CN106407566A - A complex terrain wind power plant integration optimization method - Google Patents

A complex terrain wind power plant integration optimization method Download PDF

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CN106407566A
CN106407566A CN201610836246.1A CN201610836246A CN106407566A CN 106407566 A CN106407566 A CN 106407566A CN 201610836246 A CN201610836246 A CN 201610836246A CN 106407566 A CN106407566 A CN 106407566A
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wind energy
road
turbine set
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许昌
陈丹丹
薛飞飞
韩星星
郝辰妍
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Hohai University HHU
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Abstract

The invention provides a complex terrain wind power plant integration optimization method comprising: a first step of micro-siting optimization: with the maximization of the generating capacity of a wind power plant as the target, calculating the position coordinates of each wind turbine iteratively by using the genetic algorithm; a second step of electricity collection line optimization: calculating a scheme with the minimum total path weight by using the Dijkstra algorithm and the minimum spanning tree algorithm and determining the connecting loops between the wind turbines; a third step of maintenance road optimization: based on the connecting paths between the wind turbines obtained in the last step of electricity collection line optimization, calculating the fill and cut generated by the distance of the paths, and, by using the minimum spanning tree algorithm, calculating the scheme with the lowest cost and determining the road connection between the wind turbines. The method has the advantages of high convergence rate, stable generalization performance and high prediction accuracy. Through micro-siting optimization, the method optimizes the layout of wind turbines and can increase the generating capacity of wind power plants and reduce the economic cost; through electricity collection line optimization, the method is economical and reasonable; through maintenance road optimization, the working efficiency is increased and the cost is reduced.

Description

Complicated landform wind energy turbine set Integrated Optimization
Technical field
The invention belongs to complicated landform wind energy turbine set microcosmic structure technical field, it is related to a kind of complexity landform wind energy turbine set integration Optimization method is and in particular to the layout optimization of complicated landform wind energy turbine set, current collection line optimization and maintenance road optimization.
Background technology
Wind energy is one of renewable energy utilization form the most ripe at present, and China advocates sustainable development energetically, adds Wind energy content enriches, and constantly advances the development of renewable energy source item in recent years, and wind-powered electricity generation industry development is rapid, gets more and more The area that wind energy resources enriches is expected to be planned as wind energy turbine set site.But in the exploitation of wind-powered electricity generation, microcosmic structure is it In highly important link, particularly their location is the Mountainous Regions of complicated landform.The wind energy turbine set in this kind of region, due to landform Change rises and falls greatly, and influence factor suffered by wind energy distribution situation in wind energy turbine set is numerous, increases the Mountainous Regions to complicated landform and enters The difficulty of sector-style electric field microcosmic structure, and the blower fan microcosmic structure of Mountainous Regions wind energy turbine set has also become such wind energy turbine set entirely to design In key.
The arrangement of wind energy turbine set Wind turbines affects the tortuous of current collection line route and the number of branch line.By existing Wind turbines technology, typically in 2MW, Wind turbines exit potential becomes through case and rises to after 35kV Wind turbines individual capacity Eventually enter into booster stations through collection electric line.The different loop determining collection electric line backbone of arrangement according to Wind turbines Number, the feeder number of electromechanical circuit determines the length of current collection line route and the size of conductor cross-section, directly affects whole The gross investment of engineering.
Road is the prerequisite building wind energy turbine set, is also one of principal element of Construction of Wind Power land occupation, rationally Highway layout have important impact to the investment of wind energy turbine set and operation.Highway layout is in grassland, Gobi desert or coastal wind energy turbine set Construction in be not main restricting factor, be but important ring China major part inland wind-powered electricity generation in the construction of mountain region wind energy turbine set All in the mountain area of complicated landform, problem to be considered is again a lot of for field.But at present both at home and abroad to road line selection in wind energy turbine set field Research very few, enterprise usually relies on experience and artificially carries out highway layout, and repeated workload is big and operating efficiency is relatively low, The optimization of road cannot be ensured.
In a word, it is in the wind energy turbine set in mountain area, microcosmic structure affects very greatly on the generated energy of wind energy turbine set and reliability operation, And factory road and transmission line of electricity are also very big on the construction investment impact of wind energy turbine set.
Content of the invention
It is an object of the invention to overcoming deficiency of the prior art, there is provided a kind of landform wind energy turbine set integration of complexity is excellent Change method, including microcosmic structure optimization, current collection line optimization and maintenance road optimization；Microcosmic structure optimization has optimization blower fan cloth Office's effect, it is possible to increase the generated energy of wind energy turbine set, reduces financial cost；Current collection line optimization has the suitable conductor cross-section of selection Effect is so as to economical rationality；Maintenance road optimization can meet the multiple requirements during wind energy turbine set use requirement and operation, Increase operating efficiency, reduces cost.
For solving above-mentioned technical problem, the invention provides a kind of complexity landform wind energy turbine set Integrated Optimization, it is special Levying is, comprises the following steps：
Step one, microcosmic structure optimization：Target is turned to the Energy Maximization of wind energy turbine set, using genetic algorithm iterative calculation Go out the position coordinates of each wind energy conversion system；
Step 2, current collection line optimization：Calculate after shortest path between blower fan first with dijkstra's algorithm, with shortest path Distance as the weights of tree, calculate the minimum scheme of total path weights using minimal spanning tree algorithm, determine and connect between blower fan Take back road；
Step 3, overhauls road optimization：After access path between the blower fan that previous step current collection line optimization obtains, calculate Go out the generation of this path distance fills out excavation, then calculates cost as the weights of tree, is calculated into using minimal spanning tree algorithm This minimum scheme, determines that between blower fan, road connects.
Further, in step one, the concrete calculating process of the generated energy of wind energy turbine set is：
Step 11, wind energy turbine set is angularly divided into 12 sectors by wind direction, and the Weibull parameter using each sector calculates The mean wind speed of this sector, formula is as follows：
Wherein, u is mean wind speed, and Γ (a) is Gamma function, and parameter k and c are Weibull parameter, and wherein k joins for shape Number, c is scale parameter, and k, c are all higher than 0；
Step 12, the wind accelerated factor at blower fan seat in the plane can be tried to achieve using lower relation of plane：
In formula：suxyαSpeed-up ratio at expression position (x, y), direction α；uxyαAverage at expression position (x, y), direction α Wind speed；umαRepresent the mean wind speed at equidirectional anemometer tower；
Step 13, the wake effect at blower fan seat in the plane adopts Jensen wake model, and the expression formula of the wake effect factor is：
In formula, d is the wake effect factor, CTThrust coefficient for Wind turbines；R is impeller radius；K is wake flow dissipative system Number；X is the spacing of two Wind turbines；
Step 14, wind accelerated factor and the wake effect factor are simply coupled, actual at position (x, y), direction α The calculating of wind speed adopts following formula：
Step 15, power calculation uses power curve, and single wind generator group is located at position (x, y) and wind direction α Estimated power yield is：
Wherein, vinIt is the incision wind speed of Wind turbines, generally 3m/s, voutIt is the cut-out wind speed of Wind turbines, generally 25m/s, N are annual accumulative hourages, and E (v) is the power of fan that speed v is obtained by power curve, and f (v) is by prestige cloth The frequency of speed v that your distribution obtains,
Step 16, comprehensive all directions, the net electric generation computing formula of separate unit Wind turbine is：
In formula, p (α) is the wind direction frequency of corresponding angle α；
Step 17, in wind energy turbine set, the maximum of all Wind turbine net electric generations is：
EjConsider the annual electricity generating capacity after wake effect and wind accelerated factor for jth platform wind energy conversion system, E is wind energy turbine set overall average work( Rate.
Further, in step one, in neural algorithm, target variable is the coordinate (x, y) of wind energy conversion system, using following calculation Target variable is converted between 0～1 formula, using real coding,
Wherein, xxi、yyiIt is the variate-value after normalizing, (xi,yi) represent the position of wind energy conversion system, xmin、xmaxAnd ymin、 ymaxIt is respectively the high-low limit of wind energy turbine set position coordinates.
Further, in step 2, after determining link circuit between blower fan, it may also be determined that the cross-section of cable, it is concrete Process is：1) determine separate unit blower fan electric current；2) basis《35kV and following current-carrying capacity of cable correction coefficient table》Calculate cable direct-burried Current-carrying capacity correction coefficient when laying；3) the specified current-carrying under standard laid condition according to loop works Current calculation cable Amount；4) basis《Cable is with reference to current-carrying scale》Select the cross-section of cable.
Further, in step 3, the cost of maintenance road includes road structure layer material cost and the road cubic metre of earth and stone Quantities cost；Road structure layer material cost is decomposed into road structure layer volume and is multiplied by material unit price, and formula is：
MC=H*L*W*MP
Wherein, MCFor road structure layer material total cost, H is structure thickness, and L is link length, and W is road width, MPFor Structural material unit price；Road earthwork volume cost is decomposed into a cubic meter volume and is multiplied by a cubic meter unit cost, tool Body formula is as follows：
EC=VC*CP(VC≥VF)
EC=(VF-VC)*FP+VC*CP(VC＜ VF)
Wherein, ECFor cubic metre of earth and stone cost, VCFor amount of excavation, VFFor amount of fill, CPFor excavation unit price, FPFor embankment unit Cost.
Compared with prior art, the beneficial effect that the present invention is reached is：The method of optimization of the present invention includes carrying out successively Microcosmic structure optimization, current collection line optimization and maintenance road optimization.In microcosmic structure optimizes, apply wake model and landform Impact model, wake losses that can be in Accurate Prediction complexity landform between different wind energy conversion systems, power module adopts probability density Algorithm, discrete by wind direction and wind speed, probabilistic forecasting can be made more accurate from microcosmic angle；Wind energy turbine set current collection line optimization sets The method of meter, reasonable selection current collection line cord and calculating cost, improve the security that wind energy turbine set collection electric line is run further And reliability.
Brief description
Fig. 1 is the flow chart of the inventive method.
Fig. 2 is the flow chart of genetic algorithm in microcosmic structure Optimizing Flow.
Fig. 3 is wind energy conversion system coordinate distribution schematic diagram in the embodiment of the present invention.
Fig. 4 is the path schematic diagram of wind energy turbine set collection electric line after current collection line optimization in Fig. 3 embodiment.
Fig. 5 is road schematic diagram after wind energy turbine set maintaining roadway road optimization in Fig. 3 embodiment.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following examples are only used for clearly illustrating the present invention Technical scheme, and can not be limited the scope of the invention with this.
As shown in figure 1, a kind of complexity landform wind energy turbine set Integrated Optimization of the present invention, it is characterized in that, including following Step：
Step one, microcosmic structure optimization：With the maximum net generated output of wind energy turbine set as target, using genetic algorithm iteration meter Calculate the position coordinates of each wind energy conversion system；
The process that simulation calculates the maximum net generated output of wind energy turbine set is, first from the distinguished and admirable simulation softward in wind-powered electricity generation place, As Meteodyn WT and Windsim software, read the indexing wind energy resources output of wind energy turbine set each wind direction, extract wind energy turbine set respectively each The parameters such as the whole audience wind speed of wind direction indexing, wind direction and turbulent flow.Again wind-resources grid result is calculated using CFD.In wind-resources net In lattice result, the wind-resources mesh point of anemometer tower position represents datum mark, and in wind-resources grid file, other points is average Wind speed relative datum point have a ratio, this ratio is referred to as accelerated factor, bent for calculating during generated energy input power Wind-resources correction at blower fan seat in the plane before line.Calculate the mean wind speed of full blast electric field according to CFD, using accelerated factor and tail Flow model is modified to speed.Finally, probability density algorithm is based on according to the power curve that blower fan manufacturer provides and calculates wind-powered electricity generation The wind turbine power generation amount of field.
The wind-resources grid result that CFD calculates, form is .wrg, and its Main Function is using anemometer tower position Wind speed calculates the mean wind speed of other positions.CFD calculates and considers that wind blows from all directions, and each wind direction and wind velocity is unequal.Wind Electric field is angularly divided into 12 sectors by wind direction, and each interval arrives stream wind speed that CFD calculates is defined as the average of this sector Wind speed.Wind direction is divided into 12 intervals, for wind direction, first wind direction false wind blows to due south from direct north, previous Individual wind direction turns clockwise 30 ° and obtains a rear wind direction sector.The wind speed source of each wind direction of each of wind energy turbine set point Result in the simulation of wind energy turbine set wind-resources.Wind speed profile is generally normal state partial velocities, and the most extensive in wind energy application in calculation Be two parameter Weibull distribution.The Weibull parameter through conventional two parameter for the variation characteristic of wind speed to describe.Weibull divides Two parameters k of cloth and c, wherein k is form parameter, and c is scale parameter, and k, c are all higher than 0.From wind-resources grid wrg file Can get k, the parameter value of c, k and c is the mean value of each Direction interval, any pair of k and c can be using following gamma Function calculates the interval mean wind speed of respective direction：
Wherein, Γ (a) is Gamma function.
The wind speed and direction that actual wind speed profile is measured except anemometer tower, also will be in conjunction with the shadow of complicated orographic winds acceleration effect Ring, at blower fan seat in the plane, the wind accelerated factor in each direction can be tried to achieve using lower relation of plane：
In formula：suxyαSpeed-up ratio at expression position (x, y), direction α；uxyαRepresent position (x, y), direction α (0 °～ 360 °) mean wind speed at place, calculated by formula (1)；umαRepresent the mean wind speed at anemometer tower at the α of direction, be equidirectional The mean value of the wind speed that anemometer tower is measured.Wherein the scope of direction α is 0 °～360 °.
The experience wake model commonly used both at home and abroad at present has Park wake model, Jensen wake model, Frandsen tail Flow model and Larsen wake model etc., herein using more conventional Jensen wake model.The expression of the wake effect factor Formula is：
In formula, d is the wake effect factor, CTThrust coefficient for Wind turbines；R is impeller radius；K is wake flow dissipative system Number；X is the spacing of two Wind turbines.
The wind speed that actual wind power generating set is subject to not only wake effect to be considered, it is also contemplated that the impact of landform, by public affairs Wind accelerated factor that formula (2) is calculated and the wake effect factor that formula (3) calculates simply are coupled, position (x, Y), at the α of direction, the calculating of wind speed adopts following formula：
Power calculation uses power curve, the result being calculated using formula (4) when wind speed, single wind generator group position In single position (x, y) and wind direction α it is contemplated that power yield be：
vinIt is the incision wind speed of Wind turbines, generally 3m/s, voutIt is the cut-out wind speed of Wind turbines, generally 25m/ S, N are annual accumulative hourages, and E (v) is the power of fan that speed v is obtained by power curve, and f (v) is to be divided by Weibull The frequency of speed v that cloth obtains,
Before, wind direction is separated into different sectors, is sued for peace in sector now.Comprehensive all directions, separate unit wind-force The net electric generation computing formula of unit is：
In formula, p (α) is the wind direction frequency of corresponding angle α, is obtained by anemometer tower measurement wind direction statistics.
When wind speed v in formula (6)xyαChange u intoxyα, you can try to achieve the theoretical generated energy of wind energy turbine set by formula (6), that is, Do not consider the generated energy of wind energy turbine set when wake flow and the influence of topography.
Microcosmic structure optimization in complicated landform wind energy turbine set Integrated Optimization, is multivariable nonlinearity optimization problem, Optimized variable is the position coordinates of each wind energy conversion system.To obtain the layout of the wind energy conversion system of optimum using genetic algorithm in the present invention, Genetic algorithm belongs to prior art, and concrete steps are as shown in Fig. 2 with wind energy turbine set maximum net generated output for object function be：
E in formulajConsider the net electric generation after wake flow and the influence of topography for jth platform wind energy conversion system.
First set up object function, that is, Power Output for Wind Power Field reaches maximum, wake losses is minimum；Target variable is wind energy conversion system Coordinate (x, y), using following formula, target variable is converted between 0～1, using real coding,
Wherein, xxi、yyiIt is the variate-value after normalizing, (xi,yi) represent the position of wind energy conversion system, xmin, xmax and ymin、 ymaxIt is respectively the high-low limit of wind energy turbine set position coordinates.
Wind energy turbine set is carried out during cloth machine it is considered to the impact of wake flow, it is to avoid between wind energy conversion system, spacing is too small, and coordinate will meet simultaneously Border and spacing constraint.Hypothesis blower fan spacing is L, to the constraints of wind energy conversion system is therefore：
In constraints above, (xi,yi) representing the position of wind energy conversion system, N represents the number of units of wind-driven generator, xmin、xmax And ymin、ymaxIt is respectively the high-low limit of wind energy turbine set position coordinates.In addition, constraints also comprises some conventional pacts Installing space between bundle, such as wind energy conversion system, blower fan number of units and allow the gradient, as the restriction of arrangement blower fan, handle in genetic algorithm The layout scenarios being unsatisfactory for constraints are rejected.
Initial population, using the random method generating initial population, sets up fitness functionPopulation at individual It is randomly generated, therefore it cannot be guaranteed that all of individuality all meet the constraint conditions, this algorithm is not to meeting spacing condition and side The individuality of boundary's condition carries out " punishment " so as to fitness value reduces, and is easy to eliminate these individualities in " selection operation ".Specifically Operate and be：Select undesirable individuality x, corresponding fitness value is f (x)；After punishment, new fitness value be f (x)- Ω.Wherein Ω is sufficiently large real number it is ensured that new fitness value is sufficiently small is eliminated so as in the operation below.Warp After selecting, intersect and making a variation, complete the iteration of a genetic algorithm, according to the sequence of fitness size, select suitable population, enter Enter next round iteration, until last generation, in the present embodiment, iterations is 3000, obtains the position coordinates of each wind energy conversion system.
Below by certain complicated landform wind energy turbine set real data, model is verified, landform is as shown in figure 3, direction of warp and weft The scope of X and Y is respectively 712536～724156m and 4624300～463900m, and the scope of height Z is 1474～1580m.Survey The position that wind tower is located is (715828,4631910).It is expected that 33 rated power of arrangement are the wind energy conversion system of 2MW in wind energy turbine set. The wind-powered electricity generation field parameters of concrete setting and wind energy conversion system parameter are as shown in table 1.Using this optimization method, calculate this wind energy turbine set year reason Reach 292.431GWh by gross generation, year net gross generation reaches 287.043GWh, overall wake losses is 1.84%.Optimize Wind energy conversion system situation afterwards is as shown in Figure 3.
Table 1：Fan parameter
Step 2, current collection line optimization：Calculate after beeline between blower fan first with dijkstra's algorithm, with shortest path Distance as the weights of tree, calculate the minimum scheme of total path weights using minimal spanning tree algorithm, determine and connect between blower fan Take back road.
Current collection line optimization is to determine the link circuit in blower fan group between blower fan, needs first to determine two wind energy conversion systems Distance, in complicated landform, the distance of two wind energy conversion systems is no longer simple air line distance, and adopts signal source shortest path algorithm Dijkstra is calculated.Dijkstra's algorithm belongs to prior art, calculates beeline between blower fan using dijkstra's algorithm Referring to prior art.
Calculated after beeline between blower fan using dijkstra's algorithm, with the distance of shortest path between two blower fans that calculate As the weights of tree, realize wind energy turbine set collection electric line Automatic Optimal scheme with minimal spanning tree algorithm.Minimal spanning tree algorithm Using prior art, the process of implementing is when known to feeder number, in a loop, to have the wind-force in m Weighted Coefficients path Generating set, can set up m (m-1)/2 paths, and these paths constitute a Undirected graph.In these possible paths In, select m-1 paths to constitute a network it is desirable to every Fans in this network-in-dialing loop, and the total weights in path Minimum, that is, reach optimum, the optimal path calculating is optimal current collection line route.
Simultaneously in optimization process, the selection of conductor cross-section is extremely important, and the expense investment of wire accounts for whole engineering and throws The 10% about of money cost, to the same circuit therefore under conditions of technology maturation, piecewise different capabilities are using economy, different The conductor combination in section is necessary.General in whole engineering the wire from most three kinds of models be advisable, excessive wire Model can increase the difficulty of construction of engineering, and the operation maintenance to later stage circuit also brings along inconvenience.According to permission heating strip Part selects, that is, during by allowing current-carrying capacity to select the cross-section of cable, needs meet little more than thermally-stabilised smallest cross-sectional, the loss of voltage Should be greater than 15 times of cable sizes in setting value and Cable Bending Radius.Specifically chosen process is as follows：1) determine separate unit blower fan electric current； 2) basis《35kV and following current-carrying capacity of cable correction coefficient table》Calculate current-carrying capacity correction coefficient during cable directly buried installation；3) press According to rated current-carrying capacity under standard laid condition for the loop works Current calculation cable；4) basis《Cable is with reference to current-carrying scale》Choosing Select the suitable cross-section of cable.And collect electric line and consider that single retroreflective connects, do not intersect as far as possible.In wind energy turbine set, longer section (exceedes When cable producer largest production disk is long) cross-section of cable is typically no more than 300mm2, when unit cable overall length exceeds single-deck length Increase transition joint.
In an embodiment, in order to be more clearly visible that the arrangement of collection electric line, the seat in the plane of blower fan is more evenly distributed, In the present embodiment, current collection line route figure is as shown in Figure 4.Collection electric line is divided into three single loops, and 11 typhoons are accessed in every loop Unit.Current collection line parameter circuit value and cable data are as shown in table 2.Conductor material selects copper cash, and the soil laying selects dry loess.Rise Pressure station coordinates is (715828,4631910), and the outer trench starting point coordinate of booster stations is (715800,4631900).Consider to use copper Line, as cable material, provides the information of the different cross-sections of cable, after optimization, the cross-section of cable chooses 95mm, 120mm, 150mm respectively, Length is respectively 47.41km, 3.09km, 10.72km.Booster stations electric energy loss is 757.35kW, and cable electric energy loss is 7.75kW, overall loss accounts for 0.01%.
Table 2：Current collection line parameter circuit value and cable data
Step 3, overhauls road optimization：After access path between the blower fan that previous step current collection line optimization obtains, calculate Go out the generation of this distance fills out excavation, then calculates cost as the weights of tree, calculates cost minimization using minimal spanning tree algorithm Scheme, determine between blower fan that road connects.
Maintenance road optimization is the optimization based on maintenance road minimum cost for target.Therefore, the optimization ginseng of maintenance road Number is made up of two aspects：One is road structure layer material cost；Two is road earthwork volume cost.Road structure layer material Material cost is decomposed into road structure layer volume and is multiplied by material unit price, and road structure layer material cost formula is：
MC=H*L*W*MP(10)
Wherein, MCFor road structure layer material total cost, H is structure thickness, and L is link length, and W is road width, MPFor Structural material unit price.Road earthwork volume cost is decomposed into a cubic meter volume and is multiplied by a cubic meter unit cost.Soil Cubic meter of stone cost calculates and is divided into following several situation：(1) when road embankment and excavation population equilibrium it is believed that the earthwork is in 1km scope Interior can allocate balance, earthwork volume cost is multiplied by excavation unit price equal to amount of excavation；(2) when excavation is more than embankment, Calculate by excavation, earthwork volume cost is multiplied by excavation unit price equal to amount of excavation, and spoir expense has been contained in excavation unit In cost；(3) when embankment is more than excavation, earthwork volume cost subtracts excavation equal to embankment and is multiplied by embankment unit price, its knot Fruit is multiplied by excavation unit price along with amount of excavation：Concrete formula is as follows：
Wherein, ECFor cubic metre of earth and stone cost, VCFor amount of excavation, VFFor amount of fill, CPFor excavation unit price, FPFor embankment unit Cost.
In optimization process, some constraintss to be met：(1) overhaul road design grade.Maintenance road presses factories and miness four Level highway layout, that is, Path limit least limit circular curve radius are 15m, vertical curve least radius 100m, and vertical curve is minimum long Degree 20m.(2) road longitudinal grade limits.In the arduous mountain ridge of engineering, hilling terrain, typically require in easy flights in 10%.(3) place Land character limits.General wind field scope ratio is larger, in the range of unavoidably have arable land, forest land etc., optimal routing should be according to reality Border situation avoids these farming lands.(4) place atural object limits.Atural object in the range of wind field includes cultural relics and historic sites, grave, a pile of stones, village Village etc., optimal routing should optionally avoid.Highway sideline avoids and meets certain distance requirement, typically presses 100m and considers.
To overhaul road minimum cost as object function:
OPT=min (MC+EC) (12)
In wind energy turbine set, highway layout Automatic Optimal Routing Algorithm is similar with current collection line optimization, is being calculated with dijkstra's algorithm After beeline between blowing machine, calculate the generation of this distance fills out excavation, and calculates cost as tree according to formula (10) (11) Weights, then calculate the scheme of cost minimization with minimal spanning tree algorithm.
In the present embodiment, in wind energy turbine set field, highway layout parameter is as shown in table 3.It is carried out with road optimized maintenance, excellent After change, the road plane design of this wind energy turbine set, as shown in figure 5, passing through optimization and the reasonable line arrangement of circuit, designs road overall length 56km, amount of excavation 74244.35m3, amount of fill 76926.06m3, 2866.224 ten thousand yuan of total cost.
Table 3：Highway layout parameter in wind energy turbine set field
During wind energy turbine set Integrated optimization, the Automatic Optimal selection method of highway layout in wind energy turbine set field, can be right In the wind energy turbine set field of complicated landform, road carries out rapid Optimum, reduces workload, improves the efficiency that optimal route selects, to reach Reduce the purpose of cost.
The above is only the preferred embodiment of the present invention it is noted that ordinary skill people for the art For member, on the premise of without departing from the technology of the present invention principle, some improvement and modification can also be made, these improve and modification Also should be regarded as protection scope of the present invention.

Claims (5)

1. a kind of complexity landform wind energy turbine set Integrated Optimization, is characterized in that, comprise the following steps：
Step one, microcosmic structure optimization：Target is turned to the Energy Maximization of wind energy turbine set, is iterated to calculate out often using genetic algorithm The position coordinates of individual wind energy conversion system；
Step 2, current collection line optimization：Calculate after shortest path between blower fan first with dijkstra's algorithm, with shortest path away from From the weights as tree, calculate the minimum scheme of total path weights using minimal spanning tree algorithm, determine and connect back between blower fan Road；
Step 3, overhauls road optimization：After access path between the blower fan that previous step current collection line optimization obtains, calculate this What path distance produced fills out excavation, then calculates cost as the weights of tree, calculates cost using minimal spanning tree algorithm Little scheme, determines that between blower fan, road connects.
2. complexity landform wind energy turbine set Integrated Optimization according to claim 1, is characterized in that, wind-powered electricity generation in step one The concrete calculating process of generated energy be：
Step 11, wind energy turbine set is angularly divided into 12 sectors by wind direction, calculates this fan using the Weibull parameter of each sector The mean wind speed in area, formula is as follows：
u = c * Γ ( 1 + 1 k )
Wherein, u is mean wind speed, and Γ (a) is Gamma function, and parameter k and c are Weibull parameter, and wherein k is form parameter, c For scale parameter, k, c are all higher than 0；
Step 12, the wind accelerated factor at blower fan seat in the plane can be tried to achieve using lower relation of plane：
su x y α = u x y α u m α
In formula：suxyαSpeed-up ratio at expression position (x, y), direction α；uxyαAverage wind at expression position (x, y), direction α Speed；umαRepresent the mean wind speed at equidirectional anemometer tower；
Step 13, the wake effect at blower fan seat in the plane adopts Jensen wake model, and the expression formula of the wake effect factor is：
d = [ 1 - ( 1 - C T ) 1 / 2 ] ( R R + k X ) 2
In formula, d is the wake effect factor, CTThrust coefficient for Wind turbines；R is impeller radius；K is wake flow dissipation factor；X Spacing for two Wind turbines；
Step 14, wind accelerated factor and the wake effect factor are simply coupled, actual wind speed at position (x, y), direction α Calculating adopt following formula：
v x y α = u x y α * ( 1 - d + su x y α 2 )
Step 15, power calculation uses power curve, and it is estimated that single wind generator group is located at position (x, y) and wind direction α Power yield be：
e = ∫ v i n v o u t N * E ( v ) * f ( v ) d v
Wherein, vinIt is the incision wind speed of Wind turbines, generally 3m/s, voutIt is the cut-out wind speed of Wind turbines, generally 25m/ S, N are annual accumulative hourages, and E (v) is the power of fan that speed v is obtained by power curve, and f (v) is to be divided by Weibull The frequency of speed v that cloth obtains,
Step 16, comprehensive all directions, the net electric generation computing formula of separate unit Wind turbine is：
E = ∫ 0 360 e * p ( α ) d α
In formula, p (α) is the wind direction frequency of corresponding angle α；
Step 17, in wind energy turbine set, the maximum of all Wind turbine net electric generations is：
E = m a x ( Σ j = 1 N E j )
EjConsider the annual electricity generating capacity after wake effect and wind accelerated factor for jth platform wind energy conversion system, E is wind energy turbine set total mean power.
3. complexity landform wind energy turbine set Integrated Optimization according to claim 1, is characterized in that, in step one, god It is the coordinate (x, y) of wind energy conversion system through target variable in algorithm, using following formula, target variable is converted between 0～1, adopt Real coding,
xx i = x i - x m i n x max - x min
yy i = y i - y min y max - y m i n
Wherein, xxi、yyiIt is the variate-value after normalizing, (xi,yi) represent the position of wind energy conversion system, xmin、xmaxAnd ymin、ymaxRespectively High-low limit for wind energy turbine set position coordinates.
4. complexity landform wind energy turbine set Integrated Optimization according to claim 1, is characterized in that, in step 2, really After determining link circuit between blower fan, it may also be determined that the cross-section of cable, its detailed process is：1) determine separate unit blower fan electric current；2) root According to《35kV and following current-carrying capacity of cable correction coefficient table》Calculate current-carrying capacity correction coefficient during cable directly buried installation；3) according to return Road operating current calculates rated current-carrying capacity under standard laid condition for the cable；4) basis《Cable is with reference to current-carrying scale》Select electricity Cable section.
5. complexity landform wind energy turbine set Integrated Optimization according to claim 1, is characterized in that, in step 3, inspection The cost repaired roads includes road structure layer material cost and road earthwork volume cost；Road structure layer material cost is divided Solve and be multiplied by material unit price for road structure layer volume, formula is：
MC=H*L*W*MP
Wherein, MCFor road structure layer material total cost, H is structure thickness, and L is link length, and W is road width, MPFor structure Layer material unit price；Road earthwork volume cost is decomposed into a cubic meter volume and is multiplied by a cubic meter unit cost, specifically public Formula is as follows：
EC=VC*CP(VC≥VF)
EC=(VF-VC)*FP+VC*CP(VC＜ VF)
Wherein, ECFor cubic metre of earth and stone cost, VCFor amount of excavation, VFFor amount of fill, CPFor excavation unit price, FPMake for embankment unit Valency.
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Cited By (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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US11441915B2 (en) 2019-06-18 2022-09-13 M. A. Mortenson Company Circuits for electricity-generating units
WO2023087520A1 (en) * 2021-11-19 2023-05-25 中国华能集团清洁能源技术研究院有限公司 Wind farm layout optimization method and system based on local search strategy

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102142103A (en) * 2011-04-15 2011-08-03 河海大学 Real-coded genetic algorithm-based optimizing method for micrositing of wind power station
CN103106300A (en) * 2013-01-21 2013-05-15 同济大学 Automatic optimization route-selection method for route design in wind power plant
CN105447245A (en) * 2015-11-20 2016-03-30 中国能源建设集团云南省电力设计院有限公司 Microcosmic location selection system of high-altitude mountain wind power plant
CN105656076A (en) * 2015-11-26 2016-06-08 国网江苏省电力公司经济技术研究院 Offshore wind plant submarine cable wiring acquisition method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102142103A (en) * 2011-04-15 2011-08-03 河海大学 Real-coded genetic algorithm-based optimizing method for micrositing of wind power station
CN103106300A (en) * 2013-01-21 2013-05-15 同济大学 Automatic optimization route-selection method for route design in wind power plant
CN105447245A (en) * 2015-11-20 2016-03-30 中国能源建设集团云南省电力设计院有限公司 Microcosmic location selection system of high-altitude mountain wind power plant
CN105656076A (en) * 2015-11-26 2016-06-08 国网江苏省电力公司经济技术研究院 Offshore wind plant submarine cable wiring acquisition method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
许昌等: "复杂地形风电场微观选址优化", 《中国电机工程学报》 *

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