Evaluation method and device for wind energy resources of wind power plant

Abstract

The invention discloses a wind power plant wind energy resource assessment method and device, and belongs to the technical field of wind resource assessment. The method comprises the following steps: acquiring mesoscale meteorological analysis data of a wind power plant region by using a mesoscale WRF mode; according to the mesoscale meteorological analysis data, introducing a Taylor frozen flow hypothesis, and generating a microscopic turbulence structure of the wind power plant region; determining a local complex terrain area of the wind power plant area, and driving the CFD model to perform refined numerical simulation on the local complex terrain area based on the micro turbulence structure as an inflow boundary condition of the micro-scale CFD model to obtain simulation data of the local complex terrain area; and performing wind energy resource assessment by using the simulation data of the micro turbulence structure and the local complex terrain area. According to the invention, the evaluation precision and the evaluation efficiency can be balanced.

Description

Technical Field

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

[0002] In related technologies, the methods for wind resource assessment mostly adopt numerical simulations at a single scale, such as mesoscale meteorological models (WRF) and microscale computational fluid dynamics models (CFD). However, a single scale cannot balance the assessment accuracy and assessment speed in practical applications. Therefore, there is an urgent need to provide an assessment method that takes into account both assessment accuracy and assessment efficiency. Summary of the Invention

[0003] The present invention provides a method and device for assessing wind energy resources in a wind farm, which can solve the problem that the related technologies cannot take into account both assessment accuracy and assessment efficiency at the same time. The technical solutions are as follows: On the one hand, a method for assessing wind energy resources in a wind farm is provided, and the method includes: Obtaining mesoscale meteorological analysis data of the wind farm area by using the mesoscale WRF model; Introducing the Taylor frozen flow hypothesis according to the mesoscale meteorological analysis data to generate the microscale turbulent structure of the wind farm area; Determining the local complex terrain area of the wind farm area, and based on the microscale 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 to obtain the simulation data of the local complex terrain area; Assessing the wind energy resources by using the microscale turbulent structure and the simulation data of the local complex terrain area.

[0004] In a possible implementation manner, the step of introducing the Taylor frozen flow hypothesis according to the mesoscale meteorological analysis data to generate the microscale turbulent structure includes: Extracting key variables for generating the microscale turbulent structure from the mesoscale meteorological analysis data; the key variables at least include the average wind speed and the average wind direction; Generating time-series turbulent pulsation data based on the average wind speed, and converting the time-series turbulent pulsation data into spatial distribution data along the spatial variation of the average wind direction, and performing lateral and vertical expansion on the spatial distribution data to construct the microscale turbulent structure.

[0005] In a possible implementation manner, before generating the time-series turbulent pulsation data, it further includes: calculating a correction factor based on the WRF terrain data and adjusting the average wind speed by using the correction factor.

[0006] In a possible implementation, after obtaining the simulation data of the local complex terrain area, it further includes: Using the true wind speed distribution and WRF terrain data in the simulation data, calculate the calibrated correction factor; Calibrate the average wind speed using the calibrated correction factor, and use the calibrated average wind speed to execute the generation of the time series of turbulent pulsation data.

[0007] In a possible implementation, the conversion of the time series of turbulent pulsation data into spatial distribution data includes: Implement according to the following conversion formula: ; Wherein, is the turbulent pulsation data at the spatial position downstream of the target point x , is the average wind speed, is the turbulent pulsation data corresponding to the time series t.

[0008] In a possible implementation, the obtaining of the mesoscale meteorological analysis data of the wind farm area by using the mesoscale WRF model includes: Obtain meteorological reanalysis data from the 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.

[0009] On the other hand, an evaluation device for the wind energy resource of a wind farm is provided. The device includes: An obtaining unit, configured to obtain mesoscale meteorological analysis data of the wind farm area by using the mesoscale WRF model; A generating unit, configured to generate a microscopic turbulent structure of the wind farm area according to the mesoscale meteorological analysis data by introducing the Taylor frozen flow hypothesis; A simulation unit, configured to determine the local complex terrain area of the wind farm area, and based on the microscopic turbulent 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 to obtain simulation data of the local complex terrain area; An evaluation unit, configured to perform wind energy resource evaluation by using the microscopic turbulent structure and the simulation data of the local complex terrain area.

[0010] On the other hand, a computer device is provided. 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 on the memory to implement the steps of the above-mentioned method for evaluating the wind energy resource of a wind farm.

[0011] On the other hand, a computer-readable storage medium is provided, in which a computer program is stored. 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.

[0012] On the other hand, a computer program product is provided, including a computer program. 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.

[0013] The technical solution provided by the present invention can at least bring the following beneficial effects: By using the mesoscale WRF model 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 microscale turbulence structure of the wind farm area. Considering that the accuracy of the microscale turbulence structure in the complex terrain area is poor, therefore, for the local complex terrain area, the microscale CFD model is used for refined numerical simulation, and the quickly generated microscale turbulence structure is used as the inflow boundary condition of the microscale CFD model, so that the simulation data of the local complex terrain area can be quickly obtained, and then the wind energy resources are evaluated by using the microscale turbulence structure and the simulation data of the local complex terrain area, which can achieve the balance of evaluation accuracy and evaluation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1 is a flowchart of a method for evaluating wind energy resources in a wind farm provided by an embodiment of the present invention; Figure 2 is a flowchart of a method for generating a microscale turbulence structure provided by an embodiment of the present invention; Figure 3 is a structural diagram of an apparatus for evaluating wind energy resources in a wind farm provided by an embodiment of the present invention; Figure 4 is a hardware architecture diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] To make the objectives, 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 with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] Please refer to Figure 1 , an evaluation method for wind energy resources in a wind farm provided by an embodiment of the present invention, the method includes: Step 100, obtaining mesoscale meteorological analysis data of the wind farm area by using the mesoscale WRF model; Step 102, introducing the Taylor frozen flow hypothesis according to the mesoscale meteorological analysis data to generate the microscale turbulence structure of the wind farm area; Step 104, determining the local complex terrain area of the wind farm area, and based on the microscale turbulence 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 to obtain the simulation data of the local complex terrain area; Step 106, performing wind energy resource evaluation by using the microscale turbulence structure and the simulation data of the local complex terrain area.

[0018] In the embodiments of the present invention, mesoscale meteorological analysis data of the wind farm area is obtained by using the mesoscale WRF model. After obtaining the mesoscale meteorological analysis data, the Taylor frozen flow hypothesis is introduced to quickly generate the microscale turbulence structure of the wind farm area. Considering that the accuracy of the microscale turbulence structure in the complex terrain area is poor, a microscale CFD model is used to perform refined numerical simulation on the local complex terrain area, and the quickly generated microscale turbulence structure is used as the inflow boundary condition of the microscale CFD model, so that the simulation data of the local complex terrain area can be quickly obtained. Furthermore, wind energy resource evaluation is performed by using the microscale turbulence structure and the simulation data of the local complex terrain area, which can achieve the balance of evaluation accuracy and evaluation efficiency.

[0019] The following describes Figure 1 the execution manners of the following steps.

[0020] First, for step 100, mesoscale meteorological analysis data of the wind farm area is obtained by using the mesoscale WRF model.

[0021] In the embodiments of the present invention, meteorological reanalysis data can be obtained from the reanalysis database, and the meteorological reanalysis data is used as the background field of the mesoscale WRF model to perform numerical simulation on the wind farm area to obtain mesoscale meteorological analysis data.

[0022] Among them, the mesoscale meteorological analysis data includes at least hourly time series data for more than one year. Among them, the time series data includes wind force data such as wind speed and wind direction.

[0023] Then, steps 102 "According to the mesoscale meteorological analysis data, introduce the Taylor frozen flow hypothesis to generate a micro-turbulence structure" and step 104 "Determine the local complex terrain area in the wind farm area, and based on the micro-turbulence structure as the inflow boundary condition of the micro-scale CFD model, drive the CFD model to perform refined numerical simulation on the local complex terrain area to obtain the simulation data of the local complex terrain area" are described simultaneously.

[0024] The Taylor frozen flow hypothesis holds that when the turbulent structure of the flow advects at a certain speed in the mean flow field, it can be assumed that the turbulent field is frozen in a short period of time, that is, it does not change with time. In this way, spatial measurement data can be inferred from time changes.

[0025] The assessment of wind energy resources usually requires measuring the wind speed at different heights or setting up wind measurement towers at different locations. The Taylor frozen flow hypothesis can be used to convert the time series data at a single point into spatial information. For example, assuming that the turbulent structure is advected by the mean wind speed, the wind speed change at different locations can be estimated by time delay. For example, if the mean wind speed is U, then at a downstream point at a distance x the time change of the wind speed may be equivalent to the time series delay of the upstream point x / U. 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.

[0026] Specifically, please refer to Figure 2 This step may include: Step 1020: Extract the key variables for generating the micro-turbulence structure from the mesoscale meteorological analysis data; the key variables include at least the mean wind speed and the mean wind direction; Step 1022: Generate time series turbulent pulsation data based on the mean wind speed, and convert the time series turbulent pulsation data into spatial distribution data along the spatial variation of the mean wind direction, and perform lateral and vertical expansion on the spatial distribution data to construct a micro-turbulence structure.

[0027] In the embodiment of the present invention, when generating the turbulent pulsation data, at least the following two methods may be included: Method 1, if there are measured data provided by a wind measurement tower, the measured data is used to generate the turbulent pulsation data; In this method 1, the pulsation 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; Method 2: If there is no measured data provided by the anemometer tower, the mesoscale meteorological analysis data is used to generate the turbulent pulsation data.

[0028] In this Method 2, 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 turbulent pulsation data of the time series is synthesized.

[0029] After obtaining the turbulent pulsation data of the time series in the above two methods, according to the Taylor frozen flow hypothesis, along the spatial variation of the mean wind direction ( x axis), the separated pulsation component is mapped from the time series to the spatial distribution: u ′( x ) = u ′( t = x / U WRF ). u ′( x ) is the turbulent pulsation data at the spatial position downstream x from the target point. In Method 1, the target point is the actual anemometry point, and in Method 2, the target point is the virtual anemometry point.

[0030] After obtaining the spatially distributed data through transformation, when performing three-dimensional expansion in the transverse (y-axis) direction, coherent pulsations can be generated through the exponential correlation function; when performing three-dimensional expansion in the longitudinal (z-axis) direction, the turbulent profile can be adjusted based on the WRF wind shear.

[0031] Furthermore, considering that the actual terrain is not completely flat, complex terrains (mountains, canyons) will affect the wind field, resulting in uneven spatial distribution of wind speed. For example, when the wind passes over a mountain ridge, it will accelerate due to the terrain uplift effect, while in the leeward slope or canyon, the wind speed will decrease and turbulence will be generated. Thus, the advection velocity directly generated using the Taylor frozen flow hypothesis cannot accurately reflect the actual wind speed distribution because the wind speed changes with position under complex terrains. Considering that the terrain data in WRF usually includes parameters such as surface elevation, slope, and roughness, these parameters have a significant impact on the local wind field. For example, higher terrain may cause the wind speed to accelerate (terrain acceleration effect), while rough surfaces (such as forests) will increase surface friction and reduce the wind speed. When generating the micro-turbulence structure using the Taylor frozen flow hypothesis, if the uncorrected WRF average wind speed is directly used, the terrain effect will be ignored, resulting in the micro-turbulence field deviating from the actual situation. Therefore, the impact of the terrain data provided by WRF on the average wind speed U needs to be considered.

[0032] Based on this, in one embodiment of the present invention, before generating time series turbulent pulsation data based on the average wind speed, it may further include: calculating a correction factor based on WRF terrain data, and adjusting the average wind speed using the correction factor. The adjustment method is as follows: ; where is the adjusted average wind speed, is the correction factor. In this way, the actual influence of the terrain on the wind speed can be more accurately reflected.

[0033] 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 velocity so that the spatial distribution of the turbulent structure generated by Taylor's hypothesis is more in line with the actual situation.

[0034] In the embodiment of the present invention, the correction factor of the terrain can be calibrated by the wind speed data simulated by WRF and the actual observed data, or by the refined simulation of the CFD model in the local area. Considering that the CFD model is also needed to perform refined simulation on the local complex terrain area later, therefore, the correction factor in the embodiment of the present invention can be calibrated by the simulation data of the refined simulation of the CFD model in the local complex terrain area.

[0035] Specifically, in the embodiment of the present invention, by determining the local complex terrain area of the wind farm area, and then based on the micro-turbulent structure as the inflow boundary condition of the micro-scale CFD model, driving the CFD model to perform refined numerical simulation on the local complex terrain area to obtain the simulation data of the local complex terrain area; In one implementation manner, the local complex terrain area of the wind farm area can be determined by WRF terrain data; in another implementation manner, the local complex terrain area of the wind farm area can also be determined by satellite data.

[0036] After determining the local complex terrain area, the current micro-turbulent structure can be directly used as the inflow boundary condition of the micro-scale CFD model to drive the CFD model to perform refined numerical simulation on the local complex terrain area to obtain the 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 true wind speed distribution of 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 to generate the micro-turbulent structure using the adjusted average wind speed again.

[0037] That is to say, in the preferred embodiment of the present invention, the generation method of the micro-turbulent structure includes: S1: Extract key variables for generating microscale turbulence structures from the mesoscale meteorological analysis data; the key variables include at least the mean wind speed and the mean wind direction; S2: Calculate a correction factor using the WRF terrain data, and correct the mean wind speed using the correction factor; S3: Generate time-series turbulent pulsation data based on the corrected mean wind speed, and convert the time-series turbulent pulsation data into spatial distribution data along the spatial variation of the mean wind direction, and perform lateral and vertical expansion on the spatial distribution data to construct a microscale turbulence structure; S4: Determine the local complex terrain area in the wind farm area, and based on the microscale turbulence structure as the inflow boundary condition of the microscale CFD model, drive the CFD model to perform refined numerical simulation for the local complex terrain area to obtain simulation data for the local complex terrain area; S5: Calculate a calibrated correction factor based on the actual wind speed distribution in the simulation data and the WRF terrain data; S6: Calibrate the mean wind speed using the calibrated correction factor, and perform S3 using the calibrated mean wind speed, so as to construct a first-calibrated microscale turbulence structure.

[0038] Furthermore, the first-calibrated microscale turbulence structure can be used to continue to execute step S4, and the first-calibrated microscale turbulence structure is used as the inflow boundary condition of the microscale CFD model to drive the CFD model to perform refined numerical simulation for the local complex terrain area to obtain the first-calibrated simulation data for the local complex terrain area.

[0039] Even further, the first-calibrated simulation data can be used to continue to execute steps S5, S6, and S3, so as to construct a second-calibrated microscale turbulence structure.

[0040] After at least two calibrations, a more accurate microscale turbulence structure can be obtained. Then, use the microscale turbulence structure after at least two calibrations to execute step 104, so as to obtain more accurate simulation data for the local complex terrain area.

[0041] In step 106, the wind energy resource assessment can be carried out jointly through the simulation data of the local complex terrain area and the microscale turbulence structure of the remaining area, or the microscale turbulence structure of the wind farm area can be directly used for assessment.

[0042] In the embodiments of the present invention, by introducing a correction factor and calibrating the correction factor, the topographic coupling of the mesoscale meteorological field and the micro-turbulence generation process can be achieved, which can improve the restoration of the actual wind field and ensure the reliability of subsequent micro-turbulence analysis and wind turbine layout optimization. And by introducing the Taylor frozen flow hypothesis and combining it with the mesoscale and microscale, the balance between evaluation accuracy and evaluation efficiency can be achieved.

[0043] Please refer to Figure 3 , embodiments of the present invention provide an evaluation device for wind energy resources in a wind farm, and the device includes: An acquisition unit 300, configured to obtain mesoscale meteorological analysis data of a wind farm area by using a mesoscale WRF model; A generation unit 302, configured to introduce the Taylor frozen flow hypothesis according to the mesoscale meteorological analysis data to generate a micro-turbulence structure of the wind farm area; A simulation unit 304, configured to determine a locally complex terrain area of the wind farm area, and based on the micro-turbulence structure as an inflow boundary condition of a microscale CFD model, drive the CFD model to perform refined numerical simulation on the locally complex terrain area to obtain simulation data of the locally complex terrain area; An evaluation unit 306, configured to perform wind energy resource evaluation by using the micro-turbulence structure and the simulation data of the locally complex terrain area.

[0044] In an embodiment of the present invention, the generation unit is specifically configured to: extract key variables for generating a micro-turbulence structure from the mesoscale meteorological analysis data; the key variables at least include an average wind speed and an average wind direction; generate time-series turbulent pulsation data based on the average wind speed, and convert the time-series turbulent pulsation data into spatial distribution data along the spatial variation of the average wind direction, and perform lateral and vertical expansion on the spatial distribution data to construct a micro-turbulence structure.

[0045] In an embodiment of the present invention, the generation unit is further configured to calculate a correction factor based on WRF terrain data and adjust the average wind speed by using the correction factor.

[0046] In an embodiment of the present invention, the generation unit is further configured to calculate a calibrated correction factor by using the real wind speed distribution in the simulation data and the WRF terrain data; calibrate the average wind speed by using the calibrated correction factor, and execute the generation of the time-series turbulent pulsation data by using the calibrated average wind speed.

[0047] In an embodiment of the present invention, when the generation unit executes the conversion of the time-series turbulent pulsation data into spatial distribution data, it specifically includes: implementing according to the following conversion formula: ; Among them, is the turbulent pulsation data at the spatial position downstream of the target point x , and is the average wind speed, and

[0048] In an embodiment of the present invention, the obtaining unit is specifically configured 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.

[0049] It should be noted that: the evaluation device for wind farm wind energy resources provided in the above embodiment is only illustrated by dividing the above functional modules. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the evaluation device for wind farm wind energy resources provided in the above embodiment and the embodiment of the evaluation method for wind farm wind energy resources belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be elaborated here.

[0050] An embodiment of the present application further provides a computer device. Please refer to Figure 4 , the computer device includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the evaluation method for wind farm wind energy resources provided in the above method embodiments.

[0051] An embodiment of the present application further provides a computer-readable storage medium. At least one instruction, at least one program, a code set or an instruction set is stored on the computer-readable storage medium, and at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the evaluation method for wind farm wind energy resources provided in the above method embodiments.

[0052] An embodiment of the present application further provides a computer program product. The computer program product includes a computer program. The processor of the computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the evaluation method for wind farm wind energy resources described in any one of the above embodiments.

[0053] For the convenience of description, when describing the above system or device, various modules or units are described separately according to functions. Of course, when implementing the present application, the functions of each unit can be realized in the same or multiple software and / or hardware.

[0054] As can be seen from the description of the above embodiments, 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 such an understanding, the technical solution of the present application, in essence, or the part that makes contributions to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present application.

[0055] Finally, it should also be noted that in this article, relational terms such as first, second, third, and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0056] The above are only the preferred embodiments of the present application. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope 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 in the wind farm area; Based on the mesoscale meteorological analysis data, the Taylor freezing flow hypothesis is introduced to generate the microscopic turbulence structure of the wind farm area; Determine the local complex terrain area of ​​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; The wind energy resource assessment is carried out using the simulated data of the microscopic turbulence structure and the local complex terrain area.

2. The method according to claim 1, characterized in that: The method introduces the Taylor frozen flow hypothesis based on the mesoscale meteorological analysis data to generate a microscopic turbulent structure, including: Extracting key variables for generating microscopic turbulence structure from the mesoscale meteorological analysis data; the key variables at least include average wind speed and average wind direction; Based on the average wind speed, a time series of turbulence pulsation data is generated, and along the spatial variation of the average wind direction, the time series of turbulence pulsation data is converted into spatial distribution data, and the spatial distribution data is expanded laterally and vertically to construct a microscopic turbulence structure.

3. The method according to claim 2, characterized in that 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.

4. The method according to claim 3, characterized in that After obtaining the simulation data of the local complex terrain area, the method further includes: Calculating a calibrated correction factor using the actual wind speed distribution in the simulation data and WRF terrain data; 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.

5. The method according to claim 2, characterized in that: The step of converting the time series turbulence pulsation data into spatial distribution data comprises: This is achieved according to the following conversion formula: ； in, Downstream distance from the target point x Turbulence pulsation data at the spatial position of is the average wind speed, is the turbulent pulsation data corresponding to the time series t.

6. The method according to any one of claims 1 to 5, characterized in that: The method of obtaining mesoscale meteorological analysis data of the wind farm area by 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 simulation on the wind farm area and obtain mesoscale meteorological analysis data.

7. 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 by using a mesoscale WRF model; A generating unit, used for introducing Taylor frozen flow hypothesis according to the mesoscale meteorological analysis data to generate a microscopic turbulent structure of 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 refined numerical simulation on the local complex terrain area to obtain simulation data of the local complex terrain area; An evaluation unit is used to evaluate wind energy resources using the simulated data of the microscopic turbulence structure and the local complex terrain area.

8. 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 1-6.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

10. 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 6 are implemented.

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