A wind profile fitting method suitable for complex mountainous wind field time series prediction

Abstract

The application protects a wind profile fitting method suitable for complex mountainous wind field time series prediction, including the steps of obtaining terrain data and meteorological data, determining the target area wind shear coefficient, and constructing a wind profile fitting model; obtaining real-time terrain data of the target area and real-time meteorological data of the target area, the wind profile fitting model receiving the real-time terrain data of the target area and the real-time meteorological data of the target area, and generating a wind profile set; for the target area, the wind profile parameters are dynamically adjusted in real time to generate a wind field time series prediction result. The application considers the complex mountainous topography, meteorological factors and the spatio-temporal variation characteristics of the wind field, realizes accurate fitting of the wind profile of different topographic sub-regions based on the terrain and meteorological data set, makes the wind profile fitting more in line with the actual situation, on the one hand, updates the terrain and meteorological data in real time, on the other hand, establishes a dynamic self-adaptive updating mechanism for wind field time series prediction, and ensures the rationality of the calculation input.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind field time series prediction, and particularly relates to a wind profile fitting method suitable for complex mountainous wind field time series prediction. BACKGROUND

[0002] In order to better utilize wind resources in mountainous areas, it is necessary to finely predict the wind field in complex mountainous areas. Among them, the setting of accurate inflow boundary conditions is the key to the fine simulation of the wind field. The complex mountainous wind field is affected by factors such as terrain slope, roughness, valley direction, etc., and the wind speed law in the region with severe terrain undulation presents significant spatiotemporal non-uniformity. The traditional wind field simulation method is mostly based on the simulation of the assumption of flat terrain, ignoring the mountain flow, climbing effect, local circulation, etc., and the terrain adaptability is insufficient, resulting in large prediction error of the wind speed in complex mountainous areas. In addition, the traditional method focuses on single-point static fitting and does not capture the time series law of the wind field with diurnal temperature difference and seasonal change, and the missing time series dynamic characteristics result in large difference in wind speed prediction accuracy at different times. Therefore, the present application is proposed. SUMMARY

[0003] The present application protects a wind profile fitting method suitable for complex mountainous wind field time series prediction to solve the problem of low wind field prediction accuracy for complex terrain in the prior art.

[0004] A wind profile fitting method suitable for complex mountainous wind field time series prediction, comprising the steps of: obtaining terrain data and meteorological data, and constructing a terrain meteorological data set; determining a target area wind shear coefficient based on the terrain meteorological data set, and constructing a wind profile fitting model; obtaining real-time terrain data of the target area and real-time meteorological data of the target area, the wind profile fitting model receiving the real-time terrain data of the target area and the real-time meteorological data of the target area, and generating a wind profile set; dynamically adjusting the wind profile parameters in real time for the target area; performing numerical solution for the calculation domain to generate a wind field time series prediction result.

[0005] Further, before the step of constructing the terrain meteorological data set, it further comprises: identifying terrain data outliers, fitting a neighborhood terrain trend surface of the outliers, fitting and correcting the outliers and replacing the outliers with the fitted and corrected outliers, wherein the fitting and correction is wherein H corr is the terrain data correction value, H j is the neighborhood point elevation, ω j is the distance weight, and n is the number of neighborhood points.

[0006] Further, before the step of constructing the terrain meteorological data set, the method further comprises: identifying meteorological data outliers, wherein the meteorological data outliers comprise wind speed data outliers, and performing weighted correction on the wind speed data outliers according to terrain representativeness of the observation site and consistency of multi-source data, wherein the weighted correction is wherein, V corr is a wind speed correction value, ω obs is a terrain representativeness weight coefficient, V obs is an original observed wind speed value collected by a mountain meteorological station or a wind tower, ω i is a weight coefficient of a certain meteorological data source, V i is a wind speed value of a certain meteorological data source, n is a type of meteorological data source, and n≥1.

[0007] Further, the step of determining the target area wind shear coefficient based on the terrain meteorological data set comprises: obtaining similar terrain historical wind observation data, performing power law wind profile model equation fitting on the similar terrain historical wind observation data, and inversely calculating a similar terrain historical average wind shear coefficient, wherein the power law wind profile model equation is , h is a height of a wind speed to be fitted in a surface boundary layer, V is a wind speed at a height h, V r is a reference height h ref is an average wind speed at the reference height, α is a wind shear coefficient; obtaining terrain roughness of a similar terrain area and terrain roughness of the target area, generating a target area wind shear coefficient initial value range according to the similar terrain historical average wind shear coefficient, and specifically: wherein ， α hist is a similar terrain historical wind shear coefficient, α range is a target area wind shear coefficient initial value range ，k is a roughness influence coefficient, Z 0,target is terrain roughness of the target area, Z 0,similar is terrain roughness of the similar terrain area.

[0008] Further, the step of determining the target area wind shear coefficient based on the terrain meteorological data set comprises: obtaining target area short-term high-frequency wind observation data, setting the target area wind shear coefficient initial value range as a model constraint condition, constructing a power law wind profile model, and inversely calculating a target area average wind shear coefficient.

[0009] Further, the power law wind profile model is constructed, and the target area average wind shear coefficient is inversely calculated, including: based on the power law wind profile model, correcting the height difference of terrain undulation, according to the first correction model , correction is carried out, wherein, V is the wind speed at height h, H is the height of meteorological data that can be trusted, V H is the trusted speed, dH is the height difference of the terrain relative to the baseline, used to correct the influence of terrain undulation on the vertical distribution of wind speed; h is the height of the wind speed to be fitted in the surface boundary layer, α 1 is the target area average wind shear coefficient.

[0010] Further, the step of constructing the wind profile fitting model includes: according to the terrain meteorological data, based on the terrain characteristics of the target area, the target terrain area is divided to generate sub-regions; for different sub-regions, identify the wind speed gradient mutation height, determine the height of the meteorological data that can be trusted; according to the terrain correction coefficient β, the first correction model is optimized for the terrain difference of the sub-region, and the second correction model is generated, wherein, V is the wind speed at height h, H is the height of meteorological data that can be trusted, V H is the trusted speed, dH is the height difference of the terrain relative to the baseline, used to correct the influence of terrain undulation on the vertical distribution of wind speed; h is the height of the wind speed to be fitted in the surface boundary layer, α 1 is the target area average wind shear coefficient, and β is the terrain correction coefficient.

[0011] Further, determining the height of the meteorological data that can be trusted includes: for the wind speed sequence , the wind speed difference between adjacent heights is calculated: when , take h i The above stable layer height is the height of the meteorological data that can be trusted, h i is the height of the wind speed sequence, is the wind speed difference limit threshold of the adjacent height interval.

[0012] Further, the sub-region includes at least one of a high mountain valley area, a low mountain gentle slope area, and a mountain platform area.

[0013] The beneficial effects of the present application are: (1) the present application determines the target area wind shear coefficient by acquiring the terrain meteorological data set, constructs the wind profile fitting model, and makes the wind profile fitting more in line with the actual situation; (2) a dynamic adaptive updating mechanism is established for the time sequence prediction of the wind field, the wind profile parameter is adjusted in real time, the boundary conditions are updated, the rationality of the calculation input is ensured, and the accuracy of the complex mountain wind field prediction is further improved. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0015] Figure 1 A whole flow chart of one mode of the present application;

[0016] Figure 2 A wind profile fitting model schematic diagram in a certain complex terrain in one mode of the present application;

[0017] Figure 3 A wind profile fitting model schematic diagram in different sub-regions in a certain complex terrain in one mode of the present application;

[0018] Figure 4 A meteorological data reliable layer height determination schematic diagram of one embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings of the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0020] The drawings, the technical solutions in the embodiments of the present application are clearly and completely described. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0021] The terms used in the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein means and includes any or all possible combinations of one or more associated listed items.

[0022] The application protects a wind profile fitting method suitable for complex mountainous wind field time series prediction to solve the problem of low accuracy of wind field prediction for complex terrain in the prior art.

[0023] Complex terrain conditions can refer to areas with significant spatial heterogeneity in surface morphology, such as mountains (slope ≥ 15°), valleys (width < 500m), plateaus, hills, etc., whose terrain undulations and roughness changes will significantly affect airflow movement and pollutant diffusion paths.

[0024] Digital Elevation Model, DEM for short, is a digital simulation of the terrain (i.e. digital expression of the terrain surface morphology) through limited terrain elevation data, which is a solid ground model represented by an ordered numerical array form of ground elevation.

[0025] Wind profile refers to the distribution curve of wind speed change with height, mainly used to study the wind speed law in the atmospheric boundary layer.

[0026] Trusted speed refers to the incoming flow wind speed above a certain height that is stable and reliable meteorological data, representing the airflow speed not affected by complex terrain near-surface disturbances.

[0027] Trusted height refers to the height measured by devices such as lidar, where the airflow is less affected by complex terrain near-surface friction, terrain undulations, and other disturbances, and the meteorological data tends to be stable. Above this height, meteorological data can be considered as uniform and reliable incoming flow data.

[0028] Wind shear coefficient refers to the rate of change of wind speed with height. Due to differences in terrain undulations, roughness, and other differences, wind shear coefficients are different, reflecting the degree of terrain influence on wind vertical distribution.

[0029] Reference Figure 1 As shown, the application protects a wind profile fitting method suitable for complex mountainous wind field time series prediction, which includes the following steps: S1, obtain terrain data and meteorological data, and construct a terrain-meteorological data set; S2, based on the terrain-meteorological data set, determine the target area wind shear coefficient, and construct a wind profile fitting model; S3, obtain real-time terrain data of the target area and real-time meteorological data of the target area, the wind profile fitting model receives the real-time terrain data of the target area and the real-time meteorological data of the target area, and generates a wind profile set; S4, dynamically adjust the wind profile parameters in real time for the target area; S5, perform numerical solution for the calculation domain to generate wind field time series prediction results.

[0030] By using the above scheme, the method is based on terrain meteorological data, so that the wind profile fitting is more in line with the actual situation; on the one hand, the terrain and meteorological data are updated in real time, and on the other hand, a dynamic adaptive updating mechanism is established for the time sequence prediction of the wind field, so that the wind profile parameters are adjusted and the boundary conditions are updated in real time, so that the rationality of the calculation input is ensured, and the accuracy of the complex mountain wind field prediction is further improved. The meteorological station, wind tower, etc. can be used to collect meteorological data such as wind speed and wind direction at different heights, and the elevation, slope, and undulating shape of the terrain can be obtained through satellite digital elevation model (DEM) or geographic information system (GIS) and the like. The calculation domain can be part of the target area that needs to be solved.

[0031] In another embodiment of the present application, before the step of constructing the terrain meteorological data set, the terrain data such as DEM data and the meteorological data such as satellite remote sensing meteorological data, numerical prediction data, etc. are cleaned. For example, if the deviation between the data source and the measured data is greater than 15%, it is determined as error data and is removed.

[0032] In another embodiment of the present application, before the step of constructing the terrain meteorological data set, the terrain data abnormal value is identified, the neighborhood terrain trend surface of the abnormal point is fitted, the fitting correction is performed and the terrain data abnormal value is replaced, and the fitting correction is , wherein H corr is the terrain data correction value, H j is the neighborhood point elevation, ω j is the distance weight, and n is the number of neighborhood points.

[0033] In another embodiment of the present application, before the step of constructing the terrain meteorological data set, the terrain data abnormal value is identified, the neighborhood terrain trend surface of the abnormal point is fitted, the fitting correction is performed and the terrain data abnormal value is replaced, and the fitting correction is , wherein V corr is the wind speed correction value, ω obs is the terrain representative weight coefficient, V obs is the original observed wind speed value collected by the mountain meteorological station or wind tower, ω i is the weight coefficient of a certain meteorological data source, V i is the wind speed value of a certain meteorological data source, n is the type of meteorological data source, and n≥1.

[0034] By using the above scheme, the data authenticity and accuracy can be improved by correcting abnormal values, removing error data and filling missing data.

[0035] In another embodiment of the present application, the step of determining the wind shear coefficient of the target area based on the terrain meteorological data set comprises: obtaining similar terrain historical wind measurement data, performing power law wind profile model equation fitting on the similar terrain historical wind measurement data, and inversely calculating the similar terrain historical average wind shear coefficient. The power law wind profile model equation is , h is the height of the ground boundary layer to be fitted for wind speed, V is the wind speed at height h, V r is the reference height h ref is the average wind speed at height α is the wind shear coefficient; obtaining the terrain roughness of the similar terrain area and the terrain roughness of the target area, generating the initial value range of the wind shear coefficient of the target area according to the similar terrain historical average wind shear coefficient, specifically: wherein ， α hist is the similar terrain historical wind shear coefficient, α range is the initial value range of the wind shear coefficient of the target area ，k is the roughness influence coefficient, Z 0,target is the terrain roughness of the target area, Z 0,similar is the terrain roughness of the similar terrain area.

[0036] By using the above scheme, the similar terrain historical wind measurement data can be historical long-term wind measurement data of at least two height layers for more than 5 years. The data of each period, such as monthly / quarterly / yearly, can be fitted. By using the above scheme, the accuracy and authenticity of the wind shear coefficient can be improved.

[0037] In another embodiment of the present application, the step of determining the wind shear coefficient of the target area based on the terrain meteorological data set comprises: obtaining target area short-term high-frequency wind measurement data, setting the initial value range of the wind shear coefficient of the target area as a model constraint condition, constructing a power law wind profile model, and inversely calculating the average wind shear coefficient of the target area.

[0038] By using the above scheme, the accuracy and authenticity of the wind shear coefficient can be improved by using the initial value range of the wind shear coefficient of the target area as a model constraint.

[0039] In an embodiment of the present application, the power-law wind profile model is constructed, and the target area average wind shear coefficient is inversely calculated, including: based on the power-law wind profile model, the height difference of terrain undulation is corrected, and the first correction model is generated according to the first correction model , wherein, V is the wind speed at height h, H is the meteorological data reliable height, V H is the reliable speed, dH is the height difference of terrain relative to the baseline, used for correcting the influence of terrain undulation on the vertical distribution of wind speed; h is the height of the wind speed to be fitted in the surface boundary layer, α 1 is the target area average wind shear coefficient.

[0040] The above scheme is adopted, and a wind profile fitting model schematic diagram in a certain complex terrain in an embodiment of the present application is shown in FIG. Figure 2 , considering the influence of the height difference of terrain undulation, the correction can improve the accuracy and authenticity of the target area average wind shear coefficient.

[0041] In an embodiment of the present application, the step of constructing the wind profile fitting model includes: according to the terrain meteorological data, based on the terrain characteristics of the target area, the target terrain area is divided to generate sub-areas; the wind speed gradient mutation height is identified for different sub-areas, and the different meteorological data reliable layer heights are determined; according to the terrain correction coefficient β, the first correction model is optimized for the terrain difference of the sub-area, and the second correction model is generated, wherein, V is the wind speed at height h, H is the meteorological data reliable height, V H is the reliable speed, dH is the height difference of terrain relative to the baseline, used for correcting the influence of terrain undulation on the vertical distribution of wind speed; h is the height of the wind speed to be fitted in the surface boundary layer, α 1 is the target area average wind shear coefficient, and β is the terrain correction coefficient.

[0042] The above scheme is adopted, and a wind profile fitting model schematic diagram in a certain complex terrain in an embodiment of the present application is shown in FIG. Figure 3 , by dividing different sub-areas in the target area, the fitting can be performed in combination with the actual situation of different sub-areas, so that the fitting result is more accurate and conforms to the actual situation; wherein the terrain correction coefficient β can be valued according to the terrain stability of the sub-area, for example, the value of β in the canyon area is 1.2-1.5, and the value of β in the gentle area is 0.8-1.0.

[0043] In an embodiment of the present application, determining the different meteorological data reliable layer heights includes: for the wind speed sequence , the wind speed difference of adjacent heights is calculated: When , take h i The above stable layer height is the meteorological data reliable height, h i The wind speed sequence height, The adjacent height interval wind speed difference limit threshold.

[0044] With the above scheme, as Figure 4 The meteorological data reliable layer height determination schematic diagram can make the meteorological data reliable height more accurate, and the wind speed sequence can be obtained by acquiring terrain data and meteorological data.

[0045] In another embodiment of the application, the sub-region includes at least one of high mountain valley area, low mountain gentle slope area, and mountain platform area.

[0046] With the above scheme, the terrain and historical wind data can be combined to divide the sub-region type. The high mountain valley area has a slope greater than 30°, a maximum terrain fluctuation height difference greater than 500m, a dominant wind along the valley, and a large wind speed gradient. The low mountain gentle slope area has a slope of 10°≤ slope ≤ 30°, a maximum terrain fluctuation height difference of 100m≤ maximum terrain fluctuation height difference ≤ 500m, and a relatively uniform wind field. The mountain platform area has a slope < 10°, a maximum terrain fluctuation height difference < 100m, a small wind speed gradient, and obvious turbulence characteristics.

[0047] The wind profile fitting method suitable for complex mountain wind field time series prediction provided in the application considers the complex mountain terrain, meteorological factors, and the spatio-temporal variation characteristics of the wind field. Compared with the traditional wind profile model which is only applicable to flat terrain, the method is based on terrain meteorological data set and realizes accurate fitting of the wind profile of different terrain sub-regions such as ridges and valleys based on terrain meteorological data, so that the wind profile fitting is more in line with the actual situation. On the one hand, the terrain and meteorological data are updated in real time, and on the other hand, a dynamic self-adaptive updating mechanism is established for the time series prediction of the wind field, the wind profile parameters are adjusted in real time, the boundary conditions are updated, the rationality of the calculation input is ensured, and the accuracy of the complex mountain wind field prediction is further improved. The meteorological station, wind tower, etc. can be used to collect meteorological data such as wind speed and direction at different heights, and satellite digital elevation model (DEM) or geographic information system (GIS) can be used to obtain terrain data such as altitude, slope, and fluctuation form.

Claims

1. A wind profile fitting method suitable for time-series prediction of wind fields in complex mountainous areas, characterized in that, Including the following steps: Acquire topographic and meteorological data to construct a topographic-meteorological dataset; Based on topographic and meteorological datasets, the wind shear coefficient of the target area is determined, and a wind profile fitting model is constructed. The system acquires real-time terrain data and real-time meteorological data of the target area. The wind profile fitting model receives the real-time terrain data and real-time meteorological data of the target area and generates a set of wind profiles. For the target area, adjust the wind profile parameters dynamically in real time; Numerical solutions are performed on the computational domain to generate wind field time series prediction results; The steps, based on topographic and meteorological datasets, determine the wind shear coefficient for the target area, including: Obtain historical wind measurement data for similar terrain, and generate power-law wind profiles based on the historical wind measurement data for similar terrain. Model equation fitting, inverse calculation of historical average wind shear coefficient for similar terrain, the power-law wind profile model equation for , h The height of the wind speed to be fitted within the surface boundary layer. V For height h Wind speed at the location, V r For reference height h ref The average wind speed at that location α It is the wind shear coefficient; to obtain the terrain roughness of similar terrain areas. Based on the target area's topographic roughness and historical average wind shear coefficient of similar terrain, the wind speed of the target area is generated. The initial range of the shear coefficient is as follows: ， in ，α hist Historical wind shear coefficient for similar terrain ，α range The initial range of wind shear coefficient for the target area ，k The roughness coefficient is the factor that influences roughness. Z 0,target For the terrain roughness of the target area, Z 0,similar For terrain roughness in similar terrain regions; The steps, based on topographic and meteorological datasets, determine the wind shear coefficient for the target area, including: Acquire short-term high-frequency wind measurement data of the target area, set the initial value range of the wind shear coefficient of the target area as the model constraint, construct a power-law wind profile model, and inversely calculate the average wind shear coefficient of the target area. The steps for constructing a wind profile fitting model include: Based on topographic and meteorological data, and taking into account the topographic features of the target area, sub-regions are generated for the target topographic region. Identify the height of abrupt changes in wind speed gradients for different sub-regions and determine the height of the reliable layer for different meteorological data; To address the differences in terrain within sub-regions, the first correction model is optimized based on the terrain correction coefficient β, and a second correction model is generated. , in, V For height h Wind speed, H The height of the credibility layer for meteorological data. V H For reliable speed, dH The elevation difference between the terrain and the baseline is used to correct for the influence of terrain undulation on the vertical distribution of wind speed; h is the height of the wind speed to be fitted within the surface boundary layer. α 1 represents the average wind shear coefficient of the target area, and β represents the terrain correction coefficient.

2. The wind profile fitting method for time-series prediction of complex mountain wind fields according to claim 1, characterized in that, The steps before constructing the topographic and meteorological dataset also include: Identify outliers in terrain data, fit a neighborhood terrain trend surface to the outliers, and then fit and correct the outliers to replace them. The fitted correction is... ,in H corr These are correction values ​​for terrain data. H j Elevation of neighboring points ω j is the distance weight, and n is the number of neighboring points.

3. The wind profile fitting method for time-series prediction of complex mountain wind fields according to claim 1, characterized in that, The steps before constructing the topographic and meteorological dataset also include: Identify outliers in meteorological data, including wind speed data outliers. Perform a weighted correction on the wind speed data outliers based on the representativeness of the terrain at the measured stations and the consistency of multi-source data. The weighted correction is as follows: ,in, V corr This is the wind speed correction value. ω obs This is the terrain representativeness weighting coefficient. V obs Raw observed wind speed values ​​collected from mountain weather stations and wind measurement towers. ω i For a certain meteorological data source, weighting coefficient V i The wind speed value is a data source for a certain meteorological event. n Let n represent the types of meteorological data sources, where n ≥ 1.

4. The wind profile fitting method for time-series prediction of complex mountain wind fields according to claim 1, characterized in that, Constructing a power-law wind profile model and inversely calculating the average wind shear coefficient of the target area includes: Based on the power-law wind profile model, the terrain undulation height difference is corrected according to the first correction model. Make corrections, among which... V Let h be the wind speed. H To ensure the reliability of meteorological data, V H For reliable speed, dH The elevation difference between the terrain and the baseline is used to correct for the influence of terrain undulation on the vertical distribution of wind speed; h is the height of the wind speed to be fitted within the surface boundary layer. α 1 represents the average wind shear coefficient of the target area.

5. The wind profile fitting method for time-series prediction of complex mountain wind fields according to claim 1, characterized in that, Determining the confidence level of different meteorological data includes: for wind speed sequences Calculate the wind speed difference between adjacent heights: ,when At that time, take h i The above stable layer heights are the reliable layer heights for meteorological data. h i For wind speed sequence height, The threshold for limiting the wind speed difference between adjacent height intervals.

6. The wind profile fitting method for time-series prediction of wind fields in complex mountainous areas according to any one of claims 1-5, characterized in that, The sub-regions include at least one of the following: high mountain canyon area, low mountain gentle slope area, and mountain plateau area.

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