Optimal Design Method for Layout of Multi-Type Offshore Wind Farms Considering Similar Aggregation

Abstract

The present invention relates to an optimized layout design method for multi-type offshore wind farms considering the aggregation of the same type, which includes: obtaining wind farm information, model parameters and wind measurement data, wherein the wind farm information includes the installation positions of wind turbines in the field area, the models planned to be assembled and the quantities of each model; based on the wind measurement data, determining the representative annual wind resource data set and the wind shear index at the representative height in the area where the wind farm is located; taking the wind turbine models at each wind turbine installation position in the field area as design variables, aiming at maximizing the annual power generation of the wind farm, combining the constraint conditions, and using an intelligent optimization algorithm for iterative optimization to determine the optimal layout design scheme. The present invention effectively improves the calculation accuracy of the annual power generation, enables the optimized layout design scheme to better match the wind energy resource distribution characteristics in the field area, and thus ensures that the power generation effect during the actual operation of the wind farm reaches the expectation. The present invention is applicable to the field of wind power planning and design.

Description

Technical Field

[0001] The present invention relates to an optimal layout design method for multi-type offshore wind farms considering similar aggregation, which is applicable to the field of wind power planning and design. Background Technique

[0002] For offshore wind farms, due to the relatively low inflow turbulence intensity, the recovery rate of the wind speed deficit in the wake area of wind turbines slows down, which in turn triggers a strong wake interference effect, seriously affecting the overall power generation of the wind farm. Due to the differences in the structural dimensions of different types of wind turbines, through reasonable layout, the wind turbine rotor disks in the downwind direction can avoid the low-speed wake area of the upstream wind turbines to a certain extent, thereby weakening the adverse effects of the wake effect and improving the overall wind energy capture efficiency of the whole field. Based on this, the application of the multi-type hybrid layout design scheme in offshore wind power projects is becoming increasingly widespread.

[0003] In the field of wind farm layout design, current engineering practices mainly rely on manual layout methods. This method not only overly relies on the personal experience of engineers, but is also limited by the limited range of scheme comparison, making it difficult to obtain the global optimal solution. For this reason, intelligent optimization algorithms have been widely used in wind farm layout design in recent years. However, through a full investigation of existing research, it is found that for the layout optimization design problem in the case of multi-type mixed layout, the layout schemes generated based on intelligent optimization algorithms have significant limitations in practical engineering applications, which are specifically manifested in the following two aspects:

[0004] (1) In real wind power projects, in order to facilitate the installation and operation and maintenance of wind turbines, it is often required that wind turbines of the same type adopt an "aggregated layout" mode, while the existing layout optimization algorithms do not fully consider this engineering constraint;

[0005] (2) Since the annual power generation is a key indicator determining the development benefits of wind power projects, the layout optimization design goal is often set to maximize the annual power generation or its related function. However, whether it is industrial software or academic research, the existing methods for calculating the annual power generation of multi-type mixed layout offshore wind farms have many deficiencies.

[0006] Taking the widely used offshore wind farm design software WAsP as an example, based on the classical Park model, it uses a "top hat distribution" that ignores the spanwise variation to describe the velocity loss in the wake area of wind turbines. This not only seriously does not conform to the actual situation, but also makes the calculation framework of the WAsP software fail to fully consider the influence of wind shear. In fact, with the development of wind turbines towards large-scale, the increase in the rotor diameter makes the wind speed difference between the upper and lower end points of the rotor disk more and more obvious. Therefore, in order to improve the calculation accuracy of power generation, the influence of the wind shear effect cannot be ignored.

[0007] At present, although various calculation models considering wind shear proposed in the academic community claim to have higher accuracy, the test results show that this is based on the premise of reasonable input parameter values. These relatively new calculation models often contain multiple parameters, and the value of some parameters depends heavily on wind tunnel experiments or high-precision CFD simulations. Restricted by this, the engineering practicability of these relatively new calculation models is greatly reduced. Summary of the Invention

[0008] The technical problem to be solved by the present invention is: in view of the above problems, to provide an optimized layout design method for multi-type offshore wind farms considering the aggregation of the same type.

[0009] The technical solution adopted by the present invention is: an optimized layout design method for multi-type offshore wind farms considering the aggregation of the same type, including:

[0010] Obtain the wind farm information, model parameters, and wind measurement data, where the wind farm information includes the installation positions of wind turbines in the field, the models planned to be assembled, and the quantity of each model;

[0011] Based on the wind measurement data, determine the representative annual wind resource data set and the wind shear index at the representative height in the area where the wind farm is located;

[0012] Taking the wind turbine models at each wind turbine installation position in the field as design variables, aiming at maximizing the annual power generation of the wind farm, combined with the constraint conditions, use an intelligent optimization algorithm for iterative optimization to determine the optimal layout design scheme;

[0013] The constraint conditions include: the quantity constraint of each model and the number of wind turbines belonging to each model, and the aggregation constraint of the same type for the layout positions of each model;

[0014] The calculation of the annual power generation of the wind farm includes: based on the wind turbine models at each wind turbine installation position and each model parameter, combined with the representative annual wind resource data set and the wind shear index at the representative height, determine the annual power generation of the wind farm.

[0015] The method of taking the wind turbine models at each wind turbine installation position in the field as design variables, aiming at maximizing the annual power generation of the wind farm, combined with the constraint conditions, using an intelligent optimization algorithm for iterative optimization to determine the optimal layout design scheme includes:

[0016] S410. Initialize the population, and each individual in the population corresponds to a layout scheme;

[0017] S420. Judge whether the layout scheme corresponding to each individual meets the constraint conditions;

[0018] S430. Calculate the annual power generation of the wind farm for individuals that meet the constraint conditions; for individuals that do not meet the constraint conditions, use a preset minimum value as the corresponding annual power generation of the wind farm.

[0019] S440. Determine the fitness of each individual based on the annual power generation of the wind farm corresponding to each individual, update the population based on the fitness of each individual, and return to step S420 until a preset iteration termination condition is met.

[0020] The judgment of whether the loom arrangement plan corresponding to each individual meets the constraint conditions includes:

[0021] Judge whether the number of turbine models and the number of wind turbines belonging to each model in the individual meet the preset number of turbine models and the constraint on the number of wind turbines belonging to each model.

[0022] If the quantity constraint is met, based on the coordinates of the installation points of the wind turbines corresponding to each wind turbine model, judge whether each wind turbine model meets the linear homogeneous aggregation constraint.

[0023] If a certain wind turbine model does not meet the linear homogeneous aggregation constraint, then based on the coordinates of the installation points of the wind turbines corresponding to the certain wind turbine model, judge whether the certain wind turbine model meets the polygon homogeneous aggregation constraint.

[0024] If each wind turbine model in the individual meets the linear homogeneous aggregation constraint or the polygon homogeneous aggregation constraint, then the individual meets the homogeneous aggregation constraint.

[0025] The judgment of whether each wind turbine model meets the linear homogeneous aggregation constraint based on the coordinates of the installation points of the wind turbines corresponding to each wind turbine model includes:

[0026] Sort the installation points of the wind turbines corresponding to each wind turbine model by size to determine the installation points of the first and last wind turbines of each model.

[0027] Based on the positional relationship between the installation points of the first and last wind turbines of each model and the installation points of the remaining wind turbines in the corresponding model, judge whether the installation points corresponding to the same model can be connected into a straight line.

[0028] If they cannot be connected into a straight line, then judge that the corresponding model does not meet the linear homogeneous aggregation constraint; if they can be connected into a straight line, then based on the positional relationship between the installation points of the first and last wind turbines of the corresponding model and the installation points of the remaining models in the individual, judge whether there are any remaining models on the connected straight line.

[0029] If there are, then the models connected into a straight line do not meet the linear homogeneous aggregation constraint; if not, then judge that the models connected into a straight line meet the linear homogeneous aggregation constraint.

[0030] Judging whether the certain wind turbine model meets the polygon - type same - type aggregation constraint based on the coordinates of the wind turbine installation points corresponding to the certain wind turbine model includes:

[0031] Based on the coordinates of the wind turbine installation points corresponding to the certain wind turbine model, obtaining the effective sides that enclose the circumscribed polygon;

[0032] Based on the coordinates of the wind turbine installation points corresponding to the other models within the individual, judging whether there are wind turbines of other models within the circumscribed polygon;

[0033] If there are, it is judged that the certain wind turbine model does not meet the polygon - type same - type aggregation constraint; if not, it is judged that the certain wind turbine model meets the polygon - type same - type aggregation constraint.

[0034] The obtaining of the effective sides that enclose the circumscribed polygon based on the coordinates of the wind turbine installation points corresponding to the certain wind turbine model includes:

[0035] Traverse the point pairs of all wind turbine installation points corresponding to the certain wind turbine model, calculate the Euclidean distance, and take as 1 / 2 of the average value of the Euclidean distances of all point pairs, and let ;

[0036] Construct a circle passing through the wind turbine installation points , and with a radius of The equation of the circle can be determined by the following method:

[0037] The equation of the perpendicular bisector of the candidate side is:

[0038] ;

[0039] Since the distance from the center of the circle to is therefore, there is:

[0040] ;

[0041] Solve the above two equations simultaneously to obtain the center coordinates and then the equation of the circle can be obtained:

[0042] ;

[0043] When, among all the wind turbine installation points corresponding to the certain wind turbine model, except , there is a certain point inside the circle that is, it satisfies:

[0044] ,

[0045] Then the candidate edge is an invalid edge; conversely, it is a valid edge.

[0046] Based on the coordinates of the wind turbine installation points corresponding to the other models within the individual, determining whether there are wind turbines of other models within the circumscribed polygon includes:

[0047] Based on the coordinates of each boundary point of the circumscribed polygon, calculating the area of the circumscribed polygon;

[0048] Based on the coordinates of the wind turbine installation points of other models and the coordinates of the two boundary points of any side of the circumscribed polygon, determining the area of the triangle formed by the three points;

[0049] Based on the areas of the triangles corresponding to each side of the circumscribed polygon, calculating the sum of the areas, and comparing the sum of the areas with the area of the circumscribed polygon to determine whether the wind turbine installation points of other models are located within the circumscribed polygon.

[0050] Based on the wind turbine models at each wind turbine installation point and the parameters of each model, combined with the representative annual wind resource data set and the wind shear index at the representative height, determining the annual power generation of the wind farm includes:

[0051] Based on the wind speed and wind direction, dividing the basic wind conditions, determining the basic wind conditions corresponding to each period of the representative year, and determining the representative wind speed and representative wind direction at the representative height of each basic wind condition, and counting the proportion of each basic wind condition;

[0052] Based on the wind turbine models at each wind turbine installation point and the representative wind direction, considering the influence of the wake of the upwind wind turbine, determining the wind speed loss at each discrete point on the wind turbine disk;

[0053] Based on the wind shear index, combined with the representative wind speed at the representative height, determining the inflow wind speed at the vertical height where each discrete point is located;

[0054] Based on the inflow wind speed at the vertical height where each discrete point on the wind turbine disk is located, combined with the wind speed loss at each discrete point, determining the wind speed at each discrete point, and further determining the effective wind speed of the wind turbine;

[0055] Based on the effective wind speed of each wind turbine, determining the power generation of each wind turbine in each basic wind condition;

[0056] Based on the power generation of each wind turbine, determining the power generation of the wind farm in each basic wind condition, and further combining the proportion of the basic wind conditions to determine the annual power generation of the representative year.

[0057] The dividing of the basic wind conditions based on the wind speed and wind direction includes:<00>

[0058] Based on the wind speed - aerodynamic parameter list of each wind turbine type in the wind farm, determine the maximum and minimum cut - in wind speeds and cut - out wind speeds, and divide multiple wind speed intervals between the maximum and minimum values;

[0059] Evenly divide the 0 - 360° wind direction angle and cut out multiple wind direction sectors;

[0060] Combine the wind speed intervals and wind direction sectors in pairs to form multiple basic wind conditions.

[0061] The method of determining the wind speed at each discrete point based on the inflow wind speed at the vertical height where each discrete point on the wind turbine disk is located, and combining the wind speed loss at each discrete point, and then determining the effective wind speed of the wind turbine includes:

[0062] ;

[0063] Among them, is the wind speed loss at the discrete point m on the wind turbine i, represents the number of the up - wind wind turbine, and is the wind speed loss of the isolated wake of the up - wind wind turbine at the discrete point m of the affiliated wind turbine i. The calculation formula is:

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] Among them, are the horizontal, vertical, and vertical coordinates of the discrete point m of the affiliated wind turbine i in the relative coordinate system with the positive x - axis pointing to the representative wind direction respectively, are the horizontal, vertical, and vertical coordinates of the center point of the wind turbine disk of the wind turbine j in the relative coordinate respectively, and successively represent the wind turbine diameter and thrust coefficient of the wind turbine j, is the wake expansion coefficient of the wind turbine j, which affects the wake width of the isolated wake of the wind turbine j at the wind turbine i, and thus affects the wind speed loss.

[0070] The wake expansion coefficient of the wind turbine j , includes:

[0071] ;

[0072] Among them, and are adjustable parameters. When j = 1:

[0073] ;

[0074] When :

[0075] ;

[0076] ;

[0077] Among them, represents the wind turbine upwind of wind turbine j When operating in isolation, the overlapping area of its wake influence area and the wind turbine wind wheel disk surface, is the wind turbine the additional turbulence intensity in the isolated wake of is at the wind turbine The size at is calculated by the formula:

[0078] ;

[0079] ;

[0080] Among them, and respectively refer to the wind wheel diameter and thrust coefficient of wind turbine k, is the wind turbine the effective turbulence intensity at, [[ID=5l]]、 are the abscissas of wind turbines j and k in the relative coordinate system with the positive x-axis pointing in the direction of the wind.

[0081] An optimization design device for the layout of multi-type offshore wind farms considering homogeneous aggregation, including:

[0082] A data acquisition module for acquiring wind farm information, model parameters, and wind measurement data, where the wind farm information includes the installation positions of wind turbines in the field area, the models planned to be assembled, and the quantities of each model;

[0083] A data processing module for determining the representative annual wind resource data set and wind shear index at the representative height in the area where the wind farm is located based on the wind measurement data;

[0084] A layout optimization module for taking the wind turbine models at each wind turbine installation position in the field area as design variables, aiming to maximize the annual power generation of the wind farm, and combining constraint conditions, using an intelligent optimization algorithm for iterative optimization to determine the optimal layout design plan;

[0085] The constraint conditions include: the quantity constraint of each model, and the homogeneous aggregation constraint of the layout positions of each model;

[0086] The calculation of the annual power generation of the wind farm includes: based on the wind turbine models and their parameters at each wind turbine installation point, combined with the representative annual wind resource data set and the wind shear exponent at the representative height, determining the annual power generation of the wind farm.

[0087] A storage medium stores a computer program executable by a processor. When the computer program is executed, it implements the steps of the multi-type offshore wind farm layout optimization design method considering the aggregation of the same type.

[0088] A multi-type offshore wind farm layout optimization design device has a memory and a processor. The memory stores a computer program executable by the processor. When the computer program is executed, it implements the steps of the multi-type offshore wind farm layout optimization design method considering the aggregation of the same type.

[0089] The beneficial effects of the present invention are as follows: Based on the actual engineering requirements, with the maximization of the annual power generation of the wind farm as the optimization goal, taking the wind turbine models at each wind turbine installation point as design variables, and under the conditions of meeting the quantity constraints of each model and the aggregation constraint of the same type of wind turbine layout positions, an intelligent optimization algorithm is used for iterative optimization. Finally, the best layout design scheme that meets the engineering requirements of "aggregated layout" of the same type of wind turbines is obtained.

[0090] In the optimization calculation process of the present invention, for the calculation of the annual power generation of each layout scheme, the present invention starts from two aspects: the inflow condition and the wake effect, and scientifically quantifies the influence of wind shear on the wake loss of the multi-type offshore wind farm. Compared with the prior art, the present invention effectively improves the calculation accuracy of the annual power generation, so that the optimized layout design scheme can better match the wind energy resource distribution characteristics in the field area, thereby ensuring that the power generation effect during the actual operation of the wind farm reaches the expected value.

[0091] In summary, the present invention provides a technical path that combines science and practicality for the layout optimization design of multi-type offshore wind farms in the case of multi-type mixed layout in wind power engineering practice, and has important engineering application value. Description of the Drawings

[0092] Figure 1 It is the flow chart of the layout optimization design of the multi-type offshore wind farm in the embodiment.

[0093] Figure 2 It is the schematic diagram for determining the linear type of the same type aggregation; among them, (a) and (b) respectively refer to the scenarios that meet and do not meet the linear type of the same type aggregation.

[0094] Figure 3 It is the schematic diagram for determining the polygon type of the same type aggregation; among them, (a) and (b) respectively refer to the scenarios that meet and do not meet the polygon type of the same type aggregation.

[0095] Figure 4 It is a schematic diagram for determining the relative position relationship between the wind turbine positions and the polygon. Among them, (a) and (b) respectively refer to the scenarios where the wind turbine is inside and outside the polygon.

[0096] Figure 5 It is a flowchart for calculating the annual power generation of a multi-type offshore wind farm considering the influence of wind shear in the embodiment. Specific implementation manners

[0097] 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 denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring 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 elaboration and explanation, 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.

[0098] 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 should not be construed 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 art of this technology.

[0099] As Figure 1 shown, this embodiment is a method for optimizing the layout design of a multi-type offshore wind farm considering the same-kind aggregation, which specifically includes the following steps:

[0100] S100. Obtain the wind farm information, model parameters, and wind measurement data.

[0101] In this embodiment, the wind farm information includes the wind turbine installation positions for installing wind turbines in the field area, the models planned to be assembled, and their respective quantities.

[0102] Let the number of wind turbine positions be , and plan to assemble types of models, and the number of wind turbines corresponding to the model is . Then, according to the definition, obviously .

[0103] The model parameters include the hub height, rotor diameter, and wind speed-aerodynamic parameter list. The wind speed-aerodynamic parameter list is usually obtained from the complete machine manufacturer and provides the thrust coefficient and power at each typical wind speed within the normal operating power generation wind speed range (cut-in wind speed to cut-out wind speed) of the corresponding model.

[0104] The wind measurement data includes wind speeds, wind directions, and turbulence intensities at multiple time periods at multiple different wind measurement heights.

[0105] S200. Process the wind measurement data to determine the representative annual wind resource dataset, representative turbulence intensity, and wind shear exponent at the representative height in the area where the wind farm is located.

[0106] The processing of the wind measurement data mainly includes data integrity verification, rationality verification, as well as the elimination, interpolation, extension, and representative annual correction of unreasonable data and missing measurement data. The goal is to obtain a set of representative wind resource data that can reflect the long-term average level of the wind farm.

[0107] The representative height is referred to by It is recommended to select one that is closest to the average value of the hub heights of all turbine models in the wind farm among the multiple different wind measurement heights described in step S100.

[0108] The representative annual wind resource dataset at the representative height includes wind speeds, wind directions, and turbulence intensities at 8,760 time periods throughout the entire year at the representative height.

[0109] The representative turbulence intensity is referred to by and is obtained by calculating the average value of the turbulence intensities at all 8,760 time periods in the representative annual wind resource dataset at the representative height;

[0110] The wind shear exponent is calculated with reference to the following formula:

[0111] ;

[0112] where and respectively refer to the average wind speeds at each time period in the representative annual wind resource dataset at the vertical heights and . To enhance the reliability and representativeness of the wind shear fitting results, regarding and , it is recommended to select two heights that meet the following constraint conditions among the multiple different wind measurement heights described in step 1:

[0113] ;

[0114] ;

[0115] ;

[0116] where represents the number of turbine models in the wind farm, is the hub height of the th turbine model.

[0117] S300. Divide the basic wind conditions based on wind speed and wind direction, determine the corresponding basic wind conditions for each period of the representative year, determine the representative wind speed and representative wind direction at the representative height of each basic wind condition, and count the proportion of each basic wind condition.

[0118] S310. Based on the wind speed - aerodynamic parameter list of each wind turbine model in the wind farm, determine the maximum and minimum cut-in wind speeds and cut-out wind speeds, and divide multiple wind speed intervals between the maximum and minimum values.

[0119] Traverse the wind speed - aerodynamic parameter list of each model in the wind farm obtained in step S100 to determine the minimum value of the wind speed and the maximum value , and perform floor operation on them respectively and ceiling operation processing, and then use and as the lower limit and upper limit, and according to the set wind speed calculation interval , divide multiple distinct wind speed intervals. According to the definition, the total number of wind speed intervals , where the th wind speed interval can be expressed as ;

[0120] The wind speed calculation interval , it is recommended that the value does not exceed the difference between adjacent wind speeds in the wind speed - aerodynamic parameter list of any model.

[0121] S320. Uniformly divide the 0 - 360° wind direction angle to cut out multiple wind direction sectors.

[0122] According to the set number of wind direction sectors , uniformly divide the 0 - 360° wind direction angle to cut out multiple distinct wind direction sectors. According to the definition, the size of each wind direction sector is . If is used as the middle value of the first wind direction sector, then the th wind direction sector can be expressed as: ;

[0123] Referring to the conventional practices in wind power engineering, it is recommended that take the value of 12 or 16.

[0124] S330. Combine the wind speed intervals and wind direction sectors in pairs to form multiple basic wind conditions.

[0125] Combine the wind speed intervals and wind direction sectors obtained by the division in steps S310 and S320 in pairs, and thus multiple distinct basic wind conditions can be obtained. According to the definition, the total number of basic wind conditions is , the corresponding number sequence can be expressed as , where represents the th basic wind condition.

[0126] S340. Take the intermediate values of the wind direction sector and wind speed range of the basic wind condition as the representative wind speed and representative wind direction of the basic wind condition .

[0127] S350. Process the representative annual wind resource dataset at the representative height and count the proportion of each basic wind condition;

[0128] Specifically, according to the wind speed and wind direction of each time period in the representative annual wind resource dataset at the representative height, and according to the interval range to which it belongs, classify this time period into the corresponding basic wind condition in step S330. After traversing all time periods in the dataset and completing the above operations, calculate the ratio of the number of time periods under each basic wind condition to the number of time periods in the representative annual wind resource dataset at the representative height, and the proportion of each basic wind condition can be obtained. The corresponding sequence can be expressed as , where refers to the proportion of the th basic wind condition . According to the definition, the calculation formula is as follows:

[0129] ;

[0130] Among them, is the number of time periods classified into the basic wind condition at the representative height in the representative annual wind resource dataset.

[0131] As for why the statistical results of the annual wind resource dataset at the representative height can be used for all wind turbines in the whole field, the explanation is as follows: In wind power engineering practice, when designing the mixed layout of multiple types of models in the same area, the hub heights of the selected models usually do not vary greatly, generally not exceeding 10 m. The reasons are as follows: (1) The offshore wind shear is relatively small. The additional benefit of obtaining a larger inflow wind speed by increasing the hub height of a certain model and thus increasing the power generation may not cover the investment in a larger bottom support platform caused by the increase in hub height (a larger hub height requires a larger-sized bottom support platform, and correspondingly, the investment cost will increase); (2) With the development of the large-scale of wind turbines, although the rated powers of different models are different, their rotor diameters usually vary little. Correspondingly, the hub heights for supporting this rotor diameter also vary little. For this reason, in this embodiment, following the conventional treatment method in wind power engineering, only the influence of wind shear in the inflow on the wind speed at different vertical heights is considered, and the wind direction at the representative height is applied to various different models.

[0132] S400. Taking the wind turbine models at the installation points of each wind turbine in the field as design variables, with the goal of maximizing the annual power generation of the wind farm, combined with the constraint conditions, an intelligent optimization algorithm is used for iterative optimization to determine the optimal layout design scheme.

[0133] Taking any point in the wind farm as the coordinate origin, 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°), a two-dimensional rectangular coordinate system is established, also known as the geodetic coordinate system. Under this coordinate system, the coordinates of each wind turbine point obtained in step S100 are determined to form a set T, which is expressed as

[0134] ;

[0135] Since the annual power generation is an important basis for determining the development benefits of wind power projects, in this embodiment, maximizing the annual power generation is taken as the goal, and an intelligent optimization algorithm is used to construct an optimization calculation model by continuously changing the models at each point. Specifically, in order to represent the models at each point, integer design variables are set, and the corresponding set is expressed as , where refers to the model at the point with the coordinate .

[0136] The layout scheme that meets the output standard in this embodiment needs to meet the following two constraint conditions:

[0137] (1) The quantity constraint of each model. The quantity of each model and the number of wind turbines belonging to each model need to conform to the required quantity set in step S100;

[0138] (2) Similar clustering constraints for the locations of each type of wind turbine. Each wind turbine belonging to the same type of wind turbine must meet the similar clustering constraints required in the wind power project. There are two forms: one is linear similar clustering, that is, the locations of each wind turbine belonging to the same type of wind turbine form a straight line segment, and there are no other types of wind turbines in the straight line segment; the other is polygonal similar clustering, that is, there are no other types of wind turbines inside the polygon formed by the locations of each wind turbine belonging to the same type of wind turbine.

[0139] S410: Initialize the population, where each individual in the population corresponds to a machine layout plan.

[0140] According to the population size M set in the intelligent optimization algorithm, that is, the number of individuals in each generation of the population, where each individual corresponds to a set of integer design variable values (that is, a layout plan), the initial generation population that meets the size requirements is obtained.

[0141] S420: Determine whether the layout plan corresponding to each unit satisfies the constraint conditions.

[0142] S421: Determine whether the number of models in the individual and the number of wind turbines belonging to each model meet the preset number of models and the number of wind turbines belonging to each model constraints.

[0143] For a set of design variable values corresponding to the target individual, first determine whether the type of machine model under the corresponding layout plan is the same as the number of models set in step S100; then, further determine whether the number of wind turbines belonging to each model is the same as the respective demand. If all the above conditions are met, select one of the models with no less than 2 wind turbines and proceed to step S422 to determine whether the linear homogeneous clustering constraint is met. Otherwise, the individual is classified as an individual that does not meet the constraint and proceed to step S430.

[0144] S422: Determine whether the linear homogeneous aggregation constraint is satisfied.

[0145] For model T k For example, let's summarize what it means to satisfy the linear-homogeneous clustering constraint, such as Figure 2 As shown, the red line represents the outer boundary of the wind farm. The model T is arranged at the point corresponding to the red point. k wind turbines, and other types of wind turbines are arranged at the points shown by black dots. Figure 2 In the case of (a), due to the model T k The wind turbines are located on the same straight line, and the straight line segment is closed, that is, there are no other types of wind turbines inside it, so it can be regarded as satisfying the linear homogeneous aggregation constraint. Figure 2 In the case shown in (b), although it belongs to model T kThe wind turbines of each type are on the same straight line. However, since other types of wind turbines are interspersed within this straight line segment, it does not meet the constraint of linear homogeneous aggregation. In addition, when the wind turbines of the affiliated type T k are not on the same straight line, it can be directly determined that it does not meet the constraint of linear homogeneous aggregation.

[0146] S4221. Extract the installation positions of the wind turbines corresponding to each wind turbine type in the individual. Based on the coordinates of the installation positions of the wind turbines corresponding to each wind turbine type, determine whether each wind turbine type meets the constraint of linear homogeneous aggregation.

[0147] a. Sort the installation position coordinates of the wind turbines corresponding to each wind turbine type in terms of size to determine the installation positions of the first and last wind turbines of each type.

[0148] Extract the position coordinates of the wind turbines of the affiliated type T k . Arrange them in ascending order of the abscissa (when the abscissas are the same, then in ascending order of the ordinate) to form a set:

[0149] ;

[0150] wherein, represents the number of wind turbines of the affiliated type T k , and are the two position coordinates with the smallest and largest abscissas (or when the abscissas are the same, the smallest and largest ordinates) respectively; if the coordinate points in the set are on the same straight line segment, it can be known that and are the coordinates of the two endpoints of this straight line segment.

[0151] The set of the position coordinates of the wind turbines other than the type T[[ID=3�]] k is denoted as S. According to the definition, for the sets U, S, and the set T of the position coordinates of all wind turbines, the following relationship exists: S = T - U.

[0152] b. Based on the positional relationship between the installation positions of the first and last wind turbines of each type and the installation positions of the remaining wind turbines in the corresponding type, determine whether the installation positions corresponding to the same type can be connected into a straight line.

[0153] When the number of wind turbines in the set U is 2, this step can be skipped. Otherwise, calculate the vector constructed by the position coordinates of the first and last wind turbines in the set U, and then traverse the remaining position coordinates in the set U, such as , and calculate the cross product modulus of the vector and :

[0154] ;

[0155] If, after calculation, , , it can be determined that the points within the set U are connected to form a straight line, and proceed to step c to further determine whether a certain point within this straight line segment is installed with wind turbines of other models; otherwise, it is determined that the corresponding model does not meet the straight-line type of similar aggregation constraint, and proceed to step S430.

[0156] c. Based on the positional relationship between the installation points of the first and last wind turbines of the corresponding model and the installation points of the remaining models in the individual, determine whether there are any remaining models on the connected straight line.

[0157] Traverse the coordinate points of each point within the set S. If , combined with the endpoint coordinates of the straight line segment where the wind turbine points of the affiliated model T k are located in step a, that is and , calculate the cosine value: ;

[0158] If , it can be determined that the point is not within the straight line segment formed by the points within the set U. Conversely, it is within the straight line segment.

[0159] If, after calculation, for all the point coordinates within the set S, there is , it can be determined that the wind turbine points of the affiliated model T k meet the straight-line type of similar aggregation constraint; otherwise, further determine whether the number of wind turbines of the affiliated model T k is not less than 3. If it is satisfied, proceed to step S423. If not, classify this individual as an individual that does not meet the constraint conditions and proceed to step S430.

[0160] S423. Determine whether it meets the polygon type of similar aggregation constraint.

[0161] S4231. Based on the coordinates of the wind turbine installation points corresponding to a certain wind turbine model, obtain the effective sides that enclose the circumscribed polygon.

[0162] First, traverse all the point pairs within the set U, calculate the Euclidean distance , and take as 1 / 2 of the average value of the Euclidean distances of all point pairs. Let ; judge one by one whether holds. If it holds, add the side to the candidate side set E 1.

[0163] Next, for each candidate edge E in set 1, determine whether it is a valid edge.

[0164] Taking the judgment of candidate edge as an example, the steps are described in detail:

[0165] Construct a circle passing through points , , and with a radius of. The equation of the circle can be determined by the following method:

[0166] The equation of the perpendicular bisector of candidate edge is:

[0167] ;

[0168] Since the distance from the center of the circle to is , we have:

[0169] ;

[0170] Solve the above two equations simultaneously to obtain the coordinates of the center of the circle , and then the equation of the circle can be obtained:

[0171] ;

[0172] When there exists a point k in the set U formed by all the wind turbine positions of the affiliated model T , except that is located inside the circle , that is, it satisfies:

[0173] ,

[0174] then candidate edge is an invalid edge; otherwise, it is a valid edge.

[0175] Refer to the above steps to traverse the judgment of each candidate edge in set E 1. All the valid edges obtained in this way are the boundary lines of the polygon formed by the wind turbine positions of the affiliated model T k .

[0176] S4232. Based on the coordinates of the wind turbine installation points corresponding to the other models within the individual, determine whether there are wind turbines of other models within the circumscribed polygon. If so, determine that the certain wind turbine model does not meet the same-type aggregation constraint of the polygon type; if not, determine that the certain wind turbine model meets the same-type aggregation constraint of the polygon type.

[0177] Traverse the coordinates of each wind turbine point in set S, as and determine its relative position relationship with the polygon formed by the wind turbines of the affiliated model T k . As shown in (a) of Figure 4 , the wind turbine point W is located within the polygon , while in (b) of Figure 4 , the wind turbine point W is outside the polygon . The determination method is as follows:

[0178] ①. Based on the coordinates of each boundary point of the circumscribed polygon, calculate the area of the circumscribed polygon.

[0179] According to the coordinates of each boundary point of the polygon, use the vector cross product to calculate the area of the circumscribed polygon

[0180] , where the area of the triangle .

[0181] ②. Based on the coordinates of the wind turbine installation points of other models and the coordinates of the two boundary points of any side of the circumscribed polygon, determine the area of the triangle formed by the three points.

[0182] According to the coordinates of the wind turbine point W and the coordinates of each boundary point of the polygon, use the vector cross product to calculate the area of the triangle corresponding to each side of the circumscribed polygon .

[0183] ③. Based on the areas of the triangles corresponding to each side of the circumscribed polygon in step ②, calculate the sum of the areas, and compare the sum of the areas with the area of the circumscribed polygon in step ① to determine whether the wind turbine installation points of other models are located within the circumscribed polygon.

[0184] Compare the sum of the areas of the triangles calculated above with the area of the polygon. If the two are equal and both are greater than or equal to 0, it can be known that the wind turbine point W is located within (including the boundary) the polygon ; otherwise, the wind turbine point W is located outside (excluding the boundary) the polygon .

[0185] When all the wind turbine points in set S are located within the polygon formed by the wind turbines of the affiliated model T kWhen outside the polygon formed by the wind turbines, determine the turbine type T k Meet the same-type aggregation constraint of the polygon type; otherwise, classify the target individual as an individual that does not meet the constraint conditions and proceed to step S430.

[0186] S424. Replace with another turbine type and repeat steps S422 and S423 until all turbine types with no less than 2 wind turbines have been traversed; if it is determined that each of the foregoing turbine types meets the "linear type - same-type aggregation" or "polygon type - same-type aggregation" constraint, classify the target individual as an individual that meets the constraint conditions and proceed to step S430.

[0187] S430. For individuals that meet the constraint conditions, calculate the annual power generation of the wind farm; for individuals that do not meet the constraint conditions, use a preset minimum value as the corresponding annual power generation of the wind farm.

[0188] For individuals that do not meet the constraint conditions in step S420, take the corresponding annual power generation as a minimum value, such as 0.001, while for those that meet the conditions, consider the influence of wind shear on the inflow perceived by the wind turbines and the wake effect between the wind turbines, and use an analytical model to calculate the annual power generation of the corresponding layout scheme.

[0189] As Figure 5 shown, in this embodiment, the annual power generation of the layout scheme is calculated through the following steps, including:

[0190] Ⅰ. For the wind speed - aerodynamic parameter list of each turbine type obtained in step S100, supplement the aerodynamic parameters in the wind speed intervals below the cut-in wind speed and above the cut-out wind speed.

[0191] Since the cut-in wind speed and cut-out wind speed of different turbine types may vary, in some of the basic wind conditions divided in step S300, the following scenarios may occur: the representative wind speed is greater than the cut-in wind speed of one turbine type and less than the cut-in wind speed of other turbine types, or the representative wind speed is less than the cut-out wind speed of one turbine type and greater than the cut-out wind speed of other turbine types. At this time, for the upwind wind turbines in the wind farm belonging to the latter turbine type, when determining their aerodynamic parameters according to the magnitude of the wind speed they perceive, it may occur that the wind speed is not within the wind speed interval covered by the wind speed - aerodynamic parameter list obtained in step S100, thus causing a calculation overflow error.

[0192] In view of this, it is necessary to supplement the aerodynamic parameters in the wind speed intervals below the cut-in wind speed and above the cut-out wind speed for the wind speed - aerodynamic parameter list of each turbine type obtained in step S100. Specifically, in this embodiment, it is recommended that in the wind speed interval below the cut-in wind speed, the thrust coefficient takes a large value close to 1, such as 0.9999, and the power value is 0; in the wind speed interval above the cut-out wind speed, the thrust coefficient takes a small value close to 0, such as 0.0001, and the power value is 0.

[0193] II. Coordinate system conversion.

[0194] Arrange each wind turbine according to the front - rear relative position along the representative wind direction and number them starting from 1. The corresponding number sequence can be expressed as , where is the number of the wind turbine ranked , and represents the total number of wind turbines in the wind farm.

[0195] The front - rear relative position along the representative wind direction can be obtained by comparing the abscissa values of each wind turbine in the relative coordinate system. When the abscissa is larger, the position of the wind turbine is more backward;

[0196] The relative coordinate system is obtained by rotating the geodetic coordinate system. They share the same coordinate origin, but the directions of the coordinate axes are different. The positive direction of the x - axis in the relative coordinate system points to the representative wind direction ;

[0197] The geodetic coordinate system takes any point in the wind farm as the coordinate origin, the due - east direction as the positive direction of the x - axis (corresponding to the wind direction angle of 270°), and the due - north direction as the positive direction of the y - axis (corresponding to the wind direction angle of 180°), and establishes a two - dimensional rectangular coordinate system.

[0198] Taking the wind turbine numbered i as an example, the abscissa in the relative coordinate system is calculated as:

[0199] ;

[0200] where and are the abscissa and ordinate of the wind turbine numbered i in the geodetic coordinate system and the relative coordinate system respectively.

[0201] III. Based on the wind turbine models and the representative wind direction at the installation positions of each wind turbine, considering the influence of the wake of the upwind wind turbines, determine the wind speed loss at each discrete point on the wind turbine disk surface; based on the wind shear exponent, combined with the representative wind speed at the representative height, determine the incoming flow wind speed at the vertical height where each discrete point is located; based on the incoming flow wind speed at the vertical height where each discrete point on the wind turbine disk surface is located, combined with the wind speed loss at each discrete point, determine the wind speed at each discrete point, and then determine the effective wind speed of the wind turbine.

[0202] In this embodiment, in the order of increasing numbers, using the analytical model, calculate the wind speed loss caused by the wake effect at each wind turbine in the wind farm in turn. Then, combined with the representative wind speed and the wind shear coefficient, the effective wind speed can be calculated;

[0203] Taking the wind speed loss and effective wind speed at the wind turbine numbered i as an example, the detailed process is as follows.

[0204] First, according to the set resolution, the wind turbine disk of the target wind turbine i is discretized into grids. After calculating the coordinate values of each discrete point in the relative coordinate system, the discrete point coordinate set in the following form is integrated:

[0205] , where, is the total number of discrete points belonging to wind turbine i.

[0206] For the resolution, when its value is larger, the number of discrete points in the wind turbine disk obtained by segmentation is smaller. Correspondingly, the time for wake calculation is shorter, but the representativeness of the calculation results may be insufficient. In this embodiment, considering both timeliness and calculation accuracy requirements, it is recommended to set the resolution to 0.25 times the wind turbine diameter of the corresponding model.

[0207] Wind turbine 's effective wind speed is obtained by calculating the average value of the wind speeds at all discrete points within its wind turbine disk. The calculation formula is:

[0208] ;

[0209] Since the thrust coefficient is a key factor affecting the wake evolution of the wind turbine and is also the most important input parameter in the analytical model for calculating the wind turbine wake, according to the effective wind speed of the wind turbine, combined with the wind speed-aerodynamic parameter list of its corresponding model, its thrust coefficient is obtained through interpolation calculation:

[0210] ;

[0211] where, and correspond to the two wind speeds closest to in the wind speed-aerodynamic parameter list of the wind turbine , satisfying , while is the thrust coefficient corresponding to and in the list.

[0212] is the wind speed at the discrete point numbered within the wind turbine disk. The calculation formula is:

[0213] ;

[0214] where, Is a discrete point The inflow wind speed at the vertical height where it is located, and the calculation formula is:

[0215] ;

[0216] Wherein, And Are respectively the representative height and the wind shear exponent described in step 2;

[0217] Is a discrete point At the place, the wind speed loss after considering the wake influence of all wind turbines upwind. When the number of the target wind turbine is , because there are no other wind turbines upwind of it and it is not affected by the wake interference, there is ; And when , in this embodiment, a linear superposition method is used to handle the overlapping effect of the wakes of multiple wind turbines upwind, and it is calculated by the following formula :

[0218] ;

[0219] Wherein, Represents the number of the wind turbine upwind, Is the wind speed loss of the isolated wake of the upwind wind turbine At the discrete point m of the affiliated wind turbine i, and the calculation formula is:

[0220] ;

[0221] ;

[0222] ;

[0223] ;

[0224] ;

[0225] Wherein, Are respectively the horizontal, vertical and vertical coordinates of the discrete point m of the affiliated wind turbine i in the relative coordinate system, Are respectively the horizontal, vertical and vertical coordinates of the center point of the wind turbine disk of the wind turbine j in the relative coordinate, And Successively refer to the wind turbine diameter and the thrust coefficient of the wind turbine j, Is the wake expansion coefficient of the wind turbine j, which affects the wake width of the isolated wake of the wind turbine j at the wind turbine i, and thus affects the wind speed loss. Referring to existing research, The value of is related to the effective turbulence intensity at the wind turbine j Closely related and approximately satisfy the following relationship:

[0226] ;

[0227] Among them, and are adjustable parameters. Referring to existing research, in this embodiment, it is recommended that and take the values of 0.38 and 0.004 respectively. When j = 1, it means that there are no other wind turbines upwind of wind turbine j. Therefore, the effective turbulence intensity it senses is approximately equal to the turbulence intensity at the representative height in the inflow wind, that is:

[0228] ;

[0229] And when , for wind turbine j, in addition to the turbulence intensity in the inflow, the additional turbulence intensity generated by the operation of the upwind wind turbine needs to be considered. The calculation formula is:

[0230] ;

[0231] ;

[0232] Among them, represents the overlapping area between the wake influence area of wind turbine operating alone upwind of wind turbine j and the wind turbine rotor disk, is the magnitude of the additional turbulence intensity in the isolated wake of wind turbine at wind turbine . The calculation formula is:

[0233] ;

[0234] ;

[0235] Among them, and respectively refer to the rotor diameter and thrust coefficient of wind turbine k, is the effective turbulence intensity at wind turbine .

[0236] IV. Based on the effective wind speed of each wind turbine, determine the power generation of each wind turbine in each basic wind condition; based on the power generation of each wind turbine, determine the power generation of the wind farm in each basic wind condition, and then combine the proportion of the basic wind conditions to determine the annual power generation of the representative year.

[0237] Based on the effective wind speed at each wind turbine, combined with the wind speed-aerodynamic parameter list corresponding to their respective models, calculate their power generation, and then through summation, the basic wind condition can be obtained. The wind farm power generation under is calculated as follows:

[0238] ;

[0239] where is the total number of wind turbines in the wind farm, is the power generation of the wind turbine numbered , and the calculation formula is:

[0240] ;

[0241] where and respectively correspond to the two typical wind speeds closest to in the wind speed-aerodynamic parameter list of the model to which the wind turbine belongs, satisfying , and and are the powers corresponding to and in the said list.

[0242] Traverse all the basic wind conditions divided in step S300, and then through summation calculation, obtain the annual power generation of the wind farm , and the calculation formula is:

[0243] ;

[0244] where 8760 represents the number of hours in a year, is the total number of basic wind conditions, and are the proportion of the basic wind condition numbered and the wind farm power generation under this basic wind condition.

[0245] S440. Based on the annual power generation of the wind farm corresponding to each individual, determine the fitness of the individual, update the population based on the fitness of each individual, and return to step S420 until the preset iteration termination condition is met.

[0246] Judge whether the result of the current calculation step meets the convergence criterion. If it meets, output the calculation result. If it does not meet, further judge whether the maximum allowable number of iterations is reached. If it does not meet, select the larger one among the fitness values of all individuals in the population of the current calculation step as the parent, update the population, and return to step S420. If it meets, output the calculation result.

[0247] This embodiment also provides a multi-type offshore wind farm layout optimization design device considering similar aggregation, which includes:

[0248] A data acquisition module, configured to acquire wind farm information, model parameters, and wind measurement data, where the wind farm information includes the installation positions of wind turbines in the field area, the models planned to be assembled, and the quantities of each model;

[0249] A data processing module, configured to determine the representative annual wind resource data set and the wind shear index at the representative height in the area where the wind farm is located based on the wind measurement data;

[0250] A layout optimization module, configured to use the wind turbine models at each wind turbine installation position in the field area as design variables, with the goal of maximizing the annual power generation of the wind farm, and combining the constraint conditions, and using an intelligent optimization algorithm for iterative optimization to determine the optimal layout design scheme;

[0251] The constraint conditions include: the quantity constraint of each model and the similar aggregation constraint of the layout positions of each model;

[0252] The calculation of the annual power generation of the wind farm includes: determining the annual power generation of the wind farm based on the wind turbine models at each wind turbine installation position and the model parameters of each model, in combination with the representative annual wind resource data set and the wind shear index at the representative height.

[0253] This embodiment also provides 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 above-mentioned multi-type offshore wind farm layout optimization design method considering similar aggregation are implemented.

[0254] This embodiment also provides a multi-type 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, and when the computer program is executed, the steps of the above-mentioned multi-type offshore wind farm layout optimization design method considering similar aggregation are implemented.

[0255] In some alternative embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two consecutive blocks shown may actually be executed substantially simultaneously, or the above-mentioned blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of the present invention are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logical flows presented herein. Alternative embodiments are expected, in which the order of various operations is changed and the sub-operations described as part of a larger operation are executed independently.

[0256] 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.

[0257] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an 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 such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0258] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definitional sequence of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by or in connection with 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. 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 connection with an instruction execution system, apparatus, or device.

[0259] 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.

[0260] 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 appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0261] 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 embodiments or examples 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.

[0262] 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.

[0263] The above has specifically described 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 within the scope defined by the claims of this application.

Claims

1. A multi-type offshore wind farm layout optimization design method considering homogeneous aggregation, characterized in that Including: Obtain wind farm information, turbine type parameters, and wind measurement data, where the wind farm information includes the installation locations of wind turbines in the farm area, the planned installed turbine types, and the quantity of each turbine type; Based on the wind measurement data, determine the representative annual wind resource dataset and the wind shear exponent at the representative height in the area where the wind farm is located; Taking the turbine types at each wind turbine installation location in the farm area as design variables, with the goal of maximizing the annual power generation of the wind farm, combined with constraint conditions, use an intelligent optimization algorithm for iterative optimization to determine the optimal turbine layout design scheme; The constraint conditions include: the quantity constraints of the turbine types and the number of wind turbines belonging to each turbine type, and the similar aggregation constraint of the layout positions of each turbine type; The calculation of the annual power generation of the wind farm includes: based on the turbine types at each wind turbine installation location and the parameters of each turbine type, combined with the representative annual wind resource dataset and the wind shear exponent at the representative height, determine the annual power generation of the wind farm; The process of taking the turbine types at each wind turbine installation location in the farm area as design variables, with the goal of maximizing the annual power generation of the wind farm, combined with constraint conditions, using an intelligent optimization algorithm for iterative optimization to determine the optimal turbine layout design scheme includes: S410. Initialize the population, where each individual in the population corresponds to a turbine layout scheme; S420. Determine whether the turbine layout scheme corresponding to each individual satisfies the constraint conditions; S430. For the individuals that satisfy the constraint conditions, calculate the annual power generation of the wind farm; for the individuals that do not satisfy the constraint conditions, use a preset minimum value as the corresponding annual power generation of the wind farm; S440. Based on the annual power generation of the wind farm corresponding to each individual, determine the fitness of the individual, update the population based on the fitness of each individual, and return to step S420 until the preset iteration termination condition is satisfied; The determination of whether the turbine layout scheme corresponding to each individual satisfies the constraint conditions includes: Determine whether the quantity of turbine types and the number of wind turbines belonging to each turbine type in the individual satisfy the preset quantity constraints of the turbine types and the number of wind turbines belonging to each turbine type; If the quantity constraints are satisfied, based on the coordinates of the wind turbine installation locations corresponding to each wind turbine type, determine whether each wind turbine type satisfies the linear similar aggregation constraint; If a certain wind turbine type does not satisfy the linear similar aggregation constraint, based on the coordinates of the wind turbine installation locations corresponding to the certain wind turbine type, determine whether the certain wind turbine type satisfies the polygon similar aggregation constraint; If each wind turbine type in the individual satisfies the linear similar aggregation constraint or the polygon similar aggregation constraint, then the individual satisfies the similar aggregation constraint; The determination of whether each wind turbine type satisfies the linear similar aggregation constraint based on the coordinates of the wind turbine installation locations corresponding to each wind turbine type includes: Sort the coordinates of the wind turbine installation locations corresponding to each wind turbine type in terms of size to determine the installation locations of the first and last wind turbines of each type; Based on the positional relationship between the installation locations of the first and last wind turbines of each type and the installation locations of the remaining wind turbines in the corresponding type, determine whether the installation locations corresponding to the same wind turbine type can form a straight line; If they cannot be connected into a straight line, it is determined that the corresponding model does not meet the linear type same-kind aggregation constraint; if they can be connected into a straight line, based on the positional relationship between the installation positions of the first and last wind turbines of the corresponding model and the installation positions of the remaining models in the individual, it is determined whether there are any remaining models on the connected straight line; If there are, the models connected into a straight line do not meet the linear type same-kind aggregation constraint; if not, it is determined that the models connected into a straight line meet the linear type same-kind aggregation constraint; The determining whether the certain wind turbine model meets the polygon type same-kind aggregation constraint based on the coordinates of the wind turbine installation positions corresponding to the certain wind turbine model includes: Based on the coordinates of the wind turbine installation positions corresponding to the certain wind turbine model, obtaining the effective sides that enclose the circumscribed polygon; Based on the coordinates of the wind turbine installation positions corresponding to the remaining models in the individual, determining whether there are other wind turbine models inside the circumscribed polygon; If there are, it is determined that the certain wind turbine model does not meet the polygon type same-kind aggregation constraint; if not, it is determined that the certain wind turbine model meets the polygon type same-kind aggregation constraint.

2. The multi-type offshore wind farm layout optimization design method considering homogeneous aggregation according to claim 1, wherein, The obtaining the effective sides that enclose the circumscribed polygon based on the coordinates of the wind turbine installation positions corresponding to the certain wind turbine model includes: Traverse the point pairs of all wind turbine installation points corresponding to a certain type of wind turbine, calculate the Euclidean distance, and take α as the average value of the Euclidean distances of all point pairs Let r = 1 / α; Make a circle passing through the wind turbine installation point W i ,W j , and with a radius of r. The equation of the circle can be determined by the following method: Candidate edge The equation of its perpendicular bisector is: Since the distance from the center of the circle (x c , y c ) to W i is r, we have: Solve the above two equations simultaneously to obtain the coordinates of the center of the circle \((x c , y c ), and then the equation of the circle can be obtained: (x - x c ) 2 +(y - y c ) 2 = r 2 When, among all the wind turbine installation points corresponding to a certain type of wind turbine, there exists a point (x i , W j ), except for W, located inside the circle (x - x k , y k )^2 + (y - y c )^2 2 = r c )^2 2 = r 2 ^2, that is, it satisfies: (x k - x c ) 2 +(y k - y c ) 2 <r 2 Then the candidate edge is an invalid edge; otherwise, it is a valid edge.

3. The multi-type offshore wind farm layout optimization design method considering homogeneous aggregation according to claim 1, characterized in that The determining whether there are other wind turbine models inside the circumscribed polygon based on the coordinates of the wind turbine installation positions corresponding to the remaining models in the individual includes: Based on the coordinates of each boundary point of the circumscribed polygon, calculating the area of the circumscribed polygon; Based on the coordinates of the wind turbine installation position of other models and the coordinates of the two boundary points of any side of the circumscribed polygon, determining the area of the triangle formed by the three points; Based on the areas of the triangles corresponding to each side of the circumscribed polygon, calculating the sum of the areas, and comparing the sum of the areas with the area of the circumscribed polygon to determine whether the wind turbine installation positions of other models are located inside the circumscribed polygon.

4. The multi-type offshore wind farm layout optimization design method considering homogeneous aggregation according to claim 1, characterized in that The determining the annual power generation of the wind farm based on the wind turbine models at each wind turbine installation position, each model parameter, the representative annual wind resource data set and the wind shear index at the representative height includes: Based on the wind speed and wind direction, dividing the basic wind conditions, determining the basic wind conditions corresponding to each period of the representative year, and determining the representative wind speed and representative wind direction at the representative height of each basic wind condition, and counting the proportion of each basic wind condition; Based on the wind turbine models at each wind turbine installation position and the representative wind direction, considering the wake effect of the upwind wind turbines, determining the wind speed loss at each discrete point on the wind turbine disk surface; Based on the wind shear index, combined with the representative wind speed at the representative height, determining the incoming flow wind speed at the vertical height where each discrete point is located; Based on the incoming flow wind speed at the vertical height where each discrete point on the wind turbine disk surface is located, combined with the wind speed loss at each discrete point, determining the wind speed at each discrete point, and further determining the effective wind speed of the wind turbine; Based on the effective wind speeds of each wind turbine, determining the power generation of each wind turbine in each basic wind condition; Based on the power generation of each wind turbine, determining the power generation of the wind farm in each basic wind condition, and further combining the proportion of the basic wind conditions to determine the annual power generation of the representative year.

5. The multi-type offshore wind farm layout optimization design method considering homogeneous aggregation according to claim 4, characterized in that, The dividing the basic wind conditions based on the wind speed and wind direction includes: Based on the wind speed - aerodynamic parameter list of each wind turbine model in the wind farm, determine the maximum and minimum cut - in wind speeds and cut - out wind speeds, and divide multiple wind speed intervals between the maximum and minimum values; Evenly divide the 0 - 360° wind direction angle and cut out multiple wind direction sectors; Pair - wise combine the wind speed intervals and wind direction sectors to form multiple basic wind conditions.

6. The multi-type offshore wind farm layout optimization design method considering homogeneous aggregation according to claim 4, characterized in that The method for determining the wind speed at each discrete point based on the inflow wind speed at the vertical height where each discrete point on the wind turbine disk surface is located, and combining the wind speed loss at each discrete point, and then determining the effective wind speed of the wind turbine includes: where, Δu i-m is the wind speed loss at discrete point m on wind turbine i, j represents the number of the upwind wind turbine, is the wind speed loss at discrete point m on wind turbine i caused by the isolated wake of upwind wind turbine j, and the calculation formula is: where x' i-m , y' i-m , z' i-m are the horizontal, vertical, and vertical coordinates of the discrete point m belonging to wind turbine i in the relative coordinate system with the positive x-axis pointing in the direction of the representative wind direction, respectively. x' j , y' j , H' j are the horizontal, vertical, and vertical coordinates of the center point of the wind turbine j's rotor disk in the relative coordinate system, respectively. D j and CT j successively refer to the rotor diameter and thrust coefficient of wind turbine j, respectively. k j is the wake expansion coefficient of wind turbine j, which affects the wake width of the isolated wake of wind turbine j at wind turbine i and thus affects the wind speed loss.

7. The method for optimizing the layout design of multi-type offshore wind farms considering homogeneous aggregation according to claim 6, wherein The wake expansion coefficient k of the wind turbine j j , including: k j = k a I j + k b where k a and k b are adjustable parameters. When j = 1: I j = I ref And when j > 1: Among them, A kj represents the overlapping area between the wake influence area of wind turbine k operating alone upstream of wind turbine j and the rotor disk plane of wind turbine j, I +kj is the magnitude of the additional turbulence intensity in the isolated wake of wind turbine k at wind turbine j, and the calculation formula is: Among them, D k and C Tk respectively refer to the rotor diameter and thrust coefficient of the wind turbine k, I k is the effective turbulence intensity at the wind turbine k, x' j and x' k are the abscissas of the wind turbines j and k in the relative coordinate system with the positive x-axis pointing in the direction of the wind direction.

8. An optimized design device for the layout of multi-type offshore wind farms considering the aggregation of similar types, characterized in that, including: A data acquisition module, which is used to acquire wind farm information, model parameters, and wind measurement data. The wind farm information includes the installation positions of wind turbines in the field, the models planned to be assembled, and the quantity of each model; A data processing module, which is used to determine the representative annual wind resource data set and wind shear exponent at the representative height in the area where the wind farm is located based on the wind measurement data; A layout optimization module, which takes the wind turbine models at each wind turbine installation position in the field as design variables, aims to maximize the annual power generation of the wind farm, combines the constraint conditions, and uses an intelligent optimization algorithm for iterative optimization to determine the optimal layout design scheme; The constraint conditions include: the quantity constraint of each model and the similar - type aggregation constraint of the layout positions of each model; The calculation of the annual power generation of the wind farm includes: based on the wind turbine models at each wind turbine installation position and each model parameter, combining the representative annual wind resource data set and wind shear exponent at the representative height to determine the annual power generation of the wind farm; The method of taking the wind turbine models at each wind turbine installation position in the field as design variables, aiming to maximize the annual power generation of the wind farm, combining the constraint conditions, and using an intelligent optimization algorithm for iterative optimization to determine the optimal layout design scheme includes: S410. Initialize the population, and each individual in the population corresponds to a layout scheme; S420. Judge whether the layout scheme corresponding to each individual meets the constraint conditions; S430. For the individuals that meet the constraint conditions, calculate the annual power generation of the wind farm; for the individuals that do not meet the constraint conditions, take a preset minimum value as the corresponding annual power generation of the wind farm; S440. Based on the annual power generation of the wind farm corresponding to each individual, determine the fitness of the individual, update the population based on the fitness of each individual, and return to step S420 until the preset iteration termination condition is met; The judgment of whether the layout scheme corresponding to each individual meets the constraint conditions includes: Judge whether the number of models in the individual and the number of wind turbines belonging to each model meet the preset number of models and the number of wind turbines belonging to each model constraint; If the quantity constraint is met, then based on the coordinates of the wind turbine installation positions corresponding to each wind turbine model, judge whether each wind turbine model meets the linear similar - type aggregation constraint; If a certain wind turbine model does not meet the linear similar - type aggregation constraint, then based on the coordinates of the wind turbine installation positions corresponding to the certain wind turbine model, judge whether the certain wind turbine model meets the polygon - type similar - type aggregation constraint; If each wind turbine model in the individual meets the linear similar - type aggregation constraint or the polygon - type similar - type aggregation constraint, then the individual meets the similar - type aggregation constraint; Judging whether each wind turbine model meets the linear same-type aggregation constraint based on the coordinates of the wind turbine installation points corresponding to each wind turbine model includes: Performing a size sorting based on the coordinates of the wind turbine installation points corresponding to each wind turbine model to determine the installation points of the first and last wind turbines of each model; Based on the positional relationship between the installation points of the first and last wind turbines of each model and the installation points of the remaining wind turbines in the corresponding model, judging whether the installation points corresponding to the same model can be connected into a straight line; If they cannot be connected into a straight line, it is judged that the corresponding model does not meet the linear same-type aggregation constraint; if they can be connected into a straight line, then based on the positional relationship between the installation points of the first and last wind turbines of the corresponding model and the installation points of the remaining models in the individual, judging whether there are other models on the connected straight line; If there are, the models connected into a straight line do not meet the linear same-type aggregation constraint; if not, it is judged that the models connected into a straight line meet the linear same-type aggregation constraint; Judging whether a certain wind turbine model meets the polygon-type same-type aggregation constraint based on the coordinates of the wind turbine installation points corresponding to the certain wind turbine model includes: Based on the coordinates of the wind turbine installation points corresponding to the certain wind turbine model, obtaining the effective sides enclosing the circumscribed polygon; Based on the coordinates of the wind turbine installation points corresponding to the remaining models in the individual, judging whether there are wind turbines of other models inside the circumscribed polygon; If there are, it is judged that the certain wind turbine model does not meet the polygon-type same-type aggregation constraint; if not, it is judged that the certain wind turbine model meets the polygon-type same-type aggregation constraint.

9. A storage medium having stored thereon a computer program executable by a processor, characterized in that: When the computer program is executed, it implements the steps of the multi-model offshore wind farm layout optimization design method considering same-type aggregation according to any one of claims 1 to 7.

10. A multi-type offshore wind farm layout optimization design device, having a memory and a processor, and a computer program capable of being executed by the processor is stored on the memory, characterized in that: When the computer program is executed, it implements the steps of the multi-model offshore wind farm layout optimization design method considering same-type aggregation according to any one of claims 1 to 7.

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