Typhoon wind field prediction method driven by fusion of model and measured data

Abstract

The invention provides a typhoon wind field prediction method driven by fusion of a model and measured data, and the method comprises the steps: obtaining basic parameters of a target typhoon model, and carrying out the estimation of model parameters; constructing a typhoon model based on the basic parameters of the target typhoon model; setting a typhoon model simulation output time resolution and calculating a typhoon wind field wind speed time sequence mean value; collecting original data of a typhoon wind field, and extracting wind speed time sequence data during a typhoon; processing the wind speed time sequence data in the typhoon period to obtain a wind speed mean value and a wind speed standard deviation of the typhoon wind field measured data; typhoon wind field turbulence intensity calibration is carried out based on the wind speed mean value and the wind speed standard deviation, and a turbulence intensity model is constructed; based on the wind speed and wind direction time sequence data during the typhoon and the turbulence intensity under the typhoon working condition, complete typhoon wind field wind speed time sequence data during the typhoon are obtained through calculation, and prediction of the typhoon wind field is completed. According to the method, the typhoon wind speed time sequence can be accurately predicted, so that the precision of extreme working condition numerical simulation of the offshore wind turbine is improved.

Description

Technical Field

[0001] The present application belongs to the field of marine engineering technology and relates to a typhoon wind field prediction method, and in particular to a typhoon wind field prediction method driven by the fusion of a model and measured data. Background Art

[0002] Offshore wind power is an important part of the global renewable energy sector and is of great significance for reducing carbon emissions and mitigating climate change.

[0003] However, offshore wind turbines are affected by various natural environmental factors during operation, especially typhoons. During typhoons, wind speed and direction change dramatically, and are often accompanied by heavy rain and waves, which not only cause direct damage to offshore wind turbines, but also increase the fatigue loss of wind turbine blades and shorten their service life.

[0004] Therefore, accurate simulation of wind fields under the influence of typhoons is of great practical significance for evaluating wind turbine operation safety and fatigue loss. However, traditional wind field simulation methods are usually based on meteorological models and fluid dynamics models for calculation, but these models have certain limitations when dealing with complex meteorological phenomena such as typhoons.

[0005] Therefore, in the existing typhoon prediction technology, due to the direct destructive effect of typhoons on offshore wind turbines, the safety of wind turbine operation is low and the service life of the equipment is reduced, resulting in the problem of low numerical simulation accuracy of wind field simulation. Summary of the invention

[0006] In view of the shortcomings of the prior art mentioned above, the purpose of the present application is to provide a typhoon wind field prediction method driven by the fusion of model and measured data, which is used to solve the problem in the existing typhoon prediction technology that the direct destructive effects of typhoons on offshore wind turbines lead to low safety of wind turbine operation and reduced equipment service life, resulting in low numerical simulation accuracy of wind field simulation.

[0007] To achieve the above-mentioned purpose and other related purposes, in a first aspect, the present application provides a typhoon wind field prediction method driven by the fusion of model and measured data, comprising the following steps: obtaining basic parameters of a target typhoon model; constructing a typhoon model based on the basic parameters of the target typhoon model, and obtaining a two-dimensional wind field within a typhoon cycle; extracting time series data of wind speed and direction during the typhoon in the target area based on the two-dimensional wind field within the typhoon cycle; collecting original data of the typhoon wind field, and extracting time series data of wind speed during the typhoon; processing the time series data of wind speed during the typhoon to obtain the wind speed mean and wind speed standard deviation of the measured data of the typhoon wind field; calibrating the turbulence intensity of the typhoon wind field based on the wind speed mean and wind speed standard deviation, and constructing a turbulence intensity model; based on the time series data of wind speed and direction during the typhoon and the turbulence intensity under typhoon conditions, calculating the complete typhoon wind field wind speed time series data during the typhoon period to complete the prediction of the typhoon wind field.

[0008] In an implementation of the first aspect, obtaining the basic parameters of the target typhoon model includes the following steps: obtaining the typhoon evolution parameters of the target sea area; the typhoon evolution parameters include: any one or more combinations of typhoon path, central air pressure, and maximum wind speed; analyzing and estimating based on the typhoon characteristics of the target sea area and the typhoon evolution parameters to obtain the basic parameters of the target typhoon model.

[0009] In an implementation of the first aspect, a typhoon model is constructed based on the basic parameters of the target typhoon model, and the two-dimensional wind field within the typhoon cycle is obtained, including the following steps: using the Young&Sobey model and the Holland model to calculate the cyclone wind gradient and air pressure of the target typhoon to obtain a typhoon model; correcting the typhoon model by combining a geostrophic correction method, a predecessor motion asymmetry correction method, and a wind inflow angle correction method to obtain a two-dimensional wind field for the entire typhoon cycle; setting the time resolution of the typhoon model simulation output; and calculating the two-dimensional wind field of the target typhoon within the typhoon cycle based on the typhoon model.

[0010] In an implementation of the first aspect, a cyclone wind gradient calculation formula based on the Young & Sobey model is:

[0011]

[0012] The air pressure calculation formula based on the Young&Sobey model is:

[0013]

[0014] The calculation formula of cyclone wind gradient based on the Holland model is:

[0015]

[0016] The air pressure calculation formula based on the Holland model is:

[0017]

[0018] Among them, V g (r) represents the cyclone wind gradient of the typhoon model; V max Indicates the maximum wind speed; R mw represents the maximum wind speed gradient; r represents the radius of the cyclone; P(r) represents the air pressure value; f represents the Coriolis force coefficient; P c Indicates the central pressure of the cyclone; P n represents the ambient pressure; B represents the additional coefficient used to couple different cyclone pressure models; ρ A Indicates the air density.

[0019] In an implementation of the first aspect, extracting the time series data of wind speed and direction in a target area during a typhoon based on the two-dimensional wind field in the typhoon period includes the following steps: using an interpolation method to extract the two-dimensional wind field in the typhoon period as a wind vector of a target coordinate, and obtaining a U component and a V component of the target coordinate; using a vector to scalar method to convert the wind vector into a wind speed scalar and a wind direction scalar; constructing a wind speed and direction data set of the target coordinate based on the wind speed scalar and the wind direction scalar; and using an interpolation method to fill in missing values ​​to obtain the time series data of wind speed and direction in the target area during the typhoon.

[0020] In an implementation of the first aspect, processing the wind speed time series data during a typhoon to obtain the wind speed mean and wind speed standard deviation of the typhoon wind field measured data includes the following steps: dividing the wind speed time series data during a typhoon into a plurality of wind speed time series segments during a typhoon according to a preset time period; wherein each wind speed time series segment during a typhoon contains a plurality of one-to-one corresponding wind speed data and wind direction data; constructing a data set of wind speed time series segments during a typhoon; and calculating the wind speed mean and wind speed standard deviation of the typhoon wind field measured data of the wind speed time series segment data set during a typhoon.

[0021] In an implementation of the first aspect, the calculation formula for the wind speed standard deviation of the typhoon wind field measured data is:

[0022]

[0023] Among them, σ i represents the wind speed standard deviation of the ith wind speed time series segment of the typhoon wind field measured data; μ i represents the mean wind speed of the ith wind speed time series segment of the typhoon wind field measured data; x j,i represents the j-th wind speed value of the i-th wind speed time series segment during the typhoon; n represents the number of wind speed time series segments during the i-th wind speed time series segment, and n>600.

[0024] In an implementation of the first aspect, the turbulence intensity of the typhoon wind field is calibrated based on the wind speed mean and the wind speed standard deviation, and the construction of the turbulence intensity model includes the following steps: using the wind speed mean as the horizontal coordinate and the wind speed standard deviation as the vertical coordinate to draw a scatter plot of the wind speed time series data set during the typhoon; dividing the scatter plot equally with the wind speed as the center, dividing the scattered points in the scatter plot, calculating the mean and standard deviation of all wind speed standard deviations in each segment, and obtaining the first wind speed mean and standard deviation; calculating the turbulence intensity of the typhoon wind field based on the first wind speed mean and standard deviation; performing scatter point fitting on the first wind speed mean and standard deviation through regression analysis to obtain a fitting straight line of the first wind speed mean and standard deviation; generating the turbulence intensity curve based on the fitting straight line of the first wind speed mean and standard deviation, and constructing a turbulence intensity model; wherein the formula of the fitting straight line of the first wind speed mean and standard deviation is:

[0025] σ=α1μ+α2

[0026] The turbulence intensity curve of the first wind speed mean and standard deviation is:

[0027]

[0028] Among them, α1 represents the slope of the fitted line; α2 represents the intercept of the fitted line; μ represents the expectation of the wind speed time series segment data set during the typhoon.

[0029] In an implementation of the first aspect, based on the wind speed and wind direction time series data during the typhoon and the turbulence intensity under typhoon conditions, the complete typhoon wind field wind speed time series data during the typhoon is calculated to complete the prediction of the typhoon wind field, including the following steps: based on the wind speed and wind direction time series data during the typhoon, design load conditions of multiple wind turbines under the action of the typhoon, and obtain the average wind speed time series data during the typhoon; input the average wind speed in each design load condition into the turbulence intensity model to obtain the turbulence intensity under typhoon conditions; calculate the typhoon wind field time series data during the typhoon based on the turbulence intensity under the typhoon conditions.

[0030] As described above, the typhoon wind field prediction method driven by the fusion of the model and measured data of the present application has the following beneficial effects:

[0031] The present application provides a typhoon wind field prediction method driven by the fusion of model and measured data. By processing and analyzing the measured data, a turbulence intensity model based on the measured data is fitted. Combined with the model data, a typhoon wind field wind speed time series is constructed to improve the accuracy of numerical simulation and more accurately simulate and analyze the wind field under the influence of typhoon. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1Shown is a flow chart of a typhoon wind field prediction method driven by the fusion of the model and measured data described in this application in one embodiment.

[0033] Figure 2 Shown is a flow chart of S1 in the typhoon wind field prediction method driven by the fusion of the model and measured data described in this application.

[0034] Figure 3 Shown is a flow chart of S2 in the typhoon wind field prediction method driven by the fusion of the model and measured data described in this application.

[0035] Figure 4 Shown is a flow chart of S3 in the typhoon wind field prediction method driven by the fusion of the model and measured data described in this application.

[0036] Figure 5 Shown is a schematic diagram of wind speed observation data during a typhoon period in an embodiment of a typhoon wind field prediction method driven by the fusion of the model and measured data described in this application.

[0037] Figure 6 Shown is a flow chart of S5 in the typhoon wind field prediction method driven by the fusion of the model and measured data described in this application.

[0038] Figure 7 Shown is a flow chart of S6 in the typhoon wind field prediction method driven by the fusion of the model and measured data described in this application.

[0039] Figure 8 Shown is a schematic diagram of wind speed standard deviation-average wind speed fitting in one embodiment of a typhoon wind field prediction method driven by the fusion of the model and measured data described in this application.

[0040] Fig. 9 Shown is a schematic diagram of a turbulence intensity curve in an embodiment of a typhoon wind field prediction method driven by the fusion of the model and measured data described in this application.

[0041] Fig.10 Shown is a flow chart of S7 in the typhoon wind field prediction method driven by the fusion of the model and measured data described in this application. DETAILED DESCRIPTION

[0042] The following specific examples are used to illustrate the embodiments described in this application. Those skilled in the art can easily understand the other advantages and effects described in this application from the contents disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit described in this application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0043] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept described in the present application. Therefore, the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0044] The following will describe in detail a typhoon wind field prediction method driven by the fusion of a model and measured data provided in an embodiment of the present application in conjunction with the accompanying drawings in the embodiment of the present application.

[0045] See also Figure 1 , which is a flow chart of a typhoon wind field prediction method driven by the fusion of the model and measured data of the present application in one embodiment. Figure 1 As shown, this embodiment provides a typhoon wind field prediction method driven by the fusion of model and measured data.

[0046] The typhoon wind field prediction method driven by the fusion of a model and measured data specifically comprises the following steps:

[0047] S1, obtain the basic parameters of the target typhoon model. Figure 2 , which is a flow chart of S1 in the typhoon wind field prediction method driven by the fusion of the model and measured data described in this application. Figure 2 As shown, the S1 comprises the following steps:

[0048] Typhoon model parameter estimation is a highly specialized process that closely relies on an in-depth understanding and detailed analysis of the typhoon characteristics in the target sea area.

[0049] S11, obtaining typhoon evolution parameters of the target sea area, wherein the typhoon evolution parameters include but are not limited to: typhoon path, central air pressure, maximum wind speed, etc.

[0050] In this embodiment, the specific target sea area to be monitored is determined according to the research needs. The required typhoon-related meteorological data is then obtained through methods such as meteorological satellites, meteorological radars, ocean observation stations, and meteorological data released by meteorological departments. Then, according to the selected data source, the typhoon-related data of the target sea area within a specific time period is queried and downloaded through official websites, data interfaces, or data service platforms. The data usually includes: typhoon path, center location, intensity level, wind speed and direction, etc.

[0051] Specifically, in this embodiment, the process first focuses on the evolution parameters of the target typhoon, including but not limited to its detailed path trajectory, the continuous change trend of the central air pressure, and the spatiotemporal distribution characteristics of the maximum wind speed. By fully capturing and accurately analyzing these key parameters, solid basic data can be provided for the construction of the typhoon model.

[0052] For example, the evolution parameters of the target typhoon in the present application are preferably data such as typhoon path, center location, intensity level, wind speed and direction, etc.

[0053] S12, analyzing and estimating the typhoon characteristics in the target sea area and the typhoon evolution parameters to obtain basic parameters of the target typhoon model.

[0054] In this embodiment, the basic parameters of the typhoon model are obtained according to the characteristics of the typhoon in the target sea area and in combination with the evolution parameters of the target typhoon, such as the typhoon path, central air pressure, maximum wind speed, etc.

[0055] For example, taking the engineering software MIKE21 developed by the Danish Hydraulic Institute (DHI) which is widely used in the industry as an example, the typhoon model parameters are calibrated through observation data during the typhoon, such as: typhoon maximum wind speed radius, cyclone forward wind speed correction factor, typhoon shape parameters, etc., so that subsequent typhoon models can be closer to the actual situation.

[0056] S2, constructing a typhoon model based on the basic parameters of the target typhoon model, and obtaining a two-dimensional wind field within the typhoon period. Figure 3 , which is a flow chart of S2 in the typhoon wind field prediction method driven by the fusion of the model and measured data described in this application. Figure 3 As shown, S2 comprises the following steps:

[0057] S21, the cyclone wind gradient and pressure of the target typhoon are calculated using the Young & Sobey model and the Holland model to obtain the typhoon model;

[0058] S22, correcting the typhoon model by combining a geostrophic correction method, a predecessor motion asymmetry correction method, and a wind inflow angle correction method to obtain a two-dimensional wind field throughout the typhoon cycle;

[0059] S23, set the time resolution of the typhoon model simulation output;

[0060] S24, calculating the two-dimensional wind field of the target typhoon within the typhoon period based on the typhoon model.

[0061] In this embodiment, the cyclone wind gradient V of the Young & Sobey or Holland model is firstly used based on the path coordinates, central pressure, maximum wind speed and other parameters of the target typhoon. gThe two-dimensional wind field of the entire typhoon cycle is obtained by combining the geostrophic correction, forward motion asymmetry correction, and wind inflow angle correction.

[0062] Specifically, the calculation formula for the cyclone wind gradient based on the Young & Sobey model is:

[0063]

[0064] The air pressure calculation formula based on the Young&Sobey model is:

[0065]

[0066] The calculation formula of cyclone wind gradient based on the Holland model is:

[0067]

[0068] The air pressure calculation formula based on the Holland model is:

[0069]

[0070] Among them, V g (r) represents the cyclone wind gradient of the typhoon model; V max Indicates the maximum wind speed; R mw represents the maximum wind speed gradient; r represents the radius of the cyclone; P(r) represents the air pressure value; f represents the Coriolis force coefficient; P c Indicates the central pressure of the cyclone; P n represents the ambient pressure; B represents the additional coefficient used to couple different cyclone pressure models; ρ A Indicates the air density.

[0071] The three methods for correcting and optimizing the wind field are as follows:

[0072] First, determine the geostrophic wind. Using the balance between the pressure gradient force and the geostrophic force, calculate the geostrophic wind field around the typhoon center based on the pressure field data; considering the influence of the earth's rotation, perform a geostrophic correction on the wind field to eliminate the deviation caused by the earth's rotation. Compare the calculated geostrophic wind field with the initial wind field of the model, and adjust the model parameters to reduce the difference between the two.

[0073] Reanalyze asymmetry. Use observational data to analyze the asymmetric characteristics of typhoon predecessors (such as tropical disturbances, tropical depressions, etc.) during their movement; focus on the uneven distribution and evolution of key areas such as typhoon eyewalls and spiral rain belts. Based on the asymmetric characteristics, modify the structure and parameters of the typhoon model to more accurately reflect the true form and movement of the typhoon; in particular, make fine adjustments to the intensity and position of the typhoon eyewall and spiral rain belt.

[0074] Next, the wind inflow angle correction method is used to calculate the wind inflow angle and perform model optimization. Using the observed data, the wind inflow angle of the typhoon's peripheral airflow entering the typhoon core area is calculated; considering the influence of the wind inflow angle on the internal flow field structure of the typhoon, accurate calculations are performed. The calculated wind inflow angle is used as an input parameter to further optimize the typhoon model; the relevant parameters and boundary conditions in the model are adjusted to ensure that the model can accurately simulate the airflow structure and wind field distribution inside the typhoon.

[0075] Finally, the correction results of the geostrophic correction method, the predecessor motion asymmetry correction method and the wind inflow angle correction method are combined to obtain a more accurate typhoon model. The corrected typhoon model is verified using independent observation data, including comparing the errors between the model prediction results and the actual observation results. The corrected typhoon model is used to generate two-dimensional wind field data for the entire typhoon cycle. The output wind field data should contain key information such as wind speed and wind direction, and meet specific accuracy requirements.

[0076] In summary, the three key correction and optimization methods mentioned above are: first, geostrophic correction, which takes into account the impact of the earth's rotation on the typhoon wind field and effectively corrects the geostrophic force effect in the wind field; second, forward motion asymmetry correction, which adjusts the asymmetric characteristics of the wind field distribution during the movement of the typhoon, and enhances the model's ability to capture the complexity of the actual wind field; and finally, wind inflow angle correction, which improves the model's simulation accuracy of the internal airflow movement of the typhoon by accurately calculating the wind inflow angle and applying it to the wind field simulation. The comprehensive application of these three correction methods makes the two-dimensional wind field simulation results of the entire typhoon cycle closer to actual observations, providing a more solid data foundation and technical support for typhoon forecasting, risk assessment and disaster response.

[0077] S3, extracting the time series data of wind speed and direction during the typhoon period in the target area based on the two-dimensional wind field in the typhoon period. Figure 4 , which is a flow chart of S3 in the typhoon wind field prediction method driven by the fusion of the model and measured data described in this application. Figure 4 As shown, S3 comprises the following steps:

[0078] S31, extracting the two-dimensional wind field within the typhoon period as a wind vector of the target coordinates by using an interpolation method, and obtaining a U component and a V component of the target coordinates;

[0079] S32, converting the wind vector into a wind speed scalar and a wind direction scalar by a vector-to-scalar method;

[0080] S33, constructing a wind speed and direction data set of the target coordinates based on the wind speed scalar and the wind direction scalar;

[0081] S34, using interpolation method to fill missing values ​​and obtain the time series data of wind speed and direction in the target area during the typhoon.

[0082] In this embodiment, the time resolution of the typhoon model simulation output is preferably set to ten minutes. The ten-minute average two-dimensional wind field of the target typhoon can be obtained by typhoon model calculation. The two-dimensional wind field is extracted as wind vectors u and v of the target coordinates by interpolation methods such as bilinear interpolation and nearest distance interpolation. By the method of vector to scalar, the ten-minute wind direction and wind speed time series data of the target coordinates during the typhoon are obtained. That is, the ten-minute average wind speed and wind direction time series data of the target area during the typhoon period are obtained by simulation.

[0083] Specifically, the specific coordinate point or area where the wind vector needs to be extracted is first determined, and the time period to be studied (ie, during the typhoon) is determined; in this embodiment, ten minutes is preferably used as the time period to be studied.

[0084] According to the grid distribution of the two-dimensional wind field data, an interpolation grid including the target coordinates is constructed. Then, according to the characteristics and requirements of the data, a suitable interpolation method is selected, such as bilinear interpolation, inverse distance weighted interpolation, Kriging interpolation, etc., to perform interpolation calculations, substitute the target coordinates into the interpolation model, and use the wind speed u and v component data of the surrounding grid points to obtain the wind speed u and v component values ​​of the target coordinate points through interpolation calculations.

[0085] Then, by using the vector-to-scalar method, using the wind speed u and v component values, the wind speed vector is converted into a wind speed scalar through the vector modulus calculation formula, and the wind direction is determined at the same time; and the wind speed vector and wind direction vector of the wind vector are converted into wind speed scalar and wind direction scalar. Based on all wind speed scalars and wind direction scalars, a wind speed and direction dataset of the target coordinates is constructed.

[0086] Finally, the interpolation method is used to fill in the missing values ​​and obtain the time series data of wind speed and direction in the target area during the typhoon.

[0087] S4, collect the original data of typhoon wind field and extract the time series data of wind speed during typhoon. Figure 5 , showing a schematic diagram of wind speed observation data during a typhoon period in one embodiment of a typhoon wind field prediction method driven by the fusion of the model and measured data described in this application.

[0088] In this embodiment, firstly, based on historical typhoon path data, a site facing the sea, open and without any obstructions is selected for collecting original wind field data.

[0089] Specifically, a small meteorological station is installed at the site, where the anemometer and wind vane should be placed in a position that matches the main wind direction of the wind field to avoid mutual interference between the observation equipment. High-frequency sampling accuracy is adopted to synchronously record the wind speed and wind direction information in the same wind speed process. The data collection cycle covers at least one full year and fully records several typhoon processes.

[0090] Then, after completing the data collection, the reliability of the original data is evaluated, including checking the data integrity and smoothness, drawing a wind rose diagram to verify the consistency of the main wind direction distribution, calculating the monthly average wind speed and direction and comparing it with the local meteorological data over the years, in order to preliminarily judge the reliability and accuracy of the data set.

[0091] Finally, after confirming the accuracy of the observation data, the wind speed time series during the typhoon was extracted for post-processing based on the time when the typhoon passed through the target area and combined with the change process of wind speed in the wind speed time series.

[0092] S5, processing the wind speed time series data during the typhoon to obtain the wind speed mean and wind speed standard deviation of the typhoon wind field measured data. Figure 6 , which is a flow chart of S5 in the typhoon wind field prediction method driven by the fusion of the model and measured data described in this application. Figure 6 As shown, S5 comprises the following steps:

[0093] S51, dividing the typhoon wind speed time series data into a plurality of typhoon wind speed time series segments according to a preset time period; wherein each typhoon wind speed time series segment includes a plurality of one-to-one corresponding wind speed data and wind direction data;

[0094] S52, construct a dataset of wind speed time series fragments during typhoons;

[0095] S53, calculating the wind speed mean and wind speed standard deviation of the typhoon wind field measured data of the wind speed time series segment data set during the typhoon period.

[0096] In this embodiment, the typhoon wind speed data is divided into several data time series segments according to a period of ten minutes. Each time series segment contains n (n>600) one-to-one corresponding wind speed and wind direction data points. For the wind speed time series segment data sets, their wind speed means and standard deviations are calculated respectively.

[0097] The standard deviation is a statistical measure of the dispersion of a data set, which describes the degree of fluctuation of the data set. By calculating the standard deviation of wind speed data, we can assess the turbulence intensity of the wind field.

[0098] Specifically, the calculation formula for the wind speed standard deviation of the typhoon wind field measured data is:

[0099]

[0100] Among them, σ i represents the wind speed standard deviation of the ith wind speed time series segment of the typhoon wind field measured data; μ i represents the mean wind speed of the ith wind speed time series segment of the typhoon wind field measured data; x j,i represents the j-th wind speed value of the i-th wind speed time series segment during the typhoon; n represents the number of wind speed time series segments during the i-th wind speed time series segment, and n>600.

[0101] Finally, after processing the measured data of the typhoon wind field, a series of one-to-one corresponding sets of wind speed means and standard deviations are obtained.

[0102] S6, calibrating the turbulence intensity of the typhoon wind field based on the wind speed mean and wind speed standard deviation, and constructing a turbulence intensity model. Figure 7 , which is a flow chart of S6 in the typhoon wind field prediction method driven by the fusion of the model and measured data described in this application. Figure 7 As shown, S6 comprises the following steps:

[0103] S61, drawing a scatter plot of the wind speed time series data set during the typhoon period, with the wind speed mean as the horizontal coordinate and the wind speed standard deviation as the vertical coordinate.

[0104] S62, dividing the scattered points in the scatter plot into equal parts with the wind speed as the center, calculating the mean and standard deviation of all wind speed standard deviations in each divided segment, and obtaining a first wind speed mean and standard deviation;

[0105] S63, calculating the turbulence intensity of the typhoon wind field based on the first wind speed mean and standard deviation;

[0106] S64, performing scatter point fitting on the first wind speed mean and standard deviation by regression analysis to obtain a fitting straight line of the first wind speed mean and standard deviation;

[0107] S65: Generate the turbulence intensity curve based on the fitting straight line of the first wind speed mean and standard deviation, and construct a turbulence intensity model.

[0108] See also Figure 8 and Fig. 9 , which respectively show a wind speed standard deviation-average wind speed fitting schematic diagram of a typhoon wind field prediction method driven by the fusion of the model and measured data described in the present application in one embodiment, and a turbulence intensity curve schematic diagram of a typhoon wind field prediction method driven by the fusion of the model and measured data described in the present application in one embodiment.

[0109] In this embodiment, a scatter plot of the wind speed time series data set is drawn with the wind speed mean as the horizontal coordinate and the wind speed standard deviation as the vertical coordinate.

[0110] Specifically, the horizontal axis is divided equally with the mean of each wind speed as the center and 1m / s as the range. For example, with 5m / s as the center, the scattered points are divided from 4.5m / s to 5.5m / s. According to IEC specifications, the turbulence intensity is calculated based on the standard deviation of the wind speed at the 90th percentile. Therefore, the standard deviation of all wind speeds in each segment is calculated separately. i The mean μ σ and standard deviation σ σ . Wind speed standard deviation σ i The distribution of is Gaussian, so the standard deviation of the 90th percentile wind speed σ 90 The corresponding Z value is 1.28.

[0111] 90th percentile wind speed standard deviation σ 90 The calculation formula is:

[0112] σ 90 =μ σ +1.28σ σ

[0113] The turbulence intensity represented by each segment is composed of the central wind speed μ and the 90th percentile wind speed standard deviation σ of the segment. 90 to calculate.

[0114] The calculation formula of the turbulence intensity is:

[0115] TI=σ 90 / μ

[0116] Where TI represents the turbulence intensity; σ 90 represents the 90th percentile wind speed standard deviation; μ represents the central wind speed of the segment.

[0117] Specifically, a scatter plot of the turbulence intensity data set is drawn with the wind speed mean as the horizontal coordinate and the turbulence intensity as the vertical coordinate. Through regression analysis, the wind speed mean and standard deviation scatter plot can be fitted by a first-order function. The fitted straight line can provide a prediction and estimate of the wind speed standard deviation. Through calculation, the change curve of turbulence intensity can be obtained, that is, the turbulence intensity model.

[0118] The formula for the fitted straight line of the first wind speed mean and standard deviation is:

[0119] σ=α1μ+α2

[0120] The turbulence intensity curve of the first wind speed mean and standard deviation is:

[0121]

[0122] Among them, α1 represents the slope of the fitted line; α2 represents the intercept of the fitted line; μ represents the expectation of the wind speed time series segment data set during the typhoon.

[0123] S7, based on the wind speed and wind direction time series data during the typhoon period and the turbulence intensity under the typhoon condition, calculate the complete typhoon wind field wind speed time series data during the typhoon period to complete the prediction of the typhoon wind field. Fig.10 , which is a flow chart of S7 in the typhoon wind field prediction method driven by the fusion of the model and measured data described in this application. Fig.10 As shown, the S7 comprises the following steps:

[0124] S71, based on the wind speed and wind direction time series data during the typhoon, designing design load conditions of multiple wind turbines under the typhoon, and obtaining the average wind speed time series data during the typhoon;

[0125] S72, inputting the average wind speed in each design load condition into the turbulence intensity model to obtain the turbulence intensity under the typhoon condition;

[0126] S73, calculating the typhoon wind field time series data during the typhoon period based on the turbulence intensity under the typhoon condition.

[0127] In this embodiment, according to steps S1 to S3, the ten-minute average wind speed time series data during the typhoon period can be obtained by engineering simulation software, and combined with relevant specifications, the design load cases (Design Load Case, DLC) of several wind turbines under the action of typhoon can be designed. Through steps 4 to 7, the turbulence intensity model of the actual typhoon wind field in the observation area can be obtained. The ten-minute average wind speed μ in each design load case is 10min Substitute the first wind speed mean and standard deviation into the turbulence intensity curve to determine the turbulence intensity TI under typhoon conditions 10min . Use wind farm software, such as TurbSim, to calculate the average wind speed μ 10min and turbulence intensity TI 10min By inputting it, you can get the complete 10-minute typhoon wind field wind speed time series.

[0128] The present application provides a typhoon wind field prediction method driven by the fusion of model and measured data. By processing and analyzing the measured data, a turbulence intensity model based on the measured data is fitted, and the typhoon wind field wind speed time series is constructed in combination with the model data, thereby improving the accuracy of numerical simulation and more accurately simulating and analyzing the wind field under the influence of typhoon.

[0129] The protection scope of the typhoon wind field prediction method driven by the fusion of the model and measured data described in the embodiment of the present application is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0130] In summary, the typhoon wind field prediction method driven by the fusion of model and measured data provided in this application has the following beneficial effects:

[0131] The present application provides a typhoon wind field prediction method driven by the fusion of model and measured data. By processing and analyzing the measured data, a turbulence intensity model based on the measured data is fitted, and the typhoon wind field wind speed time series is constructed in combination with the model data, thereby improving the accuracy of numerical simulation and more accurately simulating and analyzing the wind field under the influence of typhoon.

[0132] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A typhoon wind field prediction method driven by the fusion of model and measured data, characterized in that: The following steps are involved: Obtain the basic parameters of the target typhoon model; Constructing a typhoon model based on the basic parameters of the target typhoon model, and obtaining a two-dimensional wind field within the typhoon period; Extracting the time series data of wind speed and direction during the typhoon period in the target area based on the two-dimensional wind field in the typhoon period; Collect the original data of typhoon wind field and extract the time series data of wind speed during typhoon; Processing the wind speed time series data during the typhoon to obtain the wind speed mean and wind speed standard deviation of the typhoon wind field measured data; Calibrate the turbulence intensity of the typhoon wind field based on the wind speed mean and wind speed standard deviation, and construct a turbulence intensity model; Based on the wind speed and wind direction time series data during the typhoon and the turbulence intensity under typhoon conditions, the complete typhoon wind field wind speed time series data during the typhoon period is calculated to complete the prediction of the typhoon wind field.

2. The typhoon wind field prediction method driven by the fusion of model and measured data according to claim 1 is characterized in that: Obtaining the basic parameters of the target typhoon model includes the following steps: Acquire typhoon evolution parameters of the target sea area; the typhoon evolution parameters include: any one or more combinations of typhoon path, central air pressure, and maximum wind speed; Based on the typhoon characteristics of the target sea area and the typhoon evolution parameters, analysis and estimation are performed to obtain the basic parameters of the target typhoon model.

3. The typhoon wind field prediction method driven by the fusion of model and measured data according to claim 1 is characterized in that: Constructing a typhoon model based on the basic parameters of the target typhoon model and obtaining a two-dimensional wind field within a typhoon period includes the following steps: The Young&Sobey model and the Holland model are used to calculate the cyclone wind gradient and pressure of the target typhoon to obtain the typhoon model; The typhoon model is corrected by combining the geostrophic correction method, the predecessor motion asymmetry correction method and the wind inflow angle correction method to obtain the two-dimensional wind field of the entire typhoon cycle; Set the time resolution of typhoon model simulation output; The two-dimensional wind field of the target typhoon within the typhoon period is calculated based on the typhoon model.

4. The typhoon wind field prediction method driven by the fusion of model and measured data according to claim 3 is characterized in that: The calculation formula of cyclone wind gradient based on Young&Sobey model is: The air pressure calculation formula based on the Young&Sobey model is: The calculation formula of cyclone wind gradient based on the Holland model is: The air pressure calculation formula based on the Holland model is: Among them, V g (r) represents the cyclone wind gradient of the typhoon model; V max Indicates the maximum wind speed; R mw represents the maximum wind speed gradient; r represents the radius of the cyclone; P(r) represents the air pressure value; f represents the Coriolis force coefficient; P c Indicates the central pressure of the cyclone; P n represents the ambient air pressure; B represents the additional coefficient used to couple different cyclone pressure models; ρ A Indicates the air density.

5. The typhoon wind field prediction method driven by the fusion of model and measured data according to claim 1 is characterized in that: Extracting the time series data of wind speed and direction during the typhoon period in the target area based on the two-dimensional wind field within the typhoon period includes the following steps: The two-dimensional wind field in the typhoon period is extracted as a wind vector of the target coordinates by using an interpolation method to obtain a U component and a V component of the target coordinates; The wind vector is converted into a wind speed scalar and a wind direction scalar by a vector-to-scalar method; Constructing a wind speed and direction data set of the target coordinates based on the wind speed scalar and the wind direction scalar; The interpolation method is used to fill the missing values ​​and obtain the time series data of wind speed and direction in the target area during the typhoon.

6. The typhoon wind field prediction method driven by the fusion of model and measured data according to claim 1 is characterized in that: Processing the wind speed time series data during the typhoon to obtain the wind speed mean and wind speed standard deviation of the typhoon wind field measured data includes the following steps: Dividing the typhoon-period wind speed time series data into a plurality of typhoon-period wind speed time series segments according to a preset time period; wherein each typhoon-period wind speed time series segment includes a plurality of one-to-one corresponding wind speed data and wind direction data; Construct a dataset of wind speed time series fragments during typhoons; Calculate the wind speed mean and wind speed standard deviation of the typhoon wind field measured data of the wind speed time series segment data set during the typhoon.

7. The typhoon wind field prediction method driven by the fusion of model and measured data according to claim 6 is characterized in that: The calculation formula for the wind speed standard deviation of the measured data of the typhoon wind field is: Among them, σ i represents the wind speed standard deviation of the ith wind speed time series segment of the typhoon wind field measured data; μ i represents the mean wind speed of the ith wind speed time series segment of the typhoon wind field measured data; x j,i represents the j-th wind speed value of the i-th wind speed time series segment during the typhoon; n represents the number of wind speed time series segments during the i-th wind speed time series segment, and n>600.

8. The typhoon wind field prediction method driven by the fusion of model and measured data according to claim 1 is characterized in that: Based on the wind speed mean and wind speed standard deviation, the turbulence intensity of the typhoon wind field is calibrated, and the turbulence intensity model is constructed, which includes the following steps: With the wind speed mean as the horizontal coordinate and the wind speed standard deviation as the vertical coordinate, a scatter plot of the wind speed time series data set during the typhoon is drawn; Divide the scattered points in the scatter plot into equal parts with the wind speed as the center, calculate the mean and standard deviation of all wind speed standard deviations in each divided segment, and obtain a first wind speed mean and standard deviation; Calculating the turbulence intensity of the typhoon wind field based on the first wind speed mean and standard deviation; Performing scatter point fitting on the first wind speed mean and standard deviation by regression analysis to obtain a fitting straight line of the first wind speed mean and standard deviation; Generating the turbulence intensity curve based on the fitting straight line of the first wind speed mean and standard deviation, and constructing a turbulence intensity model; in, The formula for the fitted straight line of the first wind speed mean and standard deviation is: σ=α1μ+α2 The turbulence intensity curve of the first wind speed mean and standard deviation is: Among them, α1 represents the slope of the fitted line; α2 represents the intercept of the fitted line; μ represents the expectation of the wind speed time series segment data set during the typhoon.

9. The typhoon wind field prediction method driven by the fusion of model and measured data according to claim 1 is characterized in that: Based on the wind speed and wind direction time series data during the typhoon and the turbulence intensity under the typhoon conditions, the complete typhoon wind field wind speed time series data during the typhoon period is calculated to complete the prediction of the typhoon wind field, including the following steps: Based on the wind speed and wind direction time series data during the typhoon, design load conditions of multiple wind turbines under the typhoon, and obtain the average wind speed time series data during the typhoon; The average wind speed in each design load condition is input into the turbulence intensity model to obtain the turbulence intensity under the typhoon condition; The typhoon wind field time series data during the typhoon period is calculated based on the turbulence intensity under the typhoon conditions.

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