A wind power base site division method and device and readable storage medium thereof

Abstract

The application discloses a wind power base site division method and device and a readable storage medium. The wind power base comprises multiple wind power plants. The division method comprises the following steps: obtaining wind power base boundary information, wind resource information and generation metadata; generating a Voronoi diagram according to the wind power base boundary information and the generation metadata to obtain wind power plant boundary information; obtaining the number of wind turbines of the wind power plant according to the wind resource information and the wind power plant boundary information; optimizing the number of wind turbines and the wind power plant boundary information by using a six-parameter layout construction method to obtain the optimized layout of the wind turbines of the wind power plant and the economic benefit of the wind power base; and optimizing the generation metadata by using a genetic algorithm with the maximum economic benefit of the wind power base as the optimization target to obtain a wind power plant layout scheme. The purpose of the application is to take the maximum economic benefit of the wind power base as the optimization target and realize the site division and layout of the wind power plant.

Description

Technical Field

[0001] This application relates to the field of wind power generation technology, and in particular to a method and apparatus for dividing wind power base sites, and a readable storage medium. Background Technology

[0002] As a renewable and clean energy source, wind energy development and utilization has become an important trend. To achieve efficient and concentrated utilization of wind energy resources, large-scale wind power bases are typically planned in areas with abundant wind resources and suitable geographical conditions. A large-scale wind power base usually consists of multiple geographically adjacent and independently located wind farms.

[0003] Currently, the site allocation for wind farms typically involves first dividing the land into zones, and then optimizing them. Specifically, technicians use geospatial data and simulation techniques to divide the wind farm sites; then, for each wind farm, the design is optimized with the goal of maximizing its power generation. While this approach can ensure that the economic benefits of an individual wind farm are maximized, it lacks a holistic consideration of the wind farm site allocation from the perspective of the entire wind power base. Summary of the Invention

[0004] The purpose of this application is to provide a method and apparatus for site division of wind power bases, as well as a readable storage medium, with the goal of maximizing the economic benefits of wind power bases, to achieve site division and layout of wind farms.

[0005] To achieve the above objectives, this application employs the following technical solution:

[0006] Firstly, this application provides a method for dividing wind power base sites, wherein the wind power base includes multiple wind farms, and the method includes:

[0007] Acquire wind power base boundary information, wind resource information, and generate metadata;

[0008] A Voronoi diagram is generated based on the wind farm boundary information and generated metadata to obtain the wind farm boundary information;

[0009] The number of wind turbines in the wind farm is obtained based on wind resource information and wind farm boundary information;

[0010] The six-parameter layout construction method is used to optimize the wind turbine layout based on the number of wind turbines and the boundary information of the wind farm, so as to obtain the economic benefits of the wind farm.

[0011] With the goal of maximizing the economic benefits of wind power bases, a genetic algorithm is used to optimize the generated metadata to obtain a wind farm layout scheme.

[0012] Furthermore, based on wind resource information and wind farm boundary information, the number of wind turbines in the wind farm is determined to include:

[0013] By fitting the Larsen wake model, the relationships between volume density and average wind speed and between specific power and volume density are obtained.

[0014] The capacity density of the wind farm is calculated based on the relationship between the average wind speed and the capacity density-average wind speed of the wind resource information.

[0015] Based on the capacity density and wind farm boundary information, the planarable capacity of the wind farm is obtained;

[0016] The target specific power is obtained by calculating based on the capacity density and the specific power-capacity density relationship.

[0017] Select the fan model based on the target specific power;

[0018] The number of wind turbines in a wind farm is determined based on the planned capacity and the rated power of the turbine model.

[0019] Furthermore, a six-parameter layout construction method is used to optimize the wind turbine layout based on the number of wind turbines and the boundary information of the wind farm, thereby obtaining the optimal wind turbine layout of the wind farm and the economic benefits of the wind power base, including:

[0020] Based on the number of wind turbines and the boundary information of the wind farm, six parameter data are obtained;

[0021] Based on the wind farm boundary information, the grid area range of the wind farm is obtained;

[0022] Based on the grid area, a six-parameter layout construction method is used to perform calculations based on the six-parameter data to obtain the optimal layout of the wind turbine;

[0023] Based on the optimized layout of wind turbines and wind resource information, the economic benefits of the wind power base are obtained;

[0024] The six parameters include the azimuth angle α of the long side, the angle β between the long side and the short side, the length of the long side unit d1, the length of the short side unit d2, the relative offset of the long side Δd1, and the relative offset of the short side Δd2.

[0025] Furthermore, based on the number of wind turbines and the boundary information of the wind farm, the six-parameter data are obtained, including:

[0026] Based on the number of wind turbines and the boundary information of the wind farm, determine the length d1 of the long side unit and the length d2 of the short side unit;

[0027] Based on the length d1 of the long side element, the relative offset Δd1 of the long side is obtained;

[0028] Based on the length d2 of the short side element, the relative offset Δd2 of the short side is obtained.

[0029] Furthermore, with maximizing the economic benefits of wind power bases as the optimization objective, a genetic algorithm is used to optimize the generated metadata, resulting in wind farm layout schemes including:

[0030] Determine whether the number of generated metadata is a fixed value;

[0031] When the number of generators is a fixed value, a genetic algorithm is used to optimize the generator coordinates of the generated metadata; or

[0032] When the number of generators is not a fixed value, a genetic algorithm is used to optimize the number of generators and their coordinates.

[0033] Furthermore, a Voronoi diagram is generated based on the wind farm boundary information and generated metadata to obtain the wind farm boundary information, including:

[0034] A Voronoi diagram is generated based on the wind power base boundary information and generated metadata.

[0035] An expansion operation is performed on the Voronoi diagram to obtain a wake recovery Voronoi diagram, so that a wake recovery zone is formed between the boundaries of two adjacent wind farms;

[0036] The wind farm boundary information is obtained by reconstructing the Voronoi diagram from the wake.

[0037] Secondly, this application also provides a wind power base site delineation device, comprising:

[0038] The data acquisition unit is used to acquire wind power base boundary information, wind resource information, and generate metadata.

[0039] The wind farm boundary information processing unit is used to generate a Voronoi diagram based on the wind farm base boundary information and generated metadata to obtain the wind farm boundary information.

[0040] The wind turbine count calculation unit is used to obtain the number of wind turbines in a wind farm based on wind resource information and wind farm boundary information.

[0041] The optimized layout unit is used to optimize the layout of the wind farm based on the number of wind turbines and the boundary information of the wind farm using the six-parameter layout construction method, so as to obtain the optimized layout of the wind turbines and the economic benefits of the wind power base.

[0042] The genetic algorithm unit is used to optimize the generated metadata with the goal of maximizing the economic benefits of the wind power base, and obtain the wind farm layout scheme.

[0043] Thirdly, this application also provides a readable storage medium storing a computer-executable program, which, when executed, can implement the wind power base site allocation method provided in the first aspect.

[0044] Fourthly, this application also provides an electronic device, including a memory storing computer-executable instructions and a processor, which, when executed by the processor, causes the device to perform the wind power base site allocation method provided in the first aspect.

[0045] Compared with the prior art, the beneficial effects achieved by this application are as follows:

[0046] 1. By using the generated metadata as an optimization variable and taking the maximization of the overall economic benefits of the wind power base as the optimization objective, a genetic algorithm was used for optimization, achieving global optimization from the overall perspective of the wind power base. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart of a wind power base site division method provided in an embodiment of this application.

[0049] Figure 2 This is an example diagram of the fitting curve of the land-based sum of squares superposition model provided in the embodiments of this application.

[0050] Figure 3 This is an example diagram of the fitting curve of the superposition model of land energy conservation provided in the embodiments of this application.

[0051] Figure 4 This is an example diagram of the fitting curve of the marine square sum superposition model provided in the embodiments of this application.

[0052] Figure 5 This is an example diagram of the onshore power-capacity density fitting curve provided in the embodiments of this application.

[0053] Figure 6 This is an example diagram of the marine specific power-capacity density fitting curve provided in the embodiments of this application.

[0054] Figure 7 This is an example diagram of the first Voronoi diagram provided in the embodiments of this application.

[0055] Figure 8This is an example diagram of the second Voronoi diagram provided in the embodiments of this application.

[0056] Figure 9 These are example diagrams of multiple Voronoi diagrams provided in the embodiments of this application.

[0057] Figure 10 This is an example diagram of the parallelogram division of the wind farm grid area provided in the embodiments of this application.

[0058] Figure 11 This is an example diagram of the six-parameter layout construction method provided in the embodiments of this application.

[0059] Figure 12 This is an example diagram of individual i and individual j provided in the embodiments of this application.

[0060] Figure 13 This is an example diagram of cross-operation provided in the embodiments of this application.

[0061] Figure 14 This is an example diagram of the mutation operation provided in the embodiments of this application.

[0062] Figure 15 This is an example diagram of individual value-added services provided in the embodiments of this application.

[0063] Figure 16 This is an example diagram of viral replication provided in the embodiments of this application. Detailed Implementation

[0064] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this application or its application or use.

[0065] Example 1:

[0066] This application provides a method for dividing the site of a wind power base, wherein the wind power base includes multiple wind farms.

[0067] like Figure 1 As shown, Figure 1 This is a flowchart illustrating a wind power base site allocation method according to Embodiment 1 of this application. This flowchart merely shows the logical sequence of the method described in this embodiment; however, in other possible embodiments of the invention, different methods may be used, provided they do not conflict. Figure 1 Complete the steps shown or described in the order indicated.

[0068] The partitioning method provided in this embodiment includes:

[0069] S100: Acquire wind power base boundary information, wind resource information, and generate metadata;

[0070] Specifically, the boundary information of a wind power base is a closed polygon consisting of a set of continuous latitude and longitude coordinates. The boundary information of a wind power base can be obtained directly through surveying or mapping or a GIS database.

[0071] Specifically, wind resource information includes average wind speed. Average wind speeds within each area of ​​the wind farm can be collected through on-site wind measurements.

[0072] S200: Generate a Voronoi diagram based on the wind farm boundary information and generated metadata to obtain the wind farm boundary information;

[0073] Specifically, a Voronoi diagram is used to divide the wind farms within the wind power base. A Voronoi diagram is a spatial partitioning method that divides a given plane into several regions based on generators. Each region contains all points that are closer to generators within that region than to other generators. Generator metadata includes generator coordinates and the number of generators. The number of generators is a preset number that can be set by technicians based on various factors such as wind resources, topography, and administrative jurisdiction of the wind power base. In this embodiment, the number of generators is set to three. Then, a corresponding number of random generators are randomly generated within the wind power base based on the number of generators, and the coordinates of these generators are obtained. A Voronoi diagram is generated using the wind power base boundary information and the generator metadata, thereby achieving the division of wind farms within the wind power base.

[0074] S300: Based on wind resource information and wind farm boundary information, the number of wind turbines in the wind farm is obtained;

[0075] S400: The six-parameter layout construction method is used to optimize the wind turbine layout based on the number of wind turbines and the boundary information of the wind farm, so as to obtain the economic benefits of the wind farm.

[0076] S500: With the goal of maximizing the economic benefits of wind power bases, a genetic algorithm is used to optimize the generated metadata to obtain a wind farm layout scheme.

[0077] Furthermore, the S300 includes:

[0078] By fitting the Larsen wake model, the relationships between volume density and average wind speed and between specific power and volume density are obtained.

[0079] Specifically, wind farm capacity density measures the ability of a wind farm to install wind turbine generators on a unit area of ​​land. It is defined as the ratio of the wind farm's rated capacity to its land area, and the unit of capacity density is megawatts per square kilometer (MW / km²). 2 The formula for defining capacity density is shown below:

[0080]

[0081] In the formula, For capacity density, For rated capacity, This refers to the area occupied.

[0082] Specifically, when wind blows across the wind turbine blades, some of the wind's kinetic energy is converted into electrical energy, resulting in a decrease in wind speed downstream of the turbine and the generation of turbulent structures, i.e., the turbine's wake. If a downstream turbine is located within the wake region of an upstream turbine, the wind speed input to the downstream turbine will decrease, leading to a reduction in power generation. The higher the capacity density and the closer the turbines are arranged, the more significant the impact of the wake generated by the upstream turbine on the downstream turbine, and the greater the wake loss. To quantify the impact of wake loss, this embodiment uses the Larsen wake model to calculate the wake effect of the wind farm.

[0083] Specifically, based on the calculation results of the Larsen wake model, the trend of wake loss in a wind farm as a function of capacity density can be characterized, and capacity density schemes with large wake losses can be filtered out. For onshore wind farms, a wake loss threshold is set as the screening criterion. In this embodiment, the wake loss threshold is set to 10%, meaning that capacity densities with wake losses greater than 10% will be deleted. When calculating the total wake loss of the entire wind farm, since the wakes of multiple wind turbines overlap, a wake superposition model is used to synthesize the wake effects of individual wind turbines into a unified effect. In this embodiment, a sum-of-squares superposition model and an energy conservation superposition model are used respectively. By calculating different combinations of average wind speed and capacity density, data points with wake losses exceeding 10% are deleted.

[0084] Specifically, such as Figure 2 and Figure 3 As shown, data points are plotted in a two-dimensional coordinate system, where the horizontal axis represents average wind speed and the vertical axis represents volume density. For data points with different average wind speeds and volume densities, the horizontal axis represents average wind speed and the vertical axis represents volume density. The wake loss for each data point is calculated using the Larsen wake model, and the size of the data point represents its wake loss.

[0085] Specifically, such as Figure 2 and Figure 3As shown, the distribution of data points exhibits a linear trend. This indicates that, under the condition that the wake loss is less than 10%, there is a linear relationship between the wind farm's capacity density and the average wind speed in the region. By fitting the data points using a linear regression method, a mathematical expression representing the data points is obtained, which is the onshore capacity density-average wind speed relationship. Two fitting expressions are obtained in this embodiment as follows:

[0086] The fitting equation for the sum of squares on land:

[0087]

[0088] The superposition and fitting equation for energy conservation on land:

[0089]

[0090] In the formula, x is the average wind speed and y is the bulk density.

[0091] It should be noted that, in order to eliminate the influence of physical units on the fitting, both x and y are dimensionless when substituted into the above fitting formula for calculation. This means that in practical applications, the average wind speed needs to be divided by a baseline value of the average wind speed to obtain the dimensionless x, which is then substituted into the formula for calculation. Finally, the calculated dimensionless y is multiplied by the baseline value of the volume density to obtain the volume density.

[0092] Specifically, for offshore wind farms, offshore wind resources are more abundant and stable, and are not obstructed by terrain. Therefore, offshore wind farms tend to use larger capacity turbines and more densely arranged turbines. In this embodiment, the wake effect of offshore wind farms is calculated using the Larsen wake model, but only the sum-of-squares superposition model is used for the wake superposition model. Since the capacity density of offshore wind farms is higher than that of onshore wind farms, and the turbine spacing is relatively closer, this embodiment sets the wake loss threshold for offshore wind farms to 12%. In the specific calculation process, for data points with different average wind speeds and capacity densities, the wake loss of each data point is calculated using the Larsen model and the sum-of-squares superposition model, and then data points whose wake loss exceeds the wake loss threshold are deleted.

[0093] Specifically, such as Figure 4 As shown, a fitting analysis is performed on the remaining data points. By plotting the data points in a two-dimensional coordinate system and fitting the data points, a mathematical expression is obtained. This mathematical expression is the relationship between sea volume density and average wind speed:

[0094]

[0095] In the formula, x is the average wind speed and y is the bulk density.

[0096] Specifically, the specific power of a wind turbine is the relationship between its rated power and the rotor diameter. This is achieved through statistical analysis of actual operating data from existing wind farms, such as... Figure 5 As shown, this application discovers an increasing relationship between the specific power of a wind turbine and the capacity density of a wind farm. For onshore wind farms, the fitted specific power-capacity density relationship is:

[0097]

[0098] In the formula, x represents the volumetric density and y represents the specific power. It should be noted that both x and y are dimensionless numbers when substituted into the calculation.

[0099] Specifically, such as Figure 6 As shown, for offshore wind farms, the fitted specific power-capacity density relationship is:

[0100]

[0101] In the formula, x represents the volumetric density and y represents the specific power. It should be noted that both x and y are dimensionless numbers when substituted into the calculation.

[0102] The capacity density of the wind farm is calculated based on the relationship between the average wind speed and the capacity density-average wind speed of the wind resource information.

[0103] Preferably, the capacity density-average wind speed relationship is selected based on the type of wind farm, whether it is onshore or offshore. For onshore wind farms, the capacity density-average wind speed relationship can be either a sum-of-squares superposition fitting relationship or an energy conservation superposition fitting relationship. In this embodiment, the sum-of-squares superposition fitting relationship for onshore wind farms is selected for calculation.

[0104] Based on the capacity density and wind farm boundary information, the planarable capacity of the wind farm is obtained;

[0105] Specifically, the boundary information of a wind farm is a closed polygon formed by a set of continuous latitude and longitude coordinates. The land area of ​​the wind farm can be obtained from this boundary information. Substituting the capacity density and land area into the capacity density definition formula yields the planarable capacity of the wind farm.

[0106] The target specific power is obtained by calculating based on the capacity density and the specific power-capacity density relationship.

[0107] Preferably, the specific power-capacity density relationship for onshore or offshore wind farms is selected for calculation based on the type of wind farm.

[0108] Select the fan model based on the target specific power;

[0109] Specifically, technicians calculate the actual specific power of wind turbines based on their rated power and rotor diameter. Then, they compare the actual specific power of the wind turbine with the target specific power and select one or more turbines that are closest to it as the recommended model.

[0110] The number of wind turbines in a wind farm is determined based on the planned capacity and the rated power of the turbine model.

[0111] Specifically, the number of wind turbines required for the wind farm is obtained by dividing the planarable capacity by the rated power of the turbine model. Since the number of turbines must be an integer, the calculated number is rounded down.

[0112] Furthermore, the S400 includes:

[0113] Based on the number of wind turbines and the boundary information of the wind farm, six parameter data are obtained;

[0114] Specifically, the six-parameter data includes: the azimuth angle α of the long side, the angle β between the long side and the short side, the length of the long side element d1, the length of the short side element d2, the relative offset of the long side Δd1, and the relative offset of the short side Δd2. The geometric relationship of the six-parameter data is as follows: Figure 11 As shown, the azimuth angle α of the long side is the angle between the row direction of the wind turbine and the due east direction; the angle β between the long side and the short side is the angle between the row direction and the column direction of the wind turbine; the length d1 of the long side element is the row spacing of the wind turbine; the length d2 of the short side element is the column spacing of the wind turbine; and the relative offsets Δd1 and Δd2 of the long side and short side respectively represent the offsets of the entire parallelogram along the row and column directions within the grid area. By using the relative offsets Δd1 and Δd2 of the long side and short side, the entire array representing the wind turbine is translated within the grid area, ensuring that the first row of turbines in the prevailing wind direction is as close as possible to the upwind boundary of the wind farm. This allows the wind farm to fully utilize wind energy and avoids wasting wind energy resources in the boundary area due to improper layout. A wind turbine is a unit composed of multiple wind turbines.

[0115] Based on the wind farm boundary information, the grid area range of the wind farm is obtained;

[0116] Based on the grid area, a six-parameter layout construction method is used to perform calculations based on the six-parameter data to obtain the optimal layout of the wind turbine;

[0117] Specifically, a six-parameter layout construction method is used to arrange the wind turbines within the wind farm. A grid area is generated based on the wind farm boundary information. Within the grid area, the specific locations of the wind turbines are arranged in parallelograms. After deleting the wind turbine locations in sensitive areas of the wind farm, the remaining locations are the optional wind turbine locations, forming one wind turbine layout.

[0118] Specifically, based on the set six-parameter data, the grid area is divided into parallelograms to form a point matrix covering the entire grid area, with each vertex representing a wind turbine location. Finally, the point matrix is ​​spatially overlaid with the actual boundary of the wind farm and its sensitive areas, and all points falling outside the boundary or within the sensitive areas are deleted. The resulting wind turbine locations yield the optimized wind turbine layout for the wind farm. Figure 10 As shown in the figure, the irregular closed curves represent the boundaries of the wind farm. The lattice inside is the wind turbine location generated by the six-parameter layout construction method, while the blank areas are those that were removed because they are located in sensitive areas.

[0119] Based on the optimized layout of wind turbines and wind resource information, the economic benefits of the wind power base are obtained;

[0120] Specifically, by utilizing wind resource information, optimized wind turbine layout, and the turbine model and its rated power, the power generation can be calculated. After completing the calculations for all wind farms within the wind power base, the power generation of all wind farms is summed to obtain the total power generation of the wind power base. Finally, based on the power generation and costs of the wind power base, the economic benefits of the wind power base are obtained.

[0121] Furthermore, based on the number of wind turbines and the boundary information of the wind farm, the six-parameter data are obtained, including:

[0122] Based on the number of wind turbines and the boundary information of the wind farm, determine the length d1 of the long side unit and the length d2 of the short side unit;

[0123] Based on the length d1 of the long side element, the relative offset Δd1 of the long side is obtained;

[0124] Based on the length d2 of the short side element, the relative offset Δd2 of the short side is obtained.

[0125] Preferably, based on the prevailing wind direction, to ensure that the wake effect of the wind turbine is minimized in the prevailing wind direction, the azimuth angle α of the long side and the angle β between the long side and the short side are set between 60° and 120°.

[0126] Preferably, the lengths of the long side unit d1 and the short side unit d2 are determined based on the wind farm boundary information and the number of wind turbines to ensure that the distance between wind turbines is maximized within a limited range; the ranges of the relative offset of the long side Δd1 and the relative offset of the short side Δd2 are determined according to the lengths of d1 and d2, where Δd1 is one-fifth of d1 and Δd2 is one-fifth of d2.

[0127] Furthermore, the S500 includes:

[0128] Determine whether the number of generated metadata is a fixed value;

[0129] When the number of generators is a fixed value, a genetic algorithm is used to optimize the generator coordinates of the generator data; or

[0130] When the number of generators is not a fixed value, a genetic algorithm is used to optimize the number of generators and the generator coordinates.

[0131] Specifically, with maximizing the economic benefit of the wind power base as the optimization objective, this embodiment iteratively optimizes the generator data through a genetic algorithm. Specifically, one individual in the genetic algorithm is regarded as a set of generator coordinates, all coordinate positions of one set of generators are taken as a genetic sequence recorded in one individual of the genetic algorithm, and the horizontal and vertical coordinates of each generator are genetic sequence fragments, as Figure 12 shown, Figure 12 are genetic sequence fragments of individual i and individual j, each individual records the genetic sequence of generator coordinates: x0, y0, x1, y1, x3, y3, wherein x0 and y0 represent the coordinates of the first generator among the generator coordinates, x1 and y1 represent the coordinates of the second generator, and x3 and y3 represent the coordinates of the third generator.

[0132] Specifically, the genetic algorithm comprises the following steps:

[0133] (1) set the evolution generation counter g = 0, set the maximum evolution algebra G, and randomly generate NP individuals as the initial population P(0);

[0134] (2) calculate the fitness of each individual in the population P(t);

[0135] (3) apply the selection operator to the population, and select excellent individuals to inherit to the next generation population according to the fitness of individuals by means of roulette wheel selection and elite retention selection;

[0136] (4) apply the crossover operator to the population, and for the selected pairs of parent individuals, exchange part of their gene fragments with a set crossover probability to generate new individuals;

[0137] (5) apply the mutation operator to the population, and for the selected individuals, change one or some gene values on their chromosomes to other alleles with a set mutation probability to generate new individuals. After the population P(t) goes through selection, crossover and mutation operations, the next generation population P(t+1) is obtained, the fitness value is calculated, and sorting is performed according to the fitness value to prepare for the next genetic operation;

[0138] (6) if g < G, then g = g + 1, go to step 2; otherwise, the individual with the maximum fitness obtained in the evolution process is output as the optimal solution, and the calculation is terminated.

[0139] Specifically, as Figure 13 shown, Figure 13 This is an example diagram of crossover operation. Crossover operation involves exchanging partial generator coordinates between two parent individuals. For example, the corresponding generator coordinates in parent individual i are exchanged with the corresponding generator coordinates in parent individual j, resulting in two child individuals that combine the advantages of both. Figure 13 For the two new offspring individuals after the exchange; such as Figure 14 As shown, Figure 14 This is an example diagram of the mutation operation. The mutation operation modifies a generator in an individual, for example, randomly changing the coordinates xi0 and yi0 of individual j to xj0 and yj0.

[0140] Specifically, the iterative process of the above genetic algorithm is performed with the number of generators as a fixed value. In order to find the optimal solution for wind farm site allocation under different numbers of generators, it is necessary to consider both the number of wind farms and the site allocation layout.

[0141] Specifically, the iteration of the number of generators can be achieved through individual self-renewal. Maximum and minimum limits can be set for the number of generators, i.e., the longest and shortest lengths of the genetic sequence. Within these lengths, a set of genetic sequences can be added or deleted from both sides of the individual to add or delete a generator coordinate, thereby changing the number of wind farms. Figure 15 As shown, Figure 15 In an example diagram of individual value enhancement, adding generator coordinates xk0 and yk0 to the right of individual i results in a new individual with four generators; alternatively, through viral replication, individuals can extend their genetic sequence length by directly copying partial generator coordinates from other individuals. Figure 16 As shown, Figure 16 The diagram illustrates viral replication. Individual i replicates the generator coordinates xj3 and yj3 from individual j, resulting in a new individual with four generators.

[0142] Furthermore, a Voronoi diagram is generated based on the wind farm boundary information and generated metadata to obtain the wind farm boundary information, including:

[0143] A Voronoi diagram is generated based on the wind power base boundary information and generated metadata.

[0144] An expansion operation is performed on the Voronoi diagram to obtain a wake recovery Voronoi diagram, so that a wake recovery zone is formed between the boundaries of two adjacent wind farms;

[0145] The wind farm boundary information is obtained by reconstructing the Voronoi diagram from the wake.

[0146] Specifically, to avoid wake effects between wind turbines, wake recovery zones need to be established between wind farms. Based on the Voronoi diagram, for each edge of each polygon, it is translated outward by a distance *d*, forming a parallel line parallel to the original edge. For each vertex of the polygon, an arc is drawn with that vertex as the center and *d* as the radius, connecting the parallel lines created by the translation of adjacent edges. Ultimately, the new parallel lines and the new arcs form a new polygon with a larger area than the original polygon. These new polygons overlap to form an overlapping region, the width of which is the sum of the two unilateral expansion distances *d*. This overlapping region is the wake recovery zone between wind farms. Figure 7 As shown, Figure 7 This is an example of a Voronoi diagram, in which the three Voronoi polygons represent the boundaries of three wind farms, and the overlapping area between the wind farms is the wake recovery zone.

[0147] Specifically, such as Figure 8 and Figure 9 As shown, Figure 8 To determine the site division of a different wind farm, different Voronoi diagrams are obtained by iteratively generating the coordinates of the generators. However, when the number of generators is not a fixed value, the wind farm division also differs, such as... Figure 9 As shown, Figure 9 The system includes four Voronoi diagrams: (a), (b), (c), and (d). In (a), there are 2 generators; in (b), there are 3 generators; in (c), there are 4 generators; and in (d), there are 5 generators. The wind power base is divided into 2, 3, 4, and 5 regions based on the number of generators.

[0148] Example 2:

[0149] This embodiment provides a wind power base site delineation device, including:

[0150] The data acquisition unit is used to acquire wind power base boundary information, wind resource information, and generate metadata.

[0151] The wind farm boundary information processing unit is used to generate a Voronoi diagram based on the wind farm base boundary information and generated metadata to obtain the wind farm boundary information.

[0152] The wind turbine count calculation unit is used to obtain the number of wind turbines in a wind farm based on wind resource information and wind farm boundary information.

[0153] The optimized layout unit is used to optimize the layout of the wind farm based on the number of wind turbines and the boundary information of the wind farm using the six-parameter layout construction method, so as to obtain the optimized layout of the wind turbines and the economic benefits of the wind power base.

[0154] The genetic algorithm unit is used to optimize the generated metadata with the goal of maximizing the economic benefits of the wind power base, and obtain the wind farm layout scheme.

[0155] Specifically, the wind power base site division device provided in this embodiment includes an acquisition unit, a first calculation unit, a second calculation unit, an optimized layout unit, a third calculation unit, and a genetic algorithm unit, which work together to implement the wind power base site division method as described in Embodiment 1 above, and will not be repeated here.

[0156] Example 3:

[0157] This embodiment provides a readable storage medium storing a computer-executable program. When the program is executed, it can implement the wind power base site allocation method as described in Embodiment 1.

[0158] The aforementioned readable storage medium may be any combination of one or more computer-readable media. A readable storage medium may be a computer-readable signal medium or a computer-readable storage medium. A readable storage medium may be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0159] Example 4:

[0160] This embodiment provides an electronic device, including a memory storing computer-executable instructions and a processor. When the computer-executable instructions are executed by the processor, the device performs the wind power base site allocation method as described in Embodiment 1.

[0161] Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0162] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for dividing wind power base sites, characterized in that, The wind power base includes multiple wind farms, and the division method includes: Acquire wind power base boundary information, wind resource information, and generate metadata; A Voronoi diagram is generated based on the wind farm boundary information and the generated metadata to obtain the wind farm boundary information. The number of wind turbines in the wind farm is obtained based on the wind resource information and the wind farm boundary information. The six-parameter layout construction method is used to optimize the wind turbine layout of the wind farm based on the number of wind turbines and the boundary information of the wind farm, so as to obtain the economic benefits of the wind power base. With the goal of maximizing the economic benefits of the wind power base, a genetic algorithm is used to optimize the generated metadata to obtain a wind farm layout scheme.

2. The wind power base site delineation method according to claim 1, characterized in that, The step of determining the number of wind turbines in the wind farm based on the wind resource information and the wind farm boundary information includes: By fitting the Larsen wake model, the relationships between volume density and average wind speed and between specific power and volume density are obtained. The capacity density of the wind farm is calculated based on the average wind speed of the wind resource information and the relationship between capacity density and average wind speed. Based on the capacity density and the wind farm boundary information, the planarable capacity of the wind farm is obtained; The target specific power is obtained by calculating based on the capacity density and the specific power-capacity density relationship. Select the fan model based on the target specific power; The number of wind turbines in the wind farm is obtained based on the planned capacity and the rated power of the wind turbine model.

3. The wind power base site division method according to claim 1, characterized in that, The six-parameter layout construction method optimizes the wind turbine layout based on the number of wind turbines and the boundary information of the wind farm to obtain the optimized wind turbine layout of the wind farm and the economic benefits of the wind power base, including: Based on the number of wind turbines and the boundary information of the wind farm, six parameter data are obtained; Based on the wind farm boundary information, the grid area range of the wind farm is obtained; Based on the grid area range, the six-parameter layout construction method is used to perform calculations based on the six-parameter data to obtain the optimized wind turbine layout; Based on the optimized layout of the wind turbines and the wind resource information, the economic benefits of the wind power base are obtained; The six parameters include the azimuth angle α of the long side, the angle β between the long side and the short side, the length of the long side unit d1, the length of the short side unit d2, the relative offset of the long side Δd1, and the relative offset of the short side Δd2.

4. The wind power base site division method according to claim 3, characterized in that, The six-parameter data obtained based on the number of wind turbines and the wind farm boundary information include: Based on the number of wind turbines and the boundary information of the wind farm, determine the length d1 of the long side unit and the length d2 of the short side unit; Based on the length d1 of the long side unit, the relative offset Δd1 of the long side is obtained; The relative offset Δd2 of the short side is obtained based on the length d2 of the short side unit.

5. The wind power base site delineation method according to claim 1, characterized in that, The optimization of the generated metadata using a genetic algorithm, with the goal of maximizing the economic benefits of the wind power base, to obtain a wind farm layout scheme includes: Determine whether the number of generated metadata is a fixed value; When the number of generators is a fixed value, the genetic algorithm is used to optimize the generator coordinates of the generated data; or When the number of generators is not a fixed value, the genetic algorithm is used to optimize the number of generators and the coordinates of the generators.

6. The wind power base site delineation method according to claim 1, characterized in that, The step of generating a Voronoi diagram based on the wind farm boundary information and the generated metadata to obtain the wind farm boundary information includes: The Voronoi diagram is generated based on the wind power base boundary information and the generated metadata. An expansion operation is performed on the Voronoi diagram to obtain a wake recovery Voronoi diagram, so that a wake recovery zone is formed between the boundaries of two adjacent wind farms; The wind farm boundary information is obtained by reconstructing the Voronoi diagram based on the wake.

7. A wind power base site delineation device, characterized in that, include: The data acquisition unit is used to acquire wind power base boundary information, wind resource information, and generate metadata. The wind farm boundary information calculation unit is used to generate a Voronoi diagram based on the wind farm base boundary information and the generated metadata to obtain the wind farm boundary information. The wind turbine count calculation unit is used to obtain the number of wind turbines in the wind farm based on the wind resource information and the wind farm boundary information. An optimized layout unit is used to optimize the layout of the wind turbines based on the number of wind turbines and the boundary information of the wind farm using a six-parameter layout construction method, so as to obtain the optimized layout of the wind turbines in the wind farm and the economic benefits of the wind power base. The genetic algorithm unit is used to optimize the generated metadata with the goal of maximizing the economic benefits of the wind power base, and to obtain a wind farm layout scheme.

8. A readable storage medium, characterized in that, The readable storage medium stores a computer-executable program, which, when executed, implements the wind power base site allocation method as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, It includes a memory storing computer-executable instructions and a processor, which, when executed by the processor, causes the device to perform the wind power base site allocation method as described in any one of claims 1 to 6.

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