Offshore wind power plant layout optimization design method suitable for class field group situation

Abstract

The invention relates to an offshore wind power plant layout optimization design method suitable for class group situations. The method is suitable for wind power planning design. According to the technical scheme, the offshore wind power plant arrangement optimization design method suitable for the class field group situation comprises the steps that a wind power plant information array is constructed, and the wind power plant information array comprises the wind turbine positions, the machine type numbers and the machine type parameter information of all wind turbines in a planning field area and a peripheral built field area and the weights of all the wind turbines; constructing a model aerodynamic parameter dictionary file, wherein the model aerodynamic parameter dictionary file comprises model numbers and a corresponding wind speed-aerodynamic parameter list; constructing a multi-dimensional wind resource key statistical information dictionary file, wherein the file comprises the model number and the probability density of each basic wind regime; and taking the position of each wind turbine in the planning field as a variable, taking maximization of the annual energy output of the planning field as a target, combining with a preset wind turbine position constraint in the planning field, performing iterative calculation by adopting an optimization algorithm, and determining the position of each wind turbine in the planning field.

Description

Technical Field

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

[0002] For the sake of intensive use of the sea and convenient power transmission, the newly planned offshore wind farm site is likely to be arranged near the surrounding existing sites. Due to the close distance, the wake of the wind turbines in the surrounding existing sites is likely to affect the wind turbines in the newly planned site. Therefore, when optimizing the layout of the newly planned site, it is necessary to consider the impact of the wind turbines in the surrounding existing sites on the wind turbines in the newly planned site.

[0003] In addition, with the development of large-scale wind turbines, the models of wind turbines planned to be installed in the newly planned area may be different from those in the surrounding wind farms. The differences in 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: (1) Affected by the inflow wind shear, the wind energy resource distribution perceived by wind turbines of different types with different hub heights is significantly different; (2) The effective wind speed ranges for normal operation and power generation of wind turbines of different models may not be consistent. As a result, under certain wind conditions, only some wind turbines of certain models can generate electricity normally, while other wind turbines are in a shutdown state.

[0004] In addition to the above-mentioned site cluster situations, there are many "site cluster-like" situations in wind power projects (there are existing sites near the new site, and there are many types of wind turbines). For example, when old wind farms are renovated and upgraded in batches, the optimization of wind turbine layout in the renovated area needs to consider the impact of wind turbines in the surrounding unrenovated areas on the wind turbines in the renovated area.

[0005] Although intelligent optimization algorithms have been widely used in the field of wind farm layout design, and a large number of research results have been achieved for the actual needs in different scenarios, existing research focuses on the layout optimization of a single model, and rarely involves the optimization of mixed layouts of multiple models. There is still a lack of effective technical solutions for the layout optimization design of target sites in complex "class-site cluster" situations.

[0006] Annual power generation is the core indicator that determines the development income of wind power projects. Therefore, when using intelligent optimization algorithms for machine layout optimization design, the goal is often to maximize annual power generation or its related functions. When using intelligent optimization algorithms to calculate annual power generation, once the necessary parameters and information are entered, the optimization process completely relies on the algorithm to execute autonomously, and no human intervention is allowed. Therefore, in order to ensure the accuracy, precision and efficiency of the algorithm, it is necessary to focus on the following two aspects: (1) How to enable the algorithm to autonomously distinguish between the wind turbines planned to be installed in the planning area and the existing wind turbines in the surrounding area, and only consider the impact of the latter's wake on the former, without taking the latter's power generation into account in the objective function, and excluding their coordinates from the optimization variable sequence; (2) Since the calculation accuracy of annual electricity directly determines the reliability of the optimization results, and the calculation time affects the overall efficiency of the optimization process, how to scientifically deal with the impact of wind shear on the annual power generation of multiple models, ensure that the wind conditions that contribute to the annual power generation of the planned site are fully considered, and avoid including invalid wind conditions in the calculation range, is another key point in algorithm design. Summary of the invention

[0007] 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 applicable to similar wind farm cluster situations is provided.

[0008] The technical solution adopted by the present invention is: an offshore wind farm layout optimization design method applicable to a field group situation, comprising: 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 wind speed-aerodynamic parameter list in the model aerodynamic parameter dictionary file is compared to determine the power of each wind turbine under each basic wind condition; 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.

[0009] 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.

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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, determining the corresponding relationship between each wind turbine and each element in the contribution amount array; 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.

[0019] An offshore wind farm layout optimization design device applicable to a field group situation, comprising: 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 wind speed-aerodynamic parameter list in the model aerodynamic parameter dictionary file is compared to determine the power of each wind turbine under each basic wind condition; 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.

[0020] A storage medium stores a computer program executable by a processor, wherein the computer program, when executed, implements the steps of the offshore wind farm layout optimization design method applicable to the field group situation.

[0021] An 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 offshore wind farm layout optimization design method applicable to the field group situation are implemented.

[0022] The beneficial effects of the present invention are: For the problem of wind turbine layout optimization design only for the target site under the "quasi-site group" situation, which is characterized by a large number of units, various types of units, fixed point coordinates of some units and the need to consider their impact on other wind turbines with dynamically changing point coordinates, the present invention has carried out a systematic design, adopting a binary weight allocation strategy, setting the weights of each wind turbine to be installed in the planned site and the surrounding area to 1 and 0 respectively, and constructing a mapping association between each model and the key statistical characteristics of wind resources at the corresponding hub height and the wind speed-aerodynamic parameter list, so that the wind turbine layout optimization model established based on the optimization algorithm can autonomously identify the ownership of each wind turbine under the above-mentioned complex conditions and match it with accurate wind conditions and aerodynamic parameter information.

[0023] The present invention sets weights of 0 or 1 for wind turbines in different areas, which can be used to distinguish the ownership of wind turbines, so that the intelligent optimization algorithm can accurately determine which wind turbine locations to optimize; the present invention takes the weight of wind turbines in the planned area as 1 and the weight of surrounding wind turbines as 0, so that in subsequent steps, it is only necessary to calculate the new weighted sum of the power generation of each wind turbine without judging the ownership of each wind force, and the target wind farm power generation can be obtained.

[0024] The present invention systematically considers the impact of surrounding wind turbines on the annual power generation of the target planning area from multiple dimensions, from basic wind condition division to wake effect assessment. In order to improve the calculation accuracy, the present invention is cleverly designed from the following two aspects: First, based on the model, the impact of wind shear in the inflow on the difference in wind energy resource distribution perceived by each wind turbine is considered; second, in the basic wind condition division, the wind speed-aerodynamic parameter list of each model in the planned area and the surrounding built areas is traversed to determine the wind speed range, ensuring that all wind conditions that contribute to the annual power generation of the target area are included in the calculation range.

[0025] In order to meet the strict requirements of wind power projects on timeliness, the present invention, under various basic wind conditions representing different wind directions, stops the wake loss assessment at the last wind turbine (serial number Nobj_wt_max) along the representative wind direction in the planned site, thereby significantly reducing the irrelevant calculation time. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flowchart of an embodiment.

[0027] Figure 2 Schematic diagram of a dictionary file of multi-dimensional wind resource key statistical information in an embodiment.

[0028] Figure 3 This is a flow chart of optimizing the wind turbine position in the embodiment.

[0029] Figure 4 Schematic diagram of the method for determining the relative position relationship between the wind turbine location and the planned site in the embodiment; wherein (a) and (b) refer to the situations where the wind turbine location is outside the planned site and inside the planned site, respectively.

[0030] Figure 5 This is a flow chart for calculating the annual power generation of the planned site in the embodiment. DETAILED DESCRIPTION

[0031] 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.

[0032] 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.

[0033] Example 1: Figure 1 As shown, this embodiment is an offshore wind farm layout optimization design method applicable to a field group situation, which specifically includes the following steps:

[0034] S100: Obtain planning information of a wind farm in a planned area and information of wind farms in surrounding built areas, and obtain a representative annual wind resource data set at hub height of each model.

[0035] In this embodiment, the wind farm planning information of the planned site includes the planning area, the type of wind turbines to be installed, the number of each type, and the parameter information of each type. The surrounding built site is a wind farm area within a preset range around the planned site, and its wind farm information includes the wind turbine position (horizontal coordinate, vertical coordinate) of each wind turbine in the site, the model of each wind turbine, and the parameter information of each type.

[0036] In this example, the planning area includes the positions of the boundary points that enclose the planning area. When there is a restricted area where wind turbines cannot be arranged, it also includes the positions of the boundary points that enclose the restricted area.

[0037] The model parameter information in this embodiment includes the rotor diameter, hub height and wind speed-aerodynamic parameter list of the model, wherein the wind speed-aerodynamic parameter list is often provided by the whole machine manufacturer, covering the thrust coefficient and power data at each typical wind speed within the normal operating power generation range of the wind turbine (i.e., between the cut-in wind speed and the cut-out wind speed).

[0038] With regard to the multi-machine mixed arrangement scenario involved in this embodiment, it should be noted that the effective wind speed ranges for normal operation and power generation of different machines may be different. In this multi-machine mixed arrangement scenario, there may be a situation where the inflow wind speed is lower than the cut-in wind speed of a certain machine model planned to be installed in the planned site, but higher than the cut-in wind speed of other machines. In this case, when determining the aerodynamic parameters of the wind turbine based on the wind speed, the wind speed may not be included in the wind speed-aerodynamic parameter list, thereby causing a calculation overflow error.

[0039] In response to the above calculation overflow problem, this embodiment supplements the aerodynamic parameters of each model in the interval 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 larger value close to 1, such as 0.9999, and the power takes 0; while for the wind speed interval above the cut-out wind speed, the thrust coefficient takes a smaller value close to 0, such as 0.0001, and the power also takes 0.

[0040] In this example, the representative annual wind resource datasets at the hub height of each aircraft model in the planning area were obtained after the integrity and rationality inspection of the time series observation data of the floating wind laser radar in the area, as well as the elimination, interpolation, extension, and representative year correction of unreasonable and missing data. Each dataset contains wind speed and direction for 8760 time periods.

[0041] S200: Based on the wind farm planning information of the planned area and the wind farm information of the surrounding built areas, a wind farm information array including key characteristic quantities of each wind turbine planned to be installed in the planned area and the surrounding wind turbines is constructed.

[0042] In this embodiment, the number of array 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 features of the corresponding wind turbine. The number of array columns is 7, which correspond to the wind turbine number, horizontal coordinate, vertical coordinate, model number, hub height, rotor diameter and weight.

[0043] In this example, the wind turbine numbers may be numbered starting from 1 and increasing one by one according to the order in which the wind turbines are read when acquiring data in step S100 .

[0044] The horizontal and vertical coordinates in the wind farm information array can be filled in after determining the coordinate values ​​of the wind turbines in the geodetic coordinate system according to the manual layout plan for each wind turbine planned to be installed in the planned site; and for each wind turbine in the surrounding built site, the coordinate values ​​in the geodetic coordinate system can be calculated according to the position information obtained in step S100 and filled in.

[0045] In this example, the geodetic coordinate system is a two-dimensional rectangular coordinate system established by taking any point in the planning area as the origin, with the due east direction as the positive direction of the x-axis (corresponding to a wind direction angle of 270°) and the due north direction as the positive direction of the y-axis (corresponding to a wind direction angle of 180°).

[0046] In this embodiment, the model number is specified by the serial number according to the order in which the models are read when acquiring data in step S100. By binding the model 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 that match each wind turbine model.

[0047] The weight of each wind turbine in this example is used to distinguish the relative relationship between the wind turbine and the planned site. For each wind turbine planned to be installed in the planned site, the weight is 1, while for each wind turbine in the surrounding built site, the weight is 0.

[0048] Assume that the number of wind turbines to be installed in the planned area is , including M types of models, the model numbers are [1,…,M]; the number of wind turbines in the surrounding built areas is , covering a total of P types of aircraft, and assuming that the aircraft models used in the planning area are completely different, the model numbers are [M+1,…,M+P] in sequence.

[0049] For each model, the hub height and rotor diameter are expressed as and , where the subscript i represents the model number, i∈[1,M+P]; and for each wind turbine, its horizontal and vertical coordinates are respectively and refers to, where the subscript j represents the wind turbine number, i.e. j∈[1 + ].

[0050] Assume that wind turbines are numbered 1 to Corresponding to the wind turbines to be installed in the planned area, and numbered 1 and The model numbers of the wind turbines are 1 and M; and the wind turbine numbers to (in ) correspond to the existing wind turbines in the surrounding areas, and are numbered and The model numbers of the wind turbines are M+1 and M+P respectively.

[0051] Based on the above definition, the wind farm information array has the following form in Table 1: Table 1 .

[0052] S300: To facilitate subsequent calculations, this example constructs a model aerodynamic parameter dictionary file. The dictionary file is a commonly used data structure used to store data with a mapping relationship, in which each key corresponds to a value, thereby forming a one-to-one mapping relationship.

[0053] Specifically, in the model aerodynamic parameter dictionary file in this embodiment, the key is the model number, and the value is a multidimensional array used to store the wind speed-aerodynamic parameter information of the model.

[0054] Following the models involved in the above wind farm information array, examples are given below: Since the model number is [1,…,M+P], the model aerodynamic parameter dictionary file format is as follows: {1:Array_1;…;M+P:Array_M+P}. Taking Array_1 as an example, it is an array storing the wind speed-aerodynamic parameter information of the model numbered 1, in the format of the following Table 2; Table 2 ; In Table 2 above, , and It refers to the kth typical wind speed in the wind speed-aerodynamic parameter list of the aircraft model numbered A=1 and the thrust coefficient and power at this wind speed.

[0055] S400, based on wind direction and wind speed, divide the basic wind conditions, and based on the representative annual wind resource data set of each model in the planning area, determine the probability density of each basic wind condition representing each model in the year, and build a multi-dimensional wind resource key statistical information dictionary file based on the model, basic wind conditions and probability density.

[0056] S410. Evenly divide the wind direction angle of 0-360° according to the set number of wind direction sectors to generate multiple different wind direction sectors.

[0057] According to the set number of wind direction sectors (The possible values ​​are 12 or ), evenly divide the wind direction angle from 0 to 360°, thus obtaining multiple different wind direction sectors. Assume that the center value of the first wind direction sector is 0, then according to the definition, the first The center value of the wind direction sector is .

[0058] S420, traverse the wind speed-aerodynamic parameter list of the wind turbine models to be installed in the planning area, determine the minimum cut-in wind speed and the maximum cut-out wind speed, and determine the wind speed range based on the minimum cut-in wind speed and the maximum cut-out wind speed, and divide the wind speed range in combination with the preset wind speed calculation interval to generate multiple different wind speed intervals.

[0059] Traverse the wind speed-aerodynamic parameter list of each wind turbine model planned to be installed in the planning area and determine the minimum cut-in wind speed and the maximum cut-out wind speed , the calculation formula is: ; ; In the formula, M refers to the number of models planned to be assembled in the planning area, and Respectively represent the Cut-in wind speed and cut-out wind speed for each model.

[0060] contrast The larger of the cut-out wind speeds of the wind turbines in the surrounding built areas is used to update the .

[0061] Respectively and After rounding down and rounding up, the result can be expressed as , , and use this as the lower and upper limits, combined with the set wind speed calculation interval , dividing the wind speed range into multiple different ranges.

[0062] By definition, the total number of wind speed intervals is , let the center value of the first wind speed interval be , then The center value of the wind speed interval is .

[0063] about It is recommended that its value should 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 installed in the planned site, so as to ensure that the data in the list can be fully utilized.

[0064] The reason why the cut-out wind speed of surrounding wind turbines is needed to determine the upper limit of the wind speed range is The reason is that when the cut-out wind speed of the surrounding wind turbines is higher than the cut-out wind speed of the wind turbines of various models planned to be installed in the planned area, if the inflow wind speed is lower than the former but higher than the latter, and the surrounding wind turbines are located upwind, they absorb part of the wind energy in the inflow during operation and power generation, so the wind speed perceived by the wind turbines in the planned area will be lower than the inflow wind speed and may be lower than the cut-out wind speed of the model to which they belong. 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 take it into consideration.

[0065] S430, combining the multiple wind direction sectors divided in step S410 and the multiple wind speed intervals divided in step S420 in pairs, thereby obtaining multiple basic wind conditions that are different from each other, and the total number of basic wind conditions is .

[0066] Based on the basic wind conditions obtained from the above operations, while ensuring that all wind conditions that contribute to the annual power generation of the planned site are fully considered, the composition of the basic wind conditions corresponding to each model is also exactly the same, which facilitates the calculation of annual power generation in subsequent steps.

[0067] For each basic wind condition, Indicates the basic wind condition numbered r, r=1,2,3... , basic wind conditions The wind direction sector and wind speed interval number are represented as and , and basic wind conditions The representative wind direction and representative wind speed are given by and Refers to, they take the wind direction sector respectively and wind speed range The middle value of .

[0068] S440: Based on the basic wind conditions, the representative annual wind resource data set of the hub height of each aircraft model in step S100 is processed to construct a multi-dimensional wind resource key statistical information dictionary file.

[0069] The following is an example of processing a representative annual wind resource dataset at hub height for model t to describe the process in detail:

[0070] S441, according to the wind direction of each time period in the representative annual wind resource data set, classifying the time period into the corresponding wind direction sector in step S410;

[0071] S442. After the traversal processing of the wind direction information of all time periods is completed, the ratio of the number of time periods assigned to each wind direction sector to the number of time periods in the representative annual wind resource data set is calculated, thereby obtaining the proportion of each wind direction sector, also known as the wind direction frequency. According to the definition, there is: ; in, For the The proportion of wind direction sectors.

[0072] S443. The wind speed data of all time periods assigned to each wind direction sector are fitted using the Weibull function to obtain shape parameters and scale parameters. Based on the Weibull fitting results, the probability density of each wind speed interval under each wind direction sector, also known as the wind speed frequency, can be calculated. Suppose the probability density of the nth wind speed interval under the mth wind direction sector is expressed as .

[0073] Referring to the above process, after completing the traversal processing of the representative annual wind resource data set of all models in the planning area, a multi-dimensional wind resource key statistical information dictionary file containing the multi-dimensional key statistical information of the above basic wind conditions is constructed.

[0074] In this example, the multidimensional wind resource key statistical information dictionary file adopts a nested hierarchical structure, which contains three dimensions for efficient storage and retrieval of wind resource statistical information. Its overall structure is as follows: Figure 2 As shown: The first dimension is the model dimension, which contains multiple key-value pairs. Each key takes the model number, expressed as t (t∈[1,M], M is the total number of models to be assembled in the planning area); The second dimension contains the dictionary file of basic wind condition statistics, 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, expressed as , and the other key is “wind condition information”.

[0075] The wind condition information dictionary file corresponding to model t, which constitutes the third dimension, contains the following key-value pairs, each key takes the wind direction sector number, represented by m (m∈[1, ]), value is an array used to store the wind speed frequency of each wind speed interval of model t in wind direction sector m, expressed as .

[0076] 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.

[0077] 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.

[0078] 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.

[0079] like Figure 3 As shown, the specific method for optimizing the wind turbine position in this embodiment includes:

[0080] 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.

[0081] 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: ; 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.

[0082] 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: Constraint 1: The wind turbine locations of all wind turbines in the planning area are within the boundary of the planning area.

[0083] 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.

[0084] By Collection Internal wind turbine Compared with the planned area OABCD (there are also restricted areas inside it ) For example, in Figure 4 In the case shown in (a), the wind turbine location Located outside the planned area OABCD, Figure 4 In (b), the wind turbine location Located in the planned area OABCD, the specific methods are as follows: Calculate wind turbine location The area of ​​the triangle formed by two adjacent points in the boundary points of the planned site OABCD as vertices is equal to the area of ​​the planned site OABCD, then the wind turbine It is located within the planned site OABCD, otherwise, it is outside the planned site OABCD; When there are restricted areas where wind turbines cannot be placed within the planned site, the above method can also be used to determine the location of wind turbines. Restricted Area The relative relationship of When wind turbine When it is located within the planned area and outside the restricted area at the same time, it is determined to meet the affiliation requirements.

[0085] Constraint 2: The distance between any wind turbine in the planning area and any other wind turbine in the planning area is greater than the preset minimum distance .

[0086] In this embodiment, the distance between the wind turbine position in the layout plan and any other wind turbine in the planned site is calculated; if any distance is greater than the preset minimum distance, the layout plan does not meet the preset wind turbine position constraint in the site.

[0087] The calculation formula is: ; In the formula, , Refers to the collection separately The first The first and nth wind turbines.

[0088] In this embodiment, the distance between the planned site and the surrounding built sites is greater than the preset minimum distance Therefore, the distance between the wind turbines in the planned area and the wind turbines in the surrounding built areas does not need to be considered.

[0089] If both of the above constraints are satisfied, then the set The corresponding layout plan is marked as "qualified", otherwise, it is marked as "unqualified".

[0090] S540, for the layout scheme marked as "unqualified", directly take the annual power generation of the planned site as 0.001, and for the layout scheme marked as "qualified", calculate the annual power generation of the planned site according to the following steps, such as Figure 5 shown.

[0091] S541. Obtain representative wind speed and representative wind direction of each basic wind condition in the multi-dimensional wind resource key statistical information dictionary file.

[0092] S542. Based on the representative wind direction of the basic wind condition, determine the front and rear relative positions of each wind turbine in the planned site and the surrounding built sites based on the representative wind direction under the basic wind condition.

[0093] Wind conditions along the base Representative trend Sort the wind turbines by their relative positions, update the wind farm information array, and determine the maximum number of wind turbines in the planned area, expressed as Nobj_wt_max; The reason why they need to be sorted by 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.

[0094] In this embodiment, the position of each wind turbine is converted to a relative coordinate system through coordinate conversion. The positive direction of the x-axis of the relative coordinate system represents the wind direction. The front and rear relative positions of each wind turbine can be determined by the size of the horizontal coordinate in the relative coordinate system. When the horizontal coordinate is smaller, the wind turbine is closer to the front.

[0095] The relative coordinate system shares the same coordinate origin with the geodetic coordinate system established in step S200, and is based on the representative wind direction. , obtained by rotating the geodetic coordinate system so that in the relative coordinate system, the positive direction of the x-axis represents the wind direction.

[0096] The abscissa in the relative coordinate system is obtained according to the abscissa and ordinate of each wind turbine in the wind farm information array in combination with the following conversion matrix. Taking the wind turbine with index i in the wind farm information array as an example, the calculation formula is: ; In the formula, and are the horizontal and vertical coordinates of the wind turbine with index i in the geodetic coordinate system and the relative coordinate system respectively.

[0097] After calculating the horizontal and vertical coordinates of each wind turbine in the relative coordinate system and determining their order, the wind farm information array is updated in the order of the serial numbers from small to large, so that in the updated wind farm information array, the 1st, ..., i, ..., N wt The rows are sorted as 1,…,i,…,N wt Wind turbine information.

[0098] Determine the target basic wind condition based on the maximum value of the row index with a weight of 1 in the updated wind farm information array The maximum value of the serial number of each wind turbine to be installed in the next planning area is recorded as Nobj_wt_max. The reason for performing this operation is that the wind turbines with a ranking index greater than Nobj_wt_max are located outside the planning area, and their power generation does not contribute to the annual power generation of the planning area. Therefore, there is no need to spend additional time to evaluate their wake losses and power generation, so as to reduce the calculation time and better meet the high requirements of engineering practice for timeliness.

[0099] Starting from the wind turbine with the ranking index of 1 and ending with the wind turbine with the ranking index of Nobj_wt_max, the contribution of each wind turbine to the annual power generation of the planned site is calculated in turn after considering the influence of the wake of other wind turbines in the upwind direction.

[0100] S543, build basic wind conditions The corresponding array , used to store basic wind conditions The contribution of each wind turbine in the planned area to the annual power generation of the planned area is calculated and initialized to an all-zero array, that is: ; in, The number of elements in is the same as the number of rows in the wind farm information array.

[0101] In this embodiment, the correspondence between each wind turbine and each element in the contribution array is determined based on the position order of each wind turbine along the wind direction represented by the basic wind condition; and each element in the contribution array is updated based on the calculated contribution of each wind turbine to the annual power generation of the planned site under each basic wind condition.

[0102] S544. Based on the wind turbine positions of each wind turbine in the planned area and the surrounding built areas in the wind farm information array, the wind speed loss of each wind turbine in the planned area affected by the wake of the upwind wind turbine is calculated.

[0103] Below, based on the basic wind conditions Take the wind turbine with index i in the lower row as an example and describe the calculation process in detail: 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, ; when When there are (i-1) wind turbines in the upwind direction, when calculating the effective wind speed of the wind turbine with 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:

[0104] In this embodiment, based on the wind turbine positions of each wind turbine in the planned area and the surrounding built area 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 the "area projection method" is used to quantify the shielding effect of their respective wakes on the wind turbine at the row index i; ; 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 the calculation formula is ,in, is the diameter of the wind turbine rotor 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; The calculation formula is: ; In the formula, is the effective wind speed of the wind turbine with index j, and are the thrust coefficient and rotor diameter (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") increases approximately linearly as it moves away from the rotor disk. 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.

[0105] In the original Park model, it is assumed that the wake influence area at any downstream position behind the wind rotor disk is circular, and the wake width (i.e., the diameter of the circular influence area) increases approximately linearly with the distance from the wind rotor disk, and the growth rate is expressed as k= ,in, refers to the wake width, is the downstream distance relative to the rotor disk. The original Park model assumes that the wake growth rate k at any downstream position behind the rotor remains consistent. However, in fact, a large number of studies in recent years have pointed out that the additional turbulence intensity generated by the operation of the wind turbine will have a significant impact on the evolution of the wake, and when the turbulence intensity is greater, the expansion rate of the wake influence area is also faster (correspondingly, the k value is also larger). In view of this, in this embodiment, the additional turbulence intensity at the position x away from the rotor disk in the wind turbine wake area is calculated by the method of Frandsen et al.: ; in, and are the rotor diameter and thrust coefficient of the wind turbine respectively.

[0106] Combined with the consideration of turbulence intensity in free flow According to the following formula, the effective turbulence intensity at x behind the wind wheel disk can be obtained: ; Take k=AI, where A is a constant and substitute it into k= , and after numerical integration we can get: ; By comparing the actual operation results of the wind farm, this embodiment recommends taking A=0.6.

[0107] Specific to , the calculation formula is: ; In the formula, Represents the flow spacing of wind turbines with row indices j and i along the wind direction.

[0108] S545. Based on the representative wind direction of the basic wind conditions and the wind speed loss of each wind turbine in the planning area affected by the wake of the upwind wind turbine, calculate the effective wind speed of each wind turbine in the planning area under each basic wind condition, including: ; In the formula, is the effective wind speed of the wind turbine with index i; Basic wind conditions The representative wind speed is It represents the wind speed loss of wind turbine with row index i due to the isolated wake of wind turbine with row index j in the upwind direction.

[0109] S546. Based on the effective wind speed of each wind turbine in the planning 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 planning area under each basic wind condition.

[0110] When referring to the above steps to obtain the effective wind speed of the wind turbine with index i Then, the model number in the row index i (by Refers to) as a key, combined with the model aerodynamic parameter dictionary file and the multi-dimensional wind resource key statistical information dictionary file, the corresponding model can be determined Wind speed-aerodynamic parameter information.

[0111] Based on the wind speed-aerodynamic parameter information obtained above, the thrust coefficient of the wind turbine with index i can be obtained through interpolation calculation: (used to calculate wind speed loss for downwind wind turbines) and power , the calculation formula is: ; ; In the formula, and The wind speed is the closest to the aerodynamic parameter list. Two typical wind speeds (satisfying ), as well as Corresponding to and thrust coefficient and power.

[0112] S547. Based on the power of each wind turbine in the planned area under basic wind conditions and combined with the probability density of each wind turbine under basic wind conditions 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 basic wind conditions.

[0113] Based on the above power The basic wind conditions can be quantified using the following formula: The contribution of the wind turbine with index i in the lower row to the annual power generation of the planned site : ; In the formula, is the wind turbine model with index i In basic wind conditions Wind direction frequency under is the wind turbine model with index i In basic wind conditions The wind speed frequency of the lower pair; is the weight of the wind turbine with row index i in the wind farm information array.

[0114] According to the above formula, only the wind turbines to be installed 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, the corresponding is 0, so =0; For the surrounding wind turbines with i> Nobj_wt_max, since the effective wind speed and power generation calculations are not carried out, Keep the initial value as 0.

[0115] When getting Then, update the array The value of the i-th element in . At this time, the array The order of the data elements in the table corresponds to the basic wind conditions along the wind turbine. Represents the order of wind directions.

[0116] S548, when the basic wind conditions are calculated After planning the contribution of all wind turbines in the area, the array After all elements in the formula are updated in sequence, a basic wind condition is replaced, and steps S542 to S547 are repeated to calculate the contribution of each wind turbine to the annual power generation of the planned site under each pair of basic wind conditions.

[0117] S549. Determine the annual power generation of the planned site based on the contribution of each wind turbine to the annual power generation of the planned site under each basic wind condition.

[0118] Pair Array Rearrange so that under each basic wind condition representing different wind directions, the array The arrangement order of the data elements in the data set is kept consistent, which facilitates the calculation of the annual power generation of the planned site in the subsequent steps, avoids calculation errors due to inconsistent sorting, and reduces the time for data search and matching.

[0119] The specific process is as follows: The wind turbine numbers of each row in the wind farm information array are updated according to the sorting results of each wind turbine along the wind direction, forming an array Index_array; Apply a sorting function (such as the argsort() function of the Python Numpy module) to the array Index_array to generate an address index array loc_array. When the sorting function is used for an array containing multiple data elements, it returns a new array containing the address indexes of all elements in ascending order of element values. Suppose Index_array = [1, 4, 2, 3], then the corresponding loc_array = [1, 3, 4, 2].

[0120] According to the guidance of the address index array loc_array, the array After this adjustment, the array The data elements in the data will be arranged in ascending order according to the wind turbine number.

[0121] After traversing all basic wind conditions, the contribution of each wind turbine to the annual power generation of the planned site under each basic wind condition is determined, and then the annual power generation of the planned site is determined. The annual power generation of the planned site can be obtained by summing the following formula: , the calculation formula is: ; In the formula, is the total number of basic wind conditions, For the number Basic wind conditions The wind turbine power generation array below; is the total number of wind turbines, For array The value of the i-th data element in .

[0122] S550, determine whether the calculation result of step S540 meets the convergence standard. If so, output the value of the design variable in the current calculation step. If not, further determine whether the preset maximum number of iterations has been reached. If so, output the value of the design variable in the current calculation step as above. If still not, calculate new design variable values ​​according to the update rules of the SLSQP algorithm, that is, the horizontal and vertical coordinates of each wind turbine to be installed in the planning area, and fill the results in the corresponding positions of each wind turbine in the planning area in the wind farm information array in sequence, and then feed the updated wind farm information array back to step S510 for the next round of optimization calculation.

[0123] Embodiment 2: This embodiment is an offshore wind farm layout optimization design device applicable to a field group situation, comprising: 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 wind speed-aerodynamic parameter list in the model aerodynamic parameter dictionary file is compared to determine the power of each wind turbine under each basic wind condition; 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.

[0124] Embodiment 3: This embodiment is 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 offshore wind farm layout optimization design method applicable to the field group situation described in Embodiment 1 are implemented.

[0125] Embodiment 4: This embodiment is an offshore wind farm layout optimization design device, which 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 offshore wind farm layout optimization design method applicable to the field group situation described in Embodiment 1 are implemented.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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. 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, determining the corresponding relationship between each wind turbine and each element in the contribution amount array; 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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