Wind farm arrangement optimization method and device, equipment and storage medium

Abstract

This invention discloses a method, apparatus, equipment, and storage medium for optimizing wind farm layout, relating to the field of wind farm design and optimization technology. The method includes: acquiring environmental turbulence intensity and wind turbine thrust coefficient, and constructing an omnidirectional wind resource map; inputting the environmental turbulence intensity and the wind turbine thrust coefficient into a TurbOPark wake model to calculate wake deficit and obtain the inflow wind speed for each wind turbine under various wind direction and speed combinations; using the wind frequency in the omnidirectional wind resource map as weight, performing weighted summation on the single-unit power obtained by converting the inflow wind speed through a power curve to obtain an objective function that maximizes the total power generation of the entire farm; using the boundary polygon coordinates and minimum spacing requirements as constraints, iteratively updating the wind turbine positions 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 corresponding total power generation of the entire farm, significantly improving optimization accuracy and engineering efficiency.

Description

Technical Field

[0001] This invention relates to the field of wind farm design and optimization technology, and in particular to a method, apparatus, equipment and storage medium for optimizing wind farm layout. Background Technology

[0002] With global warming, efforts to develop new energy sources, including wind power, have intensified. Therefore, how to efficiently optimize the layout and design of specific wind farms and accurately assess wind resources and power generation has become a long-term and ongoing research topic in the wind energy industry.

[0003] Currently, the core of wind farm layout optimization involves using a simplified contour wind resource map, combined with an analytical wake model, to iteratively solve a problem using a single optimization algorithm, targeting a specific wind farm layout optimization objective. However, almost all mature commercial software, for efficiency reasons, often uses simplified Weibull fitting parameters based on wind direction for the wind resource map, and the wake model often does not consider turbulence intensity. The layout optimization result often depends on the accuracy of the Weibull fitting of the wind resource map. For bimodal distributions, or situations where insufficient sample size in each sector leads to significant deviations in the Weibull fitting, and in scenarios where turbulence intensity has a significant impact on wake assessment, the optimized wind farm layout may deviate significantly from the optimal layout. Furthermore, the calculated power generation cannot be used directly; the optimized layout must be entered into the commercial software for comprehensive calculation to obtain a relatively reliable power generation result, causing confusion for users and unnecessarily increasing workload.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, equipment, and storage medium for optimizing wind farm layout, aiming to solve the technical problem of how to integrate layout and power generation assessment in large-scale wind farms to improve overall energy utilization efficiency.

[0006] To achieve the above objectives, the present invention provides a method for optimizing the layout of wind farms, the method comprising the following steps:

[0007] Obtain environmental turbulence intensity and wind turbine thrust coefficient, and construct an omnidirectional wind resource map;

[0008] The environmental turbulence intensity and the wind turbine thrust coefficient are input into the TurbOPark wake model to calculate the wake deficit and obtain the inflow wind speed of each wind turbine under various wind direction and wind speed combinations.

[0009] Using the wind frequency in the omnidirectional wind resource map as the weight, the single-unit power obtained by converting the inflow wind speed through the power curve is weighted and summed to obtain the objective function that maximizes the total power generation of the entire field.

[0010] Constrained by the coordinates of the boundary polygon and the minimum spacing requirement, the wind turbine coordinates are randomly disturbed and the objective function before and after the disturbance is compared to iteratively update the wind turbine position, 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.

[0011] In one embodiment, the step of constructing an omnidirectional wind resource map includes:

[0012] Read the coordinates of grid points and the wind speed range-wind direction sector wind frequency matrix corresponding to each grid point;

[0013] The grid point coordinates are mapped to the wind frequency matrix of the wind speed range-wind direction sector based on the grid index to generate a wind resource data table with the grid points as units.

[0014] Write the number of wind direction sectors, the number of wind speed intervals, and the wind frequency from the wind resource data table into a memory matrix to obtain an omnidirectional wind resource map.

[0015] In one embodiment, the step of inputting the environmental turbulence intensity and the wind turbine thrust coefficient into the TurbOPark wake model, calculating the wake deficit, and obtaining the inflow wind speed for each wind turbine under various wind direction and speed combinations includes:

[0016] Using the current wind direction as a reference, rotate the wind turbine coordinates to the flow direction coordinate system, and sort them according to the flow direction coordinates to obtain a wind turbine sequence list;

[0017] Read the directional and lateral distances of adjacent wind turbines in the wind turbine sequence list, and input the directional distances, lateral distances, environmental turbulence intensity, and wind turbine thrust coefficients into the TurbOPark wake model to obtain the wake center deficit;

[0018] The average rotor loss is obtained by averaging the loss at the wake center on the downstream impeller disk surface using an area-weighted method.

[0019] The inflow velocity is obtained by superimposing the rotor average loss with the free flow velocity.

[0020] In one embodiment, the step of using the wind frequency in the omnidirectional wind resource map as the weight to perform a weighted summation of the single-unit power obtained by converting the inflow wind speed through the power curve, and obtaining the objective function that maximizes the total power generation of the entire field, includes:

[0021] Traverse each wind sector and read the wind frequency corresponding to the wind sector from the omnidirectional wind resource map;

[0022] Within each wind direction sector, traverse each wind speed range and read the probability of the wind speed range appearing in the wind direction sector from the omnidirectional wind resource map;

[0023] By inputting the inflow wind speed into the power curve, the single-unit power of each fan in the corresponding wind direction sector and the corresponding wind speed range can be obtained;

[0024] The power of each unit 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;

[0025] 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.

[0026] In one embodiment, prior to the step of constraining the boundary polygon coordinates and minimum spacing requirements, the method further includes:

[0027] Obtain the coordinates of the boundary inflection point and the impeller diameter;

[0028] By connecting the coordinates of the boundary inflection points, a polygonal boundary is obtained;

[0029] The minimum spacing requirement is obtained by multiplying the impeller diameter by a preset factor.

[0030] In one embodiment, the step of obtaining the target wind turbine coordinates and the total power generation corresponding to the target wind turbine coordinates by randomly perturbing the wind turbine coordinates and comparing the objective function before and after the perturbation to iteratively update the wind turbine position includes:

[0031] Randomly select one fan in the current arrangement and generate a random displacement vector within a preset moving radius;

[0032] The random displacement vector is superimposed on the current coordinates of the wind turbine to obtain candidate coordinates;

[0033] Determine whether the candidate coordinates are located inside the boundary polygon and whether the distance between them and any other wind turbine is greater than the minimum distance requirement, and obtain the judgment result;

[0034] 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, and a new objective function value is obtained;

[0035] 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.

[0036] When the preset number of iterations 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.

[0037] 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:

[0038] After the iteration terminates, the target wind turbine coordinates are mirrored, that is, mirrored wind turbine coordinates are generated at the ground symmetrical points of each wind turbine location.

[0039] 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.

[0040] The output includes the final total power generation of the entire field, including the ground effect, and the corresponding target wind turbine coordinates.

[0041] Furthermore, to achieve the above objectives, the present invention also proposes a wind farm layout optimization device, the device comprising:

[0042] The acquisition module is used to acquire environmental turbulence intensity and wind turbine thrust coefficient, and to construct an omnidirectional wind resource map;

[0043] The calculation module is used to input the environmental turbulence intensity and the wind turbine thrust coefficient into the TurbOPark wake model, calculate the wake deficit, and obtain the inflow wind speed of each wind turbine under various wind direction and wind speed combinations.

[0044] The objective function establishment module is used to perform weighted summation on the single-unit 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 that maximizes the total power generation of the entire field.

[0045] The output module 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, with the boundary polygon coordinates and minimum spacing requirements as constraints.

[0046] Furthermore, to achieve the above objectives, the present invention also proposes a wind farm layout optimization device, the device comprising: a memory, a processor, and a wind farm layout optimization program stored in the memory and executable on the processor, the wind farm layout optimization program being configured to implement the steps of the wind farm layout optimization method described above.

[0047] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing a wind farm layout optimization program, wherein when the wind farm layout optimization program is executed by a processor, it implements the steps of the wind farm layout optimization method described above.

[0048] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the wind farm layout optimization method described above.

[0049] One or more technical solutions proposed in this application have at least the following technical effects:

[0050] This invention replaces the traditional Weibull fitting with a wind speed range-wind direction sector matrix and directly incorporates the environmental turbulence intensity and thrust coefficient into the TurbOPark wake model, achieving synchronous coupling of wind resource characterization, wake deficit calculation, and power generation assessment. During the optimization process, the total power generation of the entire field is used as a single objective function, driving the iteration together with boundary and spacing constraints. This eliminates the systematic bias caused by fitting errors and missing turbulence in the traditional two-stage method, significantly improving the accuracy and computational efficiency of the layout scheme. Attached Figure Description

[0051] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart illustrating an embodiment of the wind farm layout optimization method of this application.

[0054] Figure 2 This is a flowchart of wind farm layout optimization provided in Embodiment 1 of the wind farm layout optimization method of this application;

[0055] Figure 3 This is a flowchart illustrating Embodiment 2 of the wind farm layout optimization method of this application;

[0056] Figure 4 This is a schematic diagram of the module structure of the wind farm layout optimization device according to an embodiment of this application;

[0057] Figure 5This is a schematic diagram of the equipment structure of the hardware operating environment involved in the wind farm layout optimization method in the embodiments of this application.

[0058] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0059] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0060] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0061] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or wind farm layout optimization device capable of performing the above functions. The following description uses a wind farm layout optimization device as an example to illustrate this embodiment and the subsequent embodiments.

[0062] Based on this, embodiments of this application provide a method for optimizing wind farm layout, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the wind farm layout optimization method of this application.

[0063] In this embodiment, the wind farm layout optimization method includes steps S10 to S40:

[0064] Step S10: Obtain the environmental turbulence intensity and wind turbine thrust coefficient, and construct an omnidirectional wind resource map;

[0065] It should be noted that environmental turbulence intensity is an indicator describing the degree of wind speed fluctuation and has a significant impact on wake diffusion and wind speed recovery. Its data can be obtained through actual measurements from anemometer towers or from professional mesoscale simulation data. 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; the data usually comes from thrust curve files provided by the turbine manufacturer. The omnidirectional wind resource map integrates wind speed, wind direction, and frequency information, providing fundamental data for subsequent power generation calculations.

[0066] In one feasible implementation, the step of constructing the omnidirectional wind resource map in step S10 further includes steps A11 to A13:

[0067] A11: Read the grid point coordinates and the wind speed range-wind direction sector wind frequency matrix corresponding to each grid point;

[0068] It should be noted that the coordinate information of each grid point, along with the corresponding wind speed and direction data, is read from the wind resource data file of the wind farm (usually in WRG or RSF format). This data is stored in matrix form, where rows represent wind direction sectors, columns represent wind speed ranges, and the value of each element indicates the frequency of that wind speed and direction combination, providing a data foundation for subsequent processing.

[0069] A12: The grid point coordinates are mapped to the wind frequency matrix of wind speed range-wind direction sector based on the grid index to generate a wind resource data table with grid points as units;

[0070] 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.

[0071] A13: Write the number of wind direction sectors, the number of wind speed intervals, and the wind frequency from the wind resource data table into the memory matrix to obtain the omnidirectional wind resource map;

[0072] It should be noted that the omnidirectional wind resource map integrates information on wind speed, wind direction, and frequency of occurrence, providing data support for subsequent wake calculations and power generation assessments.

[0073] Step S20: Input the environmental turbulence intensity and the wind turbine thrust coefficient into the TurbOPark wake model, calculate the wake deficit and obtain the inflow wind speed of each wind turbine under each wind direction and wind speed combination.

[0074] It should be noted that the TurbOPark wake model is a Gaussian wake model used to simulate the wake effect of wind turbines. It can calculate the impact of the wake on the inflow velocity of the downstream wind turbine based on input parameters such as environmental turbulence intensity, turbine thrust coefficient, and turbine coordinates. The calculation process is described in detail in steps A21-A24.

[0075] Wake loss refers to the phenomenon where the downstream wind speed decreases after the wind passes through a wind turbine due to energy absorption by the turbine and wake diffusion. Inflow velocity, on the other hand, refers to the actual wind speed received by each wind turbine, which is affected by the wake of the upstream turbine. After calculating the wake loss using the TurbOPark wake model, combining it with the free-flow velocity yields the actual inflow velocity for each wind turbine.

[0076] In one feasible implementation, step S20 includes steps A21 to A24:

[0077] A21: Using the current wind direction as a reference, rotate the wind turbine coordinates to the flow direction coordinate system, and sort them according to the flow direction coordinates to obtain a wind turbine sequence list;

[0078] It should be noted that, based on the current wind direction, the wind turbine coordinates are transformed into a flow direction coordinate system based on the wind direction. Then, the wind turbines are sorted according to the flow direction coordinates (i.e., the direction from which the wind is blowing), resulting in a wind turbine sequence list. This sorting process helps determine the order in which the wake affects the wind turbines, that is, the influence of upstream wind turbines on downstream wind turbines.

[0079] It should be understood that, for a given wind direction Cartesian coordinates of wind turbine Rotated to a new coordinate system ,in Align the axis with the wind direction so that the wind vector is along the positive direction. Axial direction:

[0080]

[0081] Subsequently, the wind turbines were installed from the upwind side ( From minimum value to downwind ( Sort by the largest value. The indexes used below assume this sorting, therefore for ,have By definition, located at Wind turbine 1 has no wake because there are no other wind turbines upstream of it.

[0082] A22: Read the directional and lateral distances of adjacent wind turbines in the wind turbine sequence list, and input the directional distance, lateral distance, environmental turbulence intensity, and wind turbine thrust coefficient into the TurbOPark wake model to obtain the wake center loss;

[0083] It should be noted that the directional and lateral distances between adjacent wind turbines are read from the sorted turbine sequence list. These parameters, along with the environmental turbulence intensity and turbine thrust coefficient, are input into the Gaussian wake formula to calculate the wind speed loss at the wake center. The wake center loss reflects the degree of direct impact of the wake on wind speed reduction.

[0084] It should be understood that, at reference positions and heights The free-flow wind speed at the location is The reference location could be, for example, the wind location and altitude obtained before the construction of the wind farm. Wind turbine unit. The wakeless wind speed at its position and hub height is:

[0085]

[0086] Here From the known Reference position to wind turbine The location-based wind speed scaling factor. It depends on the location of the wind turbine and the hub height. Compared to The location. Wind speed scaling includes a parameter vector. This may include wind direction, shear coefficient, and other variables that could affect wind speed variations within the 3D space surrounding the wind turbine and reference location. In the provided example, the wind speed correction factor includes horizontal gradient and shear correction to adjust for free-flow wind speeds from the reference height to the wind turbine hub height.

[0087] A23: The average rotor loss is obtained by averaging the area-weighted loss of the wake center on the downstream impeller disk.

[0088] It should be noted that, considering the area and shape of the wind turbine impeller disk, the wake center loss is averaged across the impeller disk area to obtain the rotor average loss. This rotor average loss more accurately reflects the degree to which the wind turbine blades are affected by the wake as a whole.

[0089] It should be understood that wind speed deficit is defined as:

[0090]

[0091] wind turbine The wind speed in the wake is In this formula, the background flow velocity... Explicitly dependent on location .

[0092] TurbOPark uses a Gaussian wind deficit model. Downstream distance is introduced. The formula can be written as:

[0093]

[0094] Here It is the characteristic wake width, which describes the wind speed deficit from its peak value. The velocity decreases towards zero as the wake centerline moves radially. The radial distance to the wake centerline is given by the following formula:

[0095]

[0096] Note that the definition of wake loss only applies to... Peak wind speed deficit is derived using the law of conservation of momentum:

[0097]

[0098] Here It is a wind turbine. The inflow velocity, and its rotor diameter is If the wind turbine is not affected by the wake, then Otherwise, the inflow wind speed is calculated in the wake of the upstream wind turbine. Due to the expansion of the wake, along with It increases monotonically with the increase of [something]. Therefore, the peak velocity deficit [is significant]. It decays with increasing downstream distance and gradually approaches zero. According to the above definition, this means that when... hour .

[0099] Wake spread and its functional form This is a unique characteristic of TurbOPark. In TurbOPark, the wake extension is caused by ambient atmospheric turbulence (with turbulence intensity...). The characteristic wake width is driven by both the wake itself and the turbulence generated by the wake. The contribution of the latter to the turbulence intensity is modeled by an expression proposed by Frandsen, which depends on the wind turbine thrust coefficient and the downstream distance. By integrating the wake spread rate along the downstream path, an analytical expression for the characteristic wake width is obtained:

[0100]

[0101] Initial characteristic wake width (in) (Time) is:

[0102]

[0103] The given model parameters are applicable to the specific model settings described in this specification. Modifying the wake stacking method or the treatment of ground effects will require model recalibration.

[0104] A24: The inflow velocity is obtained by superimposing the rotor average loss with the free flow velocity.

[0105] It should be noted that, considering the area and shape of the wind turbine impeller disk, the wake center loss is averaged across the impeller disk area to obtain the rotor average loss. This rotor average loss more accurately reflects the degree to which the wind turbine blades are affected by the wake as a whole.

[0106] It should be understood that wind turbine units In wind turbine Downstream ( The wind speed deficit at location ) is:

[0107]

[0108] The flow direction separation distance between the two wind turbines is The lateral position remains the same. (Wind turbine unit) 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 deficit on the downstream rotor disks is used:

[0109]

[0110] Integral coverage of wind turbine units All inside the rotor disc Point, rotor disk area is We calculate this integral using numerical methods and implement it as a lookup table based on the discretized values ​​of the lateral separation distance between the two wind turbines and the rotor radius of the downstream wind turbine, both of which are normalized by the characteristic wake width.

[0111] The distance between the rotor centers of the two wind turbines is:

[0112]

[0113] If this distance is related to the characteristic wake width If the difference is large, then the average rotor deficit can be ignored. For efficiency calculations, if... We do not include wind turbines. For wind turbines The wake effect.

[0114] Rotor mean wind speed deficit is defined That is, wind turbine units In wind turbine Average rotor velocity in the wake of the location:

[0115]

[0116] Multiple overlapping wakes are superimposed using various methods to provide the wind turbine generator. Overall wind speed deficit :

[0117]

[0118] Considering the effective rotor-average wake velocity of the overlapping wakes of all upstream wind turbines Thus defining wind turbine units Inflow velocity:

[0119]

[0120] This wind speed is used to pass through the power curve. Calculate the wake power of the wind turbine and use it together with the thrust curve to pass through Calculate the wind speed deficit in the wake generated by the wind turbine.

[0121] Step S30: Using the wind frequency in the omnidirectional wind resource map as the weight, perform weighted summation on the single-unit power obtained by converting the inflow wind speed through the power curve to obtain the objective function that maximizes the total power generation of the entire field.

[0122] It should be noted that the power curve describes the output power characteristics of a wind turbine at different inflow wind velocities. It reflects the turbine's ability to convert wind energy into electrical energy and is a key indicator for evaluating the turbine's power generation performance. Power curves are typically provided by the wind turbine manufacturer to convert inflow wind velocity into single-unit power output.

[0123] The total power generation of the entire wind farm is obtained by weighted summing of the individual unit power of each wind turbine under different wind speeds and directions. Wind frequency, as a weighting factor, reflects the frequency of occurrence of different wind speeds and directions. The objective function for maximizing the total power generation of the entire farm is constructed from the result of this weighted sum, which will serve as the basis for optimizing the wind turbine layout.

[0124] It should be understood that wind farm layout optimization requires a hub-high wind resource map, which depicts the wind resources (wind frequency distribution by wind direction, approximated here by a Weibull distribution) at different locations of the wind turbines, without considering wake. The optimization module performs optimization calculations to optimize the overall power generation considering the wake. For a given cluster of wind turbines, the power curve... Thrust coefficient curve The cut-in wind speed and cut-out wind speed are respectively and For a given wind direction This allows us to obtain the wake model. The wind speed after the wake... Depends on wind speed ,wind direction and wind field WF layout The derivation yields Specific wind speed ,wind direction wind frequency .

[0125] The objective function is defined and calculated as follows.

[0126]

[0127]

[0128] 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 turbines depends only on the wind farm layout. Therefore, Ptot can be denoted as .

[0129]

[0130] in Indicates wind direction downwind speed wind frequency; and These represent the number of intervals for wind speed and wind direction, respectively. Furthermore, statistical wind data can be fitted to a definite probability distribution by sector, typically a Weibull distribution, which can be characterized as:

[0131]

[0132] This patent considers the poor fitting effect of the Weibull distribution on bimodal and non-strict Weibull distributions, and uses a wind frequency matrix of wind speed interval-wind direction sector to characterize it. .

[0133] Along the wind direction , right The wake influence factor is defined as follows:

[0134]

[0135] The rotor mean wind speed deficit is defined as follows: ;

[0136] This application aims to maximize total power generation, and the objective function is as follows:

[0137]

[0138] Among them, the arrangement Subject to constraints.

[0139] In one feasible implementation, step S30 includes steps A31 to A35:

[0140] A31: Traverse each wind sector and read the wind frequency corresponding to the wind sector from the omnidirectional wind resource map;

[0141] It should be noted that the process iterates through all wind direction sectors and wind speed ranges in the omnidirectional wind resource map, and reads the corresponding wind frequency and occurrence probability for each sector. Wind frequency represents the frequency of occurrence of a certain wind direction, while occurrence probability represents the frequency of occurrence of a certain wind speed within a certain wind direction and wind speed range.

[0142] A32: Within each wind direction sector, traverse each wind speed range and read the probability of the wind speed range appearing in the wind direction sector from the omnidirectional wind resource map.

[0143] It should be noted that the probability of occurrence refers to the frequency of wind speed occurrence within a certain wind direction and wind speed range.

[0144] A33: Input the inflow velocity into the power curve to obtain the single-unit power of each fan in the corresponding wind direction sector and the corresponding wind speed range;

[0145] It should be noted that, based on the inflow velocity and power curve of each fan, the single-unit power output of each fan at the corresponding wind speed and wind direction is calculated.

[0146] A34: The power of a single unit is accumulated according to the wind turbine number to obtain the instantaneous power generation of the entire field under the wind direction sector and wind speed range;

[0147] It should be noted that the individual power of all wind turbines is summed up according to the turbine number to obtain the instantaneous power generation of the entire site under a specific combination of wind speed and direction.

[0148] A35: 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.

[0149] It should be noted that the total power generation is obtained by multiplying the instantaneous power generation of the entire field by the corresponding probability of occurrence, and then summing up the results for all wind speed and direction combinations. This total power generation serves as the objective function for evaluating and optimizing the wind turbine layout scheme.

[0150] Step S40: With the boundary polygon coordinates and minimum spacing requirements as constraints, the wind turbine coordinates are randomly disturbed and the objective function before and after the disturbance is compared to iteratively update the wind turbine position, 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.

[0151] It should be noted that the boundary polygon coordinates define the geographical extent of the wind farm, ensuring that all wind turbines are located within the farm's boundaries. The boundary polygon is typically determined by the boundary inflection point coordinates from a Geographic Information System (GIS) file provided by the project or a site survey report.

[0152] Minimum spacing requirements are to ensure sufficient distance between wind turbines to reduce wake interference and avoid collisions between mechanical structures. It is typically determined based on the turbine impeller diameter and the manufacturer's recommendations.

[0153] Under the aforementioned constraints, a new wind turbine layout scheme is generated by randomly perturbing the turbine coordinates. Then, the objective function (total power generation) under the new layout scheme is calculated and compared with the scheme before perturbation. If the objective function of the new scheme is larger, the new scheme is accepted, and iterative updates continue until a preset number of iterations is reached or the objective function converges. The final wind turbine coordinates and their corresponding total power generation are the optimization results.

[0154] It should be understood that, based on stochastic optimization algorithms, the goal of stochastic optimization is to update the coordinates of the turbine locations. To fully consider every possible arrangement, stochastic optimization needs to include all variables that may affect the arrangement, including the number of turbines, turbine serial numbers, and direction of movement. The main optimization steps include:

[0155] Randomly select a fan and move its position in a random direction with a random step size;

[0156] The feasibility of random movement of the wind turbine is tested by using constraints. If it is not feasible, the random movement (1) is repeated and iterated.

[0157] Calculate the optimal value of the feasible solution ,if This process is repeated iteratively.

[0158] It should be noted that the algorithm includes a "feasibility check" step, which addresses the constraints of the problem, namely the wind farm layout constraints and the wind farm boundary constraints and minimum distance constraints given in the assumptions. Since the algorithm attempts to randomly move one wind turbine (WT) in the "random move" step, it cannot guarantee that the new layout will satisfy all constraints; therefore, this explicit feasibility check is necessary. Another characteristic to note is that the number of wind turbines in this algorithm is fixed and the same as the number in the initial layout.

[0159] This algorithm is simple, intuitive, and easy to implement. It can be used as a final-stage optimization tool to improve the results obtained by any other algorithm, or as an optimization tool to optimize the layout of a wind farm with a given number of wind turbines. If the number of wind turbines is not specified, the layout optimization problem can be solved by running the random search (RS) algorithm multiple times, each time with a different number of wind turbines, and then finding the overall optimal layout. It should also be noted that the algorithm uses randomness to search for better solutions, and the process stops after a certain number of evaluations. Therefore, for the same problem, it often yields different results in different runs. Thus, it is usually necessary to run the algorithm multiple times and study its performance variations.

[0160] Wind farm layout optimization involves determining the optimal location and arrangement of wind turbines based on wind resource conditions, as well as the type and number of wind turbines. This aims to maximize the power generation of the entire wind turbine cluster while ensuring the safety of the turbines. For example... Figure 2 As shown, the main contents of wind turbine array optimization include initial modeling, turbine location determination (constraint determination) and stochastic optimization, wake and power generation calculation, power generation determination and iteration step determination.

[0161] In one feasible implementation, steps A41 to A43 are included before step S40:

[0162] A41: Obtain the coordinates of the boundary inflection point and the impeller diameter;

[0163] It should be noted that the boundary inflection point coordinates of the wind farm and the rotor diameter of the wind turbine 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 requirement.

[0164] A42: Connect the coordinates of the boundary inflection points to obtain the polygon boundary;

[0165] It should be noted that connecting the obtained boundary inflection point coordinates forms a polygonal boundary. This boundary defines the geographical extent of the wind farm and is used to constrain the location of the wind turbines during iterative updates, ensuring that the turbines are not placed outside the boundary.

[0166] A43: Multiply the impeller diameter by a preset factor to obtain the minimum spacing requirement.

[0167] It should be noted that the minimum spacing requirement between wind turbines is calculated based on the impeller diameter and a preset multiple (usually recommended by the wind turbine manufacturer). This minimum spacing requirement will be used to constrain the distance between wind turbines during iterative updates, preventing adverse effects caused by excessively close proximity.

[0168] It should be understood that wind farm development is subject to various constraints, which may stem from technical, logistical, environmental, economic, legal, and even social considerations (residential areas, mineral deposits, forestry land, transmission lines, waterways, military bases, etc.). In terms of layout optimization, two constraints are typically considered: the boundary of the wind farm and the minimum distance between wind turbine generators. For wind farms with irregular shapes and / or infeasible areas within their boundaries, boundary constraints can be addressed using a single / multi-boundary model combining the ray intersection method proposed by Gu et al. In this study, the boundary is assumed to be a polygon, and the feasible location region for wind turbine generators is defined as a polyhedron.

[0169]

[0170] Among them, S feasible Let (x, y) represent the feasible region of the wind farm, which is the set of wind turbine locations that satisfy all boundary constraints. Let (x, y) represent the location coordinates of the wind turbines. k ,b k ,c k These are the coefficients of the boundary constraints, used to define the linear equations of the wind farm boundaries. For each boundary edge k of the wind farm (there are m edges in total), a... k x+b k y≤c kk=1,…,m indicates that constraint conditions are checked for each boundary edge of the wind farm.

[0171] Boundary constraints can be expressed as:

[0172]

[0173] Among them, (x i ,y i ) represents the position coordinates of the i-th wind turbine, N wt This represents the total number of wind turbines, i = 1, 2, ..., N. wt This indicates that the location of each wind turbine is constrained to ensure that all wind turbines are located within the feasible region S. feasible Inside.

[0174] Because close proximity between wind turbines leads to significant wake losses and high turbulence intensity, which in turn increases blade fatigue and shortens turbine lifespan, wind turbines are typically arranged according to minimum distance constraints. These constraints can be provided by the wind turbine manufacturer and also depend on the specific terrain features and wind resource characteristics of a given site. In this study, we simply assume a minimum distance requirement between any two wind turbines, controlled by the following formula:

[0175]

[0176] Where, x i ,x j ,y i ,y j Let x and y represent the x-coordinate and y-coordinate of the i-th and j-th wind turbines, respectively.

[0177] The above wind farm modeling also relies on some basic assumptions, including: (1) all wind turbine generators (WTs) are of the same type, i.e., they have the same rotor size, power curve, and thrust curve; (2) all wind turbine generators are installed at the same height; these factors have been generalized in actual projects. In addition, only wake loss is considered in the power generation calculation; the minimum distance in this study is based on the definition of an ellipse to characterize anisotropy. Other factors, such as availability, electrical losses, and icing losses, 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.

[0178] This embodiment provides a wind farm layout optimization method. By replacing the traditional Weibull fitting with a wind speed interval-wind direction sector matrix, and directly incorporating the environmental turbulence intensity and thrust coefficient into the TurbOPark wake model, it achieves synchronous coupling of wind resource characterization, wake deficit 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, together with boundary and spacing constraints, drives the iteration. This eliminates the systematic bias caused by fitting errors and missing turbulence in the traditional two-stage method, significantly improving the accuracy and computational efficiency of the layout scheme.

[0179] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S40 includes steps S401 to S406:

[0180] Step S401: Randomly select a fan in the current arrangement and generate a random displacement vector within a preset moving radius;

[0181] It should be noted that randomness is introduced during the optimization process to explore new wind turbine locations within the feasible area of ​​the wind farm. The generation of random displacement vectors ensures diversity in the search process, avoids getting trapped in local optima, and thus increases the likelihood of finding the global optimum.

[0182] Step S402: Superimpose the random displacement vector onto the current coordinates of the wind turbine to obtain candidate coordinates;

[0183] It should be noted that generating potential new locations for the wind turbine through coordinate transformation provides a basis for subsequent assessments of the feasibility and power generation of these locations. The candidate coordinates represent possible new locations for the wind turbine, and their validity needs to be verified through subsequent constraint checks.

[0184] Step S403: Determine whether the candidate coordinates are located inside the boundary polygon and whether the distance between them and any other wind turbine is greater than the minimum distance requirement, and obtain the judgment result;

[0185] It should be noted that strict constraints are applied to the candidate coordinates. The internal constraints of the boundary polygon ensure that the wind turbines are located within the geographical area of ​​the wind farm, while the minimum spacing requirement ensures sufficient distance between turbines to avoid mutual wake interference and mechanical collisions, thereby guaranteeing safe operation and efficient power generation.

[0186] Step S404: When the judgment result is greater than, call the objective function to calculate the total power generation of the entire field corresponding to the candidate coordinates, and obtain the new objective function value;

[0187] It should be noted that the power generation of candidate coordinates that meet the constraints is evaluated. The objective function aims to maximize the total power generation of the entire site. By calculating the total power generation of the entire site under the candidate coordinates, the merits of the new layout scheme are evaluated, providing a quantitative basis for subsequent scheme updates.

[0188] Step S405: If the new objective function value is greater than the current objective function value, then replace the current coordinates of the wind turbine with the candidate coordinates and update the current objective function value;

[0189] It should be noted that when the total power generation of the new layout scheme is higher than that of the current scheme, the new scheme is accepted and the current objective function value is updated, thus driving the optimization process toward a better layout scheme and gradually approaching the global optimal solution.

[0190] Step S406: When the preset number of iterations 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.

[0191] It should be noted that by setting an iteration termination condition, the optimization process is ensured to converge to a relatively optimal arrangement scheme within a reasonable time. Presetting the number of iterations prevents the optimization process from running indefinitely, while setting a convergence threshold ensures that the optimization process terminates promptly when the objective function value changes only slightly, avoiding over-optimization.

[0192] In one feasible implementation, step S40 further includes steps A51 to A53:

[0193] A51: After the iteration terminates, the target wind turbine coordinates are mirrored, that is, the mirrored wind turbine coordinates are generated at the ground symmetrical points of each wind turbine location.

[0194] It should be noted that mirrored wind turbine processing is a method used to simulate the ground effect. The ground effect has a significant impact on the diffusion and attenuation of the wake. By generating mirrored wind turbines at symmetrical points on the ground, the physical behavior of the wake near the ground can be simulated more accurately, correcting the calculation results of the wake model and making it closer to actual operating conditions.

[0195] A52: Add the mirrored turbine coordinates to the turbine list and re-call the TurbOPark wake model to calculate the final total power generation of the entire field, including the ground effect;

[0196] It should be noted that the wake model calculations were corrected by introducing mirrored wind turbines. The addition of mirrored wind turbines simulates the reflection effect of the ground, enabling 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 evaluation basis for wind farm layout optimization.

[0197] A53: Outputs the final total power generation of the entire field, including the ground effect, and the corresponding target wind turbine coordinates.

[0198] It should be noted that the output results include not only the final coordinates of the wind turbine, but also the total power generation of the entire field after considering the ground effect, providing accurate decision-making basis for the actual construction and operation of wind farms, and ensuring that the wind farm can achieve the expected power generation efficiency and economic benefits in actual operation.

[0199] It should be understood that a mirrored wind turbine approach is used to indirectly incorporate the effects of the ground plane. At each wind turbine location, a mirrored wind turbine is added to an array, mirroring the turbine across the ground plane. The hub height of the mirrored wind turbine is the negative of the original wind turbine's hub height. These mirrored wind turbines generate their own wakes, which are then combined with all other wakes in the wind farm using a superposition method.

[0200] This embodiment provides a wind farm layout optimization method that iteratively optimizes the turbine locations using a stochastic perturbation algorithm while satisfying wind farm boundary and turbine spacing constraints. This method can find a relatively optimal turbine layout scheme within a reasonable number of iterations, increasing the total power generation of the entire farm. Simultaneously, by introducing ground effect simulation through mirrored turbine processing, the accuracy of power generation calculation is further improved, making the optimized layout scheme more consistent with actual conditions and contributing to improving the overall power generation efficiency and economic benefits of the wind farm.

[0201] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the wind farm layout optimization method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0202] This application also provides a wind farm layout optimization device, please refer to... Figure 4 The wind farm layout optimization device includes:

[0203] The acquisition module 10 is used to acquire environmental turbulence intensity and wind turbine thrust coefficient, and to construct an omnidirectional wind resource map;

[0204] Calculation module 20 is used to input the environmental turbulence intensity and the wind turbine thrust coefficient into the TurbOPark wake model, calculate the wake deficit and obtain the inflow wind speed of each wind turbine under various wind direction and wind speed combinations;

[0205] The objective function establishment module 30 is used to perform weighted summation of the single-unit power obtained by converting the inflow wind speed through the power curve, with the wind frequency in the omnidirectional wind resource map as the weight, to obtain the objective function that maximizes the total power generation of the entire field.

[0206] 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, with the boundary polygon coordinates and minimum spacing requirements as constraints.

[0207] The wind farm layout optimization device provided in this application, employing the wind farm layout optimization method described in the above embodiments, can solve the technical problem of how to integrate layout and power generation assessment in large-scale wind farms to improve overall energy utilization efficiency. Compared with 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 provided in the above embodiments, and other technical features in the wind farm layout optimization device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0208] 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;

[0209] The grid point coordinates are mapped to the wind frequency matrix of wind speed range-wind direction sector based on the grid index to generate a wind resource data table with grid points as the unit.

[0210] Write the number of wind direction sectors, the number of wind speed intervals, and the wind frequency from the wind resource data table into a memory matrix to obtain an omnidirectional wind resource map.

[0211] In one embodiment, the calculation module 20 is further configured to rotate the wind turbine coordinates to the flow direction coordinate system based on the current wind direction, and sort them according to the flow direction coordinates to obtain a wind turbine sequence list;

[0212] Read the directional and lateral distances of adjacent wind turbines in the wind turbine sequence list, and input the directional distance, lateral distance, environmental turbulence intensity, and wind turbine thrust coefficient into the TurbOPark wake model to obtain the wake center deficit;

[0213] The average rotor loss is obtained by averaging the wake center loss on the downstream wind turbine impeller disk surface using area weighting.

[0214] The inflow velocity is obtained by superimposing the rotor average loss with the free flow velocity.

[0215] In one embodiment, the objective function establishment 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;

[0216] Within each wind direction sector, traverse each wind speed range and read the probability of the wind speed range appearing in the wind direction sector from the omnidirectional wind resource map.

[0217] By inputting the inflow velocity into the power curve, the single-unit power of each fan in the corresponding wind direction sector and the corresponding wind speed range can be obtained;

[0218] The power of each unit 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;

[0219] 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.

[0220] In one embodiment, the output module 40 is further configured to obtain the boundary inflection point coordinates and the impeller diameter;

[0221] By connecting the coordinates of the boundary inflection points, the polygon boundary is obtained;

[0222] The minimum spacing requirement is obtained by multiplying the impeller diameter by a preset factor.

[0223] In one embodiment, the output module 40 is further configured to randomly select a fan in the current arrangement and generate a random displacement vector within a preset moving radius;

[0224] The random displacement vectors are superimposed onto the current coordinates of the wind turbine to obtain candidate coordinates;

[0225] Determine whether the candidate coordinates are located inside the boundary polygon and whether the distance between them and any other wind turbine is greater than the minimum distance requirement, and obtain the judgment result;

[0226] 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, and a new objective function value is obtained;

[0227] If the new objective function value is greater than the current objective function value, then the current coordinates of the wind turbine are replaced with the candidate coordinates and the current objective function value is updated.

[0228] When the preset number of iterations 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.

[0229] In one embodiment, the output module 40 is further configured to perform mirror wind turbine processing on the target wind turbine coordinates after the iteration terminates, that is, to generate mirror wind turbine coordinates at the ground symmetrical points of each wind turbine location.

[0230] Add the mirrored turbine coordinates to the turbine list and re-call the TurbOPark wake model to calculate the final total power generation of the entire field, including the ground effect.

[0231] The output includes the final total power generation of the entire field, including the ground effect, and the corresponding target wind turbine coordinates.

[0232] This 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 embodiment 1.

[0233] The following is for reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing the wind farm layout optimization device in the embodiments of this application. The wind farm layout optimization device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The wind farm layout optimization equipment shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0234] like Figure 5As shown, the wind farm layout optimization equipment may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program 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 equipment. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. 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 devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the wind farm layout optimization equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows wind farm layout optimization equipment with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0235] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0236] The wind farm layout optimization equipment provided in this application, employing the wind farm layout optimization method described in the above embodiments, can solve the technical problem of how to integrate layout and power generation assessment in large-scale wind farms to improve overall energy utilization efficiency. Compared with the prior art, the beneficial effects of the wind farm layout optimization equipment provided in this application are the same as those of the wind farm layout optimization method provided in the above embodiments, and other technical features of this wind farm layout optimization equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0237] 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 suitable manner in one or more embodiments or examples.

[0238] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0239] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the wind farm layout optimization method in the above embodiments.

[0240] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing 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.

[0241] The aforementioned computer-readable storage medium may be included in the wind farm layout optimization equipment; or it may exist independently and not be assembled into the wind farm layout optimization equipment.

[0242] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the wind farm layout optimization equipment, the wind farm layout optimization equipment: acquires the environmental turbulence intensity and the wind turbine thrust coefficient, and constructs an omnidirectional wind resource map; inputs the environmental turbulence intensity and the wind turbine thrust coefficient into the TurbOPark wake model, calculates the wake deficit, and obtains the inflow wind speed for each wind turbine under various wind direction and wind speed combinations; uses the wind frequency in the omnidirectional wind resource map as the weight, performs weighted summation on the single-unit power obtained by converting the inflow wind speed through the power curve, and obtains the objective function that maximizes the total power generation of the entire farm; uses the boundary polygon coordinates and minimum spacing requirements as constraints, iteratively updates the wind turbine positions by randomly perturbing the wind turbine coordinates and comparing the objective function before and after the perturbation, and obtains the target wind turbine coordinates and the total power generation of the entire farm corresponding to the target wind turbine coordinates.

[0243] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0244] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0245] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0246] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described wind farm layout optimization method. This solves the technical problem of how to integrate layout and power generation assessment in large-scale wind farms to improve overall energy utilization efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the wind farm layout optimization method provided in the above embodiments, and will not be elaborated upon here.

[0247] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the wind farm layout optimization method described above.

[0248] The computer program product provided in this application can solve the technical problem of how to integrate layout and power generation assessment in large-scale wind farms to improve overall energy utilization efficiency. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the wind farm layout optimization method provided in the above embodiments, and will not be repeated here.

[0249] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for optimizing wind farm layout, characterized in that, The method includes: Obtain environmental turbulence intensity and wind turbine thrust coefficient, and construct an omnidirectional wind resource map; The environmental turbulence intensity and the wind turbine thrust coefficient are input into the TurbOPark wake model to calculate the wake deficit and obtain the inflow wind speed of each wind turbine under various wind direction and wind speed combinations. Using the wind frequency in the omnidirectional wind resource map as the weight, the single-unit power obtained by converting the inflow wind speed through the power curve is weighted and summed to obtain the objective function that maximizes the total power generation of the entire field. Constrained by the boundary polygon coordinates and minimum spacing requirements, the wind turbine positions are iteratively updated by randomly perturbing the wind turbine coordinates and comparing the objective function before and after the perturbation, 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. The steps for constructing the omnidirectional wind resource map include: Read the coordinates of grid points and the wind speed range-wind direction sector wind frequency matrix corresponding to each grid point; The grid point coordinates are mapped to the wind frequency matrix of the wind speed range-wind direction sector based on the grid index to generate a wind resource data table with the grid points as units. Write the number of wind direction sectors, the number of wind speed intervals, and the wind frequency from the wind resource data table into a memory matrix to obtain an omnidirectional wind resource map. Before the step constrained by the boundary polygon coordinates and minimum spacing requirements, the following steps are also included: Obtain the coordinates of the boundary inflection point and the impeller diameter; By connecting the coordinates of the boundary inflection points, a polygonal boundary is obtained; The minimum spacing requirement is obtained by multiplying the impeller diameter by a preset factor.

2. The method as described in claim 1, characterized in that, The step of inputting the environmental turbulence intensity and the wind turbine thrust coefficient into the TurbOPark wake model, calculating the wake deficit, and obtaining the inflow wind speed for each wind turbine under various wind direction and speed combinations includes: Using the current wind direction as a reference, rotate the wind turbine coordinates to the flow direction coordinate system, and sort them according to the flow direction coordinates to obtain a wind turbine sequence list; Read the directional and lateral distances of adjacent wind turbines in the wind turbine sequence list, and input the directional distances, lateral distances, environmental turbulence intensity, and wind turbine thrust coefficients into the TurbOPark wake model to obtain the wake center deficit; The average rotor loss is obtained by averaging the loss at the wake center on the downstream wind turbine impeller surface using an area-weighted method. The inflow velocity is obtained by superimposing the rotor average loss with the free flow velocity.

3. The method as described in claim 1, characterized in that, The step of using the wind frequency in the omnidirectional wind resource map as the weight, and performing a weighted summation of the single-unit power obtained by converting the inflow wind speed through the power curve to obtain the objective function that maximizes the total power generation of the entire site includes: Traverse each wind sector and read the wind frequency corresponding to the wind sector from the omnidirectional wind resource map; Within each wind direction sector, traverse each wind speed range and read the probability of the wind speed range appearing in the wind direction sector from the omnidirectional wind resource map; By inputting the inflow wind speed into the power curve, the single-unit power of each fan in the corresponding wind direction sector and the corresponding wind speed range can be obtained; The power of each unit 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.

4. The method as described in claim 1, characterized in that, The step of obtaining 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 to iteratively update the wind turbine position includes: Randomly select one fan in the current arrangement and generate a random displacement vector within a preset moving radius; The random displacement vector is superimposed on 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 them and any other wind turbine is greater than the minimum distance requirement, and obtain the 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, and a new objective function value is obtained; 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 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.

5. The method as described in claim 1, characterized in that, 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 terminates, the target wind turbine coordinates are mirrored, that is, mirrored wind turbine coordinates are generated at the ground symmetrical points of each wind turbine location. 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. The output includes the final total power generation of the entire field, including the ground effect, and the corresponding target wind turbine coordinates.

6. A wind farm layout optimization device, characterized in that, The device includes: The acquisition module is used to acquire environmental turbulence intensity and wind turbine thrust coefficient, and to construct an omnidirectional wind resource map; The acquisition module is also used to read the coordinates of grid points and the wind speed range-wind direction sector wind frequency matrix corresponding to each grid point; The grid point coordinates are mapped to the wind frequency matrix of the wind speed range-wind direction sector based on the grid index to generate a wind resource data table with the grid points as units. Write the number of wind direction sectors, the number of wind speed intervals, and the wind frequency from the wind resource data table into a memory matrix to obtain an omnidirectional wind resource map. The calculation module is used to input the environmental turbulence intensity and the wind turbine thrust coefficient into the TurbOPark wake model, calculate the wake deficit, and obtain the inflow wind speed of each wind turbine under various wind direction and wind speed combinations. The objective function establishment module is used to perform weighted summation on the single-unit 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 that maximizes the total power generation of the entire field. The output module 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, with the boundary polygon coordinates and minimum spacing requirements as constraints; The output module is also used to obtain the coordinates of the boundary inflection point and the impeller diameter; By connecting the coordinates of the boundary inflection points, a polygonal boundary is obtained; The minimum spacing requirement is obtained by multiplying the impeller diameter by a preset factor.

7. 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, the computer program being configured to implement the steps of the wind farm layout optimization method as described in any one of claims 1 to 5.

8. 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, it implements the steps of the wind farm layout optimization method as described in any one of claims 1 to 5.

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