A wind resource assessment method for complex terrain based on correction of measured data from wind towers

Abstract

The present invention discloses a complex terrain wind resource assessment method based on the correction of measured data of a wind tower, comprising: simulating a complex terrain wind field based on a mesoscale WRF model, obtaining the mesoscale data of the innermost nested domain containing a wind farm; extracting the boundary inflow information of a microscale calculation domain based on the mesoscale data obtained in step S1, and obtaining the initial predicted field information of a complex terrain wind field based on a meso-microscale coupling model; determining the reference height of the wind tower for data correction and its corresponding observed wind speed and turbulence intensity according to the position of the wind tower and the flow field characteristics; and correcting the WRF-CFD numerical simulation results obtained in step S2 using the relevant data of the wind tower determined in step S3 to obtain the corrected wind speed and turbulence information of the complex terrain wind field. The present invention can improve the meso-microscale coupling numerical simulation results and greatly improve the accuracy of wind resource assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a complex terrain wind resource assessment method based on correction of measured data from a wind tower. Background Art

[0002] Due to the scarcity of abundant wind resources on land and the high construction cost of deep-sea wind farms, complex terrain areas also have good wind resources, so complex terrain wind farms have gradually become a major direction for wind farm site selection. Complex terrain wind farms have significant multi-scale and unsteady effects, with characteristics such as wind acceleration, flow separation, and high turbulence. The simulation method of unsteady characteristics of complex terrain based on the coupling mode of the mesoscale meteorological meteorological research and forecasting (Weather Research and Forecasting, WRF) platform and the small-scale computational fluid dynamics (Computational Fluid Dynamics, CFD) method can take into account the simulation of mesoscale weather conditions and the influence of microscale mountain flow on complex terrain wind fields. However, the accuracy of the meso-microscale coupling model is heavily dependent on the accuracy of mesoscale numerical simulation. The simulation accuracy of the mesoscale WRF (Weather Research and Forecasting, Meteorological Research and Forecasting) model is affected by weather processes and parameterization schemes. In the event of strong winds or extreme weather, its accuracy will be seriously affected.

[0003] In order to improve the accuracy of meso-microscale coupled simulation results, many scholars have focused on the optimization of parameterization schemes, grid scale sensitivity analysis, and parameter model optimization in mesoscale numerical simulations. However, the above methods are only valid in specific meteorological conditions and are not universal. Especially in the simulation of wind fields in complex terrain, terrain resolution and mountain features will also have a great impact on the numerical results. Although adding observation data to the WRF numerical simulation process through data assimilation can improve the WRF simulation accuracy to a certain extent, this method has high computational costs and a lack of high-altitude observation data, so the assimilation effect of this method is limited. In terms of microscale numerical simulations, turbulence models and grid resolution have a great impact on the numerical results of wind fields in complex terrains, but the impact of inflow boundary conditions from the mesoscale WRF simulation results is the most direct and obvious.

[0004] The invention with publication number CN109583096A discloses a wind resource calculation method based on the combination of mesoscale model and microscale model. Although it involves some mesoscale-microscale coupling simulation related content and the correction of microscale numerical simulation results, the invention does not clearly point out what kind of relationship is established between the mesoscale data and the wind tower, how to make specific corrections; nor does it clearly point out how the mesoscale-microscale is coupled. The invention with publication number CN118296981A discloses a wind energy resource assessment method, system, device, electronic device and storage medium, and proposes to combine wind speed observation data into mesoscale and microscale simulation, but the invention does not involve mesoscale-microscale coupling. The invention with publication number CN118735078A discloses a downscaling wind resource assessment method and system. Although it also involves downscaling numerical simulation methods and corrections to downscaling results, it does not clearly point out how to downscale; and this patent is not a dynamic downscaling method, but focuses more on artificial intelligence and the establishment of training models. Summary of the invention

[0005] The purpose of the present invention is to provide a complex terrain wind resource assessment method based on the correction of measured data from a wind tower. By combining high-precision numerical simulation methods and efficient data correction technology, the data results can not only reflect the mesoscale atmospheric circulation, but also describe the microscale flow details, providing more reliable data support for wind resource assessment.

[0006] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0007] A complex terrain wind resource assessment method based on correction of measured data from a wind tower, the method comprising the following steps:

[0008] S1, simulate the wind farm in complex terrain based on the mesoscale WRF model to obtain the mesoscale data of the innermost nested domain containing the wind farm;

[0009] S2, extracting the boundary inflow information of the microscale calculation domain according to the mesoscale data of the innermost nested domain obtained in step S1, and obtaining the initial prediction field information of the complex terrain wind field based on the meso-microscale coupling model;

[0010] S3, according to the location of the wind tower and the flow field characteristics, determine the wind tower related data including the reference height of the wind tower and its corresponding observed wind speed and turbulence intensity for data correction;

[0011] S4, using the wind tower related data determined in step S3 to correct the initial forecast field information of the complex terrain wind field obtained in step S2, to obtain the corrected wind speed and turbulence information of the complex terrain wind field; the correction formula is:

[0012] ;

[0013] ;

[0014] Where: and is the corrected wind speed and turbulence intensity, and are the wind speed and turbulence intensity obtained from meso-microscale numerical simulations, and is the observed wind speed and turbulence intensity corresponding to the reference height of the wind tower, and It is the WRF-CFD coupled numerical simulation result corresponding to the reference height of the wind tower.

[0015] Step S1 further comprises:

[0016] Determine the reference point longitude and latitude and time period for mesoscale numerical simulation;

[0017] Obtain conventional terrain static data and meteorological data;

[0018] Configure parameterized solutions based on local geographical environment and climate characteristics;

[0019] Obtain high-precision SRTM data containing wind farms and replace the conventional terrain data in the innermost nested domain. The outer nested domain does not need to be replaced.

[0020] Mesoscale WRF model numerical simulation of wind field in complex terrain based on terrain, meteorology and computing configuration scheme;

[0021] During the simulation, the mesoscale data of the innermost nested domain containing the wind farm is saved.

[0022] Furthermore, the process of configuring the parameterized scheme according to the local geographical environment and climate characteristics includes the following steps:

[0023] For complex mountainous areas, the Thompson format and WDM6 are selected; for longwave and shortwave radiation, the RRTMG+RRTMG scheme is selected; for the near-surface layer, the Monin-Obukhov scheme is used, combined with the MYJ planetary boundary layer scheme; for areas where the annual average humidity is greater than the preset humidity threshold, the MYNN 2.5 level TKE planetary layer scheme is selected, combined with the MYNN near-surface layer scheme.

[0024] Step S2 further comprises:

[0025] Determine the range of the microscale computational domain or the central reference point, and generate a microscale computational domain grid;

[0026] Extract the wind speed, temperature, and turbulent kinetic energy information corresponding to the boundary grid of the microscale calculation domain from the mesoscale calculation data obtained in step S1, and generate boundary inflow pulsating wind time series data;

[0027] Based on the computational domain grid and boundary inflow pulsating wind time series data, the Navier-Stokes (NS) equations for microscale calculation are numerically discretized in time and space to solve the flow field in the microscale computational domain and obtain the initial prediction field of the wind field.

[0028] Furthermore, the scope of the microscale calculation domain is determined according to the target research area and the mountain height, and there is a distance between the leeward slope and the outlet boundary to ensure stable development of turbulence on the leeward slope.

[0029] Furthermore, in step S2, the process of generating the microscale computational domain grid includes the following steps:

[0030] The meso-microscale terrain is fused to obtain a high-precision stl terrain file, and then the blockMesh tool of OpenFOAM is used to generate the background mesh. Finally, the snappyHexMesh tool is used to complete the overall mesh and near-ground mesh encryption process in the microscale calculation domain.

[0031] Furthermore, the process of generating the pulsating wind time series data includes the following steps:

[0032] The spatiotemporal series of wind speed, temperature and turbulent kinetic energy parameters corresponding to the boundary of the microscale calculation domain are extracted from the WRF simulation results. The extracted data are imported into the boundary of the CFD simulation calculation domain through time and space interpolation for microscale calculation to generate pulsating wind time series data.

[0033] Furthermore, large eddy simulation (LES) or delayed detached eddy simulation (DDES) is used to solve the turbulent field in the microscale computational domain.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The technical purpose of the present invention is to further improve the results of meso-microscale coupling numerical simulation by using the correction technology of the measured data of the wind tower, so as to improve the accuracy of wind resource assessment. Different from the meso-microscale coupling, downscaling method, and correction of wind tower data mentioned in the prior art, the present invention generates a pulsating wind that is more in line with the real atmospheric boundary layer flow through mesoscale data, and provides boundary conditions for microscale numerical simulation to achieve the refined simulation of complex terrain wind field. Considering that the accuracy of these mesoscale data is affected when it is cloudy, rainy or there is a strong wind process, which in turn affects the accuracy of meso-microscale coupling, in order to ensure the results of meso-microscale numerical simulation, the present invention further proposes a new method for correcting the measured data of the wind tower, and further improves the accuracy of wind field simulation by combining the meso-microscale simulation results. Compared with the prior art, the present invention combines high-precision numerical simulation methods and efficient data correction technology, and the data results can not only reflect the mesoscale atmospheric circulation, but also describe the details of microscale unsteady flow, providing more reliable data support for wind resource assessment. By comparing with the measured data of the wind field, it is found that the present invention can not only well correct the vertical distribution characteristics of the average wind speed and turbulent kinetic energy obtained by WRF-CFD, but also effectively correct the time-series pulsating wind speed, which can further improve the simulation accuracy of WRF-CFD coupling. The method is simple in form, easy to encode and efficient in calculation, and its calculation accuracy is better than the numerical simulation results of WRF and WRF-CFD model methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The flowchart of the wind resource assessment method for complex terrain based on the correction of measured data of a wind tower of the present invention;

[0037] Figure 2 This is a schematic diagram of the altitude distribution of complex mountain wind fields;

[0038] Figure 3 It is a schematic diagram of the mesoscale WRF nesting setup;

[0039] Figure 4a is a schematic diagram of the microscale computational domain and grid;

[0040] Figure 4b yes Figure 4a An enlarged schematic diagram of a local area A in FIG.

[0041] Figure 5a This is a schematic diagram of the correction results of the average wind speed profile at the radar position in the wind field (2022.12.21 15:10-15:50) (wind speed profile in the calculation domain);

[0042] Figure 5b This is a schematic diagram of the correction result of the average wind speed profile at the radar position of the wind field (2022.12.21 15:10-15:50) (locally enlarged wind speed profile 200 m near the ground);

[0043] Figure 6a This is a schematic diagram of the correction results of the turbulence intensity profile at the radar position in the wind field (2022.12.21 15:10-15:50) (wind speed profile in the calculation domain);

[0044] Figure 6b This is a schematic diagram of the correction results of the turbulence intensity profile at the radar position of the wind field (2022.12.21 15:10-15:50) (locally enlarged wind speed profile 200 m near the ground);

[0045] Figure 7 This is a schematic diagram of the time-series pulsating wind conditions at the radar position in the wind field (2022.12.21 15:10-15:50) (from top to bottom, the heights are 40 m, 61 m, 101 m and 143 m respectively). DETAILED DESCRIPTION

[0046] The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0047] The present invention discloses a complex terrain wind resource assessment method based on correction of measured data from a wind tower, the method comprising the following steps:

[0048] S1, simulate the wind farm in complex terrain based on the mesoscale WRF model to obtain the mesoscale data of the innermost nested domain containing the wind farm;

[0049] S2, extracting the inflow information of the microscale calculation domain boundary according to the mesoscale data obtained in step S1, and obtaining the initial prediction field information of the complex terrain wind field based on the mesoscale-microscale coupling model;

[0050] S3, according to the location of the wind tower and the flow field characteristics, determine the reference height of the wind tower used for data correction and its corresponding observed wind speed and turbulence intensity;

[0051] S4, using the wind tower related data determined in step S3 to correct the WRF-CFD numerical simulation results obtained in step S2, to obtain the corrected wind speed and turbulence information of the complex terrain wind field; the correction formula is:

[0052] ;

[0053] ;

[0054] Where: and is the corrected wind speed and turbulence intensity, and are the wind speed and turbulence intensity obtained from meso-microscale numerical simulations, and Observed wind speed and observed turbulence intensity corresponding to the reference height, and WRF-CFD coupled numerical simulation results corresponding to the reference height.

[0055] See also Figure 1 The complex terrain wind resource assessment method of the present invention specifically includes:

[0056] Step 1: Simulate the complex terrain wind farm based on the mesoscale WRF model to obtain the mesoscale data of the innermost nested domain containing the wind farm. Specifically, the following steps are included:

[0057] Determine the reference point longitude and latitude and time period for mesoscale numerical simulation.

[0058] Obtain conventional terrain static data and meteorological data;

[0059] Configure the parameterization scheme according to the local geographical environment and climate characteristics. When configuring the parameterization scheme, for complex mountains, it is recommended to choose the Thompson format (Thompson graupel scheme) and WDM6; for long-wave and short-wave radiation, the RRTMG+ RRTMG scheme is more detailed and relatively accurate, and its combination is preferred; the Monin-Obukhov scheme is generally used for the near-surface layer, and the MYJ planetary boundary layer scheme is used in combination. It is suitable for most mountain simulations and can capture detailed mountain boundary layer results. However, for areas where the annual average humidity is greater than the preset humidity threshold, the wind speed results obtained by the MYJ planetary boundary layer scheme have large deviations due to the large influence of humidity. At this time, the MYNN 2.5level TKE planetary layer scheme can be selected, and the MYNN near-surface layer scheme can be used in combination.

[0060] Obtain high-precision SRTM data containing wind farms and replace the conventional terrain data in the innermost nested domain. The outer nested domain does not need to be replaced.

[0061] Based on the terrain, meteorology and computing configuration scheme, the mesoscale WRF model is used to simulate the wind field in complex terrain. Monitoring data needs to be configured during the simulation process.

[0062] During the simulation process, the innermost nested domain data including the wind farm is saved, and the innermost nested domain data is saved every 10 minutes.

[0063] Step 2: Extract the inflow information of the microscale computational domain boundary based on the mesoscale data obtained in step S1, and obtain the initial forecast field information of the complex terrain wind field based on the mesoscale-microscale coupling model. Specifically, the following steps are included:

[0064] Determine the scope of the microscale calculation domain or the central reference point. The scope of the microscale calculation domain should be determined according to the target research area and the mountain height. There should be a distance between the leeward slope and the outlet boundary so that the turbulence development on the leeward slope is stable.

[0065] Generate micro-scale computational domain mesh. The micro-scale computational domain mesh is automatically generated using the snappyHexMesh tool in the OpenFOAM code. To ensure the accuracy of the inflow boundary conditions, meso-microscale terrain fusion is required before generating the mesh.

[0066] The wind speed, temperature, turbulent kinetic energy and other information corresponding to the boundary grid of the microscale calculation domain are extracted from the mesoscale calculation data obtained in step 1, and a time-series pulsating inlet is generated. The wind speed and turbulent kinetic energy data obtained from the mesoscale data are used to generate pulsating wind time series data to make the inflow more consistent with the real atmospheric boundary layer characteristics.

[0067] Based on the computational domain grid and the fluctuating wind speed of the boundary inflow, the Navier-Stokes (NS) equations of microscale calculation are numerically discretized in time and space to solve the flow field in the microscale computational domain, obtain the initial prediction field of the wind field, and use LES or DDES to solve the turbulent field in the microscale computational domain.

[0068] Step 3: Determine the reference height of the wind tower for data correction and its corresponding observed wind speed and turbulence intensity. Specifically, the following steps are included:

[0069] According to the location of the wind tower and the flow field characteristics, the reference height for initial prediction correction and the wind speed and turbulence intensity data corresponding to this height are determined. It is necessary to determine the meso-microscale coupled and measured reference wind speed and reference turbulence intensity information respectively.

[0070] Step 4: Use the wind tower related data determined in step S3 to correct the WRF-CFD numerical simulation results obtained in step S2 to obtain the corrected wind speed and turbulence information of the complex terrain wind field. The correction formula is defined as follows:

[0071] ;

[0072] ;

[0073] Where: and is the corrected wind speed and turbulence intensity, and are the wind speed and turbulence intensity obtained from meso-microscale numerical simulations, and Observed wind speed and observed turbulence intensity corresponding to the reference height, and WRF-CFD coupled numerical simulation results corresponding to the reference height.

[0074] It should be noted that this correction method can be used not only for correction of average wind speed and turbulence intensity profile data, but also for correction of pulsating wind.

[0075] Examples

[0076] The wind farm area used for method verification is hilly and is a good case for studying the flow characteristics of complex mountain wind farms. The wind farm terrain is composed of continuous mountains (such as Figure 2 The highest altitude is 1824 m and the lowest is 1222 m. The main vegetation types are farmland, grassland, bushes and forests, and the average surface roughness length is 0.0624 m. Figure 2 The wind resource assessment process for the wind farm area in this example includes the following steps:

[0077] Step 1: Simulate the complex terrain wind field based on the mesoscale WRF model to obtain the mesoscale data of the innermost nested domain containing the wind farm.

[0078] The reference point for the mesoscale numerical simulation is determined to be (112.70750004°E, 37.22866700°N).

[0079] The climate input data for mesoscale numerical simulation is the fifth generation data (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), with a spatial resolution of 0.25° and a temporal resolution of 1 hour. The static data of terrain category and terrain height used are taken from the data of the United States Geological Survey (USGS), with a spatial resolution of 30" (d04 is replaced by SRTM terrain data. In this case, d04 is the innermost nested domain of WRF, and d01-d03 are the outermost nested area, the second outermost nested area, and the second innermost nested area, respectively). The terrain of the target area is relatively complex, all of which are continuous mountains. The mesoscale numerical simulation adopts four-layer nesting, and the target simulation time period is from 00:00 on December 19, 2022 to 00:00 on December 22, 2022 (UTC time). The parameter schemes of the main physical processes are set as follows:

[0080] Four-layer nesting (recommended): spatial resolution: 27 km; 9 km; 3 km; 1 km; temporal resolution: 60s; 20s; 6.67s; 2.22s; vertical height layers: 56 layers;

[0081] Output time step: target simulation time period 10min;

[0082] Microphysical process scheme: WSM 6-class graupel scheme;

[0083] Long wave radiation: RRTMG scheme;

[0084] Shortwave radiation: RRTMG shortwave;

[0085] Near-ground floor scheme: Monin-Obukhov (Janjic Eta) scheme;

[0086] Land surface process scheme: Noah (Unified Noah Land Surface Model);

[0087] Planetary boundary layer scheme: MYJ (Mellor-Yamada-Janjic (Eta) TKE scheme);

[0088] Cumulus convection parameterization scheme: Kain-Fritsch (new Eta) (Kain–Fritsch Scheme).

[0089] Numerical simulation is carried out based on the WRF model, and monitoring data is configured during the simulation process.

[0090] The data of the innermost nested domain (d04) containing the wind farm is saved. The data of the innermost nested domain is saved every 10 minutes.

[0091] Step 2: extract the inflow information of the microscale calculation domain boundary according to the mesoscale data obtained in step S1, and obtain the initial prediction field information of the complex terrain wind field based on the mesoscale-microscale coupling model.

[0092] The height of the microscale computational domain is 2000 m, the horizontal domain size is set to 5 km × 5 km, and a uniform grid is generated in vertical layers: in the near-ground range covering the mountain, a fine grid is used with a horizontal resolution of 4 m and a vertical resolution of 2.5 m; above this layer, the horizontal resolution of the grid is set to 16 m and the vertical resolution is 10 m. The global grid is as follows: Figure 4a and Figure 4b shown.

[0093] After the fusion of meso- and micro-scale terrain, a high-precision stl terrain file is obtained, and then the blockMesh tool of OpenFOAM is used to generate the background mesh. Finally, the snappyHexMesh tool is used to complete the overall mesh and near-ground mesh encryption process in the micro-scale calculation domain.

[0094] The time-space series of parameters such as wind speed, temperature and turbulent kinetic energy corresponding to the boundary of the microscale calculation domain are extracted from the WRF simulation results, and then imported into the boundary of the CFD simulation calculation domain through time and space interpolation for microscale calculation. The wind speed and turbulent kinetic energy data obtained from the mesoscale data generate pulsating wind time series data to make the inflow more consistent with the characteristics of the real atmospheric boundary layer.

[0095] PISO-SIMPLE is used to solve the momentum and pressure equations "implicitly", the prediction-correction method uses the Rhie-Chow interpolation method, and the temperature equation is added to the correction loop. The time discretization uses the time domain backward difference method, the gradient term, convection term, and divergence term are discretized using the Gaussian linear interpolation format, and the Laplace term is discretized using the Gaussian linear interpolation correction format.

[0096] Step 3: Determine the reference height of the wind tower for data correction and its corresponding observed wind speed and turbulence intensity according to the location of the wind tower and the flow field characteristics.

[0097] According to the location of the wind tower and the flow field characteristics, the reference height for initial prediction correction and the wind speed and turbulence intensity data corresponding to this height are determined. It is necessary to determine the reference wind speed and reference turbulence intensity information for meso-microscale coupled simulation and actual measurement respectively.

[0098] In this case, the reference height is 101m. The measured wind speed corresponding to the reference height is 10.96m / s, and the wind speed simulated by WRF-CFD coupling is 15.10m / s. The measured turbulence intensity corresponding to the reference height is 0.3239, and the turbulence intensity simulated by WRF-CFD coupling is 0.2509.

[0099] Step 4: Use the wind tower related data determined in step S3 to correct the WRF-CFD numerical simulation results obtained in step S2 to obtain the corrected wind speed and turbulence information of the complex terrain wind field. The correction formula is defined as follows:

[0100] ;

[0101] ;

[0102] Where: and is the corrected wind speed and turbulence intensity, and are the wind speed and turbulence intensity obtained from meso-microscale numerical simulations, and Observed wind speed and observed turbulence intensity corresponding to the reference height, and WRF-CFD coupled numerical simulation results corresponding to the reference height.

[0103] In order to verify the accuracy of the correction method, the average wind speed and turbulence intensity at the radar location were corrected respectively. In addition, the time-series pulsating wind at the heights of 40m, 61m, 101m and 143m was corrected and analyzed.

[0104] After analysis Figure 5a , Figure 5b , Figure 6a , Figure 6b and Figure 7 It can be seen that:

[0105] The average wind speed profile is well consistent with the observed data after correction using the wind speed at 101m. Except for heights below 58m, the relative error of the corrected wind speed is within 1m / s. The average absolute error and root mean square error after correction are both within 1m / s, and the Pearson correlation coefficient is also above 0.95.

[0106] When the wind speed profile after wind speed correction is consistent with the observation results, the turbulence intensity after correction is also consistent with the observation results. Above 120m and below 47m, the turbulence intensity results after correction are smaller. This is mainly because the wind speed after correction is still larger in these two places, resulting in smaller turbulence intensity after correction. In addition, it may be related to the large turbulence pulsation formed on the leeward slope due to the larger wind speed during simulation. From the mean absolute error and root mean square error, it can be seen that the turbulence intensity after correction is closer to the observation data.

[0107] The corrected time series wind average is very close to the observed wind speed average, and the time series wind fluctuation amplitude is consistent with the observed result fluctuation amplitude. Since the statistical time is second-level data, the numerical simulation time series wind and the observed results are not synchronized with each other in terms of time changes. From the mean absolute error and root mean square error, it can be seen that the corrected time series wind is closer to the observed data.

[0108] By comparing with the measured data of the wind farm, the present invention finds that the newly proposed complex terrain wind resource assessment method based on the correction of the measured data of the wind tower can well simulate the vertical distribution of the average wind speed and turbulence intensity, and the corrected time-series pulsating wind is also closer to the observed data. The method is simple in form, easy to encode and efficient in calculation.

[0109] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A complex terrain wind resource assessment method based on correction of measured data from a wind tower, characterized in that: The method comprises the following steps: S1, simulate the wind farm in complex terrain based on the mesoscale WRF model to obtain the mesoscale data of the innermost nested domain containing the wind farm; S2, extracting the boundary inflow information of the microscale calculation domain according to the mesoscale data of the innermost nested domain obtained in step S1, and obtaining the initial prediction field information of the complex terrain wind field based on the meso-microscale coupling model; S3, according to the location of the wind tower and the flow field characteristics, determine the wind tower related data including the reference height of the wind tower and its corresponding observed wind speed and turbulence intensity for data correction; S4, using the wind tower related data determined in step S3 to correct the initial forecast field information of the complex terrain wind field obtained in step S2, to obtain the corrected wind speed and turbulence information of the complex terrain wind field; the correction formula is: Where: U mod and TI mod is the corrected wind speed and turbulence intensity, U mmc and TI mmc is the wind speed and turbulence intensity obtained from meso-microscale numerical simulation, U obs,ref and TI obs,ref is the observed wind speed and turbulence intensity corresponding to the reference height of the wind tower, U mmc,ref and TI mmc,ref is the WRF-CFD coupled numerical simulation result corresponding to the reference height of the wind tower; Step S2 further comprises: Determine the range of the microscale computational domain or the central reference point, and generate a microscale computational domain grid; Extract the wind speed, temperature, and turbulent kinetic energy information corresponding to the boundary grid of the microscale calculation domain from the mesoscale calculation data of the innermost nested domain obtained in step S1, and generate boundary inflow pulsating wind time series data; Based on the computational domain grid and boundary inflow pulsating wind time series data, the Navier-Stokes equations for microscale calculation are numerically discretized in time and space, the flow field in the microscale computational domain is solved, and the initial prediction field of the wind field is obtained.

2. The complex terrain wind resource assessment method based on the correction of measured data from a wind tower according to claim 1 is characterized in that: Step S1 further comprises: Determine the reference point longitude and latitude and time period for mesoscale numerical simulation; Obtain conventional terrain static data and meteorological data; Configure parameterized solutions based on local geographical environment and climate characteristics; Obtain high-precision SRTM data containing wind farms and replace the conventional terrain data in the innermost nested domain; Mesoscale WRF model numerical simulation of wind field in complex terrain based on terrain, meteorology and computing configuration scheme; Save the mesoscale data for the innermost nested domain containing the wind farm.

3. The complex terrain wind resource assessment method based on the correction of measured data from a wind tower according to claim 2 is characterized in that: The process of configuring a parameterized solution based on local geographic and climatic characteristics includes the following steps: For complex mountainous areas, the Thompson format and WDM6 are selected; for longwave and shortwave radiation, the RRTMG+RRTMG scheme is selected; for the near-surface layer, the Monin-Obukhov scheme is used in combination with the MYJ planetary boundary layer scheme; for areas where the annual average humidity is greater than the preset humidity threshold, the MYNN 2.5level TKE planetary layer scheme is selected in combination with the MYNN near-surface layer scheme.

4. The complex terrain wind resource assessment method based on the correction of measured data from a wind tower according to claim 1 is characterized in that: The scope of the microscale calculation domain is determined according to the target research area and the mountain height, and there is a distance between the leeward slope and the outlet boundary to ensure the stable development of turbulence on the leeward slope.

5. The complex terrain wind resource assessment method based on the correction of measured data from a wind tower according to claim 1 is characterized in that: In step S2, the process of generating the microscale computational domain grid includes the following steps: The meso-microscale terrain is fused to obtain a high-precision stl terrain file, and then the blockMesh tool of OpenFOAM is used to generate the background mesh. Finally, the snappyHexMesh tool is used to complete the overall mesh and near-ground mesh encryption process in the microscale calculation domain.

6. The complex terrain wind resource assessment method based on the correction of measured data from a wind tower according to claim 1 is characterized in that: The process of generating fluctuating wind time series data includes the following steps: The spatiotemporal series of wind speed, temperature and turbulent kinetic energy parameters corresponding to the boundary of the microscale calculation domain are extracted from the WRF simulation results. The extracted data are imported into the boundary of the CFD simulation calculation domain through time and space interpolation for microscale calculation to generate pulsating wind time series data.

7. The complex terrain wind resource assessment method based on the correction of measured data from a wind tower according to claim 1 is characterized in that: Large eddy simulation or delayed detached eddy simulation is used to solve the turbulent field in the microscale computational domain.

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