Multi-model offshore wind power plant distribution optimization design method considering similar aggregation

Abstract

The invention relates to a multi-model offshore wind power plant distribution optimization design method considering similar aggregation, and the method comprises the steps: obtaining wind power plant information, model parameters and wind measurement data, and the wind power plant information comprises a wind turbine installation point location in a field area, a model planned to be assembled and the number of each model; determining a representative annual wind resource data set and a wind shear index at the representative height of the region where the wind power plant is located based on the wind measurement data; and by taking the types of the wind turbines at the wind turbine installation point positions in the field as design variables, maximizing the annual energy production of the wind power plant as a target, and combining constraint conditions, performing iterative optimization by adopting an intelligent optimization algorithm, and determining an optimal arrangement design scheme. According to the method, the calculation precision of the annual energy output is effectively improved, and the optimized distribution design scheme can be better matched with the wind energy resource distribution characteristics in the field, so that the power generation effect in the actual operation period of the wind power plant is ensured to reach the expectation. The method is suitable for the field of wind power planning design.

Description

Technical Field

[0001] The present invention relates to an optimization design method for the layout of multiple offshore wind farm models taking into account the aggregation of similar models, and is applicable to the field of wind power planning and design. Background Art

[0002] For offshore wind farms, due to the relatively low intensity of inflow turbulence, the recovery rate of wind speed loss in the wake area of ​​the wind turbine is slowed down, which in turn causes a strong wake interference effect, seriously affecting the overall power generation of the wind farm. Due to the differences in the structural dimensions of different models, through reasonable arrangement, the wind rotor disk of the downwind wind turbine can avoid the low-speed wake area of ​​the upstream wind turbine to a certain extent, thereby weakening the adverse effects of the wake effect and improving the wind energy capture efficiency of the entire field. Based on this, the application of multi-model mixed arrangement design schemes in offshore wind power projects is becoming more and more widespread.

[0003] In the field of wind farm layout design, current engineering practice mainly relies on manual layout, which is not only overly dependent on the personal experience of engineers, but also limited by the limited scope of scheme comparison, making it difficult to obtain the global optimal solution. For this reason, intelligent optimization algorithms have been widely used in wind farm layout design in recent years. However, through a thorough investigation of existing research, it is found that for the layout optimization design problem under the mixed arrangement of multiple models, the layout scheme generated by the intelligent optimization algorithm has significant limitations in actual engineering applications, which are specifically manifested in the following two aspects: (1) In real wind power projects, in order to facilitate the installation and operation and maintenance of wind turbines, wind turbines of the same type are often required to adopt a "clustered arrangement" mode, but the existing layout optimization algorithm fails to fully consider this engineering constraint; (2) Since annual power generation is a key indicator for determining the development revenue of wind power projects, the optimization design goal of wind turbine layout is often set to maximize annual power generation or its related functions. However, whether in industrial software or academic research, the existing annual power generation calculation methods for multi-machine mixed offshore wind farms have many shortcomings.

[0004] Take the widely used offshore wind farm design software WAsP as an example. It is based on the classic Park model and adopts the "top hat distribution" that ignores the spanwise changes to describe the velocity loss in the wake area of ​​the wind turbine. This is not only seriously inconsistent with reality, but also makes the calculation framework of the WAsP software fail to fully consider the impact of wind shear. In fact, with the development of large-scale wind turbines, the increase in the diameter of the wind rotor makes the wind speed difference between the upper and lower ends of the wind rotor disk more and more obvious. Therefore, in order to improve the accuracy of power generation calculation, the influence of wind shear effect cannot be ignored.

[0005] At present, many types of calculation models that can take wind shear into account have been proposed in the academic community. Although they claim to have higher accuracy, test results show that this is based on the premise of reasonable input parameter values. These newer calculation models often contain multiple parameters, and the values ​​of some parameters are heavily dependent on wind tunnel experiments or high-precision CFD simulations. Due to this, the engineering practicality of these newer calculation models is greatly reduced. Summary of the invention

[0006] The technical problem to be solved by the present invention is: in view of the above-mentioned problems, a method for optimizing the layout of offshore wind farms with multiple types of machines taking into account the same type of clustering is provided.

[0007] The technical solution adopted by the present invention is: a method for optimizing the layout of multiple offshore wind farms considering the same type of aggregation, comprising: Obtain wind farm information, model parameters and wind measurement data, where the wind farm information includes the wind turbine installation points within the site, the planned models to be installed and the number of each model; Based on the wind measurement data, a representative annual wind resource data set and wind shear index at a representative height of the region where the wind farm is located are determined; Taking the wind turbine models at each wind turbine installation point in the site as design variables, maximizing the annual power generation of the wind farm is the goal. Combined with the constraints, an intelligent optimization algorithm is used to iteratively search for the best layout design solution. The constraints include: constraints on the number of models of each model and the number of wind turbines belonging to each model, and similar clustering constraints on the layout positions of each model; The calculation of the annual power generation of the wind farm includes: determining the annual power generation of the wind farm based on the wind turbine models and parameters of each wind turbine installation point, combined with a representative annual wind resource data set at a representative height and a wind shear index.

[0008] The wind turbine models at each wind turbine installation point in the site are used as design variables, the annual power generation of the wind farm is maximized as the goal, and the intelligent optimization algorithm is used to iteratively search for the best layout design scheme in combination with the constraint conditions, including: S410, initializing the population, where each individual in the population corresponds to a machine layout plan; S420, determining whether the layout plan corresponding to each individual meets the constraint conditions; S430, for individuals that meet the constraint conditions, the annual power generation of the wind farm is calculated; for individuals that do not meet the constraint conditions, the preset minimum value is used as the corresponding annual power generation of the wind farm; S440, determining the fitness of the individual based on the annual power generation of the wind farm corresponding to each individual, updating the population based on the fitness of each individual, and returning to step S420 until a preset iteration termination condition is met.

[0009] The step of judging whether the layout schemes corresponding to the individual units meet the constraint conditions includes: Determine whether the number of machine types in the individual and the number of wind turbines belonging to each machine type meet the preset number of machine types and the number of wind turbines belonging to each machine type constraints; If the quantity constraint is met, based on the coordinates of the wind turbine installation points corresponding to each wind turbine model, determine whether each wind turbine model meets the linear homogeneous aggregation constraint; If a certain wind turbine model does not satisfy the linear type homogeneous aggregation constraint, then based on the coordinates of the wind turbine installation point corresponding to the certain wind turbine model, determine whether the certain wind turbine model satisfies the polygonal type homogeneous aggregation constraint; If all wind turbine models within an individual satisfy the linear homogeneous clustering constraint or the polygonal homogeneous clustering constraint, then the individual satisfies the homogeneous clustering constraint.

[0010] The step of judging whether each wind turbine model satisfies a linear homogeneous aggregation constraint based on the coordinates of the wind turbine installation points corresponding to each wind turbine model includes: Sorting the wind turbine installation point coordinates corresponding to each wind turbine model to determine the installation points of the first and last wind turbines of each model; Based on the positional relationship between the installation points of the first and last wind turbines of each model and the installation points of the remaining wind turbines in the corresponding model, determine whether the installation points corresponding to the same model can be connected in a straight line; If they cannot be connected into a straight line, it is judged that the corresponding model does not meet the linear homogeneous aggregation constraint; if they can be connected into a straight line, based on the positional relationship between the installation points of the first and last two wind turbines of the corresponding model and the installation points of the remaining models in the individual, it is judged whether there are other models on the connected straight line; If it exists, the models connected in a straight line do not satisfy the linear type homogeneous aggregation constraint; if it does not exist, it is judged that the models connected in a straight line satisfy the linear type homogeneous aggregation constraint.

[0011] The step of judging whether the wind turbine model satisfies the polygonal homogeneous aggregation constraint based on the coordinates of the wind turbine installation point corresponding to the wind turbine model includes: Based on the coordinates of the wind turbine installation point corresponding to the wind turbine model, obtaining the valid edges of the circumscribed polygon; Based on the coordinates of the wind turbine installation points corresponding to the other models in the individual, determine whether there are other wind turbines in the circumscribed polygon; If it exists, it is determined that the certain wind turbine model does not satisfy the polygonal similar clustering constraint; if it does not exist, it is determined that the certain wind turbine model satisfies the polygonal similar clustering constraint.

[0012] The step of obtaining valid edges of a circumscribed polygon based on the coordinates of the wind turbine installation point corresponding to the wind turbine model includes: Traverse the point pairs of all wind turbine installation points corresponding to the wind turbine model, calculate the Euclidean distance, and take is 1 / 2 of the average Euclidean distance of all point pairs, let ; Passing by the wind turbine installation point , , and the radius is The equation of the circle can be determined by the following method: Candidate Edge The perpendicular bisector equation of is: ; Because the center arrive The distance is , so we have: ; Combine the above two equations to solve the coordinates of the center of the circle , and then we can get the equation of the circle: ; When the wind turbine model corresponds to all wind turbine installation points, except , There is a point outside Located in the circle When it is inside, it satisfies: , The candidate edge is an invalid edge; otherwise, it is a valid edge.

[0013] The step of judging whether there are wind turbines of other models in the circumscribed polygon based on the coordinates of the wind turbine installation points corresponding to the other models in the individual includes: Calculate the area of ​​the circumscribed polygon based on the coordinates of each boundary point of the circumscribed polygon; Based on the coordinates of the installation points of other types of wind turbines and the coordinates of the two boundary points on any side of the circumscribed polygon, determine the area of ​​the triangle enclosed by the three points; Based on the areas of the triangles corresponding to the sides of the circumscribed polygon, the sum of the areas is calculated, and the sum of the areas is compared with the area of ​​the circumscribed polygon to determine whether the installation points of wind turbines of other models are located within the circumscribed polygon.

[0014] The annual power generation of the wind farm is determined based on the wind turbine models and parameters of each wind turbine installation point, combined with the representative annual wind resource data set and wind shear index at the representative height, including: Divide the basic wind conditions based on wind speed and wind direction, determine the basic wind conditions corresponding to each period of the representative year, determine the representative wind speed and representative wind direction at the representative height of each basic wind condition, and calculate the proportion of each basic wind condition; Based on the wind turbine model and representative wind direction of each wind turbine installation point, the wind speed loss at each discrete point on the wind rotor disk is determined by considering the influence of the upwind wind turbine wake. Based on the wind shear index and the representative wind speed at the representative height, the inflow wind speed at the vertical height of each discrete point is determined; Based on the inflow wind speed at the vertical height of each discrete point on the wind rotor disk, combined with the wind speed loss at each discrete point, the wind speed at each discrete point is determined, and then the effective wind speed of the wind turbine is determined; Determine the power generation of each wind turbine in each basic wind condition based on the effective wind speed of each wind turbine; Based on the power generation of each wind turbine, the power generation of the wind farm under each basic wind condition is determined, and then combined with the proportion of the basic wind conditions, the annual power generation of the representative year is determined.

[0015] The basic wind conditions divided based on wind speed and wind direction include: Based on the wind speed-aerodynamic parameter list of each wind turbine model in the wind farm, the maximum and minimum values ​​of the cut-in wind speed and the cut-out wind speed are determined, and multiple wind speed intervals are divided between the maximum and minimum values; Evenly divide the wind direction angle from 0 to 360° and divide it into multiple wind direction sectors; The wind speed ranges and wind direction sectors are combined in pairs to form multiple basic wind conditions.

[0016] The wind speed at each discrete point is determined based on the inflow wind speed at the vertical height of each discrete point on the wind rotor disk surface, combined with the wind speed loss at each discrete point, and then the effective wind speed of the wind turbine is determined, including: ; Wherein, is the wind speed loss at discrete point m on wind turbine i, represents the number of the upwind wind turbine, is the wind speed loss of the isolated wake of the upwind wind turbine at discrete point m belonging to wind turbine i, and is calculated as: ; ; ; ; ; in, are the horizontal, vertical and vertical coordinates of the discrete point m belonging to wind turbine i in the relative coordinate system where the positive direction of the x-axis represents the wind direction, are the horizontal, vertical and vertical coordinates of the center point of the wind rotor disk of wind turbine j in the relative coordinates, and respectively refer to the rotor diameter and thrust coefficient of wind turbine j, is the wake expansion coefficient of wind turbine j, which affects the wind speed loss by affecting the wake width of the isolated wake of wind turbine j at wind turbine i.

[0017] The wake expansion coefficient of the wind turbine j ,include: ; in, and is an adjustable parameter, when j=1: ; When hour: ; ; in, Represents the wind turbine upwind of wind turbine j When operating in isolation, the wake impact area is close to the wind turbine. The overlapping area of ​​the wind wheel disks, For wind turbines The additional turbulence intensity in the isolated wake of a wind turbine The size of the place is calculated as: ; ; in, and They respectively refer to the rotor diameter and thrust coefficient of the wind turbine k, For wind turbines The effective turbulence intensity at , It is the horizontal coordinate of wind turbine j and k in the relative coordinate system where the positive direction of x-axis represents the wind direction.

[0018] An optimization design device for offshore wind farm layout of multiple wind turbines considering the same type of aggregation, comprising: The data acquisition module is used to obtain wind farm information, machine type parameters and wind measurement data, wherein the wind farm information includes the wind turbine installation points in the field, the planned installed machine types and the quantity of each machine type; A data processing module, for determining a representative annual wind resource data set and a wind shear index at a representative height of the region where the wind farm is located based on the wind measurement data; The wind turbine layout optimization module is used to take the wind turbine models at each wind turbine installation point in the site as design variables, maximize the annual power generation of the wind farm as the goal, combine the constraints, and use intelligent optimization algorithms to iteratively search for the best layout design solution; The constraints include: quantity constraints of each machine type, and similar clustering constraints of the layout positions of each machine type; The calculation of the annual power generation of the wind farm includes: determining the annual power generation of the wind farm based on the wind turbine models and parameters of each wind turbine installation point, combined with a representative annual wind resource data set at a representative height and a wind shear index.

[0019] A storage medium stores a computer program that can be executed by a processor, and when the computer program is executed, the steps of the method for optimizing the layout of multiple offshore wind farms considering the same type of aggregation are implemented.

[0020] A multi-type offshore wind farm layout optimization design device has a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, the steps of the multi-type offshore wind farm layout optimization design method considering the same type of aggregation are implemented.

[0021] The beneficial effects of the present invention are as follows: the present invention is based on actual engineering needs, takes maximizing the annual power generation of the wind farm as the optimization goal, takes the wind turbine models at each wind turbine installation point as the design variable, and uses an intelligent optimization algorithm to iteratively search for the best solution while satisfying the quantity constraints of each model and the similar clustering constraints of the layout positions of each model, and finally obtains the best layout design scheme that meets the engineering requirements of "clustered arrangement" of similar models.

[0022] In the optimization calculation process, the present invention calculates the annual power generation of each layout scheme. Starting from the two aspects of inflow conditions and wake effects, the present invention scientifically quantifies the impact of wind shear on the wake loss of multiple offshore wind farms. Compared with the prior art, the present invention effectively improves the accuracy of annual power generation calculation, so that the optimized layout design scheme can better match the distribution characteristics of wind energy resources in the field, thereby ensuring that the power generation effect during the actual operation of the wind farm reaches the expected.

[0023] In summary, the present invention provides a technical path that is both scientific and practical for the optimal design of offshore wind farm layout under the condition of mixed arrangement of multiple machine types in wind power engineering practice, and has important engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart for optimizing the layout of multiple offshore wind farm models in an embodiment.

[0025] Figure 2Schematic diagram of linear homogeneous clustering determination; (a) and (b) refer to the scenarios that satisfy and do not satisfy linear homogeneous clustering, respectively.

[0026] Figure 3 Schematic diagram of polygonal homogeneous clustering determination; (a) and (b) refer to the scenarios that satisfy and do not satisfy polygonal homogeneous clustering, respectively.

[0027] Figure 4 Schematic diagram for determining the relative position relationship between the wind turbine point and the polygon; (a) and (b) refer to the scenes where the wind turbine is inside and outside the polygon, respectively.

[0028] Figure 5 This is a flow chart for calculating the annual power generation of a multi-machine offshore wind farm taking into account the influence of wind shear in an embodiment. DETAILED DESCRIPTION

[0029] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0030] In the description of the present invention, the meaning of "a plurality" is two or more than two. If there is a description of "a first" or "a second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used in this document have the same meaning as those commonly understood by those skilled in the art.

[0031] like Figure 1 As shown, this embodiment is a method for optimizing the layout of multiple offshore wind farms considering the same type of aggregation, which specifically includes the following steps: S100: Obtain wind farm information, machine model parameters, and wind measurement data.

[0032] In this embodiment, the wind farm information includes wind turbine installation points for installing wind turbines in the farm area, the types of wind turbines planned to be installed, and their respective quantities.

[0033] Assume the number of wind turbine locations is , planned assembly Similar models, and corresponding models The number of wind turbines is , then according to the definition, obviously .

[0034] The model parameters include hub height, rotor diameter and wind speed-aerodynamic parameter list. The wind speed-aerodynamic parameter list is usually obtained from the whole machine manufacturer, and provides the thrust coefficient and power at each typical wind speed within the normal operating power generation wind speed range (cut-in wind speed to cut-out wind speed) of the corresponding model.

[0035] The wind measurement data includes wind speed, wind direction and turbulence intensity at multiple time periods at multiple different wind measurement heights.

[0036] S200 , processing wind measurement data to determine a representative annual wind resource data set, a representative turbulence intensity, and a wind shear index at a representative height of the region where the wind farm is located.

[0037] The processing of wind measurement data mainly includes data integrity check, rationality check, elimination, interpolation, extension of unreasonable data and missing data, representative year correction, etc. The goal is to obtain a set of representative wind resource data that can reflect the long-term average level of the wind farm.

[0038] The representative height is given by It means that it is recommended to select the one among the multiple different wind measurement heights described in step S100 that is closest to the average hub height of all models in the wind farm.

[0039] The representative annual wind resource data set at the representative height includes wind speed, wind direction and turbulence intensity for a total of 8760 time periods in a full year at the representative height.

[0040] represents the turbulence intensity, given by Refers to, obtained by calculating the average of the turbulence intensity of all 8760 periods in the representative annual wind resource dataset at the representative height; The wind shear index is calculated according to the following formula: ; in, and Refers to the vertical height and In order to enhance the reliability and representativeness of the wind shear fitting results, the relevant and It is recommended to select two heights that meet the following constraints from the multiple different wind measurement heights described in step 1: ; ; ; in, Represents the number of models in the wind farm, For the The wheel hub height of each model.

[0041] S300, dividing basic wind conditions based on wind speed and wind direction, determining basic wind conditions corresponding to each period of the representative year, determining representative wind speed and representative wind direction at representative heights of each basic wind condition, and counting the proportion of each basic wind condition.

[0042] S310, based on the wind speed-aerodynamic parameter list of each wind turbine model in the wind farm, determine the maximum and minimum values ​​of the cut-in wind speed and the cut-out wind speed, and divide a plurality of wind speed intervals between the maximum and minimum values.

[0043] Traverse the wind speed-aerodynamic parameter list of each type of wind farm obtained in step S100, and determine the minimum wind speed and maximum value , round them down respectively and round up Processing, and then and As lower and upper limits, the interval is calculated according to the set wind speed , dividing the wind speed into multiple different intervals. According to the definition, the total number of wind speed intervals , among which, The wind speed interval can be expressed as ; The wind speed calculation interval It is recommended that the value should not exceed the difference between adjacent wind speeds in the wind speed-aerodynamic parameter list of any model.

[0044] S320, evenly divide the wind direction angle from 0 to 360 degrees to obtain a plurality of wind direction sectors.

[0045] According to the set number of wind direction sectors , evenly divide the wind direction angle from 0 to 360°, and divide it into multiple different wind direction sectors. According to the definition, the size of each wind direction sector is If As the middle value of the first wind direction sector, The wind direction sector can be expressed as: ; Refer to the conventional practices in wind power engineering practice and recommend The value is 12 or 16.

[0046] S330. Combine wind speed intervals and wind direction sectors in pairs to form multiple basic wind conditions.

[0047] The wind speed intervals and wind direction sectors obtained in steps S310 and S320 are combined in pairs, thereby obtaining a plurality of mutually different basic wind conditions. According to the definition, the total number of basic wind conditions is , the corresponding numbering sequence can be expressed as ,in Indicates Basic wind conditions.

[0048] S340, take basic wind conditions The middle value of the wind direction sector and wind speed range is used as the basic wind condition Representative wind speed and represents wind direction .

[0049] S350, processing the representative annual wind resource data set at the representative height, and counting the proportion of each basic wind condition; Specifically, according to the wind speed and wind direction of each time period in the representative annual wind resource data set at the representative height, the time period is classified into the corresponding basic wind condition in step S330 according to the interval range to which it belongs. After traversing all time periods in the data set and completing the above operation, the ratio of the number of time periods under each basic wind condition to the number of time periods in the representative annual wind resource data set at the representative height is calculated, and the proportion of each basic wind condition can be obtained. The corresponding sequence can be expressed as ,in Refers to Basic wind conditions According to the definition, the calculation formula is as follows: ; in, The representative annual wind resource data set at the representative height is classified as the basic wind condition The number of time periods below.

[0050] As to why the statistical results of the representative annual wind resource data set at the representative height can be used for all wind turbines in the field, the explanation is as follows: In the practice of wind power engineering, when a mixed layout design is performed for multiple types of models in the same field, the hub heights of the selected models are usually not very different, generally not exceeding 10 m. The reasons are: (1) The offshore wind shear is relatively small. By increasing the hub height of a certain model to obtain a larger inflow wind speed, the additional benefits brought by the increase in power may not cover the investment in a larger bottom support platform caused by the increase in hub height (a larger hub height needs to match a larger bottom support platform, and the investment cost will increase accordingly); (2) With the development of large-scale wind turbines, although there are differences in the rated power of different models, their rotor diameters are usually small. Correspondingly, the hub height difference to support this rotor diameter is also small. For this reason, in this embodiment, following the conventional processing method in wind power engineering, only the influence of wind shear in the inflow on the wind speed at different vertical heights is considered, and the wind direction at the representative height is applied to various different models.

[0051] S400, taking the wind turbine models at each wind turbine installation point in the site as design variables, maximizing the annual power generation of the wind farm as the goal, combined with constraints, using intelligent optimization algorithms to iteratively search for the best layout design solution.

[0052] Take any point in the wind farm as the origin of the coordinates, the east direction is the positive direction of the x-axis (corresponding to a 270° wind direction angle), and the north direction is the positive direction of the y-axis (corresponding to a 180° wind direction angle), and establish a two-dimensional rectangular coordinate system, also known as the geodetic coordinate system. Under this coordinate system, determine the coordinates of each wind turbine point obtained in step S100 to form a set T, which is expressed as ; In view of the fact that annual power generation is an important basis for determining the development income of wind power projects, in this embodiment, the maximum annual power generation is taken as the goal, and the optimization calculation model is constructed by continuously changing the machine type at each point with the help of intelligent optimization algorithm. Specifically, in order to characterize the machine type at each point, an integer design variable is set, and the corresponding set is expressed as ,in, The coordinates are Model at the point.

[0053] In this embodiment, the layout solution that meets the output standard must meet the following two constraints: (1) Quantity constraints for each model. The number of models and the number of wind turbines belonging to each model must be consistent with the demand set in step S100; (2) Similar clustering constraints for the locations of each type of wind turbine. Wind turbines of the same type must meet the similar clustering constraints required in wind power projects, which include the following two forms: one is linear similar clustering, that is, the locations of wind turbines of the same type are connected to form a straight line segment, and there are no other types of wind turbines in the straight line segment; the other is polygonal similar clustering, that is, there are no other types of wind turbines inside the polygon formed by the locations of wind turbines of the same type.

[0054] S410, initializing the population, where each individual in the population corresponds to a machine layout plan.

[0055] According to the population size M set in the intelligent optimization algorithm, that is, the number of individuals in each generation of the population, where each individual corresponds to a set of integer design variable values ​​(that is, a machine layout plan), the initial population that meets the scale requirements is obtained.

[0056] S420: Determine whether the layout plan corresponding to each individual satisfies the constraint conditions.

[0057] S421, judging whether the number of machine types in the individual and the number of wind turbines belonging to each machine type meet the preset number of machine types and the number of wind turbines belonging to each machine type.

[0058] For a set of design variable values ​​corresponding to the target individual, first determine whether the type of machine under the corresponding layout plan is the same as the number of models set in step S100; then, further determine whether the number of wind turbines belonging to each model is the same as the respective demand. If all the above conditions are met, select one of the models with a number of wind turbines of not less than 2, and proceed to step S422 to determine whether the linear homogeneous clustering constraint is satisfied. Otherwise, the individual is classified as an individual that does not satisfy the constraint, and proceed to step S430.

[0059] S422: Determine whether the linear homogeneous aggregation constraint is satisfied.

[0060] For model T k For example, let's summarize what it means to satisfy the linear-homogeneous clustering constraints, such as Figure 2 As shown in the figure, the red line represents the outer boundary of the wind farm. The model T is arranged at the point corresponding to the red point. k wind turbines, and other types of wind turbines are arranged at the points indicated by black dots. Figure 2 In the case shown in (a), due to the belonging model T k The wind turbines are located on the same straight line, and the straight line segment is closed, that is, there are no other models inside it, so it can be regarded as satisfying the linear homogeneous aggregation constraint. Figure 2 In the case shown in (b), although it belongs to model T kThe wind turbines of are on the same straight line, but because other types of wind turbines are interspersed in the straight line segment, the linear homogeneous clustering constraint is not satisfied; in addition, when the affiliated type T k When the wind turbines are not on the same straight line, it can be directly determined that the linear homogeneous clustering constraint is not satisfied.

[0061] S4221. Extract the wind turbine installation points corresponding to each wind turbine model in the individual, and determine whether each wind turbine model satisfies the linear homogeneous aggregation constraint based on the coordinates of the wind turbine installation points corresponding to each wind turbine model.

[0062] a. Sort the wind turbine installation point coordinates corresponding to each wind turbine model by size, and determine the installation points of the first and last wind turbines of each model.

[0063] Extract the affiliated model T k The coordinates of each wind turbine are arranged in ascending order of the horizontal coordinate (when the horizontal coordinates are the same, they are arranged in ascending order of the vertical coordinate) to form a set: ; in, Refers to the model T k The number of wind turbines, and are the coordinates of the two points with the smallest and largest horizontal coordinates (or the smallest and largest vertical coordinates when the horizontal coordinates are the same); if the set When the coordinate points in are on the same straight line segment, we know and These are the coordinates of the two endpoints of the straight line segment.

[0064] Model T k The set of point coordinates of other wind turbines is denoted as S. According to the definition, the following relationship exists between the sets U, S and the point set T of all wind turbines: S=TU.

[0065] b. Based on the positional relationship between the installation points of the first and last wind turbines of each model and the installation points of the remaining wind turbines in the corresponding model, determine whether the installation points corresponding to the same model can be connected into a straight line.

[0066] When the number of wind turbines in set U is 2, this step can be skipped. Otherwise, the vector constructed by the point coordinates of the first and last wind turbines in set U is calculated. , and then traverse the coordinates of the remaining points in the set U, such as , calculate the vector and The cross product modulus : ; If calculated, , , then we can determine the set U The points in the straight line segment are connected into a straight line, and the process goes to step c to further determine whether a certain point in the straight line segment is equipped with a wind turbine of another model; otherwise, it is determined that the corresponding model does not meet the linear homogeneous aggregation constraint, and the process goes to step S430.

[0067] c. Based on the positional relationship between the installation points of the first and last wind turbines of the corresponding model and the installation points of the remaining models in the individual, determine whether there are other models on the connected straight line.

[0068] Traverse the coordinates of each point in the set S, such as , combined with the subordinate model T described in step a k The endpoint coordinates of the straight line segment where each wind turbine point is located, that is, and , calculate the cosine value: ; like , then the point can be determined It is not in the straight line segment formed by the points in the set U; otherwise, it is in the straight line segment.

[0069] If calculated, for all point coordinates in the set S, we have , then it can be determined that it belongs to model T k The wind turbine locations of the wind turbines satisfy the linear clustering constraint; otherwise, further determine the affiliated model T k Whether the number of wind turbines is not less than 3, if it is satisfied, proceed to step S423, and if not, classify the individual as an individual that does not meet the constraint condition and proceed to step S430.

[0070] S423, judging whether the polygon-type homogeneous aggregation constraint is satisfied.

[0071] S4231. Based on the coordinates of the wind turbine installation point corresponding to the wind turbine model, obtain the valid sides of the circumscribed polygon.

[0072] First, traverse all point pairs in set U , calculate the Euclidean distance , and take is 1 / 2 of the average Euclidean distance of all point pairs, let ; judge one by one Is it true? If so, then Add to the candidate edge set E 1.

[0073] Next, for the set E 1 Each candidate edge , determine whether it is a valid edge.

[0074] For candidate edges An example of judgment, with detailed steps: Passing point , , and the radius is a circle whose equation can be determined by the following method: Candidate Edge The perpendicular bisector equation of is: ; Because the center arrive The distance is , so we have: ; Combine the above two equations to solve the coordinates of the center of the circle , and then we can get the equation of the circle: ; When the subordinate model T k The set of all wind turbine locations U Inside, except , There is a point outside Located in the circle When it is inside, it satisfies: , The candidate edge is an invalid edge; otherwise, it is a valid edge.

[0075] Refer to the above steps to traverse the collection E 1, and all valid edges obtained are the belonging models T k The boundary line of the polygon enclosed by the wind turbine locations.

[0076] S4232. Based on the coordinates of the wind turbine installation points corresponding to the other models in the individual, determine whether there are other wind turbine models in the circumscribed polygon. If so, determine that the wind turbine model does not satisfy the polygonal type similar clustering constraint; if not, determine that the wind turbine model satisfies the polygonal type similar clustering constraint.

[0077] Traverse the coordinates of each wind turbine point in the set S, such as , determine whether it is related to the model T k The relative position relationship of the polygon formed by each wind turbine. Figure 4 As shown in (a), the wind turbine point W is located in the polygon Inside, and Figure 4 In (b), the wind turbine point W is located in the polygon The specific judgment method is as follows: ①. Calculate the area of ​​the circumscribed polygon based on the coordinates of each boundary point of the circumscribed polygon.

[0078] According to the boundary points of the polygon Coordinates, using vector outer product, calculate the area of ​​the circumscribed polygon , where the area of ​​the triangle is .

[0079] ② Based on the coordinates of the installation points of other types of wind turbines and the coordinates of the two boundary points on any side of the circumscribed polygon, determine the area of ​​the triangle enclosed by the three points.

[0080] According to the coordinates of the wind turbine point W and the boundary points of the polygon Coordinates, using vector outer product, calculate the triangles corresponding to each side of the circumscribed polygon Area .

[0081] ③. Based on the areas of the triangles corresponding to the sides of the circumscribed polygon in step ②, calculate the sum of the areas, and compare the sum of the areas with the area of ​​the circumscribed polygon in step ① to determine whether the installation points of wind turbines of other models are located within the circumscribed polygon.

[0082] Compare the triangles calculated above The area of ​​the polygon is equal to the area of ​​the polygon. are greater than or equal to 0, it can be seen that the wind turbine point W is located in the polygon inside (including the boundary); otherwise, the wind turbine point W is located within the polygon Outside (excluding borders).

[0083] When all wind turbine locations in the set S are located in the k When the wind turbine is outside the polygon, the model T k The polygonal homogeneous aggregation constraint is satisfied; otherwise, the target individual is classified as an individual that does not satisfy the constraint condition, and the process proceeds to step S430.

[0084] S424, change a model, repeat steps S422 and S423, until all models with the number of wind turbines not less than 2 are traversed; if it is determined that the aforementioned models all meet the "straight line type - similar clustering" or "polygonal type - similar clustering" constraints, then the target individual is classified as an individual that meets the constraint conditions, and proceed to step S430.

[0085] S430. For individuals that meet the constraint conditions, the annual power generation of the wind farm is calculated; for individuals that do not meet the constraint conditions, the preset minimum value is used as the corresponding annual power generation of the wind farm.

[0086] For individuals that do not meet the constraints in step S420, the corresponding annual power generation is taken as a minimum value, such as 0.001. For those that meet the constraints, the influence of wind shear on the inflow perceived by the wind turbine and the wake effect between wind turbines is considered, and the annual power generation of the corresponding layout scheme is calculated using an analytical model.

[0087] like Figure 5 As shown, in this embodiment, the annual power generation of the machine layout solution is calculated through the following steps, including: I. For the wind speed-aerodynamic parameter list of each model obtained in step S100, the aerodynamic parameters of the wind speed intervals below the cut-in wind speed and above the cut-out wind speed are supplemented.

[0088] Because the cut-in wind speed and cut-out wind speed of different models may be different, the following scenarios may occur in some basic wind conditions obtained in step S300: the representative wind speed is greater than the cut-in wind speed of a certain model, but less than the cut-in wind speed of other models, or the representative wind speed is less than the cut-out wind speed of a certain model, but greater than the cut-out wind speed of other models. At this time, for the upwind wind turbines in the wind farm belonging to the latter model, when determining their aerodynamic parameters based on the wind speed they sense, it may happen that the wind speed is not within the wind speed range covered by the wind speed-aerodynamic parameter list obtained in step S100, thereby causing a calculation overflow error.

[0089] In view of this, it is necessary to supplement the aerodynamic parameters of the wind speed intervals below the cut-in wind speed and above the cut-out wind speed for the wind speed-aerodynamic parameter list of each model obtained in step S100. Specifically, in this embodiment, it is recommended that in the wind speed interval below the cut-in wind speed, the thrust coefficient takes a large value close to 1, such as 0.9999, and the power value is 0; in the wind speed interval above the cut-out wind speed, the thrust coefficient takes a small value close to 0, such as 0.0001, and the power value is 0.

[0090] Ⅱ. Coordinate system conversion.

[0091] According to the wind direction The wind turbines are sorted according to their relative positions before and after, and numbered from 1. The corresponding numbering sequence can be expressed as ,in, Sort by The wind turbine number, Refers to the total number of wind turbines in a wind farm.

[0092] The edge represents the wind direction The front and rear relative positions can be obtained by comparing the horizontal coordinates of each wind turbine in the relative coordinate system. When the horizontal coordinate is larger, the wind turbine is located further back. The relative coordinate system is obtained by rotating the geodetic coordinate system. The two share the same origin, but the directions of the coordinate axes are different. The positive direction of the x-axis in the relative coordinate system represents the wind direction. ; The geodetic coordinate system is a two-dimensional rectangular coordinate system established by taking any point in the wind farm as the coordinate origin, the due east direction as the positive direction of the x-axis (corresponding to a 270° wind direction angle), and the due north direction as the positive direction of the y-axis (corresponding to a 180° wind direction angle).

[0093] The abscissa in the relative coordinate system is calculated by taking the wind turbine numbered i as an example: ; in, and They are the horizontal and vertical coordinates of the wind turbine numbered i in the geodetic coordinate system and the relative coordinate system respectively.

[0094] III. Based on the wind turbine model and representative wind direction of each wind turbine installation point, the wind speed loss at each discrete point on the wind rotor disk is determined by considering the wake influence of the upwind wind turbine; based on the wind shear index, combined with the representative wind speed at the representative height, the inflow wind speed at the vertical height where each discrete point is located is determined; based on the inflow wind speed at the vertical height where each discrete point on the wind rotor disk is located, combined with the wind speed loss at each discrete point, the wind speed at each discrete point is determined, and then the effective wind speed of the wind turbine is determined.

[0095] In this embodiment, the wind speed loss caused by the wake effect at each wind turbine in the wind farm is calculated in order of numbers from small to large using an analytical model, and then the effective wind speed can be calculated by combining the representative wind speed and the wind shear coefficient; Taking the calculation of wind speed loss and effective wind speed at wind turbine numbered i as an example, the detailed process is as follows.

[0096] First, the wind rotor disk of the target wind turbine i is discretized according to the set resolution. After calculating the coordinate values ​​of each discrete point in the relative coordinate system, the coordinate set of discrete points is integrated to obtain the following form: ,in, is the total number of discrete points belonging to wind turbine i.

[0097] The larger the resolution, the fewer discrete points in the wind rotor disk obtained by segmentation, and accordingly, the shorter the time for wake calculation, but the representativeness of the calculation result may be insufficient. In this embodiment, in order to take into account both timeliness and calculation accuracy requirements, it is recommended to set the resolution to 0.25 times the wind rotor diameter of the corresponding model.

[0098] Wind turbine Effective wind speed It is obtained by calculating the average wind speed at all discrete points on the wind wheel surface. The calculation formula is: ; The thrust coefficient is the key factor affecting the evolution of wind turbine wake and is also the most important input parameter in the analytical model used to calculate the wind turbine wake. Effective wind speed , combined with the wind speed-aerodynamic parameter list of the model to which it belongs, and through interpolation calculation, its thrust coefficient is obtained : ; in, and Corresponding to wind turbines The closest wind speed to the model in the aerodynamic parameter list The two wind speeds meet ,and For the list and The corresponding thrust coefficient.

[0099] The number inside the wind wheel disk is The wind speed at the discrete points is calculated as: ; in, For discrete points The inflow wind speed at the vertical height is calculated as: ; in, and are the representative height and wind shear index mentioned in step 2 respectively; For discrete points At , the wind speed loss after the influence of the wake of all wind turbines in the upwind direction is considered. When the target wind turbine is numbered When there is no other wind turbine in the upwind direction, it is not affected by the wake interference. ; and when In this embodiment, the linear superposition method is used to deal with the overlapping effect of the wakes of multiple wind turbines in the upwind direction, and the following formula is used to calculate : ; in, Represents the number of the upwind wind turbine. Upwind wind turbine The wind speed loss of the isolated wake at the discrete point m belonging to wind turbine i is calculated as: ; ; ; ; ; in, are the horizontal, vertical and vertical coordinates of the discrete point m belonging to wind turbine i in the relative coordinate system, are the horizontal, vertical and vertical coordinates of the center point of the wind rotor disk of wind turbine j in the relative coordinates, and respectively refer to the rotor diameter and thrust coefficient of wind turbine j, is the wake expansion coefficient of wind turbine j, which affects the wake width of the isolated wake of wind turbine j at wind turbine i, thereby affecting the wind speed loss. Referring to existing studies, The value of is related to the effective turbulence intensity at wind turbine j are closely related and approximately satisfy the following relationship: ; in, and It is an adjustable parameter. Referring to existing research, it is recommended in this embodiment and The values ​​are 0.38 and 0.004 respectively. When j=1, it means that there is no other wind turbine upwind of wind turbine j, so its effective turbulence intensity is approximately equal to the turbulence intensity at the representative height in the inflow wind, that is: ; When When , for wind turbine j, in addition to the turbulence intensity in the inflow, the additional turbulence intensity generated by the operation of the upwind wind turbine must also be considered, and the calculation formula is: ; ; in, Represents the wind turbine upwind of wind turbine j When operating in isolation, the wake impact area is close to the wind turbine. The overlapping area of ​​the wind wheel disks, For wind turbines The additional turbulence intensity in the isolated wake of a wind turbine The size of the place is calculated as: ; ; in, and They respectively refer to the rotor diameter and thrust coefficient of the wind turbine k, For wind turbines The effective turbulence intensity at .

[0100] IV. Based on the effective wind speed of each wind turbine, determine the power generation of each wind turbine in each basic wind condition; based on the power generation of each wind turbine, determine the power generation of the wind farm under each basic wind condition, and then determine the annual power generation in the representative year based on the proportion of the basic wind conditions.

[0101] Based on the effective wind speed at each wind turbine, combined with the wind speed-aerodynamic parameter list of each corresponding model, their power generation is calculated, and then the basic wind conditions can be obtained by summing them up. Wind farm power generation under , the calculation formula is: ; in, is the total number of wind turbines in the wind farm, For the number The power generation of the wind turbine is calculated as follows: ; in, and Corresponding to wind turbines The closest wind speed to the model in the aerodynamic parameter list Two typical wind speeds meet ,and and For the corresponding and of power.

[0102] All basic wind conditions divided in step S300 are traversed, and then the annual power generation of the wind farm is obtained through summation calculation. , the calculation formula is: ; Among them, 8760 refers to the number of hours in a year. is the total number of basic wind conditions, and For the number Basic wind conditions The proportion of wind farms and the power generation of wind farms under the basic wind conditions.

[0103] S440, determining the fitness of the individual based on the annual power generation of the wind farm corresponding to each individual, updating the population based on the fitness of each individual, and returning to step S420 until a preset iteration termination condition is met.

[0104] Determine whether the result of the current calculation step meets the convergence standard. If so, output the calculation result. If not, further determine whether the maximum allowed number of iterations has been reached. If not, select the larger one as the parent based on the fitness of all individuals in the population of the current calculation step, update the population, and return to step S420. If so, output the calculation result.

[0105] This embodiment also provides a device for optimizing the layout of multiple offshore wind farms taking into account the same type of aggregation, the device comprising: The data acquisition module is used to obtain wind farm information, machine type parameters and wind measurement data, wherein the wind farm information includes the wind turbine installation points in the field, the planned installed machine types and the quantity of each machine type; A data processing module, for determining a representative annual wind resource data set and a wind shear index at a representative height of the region where the wind farm is located based on the wind measurement data; The wind turbine layout optimization module is used to take the wind turbine models at each wind turbine installation point in the site as design variables, maximize the annual power generation of the wind farm as the goal, combine the constraints, and use intelligent optimization algorithms to iteratively search for the best layout design solution; The constraints include: quantity constraints of each machine type, and similar clustering constraints of the layout positions of each machine type; The calculation of the annual power generation of the wind farm includes: determining the annual power generation of the wind farm based on the wind turbine models and parameters of each wind turbine installation point, combined with a representative annual wind resource data set at a representative height and a wind shear index.

[0106] This embodiment also provides a storage medium on which a computer program that can be executed by a processor is stored. When the computer program is executed, the steps of the above-mentioned method for optimizing the layout of multiple offshore wind farms considering the same type of clustering are implemented.

[0107] This embodiment also provides a multi-type offshore wind farm layout optimization design device, which has a memory and a processor, and the memory stores a computer program that can be executed by the processor. When the computer program is executed, the steps of the above-mentioned multi-type offshore wind farm layout optimization design method considering similar clustering are implemented.

[0108] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0109] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified to the contrary, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0110] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the above methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0111] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0112] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the above-mentioned program is printed, since the above-mentioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in other suitable ways as necessary, and then stored in a computer memory.

[0113] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0114] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0115] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0116] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for optimizing the layout of multiple offshore wind farms considering the same type of clustering, characterized in that: include: Obtain wind farm information, model parameters and wind measurement data, where the wind farm information includes the wind turbine installation points within the site, the planned models to be installed and the number of each model; Based on the wind measurement data, a representative annual wind resource data set and wind shear index at a representative height of the region where the wind farm is located are determined; Taking the wind turbine models at each wind turbine installation point in the site as design variables, maximizing the annual power generation of the wind farm is the goal. Combined with the constraints, an intelligent optimization algorithm is used to iteratively search for the best layout design solution. The constraints include: constraints on the number of models of each model and the number of wind turbines belonging to each model, and similar clustering constraints on the layout positions of each model; The calculation of the annual power generation of the wind farm includes: determining the annual power generation of the wind farm based on the wind turbine models and parameters of each wind turbine installation point, combined with a representative annual wind resource data set at a representative height and a wind shear index.

2. The method for optimizing the layout of multiple offshore wind farms considering the same type of clustering according to claim 1 is characterized in that: The wind turbine models at each wind turbine installation point in the site are used as design variables, the annual power generation of the wind farm is maximized as the goal, and the intelligent optimization algorithm is used to iteratively search for the best layout design scheme in combination with the constraint conditions, including: S410, initializing the population, where each individual in the population corresponds to a machine layout plan; S420, determining whether the layout plan corresponding to each individual meets the constraint conditions; S430, for individuals that meet the constraint conditions, the annual power generation of the wind farm is calculated; for individuals that do not meet the constraint conditions, the preset minimum value is used as the corresponding annual power generation of the wind farm; S440, determining the fitness of the individual based on the annual power generation of the wind farm corresponding to each individual, updating the population based on the fitness of each individual, and returning to step S420 until a preset iteration termination condition is met.

3. The method for optimizing the layout of multiple offshore wind farms considering the same type of aggregation according to claim 2 is characterized in that: The step of judging whether the layout schemes corresponding to the individual units meet the constraint conditions includes: Determine whether the number of machine types in the individual and the number of wind turbines belonging to each machine type meet the preset number of machine types and the number of wind turbines belonging to each machine type constraints; If the quantity constraint is met, then based on the coordinates of the wind turbine installation points corresponding to each wind turbine model, determine whether each wind turbine model meets the linear homogeneous aggregation constraint; If a certain wind turbine model does not satisfy the linear type homogeneous aggregation constraint, then based on the coordinates of the wind turbine installation point corresponding to the certain wind turbine model, determine whether the certain wind turbine model satisfies the polygonal type homogeneous aggregation constraint; If all wind turbine models within an individual satisfy the linear homogeneous clustering constraint or the polygonal homogeneous clustering constraint, then the individual satisfies the homogeneous clustering constraint.

4. The method for optimizing the layout of multiple offshore wind farms considering the same type of aggregation according to claim 3 is characterized in that: The step of judging whether each wind turbine model satisfies a linear homogeneous aggregation constraint based on the coordinates of the wind turbine installation points corresponding to each wind turbine model includes: Sorting the wind turbine installation point coordinates corresponding to each wind turbine model to determine the installation points of the first and last wind turbines of each model; Based on the positional relationship between the installation points of the first and last wind turbines of each model and the installation points of the remaining wind turbines in the corresponding model, determine whether the installation points corresponding to the same model can be connected in a straight line; If they cannot be connected into a straight line, it is judged that the corresponding model does not meet the linear homogeneous aggregation constraint; if they can be connected into a straight line, based on the positional relationship between the installation points of the first and last two wind turbines of the corresponding model and the installation points of the remaining models in the individual, it is judged whether there are other models on the connected straight line; If it exists, the models connected in a straight line do not satisfy the linear type homogeneous aggregation constraint; if it does not exist, it is judged that the models connected in a straight line satisfy the linear type homogeneous aggregation constraint.

5. The method for optimizing the layout of multiple offshore wind farms considering the same type of clustering according to claim 3 is characterized in that: The step of judging whether the wind turbine model satisfies the polygonal homogeneous aggregation constraint based on the coordinates of the wind turbine installation point corresponding to the wind turbine model includes: Based on the coordinates of the wind turbine installation point corresponding to the wind turbine model, obtaining the valid edges of the circumscribed polygon; Based on the coordinates of the wind turbine installation points corresponding to the other models in the individual, determine whether there are other wind turbines in the circumscribed polygon; If it exists, it is determined that the certain wind turbine model does not satisfy the polygonal similar clustering constraint; if it does not exist, it is determined that the certain wind turbine model satisfies the polygonal similar clustering constraint.

6. The method for optimizing the layout of multiple offshore wind farms considering the same type of clustering according to claim 5 is characterized in that: The step of obtaining valid edges of a circumscribed polygon based on the coordinates of the wind turbine installation point corresponding to the wind turbine model includes: Traverse the point pairs of all wind turbine installation points corresponding to the wind turbine model, calculate the Euclidean distance, and take is 1 / 2 of the average Euclidean distance of all point pairs, let ; Passing by the wind turbine installation point , , and the radius is The equation of the circle can be determined by the following method: Candidate Edge The perpendicular bisector equation of is: ; Because the center arrive The distance is , so we have: ; Combine the above two equations to solve the coordinates of the center of the circle , and then we can get the equation of the circle: ； When the wind turbine model corresponds to all wind turbine installation points, except , There is a point outside Located in the circle When it is inside, it satisfies: ， The candidate edge is an invalid edge; otherwise, it is a valid edge.

7. The method for optimizing the layout of multiple offshore wind farms considering the same type of aggregation according to claim 5 is characterized in that: The step of judging whether there are wind turbines of other models in the circumscribed polygon based on the coordinates of the wind turbine installation points corresponding to the other models in the individual includes: Calculate the area of ​​the circumscribed polygon based on the coordinates of each boundary point of the circumscribed polygon; Based on the coordinates of the installation points of other types of wind turbines and the coordinates of the two boundary points on any side of the circumscribed polygon, determine the area of ​​the triangle enclosed by the three points; Based on the areas of the triangles corresponding to the sides of the circumscribed polygon, the sum of the areas is calculated, and the sum of the areas is compared with the area of ​​the circumscribed polygon to determine whether the installation points of wind turbines of other models are located within the circumscribed polygon.

8. The method for optimizing the layout of multiple offshore wind farms considering the same type of clustering according to claim 1 is characterized in that: The annual power generation of the wind farm is determined based on the wind turbine models and parameters of each wind turbine installation point, combined with the representative annual wind resource data set and wind shear index at the representative height, including: Divide the basic wind conditions based on wind speed and wind direction, determine the basic wind conditions corresponding to each period of the representative year, determine the representative wind speed and representative wind direction at the representative height of each basic wind condition, and calculate the proportion of each basic wind condition; Based on the wind turbine model and representative wind direction of each wind turbine installation point, the wind speed loss at each discrete point on the wind rotor disk is determined by considering the influence of the upwind wind turbine wake. Based on the wind shear index and the representative wind speed at the representative height, the inflow wind speed at the vertical height of each discrete point is determined; Based on the inflow wind speed at the vertical height of each discrete point on the wind rotor disk, combined with the wind speed loss at each discrete point, the wind speed at each discrete point is determined, and then the effective wind speed of the wind turbine is determined; Determine the power generation of each wind turbine in each basic wind condition based on the effective wind speed of each wind turbine; Based on the power generation of each wind turbine, the power generation of the wind farm under each basic wind condition is determined, and then combined with the proportion of the basic wind conditions, the annual power generation of the representative year is determined.

9. The method for optimizing the layout of multiple offshore wind farms considering the same type of clustering according to claim 8 is characterized in that: The basic wind conditions divided based on wind speed and wind direction include: Based on the wind speed-aerodynamic parameter list of each wind turbine model in the wind farm, the maximum and minimum values ​​of the cut-in wind speed and the cut-out wind speed are determined, and multiple wind speed intervals are divided between the maximum and minimum values; Evenly divide the wind direction angle from 0 to 360° and divide it into multiple wind direction sectors; The wind speed ranges and wind direction sectors are combined in pairs to form multiple basic wind conditions.

10. The method for optimizing the layout of multiple offshore wind farms considering the same type of aggregation according to claim 8 is characterized in that: The method of determining the wind speed at each discrete point based on the inflow wind speed at the vertical height where each discrete point on the wind rotor disk is located, combined with the wind speed loss at each discrete point, and then determining the effective wind speed of the wind turbine includes: ; in, is the wind speed loss at discrete point m on wind turbine i, Represents the number of the upwind wind turbine. Upwind wind turbine The wind speed loss of the isolated wake at the discrete point m belonging to wind turbine i is calculated as: ； ； ； ； ； in, are the horizontal, vertical and vertical coordinates of the discrete point m belonging to wind turbine i in the relative coordinate system where the positive direction of the x-axis represents the wind direction, are the horizontal, vertical and vertical coordinates of the center point of the wind rotor disk of wind turbine j in the relative coordinates, and respectively refer to the rotor diameter and thrust coefficient of wind turbine j, is the wake expansion coefficient of wind turbine j, which affects the wind speed loss by affecting the wake width of the isolated wake of wind turbine j at wind turbine i.

11. The method for optimizing the layout of multiple offshore wind farms considering the same type of aggregation according to claim 10 is characterized in that: The wake expansion coefficient of the wind turbine j ,include: ; in, and is an adjustable parameter, when j=1: ； When hour: ； ； in, Represents the wind turbine upwind of wind turbine j When operating in isolation, the wake impact area is close to the wind turbine. The overlapping area of ​​the wind wheel disks, For wind turbines The additional turbulence intensity in the isolated wake of a wind turbine The size of the place is calculated as: ; ； in, and They respectively refer to the rotor diameter and thrust coefficient of the wind turbine k, For wind turbines The effective turbulence intensity at , It is the horizontal coordinate of wind turbine j and k in the relative coordinate system where the positive direction of x-axis represents the wind direction.

12. A device for optimizing the layout of multiple offshore wind farms considering the same type of clustering, characterized in that: include: The data acquisition module is used to obtain wind farm information, machine type parameters and wind measurement data, wherein the wind farm information includes the wind turbine installation points in the field, the planned installed machine types and the quantity of each machine type; A data processing module, for determining a representative annual wind resource data set and a wind shear index at a representative height of the region where the wind farm is located based on the wind measurement data; The wind turbine layout optimization module is used to take the wind turbine models at each wind turbine installation point in the site as design variables, maximize the annual power generation of the wind farm as the goal, combine the constraints, and use intelligent optimization algorithms to iteratively search for the best layout design solution; The constraints include: quantity constraints of each machine type, and similar clustering constraints of the layout positions of each machine type; The calculation of the annual power generation of the wind farm includes: determining the annual power generation of the wind farm based on the wind turbine models and parameters of each wind turbine installation point, combined with a representative annual wind resource data set at a representative height and a wind shear index.

13. A storage medium having stored thereon a computer program executable by a processor, characterized in that: When the computer program is executed, the steps of the method for optimizing the layout of multiple offshore wind farms taking into account similar clustering as described in any one of claims 1 to 11 are implemented.

14. A multi-machine offshore wind farm layout optimization design device, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, characterized in that: When the computer program is executed, the steps of the method for optimizing the layout of multiple offshore wind farms taking into account similar clustering as described in any one of claims 1 to 11 are implemented.

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