Wind power plant planning macroscopic optimization method and system based on multiple maps and sensitive factors

Abstract

The invention belongs to the field of wind power plant planning macroscopic optimization, and provides a wind power plant planning macroscopic optimization method and system based on multiple maps and sensitive factors. The method comprises the steps that geographic information of a to-be-planned area is acquired, and a plurality of wind power plant candidate areas are determined in the to-be-planned area according to set sensitive factors; generating a corresponding wind resource map according to historical wind measurement data in each wind power plant candidate field area; determining the maximum fan number of the set fan type of each wind power plant candidate area and a fan layout scheme; according to the maximum fan number and the fan arrangement scheme of each wind power plant candidate field area, calculating the whole power generation amount and the noise map of each wind power plant candidate field area; and screening the wind power plant candidate field with the maximum generating capacity in the whole field from the wind power plant candidate field set of which the maximum area of the noise map does not exceed a set area threshold range, and taking the wind power plant candidate field with the maximum generating capacity as the optimal field of the wind power plant.

Description

Technical Field

[0001] The present invention belongs to the field of wind farm planning macro-optimization, and in particular relates to a wind farm planning macro-optimization method and system based on multiple graphs and sensitive factors. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Site selection is the foundation of wind farm projects and directly affects the performance of wind farms, such as power generation. Reasonable site selection can maximize the use of wind energy resources, improve power generation efficiency, and reduce energy waste. The existing macro-site selection of wind farm planning only considers the characteristics of wind resources and sensitive factors, but does not consider the impact of wind farm projection noise on the surrounding area, and cannot simultaneously meet the balance between the wind farm projection noise and the total power generation. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a macro-optimization method and system for wind farm planning based on multiple maps and sensitive factors, which comprehensively considers wind resource maps, noise maps and sensitive factors to perform macro-optimization of wind farm planning, and can meet the balance requirements between the projected noise of the wind farm and the power generation of the entire field.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: A first aspect of the present invention provides a macro optimization method for wind farm planning based on multiple graphs and sensitive factors.

[0006] In one or more embodiments, a macro optimization method for wind farm planning based on multiple maps and sensitive factors is provided, including: Obtain geographic information of the area to be planned, and determine several candidate wind farm sites in the area to be planned based on set sensitive factors; Generate corresponding wind resource maps based on historical wind measurement data in candidate wind farm areas; Determine the maximum number of wind turbines and the layout plan of wind turbines of the specified wind turbine type in each candidate wind farm area according to the wind resource map and geographic information of each candidate wind farm area; According to the maximum number of wind turbines and the layout plan of wind turbines in each candidate wind farm area, the total power generation and noise spectrum of each candidate wind farm area are calculated respectively; wherein the noise spectrum is synthesized by the time series projection range of all wind turbines in the candidate wind farm area; From the set of wind farm candidate sites whose maximum area of ​​the noise spectrum does not exceed the set area threshold range, the wind farm candidate site with the largest power generation in the whole field is selected as the optimal wind farm site.

[0007] As an implementation method, the sensitive factors include terrain factors, geological factors, hydrological factors, meteorological factors and set sensitive areas.

[0008] As an implementation method, the process of generating a wind resource map is as follows: The candidate wind farm area is further divided into several sub-areas. For each sub-area, a mesoscale meteorological model simulation is performed in a specified year to obtain background field data; For the background field data of each sub-area, dynamic downscaling is performed to obtain a 100-meter resolution wind resource map of a specified year; the 100-meter resolution wind resource maps of all areas of a specified year are spliced ​​to obtain a 100-meter resolution wind resource map of a specified area of ​​a specified year; the wind tower data of a specified area of ​​a specified year are obtained, and after spatial matching of the wind tower data with the 100-meter resolution wind resource map of a specified area of ​​a specified year, the 100-meter resolution wind resource map of a specified area of ​​a specified year is corrected; Obtain reanalysis data of a specified year, use the reanalysis data of a specified year as input, and use the corrected 100-meter resolution wind resource map of a specified area in a specified year as output to train a random forest regression model; obtain reanalysis data of non-specified years, perform statistical downscaling using the trained random forest regression model, and obtain a 100-meter resolution wind resource map of a candidate wind farm area in a non-specified year.

[0009] As an implementation method, the process of determining the maximum number of wind turbines of a set wind turbine type and the wind turbine layout plan for each wind farm candidate site is as follows: Based on the terrain within the candidate wind farm area, select locations that match the wind turbine layout within the candidate wind farm area and determine the wind turbine layout algorithm; Based on the wind resource map in the site area, the dominant wind direction of the site area is obtained, and the arrangement rule parameters are determined based on the dominant wind direction; Based on the model attribute parameters matched with the site area, the fan impeller diameter is extracted, and the arrangement rule parameters are determined based on the fan impeller diameter; Based on the matching wind turbine layout position and arrangement rule parameters, the maximum number of wind turbines and the maximum wind turbine layout plan are obtained through the preset wind turbine arrangement algorithm.

[0010] As an implementation method, the calculation process of the time series projection range of the wind turbine is: Discretize the ground near the wind turbine into a number of grids, divide the period to be evaluated into minute-level time periods, and obtain a first horizontal distance between the highest point of the wind turbine rotor and the ground projection point and a second horizontal distance between the lowest point of the wind turbine rotor and the ground projection point according to the azimuth of the sun in each minute-level time period, wherein the first distance and the second distance are both the horizontal distances between the ground projection point and the wind turbine; For any grid to be evaluated, determine whether it coincides with the projection direction at this time according to the direction of the center of the grid to be evaluated relative to the wind turbine rotor. If it coincides, determine whether the horizontal distance from the center of the grid to be evaluated to the wind turbine is between the second horizontal distance and the first horizontal distance. If so, determine that the grid to be evaluated is affected, add a minute-level period to the affected time, and continue to determine until the grid to be evaluated is not affected. The affected time period and / or cumulative affected time of each grid in the entire period to be evaluated are obtained, and the time series projection range of the wind turbine is obtained.

[0011] As an implementation mode, when the azimuth direction of the sun relative to the wind turbine coincides with the incoming wind direction, the backlit shadow area of ​​the circular surface swept by the wind turbine rotor is a cylinder with an axis L1 and a diameter D, wherein L1 is a straight line from the center of the wind turbine rotor to the ground parallel to the sun's rays, D is the diameter of the wind turbine rotor, and the ground at the interface of the cylinder is the shadow area of ​​the wind turbine rotor; When the sun's azimuth relative to the wind turbine does not coincide with the incoming wind direction, the wind turbine rotor tracks the wind direction and rotates. The backlit shadow area of ​​the circular surface swept by the wind turbine rotor is an elliptical cylinder, and the interface of the ground on the elliptical cylinder is the shadow area projected by the wind turbine rotor.

[0012] As an implementation mode, the major axis of the elliptical cylinder is the diameter of the wind turbine rotor, and the minor axis of the elliptical cylinder is determined in real time according to the angle of rotation of the wind turbine rotor relative to the sun; or, the direction of the wind turbine is predicted according to historical wind data for the same period and a prediction model, or, the direction of the wind turbine rotor is obtained according to a real-time sensor.

[0013] A second aspect of the present invention provides a wind farm planning macro-optimization system based on multiple spectra and sensitive factors.

[0014] In one or more embodiments, a wind farm planning macro optimization system based on multiple maps and sensitive factors includes: A wind farm candidate site determination module is used to obtain geographic information of the area to be planned and determine a number of wind farm candidate sites in the area to be planned according to set sensitive factors; A wind resource map generation module is used to generate a corresponding wind resource map based on the historical wind measurement data in each candidate wind farm area; A wind turbine layout scheme determination module is used to determine the maximum number of wind turbines and wind turbine layout schemes of a set wind turbine type in each wind farm candidate area according to the wind resource map and geographic information of each wind farm candidate area; The whole-field power generation and noise spectrum calculation module is used to calculate the whole-field power generation and noise spectrum of each wind farm candidate area according to the maximum number of wind turbines and wind turbine layout plan of each wind farm candidate area; wherein the noise spectrum is synthesized by the time-series projection range of all wind turbines in the wind farm candidate area; The wind farm optimal site determination module is used to select the wind farm candidate site with the largest power generation in the whole field as the optimal wind farm site from the set of wind farm candidate sites whose maximum area of ​​the noise spectrum does not exceed the set area threshold range.

[0015] A third aspect of the present invention provides a computer-readable storage medium.

[0016] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the wind farm planning macro-optimization method based on multiple graphs and sensitive factors as described above.

[0017] A fourth aspect of the present invention provides an electronic device.

[0018] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the macro optimization method for wind farm planning based on multiple graphs and sensitive factors as described above are implemented.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The present invention determines the corresponding wind turbine layout plan based on the wind resource map and geographical information of each wind farm candidate area, and then combines the full-field power generation and noise map of each wind farm candidate area. From the set of wind farm candidate areas whose maximum area of ​​the noise map does not exceed the set area threshold range, the wind farm candidate area with the largest full-field power generation is selected as the optimal wind farm area. The wind resource map, noise map and sensitive factors are comprehensively considered to perform macro-optimization of wind farm planning, thereby meeting the balance demand between the projected noise of the wind farm and the full-field power generation.

[0020] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0022] Figure 1 It is a flow chart of a macro optimization method for wind farm planning based on multiple graphs and sensitive factors according to an embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of a wind farm planning macro optimization system based on multiple graphs and sensitive factors according to an embodiment of the present invention; Figure 3 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0024] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0025] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0026] Figure 1 is a flow chart of a wind farm planning macro optimization method based on multiple graphs and sensitive factors in an embodiment of the present invention, such as Figure 1 The wind farm planning macro optimization method based on multiple graphs and sensitive factors in the embodiment shown may include: S101, obtaining geographic information of the area to be planned, and determining a number of candidate wind farm sites in the area to be planned according to set sensitive factors; S102, generating a corresponding wind resource map based on historical wind measurement data in each candidate wind farm area; S103, determining the maximum number of wind turbines and the wind turbine layout plan of the set wind turbine type in each wind farm candidate area according to the wind resource map and geographic information of each wind farm candidate area; S104, calculating the total power generation and noise spectrum of each candidate wind farm area according to the maximum number of wind turbines and the wind turbine layout plan of each candidate wind farm area; wherein the noise spectrum is synthesized by the time-series projection range of all wind turbines in the candidate wind farm area; S105, selecting a wind farm candidate area with the largest power generation in the entire field as the optimal wind farm area from a set of wind farm candidate areas whose maximum area of ​​the noise spectrum does not exceed a set area threshold range.

[0027] In step S101, the sensitive factors include topographic factors, geological factors, hydrological factors, meteorological factors and set sensitive areas.

[0028] In step S102, the process of generating a wind resource map is as follows: S1021: The candidate wind farm site is further divided into several sub-areas. For each sub-area, a mesoscale meteorological model simulation is performed for a specified year to obtain background field data. For example, in 2022, when there is more observation data, a mesoscale meteorological model (WRF model, Weather Research & Forecasting Model) is used to perform high-resolution simulation (abbreviated as mesoscale meteorological model simulation or WRF mesoscale simulation) to obtain background field data for the sub-area. After all sub-areas are completed, background field data with a resolution of 2km×2km for the entire range is obtained.

[0029] S1022: For the background field data of each sub-area, dynamic downscaling is performed to obtain a 100-meter resolution wind resource map of a specified year; the 100-meter resolution wind resource maps of all areas of a specified year are spliced ​​to obtain a 100-meter resolution wind resource map of a specified area of ​​a specified year; the wind tower data of a specified area of ​​a specified year are obtained, and after spatial matching between the wind tower data and the 100-meter resolution wind resource map of a specified area of ​​a specified year, the 100-meter resolution wind resource map of a specified area of ​​a specified year is corrected; Downscaling technology is a method of converting large-scale, low-resolution model output information into small-scale, high-resolution regional climate change information. Dynamical downscaling refers to regional climate models (GCMs) or global reanalysis data providing initial conditions and boundary conditions to higher-resolution regional climate models (RCMs). RCMs, through internal physical processes, can not only retain the large-scale characteristics of GCMs, but also simulate more regional-scale climate characteristics than GCMs. Statistical downscaling is based on statistical relationships to achieve the downscaling process. Statistical downscaling has a small amount of calculation and saves machine time. Its research area and specific implementation plan have greater flexibility.

[0030] Specifically, the 2km×2km background field data of each sub-area were dynamically downscaled using the CALMET (California Meteorological Model) technology to obtain 100-meter resolution wind resource maps at altitudes of 100 meters, 120 meters, 140 meters, 160 meters, and 180 meters for each sub-area within the overall range.

[0031] S1023: Obtain reanalysis data of a specified year, use the reanalysis data of a specified year as input, and use the corrected 100-meter resolution wind resource map of a specified area in a specified year as output to train a random forest regression model; obtain reanalysis data of a non-specified year, perform statistical downscaling using the trained random forest regression model, and obtain a 100-meter resolution wind resource map of a candidate wind farm area in a non-specified year.

[0032] The wind tower data is spatially matched with the downscaled wind field data to ensure that the wind tower data can correspond to the relevant model output area. The measured wind speed and wind direction data of the wind tower are compared with the wind field data output by the model, and the wind speed and wind direction are corrected at the grid points output by the model using the interpolation method.

[0033] For areas near wind towers, the downscaled wind data is partially corrected by weighted averaging and other methods (i.e., weighted averaging of wind tower data and wind field data in the 100-meter resolution wind resource map for a specified area and year) to improve its consistency with the measured data. The corrected wind field data is combined with the downscaled data to ensure that the obtained high-resolution wind resource map is more accurate, especially in areas monitored by wind towers, and can better reflect the actual wind field conditions.

[0034] The wind resource map is corrected and adjusted through the measured data of the wind tower. According to the observation position of the wind tower, the measured data of the wind tower is matched with the wind field data output by the model by using spatial interpolation or weighted average method to correct the wind speed and wind direction, ensuring that the results near the wind tower are more accurate.

[0035] In step S103, the core of the wind turbine preliminary selection algorithm is to calculate the online hours of different wind turbine models based on the results of site division and the terrain and wind resources within the site, and select the expected models for users to choose, and recommend the model with the highest online hours by default.

[0036] The process of determining the maximum number of wind turbines of a given wind turbine type and the wind turbine layout plan for each candidate wind farm site is as follows: S1031: Based on the terrain within the candidate wind farm area, select locations that match the wind turbine layout within the candidate wind farm area, and determine the wind turbine layout algorithm; in the macro site selection process of the wind farm, the initial layout of wind turbines refers to the stage of designing and determining the preliminary layout and configuration of wind turbines in the planning area after the wind farm planning area is determined, based on wind resource data and selected wind turbine models and other information.

[0037] When initially deploying wind turbines, the number of wind turbines to be deployed must be determined based on the wind resource assessment and the available space in the planning area. The most suitable arrangement of wind turbines should be selected based on the topography and land use of the planning area to maximize the use of wind energy and ensure a safe distance between wind turbines.

[0038] S1032: Based on the wind resource map in the site area, obtain the dominant wind direction of the site area, and determine the arrangement rule parameters based on the dominant wind direction; The layout of wind turbines should be adjusted according to the dominant wind direction to ensure that each wind turbine can capture the maximum amount of wind resources. The second layout rule parameters are determined based on the dominant wind direction; when the dominant wind direction is south wind, in a wind farm dominated by south wind, the second layout rule parameters are to ensure that the wind turbine row is perpendicular to the wind direction, that is, the wind turbines should be arranged in a north-south direction to ensure that the impeller of each wind turbine can capture the wind from the south to the maximum extent.

[0039] S1033: Based on the model attribute parameters matched with the site area, the wind turbine impeller diameter is extracted, and the arrangement rule parameters are determined based on the wind turbine impeller diameter; assuming that the wind turbine impeller diameter is 150 meters, the first arrangement rule parameters will require that the spacing between wind turbines is approximately 3-4 times the impeller diameter to reduce the wake effect between wind turbines, that is, the spacing between each wind turbine should be at least 450-600 meters.

[0040] S1034: Based on the positions and arrangement rule parameters of the matching fan layout, a maximum number of fans and a maximum fan arrangement plan are obtained through a preset fan arrangement algorithm.

[0041] Formulate layout strategies and optimize shielding effects. Use the second layout rule parameters and the first layout rule parameters as algorithm basic parameters, combined with other user-defined parameters set by the user, and obtain the maximum number of wind turbines and the maximum wind turbine layout plan through the wind turbine layout algorithm determined in step 401; in the process of using the wind turbine layout algorithm to layout wind turbines, avoid shielding effects between wind turbines, optimize the position and height of wind turbines so that each wind turbine can be fully affected by wind resources, improve the overall power generation efficiency, and obtain the optimized maximum number of wind turbines and the maximum wind turbine layout plan.

[0042] In step S104, the calculation process of the time series projection range of the wind turbine is: Discretize the ground near the wind turbine into a number of grids, divide the period to be evaluated into minute-level time periods, and obtain a first horizontal distance between the highest point of the wind turbine rotor and the ground projection point and a second horizontal distance between the lowest point of the wind turbine rotor and the ground projection point according to the azimuth of the sun in each minute-level time period, wherein the first distance and the second distance are both the horizontal distances between the ground projection point and the wind turbine; For any grid to be evaluated, determine whether it coincides with the projection direction at this time according to the direction of the center of the grid to be evaluated relative to the wind turbine rotor. If it coincides, determine whether the horizontal distance from the center of the grid to be evaluated to the wind turbine is between the second horizontal distance and the first horizontal distance. If so, determine that the grid to be evaluated is affected, add a minute-level period to the affected time, and continue to determine until the grid to be evaluated is not affected. The affected time period and / or cumulative affected time of each grid in the entire period to be evaluated are obtained, and the time series projection range of the wind turbine is obtained.

[0043] This embodiment takes into account the wind direction time series wind turbine light and shadow assessment strategy, simplifies the light and shadow model of the wind turbine rotor at each evaluation moment into a circular surface facing the incoming wind direction, thereby achieving a more accurate assessment of the wind turbine light and shadow, and further enabling optimal layout of photovoltaic modules near the wind turbine, thereby increasing photovoltaic power generation based on the same photovoltaic module layout area.

[0044] Specifically, when the azimuth direction of the sun relative to the wind turbine coincides with the incoming wind direction, the backlit shadow area of ​​the circular surface swept by the wind turbine rotor is a cylinder with axis L1 and diameter D, wherein L1 is a straight line from the center of the wind turbine rotor to the ground parallel to the sun's rays, D is the diameter of the wind turbine rotor, and the ground at the interface of the cylinder is the shadow area of ​​the wind turbine rotor; When the sun's azimuth relative to the wind turbine does not coincide with the incoming wind direction, the wind turbine rotor tracks the wind direction and rotates. The backlit shadow area of ​​the circular surface swept by the wind turbine rotor is an elliptical cylinder, and the interface of the ground on the elliptical cylinder is the shadow area projected by the wind turbine rotor.

[0045] The ground near the wind turbine is discretized into many small grids and a year is divided into 10 minutes (i.e. minute-level time periods). In each minute-level time period, whether the small grid is affected by the shadow is determined based on whether the direction of the center of the small grid on the ground relative to the wind turbine coincides with the direction of the shadow at this time, and the horizontal distance from the center of the small grid on the ground to the wind turbine. If affected, the affected time of the small grid will be increased by 10 minutes (i.e. one minute-level time period is added).

[0046] Calculating the solar altitude angle is the basis for studying the light and shadow flicker of wind turbines. The calculation formula for the solar altitude angle is as follows: ; Where: is the local latitude, deg ; The sun's red tail angle, deg ; is the solar hour angle, deg .

[0047] Where: Solar declination angle for: ; Where:n To calculate the sequence of days of the year for the current day.

[0048] The specific calculation time and Beijing time can be converted according to the following formula:

[0049] Where: L is the measured local latitude; E To correct the time difference, the calculation formula is as follows:

[0050] In the formula, .

[0051] After calculating the solar altitude angle, the projection length of the wind turbine rotor during this period can be calculated. First, calculate the projection distance of the top of the wind turbine rotor. L1 :

[0052] Wherein, D1 is the sum of the top of the wind turbine rotor and the elevation difference between the sensitive point and the wind turbine coordinates, in meters.

[0053] Then calculate the projection distance of the lower end of the fan rotor L2 :

[0054] in, D2 It is the sum of the lowest point of the wind turbine rotor and the elevation difference between the sensitive point and the wind turbine coordinates, in meters.

[0055] The ground near the wind turbine is discretized into many small grids and a year is divided into 10 minutes. In each 10-minute period, whether it is affected is determined based on whether the direction of the center of the small grid relative to the wind turbine coincides with the shadow direction at that time and whether the distance from the center of the grid to the wind turbine is less than the shadow length calculated according to the formula. If affected, the affected time of the small grid is added with 10 minutes. According to this method, the number of hours the ground around the wind turbine is affected can be calculated.

[0056] In this implementation, the light and shadow effects of the wind turbine wheel can be combined with the light and shadow effects of the tower model. The tower model does not rotate and can be implemented using existing methods. It only needs to superimpose the light and shadow effects of the wind turbine wheel described in this implementation.

[0057] For tall buildings and even mountains in and around wind farms, a method similar to that of a tower is used to simplify the buildings or mountains into a square to take into account the light and shadow effects. When considering the impact of wind farms on sensitive areas such as residential areas, calculate the time period of shadow impacts caused by the surrounding tall buildings and mountains on the grid representing the residential area. If there is an overlap with the period of time when the residential area is shadowed by the wind farm, the overlapping time should be subtracted from the wind farm impact time and not included in the light and shadow impacts caused by the wind farm. If the method described in this implementation method considers the shadow impacts on sensitive areas such as photovoltaic components in a wind farm, if the shadow time caused by the surrounding tall buildings and mountains on the residential area overlaps, it should be included in the time consideration range for the impact on the photovoltaic components.

[0058] This embodiment takes into account the wind direction time series wind turbine light and shadow assessment strategy, based on the relationship between wind direction and wind turbine orientation, when the sun's relative azimuth direction to the wind turbine coincides with the incoming wind direction, the backlit shadow area of ​​the circular surface swept by the wind turbine rotor is a cylinder, and the interface of the ground on the cylinder is the shadow area of ​​the wind turbine rotor; when the sun's relative azimuth direction to the wind turbine does not coincide with the incoming wind direction, the wind turbine rotor tracks the wind direction and rotates, and the backlit shadow area of ​​the circular surface swept by the wind turbine rotor is an elliptical cylinder, and the interface of the ground on the elliptical cylinder is the shadow area projected by the wind rotor. This modeling method can be used to more accurately assess the wind turbine rotor shadow.

[0059] In step S105, since the noise spectrum is a spectrum that changes with time, after eliminating the wind farm candidate sites whose maximum area of ​​the noise spectrum exceeds the set area threshold range (which can be set according to the set specifications or manually), the wind farm candidate site with the largest power generation in the whole field is selected from the remaining wind farm candidate site set as the optimal wind farm site.

[0060] Figure 2 is a schematic diagram of the structure of a wind farm planning macro optimization system based on multiple graphs and sensitive factors in an embodiment of the present invention. Figure 1 The macro optimization method of wind farm planning based on multi-atlas and sensitive factors corresponds to Figure 2 As shown, the wind farm planning macro optimization system based on multiple graphs and sensitive factors in this embodiment may include: A wind farm candidate site determination module 201 is used to obtain geographic information of the area to be planned, and determine a number of wind farm candidate sites in the area to be planned according to set sensitive factors; A wind resource map generation module 202, which is used to generate a corresponding wind resource map according to the historical wind measurement data in each candidate wind farm area; A wind turbine layout scheme determination module 203 is used to determine the maximum number of wind turbines and the wind turbine layout scheme of a set wind turbine type in each wind farm candidate area according to the wind resource map and geographic information of each wind farm candidate area; The whole-field power generation and noise spectrum calculation module 204 is used to calculate the whole-field power generation and noise spectrum of each wind farm candidate area according to the maximum number of wind turbines and wind turbine layout plan of each wind farm candidate area; wherein the noise spectrum is synthesized by the time-series projection range of all wind turbines in the wind farm candidate area; The wind farm optimal area determination module 205 is used to select the wind farm candidate area with the largest power generation as the optimal wind farm area from the set of wind farm candidate areas whose maximum area of ​​the noise spectrum does not exceed the set area threshold range.

[0061] It should be noted here that Figure 2 The various modules in the wind farm planning macro optimization system based on multi-atlas and sensitive factors are Figure 1 The various steps in the macro optimization method for wind farm planning based on multi-atlas and sensitive factors correspond one to one, and the specific implementation process is the same, which will not be repeated here.

[0062] Reference Figure 3 , a schematic diagram of an electronic device is given. It should be noted that, Figure 3 The electronic device 300 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0063] like Figure 3 As shown, the electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303. In the RAM 303, various programs and data required for system operation are also stored. The central processing unit 301, the ROM 302 and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0064] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a local area network (LAN) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage section 308 as needed.

[0065] When the central processing unit 301 in the electronic device of this embodiment executes the program, the following is achieved: Figure 1 The steps in the macro optimization method for wind farm planning based on multiple graphs and sensitive factors are shown.

[0066] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, the computer program including a computer program for executing Figure 1 In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit 301, various functions defined in the apparatus of the present application are executed.

[0067] in, Figure 1 The computer program instructions corresponding to the method shown may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0068] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0069] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A wind farm planning macro optimization method based on multiple graphs and sensitive factors, characterized in that: include: Obtain geographic information of the area to be planned, and determine several candidate wind farm sites in the area to be planned based on set sensitive factors; Generate corresponding wind resource maps based on historical wind measurement data in candidate wind farm areas; Determine the maximum number of wind turbines and the layout plan of wind turbines of the specified wind turbine type in each candidate wind farm area according to the wind resource map and geographic information of each candidate wind farm area; According to the maximum number of wind turbines and the layout plan of wind turbines in each candidate wind farm area, the total power generation and noise spectrum of each candidate wind farm area are calculated respectively; wherein the noise spectrum is synthesized by the time series projection range of all wind turbines in the candidate wind farm area; From the set of wind farm candidate sites whose maximum area of ​​the noise spectrum does not exceed the set area threshold range, the wind farm candidate site with the largest power generation in the whole field is selected as the optimal wind farm site.

2. The wind farm planning macro optimization method based on multiple graphs and sensitive factors according to claim 1, characterized in that: The sensitive factors include topographic factors, geological factors, hydrological factors, meteorological factors and set sensitive areas.

3. The wind farm planning macro optimization method based on multiple graphs and sensitive factors according to claim 1, characterized in that: The process of generating a wind resource map is as follows: The candidate wind farm area is further divided into several sub-areas. For each sub-area, a mesoscale meteorological model simulation is performed in a specified year to obtain background field data; For the background field data of each sub-area, dynamic downscaling is performed to obtain a 100-meter resolution wind resource map of a specified year; the 100-meter resolution wind resource maps of all areas of a specified year are spliced ​​to obtain a 100-meter resolution wind resource map of a specified area of ​​a specified year; the wind tower data of a specified area of ​​a specified year are obtained, and after spatial matching of the wind tower data with the 100-meter resolution wind resource map of a specified area of ​​a specified year, the 100-meter resolution wind resource map of a specified area of ​​a specified year is corrected; Obtain reanalysis data of a specified year, use the reanalysis data of a specified year as input, and use the corrected 100-meter resolution wind resource map of a specified area in a specified year as output to train a random forest regression model; obtain reanalysis data of non-specified years, perform statistical downscaling using the trained random forest regression model, and obtain a 100-meter resolution wind resource map of a candidate wind farm area in a non-specified year.

4. The wind farm planning macro optimization method based on multiple graphs and sensitive factors according to claim 1, characterized in that: The process of determining the maximum number of wind turbines of a given wind turbine type and the wind turbine layout plan for each candidate wind farm site is as follows: Based on the terrain within the candidate wind farm area, select locations that match the wind turbine layout within the candidate wind farm area and determine the wind turbine layout algorithm; Based on the wind resource map in the site area, the dominant wind direction of the site area is obtained, and the arrangement rule parameters are determined based on the dominant wind direction; Based on the model attribute parameters matched with the site area, the fan impeller diameter is extracted, and the arrangement rule parameters are determined based on the fan impeller diameter; Based on the matching wind turbine layout position and arrangement rule parameters, the maximum number of wind turbines and the maximum wind turbine layout plan are obtained through the preset wind turbine arrangement algorithm.

5. The wind farm planning macro optimization method based on multiple graphs and sensitive factors according to claim 1, characterized in that: The calculation process of the fan's timing projection range is: Discretize the ground near the wind turbine into a number of grids, divide the period to be evaluated into minute-level time periods, and obtain a first horizontal distance between the highest point of the wind turbine rotor and the ground projection point and a second horizontal distance between the lowest point of the wind turbine rotor and the ground projection point according to the azimuth of the sun in each minute-level time period, wherein the first distance and the second distance are both the horizontal distances between the ground projection point and the wind turbine; For any grid to be evaluated, determine whether it coincides with the projection direction at this time according to the direction of the center of the grid to be evaluated relative to the wind turbine rotor. If it coincides, determine whether the horizontal distance from the center of the grid to be evaluated to the wind turbine is between the second horizontal distance and the first horizontal distance. If so, determine that the grid to be evaluated is affected, add a minute-level period to the affected time, and continue to determine until the grid to be evaluated is not affected. The affected time period and / or cumulative affected time of each grid in the entire period to be evaluated are obtained, and the time series projection range of the wind turbine is obtained.

6. The wind farm planning macro optimization method based on multiple graphs and sensitive factors according to claim 5, characterized in that: When the sun's azimuth direction relative to the wind turbine coincides with the incoming wind direction, the backlit shadow area of ​​the circular surface swept by the wind turbine rotor is a cylinder with axis L1 and diameter D, where L1 is a straight line from the center of the wind turbine rotor to the ground parallel to the sun's rays, D is the diameter of the wind turbine rotor, and the ground at the interface of the cylinder is the shadow area of ​​the wind turbine rotor; When the sun's azimuth relative to the wind turbine does not coincide with the incoming wind direction, the wind turbine rotor tracks the wind direction and rotates. The backlit shadow area of ​​the circular surface swept by the wind turbine rotor is an elliptical cylinder, and the interface of the ground on the elliptical cylinder is the shadow area projected by the wind turbine rotor.

7. The wind farm planning macro optimization method based on multiple graphs and sensitive factors according to claim 6, characterized in that: The major axis of the elliptical cylinder is the diameter of the wind turbine rotor, and the minor axis of the elliptical cylinder is determined in real time according to the angle of rotation of the wind turbine rotor relative to the sun; or, the direction of the wind turbine is predicted according to historical wind data for the same period and a prediction model, or, the direction of the wind turbine rotor is obtained according to a real-time sensor.

8. A wind farm planning macro optimization system based on multiple graphs and sensitive factors, characterized in that: include: A wind farm candidate site determination module is used to obtain geographic information of the area to be planned and determine a number of wind farm candidate sites in the area to be planned according to set sensitive factors; A wind resource map generation module is used to generate a corresponding wind resource map based on the historical wind measurement data in each candidate wind farm area; A wind turbine layout scheme determination module is used to determine the maximum number of wind turbines and wind turbine layout schemes of a set wind turbine type in each wind farm candidate area according to the wind resource map and geographic information of each wind farm candidate area; The whole-field power generation and noise spectrum calculation module is used to calculate the whole-field power generation and noise spectrum of each wind farm candidate area according to the maximum number of wind turbines and wind turbine layout plan of each wind farm candidate area; wherein the noise spectrum is synthesized by the time-series projection range of all wind turbines in the wind farm candidate area; The wind farm optimal site determination module is used to select the wind farm candidate site with the largest power generation in the whole field as the optimal wind farm site from the set of wind farm candidate sites whose maximum area of ​​the noise spectrum does not exceed the set area threshold range.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the wind farm planning macro-optimization method based on multiple graphs and sensitive factors as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the wind farm planning macro-optimization method based on multiple graphs and sensitive factors as described in any one of claims 1 to 7 are implemented.

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