Optimization Design Method for Wind Turbine Layout in Offshore Wind Farms Applicable to the Case of Class Field Groups

Abstract

The present invention relates to an optimized layout design method for offshore wind farms applicable to the situation of a class of field groups, which is applicable to the field of wind power planning and design. The technical solution adopted by the present invention is: an optimized layout design method for offshore wind farms applicable to the situation of a class of field groups, including: constructing an array of wind farm information, which includes the wind turbine positions, model numbers, model parameter information of each wind turbine in the planned field area and the built field areas around it, as well as the weights of each wind turbine; constructing a dictionary file of model aerodynamic parameters, which includes the model number and the corresponding wind speed-aerodynamic parameter list; constructing a dictionary file of key statistical information of multi-dimensional wind resources, which includes the model number and the probability density of each basic wind condition; taking the positions of each wind turbine in the planned field area as variables, aiming at maximizing the annual power generation of the planned field area, and combining the preset constraints on the positions of wind turbines in the planned field area, using an optimization algorithm to perform iterative calculations to determine the positions of each wind turbine in the planned field area.

Description

Technical Field

[0001] The present invention relates to an optimized layout design method for wind turbines in an offshore wind farm applicable to the situation of a cluster of wind farms, and is applicable to the field of wind power planning and design. Background Art

[0002] For considerations such as intensive use of sea areas and convenient power transmission, it is very likely that the newly planned area of an offshore wind farm will be arranged near the existing wind farms in the vicinity. Due to the short distance, the wake of the wind turbines in the existing adjacent wind farms is very likely to affect the wind turbines in the newly planned area. Therefore, when optimizing the layout of the newly planned area, the influence of the wind turbines in the existing adjacent wind farms on the wind turbines in the newly planned area needs to be considered.

[0003] In addition, with the development of large-scale wind turbines, the models of the wind turbines planned to be installed in the newly planned area may be different from those in the adjacent wind farms. The differences in the structural dimensions and aerodynamic parameters of each model further increase the difficulty and complexity of power calculation and layout optimization design. The reasons are as follows:

[0004] (1) Affected by the wind shear in the incoming flow, the differences in the wind energy resource distributions perceived by the wind turbines of each model with different hub heights are obvious;

[0005] (2) The effective wind speed ranges corresponding to the normal operation and power generation of the wind turbines of each model may not be the same. Therefore, under certain wind conditions, only some models of wind turbines generate electricity normally, while other wind turbines are in a shutdown state.

[0006] In addition to the above-mentioned cluster of wind farms, there are also many "cluster-like" situations in wind power projects (there are existing wind farms near the new area, and there are many types of models). For example, when upgrading an old wind farm in batches, the layout optimization of the wind turbines in the renovation area needs to consider the influence of the wind turbines in the unrenovated area on the wind turbines in the renovation area.

[0007] Although intelligent optimization algorithms have been widely used in the field of wind farm layout design, and there are a large number of research results for the actual needs in different scenarios, the existing research mostly focuses on the layout optimization of a single model and rarely involves the optimization of the mixed layout of multiple models. For the layout optimization design of the target area in complex "cluster-like" situations, there is still a lack of effective technical solutions at present.

[0008] The annual power generation is the core index determining the development benefits of a wind power project. Therefore, when using intelligent optimization algorithms for layout optimization design, the maximum annual power generation or its related function is often used as the goal. When calculating the annual power generation using intelligent optimization algorithms, once the necessary parameters and information are input, the optimization process completely depends on the algorithm to execute autonomously, and no manual intervention is allowed. Therefore, to ensure the accuracy, precision, and efficiency of the algorithm, the following two aspects need to be focused on:

[0009] (1) How to enable the algorithm to autonomously distinguish each wind turbine planned to be assembled in the planned assembly area from the existing wind turbines in the surrounding areas, and only consider the influence of the wake of the latter on the former, without including the power generation of the latter in the objective function and removing its coordinates from the optimization variable sequence;

[0010] (2) Since the calculation accuracy of the annual electricity directly determines the reliability of the optimization result, and the calculation duration affects the overall efficiency of the optimization process, another key point in the algorithm design is how to scientifically handle the influence of wind shear on the annual power generation in the case of mixed arrangement of multiple types of wind turbines, ensure that the wind conditions contributing to the annual power generation of the planned assembly area are fully considered, and avoid including invalid wind conditions in the calculation scope. Summary of the Invention

[0011] The technical problem to be solved by the present invention is: aiming at the above problems, to provide an optimized layout design method for offshore wind farms applicable to the case of similar field groups.

[0012] The technical solution adopted by the present invention is: an optimized layout design method for offshore wind farms applicable to the case of similar field groups, including:

[0013] Construct an array of wind farm information, including the positions, model numbers, model parameter information of each wind turbine in the planned assembly area and the surrounding existing wind farms, and the weights of each wind turbine;

[0014] Construct a dictionary file of model aerodynamic parameters, including the model number and the corresponding wind speed - aerodynamic parameter list;

[0015] Construct a dictionary file of key statistical information of multi - dimensional wind resources, including the model number and the probability density of each basic wind condition;

[0016] Taking the positions of each wind turbine in the planned assembly area as variables, aiming at maximizing the annual power generation of the planned assembly area, and combining the preset position constraints of the wind turbines in the planned assembly area, use an optimization algorithm to perform iterative calculations to determine the positions of each wind turbine in the planned assembly area;

[0017] The model number is determined based on the model of the wind turbine; the weight is determined based on the wind farm where the wind turbine is located, with the weight corresponding to 1 for the planned assembly area and 0 for the surrounding existing wind farms; the basic wind conditions are divided based on wind direction and wind speed; the probability density of each basic wind condition corresponding to each model number is determined based on the representative annual wind resource data set at the hub height of each model;

[0018] The calculation of the annual power generation of the planned assembly area includes:

[0019] Based on the wind farm information array, considering the wake influence of the upwind wind turbines, calculate the effective wind speed of each wind turbine under each basic wind condition; based on the model numbers and effective wind speeds of each wind turbine, refer to the wind speed-aerodynamic parameter list in the model aerodynamic parameter dictionary file to determine the power of each wind turbine under each basic wind condition.

[0020] Based on the model numbers of each wind turbine and the power of each wind turbine under each basic wind condition, combined with the typical wind condition probability density in the multi-dimensional wind resource key statistical information dictionary file and the wind turbine weights in the wind farm information array, calculate the annual power generation of the planned field area.

[0021] The basic wind conditions are divided based on wind direction and wind speed, including:

[0022] According to the set number of wind direction sectors, evenly divide the 0 - 360° wind direction angle to generate multiple distinct wind direction sectors;

[0023] Traverse the wind speed-aerodynamic parameter lists of each model in the planned field area, determine the minimum cut-in wind speed and the maximum cut-out wind speed, combined with the maximum cut-out wind speed in the wind speed-aerodynamic parameter lists of each model in the surrounding built field areas, determine the wind speed range, and combined with the preset wind speed calculation interval, divide the wind speed range to generate multiple distinct wind speed intervals;

[0024] Combine the wind direction sectors and wind speed intervals in pairs to generate multiple distinct basic wind conditions.

[0025] The probability density of each basic wind condition corresponding to each model number is determined based on the representative annual wind resource data set at the hub height of each model, including:

[0026] Based on the wind direction of each time period in the representative annual wind resource data set of each model, assign each time period to the corresponding wind direction sector;

[0027] Based on the ratio of the number of time periods under each wind direction sector to the total number of time periods in the representative year, determine the wind direction frequency of each wind direction sector;

[0028] Use the Weibull function to fit the wind speed data of all time periods assigned to each wind direction sector, and based on the fitting results, calculate the probability density of each wind speed interval under each wind direction sector, and then determine the probability density of each basic wind condition corresponding to each model in the representative year.

[0029] The position constraints of the wind turbines in the planned field area include:

[0030] The positions of the wind turbines in the planned field area are within the planned area range of the planned field area;

[0031] And, the distance between each wind turbine in the planned field area and any other wind turbine in the planned field area is greater than the preset minimum distance.

[0032] Including:

[0033] Calculate the sum of the areas of the triangles formed by the position of the wind turbine and any two adjacent boundary points of the planned area

[0034] If the sum of the areas of the triangles is greater than the area of the planned area, the corresponding wind turbine position is outside the planned area, and the wind turbine positions do not meet the constraints of the wind turbine positions in the preset field

[0035] Including:

[0036] Calculate the distance between the wind turbine position in the loom layout plan and any other wind turbine in the planned field

[0037] If the distance is greater than the preset minimum distance, the wind turbine positions do not meet the constraints of the wind turbine positions in the preset field

[0038] Based on the wind farm information array, considering the influence of the wake of the upwind wind turbine, calculate the effective wind speed of each wind turbine under each basic wind condition, including:

[0039] Based on the wind turbine positions of each wind turbine in the planned field area and the surrounding built field areas in the wind farm information array, combined with the representative wind direction of the typical wind condition, calculate the wind speed loss of each wind turbine affected by the wake of the upwind wind turbine

[0040] Based on the representative wind speed of the basic wind condition and the wind speed loss of each wind turbine affected by the wake of the upwind wind turbine, calculate the effective wind speed of each wind turbine under each basic wind condition

[0041] Based on the wind turbine positions of each wind turbine in the planned field area and the surrounding built field areas in the wind farm information array, combined with the representative wind direction of the typical wind condition, calculate the wind speed loss of each wind turbine affected by the wake of the upwind wind turbine, including:

[0042] ;

[0043] In the formula, represents the wind speed loss of the wind turbine with row index i affected by the isolated wake of the wind turbine with row index j in the upwind direction, is the swept area of the wind turbine at row index i; and respectively represent the wind speed loss of the isolated wake of the upwind wind turbine j at the wind turbine with row index i and the projected area of its influence area on the wind turbine disk surface with row index i;

[0044] ;

[0045] In the formula, is the effective wind speed of the wind turbine with row index j, and They are the thrust coefficient and the rotor diameter respectively; It represents the wake width of the isolated wake of the wind turbine with row index j at the wind turbine with row index i;

[0046] ;

[0047] In the formula, A is a constant; is the turbulence intensity in the free incoming flow; It represents the flow direction spacing of the wind turbines with row indices j and i along the representative wind direction.

[0048] Based on the wind turbine positions of each wind turbine in the planned field area and the existing field areas around in the wind farm information array, combined with the representative wind direction of the typical wind conditions, calculating the wind speed loss of each wind turbine affected by the wake of the upwind wind turbines, including:

[0049] Sorting each wind turbine based on the front-back relative positions of each wind turbine along the representative wind direction of the typical wind conditions;

[0050] Determining the maximum value Nobj_wt_max of the serial numbers among the wind turbines to be installed in the planned field area under the basic wind conditions;

[0051] Calculating the wind speed loss of each wind turbine with serial numbers from 1 to Nobj_wt_max affected by the wake of the upwind wind turbines in sequence according to the sorting of each wind turbine.

[0052] Based on the model numbers of each wind turbine and the power of each wind turbine under each basic wind condition, combined with the probability density of the typical wind conditions in the multi-dimensional wind resource key statistical information dictionary file and the wind turbine weights in the wind farm information array, calculating the annual power generation of the planned field area, including:

[0053] Based on the power of each wind turbine in the planned field area under each basic wind condition, combined with the probability density under each basic wind condition corresponding to the model number of each wind turbine, calculating the contribution amount of each wind turbine to the annual power generation of the planned field area under each basic wind condition;

[0054] Based on the contribution amounts of each wind turbine to the annual power generation of the planned field area under each basic wind condition, determining the contribution amount of each basic wind condition to the annual power generation of the planned field area, and further determining the annual power generation of the planned field area.

[0055] Including:

[0056] Constructing an array of contribution amounts of power generation for each basic wind condition, and the number of elements in the contribution amount array is the same as the number of wind turbines in the wind farm information array;

[0057] Based on the position order of each wind turbine along the representative wind direction of the basic wind condition, determining the corresponding relationship between each wind turbine and each element in the contribution amount array;

[0058] Based on the contribution of each wind turbine to the annual power generation of the planned field area under each calculated basic wind condition, update each element in the contribution array.

[0059] An offshore wind farm layout optimization design device applicable to the case of a class of field groups, comprising:

[0060] An information array construction module for constructing a wind farm information array, including the wind turbine positions, model numbers, model parameter information of each wind turbine in the planned field area and the surrounding built field areas, as well as the weights of each wind turbine;

[0061] A parameter dictionary construction module for constructing a model aerodynamic parameter dictionary file, including the model number and the corresponding wind speed - aerodynamic parameter list;

[0062] An information dictionary construction module for constructing a multi - dimensional key wind resource statistical information dictionary file, including the model number and the probability density of each basic wind condition;

[0063] A position variable optimization module for taking the positions of each wind turbine in the planned field area as variables, aiming at maximizing the annual power generation of the planned field area, and combining the preset constraints on the positions of wind turbines in the planned field area, using an optimization algorithm for iterative calculation to determine the positions of each wind turbine in the planned field area;

[0064] The model number is determined based on the model of the wind turbine; the weight is determined based on the field area where the wind turbine is located, with the weight corresponding to 1 for the planned field area and 0 for the surrounding built field areas; the basic wind conditions are divided based on the wind direction and wind speed; the probability density of each basic wind condition corresponding to each model number is determined based on the representative annual wind resource data set at the hub height of each model;

[0065] The calculation of the annual power generation of the planned field area includes:

[0066] Based on the wind farm information array, considering the wake effect of the upwind wind turbine, calculate the effective wind speed of each wind turbine under each basic wind condition; based on the model number and the effective wind speed of each wind turbine, refer to the wind speed - aerodynamic parameter list in the model aerodynamic parameter dictionary file to determine the power of each wind turbine under each basic wind condition;

[0067] Based on the model number of each wind turbine and the power of each wind turbine under each basic wind condition, combined with the probability density of the typical wind condition in the multi - dimensional key wind resource statistical information dictionary file and the weight of the wind turbine in the wind farm information array, calculate the annual power generation of the planned field area.

[0068] A storage medium, on which a computer program executable by a processor is stored, and when the computer program is executed, the steps of the offshore wind farm layout optimization design method applicable to the case of a class of field groups are implemented.

[0069] An offshore wind farm layout optimization design device has a memory and a processor. A computer program that can be executed by the processor is stored on the memory. When the computer program is executed, the steps of the offshore wind farm layout optimization design method applicable to the "class field group" scenario are implemented.

[0070] The beneficial effects of the present invention are as follows: Aiming at the layout optimization design problem only for the target field area in the "class field group" scenario, which is characterized by a large number of units, diverse unit types, fixed point coordinates of some units, and the need to consider the influence of each wind turbine on the dynamically changing point coordinates of other points, the present invention conducts a systematic design. It adopts a binary weight allocation strategy, setting the weights of the wind turbines to be assembled in the planned field area and each wind turbine in the surrounding area to 1 and 0 respectively, and constructs a mapping relationship between each unit type and the key statistical characteristic quantities of wind resources and the wind speed-aerodynamic parameter list at the corresponding hub height. Thus, the layout optimization model established based on the optimization algorithm can autonomously identify the attribution of each wind turbine and match accurate wind condition and aerodynamic parameter information for it under the above complex conditions.

[0071] By setting the weight of 0 or 1 for the wind turbines in different field areas, the present invention can be used to distinguish the attribution of the wind turbines, enabling the intelligent optimization algorithm to accurately determine which wind turbine positions need to be optimized; by taking the weight of the wind turbines in the planned field area as 1 and the weight of the surrounding wind turbines as 0, the present invention can make it only necessary to calculate the new weighted sum of the power generation of each wind turbine in the subsequent steps, without having to judge the attribution of each wind turbine, and the power generation of the target wind farm can be obtained.

[0072] The present invention systematically considers the influence of surrounding wind turbines on the annual power generation of the target planned field area from multiple dimensions, from basic wind condition division to wake effect evaluation. To improve the calculation accuracy, the present invention has made a clever design from the following two aspects: First, based on the unit type, it considers the influence of wind shear in the inflow on the distribution difference of wind energy resources perceived by each wind turbine; second, in the basic wind condition division, it traverses the wind speed-aerodynamic parameter list of each unit type in the planned field area and the surrounding built field areas to determine the wind speed range, ensuring that all wind conditions contributing to the annual power generation of the target field area are included in the calculation range.

[0073] To meet the strict requirements of wind power projects for timeliness, in each basic wind condition with different representative wind directions, the present invention evaluates the wake loss up to the last wind turbine (serial number Nobj_wt_max) along the representative wind direction in the planned field area, thus significantly reducing the irrelevant calculation time. Brief Description of the Drawings

[0074] Figure 1 It is a flowchart of an embodiment.

[0075] Figure 2 It is a schematic diagram of a multi-dimensional wind resource key statistical information dictionary file in an embodiment.

[0076] Figure 3 It is a flowchart for optimizing the position of wind turbines in the embodiment.

[0077] Figure 4 It is a schematic diagram of the method for judging the relative position relationship between the positions of wind turbines and the planned field area in the embodiment; where (a) and (b) respectively refer to the situations where the positions of wind turbines are outside and inside the planned field area.

[0078] Figure 5 It is a flowchart for calculating the annual power generation of the planned field area in the embodiment. Detailed implementation manners

[0079] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation to the present invention. For the step numbers in the following embodiments, they are only set for the convenience of description and illustration, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0080] In the description of the present invention, the meaning of "a plurality" is two or more. If the first and second are described, it is only for the purpose of distinguishing technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the present invention.

[0081] Embodiment 1: As Figure 1 shown, this embodiment is an offshore wind farm layout optimization design method applicable to the situation of a class of field groups, specifically including the following steps:

[0082] S100. Obtain the wind farm planning information of the planned field area, as well as the wind farm information of the surrounding existing field areas, and obtain the representative annual wind resource data sets at the hub heights of each model.

[0083] In this embodiment, the wind farm planning information of the planned field area includes the planned area range, as well as the wind turbine models to be assembled, the quantity of each model, and the parameter information of each model. The surrounding existing field areas are the wind farm areas within a preset range around the planned field area, and their wind farm information includes the positions (abscissa, ordinate) of each wind turbine in the field area, the models of each wind turbine, and the parameter information of each model.

[0084] In this example, the scope of the planned area includes the positions of the boundary points that enclose the planned field area. When there are restricted areas where wind turbines cannot be arranged inside, it also includes the positions of the boundary points that enclose the restricted areas.

[0085] In this embodiment, the model parameter information includes the wind turbine diameter, hub height, and wind speed-aerodynamic parameter list of the model. Among them, the wind speed-aerodynamic parameter list is usually provided by the whole machine manufacturer and covers the thrust coefficient and power data at each typical wind speed within the range of normal operation and power generation of the wind turbine (i.e., between the cut-in wind speed and the cut-out wind speed).

[0086] Regarding the multi-model mixed arrangement scenario involved in this embodiment, it should be particularly noted that there may be differences in the effective wind speed ranges for different models to operate and generate electricity normally. In this multi-model mixed arrangement scenario, there may be a situation where the incoming flow wind speed is lower than the cut-in wind speed of a certain model planned to be assembled in the planned field area and higher than the cut-in wind speeds of other models. In this case, when determining the aerodynamic parameters of the wind turbine according to the wind speed, it may occur that this wind speed is not included in the wind speed-aerodynamic parameter list, resulting in a calculation overflow error.

[0087] Regarding the above calculation overflow problem, this embodiment supplements the aerodynamic parameters of each model in the intervals below the cut-in wind speed and above the cut-out wind speed. Specifically, for the wind speed interval below the cut-in wind speed, the thrust coefficient takes a relatively large value close to 1, such as 0.9999, and the power takes 0; for the wind speed interval above the cut-out wind speed, the thrust coefficient takes a relatively small value close to 0, such as 0.0001, and the power also takes 0.

[0088] In this example, the representative annual wind resource data sets at the hub height of each model in the planned field area are obtained after processing the time-series observation data of the floating wind lidar in the field area for integrity and rationality checks, as well as removing, interpolating, extending, and performing representative annual correction on unreasonable data and missing measurement data. Each data set contains the wind speed and wind direction for 8760 time periods.

[0089] S200. Based on the wind farm planning information of the planned field area and the wind farm information of the surrounding built field areas, construct a wind farm information array that includes the key characteristic quantities of each wind turbine planned to be assembled in the planned field area and the surrounding wind turbines.

[0090] In this embodiment, the number of rows of the wind farm information array is the same as the number of wind turbines. The data elements in each row are used to describe the key characteristics of the corresponding wind turbine. The number of columns of the array is 7, corresponding in sequence to the wind turbine number, abscissa, ordinate, model number, hub height, wind turbine diameter, and weight.

[0091] In this example, the wind turbine numbers can be numbered starting from 1 and incremented one by one according to the reading order of each wind turbine when obtaining data in step S100.

[0092] The abscissa and ordinate in the wind farm information array can be filled for each wind turbine planned to be installed in the planned area according to the manual layout plan after determining their coordinate values in the geodetic coordinate system; for each wind turbine in the surrounding built-up areas, the coordinate values in the geodetic coordinate system are calculated and filled in according to the position information obtained in step S100.

[0093] In this example, the geodetic coordinate system is a two-dimensional rectangular coordinate system established by taking any point in the planned area as the origin, with the due east direction as the positive x-axis (corresponding to the wind direction angle of 270°) and the due north direction as the positive y-axis (corresponding to the wind direction angle of 180°).

[0094] In this embodiment, the model numbers are specified by their serial numbers according to the reading order of each model when obtaining data in step S100. By binding the models to which each wind turbine in the wind farm information array belongs, it is convenient for subsequent calculations to enable the algorithm to accurately identify the wind conditions and aerodynamic parameter information matching each wind turbine model.

[0095] In this example, the weights of each wind turbine are used to distinguish the relative relationship between the wind turbine and the planned area. For each wind turbine planned to be installed in the planned area, the weight is taken as 1, while for each wind turbine in the surrounding built-up areas, the weight is taken as 0.

[0096] Let the number of wind turbines to be installed in the planned area be , which includes a total of M types of models, and the model numbers are successively [1,..., M]; the number of wind turbines in the surrounding built-up areas is , which covers a total of P types of models, and it is assumed that the models in the planned area are completely different, and the model numbers are successively [M + 1,..., M + P].

[0097] For each model, the hub height and rotor diameter are respectively expressed as and , where the subscript i represents the model number, that is, i ∈ [1, M + P]; for each wind turbine, its abscissa and ordinate are respectively represented by and , where the subscript j represents the wind turbine number, that is, j ∈ [1 + .

[0098] Assume that the wind turbine numbers from 1 to successively correspond to each wind turbine to be installed in the planned area, and the model numbers of the wind turbines numbered 1 and are 1 and M respectively; while the wind turbine numbers to (where ) successively correspond to each built wind turbine in the surrounding built-up areas, and the model numbers of the wind turbines numbered and are M + 1 and M + P respectively.

[0099] Based on the above definition, the wind farm information array has the form as shown in Table 1 below;

[0100] Table 1

[0101] 。

[0102] S300. For the convenience of subsequent calculations, an organic aerodynamic parameter dictionary file is constructed in this example. The dictionary file is a commonly used data structure for storing data with a mapping relationship, where each key corresponds to a value, thus forming a one-to-one mapping relationship.

[0103] Specifically for the aerodynamic parameter dictionary file of the model in this embodiment, the key is the model number, and the value is a multi-dimensional array for storing the wind speed-aerodynamic parameter information of this model.

[0104] Continuing with the models involved in the above wind farm information array, an example is given as follows:

[0105] Since the model number takes values in [1,..., M + P], the form of the model aerodynamic parameter dictionary file is as follows: {1: Array_1;...; M + P: Array_M + P}. Among them, taking Array_1 as an example, it is an array for storing the wind speed-aerodynamic parameter information of the model with model number 1, and the form is as shown in Table 2 below;

[0106] Table 2

[0107] ;

[0108] In Table 2 above, , and successively refer to the k-th typical wind speed in the wind speed-aerodynamic parameter list of the model with model number A = 1 and the thrust coefficient and power at this wind speed.

[0109] S400. Based on the wind direction and wind speed, the basic wind conditions are divided, and based on the representative annual wind resource data sets of each model in the planned area, the probability density of each model under each basic wind condition in the representative year is determined, and a multi-dimensional wind resource key statistical information dictionary file is constructed based on the model, basic wind condition, and probability density.

[0110] S410. According to the set number of wind direction sectors, the 0 - 360° wind direction angle is evenly divided to generate multiple distinct wind direction sectors.

[0111] According to the set number of wind direction sectors (which can take the value 12 or ), evenly divide the wind direction angle of 0 - 360°, and thus obtain multiple distinct wind direction sectors. Let the central value of the first wind direction sector be 0, then according to the definition, the central value of the th wind direction sector is

[0112] S420. Traverse the wind speed - aerodynamic parameter list of the wind turbine models to be assembled in the planned area, determine the minimum cut - in wind speed and the maximum cut - out wind speed, and based on the minimum cut - in wind speed and the maximum cut - out wind speed, determine the wind speed range, and combine with the preset wind speed calculation interval to divide the wind speed range and generate multiple distinct wind speed intervals.

[0113] Traverse the wind speed - aerodynamic parameter list of each wind turbine model planned to be assembled in the planned area, and determine the minimum cut - in wind speed and the maximum

[0114] cut - out wind speed. The calculation formula is:

[0115] ;

[0116] where M represents the number of models planned to be assembled in the planned area, and respectively represent the cut - in wind speed and the cut - out wind speed of the th model among them.

[0117] Compare with the cut - out wind speeds of the models to which the wind turbines in the surrounding built - up areas belong, and take the larger one among them to update .

[0118] Respectively perform floor and ceiling operations on and . The results can be expressed as , , and use these as the lower and upper limits, combined with the set wind speed calculation interval to divide multiple distinct wind speed intervals.

[0119] According to the definition, the total number of wind speed intervals is . Let the central value of the first wind speed interval be , then the central value of the th wind speed interval is .

[0120] Regarding , it is recommended that its value does not exceed the interval between two adjacent typical wind speed points in the wind speed - aerodynamic parameter list of each wind turbine model planned to be assembled in the planned area to ensure that the data in the list can be fully utilized.

[0121] The upper limit of the wind speed range needs to be determined by considering the cut-out wind speed of the surrounding wind turbines , because: when the cut-out wind speed of the surrounding wind turbines is higher than that of the wind turbines of each model planned to be installed in the planned area, if the incoming wind speed is lower than the former but higher than the latter, and the surrounding wind turbines are located upwind, since their operation and power generation absorb part of the wind energy in the incoming flow, the wind speed perceived by the wind turbines in the planned area will be lower than the incoming wind speed and may be less than the cut-out wind speed of their own model. Obviously, in this case, these wind turbines will also generate electricity and contribute to the annual power generation of the planned area, so it is necessary to consider them.

[0122] S430. Combine the multiple wind direction sectors divided in step S410 and the multiple wind speed ranges divided in step S420 in pairs, so as to obtain a number of different basic wind conditions. The total number of basic wind conditions is .

[0123] Based on the basic wind conditions obtained from the above operations, on the premise of ensuring that all wind conditions contributing to the annual power generation of the planned area are fully considered, the composition of the basic wind conditions corresponding to each model is also exactly the same, thus facilitating the calculation of the annual power generation in the subsequent steps.

[0124] Perform for each basic wind condition Indicates the basic wind condition numbered r, r = 1, 2, 3... , basic wind condition The wind direction sector and wind speed range numbers are respectively represented as and , and the representative wind direction and representative wind speed of the basic wind condition are represented by and respectively. They respectively take the intermediate values of the wind direction sector and the wind speed range .

[0125] S440. Based on the basic wind conditions, process the representative annual wind resource data set at the hub height of each model in step S100 to construct a multi-dimensional wind resource key statistical information dictionary file.

[0126] Next, taking the processing of the representative annual wind resource data set at the hub height of model t as an example, the process will be described in detail:

[0127] S441. According to the wind direction of each time period in the representative annual wind resource data set, assign this time period to the corresponding wind direction sector in step S410;

[0128] S442. After completing the traversal processing of the wind direction information for all time periods, calculate the ratio of the number of time periods assigned to each wind direction sector to the number of time periods in the representative year wind resource data set, from which the proportion of each wind direction sector, also known as the wind direction frequency, can be obtained. According to the definition, we have:

[0129] ;

[0130] where is the proportion of the th wind direction sector.

[0131] S443. Use the Weibull function to fit the wind speed data for all time periods assigned to each wind direction sector, and the shape parameter and scale parameter can be obtained; based on the Weibull fitting result, the probability density of each wind speed interval under each wind direction sector, also known as the wind speed frequency, can be calculated. Let the probability density of the nth wind speed interval under the mth wind direction sector be denoted as .

[0132] Referring to the above process, after completing the traversal processing of the representative year wind resource data sets of all models in the planned site area, a multi-dimensional wind resource key statistical information dictionary file containing the above key statistical information of each basic wind condition is constructed.

[0133] In this example, the multi-dimensional wind resource key statistical information dictionary file adopts a nested hierarchical structure, which contains three dimensions in total and is used to efficiently store and retrieve wind resource statistical information. Its overall structure is as Figure 2 shown:

[0134] The first dimension is the model dimension, which contains multiple key-value pairs. Each key takes the model number, denoted as t (t ∈ [1, M], where M is the total number of models to be installed in the planned site area);

[0135] The second dimension contains a basic wind condition statistical information dictionary file, including the following two key-value pairs. The first key is "wind direction frequency", and the value is an array used to store the wind direction frequency of model t in each wind direction sector, denoted as , and the other key is "wind condition information".

[0136] The wind condition information dictionary file corresponding to model t constitutes the third dimension, which contains the following key-value pairs. Each key takes the wind direction sector number, denoted as m (m ∈ [1, ), and the value is an array used to store the wind speed frequency of model t in each wind speed interval under wind direction sector m, denoted as .

[0137] Through the above multi-dimensional dictionary file structure, when the model number and the wind direction sector number and wind speed range number that constitute the basic wind condition are known, key statistical information such as wind direction frequency and wind speed frequency of the target model under the target basic wind condition can be efficiently retrieved and obtained.

[0138] In this embodiment, the probability density of each basic wind condition is determined based on the wind direction frequency of each wind direction sector and the wind speed frequency of each wind speed interval under the wind direction sector.

[0139] S500, taking the position of each wind turbine in the planning area as a variable, taking maximizing the annual power generation of the planning area as a goal, and combining the preset constraints on the position of the wind turbines in the planning area, a sequential least squares programming (SLSQP) algorithm is used for iterative calculation to determine the optimal wind turbine position of each wind turbine in the planning area within the planning area.

[0140] like Figure 3 As shown, the specific method for optimizing the wind turbine position in this embodiment includes:

[0141] S510, obtaining a wind farm information array, a multi-dimensional wind resource key statistical information dictionary file, and an aircraft model aerodynamic parameter dictionary file.

[0142] S520, receiving the wind farm information array, extracting the row index with a weight of 1 (the wind turbines in these rows correspond to the wind turbines to be installed in the planned site). According to the order in which the row index is read, extract the horizontal coordinate and the vertical coordinate in each row in turn to form a set:

[0143] ;

[0144] In the formula, refers to the wind turbine in the i-th row with a weight of 1, are its horizontal and vertical coordinates respectively.

[0145] S530, pair collection The constraints of the internal wind turbines are determined. In this example, the preset constraints on the location of the wind turbines in the field include the following two aspects:

[0146] Constraint 1: The wind turbine locations of all wind turbines in the planning area are within the boundary of the planning area.

[0147] In this example, the sum of the areas of the triangles formed by the wind turbine location in the layout plan and any two adjacent boundary points of the planning area is calculated; if the sum of the triangle areas is greater than the area of ​​the planning area, the corresponding wind turbine location is outside the planning area, and the layout plan does not meet the preset wind turbine location constraints within the site.

[0148] By Collection Internal wind turbine Taking the planned field area OABCD (with a restricted area inside it) as an example, in the situation shown in (a) of , the wind turbine position Figure 4 is outside the planned field area OABCD, while in (b) of , the wind turbine position Figure 4 is inside the planned field area OABCD. The specific method is as follows: Calculate the sum of the areas of the triangles formed by taking the wind turbine position

[0149] and two adjacent boundary points of the planned field area OABCD as vertices. When the sum is equal to the area of the planned field area OABCD, the wind turbine is inside the planned field area OABCD; otherwise, it is outside the planned field area OABCD. When there is also a restricted area where wind turbines cannot be arranged within the planned field area, the relative relationship between the wind turbine position

[0150] and the restricted area can also be judged by referring to the above method; When the wind turbine

[0151] simultaneously satisfies being inside the planned field area and outside the restricted area, it is determined to meet the membership requirements. Constraint 2: The distance between any wind turbine in the planned field area and any other wind turbine in the planned field area is greater than the preset minimum distance

[0152] . .

[0153] In this embodiment, calculate the distance between the position of the wind turbine in the layout plan and any other wind turbine in the planned field area; if there is a distance greater than the preset minimum distance, the layout plan does not meet the preset constraints on the position of wind turbines in the field area.

[0154] The calculation formula is:

[0155] ;

[0156] In the formula, , respectively represent the th and the nth wind turbines in the set .

[0157] In this embodiment, the distance between the planned field area and the surrounding existing field areas is greater than the preset minimum distance , so the size of the distance between the wind turbines in the planned field area and the wind turbines in the surrounding existing field areas does not need to be considered.

[0158] If both of the above two constraint conditions are met, the layout plan corresponding to the set is marked as "qualified", otherwise, it is marked as "unqualified".

[0159] S540. For the loom schemes marked as "unqualified", directly take the annual power generation of the planned field area as 0.001, while for the loom schemes marked as "qualified", calculate the annual power generation of the planned field area according to the following steps, as Figure 5 shown.

[0160] S541. Obtain the representative wind speed and representative wind direction of each basic wind condition in the multi-dimensional wind resource key statistical information dictionary file.

[0161] S542. Based on the representative wind direction of the basic wind condition, determine the front-back relative positions of each wind turbine in the planned field area and the surrounding existing field areas based on the representative wind direction.

[0162] Along the representative wind direction of the basic wind condition sort the wind turbines according to their front-back relative positions, update the wind farm information array, and at the same time determine the maximum value of the wind turbine serial number in the planned field area, denoted as Nobj_wt_max;

[0163] The reason for sorting according to the representative wind direction is that only the wake of the upwind wind turbine will affect the downwind wind turbine, while the latter will not affect the former.

[0164] In this embodiment, through coordinate transformation, the positions of the wind turbines are converted to the relative coordinate system. The positive direction of the x-axis of the relative coordinate system points to the representative wind direction. The front-back relative positions of the wind turbines can be determined by the size of the abscissa in the relative coordinate system. When the abscissa is smaller, the position of the wind turbine is more forward.

[0165] The relative coordinate system shares the same coordinate origin with the geodetic coordinate system established in step S200, and is obtained by rotating the geodetic coordinate system according to the representative wind direction , so that in the relative coordinate system, the positive direction of the x-axis points to the representative wind direction.

[0166] The abscissa in the relative coordinate system is obtained according to the horizontal and vertical coordinates of each wind turbine in the wind farm information array, combined with the following transformation matrix. Taking the wind turbine with row index i in the wind farm information array as an example, the calculation formula is:

[0167] ;

[0168] In the formula, and are the horizontal and vertical coordinates of the wind turbine with row index i in the geodetic coordinate system and the relative coordinate system respectively.

[0169] After calculating the horizontal and vertical coordinates of each wind turbine in the relative coordinate system and determining their front-to-back order, update the wind farm information array in ascending order of the serial numbers, so that in the updated wind farm information array, the 1st, …, i, …, N wt rows sequentially contain the wind turbine information with serial numbers 1, …, i, …, N wt .

[0170] Determine the target basic wind condition according to the maximum value of the row index with a weight of 1 in the updated wind farm information array and the maximum value of the serial numbers of the wind turbines to be installed in the planned field area, denoted as Nobj_wt_max. The reason for performing this operation is that the wind turbines with row indices greater than Nobj_wt_max are located outside the planned field area, and their power generation has no contribution to the annual power generation of the planned field area. Therefore, there is no need to spend additional time cost evaluating their wake losses and power generation to reduce the calculation time, so as to better meet the high requirements of engineering practice for timeliness.

[0171] Starting from the wind turbine with a row index of 1 and ending at the wind turbine with a row index of Nobj_wt_max, calculate the contribution of each wind turbine to the annual power generation of the planned field area after considering the wake influence of other wind turbines upwind in turn.

[0172] S543. Construct the array corresponding to the basic wind condition to store the contribution of each wind turbine in the planned field area to the annual power generation of the planned field area under the basic wind condition and initialize it as an all-zero array, that is:

[0173] ;

[0174] wherein, the number of elements in is the same as the number of rows in the wind farm information array.

[0175] In this embodiment, based on the position order of each wind turbine along the representative wind direction of the basic wind condition, determine the corresponding relationship between each wind turbine and each element in the contribution array; based on the calculated contribution of each wind turbine to the annual power generation of the planned field area under each basic wind condition, update each element in the contribution array.

[0176] S544. Calculate the wind speed loss of each wind turbine in the planned field area affected by the wake of the upwind wind turbines based on the wind turbine positions of each wind turbine in the planned field area and the surrounding existing field areas in the wind farm information array.

[0177] Next, taking the wind turbine with a row index of i under the basic wind condition as an example, the calculation process is described in detail:

[0178] When i = 1, since there are no other wind turbines upwind, the wind speed loss caused by the wake is . Therefore, the effective wind speed of the wind turbine with row index i = 1 is equal to the representative wind speed, that is ;

[0179] When , there are (i - 1) wind turbines upwind. When calculating the effective wind speed of the wind turbine with row index i, it is necessary to consider the wind speed loss caused by the wake interference of these upwind wind turbines. The specific steps are as follows:

[0180] In this embodiment, based on the wind turbine positions of each wind turbine in the planned field area and the built field areas around in the wind farm information array, the Park model is used to calculate the wind speed loss in the isolated wake area of any upwind wind turbine, and their respective wake shielding effects on the wind turbine at row index i are quantified by the "area projection method";

[0181] ;

[0182] In the formula, represents the wind speed loss of the wind turbine with row index i affected by the isolated wake of the wind turbine with row index j upwind; is the swept area of the wind turbine at row index i, and the calculation formula is , where is the wind turbine rotor diameter at row index i; and respectively represent the wind speed loss of the isolated wake of the upwind wind turbine j at the wind turbine with row index i and the projected area of its influence area on the wind turbine rotor disk surface at row index i;

[0183] The calculation formula of

[0184] ;

[0185] In the formula, is the effective wind speed of the wind turbine with row index j, and are its thrust coefficient and rotor diameter respectively (which can be obtained from the wind farm information array). The Park model assumes that the wake influence area is circular, and its diameter (also known as the "wake width") approximately linearly increases with the distance from the rotor disk. represents the wake width of the isolated wake of the wind turbine with row index j at the wind turbine with row index i.

[0186] In the original Park model, it is assumed that the wake influence area at any downstream position behind the rotor disk is circular, and the wake width (i.e., the diameter of the circular influence area) approximately linearly increases with the distance from the rotor disk, and the growth rate is expressed as k = , where represents the wake width, is the downstream distance relative to the wind turbine disk plane. The original Park model assumes that the wake growth rate k remains constant at any downstream position behind the wind turbine. However, in fact, a large number of recent studies have pointed out that the additional turbulence intensity generated by the operation of the wind turbine has a significant impact on the wake evolution, and the greater the turbulence intensity, the faster the expansion rate of the wake influence area (correspondingly, the larger the k value). In view of this, in this embodiment, the additional turbulence intensity at the position x from the wind turbine disk plane in the wind turbine wake area is calculated by the method of Frandsen et al.:

[0187] ;

[0188] where and are the wind turbine rotor diameter and thrust coefficient, respectively.

[0189] Combining the turbulence intensity in the free incoming flow, according to the following formula, the effective turbulence intensity at x behind the wind turbine disk plane can be obtained:

[0190] ;

[0191] Take k = AI, where A is a constant, and substitute it into k = , and through numerical integration, we can get:

[0192] ;

[0193] By comparing the actual operation results of the wind farm, this embodiment recommends taking A = 0.6.

[0194] Specifically for , the calculation formula is:

[0195] ;

[0196] In the formula, represents the flow direction spacing along the representative wind direction of the wind turbines with row indices j and i.

[0197] S545. Based on the representative wind direction of the basic wind conditions and the wind speed loss of each wind turbine in the planned field area affected by the wake of the upwind wind turbine, calculate the effective wind speed of each wind turbine in the planned field area under each basic wind condition, including:

[0198] ;

[0199] In the formula, is the effective wind speed of the wind turbine with row index i; is the representative wind speed of the basic wind condition ; It represents the wind speed loss of the wind turbine with row index i affected by the isolated wake of the wind turbine with upwind row index j.

[0200] S546. Based on the effective wind speeds of each wind turbine in the planned area under each basic wind condition, combined with the wind speed-aerodynamic parameter list of each wind turbine, determine the thrust coefficient and power of each wind turbine in the planned area under each basic wind condition.

[0201] When the effective wind speed of the wind turbine with row index i is obtained by referring to the above steps After that, using the model number in row index i (represented by ) as the key, combined with the model aerodynamic parameter dictionary file and the multi-dimensional wind resource key statistical information dictionary file, the wind speed-aerodynamic parameter information of the corresponding model can be determined.

[0202] Based on the above obtained wind speed-aerodynamic parameter information, through interpolation calculation, the thrust coefficient (used to calculate the wind speed loss of the downwind wind turbine) and power of the wind turbine with row index i can be obtained, and the calculation formulas are:

[0203] ;

[0204] ;

[0205] In the formula, and are respectively the two typical wind speeds closest to in the wind speed-aerodynamic parameter list (satisfying ), and are respectively the thrust coefficient and power corresponding to and .

[0206] S547. Based on the power of each wind turbine in the planned area under the basic wind condition, combined with the probability density of each wind turbine under the basic wind condition in the multi-dimensional wind resource key statistical information dictionary file, determine the contribution of each wind turbine to the annual power generation of the planned area under the basic wind condition.

[0207] Based on the power obtained above , the following formula can be used to quantify the contribution of the wind turbine with row index i to the annual power generation of the planned area under the basic wind condition : :

[0208] ;

[0209] In the formula, is the model of the wind turbine with row index i Under the basic wind conditions The wind direction frequency; is the wind turbine model for row index i Under the basic wind conditions The wind speed frequency for; is the weight of the wind turbine with row index i in the wind farm information array.

[0210] According to the above formula, only the wind turbines to be assembled in the planned area contribute to the annual power generation of the planned area, that is ; for the surrounding wind turbines with row index i ≤ Nobj_wt_max but located outside the planned area, because the corresponding is 0, so = 0; for the surrounding wind turbines with i > Nobj_wt_max, because the effective wind speed and power generation are not calculated, so remains the initial value of 0.

[0211] When obtaining after that, update the value of the i-th element in the array . At this time, the arrangement order of the data elements in the array corresponds to the sorting of each wind turbine along the direction represented by the basic wind conditions representing the wind direction.

[0212] S548. When the contribution of all wind turbines in the planned area under the basic wind conditions is calculated, that is, after each element in the array is updated in sequence, change to a basic wind condition and repeat steps S542~S547 to calculate the contribution of each wind turbine to the annual power generation of the planned area under each pair of basic wind conditions.

[0213] S549. Based on the contribution of each wind turbine to the annual power generation of the planned area under each basic wind condition, determine the annual power generation of the planned area.

[0214] Reorder the array so that the arrangement order of the data elements in the array remains consistent under each basic wind condition representing different wind directions, so as to facilitate the calculation of the annual power generation of the planned area in the subsequent steps, avoid calculation errors caused by inconsistent sorting, and reduce the time for data search and matching.

[0215] The specific process is as follows:

[0216] Form an array Index_array in sequence with the wind turbine numbers in each row of the wind farm information array updated according to the sorting results of each wind turbine along the wind direction represented;

[0217] Apply a sorting function (such as the argsort() function in the Numpy module in Python) to the array Index_array to generate an address index array loc_array. When the sorting function is applied to an array containing multiple data elements, it returns a new array containing the address indices of all elements in ascending order of element values. Let Index_array = [1, 4, 2, 3], then the corresponding loc_array = [1, 3, 4, 2].

[0218] According to the guidance of the address index array loc_array, reorder the array After this adjustment, the data elements in the array will be arranged in ascending order of the wind turbine numbers.

[0219] After traversing all the basic wind conditions, based on the contribution of each wind turbine to the annual power generation of the planned field area under each basic wind condition, determine the contribution of each basic wind condition to the annual power generation of the planned field area, and then determine the annual power generation of the planned field area. The summation calculation is carried out according to the following formula, and thus the annual power generation of the planned field area can be obtained The calculation formula is:[[]]

[0220] ;

[0221] In the formula, is the total number of basic wind conditions, is the wind turbine power generation array under the basic wind condition numbered ; is the total number of wind turbines, is the total number of wind turbines, is the array The value of the i-th data element in it.

[0222] S550. Determine whether the calculation result of step S540 meets the convergence criterion. If it meets, output the value of the design variable at the current calculation step. If it does not meet, further determine whether the preset maximum number of iterations has been reached. If it meets, output the value of the design variable at the current calculation step in the same way. If it still does not meet, calculate a new design variable value according to the update rule of the SLSQP algorithm, that is, the horizontal and vertical coordinates of each wind turbine to be assembled in the planned field area, and fill the results in sequence into the corresponding positions of each wind turbine in the wind farm information array in turn. Then, feedback the updated wind farm information array to step S510 for the next round of optimization calculation.

[0223] Embodiment 2: This embodiment is an offshore wind farm layout optimization design device applicable to the case of a class field group, including:

[0224] An information array construction module for constructing an information array of a wind farm, including the positions, model numbers, model parameter information of each wind turbine in the planned field area and the surrounding existing field areas, and the weights of each wind turbine;

[0225] A parameter dictionary construction module for constructing a model aerodynamic parameter dictionary file, including the model number and the corresponding wind speed - aerodynamic parameter list;

[0226] An information dictionary construction module for constructing a multi - dimensional key wind resource statistical information dictionary file, including the model number and the probability density of each basic wind condition;

[0227] A position variable optimization module for taking the positions of each wind turbine in the planned field area as variables, aiming at maximizing the annual power generation of the planned field area, and combining the preset constraints on the positions of wind turbines in the planned field area, and using an optimization algorithm for iterative calculation to determine the positions of each wind turbine in the planned field area;

[0228] The model number is determined based on the model of the wind turbine; the weight is determined based on the field area where the wind turbine is located, with a weight of 1 for the planned field area and a weight of 0 for the surrounding existing field areas; the basic wind conditions are divided based on wind direction and wind speed; the probability density of each basic wind condition corresponding to each model number is determined based on the representative annual wind resource data set at the hub height of each model;

[0229] The calculation of the annual power generation of the planned field area includes:

[0230] Based on the wind farm information array, considering the wake effect of the upwind wind turbine, calculate the effective wind speed of each wind turbine under each basic wind condition; based on the model number and the effective wind speed of each wind turbine, refer to the wind speed - aerodynamic parameter list in the model aerodynamic parameter dictionary file to determine the power of each wind turbine under each basic wind condition;

[0231] Based on the model number of each wind turbine and the power of each wind turbine under each basic wind condition, combined with the probability density of typical wind conditions in the multi - dimensional key wind resource statistical information dictionary file and the weights of wind turbines in the wind farm information array, calculate the annual power generation of the planned field area.

[0232] Example 3: This example is a storage medium on which a computer program executable by a processor is stored. When the computer program is executed, the steps of the offshore wind farm layout optimization design method for the applicable class of field group scenarios described in Example 1 are implemented.

[0233] Example 4: This example is an offshore wind farm layout optimization design device, which has a memory and a processor. A computer program executable by the processor is stored on the memory. When the computer program is executed, the steps of the offshore wind farm layout optimization design method for the applicable class of field group scenarios described in Example 1 are implemented.

[0234] In addition, although the present invention has been described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the above-described functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It should also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. Rather, given the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Thus, those skilled in the art can implement the present invention as set forth in the claims without undue experimentation. It should also be 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.

[0235] 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, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0236] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0237] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the above programs can be printed, because the above programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing them in a computer memory.

[0238] 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 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, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0239] In the above description of this specification, the descriptions referring to the terms "one embodiment / example", "another embodiment / example", or "certain embodiments / examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0240] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0241] The above is a specific description of the preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included in the scope defined by the claims of this application.

Claims

1. An offshore wind farm layout optimization design method applicable to a cluster-like situation, characterized in that: include: Construct a wind farm information array, including the wind turbine location, model number, model parameter information of each wind turbine in the planned site and surrounding built sites, and the weight of each wind turbine; Construct a dictionary file of aerodynamic parameters of the model, including the model number and the corresponding wind speed-aerodynamic parameter list; Construct a dictionary file of key statistical information of multi-dimensional wind resources, including the model number and the probability density of each basic wind condition; Taking the location of each wind turbine in the planning area as a variable and maximizing the annual power generation of the planning area as the goal, combined with the preset constraints on the location of wind turbines in the planning area, an optimization algorithm is used for iterative calculation to determine the location of each wind turbine in the planning area; The model number is determined based on the model of the wind turbine; the weight is determined based on the site where the wind turbine is located, the planned site corresponds to a weight of 1, and the surrounding built site corresponds to a weight of 0; the basic wind condition is divided based on wind direction and wind speed; the probability density of each basic wind condition corresponding to each model number is determined based on the representative annual wind resource data set at the hub height of each model; The calculation of the annual power generation of the planned site includes: Based on the wind farm information array, the effective wind speed of each wind turbine under each basic wind condition is calculated by considering the wake effect of the upwind wind turbine; Based on the model number and effective wind speed of each wind turbine, the power of each wind turbine under each basic wind condition is determined by comparing the wind speed-aerodynamic parameter list in the model aerodynamic parameter dictionary file; Based on the model number of each wind turbine and the power of each wind turbine under each basic wind condition, combined with the probability density of typical wind conditions in the multi-dimensional wind resource key statistical information dictionary file and the wind turbine weight in the wind farm information array, the annual power generation of the planned site is calculated.

2. The offshore wind farm layout optimization design method applicable to the field cluster situation according to claim 1 is characterized in that: The basic wind conditions are divided based on wind direction and wind speed, including: According to the set number of wind direction sectors, the wind direction angle of 0-360° is evenly divided to generate multiple different wind direction sectors; Traverse the wind speed-aerodynamic parameter list of each model in the planned site, determine the minimum cut-in wind speed and the maximum cut-out wind speed, combine the maximum cut-out wind speed in the wind speed-aerodynamic parameter list of each model in the surrounding built site, determine the wind speed range, and combine the preset wind speed calculation interval to divide the wind speed range and generate multiple different wind speed intervals; Wind direction sectors and wind speed ranges are combined in pairs to generate multiple different basic wind conditions.

3. The offshore wind farm layout optimization design method applicable to the field cluster situation according to claim 2 is characterized in that: The probability density of each basic wind condition corresponding to each aircraft model number is determined based on a representative annual wind resource data set at the hub height of each aircraft model, including: Based on the wind direction of each period in the representative annual wind resource data set of each model, each period is classified into the corresponding wind direction sector; Based on the ratio of the number of time periods under each wind direction sector to the total number of time periods in the representative year, the wind direction frequency of each wind direction sector is determined; The Weibull function is used to fit the wind speed data of all time periods assigned to each wind direction sector, and the probability density of each wind speed interval under each wind direction sector is calculated based on the fitting results, thereby determining the probability density of each basic wind condition corresponding to each aircraft model within the year.

4. The offshore wind farm layout optimization design method applicable to the field cluster situation according to claim 1 is characterized in that: The wind turbine location constraints within the planned site include: The wind turbine location of each wind turbine in the planned site is within the planning area of ​​the planned site; And, the distance between each wind turbine in the planned area and any other wind turbine in the planned area is greater than a preset minimum distance.

5. The offshore wind farm layout optimization design method applicable to the field cluster situation according to claim 4 is characterized in that: include: Calculate the sum of the areas of the triangles formed by the wind turbine location and any two adjacent boundary points of the planning area; If the sum of the triangle areas is greater than the area of ​​the planned area, the corresponding wind turbine location is outside the planned area, and the wind turbine location does not meet the preset wind turbine location constraints within the site.

6. The offshore wind farm layout optimization design method applicable to the field group situation according to claim 4 is characterized in that: include: Calculate the distance between the wind turbine location in the layout plan and any other wind turbine in the planned site; If there is a spacing greater than the preset minimum spacing, the wind turbine location does not meet the preset wind turbine location constraints within the site.

7. The offshore wind farm layout optimization design method applicable to the field cluster situation according to claim 1 is characterized in that: The effective wind speed of each wind turbine under each basic wind condition is calculated based on the wind farm information array and taking into account the influence of the upwind wind turbine wake, including: Based on the wind turbine positions of the planned area and the surrounding built areas in the wind farm information array, combined with the representative wind direction of the typical wind conditions, the wind speed loss of each wind turbine affected by the wake of the upwind wind turbine is calculated; Based on the representative wind speed of the basic wind condition and the wind speed loss of each wind turbine affected by the wake of the upwind wind turbine, the effective wind speed of each wind turbine under each basic wind condition is calculated.

8. The offshore wind farm layout optimization design method applicable to the field cluster situation according to claim 7 is characterized in that: The wind turbine positions of each wind turbine in the planned area and the surrounding built area in the wind farm information array are calculated in combination with the representative wind direction of the typical wind conditions to calculate the wind speed loss of each wind turbine affected by the wake of the upwind wind turbine, including: ； In the formula, represents the wind speed loss of the wind turbine with index i due to the isolated wake of the wind turbine with index j in the upwind direction, is the rotor swept area of ​​the wind turbine at index i; and They respectively represent the wind speed loss of the isolated wake of upwind wind turbine j at the wind turbine with row index i and the projected area of ​​its impact area on the rotor disk of the wind turbine with row index i; ； In the formula, is the effective wind speed of the wind turbine with index j, and are its thrust coefficient and wind rotor diameter respectively; represents the width of the isolated wake of the wind turbine with row index j at the wind turbine with row index i; ； Where A is a constant; is the turbulence intensity in the free flow; Represents the flow spacing of wind turbines with row indices j and i along the wind direction.

9. The offshore wind farm layout optimization design method applicable to the field cluster situation according to claim 7 is characterized in that: The wind turbine positions of each wind turbine in the planned area and the surrounding built area in the wind farm information array are calculated in combination with the representative wind direction of the typical wind conditions to calculate the wind speed loss of each wind turbine affected by the wake of the upwind wind turbine, including: Rank each wind turbine based on its relative position in front and behind along a wind direction representative of typical wind conditions; Determine the maximum value Nobj_wt_max of the serial number of each wind turbine to be installed in the planned site under basic wind conditions; The wind speed loss of each wind turbine from Nobj_wt_max affected by the wake of the upwind wind turbine is calculated in sequence according to the order of each wind turbine.

10. The offshore wind farm layout optimization design method applicable to the field cluster situation according to claim 1 is characterized in that: The annual power generation of the planned area is calculated based on the model number of each wind turbine and the power of each wind turbine under each basic wind condition, combined with the probability density of typical wind conditions in the multi-dimensional wind resource key statistical information dictionary file and the wind turbine weight in the wind farm information array, including: Based on the power of each wind turbine in the planned area under each basic wind condition, combined with the probability density of each wind turbine model number under each basic wind condition, calculate the contribution of each wind turbine to the annual power generation of the planned area under each basic wind condition; Based on the contribution of each wind turbine to the annual power generation of the planned area under each basic wind condition, the contribution of each basic wind condition to the annual power generation of the planned area is determined, and then the annual power generation of the planned area is determined.

11. The offshore wind farm layout optimization design method applicable to the field group situation according to claim 10 is characterized in that: include: Constructing a power generation contribution array for each basic wind condition, wherein the number of elements in the contribution array is the same as the number of wind turbines in the wind farm information array; Based on the position order of each wind turbine along the wind direction representing the basic wind condition, the corresponding relationship between each wind turbine and each element in the contribution array is determined; Based on the calculated contribution of each wind turbine to the annual power generation of the planned site under each basic wind condition, each element in the contribution array is updated.

12. An offshore wind farm layout optimization design device applicable to a group of wind farms, characterized in that: include: An information array construction module is used to construct a wind farm information array, including the wind turbine location, model number, model parameter information of each wind turbine in the planned site and surrounding built sites, and the weight of each wind turbine; The parameter dictionary construction module is used to construct the model aerodynamic parameter dictionary file, which includes the model number and the corresponding wind speed-aerodynamic parameter list; The information dictionary construction module is used to construct a dictionary file of key statistical information of multi-dimensional wind resources, including the model number and the probability density of each basic wind condition; The position variable optimization module is used to determine the position of each wind turbine in the planning area by iterative calculation using the optimization algorithm in combination with the preset wind turbine position constraints in the planning area, with the position of each wind turbine in the planning area as the variable and the goal of maximizing the annual power generation of the planning area; The model number is determined based on the model of the wind turbine; the weight is determined based on the site where the wind turbine is located, the planned site corresponds to a weight of 1, and the surrounding built site corresponds to a weight of 0; the basic wind condition is divided based on wind direction and wind speed; the probability density of each basic wind condition corresponding to each model number is determined based on the representative annual wind resource data set at the hub height of each model; The calculation of the annual power generation of the planned site includes: Based on the wind farm information array, the effective wind speed of each wind turbine under each basic wind condition is calculated by considering the wake effect of the upwind wind turbine; Based on the model number and effective wind speed of each wind turbine, the power of each wind turbine under each basic wind condition is determined by comparing the wind speed-aerodynamic parameter list in the model aerodynamic parameter dictionary file; Based on the model number of each wind turbine and the power of each wind turbine under each basic wind condition, combined with the probability density of typical wind conditions in the multi-dimensional wind resource key statistical information dictionary file and the wind turbine weight in the wind farm information array, the annual power generation of the planned site is calculated.

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 offshore wind farm layout optimization design method applicable to the field group situation described in any one of claims 1 to 11 are implemented.

14. An 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 offshore wind farm layout optimization design method applicable to the field group situation described in any one of claims 1 to 11 are implemented.

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