Wind energy resource assessment method and device for wind farm

Abstract

The present invention discloses a method and device for evaluating wind energy resources in a wind farm, belonging to the technical field of wind resource evaluation. The method comprises: obtaining mesoscale meteorological analysis data of the wind farm region using a mesoscale WRF model; introducing the Taylor frozen flow hypothesis based on the mesoscale meteorological analysis data to generate a microscopic turbulence structure of the wind farm region; determining a local complex terrain region in the wind farm region, using the microscopic turbulence structure as the inflow boundary condition of a microscale CFD model, driving the CFD model to perform refined numerical simulation of the local complex terrain region, and obtaining simulation data of the local complex terrain region; and utilizing the microscopic turbulence structure and the simulation data of the local complex terrain region to perform wind energy resource evaluation. The present invention can achieve a balance between evaluation accuracy and evaluation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of wind resource assessment, and in particular to a method and device for assessing wind energy resources in a wind farm. Background Art

[0002] Related technologies for wind resource assessment often employ single-scale numerical simulations, such as the mesoscale weather forecasting model (WRF) and microscale computational fluid dynamics (CFD). However, in practical applications, single-scale approaches cannot balance assessment accuracy and speed. Therefore, there is an urgent need for an assessment method that balances both accuracy and efficiency. Summary of the Invention

[0003] The present invention provides a method and device for evaluating wind energy resources in a wind farm, which can solve the problem in related technologies that cannot simultaneously take into account both evaluation accuracy and evaluation efficiency. The technical solution is as follows:

[0004] In one aspect, a method for evaluating wind energy resources in a wind farm is provided, the method comprising:

[0005] Use the mesoscale WRF model to obtain mesoscale meteorological analysis data for the wind farm area;

[0006] Based on the mesoscale meteorological analysis data, the Taylor frozen flow hypothesis is introduced to generate the microscopic turbulence structure of the wind farm area;

[0007] Determine the local complex terrain area in the wind farm area, use the microscopic turbulence structure as the inflow boundary condition of the microscale CFD model, drive the CFD model to perform refined numerical simulation on the local complex terrain area, and obtain simulation data of the local complex terrain area;

[0008] The simulated data of the microscopic turbulence structure and local complex terrain area are used to evaluate wind energy resources.

[0009] In a possible implementation, the method of introducing the Taylor frozen flow hypothesis based on the mesoscale meteorological analysis data to generate a microscopic turbulent structure includes:

[0010] Extracting key variables for generating microscopic turbulence structures from the mesoscale meteorological analysis data; the key variables include at least average wind speed and average wind direction;

[0011] Based on the average wind speed, a time series of turbulence pulsation data is generated. The time series of turbulence pulsation data is converted into spatial distribution data along the spatial variation of the average wind direction. The spatial distribution data is expanded horizontally and vertically to construct a microscopic turbulence structure.

[0012] In a possible implementation, before generating the time series turbulence pulsation data, the method further includes: calculating a correction factor based on WRF terrain data, and adjusting the average wind speed using the correction factor.

[0013] In a possible implementation, after obtaining the simulation data of the local complex terrain area, the method further includes:

[0014] Calculating a calibrated correction factor using the actual wind speed distribution in the simulation data and WRF terrain data;

[0015] The average wind speed is calibrated using the calibrated correction factor, and the turbulence pulsation data of the time series is generated using the calibrated average wind speed.

[0016] In a possible implementation, converting the time series turbulence pulsation data into spatial distribution data includes:

[0017] This is achieved according to the following conversion formula:

[0018] ;

[0019] in, Downstream distance from the target point x Turbulent pulsation data at the spatial position of is the average wind speed, is the turbulent pulsation data corresponding to the time series t.

[0020] In a possible implementation, the method of obtaining mesoscale meteorological analysis data of the wind farm area using the mesoscale WRF model includes:

[0021] Meteorological reanalysis data are obtained from the reanalysis database and used as the background field of the mesoscale WRF model to perform numerical simulations on the wind farm area and obtain mesoscale meteorological analysis data.

[0022] In another aspect, a device for evaluating wind energy resources in a wind farm is provided, the device comprising:

[0023] An acquisition unit, used for acquiring mesoscale meteorological analysis data of the wind farm area using a mesoscale WRF model;

[0024] a generating unit, configured to introduce a Taylor frozen flow hypothesis based on the mesoscale meteorological analysis data to generate a microscopic turbulence structure in the wind farm area;

[0025] A simulation unit is used to determine the local complex terrain area of ​​the wind farm area, and based on the microscopic turbulence structure as the inflow boundary condition of the microscale CFD model, drive the CFD model to perform a refined numerical simulation on the local complex terrain area to obtain simulation data of the local complex terrain area;

[0026] An evaluation unit is used to evaluate wind energy resources using the simulation data of the microscopic turbulence structure and the local complex terrain area.

[0027] On the other hand, a computer device is provided, which includes a memory and a processor, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the steps of the above-mentioned wind farm wind energy resource assessment method.

[0028] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored in the storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned method for evaluating wind energy resources in a wind farm are implemented.

[0029] On the other hand, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the method for evaluating wind energy resources in a wind farm are implemented.

[0030] The technical solution provided by the present invention can at least bring the following beneficial effects:

[0031] The mesoscale WRF model is used to obtain mesoscale meteorological analysis data of the wind farm area. After obtaining the mesoscale meteorological analysis data, the Taylor frozen flow hypothesis is introduced to quickly generate the micro-turbulence structure of the wind farm area. Considering that the accuracy of the micro-turbulence structure in the complex terrain area is poor, the micro-scale CFD model is used to perform refined numerical simulation on the local complex terrain area, and the rapidly produced micro-turbulence structure is used as the inflow boundary condition of the micro-scale CFD model, so that the simulation data of the local complex terrain area can be quickly obtained, and then the micro-turbulence structure and the simulation data of the local complex terrain area are used to perform wind energy resource assessment, which can achieve a balance between assessment accuracy and assessment efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1This is a flow chart of a method for evaluating wind energy resources in a wind farm provided by one embodiment of the present invention;

[0034] Figure 2 This is a flow chart of a method for generating a microscopic turbulent structure provided by one embodiment of the present invention;

[0035] Figure 3 This is a structural diagram of a wind energy resource assessment device for a wind farm provided by one embodiment of the present invention;

[0036] Figure 4 This is a hardware architecture diagram of a computer device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0038] Please refer to Figure 1 An embodiment of the present invention provides a method for evaluating wind energy resources in a wind farm, the method comprising:

[0039] Step 100, using a mesoscale WRF model to obtain mesoscale meteorological analysis data of the wind farm area;

[0040] Step 102 , based on the mesoscale meteorological analysis data, introducing the Taylor frozen flow hypothesis to generate the microscopic turbulence structure of the wind farm area;

[0041] Step 104: determining a local complex terrain region in the wind farm area, using the microscopic turbulence structure as an inflow boundary condition of a microscale CFD model, driving the CFD model to perform a refined numerical simulation on the local complex terrain region, and obtaining simulation data for the local complex terrain region;

[0042] Step 106 : Using the simulated data of the microscopic turbulence structure and the local complex terrain area to perform wind energy resource assessment.

[0043] In an embodiment of the present invention, a mesoscale WRF model is used to obtain mesoscale meteorological analysis data of the wind farm area. After obtaining the mesoscale meteorological analysis data, the Taylor frozen flow hypothesis is introduced to quickly generate the micro-turbulent structure of the wind farm area. Considering that the accuracy of the micro-turbulent structure in the complex terrain area is poor, a micro-scale CFD model is used to perform refined numerical simulation on the local complex terrain area, and the rapidly produced micro-turbulent structure is used as the inflow boundary condition of the micro-scale CFD model, so that the simulation data of the local complex terrain area can be quickly obtained, and then the micro-turbulent structure and the simulation data of the local complex terrain area are used to perform wind energy resource assessment, which can achieve a balance between assessment accuracy and assessment efficiency.

[0044] Described below Figure 1 How to perform the steps shown.

[0045] First, for step 100, the mesoscale WRF model is used to obtain mesoscale meteorological analysis data of the wind farm area.

[0046] In an embodiment of the present invention, meteorological reanalysis data can be obtained from a reanalysis database, and the meteorological reanalysis data can be used as a background field of a mesoscale WRF model to perform numerical simulation on the wind farm area to obtain mesoscale meteorological analysis data.

[0047] Mesoscale meteorological analysis data includes at least one year of hourly time series data, including wind speed, wind direction and other wind data.

[0048] Then, step 102 "introducing the Taylor frozen flow hypothesis based on the mesoscale meteorological analysis data to generate a microscopic turbulent structure" and step 104 "determining the local complex terrain area of ​​the wind farm area, using the microscopic turbulent structure as the inflow boundary condition of the microscale CFD model, driving the CFD model to perform refined numerical simulation on the local complex terrain area, and obtaining simulation data of the local complex terrain area" are explained at the same time.

[0049] The Taylor frozen flow hypothesis states that when the turbulent structure of a flow is advected at a certain velocity in the mean flow field, it can be assumed that the turbulent field is frozen for a short period of time, that is, it does not change with time. In this way, spatial measurement data can be inferred from the temporal changes.

[0050] The assessment of wind energy resources usually requires measuring wind speeds at different heights or setting up wind towers at different locations. The Taylor frozen flow hypothesis can be used to convert single-point time series data into spatial information. For example, assuming that the turbulent structure is advected by the average wind speed, the wind speed changes at different locations can be estimated by time delay. For example, if the average wind speed is U, then at a distance xAt downstream points, the temporal variation of wind speed may be equivalent to the time series delay of upstream points. x It can be seen that by introducing the Taylor frozen flow hypothesis, the mesoscale meteorological analysis data can be quickly downscaled to obtain accurate downscaled data.

[0051] Specifically, please refer to Figure 2 , this step may include:

[0052] Step 1020: extracting key variables for generating microscopic turbulence structures from the mesoscale meteorological analysis data; the key variables include at least average wind speed and average wind direction;

[0053] Step 1022: Generate time series turbulence pulsation data based on the average wind speed, convert the time series turbulence pulsation data into spatial distribution data along the spatial variation of the average wind direction, and expand the spatial distribution data horizontally and vertically to construct a microscopic turbulence structure.

[0054] In the embodiment of the present invention, when generating turbulent pulsation data, at least the following two methods may be included:

[0055] Method 1: If there is measured data provided by a wind tower, the measured data is used to generate turbulence pulsation data;

[0056] In this way, the pulsating component can be separated using the measured data: u′(t)=u(t)−U WRF ; where u′(t) is the turbulent pulsation data corresponding to the time series t, u(t) is the measured wind speed corresponding to the time series t, and U WRF is the average wind speed extracted from the mesoscale meteorological analysis data;

[0057] Method 2: If there is no measured data provided by a wind tower, the turbulence pulsation data is generated using mesoscale meteorological analysis data.

[0058] In the second method, since there is no measured data, the turbulence intensity and energy spectrum characteristics can be extracted from the mesoscale meteorological analysis data, and then the time series turbulence pulsation data can be synthesized.

[0059] After obtaining the time series turbulence pulsation data in the above two ways, we can follow the Taylor frozen flow hypothesis and calculate the average wind direction ( x axis), mapping the separated pulsating components from the time series to the spatial distribution: u ′( x )= u ′( t = x / U WRF ). u ′(x ) is the distance downstream from the target point x In method 1, the target point is the actual wind measurement point, while in method 2, the target point is the virtual wind measurement point.

[0060] After the spatial distribution data is converted, coherent pulsations can be generated through exponential correlation functions when the data is expanded three-dimensionally in the horizontal direction (y-axis); the turbulence profile can be adjusted based on the WRF wind shear when the data is expanded three-dimensionally in the vertical direction (z-axis).

[0061] Furthermore, considering that real terrain is not completely flat, complex terrain (such as mountains and canyons) can affect the wind field, leading to an uneven spatial distribution of wind speeds. For example, when wind passes over a mountain ridge, it accelerates due to the uplift of the terrain. However, on leeward slopes or in canyons, the wind speed weakens and generates turbulence. Directly using the Taylor frozen flow assumption to generate advection velocities cannot accurately reflect the actual wind speed distribution, as wind speed varies with location in complex terrain. Consider that terrain data in WRF typically includes parameters such as surface elevation, slope, and roughness, which have a significant impact on the local wind field. For example, high terrain can cause wind speed acceleration (terrain acceleration effect), while rough surfaces (such as forests) increase surface friction and reduce wind speed. When using the Taylor frozen flow assumption to generate microscopic turbulent structure, directly using the uncorrected WRF mean wind speed will ignore the terrain effect, resulting in a deviated microscopic turbulent field. Therefore, it is important to consider the impact of WRF terrain data on the mean wind speed U.

[0062] Based on this, in one embodiment of the present invention, before generating the time series turbulence pulsation data based on the average wind speed, the method may further include: calculating a correction factor based on the WRF terrain data, and adjusting the average wind speed using the correction factor, in the following manner: ;in, is the adjusted average wind speed, is the correction factor. This can more accurately reflect the actual impact of terrain on wind speed.

[0063] For example, if the terrain at a certain location causes the wind speed to accelerate by 20%, then k_terrain=1.2, thereby correcting the advection speed so that the spatial distribution of the turbulence structure generated by the Taylor hypothesis is more consistent with the actual situation.

[0064] In this embodiment of the present invention, the terrain correction factor can be calibrated using WRF-simulated wind speed data and actual observation data, or through refined simulation of a local area using a CFD model. Considering that a CFD model will subsequently be required to perform refined simulations of local complex terrain areas, the correction factor in this embodiment of the present invention can be calibrated using simulated data from a refined CFD model simulation of local complex terrain areas.

[0065] Specifically, in an embodiment of the present invention, by determining the local complex terrain area of ​​the wind farm area, and then using the microscopic turbulence structure as the inflow boundary condition of the microscale CFD model, the CFD model is driven to perform refined numerical simulation on the local complex terrain area, thereby obtaining simulation data of the local complex terrain area;

[0066] In one implementation, the local complex terrain area of ​​the wind farm area may be determined using WRF terrain data; in another implementation, the local complex terrain area of ​​the wind farm area may also be determined using satellite data.

[0067] After determining the local complex terrain area, the current microscopic turbulence structure can be directly used as the inflow boundary condition of the microscale CFD model to drive the CFD model to perform refined numerical simulation on the local complex terrain area and obtain simulation data of the local complex terrain area; further, after obtaining the simulation data of the local complex terrain area, the simulation data can characterize the actual wind speed distribution in the local complex terrain area. By comparing the CFD results and the WRF results, the calibrated correction factor is calculated, and the average wind speed is adjusted again using the calibrated correction factor, so that the microscopic turbulence structure can be generated using the readjusted average wind speed.

[0068] That is to say, in a preferred embodiment of the present invention, the method for generating the microscopic turbulent structure includes:

[0069] S1: extracting key variables for generating microscopic turbulence structure from the mesoscale meteorological analysis data; the key variables include at least average wind speed and average wind direction;

[0070] S2: Calculate the correction factor using WRF terrain data and use the correction factor to correct the average wind speed;

[0071] S3: generating time series turbulence pulsation data based on the corrected average wind speed, converting the time series turbulence pulsation data into spatial distribution data along the spatial variation of the average wind direction, and performing horizontal and vertical expansion on the spatial distribution data to construct a microscopic turbulence structure;

[0072] S4: determining a local complex terrain region in the wind farm area, using the microscopic turbulence structure as an inflow boundary condition of a microscale CFD model, driving the CFD model to perform a refined numerical simulation on the local complex terrain region, and obtaining simulation data for the local complex terrain region;

[0073] S5: Calculate the calibrated correction factor based on the real wind speed distribution in the simulation data and the WRF terrain data;

[0074] S6: Calibrate the average wind speed using the calibrated correction factor, and execute S3 using the calibrated average wind speed, thereby constructing the microscopic turbulence structure after the first calibration.

[0075] Furthermore, the microscopic turbulence structure after the first calibration can be used to continue executing step S4, and the microscopic turbulence structure after the first calibration can be used as the inflow boundary condition of the microscale CFD model to drive the CFD model to perform refined numerical simulation on the local complex terrain area, and obtain the simulation data of the local complex terrain area after the first calibration.

[0076] Furthermore, the simulation data after the first calibration may be used to continue executing steps S5, S6, and S3, so that the microscopic turbulence structure after the second calibration may be constructed.

[0077] After at least two calibrations, a more accurate microscopic turbulence structure can be obtained. Then, step 104 is performed using the microscopic turbulence structure after at least two calibrations, so that more accurate simulation data of the local complex terrain area can be obtained.

[0078] In step 106, the wind energy resource assessment may be performed by jointly assessing the simulation data of the local complex terrain area and the microscopic turbulence structure of the remaining area, or by directly assessing the microscopic turbulence structure of the wind farm area.

[0079] In this embodiment of the present invention, by introducing and calibrating correction factors, topographic coupling between the mesoscale meteorological field and the microscopic turbulence generation process can be achieved. This improves the accuracy of the actual wind field and ensures the reliability of subsequent microscopic turbulence analysis and wind turbine layout optimization. Furthermore, the introduction of the Taylor frozen flow hypothesis, combined with the mesoscale and microscale approaches, achieves a balance between assessment accuracy and efficiency.

[0080] Please refer to Figure 3 , an embodiment of the present invention provides a device for evaluating wind energy resources in a wind farm, the device comprising:

[0081] An acquisition unit 300 is configured to acquire mesoscale meteorological analysis data of a wind farm area using a mesoscale WRF model;

[0082] A generating unit 302 is configured to introduce the Taylor frozen flow hypothesis based on the mesoscale meteorological analysis data to generate a microscopic turbulence structure in the wind farm area;

[0083] The simulation unit 304 is configured to determine a local complex terrain region in the wind farm area, and based on the microscopic turbulence structure as an inflow boundary condition of the microscale CFD model, drive the CFD model to perform a refined numerical simulation on the local complex terrain region to obtain simulation data of the local complex terrain region;

[0084] The evaluation unit 306 is configured to evaluate wind energy resources using the simulation data of the microscopic turbulence structure and the local complex terrain area.

[0085] In one embodiment of the present invention, the generation unit is specifically used to: extract key variables for generating microscopic turbulence structures from the mesoscale meteorological analysis data; the key variables include at least average wind speed and average wind direction; generate time series turbulence pulsation data based on the average wind speed, and convert the time series turbulence pulsation data into spatial distribution data along the spatial variation of the average wind direction, and expand the spatial distribution data laterally and vertically to construct a microscopic turbulence structure.

[0086] In one embodiment of the present invention, the generating unit is further configured to calculate a correction factor based on WRF terrain data, and adjust the average wind speed using the correction factor.

[0087] In one embodiment of the present invention, the generation unit is further used to calculate a calibrated correction factor using the actual wind speed distribution and WRF terrain data in the simulation data; calibrate the average wind speed using the calibrated correction factor, and execute the generation of turbulent pulsation data of the time series using the calibrated average wind speed.

[0088] In one embodiment of the present invention, when the generating unit converts the time series turbulence pulsation data into spatial distribution data, the conversion process specifically includes: implementing the conversion process according to the following conversion formula:

[0089] ;

[0090] in, Downstream distance from the target point x Turbulent pulsation data at the spatial position of is the average wind speed, is the turbulent pulsation data corresponding to the time series t.

[0091] In one embodiment of the present invention, the acquisition unit is specifically used to obtain meteorological reanalysis data from a reanalysis database, and use the meteorological reanalysis data as the background field of the mesoscale WRF model to perform numerical simulation on the wind farm area to obtain mesoscale meteorological analysis data.

[0092] It should be noted that the wind farm wind energy resource assessment device provided in the above embodiment is merely illustrative of the division of the aforementioned functional modules. In actual applications, the aforementioned functions can be assigned to different functional modules as needed, i.e., the internal structure of the device can be divided into different functional modules to perform all or part of the functions described above. Furthermore, the wind farm wind energy resource assessment device provided in the above embodiment and the wind farm wind energy resource assessment method embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0093] The embodiment of the present application also provides a computer device, please refer to Figure 4 The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the wind energy resource assessment method of the wind farm provided by the above-mentioned method embodiments.

[0094] An embodiment of the present application also provides a computer-readable storage medium, on which is stored at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the wind energy resource assessment method for a wind farm provided by the above-mentioned method embodiments.

[0095] An embodiment of the present application also provides a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the method for evaluating wind energy resources in a wind farm as described in any of the above embodiments.

[0096] For the convenience of description, the above systems or devices are described as being divided into various modules or units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0097] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present application or certain parts of the embodiments.

[0098] Finally, it should be noted that, in this document, relational terms such as first, second, third, and fourth are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0099] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for evaluating wind energy resources in a wind farm, characterized in that: include: Use the mesoscale WRF model to obtain mesoscale meteorological analysis data for the wind farm area; Based on the mesoscale meteorological analysis data, the Taylor frozen flow hypothesis is introduced to generate the microscopic turbulence structure of the wind farm area; Determine the local complex terrain area in the wind farm area, use the microscopic turbulence structure as the inflow boundary condition of the microscale CFD model, drive the CFD model to perform refined numerical simulation on the local complex terrain area, and obtain simulation data of the local complex terrain area; Using the simulated data of the microscopic turbulence structure and the local complex terrain area to evaluate wind energy resources; The method of generating a microscopic turbulence structure by introducing the Taylor frozen flow hypothesis based on the mesoscale meteorological analysis data includes: extracting key variables for generating the microscopic turbulence structure from the mesoscale meteorological analysis data; the key variables include at least average wind speed and average wind direction; generating time series turbulence pulsation data based on the average wind speed, converting the time series turbulence pulsation data into spatial distribution data along the spatial variation of the average wind direction, and horizontally and vertically expanding the spatial distribution data to construct a microscopic turbulence structure; Before generating the time series turbulence pulsation data, the method further includes: calculating a correction factor based on WRF terrain data, and adjusting the average wind speed using the correction factor; After obtaining the simulation data of the local complex terrain area, it also includes: using the real wind speed distribution and WRF terrain data in the simulation data to calculate the calibrated correction factor; using the calibrated correction factor to calibrate the average wind speed, and using the calibrated average wind speed to execute the generation of time series turbulence pulsation data.

2. The method according to claim 1, characterized in that The step of converting the time series turbulence pulsation data into spatial distribution data includes: This is achieved according to the following conversion formula: u ′( x )= u ′( t = x / U WRF )； in, u ′( x ) is the distance downstream from the target point x Turbulent pulsation data at the spatial position of U WRF is the average wind speed, u ′( t ) is the turbulent pulsation data corresponding to the time series t.

3. The method according to any one of claims 1-2, characterized in that The method of obtaining mesoscale meteorological analysis data of the wind farm area using the mesoscale WRF model includes: Meteorological reanalysis data are obtained from the reanalysis database and used as the background field of the mesoscale WRF model to perform numerical simulations on the wind farm area and obtain mesoscale meteorological analysis data.

4. A wind energy resource assessment device for a wind farm, characterized in that: The device comprises: An acquisition unit, used for acquiring mesoscale meteorological analysis data of the wind farm area using a mesoscale WRF model; a generating unit, configured to introduce a Taylor frozen flow hypothesis based on the mesoscale meteorological analysis data to generate a microscopic turbulence structure in the wind farm area; A simulation unit is used to determine the local complex terrain area of ​​the wind farm area, and based on the microscopic turbulence structure as the inflow boundary condition of the microscale CFD model, drive the CFD model to perform a refined numerical simulation on the local complex terrain area to obtain simulation data of the local complex terrain area; an evaluation unit for evaluating wind energy resources using the simulated data of the microscopic turbulence structure and the local complex terrain area; The generating unit is specifically configured to extract key variables for generating a microscopic turbulence structure from the mesoscale meteorological analysis data; the key variables include at least an average wind speed and an average wind direction; generate a time series of turbulence pulsation data based on the average wind speed, convert the time series of turbulence pulsation data into spatial distribution data along the spatial variation of the average wind direction, and perform horizontal and vertical expansion on the spatial distribution data to construct a microscopic turbulence structure; The generating unit is further configured to calculate a correction factor based on WRF terrain data, and adjust the average wind speed using the correction factor; The generation unit is further configured to calculate a calibrated correction factor using the actual wind speed distribution and WRF terrain data in the simulation data; calibrate the average wind speed using the calibrated correction factor; and generate the turbulence pulsation data of the time series using the calibrated average wind speed.

5. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the steps of any one of the methods described in claims 1-3.

6. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 3.

7. A computer program product, characterized in that The method comprises a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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