Wind power plant arrangement optimization method and device, equipment and storage medium

Abstract

The invention discloses a wind power plant arrangement optimization method and device, equipment and a storage medium, and relates to the technical field of wind power plant design and optimization, and the method comprises the steps: obtaining environment turbulence intensity and a fan thrust coefficient, and constructing an omnidirectional wind resource map; inputting the environmental turbulence intensity and the fan thrust coefficient into a TurbOPark wake flow model, calculating wake flow loss and obtaining the inflow wind speed of each fan under each wind direction and wind speed combination; taking the wind frequency in the omnidirectional wind resource map as a weight, and carrying out weighted summation processing on single-machine power obtained by converting the inflow wind speed through a power curve to obtain a target function of maximizing the total power generation of the whole field; and by taking the boundary polygon coordinates and the minimum spacing requirement as constraints, randomly disturbing the fan coordinates and comparing the target function before and after disturbance to iteratively update the fan position, the target fan coordinates and the whole-field total generating capacity corresponding to the target fan coordinates are obtained, and the optimization precision and the engineering efficiency are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farm design and optimization, and in particular to a wind farm layout optimization method and device, equipment and storage medium. Background Art

[0002] As global warming continues, efforts to develop new energy sources, including wind power, are intensifying. Consequently, optimizing the efficient layout and design of specific wind farms, as well as accurately assessing wind resources and power generation, have become long-term, ongoing research topics within the wind energy industry.

[0003] Currently, the core content of wind farm layout optimization is to iteratively solve the specific wind farm layout optimization goals based on a simplified contour wind resource map and a comprehensive calculation method that considers the analytical wake model. However, in the above-mentioned wind farm layout optimization process, almost all mature commercial software often simplifies the wind resource map based on the Weibull fitting parameters of the wind direction for optimization efficiency considerations, and the wake model often does not consider turbulence intensity. The layout optimization results often depend on the accuracy of the Weibull fitting of the wind resource map. For bimodal distribution or insufficient number of sector samples, resulting in large deviations from the Boolean fitting, and in scenarios where turbulence intensity has a significant impact on wake assessment, the optimized wind farm layout may often deviate significantly from the optimal layout. In addition, the calculated power generation cannot be used. The optimized layout needs to be put into the commercial software for comprehensive calculation to obtain a relatively reliable power generation result, which brings confusion to users and increases the workload.

[0004] The above content is only used to assist in understanding the technical solution of the present invention and does not constitute an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of the present invention is to provide a wind farm layout optimization method and device, equipment and storage medium, aiming to solve the technical problem of how to integrate layout and power generation evaluation in large-scale wind farms to improve overall energy utilization efficiency.

[0006] To achieve the above object, the present invention provides a wind farm layout optimization method, which comprises the following steps: Obtain environmental turbulence intensity, wind turbine thrust coefficient, and construct an omnidirectional wind resource map; Inputting the ambient turbulence intensity and the wind turbine thrust coefficient into the TurbOPark wake model, calculating the wake loss and obtaining the inlet wind speed of each wind turbine under each wind direction and wind speed combination; Using the wind frequency in the omnidirectional wind resource map as a weight, weighted summing is performed on the single-machine power obtained by converting the inflow wind speed through the power curve to obtain an objective function for maximizing the total power generation of the entire field; With the boundary polygon coordinates and the minimum spacing requirement as constraints, the wind turbine coordinates are randomly perturbed and the objective function is iteratively updated to update the wind turbine position by comparing the coordinates before and after the perturbation, thereby obtaining the target wind turbine coordinates and the total power generation of the entire field corresponding to the target wind turbine coordinates.

[0007] In one embodiment, the step of constructing an omnidirectional wind resource map includes: Read the grid point coordinates and the wind speed range-wind direction sector wind frequency matrix corresponding to each grid point; The grid point coordinates and the wind speed interval-wind direction sector wind frequency matrix are processed correspondingly based on the grid index to generate a wind resource data table with the grid point as a unit; The number of wind direction sectors, the number of wind speed intervals, and the wind frequency in the wind resource data table are written into a memory matrix to obtain an omnidirectional wind resource map.

[0008] In one embodiment, the step of inputting the ambient turbulence intensity and the wind turbine thrust coefficient into a TurbOPark wake model, calculating the wake loss, and obtaining the inlet wind speed of each wind turbine under each wind direction and wind speed combination includes: Based on the current wind direction, the wind turbine coordinates are rotated to the flow direction coordinate system, and the wind turbine sequence list is obtained by sorting them according to the flow direction coordinates. Reading the flow distance and lateral distance of adjacent fans in the fan sequence list, and inputting the flow distance, the lateral distance, the ambient turbulence intensity, and the fan thrust coefficient into a TurbOPark wake model to obtain a wake center loss; Performing area-weighted averaging on the downstream fan impeller disc surface on the wake center loss to obtain the rotor average loss; The rotor average loss is superimposed on the free stream wind speed to obtain the inflow wind speed.

[0009] In one embodiment, the step of performing weighted summation processing on the single-machine power obtained by converting the inflow wind speed through the power curve using the wind frequency in the omnidirectional wind resource map as a weight to obtain the objective function for maximizing the total power generation of the entire field includes: Traversing each wind direction sector and reading the wind frequency corresponding to the wind direction sector from the omnidirectional wind resource map; In each wind direction sector, traverse each wind speed interval and read the occurrence probability of the wind speed interval in the wind direction sector from the omnidirectional wind resource map; The inflow wind speed is input into the power curve to obtain the single-unit power of each wind turbine in the corresponding wind direction sector and the corresponding wind speed range; The power of each wind turbine is accumulated according to the wind turbine number to obtain the instantaneous power generation of the entire field under the wind direction sector and the wind speed range; The instantaneous power generation of the entire field is weighted based on the occurrence probability to obtain the total power generation of the entire field as the objective function.

[0010] In one embodiment, before the step of using the boundary polygon coordinates and the minimum spacing requirement as constraints, the method further includes: Get the coordinates of the boundary inflection point and the impeller diameter; Connecting the coordinates of the boundary inflection points to obtain a polygonal boundary; The minimum spacing requirement is obtained by multiplying the impeller diameter by a preset multiple.

[0011] In one embodiment, the step of iteratively updating the wind turbine position by randomly perturbing the wind turbine coordinates and comparing the objective function before and after the perturbation to obtain the target wind turbine coordinates and the total power generation of the entire field corresponding to the target wind turbine coordinates includes: Randomly select a wind turbine in the current arrangement and generate a random displacement vector within the preset movement radius; Superimposing the random displacement vector onto the current coordinates of the wind turbine to obtain candidate coordinates; Determine whether the candidate coordinates are located inside the boundary polygon and whether the distance between the candidate coordinates and any other wind turbines is greater than the minimum distance requirement, and obtain a judgment result; When the judgment result is greater than, calling the objective function to calculate the total power generation of the entire field corresponding to the candidate coordinates to obtain a new objective function value; When the new objective function value is greater than the current objective function value, the current coordinates of the wind turbine are replaced with the candidate coordinates and the current objective function value is updated; When the preset number of iterations is met or the increment of the current objective function value is less than a preset convergence threshold, the target wind turbine coordinates and the total power generation of the entire field corresponding to the target wind turbine coordinates are obtained.

[0012] In one embodiment, the step of iteratively updating the wind turbine position by randomly perturbing the wind turbine coordinates and comparing the objective function before and after the perturbation further includes: After the iteration is terminated, the target wind turbine coordinates are mirrored, that is, mirrored wind turbine coordinates are generated at the ground symmetrical points of each wind turbine position; Add the mirrored wind turbine coordinates to the wind turbine list and re-call the TurbOPark wake model to calculate the final total power generation of the entire field including the ground effect; Output the final total power generation of the entire field including ground effect and the corresponding target wind turbine coordinates.

[0013] In addition, to achieve the above-mentioned purpose, the present invention further proposes a wind farm layout optimization device, the device comprising: The acquisition module is used to obtain the environmental turbulence intensity, wind turbine thrust coefficient, and construct an omnidirectional wind resource map; a calculation module, configured to input the ambient turbulence intensity and the wind turbine thrust coefficient into a TurbOPark wake model, calculate the wake loss, and obtain the inlet wind speed of each wind turbine under each wind direction and wind speed combination; An objective function establishment module is configured to perform weighted summation processing on the single-machine power obtained by converting the inflow wind speed through the power curve using the wind frequency in the omnidirectional wind resource map as a weight, to obtain an objective function that maximizes the total power generation of the entire field; The output module is used to iteratively update the wind turbine position by randomly perturbing the wind turbine coordinates and comparing the objective function before and after the disturbance based on the boundary polygon coordinates and the minimum spacing requirement, so as to obtain the target wind turbine coordinates and the total power generation of the entire field corresponding to the target wind turbine coordinates.

[0014] In addition, to achieve the above-mentioned objectives, the present invention also proposes a wind farm arrangement optimization device, which includes: a memory, a processor, and a wind farm arrangement optimization program stored in the memory and runnable on the processor, wherein the wind farm arrangement optimization program is configured to implement the steps of the wind farm arrangement optimization method described above.

[0015] In addition, to achieve the above objectives, the present invention also proposes a storage medium, which stores a wind farm arrangement optimization program. When the wind farm arrangement optimization program is executed by a processor, the steps of the wind farm arrangement optimization method described above are implemented.

[0016] In addition, to achieve the above-mentioned objectives, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the wind farm arrangement optimization method described above are implemented.

[0017] One or more technical solutions proposed in this application have at least the following technical effects: The present invention replaces the traditional Weibull fitting with the wind speed range-wind direction sector matrix and directly incorporates the ambient turbulence intensity and thrust coefficient into the TurbOPark wake model, thereby achieving the synchronous coupling of wind resource characterization, wake loss calculation and power generation assessment. During the optimization process, the total power generation of the entire field is used as the single objective function, which drives the iteration together with the boundary and spacing constraints. This eliminates the systematic deviations caused by fitting errors and turbulence loss in the traditional two-stage method, and significantly improves the accuracy and computational efficiency of the layout scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 A flow chart of the first embodiment of the wind farm layout optimization method provided in this application; Figure 2 A wind farm arrangement optimization flow chart provided in Example 1 of the wind farm arrangement optimization method of this application; Figure 3 A flow chart of the second embodiment of the wind farm layout optimization method provided in this application; Figure 4 This is a schematic diagram of the module structure of the wind farm arrangement optimization device according to an embodiment of the present application; Figure 5 Schematic diagram of the equipment structure of the hardware operating environment involved in the wind farm layout optimization method in the embodiment of the present application.

[0021] The purpose, features and advantages of this application will be further explained with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0023] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0024] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of performing the above functions, such as a wind farm layout optimization device. This embodiment and the following embodiments will be described below using a wind farm layout optimization device as an example.

[0025] Based on this, the embodiment of the present application provides a method for optimizing the layout of a wind farm. Figure 1 , Figure 1 This is a flow chart of the first embodiment of the wind farm layout optimization method of the present application.

[0026] In this embodiment, the wind farm layout optimization method includes steps S10 to S40: Step S10, obtaining the environmental turbulence intensity and the wind turbine thrust coefficient, and constructing an omnidirectional wind resource map; It should be noted that ambient turbulence intensity (ATI) is an indicator of wind speed fluctuations, significantly affecting wake dispersion and wind speed recovery. This data can be obtained through field measurements on a wind tower or from professional mesoscale simulations. The wind turbine thrust coefficient reflects the turbine's ability to convert wind energy into mechanical energy and is closely related to the aerodynamic design of the turbine blades. This data is typically derived from thrust curve files provided by the turbine manufacturer. The omnidirectional wind resource map integrates wind speed, direction, and frequency information, providing foundational data for subsequent power generation calculations.

[0027] In a feasible implementation, the step of constructing an omnidirectional wind resource map in step S10 further includes steps A11 to A13: A11: Read the grid point coordinates and the wind speed range-wind direction sector wind frequency matrix corresponding to each grid point; It's important to note that the coordinates of each grid point, along with the corresponding wind speed and direction data, are read from the wind farm's wind resource data file (typically in WRG or RSF format). This data is stored in a matrix format, with rows representing wind direction sectors and columns representing wind speed ranges. The value of each element indicates the frequency of occurrence of that wind speed and direction combination, providing a data foundation for subsequent processing.

[0028] A12: Corresponding grid point coordinates with the wind speed range-wind direction sector wind frequency matrix based on the grid index to generate a wind resource data table with grid points as units; It should be noted that by associating the grid point coordinates with the corresponding wind speed range-wind direction sector wind frequency matrix, a wind resource data table is formed to provide data support for subsequent calculations.

[0029] A13: Write the wind direction sector number, wind speed interval number, and wind frequency in the wind resource data table into the memory matrix to obtain an omnidirectional wind resource map. It should be noted that the omnidirectional wind resource map integrates wind speed, wind direction and frequency information to provide data support for subsequent wake calculation and power generation assessment.

[0030] Step S20, inputting the ambient turbulence intensity and the wind turbine thrust coefficient into the TurbOPark wake model, calculating the wake loss and obtaining the inlet wind speed of each wind turbine under each wind direction and wind speed combination; It should be noted that the TurbOPark wake model is a Gaussian wake model used to simulate the wake effects of wind turbines. It calculates the effect of the wake on the downstream wind turbine inlet velocity based on input parameters such as ambient turbulence intensity, turbine thrust coefficient, and turbine coordinates. The calculation process is described in detail in steps A21-A24.

[0031] Wake loss refers to the phenomenon whereby downstream wind speed is reduced after wind passes through a turbine due to factors such as wind energy absorption and wake diffusion. Inflow velocity refers to the actual wind speed received by each turbine, which is influenced by the wake of upstream turbines. The wake loss is calculated using the TurbOPark wake model and combined with the free stream velocity to determine the actual inflow velocity for each turbine.

[0032] In a feasible implementation, step S20 includes steps A21 to A24: A21: Based on the current wind direction, rotate the wind turbine coordinates to the flow direction coordinate system, and sort by the flow direction coordinates to obtain a wind turbine sequence list; It's important to note that, based on the current wind direction, the wind turbine coordinates are converted to a wind-direction-based flow coordinate system. Then, the wind turbines are sorted by their flow coordinates (i.e., the direction of the wind) to create a wind turbine order list. This sorting process helps determine the order in which wakes affect the wind turbines, specifically the impact of upstream wind turbines on downstream wind turbines.

[0033] It should be understood that for a given wind direction , the Cartesian coordinates of the wind turbine Rotated to a new coordinate system ,in The axis is aligned with the wind direction so that the wind vector is in the positive direction. Axis direction:

[0034] Then, the wind turbines are arranged in the order from the most upwind ( value) to the most downwind ( The index used below assumes this ordering, so for ,have By definition, the Wind turbine 1 has no wake because there are no other wind turbines upstream of it.

[0035] A22: Read the flow distance and lateral distance between adjacent fans in the fan sequence list, and input the flow distance, lateral distance, ambient turbulence intensity and fan thrust coefficient into the TurbOPark wake model to obtain the wake center loss; It should be noted that the flow direction and lateral distances between adjacent wind turbines are read from the sorted wind turbine list. These parameters, combined with the ambient turbulence intensity and wind turbine thrust coefficient, are input into the Gaussian wake formula to calculate the wind speed loss at the center of the wake. The center of the wake loss reflects the degree to which the wake directly reduces wind speed.

[0036] It should be understood that at the reference position and height The free stream wind speed at The reference position can be, for example, the wind measurement position and height obtained before the construction of the wind farm. The wake-free wind speed at its location and hub height is:

[0037] Here From the known The reference position to the wind turbine The wind speed scaling factor for the location. It depends on the location and hub height of the wind turbine. Relative to The wind speed scaling parameter vector , which may include wind direction, shear coefficient, and other variables that may affect the wind speed variation in the 3D space surrounding the wind turbine and the reference location. In the example provided, the wind speed correction factor includes horizontal gradient and shear correction to adjust the freestream wind speed from the reference height to the turbine hub height.

[0038] A23: Perform area-weighted averaging of the wake center loss on the downstream fan impeller disk to obtain the rotor average loss; It should be noted that, considering the area and shape of the fan impeller disk, the center wake loss is area-weighted averaged over the impeller disk to obtain the rotor average loss. This rotor average loss more accurately reflects the extent to which the fan blades as a whole are affected by the wake.

[0039] It should be understood that a wind speed deficit is defined as:

[0040] wind turbines The wind speed in the wake is In this formula, the background flow velocity Explicitly depends on location .

[0041] TurbOPark uses the Gaussian wind deficit model. Introducing downstream distance , the formula can be written as:

[0042] Here is the characteristic wake width, describing the wind speed deficit from its peak The velocity decreases towards zero as one moves radially from the wake centerline. The radial distance from the wake centerline is given by:

[0043] Note that the definition of wake loss only applies to The peak wind speed deficit is derived from the conservation of momentum:

[0044] Here It is a wind turbine The inflow wind speed is If the wind turbine is not affected by the wake, then Otherwise, the wind speed in the tail flow of the upstream wind turbine is calculated. Due to the expansion of the tail flow, along with Therefore, the peak velocity deficit It decays as the downstream distance increases and gradually approaches zero. According to the above definition, this means that when hour .

[0045] Wake expansion and its functional form is a unique feature of TurbOPark. In TurbOPark, the wake extension is determined by the ambient atmospheric turbulence (in terms of turbulence intensity The turbulence intensity is driven by both the wind turbine's thrust coefficient and the turbulence generated by the wake itself. The latter's contribution to the turbulence intensity is modeled by an expression proposed by Frandsen and depends on the turbine's thrust coefficient and downstream distance. By integrating the wake expansion rate along the downstream path, an analytical expression for the characteristic wake width is derived:

[0046] Initial characteristic wake width (at When:

[0047] The given model parameters apply to the specific model setup described in this note. Modifying the wake superposition method or the treatment of ground effects will require recalibration of the model.

[0048] A24: Superimpose the rotor average loss and the free stream wind speed to obtain the inflow wind speed.

[0049] It should be noted that, considering the area and shape of the fan impeller disk, the center wake loss is area-weighted averaged over the impeller disk to obtain the rotor average loss. This rotor average loss more accurately reflects the extent to which the fan blades as a whole are affected by the wake.

[0050] It should be understood that wind turbines In wind turbines Downstream ( ) is:

[0051] The flow separation distance between two wind turbines is , the horizontal position is still universal. The wake effect depends on its relationship with the wind turbine The degree of overlap of the rotors. To take this into account, the average value of the deficit on the downstream rotor disk is used:

[0052] Points cover wind turbines All the rotor disc The rotor disk area is We compute this integral numerically and implement it as a lookup table based on discretized values ​​of the lateral separation between the two turbines and the rotor radius of the downstream turbine, both normalized by the characteristic wake width.

[0053] The distance between the rotor centers of two wind turbines is:

[0054] If this distance is equal to the characteristic wake width If the rotor loss is large, the average rotor loss can be ignored. , we do not count wind turbines For wind turbines wake effect.

[0055] The rotor mean wind speed deficit defines , that is, wind turbines In wind turbines The average wind speed in the rotor wake at position:

[0056] Multiple overlapping wakes are superimposed using various methods to give the wind turbine Comprehensive wind speed deficit :

[0057] The effective rotor average wake wind speed considering the overlapping wakes of all upstream wind turbines , thus defining the wind turbine Inflow wind speed:

[0058] This wind speed is used to pass the power curve Calculate the wind turbine wake power and use it together with the thrust curve to pass Calculate the wind speed deficit in the wake produced by this wind turbine.

[0059] Step S30, using the wind frequency in the omnidirectional wind resource map as a weight, performing weighted summation on the single-machine power obtained by converting the inflow wind speed through the power curve to obtain an objective function for maximizing the total power generation of the entire field; It's important to note that a power curve describes a wind turbine's output power at different inlet wind speeds. It reflects a wind turbine's ability to convert wind energy into electricity and is a key indicator for evaluating wind turbine performance. Power curves are typically provided by wind turbine manufacturers and are used to convert inlet wind speed into unit power output.

[0060] The total power generation for the entire wind farm is calculated by weightedly summing the power output of each wind turbine at different wind speeds and directions. Wind frequency, used as a weighting factor, reflects the frequency of occurrence of different wind speeds and directions. The objective function for maximizing total power generation for the entire farm is constructed as the result of this weighted summation, which serves as the basis for optimizing the wind turbine layout.

[0061] It should be understood that wind farm layout optimization requires a hub-height wind resource map, which is used to characterize the wind resources at different locations of the wind turbines without considering the wake (distribution of wind direction and wind frequency, where the Weibull distribution is used to approximate the wind frequency distribution). The optimization module considers the optimal power generation of the entire field with the wake to perform optimization calculations. For a cluster of wind turbines of a given type, the power curve , thrust coefficient curve , the cut-in wind speed and cut-out wind speed are and For a given wind direction , we can get the wake model. The wind speed after the wake Depends on wind speed ,wind direction And wind farm WF arrangement Derived Specific wind speed ,wind direction Wind frequency .

[0062] The objective function definition and calculation process are as follows.

[0063]

[0064]

[0065] Note that for a given wind resource F and a given turbine type, the total power generation Ptot of a wind farm with a fixed number of wind turbines depends only on the wind farm layout. , so Ptot can be written as .

[0066]

[0067] in Indicates wind direction Downwind speed Wind frequency; and Represents the number of intervals of wind speed and wind direction respectively. Furthermore, the statistical wind data can be fitted into a certain probability distribution by sector, typically the Weibull distribution, which can be characterized as:

[0068] This patent considers that the Weibull distribution has poor fitting effect on the bimodal distribution and the non-strict Weibull distribution, and uses the wind speed range-wind direction sector wind frequency matrix to represent .

[0069] Along the wind direction , right Definition of wake influence factor is as follows

[0070] The rotor mean wind speed deficit is defined as ; The goal of this application is to maximize the overall power generation. The objective function is as follows:

[0071] Among them, the arrangement Subject to constraints.

[0072] In a feasible implementation, step S30 includes steps A31 to A35: A31: Traverse each wind direction sector and read the wind frequency corresponding to the wind direction sector from the omnidirectional wind resource map; It should be noted that all wind direction sectors and wind speed intervals in the omnidirectional wind resource map are traversed to read the corresponding wind frequency and occurrence probability. Wind frequency indicates how often a certain wind direction occurs, while occurrence probability indicates how often a wind speed occurs within a certain wind direction and wind speed interval.

[0073] A32: In each wind direction sector, traverse each wind speed interval and read the probability of the wind speed interval occurring in the wind direction sector from the omnidirectional wind resource map; It should be noted that the occurrence probability indicates the frequency of wind speed occurrence within a certain wind direction and wind speed range.

[0074] A33: Input the inflow wind speed into the power curve to obtain the single-unit power of each wind turbine in the corresponding wind direction sector and the corresponding wind speed range; It should be noted that the single-unit power output of each wind turbine at the corresponding wind speed and wind direction is calculated based on the inflow wind speed of each wind turbine and the power curve of the wind turbine.

[0075] A34: Accumulate the power of each wind turbine by turbine number to obtain the instantaneous power generation of the entire field under the wind direction sector and wind speed range; It should be noted that the individual power of all wind turbines is accumulated according to the wind turbine number to obtain the instantaneous power generation of the entire field under a specific wind speed and direction combination.

[0076] A35: The instantaneous power generation of the entire field is weighted based on the probability of occurrence, and the total power generation of the entire field is obtained as the objective function.

[0077] It should be noted that the total wind turbine power generation is calculated by multiplying the instantaneous power generation of the entire wind turbine by the corresponding probability of occurrence and summing the total power generation for all wind speed and direction combinations. This total power generation is used as the optimization objective function to evaluate and optimize the wind turbine layout plan.

[0078] Step S40 , using the boundary polygon coordinates and the minimum spacing requirement as constraints, randomly perturbs the wind turbine coordinates and compares the objective function before and after the perturbation to iteratively update the wind turbine position, thereby obtaining the target wind turbine coordinates and the total power generation corresponding to the target wind turbine coordinates.

[0079] It is important to note that the boundary polygon coordinates define the geographic extent of the wind farm, ensuring that all wind turbines are located within the boundaries of the wind farm. The boundary polygon is usually determined by the boundary inflection point coordinates in the project's Geographic Information System (GIS) file or site survey report.

[0080] The minimum spacing requirement is to ensure that there is sufficient distance between fans to reduce the interaction of wakes and avoid collisions between mechanical structures. It is usually determined based on the fan's impeller diameter and the manufacturer's recommended value.

[0081] Under the premise of satisfying the above constraints, the wind turbine coordinates are randomly perturbed to generate a new wind turbine layout. The objective function (total power generation) for the new layout is then calculated and compared with the pre-perturbation solution. If the new solution's objective function is greater, the new solution is accepted and iterative updates are continued until the preset number of iterations is reached or the objective function converges. The resulting wind turbine coordinates and their corresponding total power generation are the optimization results.

[0082] It should be understood that the goal of random optimization is to update the coordinates of the turbine locations. To fully consider every possible layout, random optimization must include all variables that may affect the layout, including the number of turbines, turbine numbering, and movement direction. The main steps of the optimization include: Randomly select a wind turbine and move its position in a random direction with a random step size; Use constraint conditions to check the feasibility of random movement of wind turbines. If it is not feasible, repeat random movement (1) and iterate; Calculate the optimal value of a feasible solution ,if , and iterate in sequence.

[0083] It should be noted that the algorithm includes a "feasibility check" step, which addresses the problem's constraints, specifically the wind farm layout restrictions and the wind farm boundary constraints and minimum distance constraints given in the assumptions. Because the algorithm attempts to randomly move a wind turbine (WT) in the "random move" step, there's no guarantee that the new layout will satisfy all constraints. Therefore, this explicit feasibility check is necessary. Another important point to note is that the number of wind turbines in this algorithm is fixed, the same as in the initial layout.

[0084] This algorithm is simple, intuitive, and easy to implement. It can be used as a final-stage optimization tool to improve upon the results obtained by any other algorithm, or as an optimization tool for optimizing a wind farm layout for a given number of wind turbines. If the number of wind turbines is unspecified, the layout optimization problem can be solved by running the random search (RS) algorithm multiple times, each with a different number of wind turbines, to find the overall optimal layout. It should also be noted that this algorithm uses randomness to search for better solutions and stops after a certain number of evaluations. Therefore, it often yields different results for the same problem in different runs. Therefore, it is often necessary to run the algorithm multiple times and study its performance over time.

[0085] Wind farm layout optimization is to provide an optimized layout of wind turbine locations based on the wind resource conditions of the wind farm, as well as the types and number of wind turbines, in order to maximize the power generation of the wind turbines while ensuring their safety. Figure 2 As shown in the figure, the main contents of wind turbine array optimization include initial modeling, machine site judgment (constraint judgment) and random optimization, and wake and power generation calculation, power generation judgment and iterative step judgment.

[0086] In a feasible implementation manner, before step S40, steps A41 to A43 are further included: A41: Get the coordinates of the boundary inflection point and the impeller diameter; It should be noted that the wind farm boundary inflection point coordinates and wind turbine rotor diameters are obtained from the project data. The boundary inflection point coordinates are used to construct the boundary polygon, and the rotor diameter is used to calculate the minimum spacing requirements.

[0087] A42: Connect the coordinates of the boundary inflection points to obtain the polygonal boundary; It should be noted that the coordinates of the obtained boundary inflection points are connected to form a polygonal boundary. This boundary defines the geographical scope of the wind farm and is used to constrain the location of wind turbines during the iterative update process to ensure that wind turbines are not placed outside the boundary.

[0088] A43: Multiply the impeller diameter by the preset multiple to obtain the minimum clearance requirement.

[0089] It should be noted that the minimum spacing requirement between fans is calculated based on the fan impeller diameter and a preset multiple (usually recommended by the fan manufacturer). This minimum spacing requirement will be used to constrain the distance between fans during the iterative update process to prevent fans from being too close together and causing adverse effects.

[0090] It should be understood that the development of wind farms is subject to various constraints, which may come from technical, logistical, environmental, economic, legal and even social considerations (residential areas, overburdened minerals, forestry woodlands, transmission lines, waterways, military bases, etc.). In terms of layout optimization, two constraints are usually considered. One is the boundary of the wind farm and the other is the minimum distance between wind turbines. For wind farms with irregular shapes and / or infeasible areas within the boundary, the boundary constraints can be handled by the single / multi-boundary model combined with the ray intersection method proposed by Gu et al. In this study, the boundary is assumed to be a polygon and the feasible location area of ​​wind turbines is defined as a polyhedron:

[0091] Among them, S feasible The feasible area of ​​the wind farm is the set of wind turbine locations that meet all boundary constraints. (x, y) represents the location coordinates of the wind turbine. k ,b k ,c k is the coefficient of the boundary constraint, which is used to define the straight line equation of the wind farm boundary. For each boundary edge k of the wind farm (there are m edges in total), a k x+b k y≤c k , k=1,…,m means that the constraint condition is checked on each boundary edge of the wind farm.

[0092] Boundary constraints can be expressed as:

[0093] Among them, (x i ,y i ) represents the position coordinates of the i-th fan, N wt Represents the total number of fans, i=1,2,…,N wt Indicates that the position of each wind turbine is constrained to ensure that all wind turbines are located in the feasible area Sfeasible Inside.

[0094] Because close proximity between wind turbines results in larger wake losses and higher turbulence intensity, which in turn increases blade fatigue and shortens the life of the wind turbine, it is often necessary to arrange wind turbines according to a minimum distance constraint. This constraint can be provided by the wind turbine manufacturer and also depends on the specific terrain characteristics and wind resource characteristics of a given site. In this study, it is simply assumed that there is a minimum distance requirement between any two wind turbines, which is governed by the following equation:

[0095] Among them, x i ,x j ,y i ,y j Represent the horizontal and vertical coordinates of the i-th and j-th wind turbines respectively.

[0096] The above wind farm modeling also has some basic assumptions, including: (1) all wind turbines (WTs) are of the same model, that is, they have the same rotor size, power curve and thrust curve; (2) all wind turbines are installed at the same height; these factors are generalized in actual projects. In addition, only wake losses are considered in the power generation calculation; the minimum distance in this study is based on the definition of anisotropy based on the definition of an ellipse. Other factors, such as availability, electrical losses, icing losses, etc., may also affect power generation. Although electrical losses may depend in part on the wind farm layout, most of these factors are not affected by the layout and are therefore not included in the current modeling.

[0097] This embodiment provides a wind farm layout optimization method. By replacing the traditional Weibull fitting with a wind speed range-wind direction sector matrix and directly incorporating the ambient turbulence intensity and thrust coefficient into the TurbOPark wake model, this method achieves the synchronous coupling of wind resource characterization, wake loss calculation, and power generation assessment. During the optimization process, the total power generation of the entire farm is used as a single objective function, which is jointly driven by boundary and spacing constraints to drive the iteration. This eliminates the systematic deviations caused by fitting errors and turbulence loss in the traditional two-stage method, and significantly improves the accuracy and computational efficiency of the layout solution.

[0098] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 3 Step S40 includes steps S401 to S406: Step S401: randomly select a wind turbine in the current arrangement and generate a random displacement vector within a preset movement radius; It is important to note that randomness is introduced into the optimization process to explore new wind turbine locations within the feasible region of the wind farm. The generation of random displacement vectors ensures diversity in the search process, preventing the optimization from falling into local optima, thereby increasing the likelihood of finding a global optimal solution.

[0099] Step S402 , superimposing the random displacement vector onto the current coordinates of the wind turbine to obtain candidate coordinates; It should be noted that the coordinate transformation generates potential new locations for wind turbines, providing a basis for subsequent assessment of the feasibility and energy yield of these locations. The candidate coordinates represent possible new locations for wind turbines, and their validity is verified through subsequent constraint checks.

[0100] Step S403, determining whether the candidate coordinates are located inside the boundary polygon and the distance between the candidate coordinates and any other wind turbines is greater than the minimum distance requirement, and obtaining a judgment result; It's important to note that strict constraints are applied to candidate coordinates. The internal constraints of the bounding polygon ensure that the wind turbines are located within the geographic boundaries of the wind farm, while the minimum spacing requirement ensures sufficient distance between turbines to prevent wake interactions and mechanical structure collisions, thereby ensuring safe operation and efficient power generation.

[0101] Step S404: when the result of the judgment is greater than, the objective function is called to calculate the total power generation of the entire field corresponding to the candidate coordinates to obtain a new objective function value; It should be noted that the power generation is evaluated for candidate coordinates that meet the constraints. The objective function is to maximize the total power generation of the entire field. By calculating the total power generation of the entire field under the candidate coordinates, the advantages and disadvantages of the new arrangement scheme are evaluated, providing a quantitative basis for subsequent scheme updates.

[0102] Step S405: when the new objective function value is greater than the current objective function value, the current coordinates of the wind turbine are replaced with the candidate coordinates and the current objective function value is updated; It should be noted that when the total power generation of the new arrangement scheme is higher than that of the current scheme, the new scheme is accepted and the current objective function value is updated to promote the optimization process to develop towards a better arrangement scheme and gradually approach the global optimal solution.

[0103] Step S406 , when the preset number of iterations is met or the increment of the current objective function value is less than the preset convergence threshold, the target wind turbine coordinates and the total power generation of the entire field corresponding to the target wind turbine coordinates are obtained.

[0104] It should be noted that setting the iterative termination condition ensures that the optimization process converges to a relatively optimal arrangement within a reasonable time. The preset number of iterations prevents the optimization process from running indefinitely, while the setting of the convergence threshold ensures that the optimization process is terminated in a timely manner when the objective function value changes slightly, avoiding over-optimization.

[0105] In a feasible implementation, step S40 further includes steps A51 to A53: A51: After the iteration is terminated, the target wind turbine coordinates are mirrored, that is, the mirrored wind turbine coordinates are generated at the ground symmetrical point of each wind turbine position; It should be noted that mirrored fan processing is a method used to simulate ground effect. Ground effect significantly affects the diffusion and attenuation of wakes. By generating mirrored fans at symmetrical points on the ground, the physical behavior of wakes near the ground can be more accurately simulated, and the calculated results of the wake model can be corrected to more closely match actual operating conditions.

[0106] A52: Add the mirrored wind turbine coordinates to the wind turbine list and re-call the TurbOPark wake model to calculate the final total power generation including the ground effect; It should be noted that the wake model calculation is modified by introducing mirror-image wind turbines. The addition of mirror-image wind turbines simulates the reflection effect of the ground, allowing the wake model to more accurately reflect the actual wake diffusion and wind speed recovery, thereby improving the accuracy of power generation calculations and providing a more reliable assessment basis for wind farm layout optimization.

[0107] A53: Output the final total power generation including ground effect and the corresponding target wind turbine coordinates.

[0108] It should be noted that the output results include not only the final coordinates of the wind turbines, but also the total power generation of the entire field after considering the ground effect, providing an accurate decision-making basis for the actual construction and operation of the wind farm, ensuring that the wind farm can achieve the expected power generation efficiency and economic benefits in actual operation.

[0109] It should be understood that to indirectly include the effects of the ground plane, a mirrored wind turbine approach is used. At each wind turbine location, a mirrored wind turbine is added to the array, mirroring the turbine through the ground plane. The hub height of the mirrored wind turbine is the negative of the turbine hub height. These mirrored wind turbines generate their own wakes, which are combined with all other wakes in the wind farm using a superposition method.

[0110] This embodiment provides a method for optimizing wind farm layouts, utilizing a random perturbation algorithm to iteratively optimize wind turbine positions while satisfying wind farm boundary and turbine spacing constraints. This method can identify an optimal wind turbine layout within a reasonable number of iterations, thereby increasing the total power generation of the entire farm. Furthermore, by simulating ground effects through mirrored wind turbine processing, the accuracy of power generation calculations is further improved, making the optimized layout more realistic and contributing to improved overall wind farm power generation efficiency and economic benefits.

[0111] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the wind farm layout optimization method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0112] This application also provides a wind farm layout optimization device, please refer to Figure 4 , the wind farm layout optimization device includes: An acquisition module 10 is used to obtain the environmental turbulence intensity, the wind turbine thrust coefficient, and construct an omnidirectional wind resource map; The calculation module 20 is used to input the ambient turbulence intensity and the wind turbine thrust coefficient into the TurbOPark wake model, calculate the wake loss and obtain the inlet wind speed of each wind turbine under various wind direction and wind speed combinations; The objective function establishment module 30 is used to perform weighted summation of the single-machine power obtained by converting the inflow wind speed through the power curve using the wind frequency in the omnidirectional wind resource map as the weight, to obtain the objective function of maximizing the total power generation of the entire field; The output module 40 is used to obtain the target wind turbine coordinates and the total power generation of the entire field corresponding to the target wind turbine coordinates by randomly perturbing the wind turbine coordinates and comparing the objective function before and after the perturbation, based on the boundary polygon coordinates and the minimum spacing requirement.

[0113] The wind farm layout optimization device provided in this application, utilizing the wind farm layout optimization method described in the aforementioned embodiments, can address the technical problem of integrating layout and power generation assessment in large-scale wind farms to improve overall energy efficiency. Compared to the prior art, the beneficial effects of the wind farm layout optimization device provided in this application are the same as those of the wind farm layout optimization method described in the aforementioned embodiments. Other technical features of the wind farm layout optimization device are the same as those disclosed in the aforementioned embodiments and are not further elaborated here.

[0114] In one embodiment, the acquisition module 10 is further configured to read the grid point coordinates and the wind speed interval-wind direction sector wind frequency matrix corresponding to each grid point; The grid point coordinates and the wind speed range-wind direction sector wind frequency matrix are processed correspondingly based on the grid index to generate a wind resource data table with grid points as units; The number of wind direction sectors, number of wind speed intervals and wind frequency in the wind resource data table are written into the memory matrix to obtain an omnidirectional wind resource map; In one embodiment, the calculation module 20 is further configured to rotate the wind turbine coordinates to a flow direction coordinate system based on the current wind direction, and to sort the wind turbines by the flow direction coordinates to obtain a wind turbine sequence list; Read the flow distance and lateral distance between adjacent fans in the fan sequence list, and input the flow distance, lateral distance, ambient turbulence intensity and fan thrust coefficient into the TurbOPark wake model to obtain the wake center loss; The center loss of the wake is processed by area-weighted averaging on the downstream fan impeller disc to obtain the rotor average loss; The rotor average loss is superimposed on the free stream wind speed to obtain the inflow wind speed.

[0115] In one embodiment, the objective function establishing module 30 is further configured to traverse each wind direction sector and read the wind frequency corresponding to the wind direction sector from the omnidirectional wind resource map; In each wind direction sector, each wind speed interval is traversed, and the probability of occurrence of the wind speed interval in the wind direction sector is read from the omnidirectional wind resource map; Input the inflow wind speed into the power curve to obtain the single-unit power of each wind turbine in the corresponding wind direction sector and the corresponding wind speed range; The power of each wind turbine is accumulated according to the turbine number to obtain the instantaneous power generation of the entire field under the wind direction sector and wind speed range; The instantaneous power generation of the entire field is weighted based on the occurrence probability, and the total power generation of the entire field is obtained as the objective function.

[0116] In one embodiment, the output module 40 is further used to obtain the coordinates of the boundary inflection point and the impeller diameter; Connect the coordinates of the boundary inflection points to obtain the polygonal boundary; Multiply the impeller diameter by the preset multiple to obtain the minimum clearance requirement.

[0117] In one embodiment, the output module 40 is further configured to randomly select a wind turbine in the current arrangement and generate a random displacement vector within a preset movement radius; The random displacement vector is superimposed on the current coordinate of the wind turbine to obtain the candidate coordinate; Determine whether the candidate coordinates are located inside the boundary polygon and whether the distance between the candidate coordinates and any other wind turbines is greater than the minimum distance requirement, and obtain a judgment result; When the judgment result is greater than, the objective function is called to calculate the total power generation of the entire field corresponding to the candidate coordinates to obtain a new objective function value; When the new objective function value is greater than the current objective function value, the current coordinates of the wind turbine are replaced with the candidate coordinates and the current objective function value is updated; When the preset number of iterations is met or the increment of the current objective function value is less than the preset convergence threshold, the target wind turbine coordinates and the total power generation of the entire field corresponding to the target wind turbine coordinates are obtained.

[0118] In one embodiment, the output module 40 is further configured to perform mirror image processing on the target wind turbine coordinates after the iteration is terminated, that is, to generate mirror image wind turbine coordinates at a ground symmetrical point at the position of each wind turbine; Add the mirrored wind turbine coordinates to the wind turbine list and re-call the TurbOPark wake model to calculate the final total power generation including the ground effect; Output the final total power generation of the entire field including ground effect and the corresponding target wind turbine coordinates.

[0119] The present application provides a wind farm layout optimization device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the wind farm layout optimization method in the above-mentioned embodiment 1.

[0120] Reference below Figure 5 , which shows a schematic diagram of the structure of a wind farm layout optimization device suitable for implementing the embodiments of the present application. The wind farm layout optimization device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The wind farm arrangement optimization device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0121] like Figure 5As shown, the wind farm layout optimization device may include a processing device 1001 (e.g., a central processing unit, graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in ROM (Read Only Memory) 1002 or programs loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the wind farm layout optimization device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and communication device 1009. Communication device 1009 can allow the wind farm layout optimization device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a wind farm layout optimization device with various systems, it should be understood that implementation or presence of all the illustrated systems is not required. More or fewer systems may alternatively be implemented or present.

[0122] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0123] The wind farm layout optimization device provided in this application, utilizing the wind farm layout optimization method described in the aforementioned embodiment, can address the technical problem of integrating layout and power generation assessment in large-scale wind farms to improve overall energy efficiency. Compared to the prior art, the beneficial effects of the wind farm layout optimization device provided in this application are the same as those of the wind farm layout optimization method described in the aforementioned embodiment. Other technical features of the wind farm layout optimization device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0124] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0125] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0126] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the wind farm arrangement optimization method in the above-mentioned embodiment.

[0127] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash memory), optical fiber, CD-ROM (CD-Read Only Memory), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0128] The computer-readable storage medium may be included in the wind farm arrangement optimization device; or may exist independently without being assembled into the wind farm arrangement optimization device.

[0129] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the wind farm arrangement optimization device, the wind farm arrangement optimization device is enabled to: obtain the environmental turbulence intensity, the wind turbine thrust coefficient, and construct an omnidirectional wind resource map; input the environmental turbulence intensity and the wind turbine thrust coefficient into the TurbOPark wake model, calculate the wake loss and obtain the inflow wind speed of each wind turbine under each wind direction and wind speed combination; use the wind frequency in the omnidirectional wind resource map as the weight, perform weighted summation on the single-machine power obtained by converting the inflow wind speed through the power curve, and obtain the objective function of maximizing the total power generation of the entire field; use the boundary polygon coordinates and the minimum spacing requirements as constraints, iteratively update the wind turbine position by randomly perturbing the wind turbine coordinates and comparing the objective function before and after the disturbance, and obtain the target wind turbine coordinates and the total power generation of the entire field corresponding to the target wind coordinates.

[0130] The computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a LAN (Local Area Network) or a WAN (Wide Area Network), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0131] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0132] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0133] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned wind farm layout optimization method. This computer-readable storage medium addresses the technical problem of integrating layout and power generation assessment in large-scale wind farms to improve overall energy efficiency. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the wind farm layout optimization method provided in the aforementioned embodiments and are not further elaborated here.

[0134] The present application also provides a computer program product, including a computer program, which implements the steps of the wind farm arrangement optimization method as described above when the computer program is executed by a processor.

[0135] The computer program product provided in this application addresses the technical problem of integrating layout and power generation assessment in large-scale wind farms to improve overall energy efficiency. Compared to existing technologies, the beneficial effects of the computer program product provided in this application are similar to those of the wind farm layout optimization method provided in the aforementioned embodiments, and are not further elaborated here.

[0136] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A wind farm layout optimization method, characterized in that: The method comprises: Obtain environmental turbulence intensity, wind turbine thrust coefficient, and construct an omnidirectional wind resource map; Inputting the ambient turbulence intensity and the wind turbine thrust coefficient into the TurbOPark wake model, calculating the wake loss and obtaining the inlet wind speed of each wind turbine under each wind direction and wind speed combination; Using the wind frequency in the omnidirectional wind resource map as a weight, weighted summing is performed on the single-machine power obtained by converting the inflow wind speed through the power curve to obtain an objective function for maximizing the total power generation of the entire field; With the boundary polygon coordinates and the minimum spacing requirement as constraints, the wind turbine coordinates are randomly perturbed and the objective function is iteratively updated to update the wind turbine position by comparing the coordinates before and after the perturbation, thereby obtaining the target wind turbine coordinates and the total power generation of the entire field corresponding to the target wind turbine coordinates.

2. The method according to claim 1, wherein The step of constructing an omnidirectional wind resource map includes: Read the grid point coordinates and the wind speed range-wind direction sector wind frequency matrix corresponding to each grid point; The grid point coordinates and the wind speed interval-wind direction sector wind frequency matrix are processed correspondingly based on the grid index to generate a wind resource data table with the grid point as a unit; The number of wind direction sectors, the number of wind speed intervals, and the wind frequency in the wind resource data table are written into a memory matrix to obtain an omnidirectional wind resource map.

3. The method according to claim 1, wherein The step of inputting the ambient turbulence intensity and the wind turbine thrust coefficient into the TurbOPark wake model, calculating the wake loss and obtaining the inlet wind speed of each wind turbine under each wind direction and wind speed combination includes: Based on the current wind direction, the wind turbine coordinates are rotated to the flow direction coordinate system, and the wind turbine sequence list is obtained by sorting them according to the flow direction coordinates. Reading the flow distance and lateral distance of adjacent fans in the fan sequence list, and inputting the flow distance, the lateral distance, the ambient turbulence intensity, and the fan thrust coefficient into a TurbOPark wake model to obtain a wake center loss; Performing area-weighted averaging on the downstream fan impeller disc surface on the wake center loss to obtain the rotor average loss; The rotor average loss is superimposed on the free stream wind speed to obtain the inflow wind speed.

4. The method according to claim 1, wherein The step of performing weighted summation processing on the single-machine power obtained by converting the inflow wind speed through the power curve using the wind frequency in the omnidirectional wind resource map as a weight to obtain the objective function of maximizing the total power generation of the entire field includes: Traversing each wind direction sector and reading the wind frequency corresponding to the wind direction sector from the omnidirectional wind resource map; In each wind direction sector, traverse each wind speed interval and read the occurrence probability of the wind speed interval in the wind direction sector from the omnidirectional wind resource map; The inflow wind speed is input into the power curve to obtain the single-unit power of each wind turbine in the corresponding wind direction sector and the corresponding wind speed range; The power of each wind turbine is accumulated according to the wind turbine number to obtain the instantaneous power generation of the entire field under the wind direction sector and the wind speed range; The instantaneous power generation of the entire field is weighted based on the occurrence probability to obtain the total power generation of the entire field as the objective function.

5. The method according to claim 1, wherein Before the step of constraining the boundary polygon coordinates and the minimum spacing requirement, the method further includes: Get the coordinates of the boundary inflection point and the impeller diameter; Connecting the coordinates of the boundary inflection points to obtain a polygonal boundary; The minimum spacing requirement is obtained by multiplying the impeller diameter by a preset multiple.

6. The method according to claim 1, wherein The step of randomly perturbing the wind turbine coordinates and iteratively updating the wind turbine positions by comparing the objective function before and after the perturbation to obtain the target wind turbine coordinates and the total power generation of the entire field corresponding to the target wind turbine coordinates includes: Randomly select a wind turbine in the current arrangement and generate a random displacement vector within the preset movement radius; Superimposing the random displacement vector onto the current coordinates of the wind turbine to obtain candidate coordinates; Determine whether the candidate coordinates are located inside the boundary polygon and whether the distance between the candidate coordinates and any other wind turbines is greater than the minimum distance requirement, and obtain a judgment result; When the judgment result is greater than, calling the objective function to calculate the total power generation of the entire field corresponding to the candidate coordinates to obtain a new objective function value; When the new objective function value is greater than the current objective function value, the current coordinates of the wind turbine are replaced with the candidate coordinates and the current objective function value is updated; When the preset number of iterations is met or the increment of the current objective function value is less than a preset convergence threshold, the target wind turbine coordinates and the total power generation of the entire field corresponding to the target wind turbine coordinates are obtained.

7. The method according to claim 1, wherein The step of iteratively updating the position of the wind turbine by randomly perturbing the wind turbine coordinates and comparing the objective function before and after the perturbation further includes: After the iteration is terminated, the target wind turbine coordinates are mirrored, that is, mirrored wind turbine coordinates are generated at the ground symmetrical points of each wind turbine position; Add the mirrored wind turbine coordinates to the wind turbine list and re-call the TurbOPark wake model to calculate the final total power generation of the entire field including the ground effect; Output the final total power generation of the entire field including ground effect and the corresponding target wind turbine coordinates.

8. A wind farm layout optimization device, characterized in that: The device comprises: The acquisition module is used to obtain the environmental turbulence intensity, wind turbine thrust coefficient, and construct an omnidirectional wind resource map; a calculation module, configured to input the ambient turbulence intensity and the wind turbine thrust coefficient into a TurbOPark wake model, calculate the wake loss, and obtain the inlet wind speed of each wind turbine under each wind direction and wind speed combination; An objective function establishment module is configured to perform weighted summation processing on the single-machine power obtained by converting the inflow wind speed through the power curve using the wind frequency in the omnidirectional wind resource map as a weight, to obtain an objective function that maximizes the total power generation of the entire field; The output module is used to iteratively update the wind turbine position by randomly perturbing the wind turbine coordinates and comparing the objective function before and after the disturbance based on the boundary polygon coordinates and the minimum spacing requirement, so as to obtain the target wind turbine coordinates and the total power generation of the entire field corresponding to the target wind turbine coordinates.

9. A wind farm layout optimization device, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the wind farm arrangement optimization method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the wind farm arrangement optimization method according to any one of claims 1 to 7 are implemented.

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