CN113962113B - Optimized arrangement method and system for offshore wind farm fans - Google Patents
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Abstract
The invention discloses an offshore wind farm fan optimal arrangement method and system, belonging to the technical field of offshore wind power generation, comprising the following steps: s1, uniformly dividing a wind power plant area into n multiplied by n grids, and arranging all fans at grid intersection points; s2, randomly generating M groups of arrangement schemes; s3, setting the power generation amount of the fans with the distance smaller than the distance threshold value in each group of arrangement schemes to zero, and calculating the wake efficiency of the wind power plant corresponding to each group of arrangement schemes to serve as a fitness value; if the fitness value is larger than the wake efficiency threshold, taking the arrangement scheme corresponding to the maximum value in all the fitness values as the optimal arrangement scheme; otherwise, go to S4; s4, if the circulation times is less than the circulation threshold value, executing S5; otherwise, go to S6; s5, randomly copying the M groups of arrangement schemes from the M groups of arrangement schemes, performing crossing and mutation operations, and jumping to S3; s6, let n =2n, jump to S1. The power generation efficiency of the offshore wind farm can be greatly improved.
Description
Technical Field
The invention belongs to the technical field of offshore wind power generation, and particularly relates to an optimized arrangement method and system for offshore wind farm fans.
Background
With the advance of bidding online policies in the wind power industry, the demand of increasing income and reducing cost is more and more urgent, and in the past, manual arrangement is mostly adopted in fan arrangement, so that subjective factors have great influence, and real optimization of machine position arrangement is difficult to realize. The intelligent algorithm is introduced into the fan arrangement, the optimal fan layout is automatically searched, the wind power plant resources can be more fully utilized, and the economic benefit is improved.
In the prior art, in order to avoid the situation that the fan distance is too close in the optimization process by using a random search algorithm, the grid distance is often simply specified to be eight times of the fan radius. However, due to the fact that the grid interval is too large, the number of the fan position candidate points is not enough to simulate all possible arrangement positions of the fans, the error of simulating the fan positions by utilizing the grid intersection points is too large, and when a million-kilowatt-level or even million-kilowatt-level large-scale wind power plant is optimized, the arrangement result wake flow efficiency is difficult to meet the construction requirement. By improving the grid precision of the wind power plant, the degree of freedom of fan arrangement is increased, and the wake loss can be greatly reduced; however, no effective solution is available for the problem of wake effect caused by too close fan distance in the high-precision grid.
Disclosure of Invention
Aiming at the defects and improvement requirements of the prior art, the invention provides an optimized arrangement method and system for offshore wind farm fans, aiming at improving the reasonability of fan arrangement and greatly improving the power generation efficiency of a wind farm.
In order to achieve the above object, in a first aspect, the present invention provides an optimized arrangement method for offshore wind farm fans, including:
s1, uniformly dividing the wind farm area inton×nEach fan is arranged at the intersection of the grids,nto satisfyn 2The number of the fans is not less than N, and N is the total number of the fans;
s2, randomly generating M groups of arrangement schemes;
s3, setting the power generation amount of the fans with the distance smaller than the distance threshold value in each group of arrangement schemes to zero, and calculating the wake efficiency of the wind power plant corresponding to each group of arrangement schemes to serve as a fitness value; if the fitness value is larger than the wake efficiency threshold, taking the arrangement scheme corresponding to the maximum value in all the fitness values as the optimal arrangement scheme; otherwise, go to S4;
s4, if the circulation times is less than the circulation threshold value, executing S5; otherwise, go to S6;
s5, randomly copying one group of arrangement schemes from the M groups of arrangement schemes by taking the normalized fitness value as a copy probability, repeating for M times to obtain the copied M groups of arrangement schemes, performing crossing and variation operations on the copied M groups of arrangement schemes, and jumping to S3;
s6, ordern=2nAnd jumps to S1.
Furthermore, for each group of arrangement schemes, the horizontal and vertical coordinates of the position of each fan are represented by binary numbers, and the binary numbers of the vertical coordinates are spliced on the binary numbers of the horizontal coordinates to serve as a string of genes of population individuals of the genetic algorithm.
Further, in the step S1,nto the power of 2.
Further, in S3, wind farm wake efficiency =
；
Wherein the content of the first and second substances,
is as follows
The generating capacity of the wake effect is considered by each fan,
the sum of the generated energy of the wake effect is not considered for all the fans;
electric energy production
Calculated from the following formula:
Further, it is to
Wind speed of each fan
Comprises the following steps:
wherein the content of the first and second substances,
and
are respectively the first
The radial and axial coordinates of each fan,
and
respectively to
Radial and axial coordinates of each fan;
is the fan blade radius;
the incident wind speed of the wind power plant is a constant determined by local wind resource conditions;
is as follows
The wind speed at the location of each fan,
is related to the local wind speed and the first
The thrust coefficient associated with each fan model,
is the expansion coefficient of the maximum radial influence range of the wake effect along with the change of the axial distance,
is as follows
A fan and the second
The axial distance of each fan is set according to the axial distance,kthe sea water surface friction coefficient.
Further, the distance threshold is eight times the fan blade radius.
Further, in S5, the normalized fitness value is:
wherein the content of the first and second substances,
is shown askA normalized fitness value for the seed placement plan;Fit(k) Is shown askThe fitness value of the seed placement scheme, which is not normalized, is the wake efficiency.
In a second aspect, the present invention provides an optimized arrangement system for offshore wind farm fans, comprising: a memory and at least one processor; the memory stores computer-executable instructions; the at least one processor executes computer-executable instructions stored in the memory, so that the at least one processor executes the optimal arrangement method of the offshore wind farm wind turbines according to any one of the first aspect.
Generally, by the above technical solution conceived by the present invention, the following beneficial effects can be obtained:
(1) according to the method, the generated energy of the fans with the distance smaller than the distance threshold value in each group of arrangement schemes is set to zero, the wake efficiency of the wind power plant corresponding to each group of arrangement schemes is calculated to serve as a fitness value, and the optimal arrangement scheme is obtained by iterative optimization with the maximum fitness value as a target. The invention avoids the interference of fan distance correction measures on population evolution. The problem that the distance between the fans is smaller than a distance threshold value can be effectively avoided through distance punishment measures, and grid calculation of any precision of the wind power plant can be realized;
(2) compared with the prior art, the wind speed calculation method of the fan, namely the two-dimensional wake effect model, is more concise in expression, more convenient in programming and more efficient in calculation;
(3) the gridding realized by the method can be iteratively evolved to have higher precision than that of the prior art, the wake flow efficiency higher than that of the prior art can be realized, and compared with the limited optimization effect of the prior art, the method can calculate to obtain the most efficient arrangement scheme.
Drawings
Fig. 1 is a schematic diagram of gridding according to an embodiment of the present invention.
Fig. 2 is one of the flow diagrams of the method for optimally arranging wind turbines in an offshore wind farm according to the embodiment of the present invention.
Fig. 3 is a schematic diagram of a 64 × 64 grid according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a relationship curve between power generation and wind speed of a wind turbine according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a two-dimensional wake effect model according to an embodiment of the present invention.
Fig. 6 is a second schematic flow chart of the method for optimally arranging wind turbines in an offshore wind farm according to the embodiment of the present invention.
Fig. 7 is a schematic diagram of an optimal arrangement result of fans according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In the present application, the terms "first," "second," and the like (if any) in the description and the drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
It should be noted that, in order to reduce the data volume of the wind turbine coordinates, the wind farm area is often divided into equidistant grids, and the coordinates of the grid intersection point closest to the position of the wind turbine are used to replace the wind turbine coordinates, so as to mark the wind turbine coordinates. As in FIG. 1, the A-point fan coordinates may be represented by the B-point coordinates and may be referred to by the reference numerals (16, 12). The actual coordinates of the position of the wind turbine in the wind farm can be converted in a mode of multiplying the coordinate labels by the grid intervals.
The genetic algorithm requires that the individual data of the population are binary and each binary bit is effective, so that candidate points of all fan positions in the wind power plant can be completely represented by binary numbers with a certain length, and the random generation and cross variation operation of the genetic algorithm is facilitated. For example, the grids 64 × 64 are marked with 0-63 grids in the horizontal and vertical directions, i.e., eight-bit binary numbers of 00000000-11111111. By the gridding marking method, the fan coordinates at any position in the wind power plant can be marked by the binary horizontal and vertical coordinates of the grid intersection point closest to the fan coordinates, and the binary vertical coordinates are spliced to the binary horizontal coordinates to serve as a string of genes of population individuals of the genetic algorithm to participate in optimization. The coordinates of point A in FIG. 1 can be represented by 0001000000001100 cluster of genes. The genes at all the fan positions are spliced to form a population individual in the genetic algorithm, and the selection of the population individual is the selection of the fan arrangement scheme.
Based on this, referring to fig. 2, the invention provides an optimized arrangement method of offshore wind farm fans, comprising the following steps:
s1, uniformly dividing the wind farm area inton×nEach fan is arranged at the intersection of the grids,nto satisfyn 2The number of the fans is not less than N, and N is the total number of the fans;
specifically, adoptnA horizontal line andnthe vertical lines divide the wind electric field area evenly, each fan is arranged at the intersection of each horizontal line and each vertical line,nto the power of 2. As shown in fig. 3, the grid is a 64 × 64 grid, and the intersection points of grid lines are candidate points for fan arrangement;
s2, randomly generating M groups of arrangement schemes;
in this embodiment, for each group of arrangement schemes, the horizontal and vertical coordinates of the position of each fan are represented by binary numbers, and the binary numbers of the vertical coordinates are spliced to the binary numbers of the horizontal coordinates to serve as a string of genes of population individuals of the genetic algorithm;
s3, setting the power generation amount of the fans with the distance smaller than the distance threshold value in each group of arrangement schemes to zero, and calculating the wake efficiency of the wind power plant corresponding to each group of arrangement schemes to serve as a fitness value; if the fitness value is larger than the wake efficiency threshold, taking the arrangement scheme corresponding to the maximum value in all the fitness values as the optimal arrangement scheme; otherwise, go to S4;
specifically, the generated energy of the fan is determined by the wind speed of the fan, the higher the wake efficiency is, the smaller the wake loss is, and the higher the generated energy of the wind power plant is. The aim of wind power plant fan arrangement optimization is the highest power generation, namely the maximum wake efficiency. The wake effect is not considered, namely no wake effect is assumed to exist, and the wind speed at all the fans is the input wind speed at the most upstream fan.
In this embodiment, wind farm wake efficiency =
；
Wherein the content of the first and second substances,
is as follows
The generating capacity of the wake effect is considered by each fan,
the sum of the generated energy of the wake effect is not considered for all the fans.
When the generated energy is calculated, the starting wind speed, the rated wind speed and the cut-off wind speed are set, when the wind speed is between the starting wind speed and the rated wind speed, the output index of the fan is increased along with the increase of the wind speed, when the wind speed is between the rated wind speed and the cut-off wind speed, the fan is fully started, and when the wind speed exceeds the cut-off wind speed or is less than the starting wind speed, the fan does not generate power, as shown in fig. 4, five curves from top to bottom are the power generation curves of the fans of 11MW, 9MW, 7MW, 5MW and 3MW respectively.
In the present embodiment, the amount of electric power generationP i Calculated from the following formula:
Wind power plant total generating capacity without considering wake effect
Calculated from the following formula:
the wake effect is a reduction of the downstream fan speed by the upstream fan, which is shown in figure 5, where in figure 5,
is the wind speed of the upstream fan,
is the wind speed of the downstream fan,
in order to be the diffusion radius of the wake effect,
is the axial distance between the upstream fan and the downstream fan,
is the radial distance between the upstream fan and the downstream fan. First, the
Wind speed of each fan
Comprises the following steps:
wherein the content of the first and second substances,
and
are respectively the first
The radial and axial coordinates of each fan,
and
respectively to
Radial and axial coordinates of each fan;
is the fan blade radius;
the incident wind speed of the wind power plant is a constant determined by local wind resource conditions;
is as follows
The wind speed at the location of each fan,
is related to the local wind speed and the first
The thrust coefficient associated with each fan model,
coefficient of expansion of the maximum radial influence range of wake effect as a function of axial distance
Is as follows
A fan and the second
The axial distance of each fan is set according to the axial distance,kthe sea water surface friction coefficient.
Further, the axial and radial coordinates of the wind farm are different from the horizontal and vertical coordinates. The horizontal and vertical coordinate directions are determined by the gridding division directions, but the axial direction is the wind direction, and the radial direction is the direction perpendicular to the wind direction. The relationship of the coordinates can be obtained by rotating the coordinate axes as follows.
In the above formula, the first and second carbon atoms are,
and
respectively, the horizontal and vertical coordinates are shown,xandyrespectively representing radial and axial coordinates, rotation angleθIs the anticlockwise included angle from the positive direction of the longitudinal axis to the wind direction.
The influence of the upstream fan on the downstream fan is radial, the fan wind speed in the wake radiation area of the upstream fan is greatly reduced, and the closer the distance between the fans is, the larger the influence is. To reduce wake interference between fans, it is common to provide fan spacing of no less than eight fan radii, or to provide that downstream fans are not within the wake radiation zone of upstream fans.
S4, if the circulation times is less than the circulation threshold value, executing S5; otherwise, go to S6;
s5, randomly copying one group of arrangement schemes from the M groups of arrangement schemes by taking the normalized fitness value as a copy probability, repeating for M times to obtain the copied M groups of arrangement schemes, performing crossing and variation operations on the copied M groups of arrangement schemes, and jumping to S3;
in this embodiment, the normalized fitness value is:
wherein the content of the first and second substances,
is shown askA normalized fitness value for the seed placement plan;Fit(k) Is shown askThe fitness value of the seed placement scheme, which is not normalized, is the wake efficiency.
Specifically, random selection is made among the duplicated M sets of arrangement schemesM 0Group interleaving and selectionM 1Groups were subjected to mutation.
S6, ordern=2nAnd jumps to S1.
In this embodiment, a genetic algorithm is taken as an example, the wake efficiency is taken as a fitness function, and the arrangement mode with the minimum wake loss is screened, so that the population is promoted to evolve towards the direction with smaller wake loss. The crossover and mutation operations ensure that the algorithm does not converge to a locally optimal solution. Too powerful corrective measures cannot be used in the genetic algorithm optimization process. The distance punishment measures provided by the invention can promote the genetic algorithm to develop towards the direction that the distance between the wind turbines is more than eight times of the radius of the wind turbines, so that the arrangement of the wind power plant naturally evolves until the distance between the wind turbines meets the requirement.
The implementation mode is that in the genetic algorithm optimization process, a plurality of fans which are too close to each other in each generation of arrangement mode are not used for generating electricity, after the processing, the fitness function of the arrangement mode in which the fans are too close to each other is obviously reduced, the genetic algorithm gradually eliminates the arrangement mode with low fitness function, and the arrangement mode is evolved towards the high fitness function, namely the fan distance is kept in the direction of eight times of the fan radius.
By the method, the fan distance is still ensured to be not less than eight times of the fan radius under the condition of higher grid precision. The distance penalty provides feasibility for a high-precision gridding method. The validity of the distance penalty is demonstrated by way of example below.
Example verification
Referring to fig. 2, and referring to fig. 6, taking an example of a wind farm in Fujian province, the wind farm has 48 fans of 5MW, the scale is 4km × 4km, and 8R (eight times the radius of the fan) is 440 m. In the genetic algorithm, the genetic algorithm is carried out,M=80, cycle threshold =50000,M 0=0.9×M=72，M 1=0.075 × M =6, wake efficiency threshold = 98%.
The calculations were performed using the algorithm of the present invention. In S1, an initial grid is 8 x 8 grid precision, fan arrangement is randomly generated through S2, fan arrangement is randomly generated through S3-S5 50000 times of circulation optimization, the maximum wake flow efficiency is only 94% and not greater than a wake flow efficiency threshold value 98%, S6 is carried out, the grid precision is improved to 16 x 16, fan arrangement is randomly generated through S2, through S3-S5 circulation optimization 50000 times of circulation optimization, the maximum wake flow efficiency is 97.4% and not greater than a wake flow effect threshold value 98%, S6 is carried out, the grid precision is improved to 32 x 32, fan arrangement is randomly generated through S2, through S3-S5 circulation optimization 50000 times of S6-S5, the maximum wake flow efficiency is 97.6% and not greater than a wake flow effect threshold value 98%, S6 is carried out, the grid precision is improved to 64 x 64, the fan arrangement is randomly generated through S2, through S3-S5 times of circulation optimization is carried out 50000 times, the maximum wake flow efficiency is 98.3%, the maximum wake flow efficiency is met the requirement of wake flow efficiency 98%, and the maximum threshold value adaptive to the optimal arrangement scheme is selected, wherein the maximum threshold value function arrangement.
By adopting the method provided by the invention, the accuracy of the 64 multiplied by 64 grid is optimized finally, and the fan arrangement result is shown in figure 7.
After calculation and distance punishment measures are adopted, the shortest distance between the fans is 444.44m, the requirement that the distance is larger than 8R (eight times of fan radius) =440m is met, and the fan arrangement is automatically optimized to the result that the fan distance is larger than eight times of fan diameter by the visible algorithm. The validity of the distance punishment can be fully proved through the arrangement result.
In summary, through the distance punishment measure, the wind power plant gridding with higher precision can be realized, the arrangement freedom degree of the wind power plant is greatly improved, the wake loss of the wind power plant is obviously reduced, and a foundation is laid for the arrangement of a ten-million-kilowatt-level large offshore wind power plant.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (6)
1. An optimized arrangement method for offshore wind farm fans is characterized by comprising the following steps:
s1, uniformly dividing the wind farm area inton×nEach fan is arranged at the intersection of the grids,nto satisfyn 2The number of the fans is not less than N, and N is the total number of the fans;
s2, randomly generating M groups of arrangement schemes;
s3, setting the power generation amount of the fans with the distance smaller than the distance threshold value in each group of arrangement schemes to zero, and calculating the wake efficiency of the wind power plant corresponding to each group of arrangement schemes to serve as a fitness value; if the fitness value is larger than the wake efficiency threshold, taking the arrangement scheme corresponding to the maximum value in all the fitness values as the optimal arrangement scheme; otherwise, go to S4;
wherein, wind power plant wake efficiency =
；
Wherein the content of the first and second substances,P i is as followsiThe generating capacity of the wake effect is considered by each fan,P m the sum of the generated energy of the wake effect is not considered for all the fans;
electric energy productionP i Calculated from the following formula:
v a 、v b 、v c respectively a starting wind speed, a rated wind speed and a cutting wind speed,v i is as followsiThe wind speed of each fan is controlled by the wind speed,P i0 is as followsiRated power generation of each fan;
s4, if the circulation times is less than the circulation threshold value, executing S5; otherwise, go to S6;
s5, randomly copying one group of arrangement schemes from the M groups of arrangement schemes by taking the normalized fitness value as a copy probability, repeating for M times to obtain the copied M groups of arrangement schemes, performing crossing and variation operations on the copied M groups of arrangement schemes, and jumping to S3;
s6, ordern=2nAnd jumps to S1.
2. The offshore wind farm fan optimal arrangement method according to claim 1, wherein in S2, for each group of arrangement schemes, the abscissa and ordinate of the position of each fan are represented by binary numbers, and the binary numbers of the ordinate are spliced to the binary numbers of the abscissa to serve as a string of genes of population individuals of the genetic algorithm.
3. The optimal arrangement method for offshore wind farm fans according to claim 2, wherein in S1,nis 2The power of the power.
4. The optimal arrangement method of offshore wind farm fans as claimed in claim 1, wherein the first step isiWind speed of each fanv i Comprises the following steps:
wherein the content of the first and second substances,X i andY i are respectively the firstiThe radial and axial coordinates of each fan,X j andY j respectively tojRadial and axial coordinates of each fan; r is the fan blade radius;v in the incident wind speed of the wind power plant is a constant determined by local wind resource conditions;v i is as followsiThe wind speed at the location of each fan,
is related to the local wind speed and the firstjThe thrust coefficient associated with each fan model,
is the expansion coefficient of the maximum radial influence range of the wake effect along with the change of the axial distance,d ij is as followsiA fan and the secondjThe axial distance of each fan is set according to the axial distance,kthe sea water surface friction coefficient.
5. The offshore wind farm fan optimal arrangement method according to claim 1, wherein in the step S3, the distance threshold is eight times of the fan blade radius.
6. The utility model provides an offshore wind farm fan system of optimizing arrangement which characterized in that includes: a memory and at least one processor;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method of optimizing wind turbines of an offshore wind farm according to any of claims 1 to 5.
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