Wind farm layout processing method, device, equipment and medium based on dual population

Abstract

The present application relates to the technical field of wind farm layout, and specifically provides a wind farm layout processing method, device, equipment and medium based on dual populations, which aims to solve the technical problem that the existing wind farm layout leads to low total output power and high construction cost of the wind farm. To this end, the present application includes: in multiple iterations, according to the first optimization target and the second optimization target, the fitness of the first wind turbine group population, the second wind turbine group population and the booster station population is calculated to obtain the fitness result; according to the fitness result, the first wind turbine group population, the second wind turbine group population and the booster station population are selected; the first wind turbine group optimized population, the second wind turbine group optimized population and the booster station optimized population are mutated to obtain the first wind turbine group population, the second wind turbine group population and the booster station population; according to the first optimized population of the first wind turbine group and the first optimized population of the second wind turbine group, the wind turbine group arrangement is obtained.

Description

Technical Field

[0001] The present application relates to the technical field of wind farm layout, and specifically provides a wind farm layout processing method, device, equipment and medium based on dual populations. Background Art

[0002] At present, wind energy is an important driving force for the transition to clean energy, and wind farms tend to be large-scale and base-based, which makes the layout optimization of wind farms increasingly important.

[0003] In existing technology, wind power systems primarily consist of wind turbines, booster stations, and cables. Once the installation area is determined, a regular layout is implemented, allowing for a large number of wind turbines within the wind farm and creating an aesthetically pleasing layout. However, the lack of consideration for the wake effect of the wind turbines results in low total output power and high construction costs.

[0004] Accordingly, the art needs a new wind farm layout processing method based on dual populations to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects, the present application is proposed to provide a solution or at least partially solve the technical problem in the prior art that the total output power of the wind farm is low and the construction cost is high due to the failure to consider the wake effect of the wind turbine.

[0006] In a first aspect, the present application provides a wind farm layout processing method based on dual populations, comprising:

[0007] Get the wind farm area;

[0008] According to the wind farm area, obtaining a first wind turbine generator set initial population and a second wind turbine generator set initial population;

[0009] In the i-th iteration, the i-th population of the first wind turbine group and the i-th population of the second wind turbine group are obtained; wherein, when i=1, the first population of the first wind turbine group is the initial population of the first wind turbine group, the first population of the second wind turbine group is the initial population of the second wind turbine group, the i-th population of the first wind turbine group and the i-th population of the second wind turbine group both contain multiple individuals, each individual in the i-th population of the first wind turbine group is a regularly arranged shape variable, and each individual in the i-th population of the second wind turbine group is a wind turbine coordinate vector;

[0010] According to the i-th group of the first wind turbine generator set and the i-th group of the second wind turbine generator set and the optimization constraints, the i-th group of the booster station is obtained; wherein, when i=1, the first group of the booster station is the initial group of the booster station;

[0011] performing fitness calculations on the i-th group of the first wind turbines, the i-th group of the second wind turbines, and the i-th group of the booster stations according to a first optimization objective and a second optimization objective to obtain fitness results; wherein the first optimization objective is maximizing total output power, and the second optimization objective is minimizing cable cost;

[0012] According to the fitness result, selecting the i-th population of the first wind turbine generator set, the i-th population of the second wind turbine generator set, and the i-th population of the booster station, to obtain the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station;

[0013] Performing mutation processing on the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station to obtain the i+1-th population of the first wind turbine generator set, the i+1-th population of the second wind turbine generator set, and the i+1-th population of the booster station; wherein 1≤i≤I, where I corresponds to the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station obtained after the i-th iteration;

[0014] A wind turbine arrangement is obtained according to the first optimized population of the first wind turbines and the first optimized population of the second wind turbines.

[0015] In a technical solution of the above-mentioned dual-population-based wind farm layout processing method, obtaining the first wind turbine initial population according to the wind farm area includes:

[0016] Obtaining a global center point according to the wind farm area;

[0017] According to the first preset range, the ratio of starting points in the line is obtained;

[0018] According to the preset angle range, the parallel line rotation angle is obtained;

[0019] According to the preset constraint conditions, the minimum spacing between rows and the minimum spacing within rows are obtained;

[0020] According to the second preset range, a line spacing gradient coefficient is obtained;

[0021] According to the third preset range, obtaining a gradient coefficient of the spacing within the line;

[0022] Obtaining shape variables of the regular arrangement according to the global center point coordinates, the ratio of the starting points within the row, the parallel line rotation angle, the minimum spacing between rows, the minimum spacing within a row, the row spacing gradient coefficient, and the intra-row spacing gradient coefficient;

[0023] A plurality of the regularly arranged shape variables are combined to obtain the first initial population of wind turbines.

[0024] In one technical solution of the above-mentioned dual-population-based wind farm layout processing method, obtaining the wind turbine arrangement according to the first optimized population of the first wind turbines and the first optimized population of the second wind turbines includes:

[0025] performing point processing on the regularly arranged shape variables in the first optimized population of the first wind turbine generator set to obtain an available point position vector;

[0026] In the nth cycle, according to the nth coordinate in the first optimized population of the second wind turbine generator set and the available point vector, a distance set between the nth coordinate in the first optimized population of the second wind turbine generator set and the available point vector is obtained;

[0027] Obtaining the coordinates of the nth wind turbine generator set according to the distance of the available points;

[0028] Wherein, 1≤n≤N, where N is the number of coordinates in the first optimized population of the second wind turbine generator set;

[0029] The coordinates of the first wind turbine group to the Nth wind turbine group are combined to obtain the wind turbine group arrangement.

[0030] In a technical solution of the above-mentioned dual-population-based wind farm layout processing method, obtaining the initial population of booster stations includes:

[0031] Within the wind farm area, according to preset booster station location constraints, the booster station coordinates are obtained;

[0032] The booster station coordinates are combined to obtain the initial population of booster stations.

[0033] In one technical solution of the above-mentioned wind farm layout processing method based on rule arrangement, the mutation processing of the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station to obtain the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group, and the i+1-th population of the booster station includes:

[0034] Determine whether i is less than the preset number of iterations;

[0035] If it is determined that i is less than or equal to the preset number of iterations, then for the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station, data of each individual is selected within a preset large scale and a preset small scale to obtain the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group, and the i+1-th population of the booster station;

[0036] If it is determined that i is greater than the preset number of iterations, then for the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station, the data of each individual is selected within a preset small-scale range to obtain the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group, and the i+1-th population of the booster station.

[0037] In a technical solution of the above-mentioned dual-population-based wind farm layout processing method, obtaining the total output power includes:

[0038] According to the speed loss of the upstream wind turbine at multiple points on the wind rotor of the current wind turbine, the inflow speed loss at multiple points on the wind rotor of the current wind turbine is obtained;

[0039] According to the additional flow turbulence intensity of the upstream wind turbine at multiple points on the wind rotor of the current wind turbine, the additional flow turbulence intensity of the inflow at multiple points on the wind rotor of the current wind turbine is obtained;

[0040] Obtaining the wind speed in front of the hub of the current wind turbine generator set according to the inflow velocity deficits at multiple points on the wind turbine rotor of the current wind turbine generator set;

[0041] Obtaining a corresponding relationship between the wind speed in front of the hub of the current wind turbine and the power of the current wind turbine according to the wind conditions, the additional turbulence intensity of the inflow at multiple points on the wind turbine rotor of the current wind turbine, and the wind speed in front of the hub of the current wind turbine;

[0042] The total output power is obtained according to the wind condition, the corresponding relationship, the frequency of occurrence of the wind condition, the number of wind directions, the number of wind speed segments and the number of wind turbine generator sets.

[0043] In a technical solution of the above-mentioned dual-population-based wind farm layout processing method, obtaining the cable cost includes:

[0044] According to the initial population of the booster station and the preset coastline position, the coast point closest to the booster station is obtained, and the coast point is used as the feeder cable node;

[0045] Obtaining a path layout according to the wind turbine position and the feeder cable node;

[0046] According to preset constraint conditions, the path layout is constrained to obtain a constrained path layout;

[0047] Obtaining a cable cost model according to the constraint path layout, the wind turbine location and cable information;

[0048] The cable cost is obtained according to the cable cost model.

[0049] In a second aspect, the present application provides a wind farm layout processing device based on dual populations, comprising:

[0050] An acquisition module is used to obtain the area of the wind farm;

[0051] An analysis module is configured to obtain a first initial population of wind turbines and a second initial population of wind turbines according to the wind farm area;

[0052] The acquisition module is further configured to acquire, in an i-th iteration, an i-th population of the first wind turbine group and an i-th population of the second wind turbine group; wherein, when i=1, the first population of the first wind turbine group is the initial population of the first wind turbine group, the first population of the second wind turbine group is the initial population of the second wind turbine group, the i-th population of the first wind turbine group and the i-th population of the second wind turbine group each include a plurality of individuals, each individual in the i-th population of the first wind turbine group is a regularly arranged shape variable, and each individual in the i-th population of the second wind turbine group is a wind turbine group coordinate vector;

[0053] The analysis module is further configured to obtain an i-th population of booster stations based on the i-th population of the first wind turbine generator set and the i-th population of the second wind turbine generator set and the optimization constraint; wherein, when i=1, the first population of booster stations is the initial population of booster stations;

[0054] a processing module, configured to calculate the fitness of the i-th group of the first wind turbines, the i-th group of the second wind turbines, and the i-th group of the booster stations according to a first optimization objective and a second optimization objective, to obtain a fitness result; wherein the first optimization objective is to maximize the total output power, and the second optimization objective is to minimize the cable cost;

[0055] A selection module is configured to select the i-th population of the first wind turbine generator set, the i-th population of the second wind turbine generator set, and the i-th population of the booster station according to the fitness result, to obtain the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station;

[0056] a mutation module, configured to perform mutation processing on the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station, to obtain the i+1-th population of the first wind turbine generator set, the i+1-th population of the second wind turbine generator set, and the i+1-th population of the booster station; wherein 1≤i≤I, where I corresponds to the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station obtained after the i-th iteration;

[0057] A conversion module is used to obtain a wind turbine arrangement according to the first optimized population of the first wind turbines and the first optimized population of the second wind turbines.

[0058] In a third aspect, the present application provides a wind farm layout processing device based on dual populations, comprising a processor and a storage device, wherein the storage device is suitable for storing multiple program codes, and the program codes are suitable for being loaded and run by the processor to execute the method as described in any one of the first aspects.

[0059] In a fourth aspect, the present application provides a computer-readable storage medium storing a plurality of program codes, wherein the program codes are suitable for being loaded and run by a processor to execute the method as described in any one of the first aspects.

[0060] The present application provides a wind farm layout processing method, device, equipment and medium based on dual populations, the method specifically comprising: obtaining a wind farm area range; obtaining a first wind turbine group initial population and a second wind turbine group initial population according to the wind farm area range; obtaining an i-th population of the first wind turbine group and an i-th population of the second wind turbine group in the i-th iteration; wherein, when i=1, the first population of the first wind turbine group is the initial population of the first wind turbine group, the first population of the second wind turbine group is the initial population of the second wind turbine group, the i-th population of the first wind turbine group and the i-th population of the second wind turbine group are the initial population of the second wind turbine group, and the i-th population of the first wind turbine group and the i-th population of the second wind turbine group are the initial population of the second wind turbine group. The second wind turbine group i-th group contains multiple individuals, each individual in the first wind turbine group i-th group is a regularly arranged shape variable, and each individual in the second wind turbine group i-th group is a wind turbine group coordinate vector; according to the first wind turbine group i-th group and the second wind turbine group i-th group and the optimization constraint, the booster station i-th group is obtained; wherein, when i=1, the first booster station group is the initial booster station group; according to the first optimization objective and the second optimization objective, the first wind turbine group i-th group, the second wind turbine group i-th group and the booster station i-th group are optimized. The i-th population is subjected to fitness calculation to obtain a fitness result; wherein, the first optimization objective is to maximize the total output power, and the second optimization objective is to minimize the cable cost; according to the fitness result, the i-th population of the first wind turbine group, the i-th population of the second wind turbine group, and the i-th population of the booster station are selected to obtain the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station; the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station are selected. The population is mutated to obtain the i+1th population of the first wind turbine group, the i+1th population of the second wind turbine group, and the i+1th population of the booster station; wherein 1≤i≤I, where I corresponds to the Ith optimized population of the first wind turbine group, the Ith optimized population of the second wind turbine group, and the Ith optimized population of the booster station obtained after the Ith iteration; based on the Ith optimized population of the first wind turbine group and the Ith optimized population of the second wind turbine group, a wind turbine arrangement is obtained, and then a wind turbine layout and a booster station layout that simultaneously meet the requirements of maximizing the total output power of the wind farm and minimizing the construction cost are obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The disclosure of this application will be more easily understood with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Furthermore, similar numbers in the figures represent similar components, where:

[0062] Figure 1 A schematic flow chart of a first embodiment of a wind farm layout processing method based on dual populations provided in an embodiment of the present application;

[0063] Figure 2 A schematic flow chart of a second embodiment of a wind farm layout processing method based on dual populations provided in an embodiment of the present application;

[0064] Figure 3 A flowchart of a third embodiment of a wind farm layout processing method based on dual populations provided in an embodiment of the present application;

[0065] Figure 4 A flowchart of a fourth embodiment of a wind farm layout processing method based on dual populations provided in an embodiment of the present application;

[0066] Figure 5 A flowchart of a fifth embodiment of a wind farm layout processing method based on dual populations provided in an embodiment of the present application;

[0067] Figure 6 This is a flow chart of a sixth embodiment of a wind farm layout processing method based on dual populations provided in an embodiment of the present application.

[0068] Figure 7 A flowchart of a seventh embodiment of a wind farm layout processing method based on dual populations provided in an embodiment of the present application;

[0069] Figure 8 A schematic structural diagram of a first embodiment of a wind farm layout processing device based on dual populations provided in an embodiment of the present application;

[0070] Figure 9 This is a structural diagram of a first embodiment of a wind farm layout processing device based on dual populations provided in an embodiment of the present application.

[0071] Reference Signs List :

[0072] 11: Acquisition module; 12: Analysis module; 13: Processing module; 14: Selection module; 15: Mutation module; 16: Conversion module; 21: Processor; 22: Memory. DETAILED DESCRIPTION

[0073] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the scope of protection of the present application.

[0074] In the description of this application, "module" and "processor" may include hardware, software, or a combination of both. A module may include hardware circuitry, various suitable sensors, communication ports, and memory. It may also include software components, such as program code, or a combination of software and hardware. A processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. A processor has data and / or signal processing capabilities. A processor may be implemented in software, hardware, or a combination of both. Non-transitory computer-readable storage media include any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" refers to all possible combinations of A and B, such as only A, only B, or both A and B. The terms "at least one of A or B" or "at least one of A and B" have similar meanings to "A and / or B" and may include only A, only B, or both A and B. The singular forms "a" and "the" may also include the plural forms.

[0075] In the prior art, when laying out wind turbines and booster stations in a wind farm, construction personnel perform layouts based on experience, layouts based on random positions, and layouts based on neat arrangements. Among these three layout methods, the empirical layout cannot obtain the optimal layout, the random layout causes the wind farm to have poor aesthetics, and the neat arrangement causes the overall optimization space to be limited. Therefore, these three methods cannot effectively layout the wind turbines and booster stations in the wind farm, which leads to technical problems such as low total output power of the wind farm and high construction cost of the wind farm.

[0076] Based on this, in order to solve the above technical problems, the present application provides a wind farm layout processing method based on dual populations to achieve maximum total output power and minimum construction cost.

[0077] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0078] Figure 1 This is a flow chart of a first embodiment of a wind farm layout processing method based on dual populations provided in the present application. Figure 1 Specifically, the method includes:

[0079] Step S101: Acquire the wind farm area.

[0080] In this embodiment, the wind farm area range is the coordinates of multiple vertices on the edge of an image in a plane geometric figure composed of the wind farm.

[0081] Step S102: obtaining a first initial population of wind turbines and a second initial population of wind turbines according to the wind farm area.

[0082] In this embodiment, coordinates are randomly selected within the wind farm area to obtain a first initial population of wind turbines and a second initial population of wind turbines.

[0083] Step S103: in the i-th iteration, obtaining the i-th population of the first wind turbine generator set and the i-th population of the second wind turbine generator set.

[0084] In this embodiment, when i=1, the first population of the first wind turbine group is the initial population of the first wind turbine group, the first population of the second wind turbine group is the initial population of the second wind turbine group, the i-th population of the first wind turbine group and the i-th population of the second wind turbine group both contain multiple individuals, each individual in the i-th population of the first wind turbine group is a regularly arranged shape variable, and each individual in the i-th population of the second wind turbine group is a wind turbine group coordinate vector.

[0085] Step S104: obtaining the i-th group of booster stations according to the i-th group of the first wind turbine generator set and the i-th group of the second wind turbine generator set and the optimization constraints.

[0086] In this embodiment, when i=1, the first population of booster stations is the initial population of booster stations.

[0087] In this embodiment, the optimization constraints are the installed capacity of the wind turbines and the number of wind turbines. Based on the optimization constraints, the number of booster stations is obtained, and the position of each booster station in the i-th group of booster stations is determined based on the i-th group of the first wind turbines and the i-th group of the second wind turbines.

[0088] Step S105: according to the first optimization target and the second optimization target, fitness calculation is performed on the i-th group of first wind turbines, the i-th group of second wind turbines and the i-th group of booster stations to obtain fitness results.

[0089] In this embodiment, the first optimization goal is to maximize the total output power, and the second optimization goal is to minimize the cable cost.

[0090] In this embodiment, if the i-th group of first wind turbines, the i-th group of second wind turbines, and the i-th group of booster stations in the current iteration are closer to the first optimization target and the second optimization target, their fitness is higher, and vice versa.

[0091] Step S106: According to the fitness result, the i-th population of the first wind turbine group, the i-th population of the second wind turbine group and the i-th population of the booster station are selected to obtain the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group and the i-th optimized population of the booster station.

[0092] In this embodiment, the populations with high fitness among the i-th population of the first wind turbine group, the i-th population of the second wind turbine group and the i-th population of the booster station are screened to obtain the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group and the i-th optimized population of the booster station.

[0093] Step S107: performing mutation processing on the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station to obtain the i+1-th population of the first wind turbine generator set, the i+1-th population of the second wind turbine generator set, and the i+1-th population of the booster station.

[0094] In this embodiment, the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station are populations with relatively high fitness. By mutating them, a population with relatively high fitness can be generated.

[0095] In this embodiment, the data in the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station can be randomly changed to obtain the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group, and the i+1-th population of the booster station.

[0096] In this embodiment, 1≤i≤I, where I corresponds to the Ith optimized population of the first wind turbine generator set, the Ith optimized population of the second wind turbine generator set, and the Ith optimized population of the booster station obtained after the Ith iteration.

[0097] In this embodiment, steps S103 to S107 are repeated. When it is determined that the first optimization objective and the second optimization objective have converged, the optimization process is ended. At this time, the first optimized population of the first wind turbine group, the first optimized population of the second wind turbine group, and the first optimized population of the booster station are obtained.

[0098] Step S108: Obtaining a wind turbine arrangement according to the first optimized population of the first wind turbine and the first optimized population of the second wind turbine.

[0099] In this embodiment, the scope of the wind farm area is obtained; based on the scope of the wind farm area, the initial population of the first wind turbine group and the initial population of the second wind turbine group are obtained; in the i-th iteration, the i-th population of the first wind turbine group and the i-th population of the second wind turbine group are obtained; based on the i-th population of the first wind turbine group and the i-th population of the second wind turbine group and the optimization constraint, the i-th population of the booster station is obtained; based on the first optimization target and the second optimization target, the fitness of the i-th population of the first wind turbine group, the i-th population of the second wind turbine group and the i-th population of the booster station is calculated to obtain the fitness result; based on the fitness result, the i-th population of the first wind turbine group and the i-th population of the second wind turbine group are calculated. The i-th population of wind turbines and the i-th population of booster stations are selected and processed to obtain the i-th optimized population of the first wind turbine, the i-th optimized population of the second wind turbine and the i-th optimized population of booster stations; the i-th optimized population of the first wind turbine, the i-th optimized population of the second wind turbine and the i-th optimized population of booster stations are mutated to obtain the i+1-th population of the first wind turbine, the i+1-th population of the second wind turbine and the i+1-th population of booster stations; the arrangement of wind turbines is obtained according to the i-th optimized population of the first wind turbine and the i-th optimized population of the second wind turbine. Compared with the existing technology, the wind turbine arrangement is not considered because the wake effect of the wind turbine is not considered. As for the low total output power and high construction cost of the electric farm, the present application obtains the first initial population of wind turbines and the second initial population of wind turbines according to the scope of the wind farm area, obtains the i-th population of booster stations according to the i-th population of the first wind turbines and the i-th population of the second wind turbines and the optimization constraints in the i-th iteration, and calculates the fitness of the i-th population of the first wind turbines, the i-th population of the second wind turbines and the i-th population of booster stations according to the first optimization target and the second optimization target to obtain the fitness result, and then calculates the fitness of the i-th population of the first wind turbines, the i-th population of the second wind turbines and the i-th population of booster stations according to the fitness result. The groups are selected to obtain the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station; the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station are mutated to obtain the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group, and the i+1-th population of the booster station; finally, according to the i-th optimized population of the first wind turbine group and the i-th optimized population of the second wind turbine group, the wind turbine arrangement is obtained, and then the wind turbine layout and booster station layout that simultaneously meet the requirements of maximizing the total output power of the wind farm and minimizing the construction cost are obtained.

[0100] Figure 2 This is a flow chart of a second embodiment of a wind farm layout processing method based on dual populations provided in this application. Figure 2 Specifically, in step S102, according to the wind farm area, an initial population of the first wind turbine generator set is obtained, including:

[0101] Step S201: Obtain a global center point according to the wind farm area.

[0102] In this embodiment, the wind farm area is a closed polygon. A point is randomly selected in the closed polygon and is used as the global center point.

[0103] Step S202: Obtaining the ratio of starting points within a line according to a first preset range.

[0104] In this embodiment, within the first preset range, any value is taken, and the value is the ratio of the starting points within the row.

[0105] In this embodiment, for example, the first preset range is greater than or equal to 0 and less than 1.

[0106] Step S203: Obtaining the parallel line rotation angle according to a preset angle range.

[0107] In this embodiment, within the preset angle range, any angle value is taken, and the angle value is the parallel line rotation angle.

[0108] In this embodiment, for example, the preset angle range is greater than or equal to 0 degrees and less than 360 degrees.

[0109] Step S204: Obtaining the minimum spacing between rows and the minimum spacing within a row according to preset constraints.

[0110] In this embodiment, for example, the preset constraint condition is that the diameter is greater than or equal to the diameter of the wind turbine generator set and less than 4 times the diameter of the wind turbine generator set.

[0111] In this embodiment, within the preset constraints, any two values are taken, and the two values are the minimum spacing between rows and the minimum spacing within a row, respectively.

[0112] Step S205: Obtaining a line spacing gradient coefficient according to a second preset range.

[0113] In this embodiment, within the second preset range, any two values are taken, and the two values are line spacing gradient coefficients.

[0114] In this embodiment, for example, the second preset range is greater than or equal to 0 and less than 1.

[0115] Step S206: Obtaining a gradient coefficient of the intra-row spacing according to a third preset range.

[0116] In this embodiment, within the third preset range, any two values are taken, and the two values are the intra-row spacing gradient coefficients.

[0117] Step S207: Obtain regularly arranged shape variables according to the global center point coordinates, the ratio of the starting points within the row, the parallel line rotation angle, the minimum spacing between rows, the minimum spacing within a row, the row spacing gradient coefficient, and the row spacing gradient coefficient.

[0118] In this embodiment, the global center point coordinates (x0, y0), the ratio of the starting point in the line r, the parallel line rotation angle θ, the minimum spacing between lines D lines , minimum spacing within the line D inline , the line spacing gradient coefficient (a1, b1) and the intra-line spacing gradient coefficient (a2, b2) are combined to obtain the regularly arranged shape variables (x0, y0, r, θ, D lines ,D inline ,a1,b1,a2,b2).

[0119] Step S208: combining a plurality of regularly arranged shape variables to obtain a first initial population of wind turbines.

[0120] In this embodiment, steps S201 to S207 are performed multiple times to obtain a plurality of regularly arranged shape variables, and then the plurality of regularly arranged shape variables are combined to obtain a first initial population of wind turbines.

[0121] In this embodiment, the number of executions is the number of wind turbines in the wind farm.

[0122] In this embodiment, the global center point is obtained according to the area range of the wind farm; the proportion of starting points within the row is obtained according to the first preset range; the parallel line rotation angle is obtained according to the preset angle range; the minimum spacing between rows and the minimum spacing within a row are obtained according to the preset constraints; the row spacing gradient coefficient is obtained according to the second preset range; the row spacing gradient coefficient is obtained according to the third preset range; the regularly arranged shape variables are obtained according to the coordinates of the global center point, the proportion of starting points within the row, the parallel line rotation angle, the minimum spacing between rows, the minimum spacing within a row, the row spacing gradient coefficient and the row spacing gradient coefficient; a plurality of regularly arranged shape variables are combined to obtain the first initial population of wind turbines.

[0123] Figure 3 This is a flow chart of a third embodiment of a wind farm layout processing method based on dual populations provided in the present application. Figure 3 As shown, specifically, a specific implementation of step S108 is:

[0124] Step S301: performing point processing on the regularly arranged shape variables in the first optimized population of the first wind turbine generator set to obtain an available point vector.

[0125] In this embodiment, the available point vector (x1 reg ,…,x NT reg ,y1 reg ,…,y NT reg ).

[0126] Step S302: In the nth cycle, according to the nth coordinate and the available point vector in the first optimized population of the second wind turbine generator, a distance set between the nth coordinate and the available point vector in the first optimized population of the second wind turbine generator is obtained.

[0127] In this embodiment, in the nth cycle, the nth coordinate (x n rand ,y n rand ) and the distance between each point in the available point position vector, and obtain the distance set between the nth coordinate in the I-th optimized population of the second wind turbine generator and the available point position vector.

[0128] Step S303: Obtain the coordinates of the nth wind turbine generator set according to the distance set of available points.

[0129] In this embodiment, the available point corresponding to the minimum value in the distance set of available points is used as the coordinate of the nth wind turbine generator set.

[0130] Step S304: combining the coordinates of the first wind turbine group to the coordinates of the Nth wind turbine group to obtain a wind turbine group arrangement.

[0131] In this embodiment, 1≤n≤N, where N is the number of coordinates in the first optimized population of the second wind turbine generator set.

[0132] In this embodiment, the coordinates of the first wind turbine group to the Nth wind turbine group are combined to obtain the arrangement of all wind turbine groups in the current wind farm.

[0133] In this embodiment, point processing is performed on the regularly arranged shape variables in the first optimized population of the first wind turbine group to obtain an available point position vector; in the nth cycle, based on the nth coordinate and the available point position vector in the first optimized population of the second wind turbine group, a distance set between the nth coordinate and the available point position vector in the first optimized population of the second wind turbine group is obtained; based on the distance set of the available points, the coordinates of the nth wind turbine group are obtained; the coordinates of the 1st wind turbine group to the Nth wind turbine group are combined to obtain the wind turbine arrangement.

[0134] Figure 4This is a flow chart of a fourth embodiment of a wind farm layout processing method based on dual populations provided in the present application. Figure 4 Specifically, obtaining the initial population of booster stations in step S104 includes:

[0135] Step S401: within the wind farm area, according to preset booster station location constraints, the booster station coordinates are obtained.

[0136] In this embodiment, the booster station location constraint is that the booster station location is within the wind farm area and the distance to the wind turbine is greater than n times the diameter of the wind turbine rotor.

[0137] In this embodiment, coordinate points are randomly selected within the wind farm area to obtain the coordinates of the booster station.

[0138] Step S402: Combining the booster station coordinates to obtain the initial population of booster stations.

[0139] In this embodiment, the coordinates of a booster station are regarded as an individual, and the coordinates of multiple booster stations are combined to obtain an initial population of booster stations.

[0140] In this embodiment, within the wind farm area, the booster station coordinates are obtained according to preset booster station position constraints; the booster station coordinates are combined to obtain the initial population of booster stations.

[0141] Figure 5 This is a flow chart of a fifth embodiment of a wind farm layout processing method based on dual populations provided in the present application. Figure 5 Specifically, a specific implementation of step S107 includes:

[0142] Step S501: Determine whether i is less than a preset number of iterations.

[0143] In this embodiment, if the number of iterations is less than the preset number, it is considered as the early stage of optimization, and if the number of iterations is greater than the preset number, it is considered as the late stage of optimization.

[0144] Step S502: If it is determined that i is less than or equal to the preset number of iterations, then for the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station, data of each individual is selected within a preset large scale and a preset small scale to obtain the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group, and the i+1-th population of the booster station.

[0145] In this embodiment, when it is determined that i is less than or equal to the preset number of iterations, for each individual in the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station, the data of each individual is randomly selected within the preset large scale and the preset small scale, and the set data is used as the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group, and the i+1-th population of the booster station.

[0146] Optionally, when it is determined that i is less than or equal to a preset number of iterations, for each individual in the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group and the i-th optimized population of the substation, the data of each individual is selected within a preset large scale with a preset first probability and within a preset small scale with a preset second probability, and the set data is used as the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group and the i+1-th population of the substation.

[0147] In this embodiment, each individual in the i-th optimized population of the first wind turbine, the i-th optimized population of the second wind turbine, and the i-th optimized population of the booster station is mutated within a preset large scale and a preset small scale range to prevent the occurrence of local optimality.

[0148] Step S503: If it is determined that i is greater than the preset number of iterations, for the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station, data of each individual is selected within a preset small-scale range to obtain the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group, and the i+1-th population of the booster station.

[0149] In this embodiment, it is determined whether i is less than a preset number of iterations; if it is determined that i is less than or equal to the preset number of iterations, then for the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station, within a preset large scale and a preset small scale, the data of each individual is selected to obtain the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group, and the i+1-th population of the booster station; if it is determined that i is greater than the preset number of iterations, then for the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station, within a preset small scale, the data of each individual is selected to obtain the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group, and the i+1-th population of the booster station, so as to generate a new population.

[0150] Figure 6 This is a flow chart of a sixth embodiment of a wind farm layout processing method based on dual populations provided in the present application. Figure 6 Specifically, obtaining the total output power in step S105 includes:

[0151] Step S601: obtaining the inflow velocity losses at multiple points on the wind rotor of the current wind turbine according to the velocity losses of the upstream wind turbine at multiple points on the wind rotor of the current wind turbine.

[0152] In this embodiment, according to Formula 1:

[0153] (1)

[0154] Get the inflow velocity loss ΔU at multiple points on the current wind turbine rotor i (x,y,z). When the i-th wind turbine is downstream of the j-th wind turbine, q ij is 1, in other cases, q ij is 0; N is the number of wind turbines; ΔU i (x, y, z) is the speed loss of the jth upstream wind turbine at the point (x, y, z) on the rotor of the current i-th wind turbine.

[0155] In this embodiment, the average speed loss of the wind turbine can be obtained according to the two-dimensional analytical model of the wind turbine wake of Formula 2:

[0156] (2)

[0157] Among them C T is the thrust coefficient; k=2*(0.3837*Iu+0.003678) is the wake expansion rate; 𝑅 is the rotor radius; σ=(kx+R) / 2 is the standard deviation of the spanwise distribution of the velocity loss.

[0158] According to formula (1), the inflow velocity loss of multiple points on the wind turbine rotor can be calculated for each point on the wind turbine rotor.

[0159] Step S602: obtaining the inflow additional directional turbulence intensity at multiple points on the rotor of the current wind turbine according to the additional directional turbulence intensity of the upstream wind turbine at multiple points on the rotor of the current wind turbine, for solving the wake expansion rate of the wake model.

[0160] In this embodiment, according to Formula 3:

[0161] (3)

[0162] Get the additional turbulence intensity ΔIu of the inflow at multiple points on the wind turbine rotor ij (x,y,z). Among them, ΔIu ij is the additional flow turbulence intensity of the j-th wind turbine at the point (x, y, z) on the rotor of the i-th wind turbine; when the i-th wind turbine is downstream of the j-th wind turbine, q ijis 1, otherwise, q ij is 0; N is the number of wind turbines.

[0163] In this embodiment, according to Formula 4:

[0164] (4)

[0165] The additional turbulence intensity ΔIu is obtained. Where σ is the standard deviation of the Gaussian curve with the same velocity loss model, z is the vertical height, and the flow function G(C T ,I0,x / D) is the maximum additional turbulence intensity of the wake interface at each flow direction, and δ(z) is the vertical correction function. According to Formula 5:

[0166] (5)

[0167] Get the flow function G(C T ,I0,x / D). Where, d=2.3C T -1.2 , e=I0 0.1 , q=0.7*C T -3.2 *I0 -0.45 *(1+x / D) -2 .

[0168] In this embodiment, according to Formula 6:

[0169] (6)

[0170] The spanwise function φ(r / σ) is obtained. The values of k1 and k2 are:

[0171]

[0172]

[0173] The vertical correction function δ(z) is:

[0174]

[0175] Step S603: obtaining the wind speed in front of the hub of the current wind turbine according to the inflow velocity deficits at multiple points on the wind turbine rotor of the current wind turbine.

[0176] In this embodiment, according to Formula 7:

[0177] (7)

[0178] Get the wind speed U in front of the hub of the current i-th wind turbine i Among them, U0 is the ambient wind speed; It is the average inflow velocity loss at multiple points on the wind turbine rotor.

[0179] Step S604: Obtain the corresponding relationship between the wind speed ahead of the hub of the current wind turbine and the power of the current wind turbine according to the wind conditions, the additional directional turbulence intensity of the inflow at multiple points on the rotor of the current wind turbine, and the wind speed ahead of the hub of the current wind turbine.

[0180] Step S605: Obtain the total output power according to the wind conditions, the corresponding relationship, the frequency of occurrence of the wind conditions, the number of wind directions, the number of wind speed segments, and the number of wind turbines.

[0181] In this embodiment, according to Formula 8:

[0182] (8)

[0183] Get the total output power P total Among them, u j is the wind speed; θ k W is the wind direction; jk For wind speed u j and wind direction θ k Wind conditions under P i is the wind turbine i in wind condition W jk Output power under jk ) is the wind condition W jk Frequency of occurrence; N θ is the number of wind directions, N u is the number of wind speed segments taken in a single wind direction; N is the number of wind turbines.

[0184] In this embodiment, the inflow velocity loss at multiple points on the wind rotor of the current wind turbine is obtained based on the velocity loss of the upstream wind turbine at multiple points on the wind rotor of the current wind turbine; the inflow additional flow turbulence intensity at multiple points on the wind rotor of the current wind turbine is obtained based on the additional flow turbulence intensity of the upstream wind turbine at multiple points on the wind rotor of the current wind turbine; the wind speed in front of the hub of the current wind turbine is obtained based on the inflow velocity loss at multiple points on the wind rotor of the current wind turbine; the correspondence between the wind speed in front of the hub of the current wind turbine and the power of the current wind turbine is obtained based on the wind condition, the additional flow turbulence intensity of the inflow at multiple points on the wind rotor of the current wind turbine, and the wind speed in front of the hub of the current wind turbine; the total output power is obtained based on the wind condition, the correspondence, the frequency of occurrence of the wind condition, the number of wind directions, the number of wind speed segments and the number of wind turbines, and then the total output power is obtained.

[0185] Figure 7 This is a flow chart of a seventh embodiment of a wind farm layout processing method based on rule arrangement provided in the embodiment of the present application. Figure 7Specifically, obtaining the cable cost in step S105 includes:

[0186] Step S701: According to the initial population of the booster station and the preset coastline position, the coast point closest to the booster station is obtained, and the coast point is used as the feeder cable node.

[0187] In this embodiment, the booster station positions in the initial population of booster stations are connected to the preset coastline positions to obtain multiple lines, and the point on the coastline corresponding to the shortest line among the multiple lines is used as the coast point, and the coast point is used as the feeder cable node.

[0188] Step S702: Obtain a path layout according to the wind turbine location and feeder cable nodes.

[0189] Step S703: performing constraint processing on the path layout according to preset constraint conditions to obtain a constrained path layout.

[0190] In this embodiment, for example, the preset constraint conditions are the safety distance between wind turbines and the area of the wind farm.

[0191] Step S704: Obtain a cable cost model based on the constraint path layout, wind turbine location and cable information.

[0192] Step S705: Obtain the cable cost according to the cable cost model.

[0193] In this embodiment, according to Formula 9:

[0194] (9)

[0195] Get the cable cost Cost. Among them, C unit is the cable unit price; d(i,j) is the Euclidean distance between point i and point j; sf is the safety factor; when type C cable is used between point i and point j, then x c (i,j) is 1, otherwise it is 0.

[0196] In this embodiment, based on the initial population of the substation and the preset coastline position, the coast point closest to the substation is obtained, and the coast point is used as the feeder cable node; based on the wind turbine position and the feeder cable node, a path layout is obtained; based on the preset constraint conditions, the path layout is constrained to obtain a constrained path layout; based on the constrained path layout, the wind turbine position and the cable information, a cable cost model is obtained; based on the cable cost model, the cable cost is obtained, and then the wind turbine layout and the substation layout can be optimized according to the cable cost.

[0197] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of the present application, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present application.

[0198] Furthermore, the present application also provides a wind farm layout processing device based on dual populations.

[0199] Figure 8 This is a schematic diagram of the structure of a wind farm layout processing device based on dual populations provided in an embodiment of the present application. Figure 8As shown, the apparatus in the embodiment of the present application mainly includes an acquisition module 11, an analysis module 12, a processing module 13, a selection module 14, a variation module 15, and a conversion module 16. In some embodiments, one or more of the acquisition module 11, the analysis module 12, the processing module 13, the selection module 14, the variation module 15, and the conversion module 16 can be combined into a single module. In some embodiments, the acquisition module 11 can be configured to obtain the wind farm area range. The analysis module 12 can be configured to obtain the first wind turbine initial population and the second wind turbine initial population based on the wind farm area range. The acquisition module 11 can also be configured to acquire the i-th population of the first wind turbine group and the i-th population of the second wind turbine group in the i-th iteration; wherein, when i=1, the first population of the first wind turbine group is the initial population of the first wind turbine group, the first population of the second wind turbine group is the initial population of the second wind turbine group, the i-th population of the first wind turbine group and the i-th population of the second wind turbine group each contain multiple individuals, each individual in the i-th population of the first wind turbine group is a regularly arranged shape variable, and each individual in the i-th population of the second wind turbine group is a wind turbine coordinate vector. The analysis module 12 can be configured to obtain the i-th population of the booster station based on the i-th population of the first wind turbine group and the i-th population of the second wind turbine group and the optimization constraints; wherein, when i=1, the first population of the booster station is the initial population of the booster station. The processing module 13 can be configured to perform fitness calculations on the i-th population of the first wind turbine generator set, the i-th population of the second wind turbine generator set, and the i-th population of the substations based on a first optimization objective and a second optimization objective, thereby obtaining a fitness result; wherein the first optimization objective is to maximize total output power, and the second optimization objective is to minimize cable cost. The selection module 14 can be configured to perform selection processing on the i-th population of the first wind turbine generator set, the i-th population of the second wind turbine generator set, and the i-th population of the substations based on the fitness result, thereby obtaining the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the substations. The mutation module 15 can be configured to perform a mutation process on the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the substation, to obtain the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group, and the i+1-th population of the substation; wherein 1≤i≤I, where I corresponds to the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the substation obtained after the i-th iteration. The conversion module 16 can be configured to obtain a wind turbine arrangement based on the i-th optimized population of the first wind turbine group and the i-th optimized population of the second wind turbine group.

[0200] The above-mentioned wind farm layout processing device based on dual population is used to execute Figure 1The embodiment of the wind farm layout processing method based on dual populations shown in the figure has similar technical principles, technical problems solved and technical effects produced. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the wind farm layout processing device based on dual populations can refer to the contents described in the embodiment of the wind farm layout processing method based on dual populations, and will not be repeated here.

[0201] It will be understood by those skilled in the art that all or part of the processes in the method for implementing the above-mentioned embodiments of the present application can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program code. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0202] Furthermore, the present application also provides a wind farm layout processing device based on dual populations.

[0203] Figure 9 This is a schematic diagram of a wind farm layout processing device based on dual populations according to an embodiment of the present application. Figure 9 As shown, the wind farm layout processing device based on dual populations includes at least one processor 21 and a memory 22. The memory 22 can be configured to store and execute the above Figures 1 to 7 The processor 21 can be configured to execute a program in the memory 22 for the dual-population-based wind farm layout processing method of the illustrated embodiment. This program includes, but is not limited to, a program for executing a dual-population-based wind farm layout processing method according to the aforementioned method embodiment. For ease of illustration, only the portions relevant to the present embodiment are shown. For specific technical details not disclosed, please refer to the method section of the present embodiment. The dual-population-based wind farm layout processing device can be a control device comprising various electronic devices.

[0204] Furthermore, the present application also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present application, the computer-readable storage medium can be configured to store a program for executing the dual-population-based wind farm layout processing method of the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned dual-population-based wind farm layout processing method. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.

[0205] Furthermore, it should be understood that since the configuration of each module is merely for the purpose of illustrating the functional units of the apparatus of the present application, the physical devices corresponding to these modules may be the processor itself, or a portion of the software in the processor, a portion of the hardware, or a combination of software and hardware. Therefore, the number of modules in the figure is merely illustrative.

[0206] Those skilled in the art will appreciate that the various modules in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principles of this application. Therefore, the technical solutions after splitting or merging will fall within the scope of protection of this application.

[0207] Thus far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present application.

Claims

1. A wind farm layout processing method based on dual populations, characterized in that: include: Get the wind farm area; According to the wind farm area, obtaining a first wind turbine generator set initial population and a second wind turbine generator set initial population; In the i-th iteration, the i-th population of the first wind turbine group and the i-th population of the second wind turbine group are obtained; wherein, when i=1, the first population of the first wind turbine group is the initial population of the first wind turbine group, the first population of the second wind turbine group is the initial population of the second wind turbine group, the i-th population of the first wind turbine group and the i-th population of the second wind turbine group each contain multiple individuals, each individual in the i-th population of the first wind turbine group is a regularly arranged shape variable, and each individual in the i-th population of the second wind turbine group is a wind turbine coordinate vector; According to the i-th group of the first wind turbine generator set and the i-th group of the second wind turbine generator set and the optimization constraint, the i-th group of the booster station is obtained; wherein, when i=1, the first group of the booster station is the initial group of the booster station; performing fitness calculations on the i-th group of the first wind turbines, the i-th group of the second wind turbines, and the i-th group of the booster stations according to a first optimization objective and a second optimization objective to obtain fitness results; wherein the first optimization objective is maximizing total output power, and the second optimization objective is minimizing cable cost; According to the fitness result, selecting the i-th population of the first wind turbine generator set, the i-th population of the second wind turbine generator set, and the i-th population of the booster station, to obtain the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station; Performing mutation processing on the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station to obtain the i+1-th population of the first wind turbine generator set, the i+1-th population of the second wind turbine generator set, and the i+1-th population of the booster station; wherein 1≤i≤I, where I corresponds to the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station obtained after the i-th iteration; A wind turbine arrangement is obtained according to the first optimized population of the first wind turbines and the first optimized population of the second wind turbines.

2. The method according to claim 1, characterized in that The step of obtaining a first initial population of wind turbines according to the wind farm area includes: According to the wind farm area, a global center point is obtained; According to the first preset range, the ratio of starting points in the line is obtained; According to the preset angle range, the parallel line rotation angle is obtained; According to the preset constraints, the minimum spacing between rows and the minimum spacing within rows are obtained; According to the second preset range, a line spacing gradient coefficient is obtained; According to the third preset range, obtaining the intra-line spacing gradient coefficient; Obtaining shape variables of the regular arrangement according to the global center point, the ratio of the starting points within the row, the parallel line rotation angle, the minimum spacing between rows, the minimum spacing within a row, the row spacing gradient coefficient, and the row spacing gradient coefficient; A plurality of the regularly arranged shape variables are combined to obtain the first initial population of wind turbines.

3. The method according to claim 1, characterized in that The step of obtaining a wind turbine arrangement according to the first optimized population of the first wind turbines and the first optimized population of the second wind turbines includes: performing point processing on the regularly arranged shape variables in the first optimized population of the first wind turbine generator set to obtain an available point position vector; In the nth cycle, according to the nth coordinate in the first optimized population of the second wind turbine generator set and the available point vector, a distance set between the nth coordinate in the first optimized population of the second wind turbine generator set and the available point vector is obtained; Obtaining the coordinates of the nth wind turbine generator set according to the distance set of the available points; Combining the coordinates of the first wind turbine group to the coordinates of the Nth wind turbine group to obtain the wind turbine group arrangement; Wherein, 1≤n≤N, where N is the number of coordinates in the first optimized population of the second wind turbine generator set.

4. The method according to claim 1, wherein Obtaining the initial population of the booster stations includes: Within the wind farm area, according to preset booster station location constraints, the booster station coordinates are obtained; The booster station coordinates are combined to obtain the initial population of booster stations.

5. The method according to claim 1, wherein The step of performing mutation processing on the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station to obtain the i+1-th population of the first wind turbine generator set, the i+1-th population of the second wind turbine generator set, and the i+1-th population of the booster station includes: Determine whether i is less than the preset number of iterations; If it is determined that i is less than or equal to the preset number of iterations, then for the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station, data of each individual is selected within a preset large scale and a preset small scale to obtain the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group, and the i+1-th population of the booster station; If it is determined that i is greater than the preset number of iterations, then for the i-th optimized population of the first wind turbine group, the i-th optimized population of the second wind turbine group, and the i-th optimized population of the booster station, the data of each individual is selected within a preset small-scale range to obtain the i+1-th population of the first wind turbine group, the i+1-th population of the second wind turbine group, and the i+1-th population of the booster station.

6. The method according to claim 1, characterized in that The total output power is obtained, comprising: According to the speed loss of the upstream wind turbine at multiple points on the wind rotor of the current wind turbine, the inflow speed loss at multiple points on the wind rotor of the current wind turbine is obtained; According to the additional flow turbulence intensity of the upstream wind turbine at multiple points on the wind rotor of the current wind turbine, the additional flow turbulence intensity of the inflow at multiple points on the wind rotor of the current wind turbine is obtained; Obtaining the wind speed in front of the hub of the current wind turbine generator set according to the inflow velocity deficits at multiple points on the wind turbine rotor of the current wind turbine generator set; Obtaining a corresponding relationship between the wind speed in front of the hub of the current wind turbine and the power of the current wind turbine according to the wind conditions, the additional turbulence intensity of the inflow at multiple points on the wind turbine rotor of the current wind turbine, and the wind speed in front of the hub of the current wind turbine; The total output power is obtained according to the wind condition, the corresponding relationship, the frequency of occurrence of the wind condition, the number of wind directions, the number of wind speed segments and the number of wind turbine generator sets.

7. The method according to claim 1, characterized in that Get the cable cost, including: According to the initial population of the booster station and the preset coastline position, the coast point closest to the booster station is obtained, and the coast point is used as the feeder cable node; Obtaining a path layout according to the wind turbine position and the feeder cable node; According to preset constraint conditions, the path layout is constrained to obtain a constrained path layout; Obtaining a cable cost model according to the constraint path layout, the wind turbine location and cable information; The cable cost is obtained according to the cable cost model.

8. A wind farm layout processing device based on dual populations, characterized in that: include: An acquisition module is used to obtain the area of the wind farm; An analysis module is configured to obtain a first initial population of wind turbines and a second initial population of wind turbines according to the wind farm area; The acquisition module is further configured to acquire, in an i-th iteration, an i-th population of the first wind turbine group and an i-th population of the second wind turbine group; wherein, when i=1, the first population of the first wind turbine group is the initial population of the first wind turbine group, the first population of the second wind turbine group is the initial population of the second wind turbine group, the i-th population of the first wind turbine group and the i-th population of the second wind turbine group each include a plurality of individuals, each individual in the i-th population of the first wind turbine group is a regularly arranged shape variable, and each individual in the i-th population of the second wind turbine group is a wind turbine group coordinate vector; The analysis module is further configured to obtain an i-th population of booster stations based on the i-th population of the first wind turbine generator set and the i-th population of the second wind turbine generator set and the optimization constraint; wherein, when i=1, the first population of booster stations is the initial population of booster stations; a processing module, configured to calculate the fitness of the i-th group of the first wind turbines, the i-th group of the second wind turbines, and the i-th group of the booster stations according to a first optimization objective and a second optimization objective, to obtain a fitness result; wherein the first optimization objective is to maximize the total output power, and the second optimization objective is to minimize the cable cost; A selection module is configured to select the i-th population of the first wind turbine generator set, the i-th population of the second wind turbine generator set, and the i-th population of the booster station according to the fitness result, to obtain the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station; a mutation module, configured to perform mutation processing on the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station, to obtain the i+1-th population of the first wind turbine generator set, the i+1-th population of the second wind turbine generator set, and the i+1-th population of the booster station; wherein 1≤i≤I, where I corresponds to the i-th optimized population of the first wind turbine generator set, the i-th optimized population of the second wind turbine generator set, and the i-th optimized population of the booster station obtained after the i-th iteration; A conversion module is used to obtain a wind turbine arrangement according to the first optimized population of the first wind turbines and the first optimized population of the second wind turbines.

9. A wind farm layout processing device based on dual populations, comprising a processor and a storage device, wherein the storage device is suitable for storing multiple program codes, characterized in that: The program code is suitable for being loaded and executed by the processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and executed by a processor to perform the method according to any one of claims 1 to 7.

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