturbance (the being wake flow) factor that self is caused by wind energy conversion system by the variation of the natural wind that the wind field environment causes, how to determine the arranged wind power generating set, make whole wind power plant annual electricity generating capacity maximum, thereby the production cost that reduces the energy is to obtain favorable economic benefit, in addition, the place topological design, the selection of wind energy turbine set geographical environment etc. also can be included the category of microcosmic addressing in.

The most of business softwares such as WAsP and WindFarmer that rely on of the microcosmic addressing work of present domestic wind energy turbine set, and after when the wind energy conversion system addressing, needing the microcosmic address of artificial each wind energy conversion system of layout, business software just can calculate a year maximum generated energy, generally also be to adopt the probability density method of wind speed probability density and wind direction when generated energy calculates according to trying to achieve to dispersing after the circumferential calibration, reach the result of microcosmic addressing preferably, need repeatedly manually to determine the microcosmic address, obtain after relatively through scheme then, the workload of microcosmic addressing is big, and generally can not obtain optimum result.

Understand according to the applicant, also do not optimize the patent of microcosmic addressing aspect at present about wind energy turbine set.

Summary of the invention

Technical matters: the microcosmic addressing of wind energy turbine set at present adopts the business software of introduction to carry out always, business software also requires the rule of thumb artificial experience of designer, arrange the specific address of each wind energy conversion system, obtain final microcosmic address according to objective functions such as economy or Wind Power Utilization efficient after relatively, workload is big, and can not reach generated energy or the best wind energy conversion system preferred arrangement result of economic benefit, sometimes indivedual wind-force unit annual utilization hourses of arranging are far from reaching designing requirement, make lower that the not high wind-power electricity generation benefit of investment repayment originally falls, thereby make wind-power electricity generation advantage reduction relatively the time with other power supply.The object of the invention is to provide a kind of wind energy turbine set microcosmic addressing optimization method based on Real Coding Genetic Algorithm at above-mentioned defective.

Technical scheme:

The present invention adopts following technical scheme for achieving the above object:

A kind of wind energy turbine set microcosmic addressing optimization method of the present invention based on Real Coding Genetic Algorithm, use the exponential model of relative height direction to proofread and correct to the wind energy turbine set measuring wind, obtain the wind speed of wind energy conversion system hub height, adopt linearization technique discrete to the wind energy conversion system power characteristic, utilize interpolation method to obtain the wind energy conversion system output of any wind speed, single wind energy conversion system wake flow is adopted linearizing wake model, the wind energy conversion system wind speed adopts difference side's accumulation method to find the solution in the windy power tail stream to being in, when partly being in, wind energy conversion system adopts the correction of area coefficient method in the wake flow, the optimization aim function of microcosmic addressing is when total platform number of wind energy turbine set installation is determined in wind power plant designs, with total generated energy as objective function, when total platform number of wind energy turbine set installation is not determined, answer expenditure electricity cost as objective function, employing is optimized the microcosmic that obtains each blower fan in the wind energy turbine set and is arranged the address based on the genetic algorithm of real coding.

Preferably, the wind energy conversion system power characteristic is adopted adopt linear slotting separating method to obtain when reading behind the integer wind speed point power other wind speed.

Preferably, when described total platform number of installing in wind energy turbine set when the optimization aim function of microcosmic addressing in the wind power plant design is determined, adopt total generated energy as objective function; Do not determine when total platform number of wind energy turbine set installation, spend electric cost, degree of selecting for use electricity cost minimum as objective function.

Preferably, described genetic algorithm based on real coding is optimized arranging of wind energy turbine set inner blower, and the method as a result that is optimized is as follows:

(1) determines the constant interval of objective function and each independent variable, when the platform number of wind-force unit determines that objective function is that the annual maximum generating watt summation of all wind energy conversion systems in the wind energy turbine set reaches maximum, promptly

$F(x,y)=\mathrm{Max}\left(\underset{M}{\mathrm{\Σ}}\underset{\mathrm{year}}{\mathrm{\Σ}}f(x_i,y_i)\right)---\left(1\right)$

Wherein M is a wind-force board number, f (x _i, y _i) be hour generated energy of separate unit wind energy conversion system;

When wind-force board number was not determined yet, objective function was the electric cost of degree:

F(x，y)＝MinC(M，x _i，y _i) (2)

Wherein, M is a wind-force board number, obtains after needing to optimize, and also will obtain the microcosmic address (x of corresponding optimization simultaneously _i, y _i).

In addition, the optimizing process independent variable is that constraint condition that the microcosmic address of wind energy conversion system is arranged is that distance between any two blower fans needs to satisfy the rotor diameter greater than 4 times, that is:

(x _i-x _j) ²+(y _i-y _j) ²≥64R ² (3)

i＝1，2，...，n；j＝1，2，...，n；i≠j

(2) coding and initial population: utilize the normalization linear transformation between the address spaces to 0 of optimization variable and 1;

(3) ideal adaptation degree function: determine that when the platform number of wind-force unit objective function is that the annual maximum generating watt summation of all wind energy conversion systems in the wind energy turbine set reaches maximum, corresponding ideal adaptation degree function is:

$F\left(\mathrm{fitness}\left(i\right)\right)=1/(\left(\underset{M}{\mathrm{\Σ}}\underset{\mathrm{year}}{\mathrm{\Σ}}f(x_i,y_i)\right)+c)---\left(4\right)$

C is a very little number, is in order to prevent to take place to calculate spillover when the optimization aim functional value is tending towards 0; And when belonging to wind-force board number and also determining, corresponding ideal adaptation degree function is:

F(fitness(i))＝C(M，x _i，y _i) (5)

(4) determining of genetic operator:

1) select computing: select proportionally selection mode of operator, the selection probability of parent individuality is:

$\mathrm{ps}\left(i\right)=\frac{F\left(\mathrm{fitness}\left(i\right)\right)}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}F\left(\mathrm{fitness}\left(i\right)\right)}---\left(6\right)$

Order

Sequence p (i) has been divided into n sub-range to [0,1] interval: [0, p (1)], [p (1), p (2)] ..., [p (n-1), p (n)], these sub-ranges and n parent individuality set up one-to-one relationship, generate m [0,1] random number u (k), k is [1, m], if u (k) is in [p (i-1), p (i)], i individuality (x then _i, y _i) selected; From parent colony, select i individuality like this, select m individuality (x altogether with Probability p s (i) _i, y _i), (x wherein _i, y _i) be the microcosmic address at every wind energy conversion system place of wind energy turbine set;

2) hybridization computing: the crossover operation that invention is adopted is to select two couples of parent individuality (x at random according to the selection probability of selecting operator _i ¹, y _i ¹) and (x _j ¹, y _f ¹) as parents, and carry out following linear combination at random, produce a filial generation individuality (x _p, y _p):

$\left\{\begin{array}{cc}(x_p,y_p)=u_1(x_i^1,y_i^1)+(1-u_1)(x_j^1,y_j^1)& u_3<0.5\\ (x_p,y_p)=u_2(x_i^1,y_i^1)+(1-u_2)(x_j^1,y_j^1)& u_3\&GreaterEqual;0.5\end{array}\right.---\left(7\right)$

In the formula, u ₁, u ₂And u ₃It all is random number.By such crossover operation, common property is given birth to m filial generation body (x _p, y _p);

3) variation computing: mutation operation adopts m random number with p _m=1-p _sProbability replace individuality (x _i, y _i), thereby obtain offspring individual (x _q, y _q):

$\left\{\begin{array}{cc}(x_q,y_q)=u_i& u_m<p_m\\ (x_q,y_q)=(x_i,y_i)& u_m\&GreaterEqual;p_m\end{array}\right.---\left(8\right)$

U in the formula _mAnd u _iBe random number, variation computing common property is given birth to m filial generation individuality (x _q, y _q);

(5) evolution iteration and end condition: 3m the filial generation individuality that obtains by preceding step (4) arranged from small to large by fitness, m that selects the fitness minimum as new parent colony, again it is repeated step (4) computing, microcosmic addressing until optimum is stable, and stable Rule of judgment is that maximum blower fan position deviation is not more than 0.5 meter.

Beneficial effect: compare with existing wind energy turbine set microcosmic site selecting method, the method that the present invention proposes has following benefit:

(1) wind energy turbine set microcosmic site selecting method can adopt the microcosmic address of the wind energy conversion system that certain objective function is optimized automatically;

(2) hour air speed data that adopted a year is directly optimized the microcosmic address layout that obtains wind energy conversion system, and more accurate than the annual electricity generating capacity that adopts probabilistic statistical method to obtain, forecasting reliability is higher;

(3) genetic algorithm based on real coding is adopted in the microcosmic addressing of wind energy conversion system, and robustness is good, optimizes the efficient height, and the result is accurate;

(4) the wind electric field blower cloth postpone that obtains after optimizing, the wind energy turbine set annual electricity generating capacity increases about 3%.

Description of drawings

Fig. 1 separate unit wind energy conversion system wake flow;

Fig. 2 wind energy conversion system partly is in the wind energy conversion system wake flow of upper reaches;

The optimization addressing process that Fig. 3 wind-force board number is determined;

Fig. 4 wind-force board number does not have definite optimization addressing process;

The rose diagram of Fig. 5 wind direction;

The layout result that Fig. 6 optimizes.

Embodiment

Wind energy turbine set based on genetic algorithm is optimized the microcosmic site selecting method, employing is proofreaied and correct the index method of short transverse the wind energy turbine set measuring wind, adopt linearization discrete to the wind energy conversion system power characteristic, single wind energy conversion system wake flow is adopted linearizing wake model, the wind energy conversion system wind speed adopts difference side's accumulation method in the windy power tail stream to being in, and considered that wind energy conversion system partly is in the situation in the wake flow, adopt the area coefficient correction this moment, in determining the wind power plant design during optimization aim function of microcosmic addressing, when total platform number of wind energy conversion system is determined, selecting total generated energy for use is objective function, when the total platform number of the wind energy conversion system of wind energy turbine set is not determined, in the lump under the microcosmic geologic condition of the platform number of definite wind-force unit and layout, select for use unit degree electricity cost as objective function, adopt the microcosmic layout address or the platform number of wind energy conversion system and the specific address of every wind energy conversion system that go out each blower fan in the wind energy turbine set based on the genetic algorithm optimization of real coding at last.Its basic process is as follows:

1. to the wind speed-powertrace linearization of wind energy conversion system

Wind-power electricity generation acc power output is with relevant in the powertrace of the wind speed of hub of wind power generator height and aerogenerator.And because the influence of wind shear, generally be not equal to the wind speed of test height at the wind speed of hub height, it generally calculates according to exponential relationship:

$\frac{v}{v_0}={\left(\frac{h}{h_0}\right)}^{\mathrm{\&alpha;}}---\left(1\right)$

Wherein: v refers to calculate the speed of h height, v ₀Refer to test height h ₀The speed at place, α is the wind shear exponent coefficient, and frictional resistance general and ground has relation, and the wind energy turbine set location that relates to belongs to the low rocky ground that rises and falls, and generally is taken as 1/7, and other corresponding landform has corresponding wind shear exponent.

The power discrete method that multiple aerogenerator is arranged, as segmentation secondary or repeatedly approximating method, sectional linear fitting method etc., linearization technique is adopted in invention, and the linear segmented model is as the formula (2).

$P_w\left(v\right)=\left\{\begin{array}{cc}0& v<v_{\mathrm{cutin}}\\ P_w\left(v_{i-1}\right)+(P_w\left(v_i\right)-P_w\left(v_{i-1}\right))(v-v_{i-1})& v_i>v>v_{i-1},v_i=i,i=[v_{\mathrm{cutin}}+1,v_{\mathrm{rated}}-1]i\&Element;\mathrm{int}\\ P_{\mathrm{rated}}& v_{\mathrm{cutoff}}>v>v_{\mathrm{rated}}\\ 0& v>v_{\mathrm{cutoff}}\end{array}\right.---\left(2\right)$

2. wake model

2.1 single wind energy conversion system wake model

The tail loss is a very important factor that influences the design of wind field apoplexy machine arrangement.When the wind comes from the wind field runs into blower fan, will behind blower fan, produce a wake flow that is coniform continuous expansion, as shown in Figure 1, can in a distance x thereafter, form certain speed loss, the speed of part wind can be by initial wind velocity U ₀Be reduced to U (x), the tail zone of influence of the demonstration blower fan among the figure, when distinguished and admirable go out the tail zone of influence after, wind speed can go back up to initial velocity U again ₀

Wind speed in the wake zone at x place, wind energy conversion system downstream is U (x), can be expressed as

$\frac{U\left(x\right)}{{\mathrm{<figure-callout\; id="0"\; label="U"\; filenames="HDA0000055444480000051.png,HDA0000055444480000061.png"\; state="\{\{state\}\}">U</figure-callout>}}_0}=\frac{1}{2}+\frac{1}{2}{(1-2C_T{\left(\frac{D_0}{D\left(x\right)}\right)}^2)}^{\frac{1}{2}}---\left(3\right)$

U wherein ₀Be incoming flow wind speed, D ₀Be rotor diameter, D (x) is the wake flow diameter, C _TBe the thrust coefficient of wind energy conversion system, general wind energy conversion system manufacturer can be provided at the thrust coefficient under the various wind speed, also can through type

$T=\frac{1}{8}\mathrm{\&pi;\&rho;}{\mathrm{<figure-callout\; id="0"\; label="D"\; filenames="HDA0000055444480000051.png,HDA0000055444480000061.png"\; state="\{\{state\}\}">D</figure-callout>}}_0^2{\mathrm{<figure-callout\; id="0"\; label="U"\; filenames="HDA0000055444480000051.png,HDA0000055444480000061.png"\; state="\{\{state\}\}">U</figure-callout>}}_0^2{C}_T---\left(4\right)$

Wherein ρ is the density of air.And D (x) can be by linear process

$D\left(x\right)=(1+2{\mathrm{\&alpha;}}_{\mathrm{noj}}\frac{x}{D_0})\sqrt{\mathrm{\&beta;}}{D}_0---\left(5\right)$

α _NojBe 0.05,

$\mathrm{\&beta;}=\frac{1}{2}\frac{1+\sqrt{1+C_T}}{\sqrt{1-C_T}}---\left(6\right)$

2.2 blower fan partly is in the wake flow of upstream blower fan

When the blower fan in downstream is not in the wake flow of upstream blower fan fully, as Fig. 2, the mean wind speed U of downstream blower fan then _PartialFor

${(U_{\mathrm{partial}}-U_0)}^2=\frac{4A_w}{\mathrm{\&pi;}{\mathrm{<figure-callout\; id="0"\; label="D"\; filenames="HDA0000055444480000051.png,HDA0000055444480000061.png"\; state="\{\{state\}\}">D</figure-callout>}}_0^2}{(U-U_0)}^2---\left(7\right)$

Suppose that X is two distances between the center of circle, then A _wCan get

$A_w=R^2(Y_R-\frac{\sin \left({2Y}_R\right)}{2})+r^2(Y_r-\frac{\sin \left({2Y}_r\right)}{2})$ $\&ForAll;X\&Element;[R-r,R+r]---\left(8\right)$

A _w＝0 $\&ForAll;X\&GreaterEqual;R+r$

A _w＝πr ² $\&ForAll;X\&le;R-r$

Wherein $Y_R={\cos }^{-1}\left(\frac{R^2+X^2-r^2}{2\mathrm{XR}}\right)$ $\&ForAll;X\&Element;[R-r,R+r]$

$Y_r={\cos }^{-1}\left(\frac{R^2-X^2-r^2}{2\mathrm{XR}}\right)$ $\&ForAll;X\&Element;[R-r,R+r]$

2.3 blower fan partly is in the wake flow of a plurality of blower fans in upstream

When blower fan partly is in the wake flow of a plurality of blower fans in upstream, the average velocity of blower fan is calculated as follows:

${(U-U_0)}^2=\underset{i=0}{\overset{N_{\mathrm{upstream}}}{\mathrm{\&Sigma;}}}{(U_i-U_0)}^2---\left(9\right)$

U wherein _iThe wake flow wind speed that produces when having only blower fan i for the upstream, N _UpstreamBe the quantity of upstream blower fan, when the urticaria machine has in the wake flow that partly is in the upstream blower fan instantly, need in following formula, increase weight coefficient:

${(U-U_0)}^2=\underset{i=0}{\overset{N_{\mathrm{upstream}}}{\mathrm{\&Sigma;}}}{\frac{4A_{\mathrm{wi}}}{\mathrm{\&pi;}{\mathrm{<figure-callout\; id="0"\; label="D"\; filenames="HDA0000055444480000051.png,HDA0000055444480000061.png"\; state="\{\{state\}\}">D</figure-callout>}}_0^2}(U_i-U_0)}^2---\left(10\right)$

A _WiBe upstream blower fan wake flow and the public part area of downstream blower fan.

3. optimization aim function

The optimization aim function of microcosmic addressing generally has two kinds in the wind power plant design, and a kind of is that total platform number of installing has been determined, arranges that the microcosmic address of each wind energy conversion system is just passable; Another kind is a kind of of relative complex, the total platform number that is wind energy turbine set is not determined, need to determine in the lump the platform number of wind-force unit and the microcosmic address of layout, the electronic attitude pricing of unit degree of this general employing generating, the present invention both can adopt the former, also go for the latter, the former objective function selects for use the N maximum with regard to passable (wherein N is an annual electricity generating capacity); The latter adopts unit degree electricity cost (C) minimum.

Unit degree electricity cost adopts the electronic attitude pricing of unit degree model, that is:

C＝(I _WTGCRF _WTG+I _ECRF _E+I _GCRF _G+M _WTG)/N (11)

Wherein: C is the electric cost of degree, unit/kWh; I _WTGBe the initial cost of wind power generating set, unit; I _WTG=c _WTGM, c _WTGBe the price of separate unit wind power generating set, unit/platform, m are the platform number of wind power generating set; CRF _WTGDiscount rate for wind power generating set; LT _WTGBe the life-span of wind power generating set, year; I is a bank rate; I _EBe the initial cost in soil, unit; CRF _EDiscount rate for cost of land;

LT _EBe the land use time limit, year; I _GBe the initial cost of grid-connected system, unit; CRF _GDiscount rate for grid-connected system;

LT _GFor being incorporated into the power networks tenure of use in year; M _WTGBe the annual operating cost of wind power generating set, M _WTG=0.05I _WTGN is the year gross generation of design m typhoon machine.

4. arrange with the optimization of genetic algorithm realization blower fan

Genetic algorithm is a kind of self-adaptation overall situation probabilistic search algorithm of the Darwinian biological theory of natural selection of simulation and natural biological evolution process, its characteristics are wide coverage, are beneficial to the overall situation according to qualifications, have good convergence, and computing time is few, the robustness height.The present invention adopts the genetic algorithm based on real coding arranging of wind energy turbine set inner blower to be optimized the result who is optimized.

(1) determines the constant interval of objective function and each independent variable.The objective function that the present invention adopts has two kinds, and a kind of is that the platform number of wind-force unit determines that objective function is that the annual maximum generating watt summation of all wind energy conversion systems in the wind energy turbine set reaches maximum, promptly

$F(x,y)=\mathrm{Max}\left(\underset{M}{\mathrm{\&Sigma;}}\underset{\mathrm{year}}{\mathrm{\&Sigma;}}f(x_i,y_i)\right)---\left(12\right)$

Wherein M is a wind-force board number, f (x _i, y _i) be hour generated energy of separate unit wind energy conversion system.When wind-force board number was not determined yet, objective function was:

F(x，y)＝MinC(M，x _i，y _i) (13)

Wherein, M obtains after wind-force board number need be optimized, and also will obtain the microcosmic address (x of corresponding optimization simultaneously _i, y _i).

In addition, the optimizing process independent variable is that constraint condition that the microcosmic address of wind energy conversion system is arranged is that distance between any two blower fans needs to satisfy the rotor diameter greater than 4 times, that is:

(x _i-x _j) ²+(y _i-y _j) ²≥64R ² (14)

i＝1，2，...，n；j＝1，2，...，n；i≠j

(2) coding and initial population.Because genetic algorithm can not directly be handled the data of separating of solution space, therefore must they be expressed as the genotype string structure data in hereditary space by coding, the present invention adopts real coding, utilizes linear transformation normalization between the address spaces to 0 of optimization variable and 1.Population scale is excessive, and each is also just many more for the calculated amount of needs, and this might cause speed of convergence slow excessively, and general population number is taken as 20～200, and invention suggestion population number selects 50 for use.

(3) ideal adaptation degree function.Determine that when the platform number of wind-force unit objective function is that the annual maximum generating watt summation of all wind energy conversion systems in the wind energy turbine set reaches maximum, corresponding ideal adaptation degree function is:

$F\left(\mathrm{fitness}\left(i\right)\right)=1/(\left(\underset{M}{\mathrm{\&Sigma;}}\underset{\mathrm{year}}{\mathrm{\&Sigma;}}f(x_i,y_i)\right)+c)---\left(15\right)$

C is a very little number, mainly is in order to prevent to take place to calculate spillover when the optimization aim functional value is tending towards 0.And if when belonging to wind-force board number and also determining, corresponding ideal adaptation degree function is:

F(fitness(i))＝C(M，x _i，y _i) (16)

(4) genetic operator determines.Deterministic process is as follows.

1) select computing: select proportionally selection mode of operator, the selection probability of parent individuality is:

$\mathrm{ps}\left(i\right)=\frac{F\left(\mathrm{fitness}\left(i\right)\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}F\left(\mathrm{fitness}\left(i\right)\right)}---\left(17\right)$

Order

Sequence p (i) has been divided into n sub-range to [0,1] interval: [0, p (1)], [p (1), p (2)] ..., [p (n-1), p (n)], these sub-ranges and n parent individuality set up one-to-one relationship, generate m [0,1] random number u (k), k is [1, m], if u (k) is in [p (i-1), p (i)], i individuality (x then _i, y _i) selected.From parent colony, select i individuality like this, select m individuality (x altogether with Probability p s (i) _i, y _i), (x wherein _i, y _i) be the microcosmic address at every wind energy conversion system place of wind energy turbine set.

2) hybridization computing: the crossover operation that invention is adopted is to select two couples of parent individuality (x at random according to the selection probability of selecting operator _i ¹, y _i ¹) and (x _j ¹, y _j ¹) as parents, and carry out following linear combination at random, produce a filial generation individuality (x _p, y _p):

$\left\{\begin{array}{cc}(x_p,y_p)=u_1(x_i^1,y_i^1)+(1-u_1)(x_j^1,y_j^1)& u_3<0.5\\ (x_p,y_p)=u_2(x_i^1,y_i^1)+(1-u_2)(x_j^1,y_j^1)& u_3\&GreaterEqual;0.5\end{array}\right.---\left(18\right)$

In the formula, u ₁, u ₂And u ₃It all is random number.By such crossover operation, common property is given birth to m filial generation body (x _p, y _p).

3) variation computing: mutation operation adopts m random number with p _m=1-p _sProbability replace individuality (x _i, y _i), thereby obtain offspring individual (x _q, y _q).

$\left\{\begin{array}{cc}(x_q,y_q)=u_i& u_m<p_m\\ (x_q,y_q)=(x_i,y_i)& u_m\&GreaterEqual;p_m\end{array}\right.---\left(19\right)$

U in the formula _mAnd u _iBe random number, variation computing common property is given birth to m filial generation individuality (x _q, y _q).

(5) evolution iteration and end condition: 3m the filial generation individuality that obtains by preceding step (4) arranged from small to large by fitness, m that selects the fitness minimum as new parent colony, again it is repeated step (4) computing, microcosmic addressing until optimum is stable, and stable Rule of judgment is that maximum blower fan position deviation is not more than 0.5 meter.

Obviously, if know the wind field wind speed and direction of the whole year, then can obtain gross generation and total unit degree electricity cost in the wind power plant 1 year.

Whole optimizing process such as Fig. 3 or shown in Figure 4.

5. result's output

Be approximately the planning place of closing on the seashore of circular wind field on certain border, physical features is smooth, gathers the wind-resources data in 1 year, uses the inventive method, and blower fan and ground environment parameter used in the calculating are as shown in table 1

Table 1 calculates used parameter

Wind direction distribution rose diagram as shown in Figure 5, as seen from the figure, the prevailing wind direction of wind field is 90 °～112.5 °.And by method of the present invention, the layout address of in wind energy turbine set, optimizing during different blower fan number as shown in Figure 6, as calculated, the layout of optimization approximately Duos 3% than conventional quincuncial arrangement mode annual electricity generating capacity.

Claims (4)

1. wind energy turbine set microcosmic addressing optimization method based on Real Coding Genetic Algorithm, it is characterized in that using the exponential model of relative height direction to proofread and correct to the wind energy turbine set measuring wind, obtain the wind speed of wind energy conversion system hub height, adopt linearization technique discrete to the wind energy conversion system power characteristic, single wind energy conversion system wake flow is adopted linearizing wake model, the wind energy conversion system wind speed adopts difference side's accumulation method to find the solution in the windy power tail stream to being in, when partly being in the wake flow, adopts wind energy conversion system the correction of area coefficient method, the optimization aim function of microcosmic addressing is when total platform number of wind energy turbine set installation is determined in wind power plant designs, adopt total generated energy as objective function, when total platform number of wind energy turbine set installation is not determined, employing degree electricity cost is as objective function, application is based on the genetic algorithm of real coding, and the microcosmic that obtains each blower fan in the wind energy turbine set after the optimization is arranged the address.

2. according to claims 1 described a kind of wind energy turbine set microcosmic addressing optimization method based on Real Coding Genetic Algorithm, it is characterized in that integer wind speed point power is read in employing to the wind energy conversion system power characteristic, and the power during other wind speed adopts the linear separating method of inserting to obtain.

3. according to claims 1 described a kind of wind energy turbine set microcosmic addressing optimization method based on Real Coding Genetic Algorithm, when it is characterized in that described total platform number of installing in wind energy turbine set when the optimization aim function of microcosmic addressing in the wind power plant design is determined, select for use annual electricity generating capacity as objective function, require the annual electricity generating capacity maximum; Do not determine when total platform number of wind energy turbine set installation, spend electric cost, degree of requirement electricity cost minimum as objective function.

4. according to claims 1 described a kind of wind energy turbine set microcosmic addressing optimization method based on Real Coding Genetic Algorithm, it is characterized in that adopting the genetic algorithm based on real coding that arranging of wind energy turbine set inner blower is optimized, the result's that is optimized method is as follows:

(1) determines the constant interval of objective function and each independent variable, when the platform number of wind-force unit determines that objective function is that the annual generated energy summation of all wind energy conversion systems in the wind energy turbine set reaches maximum, promptly

$F(x,y)=\mathrm{Max}\left(\underset{M}{\mathrm{\&Sigma;}}\underset{\mathrm{year}}{\mathrm{\&Sigma;}}f(x_i,y_i)\right)---\left(1\right)$

Wherein M is a wind-force board number, f (x _i, y _i) be hour generated energy of separate unit wind energy conversion system;

When wind-force board number was not determined yet, objective function was:

F(x，y)＝MinC(M，x _i，y _i) (2)

Wherein, M is a wind-force board number, obtains after needing to optimize, and also will obtain the microcosmic address (x of corresponding optimization simultaneously _i, y _i).

In addition, the optimizing process independent variable is that constraint condition that the microcosmic address of wind energy conversion system is arranged is that distance between any two blower fans needs to satisfy the rotor diameter greater than 4 times, that is:

(x _i-x _j) ²+(y _i-y _j) ²≥64R ² (3)

i＝1，2，...，n；j＝1，2，...，n；i≠j

(2) coding and initial population: the method for normalizing that utilizes linear transformation is optimization variable, and promptly address coordinate is transformed between 0 and 1;

(3) ideal adaptation degree function: determine that when the platform number of wind-force unit objective function is the annual generated energy summation of all wind energy conversion systems in the wind energy turbine set, requires the objective function maximum, corresponding ideal adaptation degree function is:

$F\left(\mathrm{fitness}\left(i\right)\right)=1/(\left(\underset{M}{\mathrm{\&Sigma;}}\underset{\mathrm{year}}{\mathrm{\&Sigma;}}f(x_i,y_i)\right)+c)---\left(4\right)$

C is a very little number, is in order to prevent to take place to calculate spillover when the optimization aim functional value is tending towards 0; And when belonging to wind-force board number and also determining, corresponding ideal adaptation degree function is:

F(fitness(i))＝C(M，x _i，y _i) (5)

Require the ideal adaptation degree to reach minimum in the genetic algorithm.

(4) determining of genetic operator:

1) select computing: select proportionally selection mode of operator, the selection probability of parent individuality is:

$\mathrm{ps}\left(i\right)=\frac{F\left(\mathrm{fitness}\left(i\right)\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}F\left(\mathrm{fitness}\left(i\right)\right)}---\left(6\right)$

Order

Sequence p (i) has been divided into n sub-range to [0,1] interval: [0, p (1)], [p (1), p (2)] ..., [p (n-1), p (n)], these sub-ranges and n parent individuality set up one-to-one relationship, generate m [0,1] random number u (k), k is [1, m], if u (k) is in [p (i-1), p (i)], i individuality (x then _i, y _i) selected; From parent colony, select i individuality like this, select m individuality (x altogether with Probability p s (i) _i, y _i), (x wherein _i, y _i) be the microcosmic address at every wind energy conversion system place of wind energy turbine set;

2) hybridization computing: the crossover operation that invention is adopted is to select two couples of parent individuality (x at random according to the selection probability of selecting operator _i ¹, y _i ¹) and (x _j ¹, y _j ¹) as parents, and carry out following linear combination at random, produce a filial generation individuality (x _p, y _p):

$\left\{\begin{array}{cc}(x_p,y_p)=u_1(x_i^1,y_i^1)+(1-u_1)(x_j^1,y_j^1)& u_3<0.5\\ (x_p,y_p)=u_2(x_i^1,y_i^1)+(1-u_2)(x_j^1,y_j^1)& u_3\&GreaterEqual;0.5\end{array}\right.---\left(7\right)$

In the formula, u ₁, u ₂And u ₃It all is random number.By such crossover operation, common property is given birth to m filial generation body (x _p, y _p);

3) variation computing: mutation operation adopts m random number with p _m=1-p _sProbability replace individuality (x _i, y _i), thereby obtain offspring individual (x _q, y _q):

$\left\{\begin{array}{cc}(x_q,y_q)=u_i& u_m<p_m\\ (x_q,y_q)=(x_i,y_i)& u_m\&GreaterEqual;p_m\end{array}\right.---\left(8\right)$

U in the formula _mAnd u _iBe random number, variation computing common property is given birth to m filial generation individuality (x _q, y _q);

(5) evolution iteration and end condition: 3m the filial generation individuality that obtains by preceding step (4) arranged from small to large by fitness, m that selects the fitness minimum as new parent colony, again it is repeated step (4) computing, microcosmic addressing until optimum is stable, and stable Rule of judgment is that maximum blower fan position deviation is not more than 0.5 meter.

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