Double-population wind power plant arrangement optimization method and device based on regular arrangement and medium

Abstract

The invention relates to the technical field of wind power plant micro-siting, and particularly provides a double-population wind power plant arrangement optimization method and device based on regular arrangement and a medium, and the method comprises the steps: obtaining the attribute information of a to-be-optimized wind power plant; randomly generating regular shape variables and bottom layer coordinates of the wind turbine generator based on the attribute information, establishing a first initial population based on the regular shape variables, establishing a second initial population based on the bottom layer coordinates of the wind turbine generator, and obtaining bottom layer coordinate vectors; based on the regular shape variable and the bottom coordinate vector, obtaining an arrangement scheme of wind turbine generators in the to-be-optimized wind power plant; optimizing the arrangement scheme based on the first initial population, the second initial population and an optimization target; when the optimization target reaches a preset convergence condition, obtaining an optimal arrangement scheme; according to the method, the mathematical optimal solution of the point location of the wind turbine generator can be realized while the overall layout is ensured to accord with the regular shape, so that the comprehensive benefit of the whole life cycle of a wind power plant is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of micro-siting of wind farms, and particularly to an optimization method, device and medium for wind turbine layout in a wind farm based on regular arrangement of a dual population. Background Art

[0002] As an important driving force for the transformation to clean energy, wind energy is promoting the construction of wind farms towards large-scale and base-scale development; moreover, as a renewable clean energy power generation method with relatively mature technology and large installed capacity, the development of wind power generation plays a key role in promoting the green and low-carbon transformation of the energy structure; the layout of wind turbines has an important impact on their operating efficiency. By reasonably designing the layout of wind turbines, the mutual influence between wind turbines can be reduced, and the wind capture and operating efficiency of wind turbines can be improved; a reasonable arrangement of wind turbines can maximize the utilization of wind energy resources and improve the overall power generation efficiency of the wind farm; this makes the importance of the research on the layout optimization of wind farms more prominent.

[0003] However, the current layout design of wind farms often relies on empirical site selection. This method is difficult to obtain the mathematical optimal solution of the positions of wind turbine units, and the completely random layout optimization of wind farms lacks aesthetics and is not conducive to the planning of operation and maintenance paths; in addition, overly regular layout forms will limit the overall optimization space. For example, reasonably reducing the number of wind turbine positions in areas with large wake effects can improve the overall efficiency, while a regularly and densely arranged layout is difficult to meet this requirement; resulting in poor comprehensive benefits of the wind farm.

[0004] Correspondingly, there is a need in the art for a new optimization scheme for wind turbine layout in a wind farm based on regular arrangement of a dual population to solve the above problems. Summary of the Invention

[0005] In order to overcome the above defects, the present invention is proposed to solve or at least partially solve the technical problems that the existing wind farm layout methods are difficult to achieve the mathematical optimal solution of the positions of wind turbine units, and the regularly and densely arranged layout form will limit the overall optimization space, resulting in poor comprehensive benefits of the wind farm.

[0006] In a first aspect, there is provided an optimization method for wind turbine layout in a wind farm based on regular arrangement of a dual population, the method comprising: Obtaining attribute information of the wind farm to be optimized; Randomly generating regular shape variables and bottom-layer coordinates of wind turbine units based on the attribute information; Establishing a first initial population based on the regular shape variables, wherein the first initial population includes a plurality of first population individuals, and each first population individual is a set of regular shape variables; Establish a second initial population based on the underlying coordinates of the wind turbines in the wind farm, where the second initial population includes multiple second population individuals, and each second population individual is respectively a vector of the underlying coordinates of the wind turbines in a group of the wind farms to be optimized; Obtain the layout scheme of the wind turbines in the wind farm to be optimized based on the rule-based shape variables and the underlying coordinate vectors; Optimize the layout scheme of the wind turbines in the wind farm to be optimized based on the first initial population, the second initial population, and the optimization objective; After the optimization objective reaches the preset convergence condition, obtain the optimal layout scheme of the wind turbines in the wind farm to be optimized.

[0007] In a technical solution of the above double-population wind farm layout optimization method based on rule-based layout, the attribute information includes the regional range of the wind farm to be optimized and the number of wind turbines; The randomly generating rule-based shape variables and the underlying coordinates of the wind turbines based on the attribute information includes: Set the distance between any two wind turbines in the wind farm to be optimized; Randomly generate rule-based shape variables based on the regional range of the wind farm to be optimized; Randomly generate the underlying coordinates of the wind turbines based on the regional range of the wind farm to be optimized, the number of wind turbines, and the distance between any two wind turbines in the wind farm to be optimized; Among them, the rule-based shape variables include one of the shape variables of the parallelogram rule, the row arrangement rule, and the circular rule, and the distance between any two wind turbines in the wind farm to be optimized is greater than n times the rotor diameter, where n is a positive integer greater than 1.

[0008] In a technical solution of the above double-population wind farm layout optimization method based on rule-based layout, the randomly generating rule-based shape variables based on the regional range of the wind farm to be optimized includes: Randomly select the global center point coordinates based on the regional range of the wind farm to be optimized; Randomly select multiple shape parameters within multiple preset shape parameter ranges based on the uniform probability distribution, where the type of the shape parameters is determined based on the shape type of the rule-based shape variables; Determine the rule-based shape variables based on the global center point coordinates and the multiple shape parameters.

[0009] In a technical solution of the above double-population wind farm layout optimization method based on rule-based layout, the obtaining the layout scheme of the wind turbines in the wind farm to be optimized based on the rule-based shape variables and the underlying coordinate vectors includes: Obtain the available position vector of the wind turbines in the wind farm to be optimized based on the shape variables of the rules, where the number of the available positions is greater than or equal to the number of the wind turbines; Based on the available position vector and the underlying coordinate vector, obtain the layout scheme of the wind turbines in the wind farm to be optimized.

[0010] In a technical solution of the above double-population wind farm layout optimization method based on rules, the obtaining the layout scheme of the wind turbines in the wind farm to be optimized based on the available position vector and the underlying coordinate vector includes: S1. Select a coordinate point from the underlying coordinate vector, obtain the distances between the coordinate point and each available position in the available position vector, extract the available position closest to the coordinate point as the coordinate point of the wind turbine in the wind farm to be optimized, and remove the available position closest to the coordinate point from the available position vector; S2. Traverse all the coordinate points in the underlying coordinate vector, repeat step S1 until the number of the coordinate points of the wind turbines in the wind farm to be optimized obtained is the same as the number of the wind turbines, and determine the layout scheme of the wind turbines in the wind farm to be optimized based on the obtained multiple coordinate points of the wind turbines in the wind farm to be optimized.

[0011] In a technical solution of the above double-population wind farm layout optimization method based on rules, the preset convergence condition is that the target value of the optimization objective is the largest; The method further includes: S3. Optimize based on the first initial population to obtain the optimized shape variables of the rules; S4. Optimize based on the second initial population to obtain the optimized underlying coordinates of the wind turbines; S5. Establish the first optimized population and the second optimized population based on the optimized shape variables of the rules and the optimized underlying coordinates of the wind turbines, and obtain the optimized layout scheme of the wind turbines; S6. Obtain the corresponding optimized optimization objective based on the optimized layout scheme of the wind turbines, and loop to execute steps S3 - S5 until the target value of the optimized optimization objective reaches the largest, then end the optimization process to obtain the optimal shape variables of the rules, the optimal underlying coordinates of the wind turbines, and the optimal layout scheme of the wind turbines in the wind farm to be optimized.

[0012] In a technical solution of the above double-population wind farm layout optimization method based on rules, the optimization objective is the total output power of the wind farm to be optimized under the layout scheme of the wind turbines; The method for obtaining the total output power of the wind farm to be optimized includes: Obtain the wind resource information of the wind farm to be optimized, where the wind resource information includes the environmental incoming flow velocity of the wind farm to be optimized; Based on the environmental incoming flow velocity of the wind farm to be optimized and the layout scheme of the wind turbines in the wind farm to be optimized, obtain the total output power of the wind farm to be optimized according to a preset wind turbine wake model.

[0013] In a technical solution of the above-mentioned double-population wind farm layout optimization method based on regular layout, the preset wind turbine wake model includes a two-dimensional analytical model of the wind turbine wake and an analytical model of the additional turbulence intensity of the wind turbine wake; The obtaining of the total output power of the wind farm to be optimized according to the preset wake model based on the environmental incoming flow velocity of the wind farm to be optimized and the layout scheme of the wind turbines in the wind farm to be optimized includes: Obtain the wake flow direction turbulence intensity of each wind turbine in the layout scheme based on the analytical model of the additional turbulence intensity of the wind turbine wake; Obtain the additional incoming flow direction turbulence intensity at multiple points on the wind wheel of each wind turbine based on the wake flow direction turbulence intensity of each wind turbine; Obtain the incoming flow velocity deficit at multiple points on the wind wheel of each wind turbine based on the two-dimensional analytical model of the wind turbine wake and the additional incoming flow direction turbulence intensity at multiple points on the wind wheel of each wind turbine; Obtain the wind speed in front of the hub of each wind turbine based on the incoming flow velocity deficit at multiple points on the wind wheel of each wind turbine and the environmental incoming flow velocity; Obtain the output power of each wind turbine based on the wind speed in front of the hub of each wind turbine and the preset power curve of each wind turbine; Obtain the total output power of the wind farm to be optimized based on the output power of each wind turbine.

[0014] In a second aspect, there is provided an electronic device, which includes at least one processor; and a memory communicatively connected to the at least one processor; wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any one of the technical solutions of the above-mentioned double-population wind farm layout optimization method based on regular layout is implemented.

[0015] In a third aspect, there is provided a computer-readable storage medium, which stores multiple program codes, and the program codes are suitable for being loaded and run by a processor to execute the method described in any one of the technical solutions of the above-mentioned double-population wind farm layout optimization method based on regular layout.

[0016] One or more of the above technical solutions of the present invention have at least one or more of the following beneficial effects: In the technical solution of the method for optimizing the layout of a dual-population wind farm based on regular arrangement provided by the present invention, the attribute information of the wind farm to be optimized is obtained; based on the attribute information, regular shape variables and the underlying coordinates of wind turbines are randomly generated. A first initial population is established based on the regular shape variables, and a second initial population is established based on the underlying coordinates of wind turbines and the underlying coordinate vector is obtained; based on the regular shape variables and the underlying coordinate vector, the layout plan of the wind turbines in the wind farm to be optimized is obtained; based on the first initial population, the second initial population and the optimization objective, the layout plan is optimized; when the optimization objective reaches the preset convergence condition, the optimal layout plan is obtained; through the above optimization method, while ensuring that the overall layout conforms to a regular shape, the present invention can achieve the mathematical optimal solution of the wind turbine positions, thereby improving the comprehensive benefits of the entire life cycle of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Referring to the accompanying drawings, the disclosure of the present invention will become more readily understood. It is easily understood by those skilled in the art that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. Among them: Figure 1 is a schematic flow chart of the main steps of the method for optimizing the layout of a dual-population wind farm based on regular arrangement according to an embodiment of the present invention; Figure 2 is a schematic flow chart of the main steps of randomly generating regular shape variables based on attribute information according to an embodiment of the present invention; Figure 3 is a schematic flow chart of the main steps of randomly generating regular shape variables based on the regional scope of the wind farm to be optimized according to an embodiment of the present invention; Figure 4 is a schematic flow chart of the main steps of obtaining the layout plan of the wind turbines in the wind farm to be optimized based on the available position vector and the underlying coordinate vector according to an embodiment of the present invention; Figure 5 is a schematic flow chart of the main steps of optimizing the layout plan of the wind turbines in the wind farm to be optimized according to an embodiment of the present invention; Figure 6 is a wind rose diagram according to an embodiment of the present invention; Figure 7 is the power curve of the unit and the thrust coefficient curve according to an embodiment of the present invention; Figure 8 is the original wind turbine positions of the wind farm to be optimized according to an embodiment of the present invention; Figure 9It is the optimization process evolution curve of the rule-based double-population wind farm layout optimization method according to an embodiment of the present invention; Figure 10 It is a schematic diagram of generating the wind turbine positions of the wind farm to be optimized by mapping the underlying coordinate points and the available points of the upper-layer parallelogram rule based on random layout according to an embodiment of the present invention; Figure 11 It is the wind turbine positions in the wind farm to be optimized under the optimal layout scheme according to an embodiment of the present invention; Figure 12 It is the main structural schematic diagram of an electronic device according to an embodiment of the present invention.

[0018] Reference numerals: 121: Memory; 122: Processor. Specific embodiments

[0019] The following describes some embodiments of the present invention with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principle of the present invention and are not intended to limit the protection scope of the present invention.

[0020] In the description of the present invention, "module" and "processor" may include hardware, software, or a combination of both. A module may include a hardware circuit, various suitable sensors, communication ports, memory, and may also include a software part, such as program code, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, in hardware, or in a combination of both. The computer-readable storage medium includes any suitable medium for storing program code, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, and so on. The term "A and / or B" represents all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B" and may include only A, only B, or A and B. The singular terms "a" and "this" may also include the plural form.

[0021] Refer to the attached Figure 1 , Figure 1 It is the main step flow schematic diagram of the rule-based double-population wind farm layout optimization method according to an embodiment of the present invention. As Figure 1 shown, the rule-based double-population wind farm layout optimization method in the embodiment of the present invention mainly includes the following steps S101 to step S105.

[0022] Step S101: Obtain the attribute information of the wind farm to be optimized.

[0023] In this embodiment, the attribute information includes the regional scope of the wind farm to be optimized and the number of wind turbines. ; In one implementation, a suitable wind turbine model can be selected according to the regional scope, the number of wind turbines, and the installed capacity requirements of the wind farm to be optimized.

[0024] Step S102: Randomly generate regular shape variables based on the attribute information, and establish a first initial population based on the regular shape variables. Among them, the first initial population includes multiple first population individuals, and each first population individual is a set of regular shape variables; corresponding uniquely determined available positions of wind farm units with regular arrangements can be generated based on a specific point-taking program.

[0025] In this embodiment, real number coding is adopted to randomly generate regular shape variables, and a first initial population is established based on the regular shape variables. The first initial population contains multiple first population individuals, and each first population individual is a set of regular shape variables. Each set of regular shape variables corresponds to an available position of wind farm units with a regular arrangement; among them, real number coding means representing each gene value of the first population individual with a floating-point number within a certain range.

[0026] In one implementation, each set of regular shape variables includes a row vector of 10 variables, for example .

[0027] Meanwhile, in this embodiment, constraint conditions for the coordinates of wind turbines are preset. The constraint conditions include the regional scope of the wind farm to be optimized and the distance between any two wind turbines in the wind farm to be optimized.

[0028] Refer to the appendix Figure 2 , Figure 2 is a schematic diagram of the main step flow for randomly generating regular shape variables based on attribute information according to an embodiment of the present invention. As Figure 2 shown, randomly generating regular shape variables based on attribute information includes: Step S201: Set the distance between any two wind turbines in the wind farm to be optimized; Among them, the distance between any two wind turbines in the wind farm to be optimized is greater than n times the wind turbine diameter, , n where is a positive integer greater than 1; Greater than four times the rotor diameter. It should be noted that the distance between any two wind turbines in the wind farm to be optimized in this embodiment Greater than four times the rotor diameter is just an example of an implementation manner and is not a limitation to this embodiment. In the actual application of the implementation manner of the present invention, the distance between any two wind turbines in the wind farm to be optimized can be set according to actual needs.

[0029] Step S202: Randomly generate regular shape variables based on the regional scope of the wind farm to be optimized; In one implementation manner, the randomly generated regularly arranged shape variables are , where the generation method of each parameter should be determined according to the regular form of the available positions of the wind turbines in the wind farm to be optimized. Among them, the regular shape variables include one of the shape variables of the parallelogram rule, the row arrangement rule, and the annular rule; Optionally, if the regular form of the available positions of the wind turbines in the wind farm to be optimized is the parallelogram rule, the shape variables of the parallelogram rule , where each parameter is: the global center point coordinates , the included angle of the parallelogram θ , the overall rotation angle γ , the side length of the parallelogram row a , the side length of the parallelogram column b , the gradient coefficient between rows and the gradient coefficient within rows ; if the regular form of the available positions of the wind turbines in the wind farm to be optimized is the row arrangement rule, the shape variables of the row arrangement rule , where each parameter is: the global center point coordinates , the starting point ratio within the row r , the rotation angle of the parallel lines θ , the minimum distance between rows , the minimum distance within the row , the gradient coefficient of the row spacing and the gradient coefficient of the spacing within the row ; if the regular form of the available positions of the wind turbines in the wind farm to be optimized is the annular rule, the shape variables of the annular rule , where each parameter is: the global center point coordinates , the starting point ratio of the ring spacing r , the ray angle of the starting point within the ring θ , the minimum distance between rings , the minimum distance within the ring , the gradient coefficient of the ring spacing and the gradient coefficient of the spacing within the ring It should be noted that the shape variables of the rules include the shape variables of the parallelogram rule, the shape variables of the row arrangement rule, and the shape variables of the annular rule. Just one example of the implementation manner does not constitute a limitation to the embodiments of the present invention. In actual applications of the embodiments of the present invention, the types of actual shape variables can be generated according to actual needs.

[0030] Refer to the appendix Figure 3 , Figure 3 is a schematic diagram of the main step flow for randomly generating the shape variables of the rules based on the regional scope of the wind farm to be optimized according to an embodiment of the present invention. As Figure 3 shown, randomly generating the shape variables of the rules based on the regional scope of the wind farm to be optimized includes: Step S301: Randomly select the global center point coordinates based on the regional scope of the wind farm to be optimized.

[0031] Within the closed polygon of the regional scope of the wind farm to be optimized, randomly take out a point coordinate as the global center point coordinate for regular arrangement ; Step S302: Randomly select multiple shape parameters within multiple preset shape parameter ranges based on a uniform probability distribution, where the types of shape parameters are determined based on the shape types of the shape variables of the rules.

[0032] Optionally, when the shape variable of the rule is the shape variable of the parallelogram rule, randomly select the parallelogram included angle θ and the overall rotation angle γ within the range of [0°, 360°], and randomly select the parallelogram row side length and the parallelogram column side length within a certain multiple range based on the distance a between any two wind turbines in the wind farm to be optimized, randomly select the inter-row gradient coefficient b and within the range of [0, 1), and randomly select the intra-row gradient coefficient and within the range of [0, 1); When the shape variable of the rule is the shape variable of the row arrangement rule, randomly select the intra-row starting point ratio within the range of [0, 1), randomly select the parallel line rotation angle r within the range of [0°, 360°), randomly select the minimum inter-row spacing θ and the minimum intra-row spacing within the range of and randomly select the inter-row spacing gradient coefficient and within the range of [0, 1), and Two parameters, randomly obtain the row spacing gradient coefficient within the range of [0, 1). and two parameters; When the shape variable of the rule is the shape variable of the circular rule, randomly obtain the starting point ratio of the ring spacing within the range of [0, 1). r , randomly obtain the starting point ray angle of the ring within the range of [0°, 360°). θ , within randomly obtain the minimum distance between rings within the range. and the minimum distance within the ring two parameters, randomly obtain the ring spacing gradient coefficient within the range of [0, 1). and two parameters, randomly obtain the gradient coefficient of the spacing within the ring within the range of [0, 1). and two parameters.

[0033] Step S303: Determine the shape variable of the rule based on the global center point coordinates and multiple shape parameters.

[0034] Optionally, when the shape variable of the rule is the shape variable of the parallelogram rule, based on the global center point coordinates , the included angle of the parallelogram θ , the overall rotation angle γ , the row side length of the parallelogram a , the column side length b of the parallelogram, the gradient coefficient between rows and the gradient coefficient within the row obtain the shape variable of the parallelogram rule ; when the shape variable of the rule is the shape variable of the row arrangement rule, based on the global center point coordinates , the starting point ratio within the row r , the rotation angle of the parallel lines θ , the minimum distance between rows , the minimum distance within the row , the gradient coefficient of the row spacing and the gradient coefficient of the spacing within the row obtain the shape variable of the row arrangement rule ; when the shape variable of the rule is the shape variable of the circular rule, based on the global center point coordinates , the starting point ratio of the ring spacing r , the starting point ray angle of the ring within θ , the minimum distance between rings , the minimum distance within the ring , the gradient coefficient of the ring spacing and the gradient coefficient of the spacing within the ring Obtain the shape variables of the circular rule .

[0035] In this embodiment, the method further includes: According to the number of wind turbines, using the generated shape variables arranged in a rule, based on a specific point-taking program, in the form of a rule for the available positions of the wind turbines in the wind farm to be optimized, generate the corresponding uniquely determined available positions of the wind turbines in the wind farm to be optimized within the area range of the wind farm to be optimized, where the number of available positions is greater than or equal to the number of wind turbines.

[0036] Optionally, if the available positions of the wind turbines in the wind farm to be optimized are obtained based on the shape variables of the parallelogram rule, the method includes: Based on the global center point coordinates and the overall rotation angle γ , taking the horizontal line of the area range of the wind farm to be optimized as the reference, obtain the first straight line; based on the global center point coordinates and the parallelogram included angle θ , taking the first straight line as the reference, obtain the second straight line; Obtain the first distance between the two intersection points of the first straight line and the boundary of the wind farm to be optimized; based on the first distance and the distance between any two wind turbines in the wind farm to be optimized, determine the maximum number of first gradient points; based on the maximum number of first gradient points, the inter-row gradient coefficient and the preset minimum distance between the first gradient points, determine the distance between the first gradient points and the coordinate points of the first gradient points on the second straight line; based on the distance between the first gradient points and the coordinate points of the first gradient points on the second straight line, generate multiple first parallel lines parallel to the first straight line; Obtain the second distance between the two intersection points of the second straight line and the boundary of the wind farm to be optimized; based on the second distance and the distance between any two wind turbines in the wind farm to be optimized, determine the maximum number of second gradient points; based on the maximum number of second gradient points, the intra-row gradient coefficient and the preset minimum distance between the second gradient points, determine the distance between the second gradient points and the coordinate points of the second gradient points on the first straight line; based on the distance between the second gradient points and the coordinate points of the second gradient points on the first straight line, generate multiple second parallel lines parallel to the second straight line; Based on the multiple first parallel lines and the multiple second parallel lines, obtain multiple intersection points, where the intersection points are the available positions of the wind turbines in the wind farm to be optimized that conform to the parallelogram rule.

[0037] Optionally, if the available positions of the wind turbines in the wind farm to be optimized that conform to the row arrangement rule are adopted, the method for obtaining the available positions includes: Based on the global center point coordinates and the parallel line rotation angle θ, taking the horizontal line of the area range of the wind farm to be optimized as the benchmark, obtaining a straight line intersecting with the area range of the wind farm to be optimized, and the length of the intersecting line segment between the two intersection points where the straight line intersects with the area range of the wind farm to be optimized; Obtain all the straight lines passing through the global center point coordinates , obtaining the lengths of the line segments between the two intersection points where all the straight lines intersect with the boundary of the wind farm to be optimized, and selecting the longest line segment; Based on the longest line segment and the minimum inter-row spacing , obtaining the maximum number of parallel line segments within the area range of the wind farm to be optimized; Based on the minimum inter-row spacing , the maximum number of parallel line segments, and the row spacing gradient coefficient , obtaining the inter-row gradient interval between the parallel line segments; Based on the intersecting line segment and the inter-row gradient interval, generating multiple parallel line segments parallel to the intersecting line segment; Starting from one end of all the parallel line segments, based on the in-row starting point ratio r and the lengths of all the parallel line segments, obtaining multiple in-row starting points corresponding on all the parallel line segments; Among them, all the parallel line segments include the intersecting line segment and multiple parallel line segments parallel to the intersecting line segment; Based on the lengths of all the parallel line segments and the minimum in-row spacing , obtaining the maximum number of gradient points on each parallel line segment; Based on the minimum in-row spacing , the maximum number of gradient points on each parallel line segment, and the in-row spacing gradient coefficient , obtaining the in-row gradient interval of each parallel line segment; Based on the in-row gradient interval of each parallel line segment, obtaining multiple gradient points on both sides of the in-row starting point of each parallel line segment, and obtaining the coordinate points of the multiple gradient points, where the gradient points are the available positions of the wind turbines in the wind farm to be optimized with a row arrangement rule.

[0038] Optionally, if the available positions of the wind turbines in the wind farm to be optimized conform to the circular rule, the method for obtaining the available positions includes: Obtain the global center point coordinates The connecting line segments between the global center point coordinates and each vertex of the boundary of the wind farm to be optimized, and the length of each connecting line segment; Based on the length of each connecting line segment and the ring spacing starting point ratio r, determining the ring spacing starting point of each connecting line segment; Obtain the global center point coordinates The shortest distance from the global center point coordinates to the boundary of the wind farm to be optimized; Based on the shortest distance, the length of each connecting line segment, and the minimum inter-ring spacing , determining the minimum spacing between the first gradient points and the maximum number of the first gradient points on each connecting line segment; Based on the ring spacing gradient coefficient 、Determine the minimum distance between the first gradient points on each connection line segment and the maximum number of first gradient points, and determine the inter-ring gradient interval on each connection line segment; based on the inter-ring gradient interval on each connection line segment, obtain multiple first gradient points on each connection line segment on both sides of the starting point of the inter-ring distance of each connection line segment, and obtain multiple rings similar to the boundary shape of the wind farm to be optimized based on the multiple first gradient points on each connection line segment; Based on the global center point coordinates and the ray angle at the starting point inside the ring θ ,using the horizontal line of the area range of the wind farm to be optimized as a reference, obtain rays; Obtain the perimeter of each ring, and determine the maximum number of second gradient points on each ring based on the minimum distance inside the ring and the perimeter of each ring; based on the maximum number of second gradient points on each ring, the minimum distance inside the ring ,the gradient coefficient of the distance inside the ring ,determine the inter-ring gradient interval on each ring; based on the inter-ring gradient interval on each ring, obtain multiple second gradient points on both sides of the starting point inside the ring corresponding to each ring, and obtain the coordinate points of the multiple second gradient points, where the second gradient points are the available positions of the wind farm units with regular rings.

[0039] Step S103: Randomly generate the bottom-layer coordinates of the wind turbines based on the attribute information, and establish a second initial population based on the bottom-layer coordinates of the wind turbines, where the second initial population includes multiple second population individuals, and each second population individual is a bottom-layer coordinate vector of the wind turbines in a wind farm to be optimized; based on a specific point mapping program, the bottom-layer coordinate vector can be converted into a wind farm unit layout plan in a regular arrangement in the first initial population; In this embodiment, based on the area range of the wind farm to be optimized, the number of wind turbines, and the distance between any two wind turbines in the wind farm to be optimized, randomly generate the bottom-layer coordinates of the wind turbines; Specifically, according to the determined number of wind turbines ,randomly generate a bottom-layer coordinate position vector with the same number as the number of units in the area range of the wind farm to be optimized ,repeat this process until the distance between any two wind turbines is greater than n times the wind wheel diameter.

[0040] In one implementation, real number coding is used, considering the constraint conditions of the area range of the wind farm to be optimized and the distance between any two wind turbines, and completely random bottom-layer coordinates are generated.

[0041] Step S104: Based on the regular shape variables and the bottom-layer coordinate vector, obtain the layout plan of the wind turbines in the wind farm to be optimized; In this embodiment, the available position vectors of the wind turbines in the wind farm to be optimized are determined based on the rule-based shape variables, where the number of available positions is greater than or equal to the number of wind turbines; Based on the available position vectors and the underlying coordinate vectors, the layout scheme of the wind turbines in the wind farm to be optimized is obtained.

[0042] Refer to the appendix Figure 4 , Figure 4 is a schematic diagram of the main steps for obtaining the layout scheme of the wind turbines in the wind farm to be optimized based on the available position vectors and the underlying coordinate vectors according to an embodiment of the present invention. As Figure 4 shown, obtaining the layout scheme of the wind turbines in the wind farm to be optimized based on the available position vectors and the underlying coordinate vectors includes: Step S401: Select a coordinate point from the underlying coordinate vectors, obtain the distances between the coordinate point and each available position in the available position vectors, extract the available position closest to the coordinate point as the coordinate point of the wind turbine in the wind farm to be optimized, and remove the available position closest to the coordinate point from the available position vectors; Step S402: Traverse all the coordinate points in the underlying coordinate vectors, repeat Step S401 until the number of coordinate points of the wind turbines in the wind farm to be optimized obtained is the same as the number of wind turbines, and determine the layout scheme of the wind turbines in the wind farm to be optimized based on the obtained multiple coordinate points of the wind turbines in the wind farm to be optimized.

[0043] In one embodiment, the first optimization population contains first population individuals, that is, rule-arranged shape variables , and each shape variable can generate a corresponding uniquely determined available position vector of the wind farm units to be optimized through a point-taking program ; the second optimization population contains second population individuals, that is, underlying coordinate vectors of the wind turbines , where ; Traverse the underlying coordinate vectors , first select the coordinate point in the underlying coordinate vectors, calculate the distances between this coordinate point and each available position in the available position vector , extract the available position closest to the coordinate point as the coordinate point of the wind turbine in the wind farm to be optimized , and remove the available position closest to the coordinate point from the available position vector, that is, sampling without replacement; Repeat the above step, i.e., for the k-th coordinate point , extract the available point position nearest to the coordinate point , and use it as the coordinate point of the wind turbine in the wind farm to be optimized , and remove it from the vector of available point positions of the unit , i.e., sampling without replacement; Traverse all the coordinate points in the bottom-layer coordinate vector, and repeat the above steps until the number of coordinate points of the wind turbines in the wind farm to be optimized is the same as the number of wind turbines , forming a vector; Determine the layout plan of the wind turbines in the wind farm to be optimized based on the obtained multiple coordinate points of the wind turbines in the wind farm to be optimized.

[0044] Step S105: Optimize the layout plan of the wind turbines in the wind farm to be optimized based on the first initial population, the second initial population, and the optimization objective; When the optimization objective reaches the preset convergence condition, obtain the optimal layout plan of the wind turbines in the wind farm to be optimized.

[0045] In this embodiment, the preset convergence condition is that the target value of the optimization objective is the largest; Refer to the appendix Figure 5 , Figure 5 is a schematic flowchart of the main steps for optimizing the layout plan of the wind turbines in the wind farm to be optimized according to an embodiment of the present invention. As Figure 5 shown, the method for optimizing the layout plan of the wind turbines in the wind farm to be optimized includes: Step S501: Optimize based on the first initial population to obtain the optimized regular shape variables; Step S502: Optimize based on the second initial population to obtain the optimized bottom-layer coordinates of the wind turbines; Step S503: Establish the first optimization population and the second optimization population based on the optimized regular shape variables and the optimized bottom-layer coordinates of the wind turbines, and obtain the optimized layout plan of the wind turbines; Step S504: Obtain the corresponding optimized optimization objective based on the optimized layout plan of the wind turbines, and loop through steps S501 - S503 until the target value of the optimized optimization objective reaches the maximum, then end the optimization process to obtain the optimal regular shape variables, the optimal bottom-layer coordinates of the wind turbines, and the optimal layout plan of the wind turbines in the wind farm to be optimized.

[0046] In one implementation, the layout plan of the wind turbines in the wind farm to be optimized is optimized based on a genetic algorithm as the optimization algorithm, and the genetic algorithm can be the NSGA-II algorithm.

[0047] In one embodiment, the regular shape variable can be cross - mutated to obtain a new shape variable based on the constraint conditions of the coordinates of the wind turbines.

[0048] In one embodiment, the optimization method for the initial population can be as follows: set the optimization target value, and through the layout optimization process based on the optimization algorithm, and combine other frameworks to calculate the optimization target, and iteratively obtain a series of layout schemes of the wind turbines in the wind farm to be optimized that meet the requirements and have a globally better or optimal optimization target value. The specific optimization process of the initial population is as follows: (1) Population initialization. Randomly generate optimization variables (if the regular layout form is adopted, it includes two optimization variables: the random bottom - layer coordinates of the wind turbines and the regular shape variable; if the mixed layout of multiple models is adopted, the unit model vector needs to be added), and form an initial population containing N individuals. Each individual is a definite layout scheme, including N unit coordinates. The optimization variables are encoded with real numbers, and the unit coordinates are randomly generated within the optimization constraints. If the unit coordinates do not fully meet the constraints, regenerate this individual (randomly generate optimization variables) until it meets the requirements (or, if the generated regular available points do not fully meet the optimization constraints, or the number of point positions is less than the required number of units, regenerate this individual until it meets the requirements).

[0049] (2) Crossover and mutation. The present invention adopts the simulated binary crossover method for crossover operation, and performs mutation operation by adding Gaussian random numbers to the optimization variables (if the regular layout form is adopted, crossover and mutation are performed on the shape variable). In the mutation operation, there is a probability of adding random numbers of two scales, large and small, to the variables. The Gaussian standard deviation of the large - scale mutation is set to , and the Gaussian standard deviation of the small - scale mutation is set to , and the mutation probabilities and are set respectively.

[0050] (3) Combine the parent and offspring generations. Combine the parent generation with the offspring population generated by crossover and mutation to form a population of 2N individuals.

[0051] (4) Fast non - dominated sorting and crowding degree calculation. Calculate the optimization target value of each individual in the combined population. According to the optimization target value, find the non - dominated individuals in the combined population of the parent and offspring generations in turn to divide the levels. In the same level, sort the individuals from small to large according to one target to obtain the serial number i , and then calculate the crowding degree of the individuals.

[0052] (5) Select the dominant population. Select N individuals from the combined population of the parent and offspring generations using the obtained levels. If adding a certain level exactly exceeds the population size, then randomly select two individuals using the elite strategy in this level , , select the one with a larger degree of crowding until the required number of individuals is obtained.

[0053] (6) Convergence judgment. Return to (2) and repeat the steps until the number of iterations meets the requirements to obtain a convergent solution set. At this time, the convergent solution set is the optimized population.

[0054] In this embodiment, the optimization objective is the total output power of the wind farm to be optimized under the layout scheme of the wind turbines; The method for obtaining the total output power of the wind farm to be optimized includes: Obtain the wind resource information of the wind farm to be optimized, where the wind resource information includes the ambient incoming flow velocity of the wind farm to be optimized; Based on the ambient incoming flow velocity of the wind farm to be optimized and the layout scheme of the wind turbines in the wind farm to be optimized, obtain the total output power of the wind farm to be optimized according to the preset wind turbine wake model.

[0055] In this embodiment, the preset wind turbine wake model includes a two-dimensional analytical model of the wind turbine wake and an analytical model of the additional turbulence intensity of the wind turbine wake; Based on the ambient incoming flow velocity of the wind farm to be optimized and the layout scheme of the wind turbines in the wind farm to be optimized, obtaining the total output power of the wind farm to be optimized according to the preset wake model includes: Obtain the wake flow direction turbulence intensity of each wind turbine in the layout scheme based on the analytical model of the additional turbulence intensity of the wind turbine wake; Obtain the additional inflow flow direction turbulence intensity at multiple points on the wind wheel of each wind turbine based on the wake flow direction turbulence intensity of each wind turbine; Obtain the inflow velocity deficit at multiple points on the wind wheel of each wind turbine based on the two-dimensional analytical model of the wind turbine wake and the additional inflow flow direction turbulence intensity at multiple points on the wind wheel of each wind turbine; Obtain the wind speed in front of the hub of each wind turbine based on the inflow velocity deficit at multiple points on the wind wheel of each wind turbine and the ambient incoming flow velocity; Obtain the output power of each wind turbine based on the wind speed in front of the hub of each wind turbine and the preset power curve of each wind turbine; Obtain the total output power of the wind farm to be optimized based on the output power of each wind turbine.

[0056] In one implementation, the method for obtaining the total output power of the wind farm to be optimized includes: Calculate the wake flow direction turbulence intensity of each wind turbine in the layout scheme. The wake flow direction turbulence intensity of the wind turbine can be obtained according to the analytical model of the additional turbulence intensity of the wind turbine wake in the following formulas (1)-(6): (1) Wherein, is the thrust coefficient; is the atmospheric environmental turbulence intensity; D is the wind turbine rotor diameter; r is the lateral distance of the rotor axis; is the standard deviation of the Gaussian curve same as the velocity deficit model; z is the vertical height.

[0057] Flow direction function is the maximum additional turbulence intensity of the wake cross-section at each flow direction position: (2) Wherein, ; ; is the correction value considering the near wake region.

[0058] Spanwise function of formula (1) is: (3) Wherein, and take values as: (4) (5) is the vertical correction function: (6) For each wind turbine in the wind farm, according to the wake flow direction turbulence intensity at multiple points on the wind turbine rotor of the upstream wind turbine of the current wind turbine, obtain the inflow additional flow direction turbulence intensity at multiple points on the wind turbine rotor of the current wind turbine: (7) Wherein, is the inflow additional flow direction turbulence intensity at point i on the wind turbine rotor of the current th unit; is the wake flow direction turbulence intensity at point j on the wind turbine rotor of the i th unit by the th unit; is a binary variable, when and only when the current i th unit is downstream of the j th unit , in other cases ; N is the number of wind turbines in the wind farm to be optimized.

[0059] The average velocity deficit of a wind turbine is obtained according to the two-dimensional analytical model of the wind turbine wake and the additional inflow directional turbulence intensity at multiple points on the wind turbine rotor: (8) Where is the thrust coefficient; is the actual expansion rate of the wake boundary; is the rotor radius; is the standard deviation of the spanwise distribution of the velocity deficit, taken as half of the wake width, which is also used as the wake radius, is the wake width.

[0060] The inflow velocity deficit at multiple points on the rotor of the current wind turbine is obtained according to the average velocity deficit at multiple points on the rotor of the upstream wind turbine of the current wind turbine: (9) Where is the inflow velocity deficit at point i on the rotor of the th unit; is the average velocity deficit at point j on the rotor of the i th unit by the th unit; is a binary variable, which is i when and only when the j th unit is downstream of the th unit, and otherwise; N is the number of wind turbines in the wind farm to be optimized.

[0061] According to the inflow velocity deficits at multiple points on the rotor of the current wind turbine, the average value is taken to obtain the wind speed in front of the hub of the current wind turbine under the preset wind conditions: (10) Where is the wind speed in front of the hub of the i th wind turbine; is the ambient inflow wind speed; At the wind speed of and the wind direction angle of , the wind speed in front of the hub of the current wind turbine and the power curve of the current wind turbine are obtained, and the output power of the current wind turbine under the above wind conditions is obtained; among them, the wind turbine power curve includes the corresponding relationship between the wind speed in front of the hub and the output power of the wind turbine.

[0062] Obtain the total output power of the wind farm to be optimized under the current layout scheme according to the corresponding output power under the above wind conditions: (11) Wherein, is the total output power of the wind farm to be optimized under the current layout scheme; is the wind speed of , and the wind direction is wind condition; is the output power of the unit i under the wind condition of ; is the frequency of the wind condition ; is the number of wind directions; is the number of wind speed segments taken under a single wind direction; N is the number of units.

[0063] In an application scenario according to an embodiment of the present invention, the regional vertices of the wind farm to be optimized can be , , and , the coordinate dimension is meters (m), the unit model is Vestas-V80 unit, the number of units is , the wind rose diagram is as shown in Figure 6 , the unit power curve and the thrust coefficient curve are as shown in Figure 7 , the original unit positions of the wind farm to be optimized are as shown in Figure 8 . It should be noted that the regional scope of the wind farm to be optimized, the coordinates of the regional vertices, the available form of the available positions of the units in the wind farm to be optimized, the unit model and the number of units, etc. are only for illustrative purposes, and in actual applications, they can be set according to needs.

[0064] The basic parameters of the wind farm to be optimized are shown in Table 1; Table 1 Wind Farm Parameters

[0065] Select the wind conditions of multiple wind directions and multiple wind speeds, and refer to Appendix Figure 6 for the probability values under each wind direction and wind speed. Figure 6 is the wind rose diagram according to an embodiment of the present invention. In Appendix Figure 6 , the central angle of the polar coordinate histogram represents the wind direction angle, and the height of the histogram represents the wind frequency. Figure 7It is the unit power curve and thrust coefficient curve according to an embodiment of the present invention. The vertical coordinate Thrust Coefficient is the thrust coefficient, Power is the power (kW), and the horizontal coordinate Wind Speed is the wind speed (m / s). The optimization constraint conditions are the regional scope of the wind farm to be optimized and the distance between any two wind turbines must be greater than 4 times the rotor diameter D . In this application scenario, flat terrain is selected without considering the influence of complex terrain. When performing the layout optimization of wind turbines, the evolution curve during the optimization process is as shown in the appendix Figure 9 . Figure 9 It is the evolution curve of the optimization process of the dual-population wind farm layout optimization method based on regular layout according to an embodiment of the present invention. The horizontal coordinate is the number of optimization iterations (iteration), and the vertical coordinate represents the maximum value of the annual power generation of the entire wind farm under different layout schemes of the wind farm to be optimized (Annual Power / MWh). It can be seen from Figure 9 that as the number of iterations increases, the maximum value of the annual power generation of the entire wind farm gradually rises and finally converges. It should be noted that the distance between any two wind turbines must be greater than 4 times the rotor diameter D , and the selection of flat terrain are all exemplary descriptions of this application scenario and can be selected according to needs in practice

[0066] Figure 8 is the original wind turbine positions of the wind farm to be optimized according to an embodiment of the present invention Figure 10 is a schematic diagram of generating the wind turbine positions of the wind farm to be optimized by mapping the underlying coordinate positions and the available positions of the upper parallelogram rules based on random layout according to an embodiment of the present invention Figure 11 is the wind turbine positions in the wind farm to be optimized under the optimal layout scheme according to an embodiment of the present invention. It can be seen from Figure 9 and Figure 11 that the optimal wind turbine positions in the wind farm have obvious regular parallelogram layout characteristics. At the same time, since the number of available positions of the upper parallelogram rules is greater than or equal to the number of random underlying coordinate positions, the final layout of the wind farm to be optimized shows the characteristics with vacancies in the field. The annual power generation corresponding to the wind turbine positions in the wind farm to be optimized under this optimal layout scheme is 268688.43 MWh. Compared with Figure 8 the annual power generation of 261616.18 MWh corresponding to the original wind turbine positions of the wind farm to be optimized, the optimized parallelogram rule layout with vacancies has an annual power generation increase of about 2.703%, which can maximize the approximation to the actual optimal random wind farm layout while ensuring that the wind turbine positions in the wind farm to be optimized are arranged in a regular parallelogram, and significantly improve the power generation of the wind farm throughout its life cycle

[0067] It should be noted that although the above embodiments describe the various steps in a specific order, those skilled in the art can understand that in order to achieve the effects of the present invention, it is not necessary for different steps to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these adjusted solutions belong to equivalent technical solutions to the technical solutions described in the present invention, and thus will also fall within the protection scope of the present invention.

[0068] Those skilled in the art can understand that all or part of the processes in the method of the above-mentioned embodiment of the present invention can also be completed by instructing 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, the steps of the above-mentioned various method embodiments can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium can include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc., that can carry the computer program code.

[0069] Another aspect of the present invention also provides a computer-readable storage medium.

[0070] In an embodiment of a computer-readable storage medium according to the present invention, the computer-readable storage medium can be configured to store a program for executing the method of optimizing the layout of a double-population wind farm based on rule arrangement in the above method embodiment. This program can be loaded and run by a processor to implement the above method of optimizing the layout of a double-population wind farm based on rule arrangement. For the sake of convenience of description, only the parts related to the embodiments of the present invention are shown. For the specific technical details not disclosed, please refer to the method part of the embodiments of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiments of the present invention is a non-transitory computer-readable storage medium.

[0071] Another aspect of the present invention also provides an electronic device.

[0072] In an embodiment of an electronic device according to the present invention, the electronic device can include at least one processor; and a memory communicatively connected to at least one processor; wherein, a computer program is stored in the memory, and when the computer program is executed by at least one processor, the method described in any of the above embodiments is implemented. Refer to the attached Figure 12 , Figure 12Exemplarily, the memory 121 and the processor 122 are communicatively connected via a bus.

[0073] In some embodiments of the present invention, the electronic device described in the present invention may be, but is not limited to, a mobile phone, a tablet computer, a desktop computer, a laptop computer, a handheld computer, a notebook computer, a vehicle-mounted device, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), an augmented reality (AR) / virtual reality (VR) device, etc. The embodiments of the present invention do not limit this.

[0074] So far, the technical solution of the present invention has been described in conjunction with an embodiment shown in the accompanying drawings. However, those skilled in the art can easily understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A dual-population wind farm layout optimization method based on regular arrangement, characterized in that: include: Obtaining attribute information of the wind farm to be optimized; Randomly generate regular shape variables and bottom-layer coordinates of wind turbines based on the attribute information; Establishing a first initial population based on the regular shape variables, wherein the first initial population includes a plurality of first population individuals, each of which is a set of regular shape variables; Establishing a second initial population based on the bottom-level coordinates of the wind turbine generator set, wherein the second initial population includes a plurality of second population individuals, each of which is a group of bottom-level coordinate vectors of the wind turbine generator set in the wind farm to be optimized; Based on the shape variables of the rule and the underlying coordinate vectors, obtaining an arrangement scheme of wind turbines in the wind farm to be optimized; Based on the first initial population, the second initial population and the optimization target, optimizing the arrangement scheme of the wind turbines in the wind farm to be optimized; When the optimization target reaches a preset convergence condition, an optimal arrangement scheme of wind turbines in the wind farm to be optimized is obtained.

2. The method for optimizing the layout of a dual-population wind farm based on rule arrangement according to claim 1, characterized in that: The attribute information includes the area scope and the number of wind turbines of the wind farm to be optimized; The shape variables and bottom-level coordinates of the wind turbine generator set randomly generated based on the attribute information include: Setting the distance between any two wind turbines in the wind farm to be optimized; Randomly generate regular shape variables based on the regional scope of the wind farm to be optimized; Randomly generate bottom-level coordinates of wind turbines based on the regional scope of the wind farm to be optimized, the number of wind turbines, and the distance between any two wind turbines in the wind farm to be optimized; The regular shape variables include one of the regular shape variables of parallelogram, regular shape variables of row arrangement and regular shape variables of ring, and the distance between any two wind turbines in the wind farm to be optimized is greater than the diameter of the wind rotor. times, is a positive integer greater than 1.

3. The method for optimizing the layout of a dual-population wind farm based on rule arrangement according to claim 2, characterized in that: The shape variables randomly generated based on the regional scope of the wind farm to be optimized include: Based on the regional scope of the wind farm to be optimized, randomly selecting the coordinates of the global center point; Based on a uniform probability distribution, a plurality of shape parameters are randomly selected within a plurality of preset shape parameter ranges, wherein the type of the shape parameters is determined based on the shape type of the regular shape variable; The shape variables of the rule are determined based on the global center point coordinates and the plurality of shape parameters.

4. The method for optimizing the layout of a dual-population wind farm based on rule arrangement according to claim 2, characterized in that: The obtaining of the arrangement scheme of the wind turbines in the wind farm to be optimized based on the shape variables of the rule and the underlying coordinate vector comprises: Obtaining available point vectors of wind turbines in the wind farm to be optimized based on the shape variables of the rule, wherein the number of available points is greater than or equal to the number of wind turbines; Based on the available point vectors and the underlying coordinate vectors, an arrangement scheme of wind turbines in the wind farm to be optimized is obtained.

5. The method for optimizing the layout of a dual-population wind farm based on rule arrangement according to claim 4 is characterized in that: The obtaining, based on the available point vector and the bottom layer coordinate vector, an arrangement scheme of wind turbines in the wind farm to be optimized comprises: S1. Select a coordinate point in the bottom coordinate vector, obtain the distance between the coordinate point and each available point in the available point vector, extract the available point closest to the coordinate point as the coordinate point of the wind turbine in the wind farm to be optimized, and remove the available point closest to the coordinate point in the available point vector; S2. Traverse all coordinate points in the underlying coordinate vector and repeat step S1 until the number of coordinate points of the wind turbine sets in the wind farm to be optimized is consistent with the number of wind turbine sets, and determine the arrangement scheme of the wind turbine sets in the wind farm to be optimized based on the obtained coordinate points of the wind turbine sets in the wind farm to be optimized.

6. The method for optimizing the layout of a dual-population wind farm based on regular arrangement according to claim 5, characterized in that: The preset convergence condition is that the target value of the optimization target is maximum; The method further comprises: S3, optimizing based on the first initial population to obtain optimized regular shape variables; S4, optimizing based on the second initial population to obtain optimized bottom-level coordinates of the wind turbine generator set; S5, establishing a first optimized population and a second optimized population based on the optimized regular shape variables and the optimized bottom-layer coordinates of the wind turbines, and obtaining an optimized arrangement scheme of the wind turbines; S6. Based on the optimized arrangement plan of the wind turbines, the corresponding optimized optimization target is obtained, and steps S3 to S5 are executed in a loop until the target value of the optimized optimization target reaches the maximum. Then, the optimization process is terminated to obtain the optimal regular shape variables, the optimal bottom-level coordinates of the wind turbines, and the optimal arrangement plan of the wind turbines in the wind farm to be optimized.

7. The method for optimizing the layout of a dual-population wind farm based on rule arrangement according to claim 6, characterized in that: The optimization target is the total output power of the wind farm to be optimized under the arrangement scheme of the wind turbines; The method for obtaining the total output power of the wind farm to be optimized comprises: Acquiring wind resource information of the wind farm to be optimized, wherein the wind resource information includes the ambient incoming wind speed of the wind farm to be optimized; Based on the ambient incoming wind speed of the wind farm to be optimized and the arrangement scheme of the wind turbines in the wind farm to be optimized, the total output power of the wind farm to be optimized is obtained according to a preset wake model of the wind turbines.

8. The method for optimizing the layout of a dual-population wind farm based on rule arrangement according to claim 7, characterized in that: The preset wind turbine wake model includes a two-dimensional analytical model of the wind turbine wake and an additional turbulence intensity analytical model of the wind turbine wake; The step of obtaining the total output power of the wind farm to be optimized according to a preset wake model based on the ambient incoming wind speed of the wind farm to be optimized and the arrangement scheme of the wind turbines in the wind farm to be optimized comprises: Based on the analytical model of additional turbulence intensity of the wind turbine wake, the turbulence intensity of the wake of each wind turbine in the arrangement scheme is obtained; Based on the wake flow turbulence intensity of each wind turbine group, obtaining the additional flow turbulence intensity of the inflow at multiple points on the wind rotor of each wind turbine group; Obtaining the inflow velocity loss at multiple points on the wind rotor of each wind turbine based on the two-dimensional analytical model of the wind turbine wake and the additional flow turbulence intensity of the inflow at multiple points on the wind rotor of each wind turbine; Based on the inflow velocity loss at multiple points on the wind rotor of each wind turbine and the ambient incoming wind speed, the wind speed in front of the hub of each wind turbine is obtained; Based on the wind speed in front of the hub of each wind turbine set and a preset power curve of each wind turbine set, the output power of each wind turbine set is obtained; Based on the output power of each wind turbine generator set, the total output power of the wind farm to be optimized is obtained.

9. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method for optimizing the layout of a dual-population wind farm based on rule arrangement as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the dual-population wind farm layout optimization method based on rule arrangement according to any one of claims 1 to 8.

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