A design wind speed analysis method and device for areas without measured wind speed data

Abstract

The present invention provides a design wind speed analysis method and device for areas without measured wind speed data. The method comprises the following steps: based on the measured wind speed data, a set time interval average wind speed data sample is simulated and generated for a set period of time and a set time interval every day; a daily maximum sample is selected from the set time interval average wind speed data sample to form a daily maximum set time interval average wind speed sequence; a wind speed sample greater than a preset threshold is selected from the daily maximum set time interval average wind speed sequence to form an over-threshold sequence; a generalized Pareto distribution model is used to fit the over-threshold sequence to determine a scale parameter σ; and a design wind speed V with a recurrence period of R years is obtained based on the scale parameter σ and the average number of samples per month of the over-threshold sequence. R ; For design wind speed V R The present invention solves the problem of determining the value of the design wind speed in areas without measured wind speed data, and provides support for the wind-resistant design of engineering structures in areas without measured wind speed data.

Description

Technical Field

[0001] The invention relates to a design wind speed analysis method and device for areas without measured wind speed data, belonging to the field of engineering wind-resistant structure design. Background Art

[0002] In the wind-resistant system of engineering structures, the assessment of wind-resistant failure risk is extremely important, and the analysis and calculation of the important parameter, the design wind speed, has a decisive influence. The design wind speed value selection method recommended by existing regulations and specifications mainly adopts the Gumbel frequency distribution model (extreme type I) and the P-III type frequency distribution model. Both frequency distribution models require at least 25 years of the maximum 10-minute average wind speed sequence for input to meet the requirements of structural wind-resistant design.

[0003] However, many engineering construction areas are far away from long-term meteorological stations and do not have the 25-year maximum 10-minute average wind speed sequence required by regulations and specifications, which makes it extremely difficult to calculate the engineering design wind speed in areas with insufficient wind measurement data. In some underdeveloped countries and regions such as overseas, many engineering areas do not have long-term wind speed observation data, making it impossible to carry out the method of relying on long-term wind measurement data from meteorological stations for engineering wind resistance design. Many engineering designers will use the operating experience and structural design of surrounding projects to estimate the design wind speed, but there are many problems with the estimation results, which will cause a waste of engineering investment and will also cause wind disasters during the operation period of the project due to insufficient representativeness of existing projects. Summary of the invention

[0004] The purpose of the present invention is to provide a design wind speed analysis method for areas without measured wind speed data, solve the problem of engineering design wind speed value selection in areas without measured wind speed data, and provide a reference for wind resistance of engineering structures.

[0005] In order to achieve the above technical objectives, the present invention adopts the following technical solutions.

[0006] On the one hand, an embodiment of the present invention provides a design wind speed analysis method for an area without measured wind speed data, comprising the following steps:

[0007] Conduct actual measurements to obtain wind speed data in a specific project area, and simulate and generate a set time-distance average wind speed data sample at a set time interval every day for a set number of years based on the obtained wind speed data;

[0008] Select the daily maximum sample from the set time interval average wind speed data samples to form the daily maximum set time interval average wind speed sequence; select the wind speed sample greater than the preset threshold from the daily maximum set time interval average wind speed sequence to form an over-threshold sequence;

[0009] The generalized Pareto distribution model is used to fit the super-threshold sequence and determine the scale parameter σ. Based on the scale parameter σ and the average number of samples per month of the super-threshold sequence λ, the design wind speed V with a return period of R years is obtained. R ;

[0010] For design wind speed V R A rationality analysis is conducted on the value of to determine the design wind speed in areas where there is no measured wind speed data in the specific project area.

[0011] In a specific embodiment, the daily setting time interval can be set hour by hour or according to needs, and the setting time interval average wind speed data sample can be a 2-minute, 5-minute or 10-minute average wind speed data sample.

[0012] Preferably, the method for determining the preset threshold u0 is as follows:

[0013]

[0014] in is the average value of the daily maximum set time-distance wind speed sequence, and S is the standard deviation of the daily maximum set time-distance wind speed sequence.

[0015] Preferably, the design wind speed V R The approximate calculation formula is as follows:

[0016] V R =u0+σln(12λR)

[0017] Where u0 is the preset threshold, σ is the scale parameter determined by fitting the super-threshold sequence using the generalized Pareto distribution model, λ is the average number of samples per month of the super-threshold sequence, λ=n /

[0018] (12T), n is the number of samples of the super-threshold sequence, and T is the set number of years.

[0019] Preferably, for the design wind speed V R The specific method for determining the design wind speed in areas without measured wind speed data for a specific project area is as follows:

[0020] The KS goodness-of-fit test method is used to test whether the threshold-exceeding series conforms to the generalized Pareto distribution model. 2 The test method tests whether the number of monthly threshold-exceeding occurrences obeys a Poisson distribution with λ as the parameter, and determines the design wind speed in areas without measured wind speed data in specific engineering areas based on the set confidence level.

[0021] Preferably, the method for simulating and generating the set time interval average wind speed data based on the acquired wind speed data is as follows:

[0022] Based on the measured wind speed data and combined with the ERA5 reanalysis data, the optimal parameterization scheme is obtained through simulation and verification of the mesoscale WRF numerical model, and the average wind speed data for the set time interval is simulated and generated.

[0023] In a second aspect, an embodiment of the present invention provides a design wind speed analysis device for areas without measured wind speed data, comprising: a simulation data generation module, an over-threshold sequence determination module, an over-threshold sequence fitting module, a design wind speed calculation module, and a rationality verification module;

[0024] The simulation data generation module is used to obtain wind speed data by actual measurement in a specific project area, and to simulate and generate a set time-distance average wind speed data sample at a set time interval for a set number of years and days based on the obtained wind speed data;

[0025] The over-threshold sequence determination module is used to select the daily maximum sample from the set time-distance average wind speed data obtained by the simulation data generation module to form a daily maximum set time-distance average wind speed sequence; select wind speed samples greater than a preset threshold from the daily maximum set time-distance average wind speed sequence to form an over-threshold sequence;

[0026] The super-threshold sequence fitting module is used to fit the super-threshold sequence determined by the super-threshold sequence determination module using a generalized Pareto distribution model to determine a scale parameter σ;

[0027] The design wind speed calculation module is used to obtain the design wind speed V with a return period of R years based on the scale parameter σ and the average number of samples per month of the super-threshold sequence λ R ;

[0028] The rationality verification module is used to verify the design wind speed V R A rationality analysis is conducted on the value of to determine the design wind speed in areas where there is no measured wind speed data in the specific project area.

[0029] Preferably, the super-threshold sequence determination module further includes a preset threshold determination submodule, and the threshold determination submodule is used to determine the preset threshold. The method for determining the preset threshold u0 is as follows:

[0030]

[0031] in is the average value of the daily maximum set time-distance average wind speed sequence, and S is the standard deviation of the daily maximum set time-distance average wind speed sequence.

[0032] Preferably, the design wind speed calculation module determines the design wind speed V R The approximate calculation formula is as follows:

[0033] V R =u0+σln(12λR)

[0034] Where u0 is the preset threshold, σ is the scale parameter σ determined by fitting the super-threshold sequence using the generalized Pareto distribution model, λ is the average number of samples per month in the super-threshold sequence, λ=n / (12T), n is the number of samples in the super-threshold sequence, and T is the set number of years.

[0035] Preferably, the rationality verification module uses the KS goodness-of-fit test method to test whether the threshold-exceeding series conforms to the generalized Pareto distribution model, using the χ 2 The test method tests whether the number of monthly threshold-exceeding occurrences obeys a Poisson distribution with λ as the parameter, and determines the design wind speed in areas without measured wind speed data in specific engineering areas based on the set confidence level.

[0036] It can be seen from the above technical solutions that the embodiments of the present invention have the following beneficial technical effects:

[0037] The present invention uses a statistical method to solve the problem of design wind speed value when the wind measurement data is insufficient, and provides support for the wind resistance design of engineering structures in areas where wind measurement data is insufficient. The calculation method provided by the present invention can shorten the required data period, and short-term wind speed measurement can be carried out in the early stage of engineering design, solving the current situation that engineering wind resistance design relies on long-term wind measurement data from meteorological stations.

[0038] The design wind speed calculation in areas without wind measurement data is solved by combining short-term temporary observation data, ERA5 global reanalysis data and mathematical statistics methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Shown is a method flow chart of a specific embodiment of the present invention;

[0040] Figure 2 The following is a diagram showing the setting of the observation point simulation area according to a specific embodiment of the present invention;

[0041] Figure 3 The figure shows the simulation test results within the observation period of the observation point of the specific embodiment of the present invention;

[0042] Figure 4 The figure shows the change diagram of the maximum 10-minute average wind speed of a certain island weather station in a specific embodiment of the present invention;

[0043] Figure 5 Shown is a PP diagram (probability diagram) of a specific embodiment of the present invention;

[0044] Figure 6 Shown is a QQ graph (quantile graph) of a specific embodiment of the present invention. DETAILED DESCRIPTION

[0045] The present invention is further described below in conjunction with the accompanying drawings and examples. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and cannot be used to limit the protection scope of the present invention.

[0046] Combination Figure 1 , further illustrating the technical solution of the present invention.

[0047] Embodiment 1: A design wind speed analysis method for an area without measured wind speed data comprises the following steps:

[0048] Conduct actual measurements to obtain wind speed data in a specific project area, and simulate and generate average wind speed data with a time interval of 10 minutes (preferably not less than 2 years) based on the obtained wind speed data;

[0049] Select the daily maximum 10-minute average wind speed sample from the 10-minute average wind speed data to form the daily maximum 10-minute average wind speed sequence {X};

[0050] Assume that the maximum 10-minute average wind speed series {X i}, the threshold is u0, and wind speed samples greater than the preset threshold are selected from the daily maximum wind speed sequence to form an ultra-threshold series. The ultra-threshold series {X|X>u0} approximately conforms to the following GPD distribution.

[0051]

[0052] In the formula, u0 is the threshold, σ is the scale parameter, and is always a positive value. F G (x) is the distribution function of GPD, P(X≤x|X>u0) is the probability that the super-threshold average wind speed sequence {X|X>u0} is less than x, and x is an arbitrary number.

[0053] Due to the randomness of wind speed, it can be considered that the occurrence of wind speed exceeding the threshold is also random. According to the classic assumption of the super-threshold model, the number of strong winds exceeding the threshold each year is a random variable m, and it obeys the Poisson distribution:

[0054]

[0055] Wherein, P(m=k) means the probability when the number of occurrences is k, k is any non-negative integer, λ1 is the average number of occurrences exceeding the threshold each year, λ1>0.

[0056] According to the classic assumption of the super-threshold model, the number of strong winds exceeding the threshold each year follows a Poisson distribution with λ1 as the parameter, so there will be λ1R strong winds exceeding the threshold in R years. The corresponding recurrence period of a strong wind once in R years is

[0057]

[0058] Where P e The frequency corresponding to the design wind speed of the GPD model.

[0059] Corresponding to the R-year design wind speed V R It can be calculated by the following formula.

[0060] V R =u0+σln(λ1R) (4)

[0061] For short-term data, the monthly maximum wind speed can be used to estimate the annual maximum wind speed distribution. Here, it is assumed that the monthly maximum wind speed of each month meets the independent and identically distributed conditions. At this time, the annual maximum distribution and monthly maximum distribution of wind speed meet the following formula:

[0062] F a (x)=[F m (X)] 12 (5)

[0063] where F a (x) and F m (X) are the annual maximum and monthly maximum wind speed distributions respectively.

[0064] The present invention is based on the GPD model, λ is the monthly average number of occurrences exceeding the threshold u0, F G (Y) is the GPD distribution of short-term wind speed data, and the following formula is obtained:

[0065] F m (X) = λF G (Y) (6)

[0066] According to formula (5), the annual design wind speed V is solved using short-term wind speed data. R , we can get the following formula.

[0067]

[0068] Where σ is the scale parameter of the GPD model for short-term wind speed data.

[0069] After the equation is transformed, the following formula is obtained:

[0070]

[0071] Considering that R>10, 1-(1-1 / R) 1 / 12 ≈1 / (12R), the approximate calculation formula for the design wind speed with different annual return periods is as follows:

[0072] V R =u0+σln(12λR) (9)

[0073] The number of samples in the sequence is n, and the average number of samples per month is λ=n / (12T).

[0074] For design wind speed V R A rationality analysis is conducted on the value of to determine the design wind speed in areas where there is no measured wind speed data in the specific project area.

[0075] Embodiment 2: Based on Embodiment 1, this embodiment describes in detail a design wind speed analysis method for an area without measured wind speed data provided by another embodiment.

[0076] The method for simulating and generating average wind speed data with a time interval of 10 minutes based on the acquired wind speed data is as follows:

[0077] Temporary wind measuring instruments are set up in the project area and short-term measurements are carried out to obtain wind speed data, using the same quality control measures as the national meteorological station. The temporary observation point in this case is set up on Xilian Island, Jiangsu, and the observation period is from 8:00 on June 1, 2020 to 8:00 on June 4, 2020.

[0078] The mesoscale model used is the Weather Research and Forcasting Model WRF4.1.5 (Weather Research and Forcasting Model) released by the National Center for Atmospheric Research in the United States. The model uses ArakawaC grid points in the horizontal direction and terrain-following mass coordinates in the vertical direction. The simulation area is designed with two nested grids centered on the Xilian Island Station (34.7769°N, 119.4481°E), with an outer spatial resolution of 9 kilometers and an inner spatial resolution of 3 kilometers. For details of the area, see Figure 2 . In the process of selecting the vertical resolution of the regional model, if the vertical layering is too coarse, it will amplify the terrain effect, generate false gravity waves in the horizontal direction, cause integral instability or simulation deviation, and if the layering is too fine, a smaller time step is required, the amount of calculation is large, and it does not improve the simulation effect. The appropriate vertical layering should consider the density in the boundary layer and near the ice-water mixing layer, which is conducive to distinguishing the water vapor convergence and latent heat release process, and can simulate the convective system and cloud microphysics more realistically. After comprehensive consideration, the vertical layering selected in this example is 38 layers. In order to avoid calculation instability, the integral time step is 20 seconds. The simulation period is selected as the temporary observation period from 8:00 on June 1, 2020 to 8:00 on June 4, 2020. The total duration of a single simulation is 72 hours. The inner layer simulation directly outputs the 10-meter wind speed at the location of the temporary observation point. The output frequency is 10 minutes, that is, the 10-minute average wind. Considering that the simulation may have a certain time drift, the maximum value of the 10-minute average wind in the entire simulation period is calculated as the simulation value of the maximum daily wind speed.

[0079] The operation of mesoscale regional models requires the provision of initial fields and boundary conditions, which are generally provided by global model results or reanalysis data. Currently, the best publicly available reanalysis data in the world is the fifth generation of global climate reanalysis data (ERA5) released by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 obtains hourly estimates of atmospheric variables at a horizontal resolution of 31km, from the surface down to 0.01hPa (about 80km from the ground) with a total of 137 model layers. The temporal resolution is 3h and the spatial resolution is 62km. The ensemble reanalysis product is used to assess atmospheric uncertainty. In addition, ERA5 uses more historical observation data, especially satellite data, in advanced data assimilation and model systems to estimate more accurate atmospheric conditions. Therefore, this example uses ERA5 data as the model background field to provide initial fields and boundary conditions for WRF simulation.

[0080] According to the characteristics of this case, it is determined that the longwave radiation scheme, shortwave radiation scheme, microphysical process parameterization scheme, land surface process parameterization scheme and boundary layer scheme are mainly used for sensitivity experiments and optimization. The introduction of each parameterization scheme is shown in Table 1.

[0081] Table 1 Basic introduction to the parameterization scheme used in this example

[0082]

[0083]

[0084] In this case, 8 sets of parameterization schemes are set, as shown in Table 2. The parameterization schemes are compared based on the maximum wind speed of 11.1 m / s during the observation period. The simulation results of the 8 sets of parameterization schemes for the maximum wind speed during the observation period vary. The wind speeds simulated by 4 sets of experiments are too high, and those simulated by 4 sets are too low. Comparing the results of case 1 and case 3, the difference between the two sets of experimental parameters lies in the microphysical process parameterization scheme. Case 1 uses the WSM5 scheme, and case 3 uses the WDM5 scheme. As an improved scheme of WSM5, WDM5 is a dual-parameter scheme. From the simulation results, the wind speeds simulated by the two sets of schemes are smaller than the observations, but the wind speed simulated by case 3 is slightly larger than that of case 1 and closer to the observations. Comparing the results of case 3 and case 5, the difference between the two sets of experimental parameters lies in the boundary layer parameterization scheme. Case 3 uses the YSU scheme, and case 5 uses the MYJ scheme. The MYJ scheme is a local scheme, which assumes that the pulsating flux at each grid point is completely determined by the average value of the physical quantity at that grid point. , while the YSU scheme comprehensively considers the influence of the grid points and their surrounding grid points on the pulsating flux. From the simulation results, the simulation effects brought by the two are very different. The wind speed value simulated by case5 is significantly higher than the observed value, while the wind speed value simulated by case3 is slightly smaller than the observed value. Comparing case1 with case8, case2 with case4, and case6 with case7, similar conclusions can be drawn. Comparing the simulation results of case4 with case5, it is found that the wind speed simulation results of the RRTMG+CAM radiation parameter scheme are higher than those of the RRTM+Dudhia scheme. Similar conclusions can be drawn through further comparisons of case7 with case8, case2 with case3, and case1 with case6. From the overall simulation effect, the simulation effect of case3 is closest to the observation, so the parameterization scheme of case3 is selected in this example.

[0085] Table 2 Main parameterization schemes for each group of experiments

[0086]

[0087]

[0088] According to the selected parameterization scheme case3, the simulation generates 10-minute average wind speed data for no less than 2 years, and outputs the daily maximum 10-minute average wind speed for the two years from July 1, 2018 to June 30, 2020. Figure 4 shown.

[0089] Embodiment 3: Based on the above embodiments, this embodiment describes in detail a design wind speed analysis method for an area without measured wind speed data provided by another embodiment. The method for calculating the preset threshold u0 in this embodiment is as follows:

[0090] The average value is obtained by statistical analysis of the daily maximum 10-minute average wind speed for two years from July 1, 2018 to June 30, 2020. Standard deviation S = 3.10, calculate threshold

[0091] Considering that there may be a strong correlation between consecutive wind speed samples, the 10-minute average maximum wind speed data for 4 days is taken as a cycle, and the maximum value of each cycle is selected. The interval between adjacent maximum values ​​cannot be less than half a cycle. The threshold series {X|X>u0} is obtained, the number of samples n=160, and the average number of samples per month λ=6.67.

[0092] The generalized Pareto distribution (GPD) model is used to fit the super-threshold sequence {X|X>u0}, and the scale parameter σ = 2.438 is obtained. When estimating the distribution parameters of the generalized Pareto distribution (GPD) model, the POT and EVIR packages of the R language are used for calculation, which is a prior art and will not be described in detail here.

[0093] Based on the above parameter calculation results, the design wind speed results are shown in the following table.

[0094] Table 3 Design wind speed calculation results of the present invention

[0095] Return period (years) 30 50 100 Design value (m / s) 30.6 31.8 33.5

[0096] In this embodiment, the KS goodness-of-fit test method is used to test whether the threshold-exceeding series conforms to the generalized Pareto distribution model. 2 The test method tests whether the number of monthly threshold-exceeding occurrences follows a Poisson distribution with λ as the parameter, with a confidence level of 95%. Both test methods are prior art and will not be described in detail here. The results in the table below show that both tests have been passed.

[0097] Table 4 KS test, χ 2 Test results

[0098]

[0099] Through the PP diagram (probability diagram, Figure 5 ), QQ chart (quantile chart, Figure 6 ), it can be seen that the GPD model with the threshold selected in the present invention has a better fitting effect on the samples and can reliably reflect the real distribution of the samples.

[0100] To further illustrate the applicability of the results of the present invention. The present invention conducted temporary observations of short-term wind speeds near 13 major national basic meteorological stations in Jiangsu, calculated using the solution provided by the present invention, and compared and analyzed the results of long-term data calculations to verify the reliability of the present invention. The design wind speed was calculated using the Gumbel frequency distribution model recommended by the regulations. The Gumbel frequency distribution model is a prior art and will not be described in detail here.

[0101] The design wind speed calculation results of the long-term and short-term data of each meteorological station show that when the design wind speed is calculated by the present invention, one meteorological station fails to pass the KS goodness of fit test with a confidence level of 95%, and two meteorological stations fail to pass the χ2 test of the Poisson distribution. 2 The details are shown in Table 5.

[0102] Table 5 Comparison of design wind speed results

[0103]

[0104]

[0105] Embodiment 4. The present invention also provides a design wind speed analysis device for areas without measured wind speed data, comprising: a simulation data generation module, an over-threshold sequence determination module, an over-threshold sequence fitting module, a design wind speed calculation module and a rationality verification module;

[0106] The simulation data generation module is used to obtain wind speed data by actual measurement in a specific project area, and to simulate and generate average wind speed data samples with a set time interval of 10 minutes per day (hourly in this embodiment) for a set period of years (not less than 2 years in this embodiment) based on the obtained wind speed data;

[0107] The super-threshold sequence determination module is used to select the daily maximum sample from the 10-minute average wind speed data samples obtained by the simulation data generation module to form a daily maximum 10-minute average wind speed sequence; select wind speed samples greater than a preset threshold from the daily maximum 10-minute average wind speed sequence to form a super-threshold sequence;

[0108] The super-threshold sequence fitting module is used to fit the super-threshold sequence determined by the super-threshold sequence determination module using a generalized Pareto distribution model to determine a scale parameter σ;

[0109] The design wind speed calculation module is used to obtain the design wind speed V with a return period of R years based on the scale parameter σ and the average number of samples per month of the super-threshold sequence λ R ;

[0110] The rationality verification module is used to verify the design wind speed V RA rationality analysis is conducted on the value of to determine the design wind speed in areas where there is no measured wind speed data in the specific project area.

[0111] The above is a detailed description of each module of a design wind speed analysis device for an area without measured wind speed data. The following is a detailed description of each additional module of a design wind speed analysis device for an area without measured wind speed data. This embodiment provides another embodiment of a design wind speed analysis device for an area without measured wind speed data, including:

[0112] The super-threshold sequence determination module further includes a preset threshold determination submodule, which is used to determine a preset threshold. The method for determining the preset threshold u0 is as follows:

[0113]

[0114] in is the average value of the daily maximum 10-minute average wind speed sequence, and S is the standard deviation of the daily maximum 10-minute average wind speed sequence.

[0115] The design wind speed calculation module determines the design wind speed V R The approximate calculation formula is as follows:

[0116] V R =u0+σln(12λR)

[0117] Where u0 is the preset threshold, σ is the scale parameter σ determined by fitting the super-threshold sequence using the generalized Pareto distribution model, λ is the average number of samples per month in the super-threshold sequence, λ=n / (12T), n is the number of samples in the super-threshold sequence, and T is the number of years.

[0118] The rationality verification module uses the KS goodness-of-fit test method to test whether the threshold-exceeding series conforms to the generalized Pareto distribution model, and uses the χ 2 The test method tests whether the number of monthly threshold-exceeding occurrences obeys a Poisson distribution with λ as the parameter, and determines the design wind speed in areas without measured wind speed data in specific engineering areas based on the set confidence level.

[0119] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described device and the units / modules / sub-modules in the device can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0120] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0121] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, the functional units in the various embodiments of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software functional units.

[0122] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0123] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A design wind speed analysis method for areas without measured wind speed data, characterized in that: The following steps are involved: Conduct actual measurements to obtain wind speed data in a specific project area, and simulate and generate a set time-distance average wind speed data sample at a set time interval every day for a set number of years based on the obtained wind speed data; Select the daily maximum sample from the set time interval average wind speed data samples to form the daily maximum set time interval average wind speed sequence ; Select wind speed samples greater than a preset threshold from the daily maximum set time-distance average wind speed sequence to form an over-threshold sequence; Using a broad Pareto Distribution model fitting super-threshold sequence, determining scale parameter σ ; Based on the scale parameter σ and the average number of samples per month for the super-threshold series λ Get the return period as R Design wind speed for the year V R ; Design wind speed V R The rationality of the value of is analyzed to determine the design wind speed in the specific project area where there is no measured wind speed data; Among them, the super-threshold sequence According to GPD distribution: ； In the formula, is the threshold value, is the scale parameter and is always positive. , is the distribution function of GPD, is the super-threshold average wind speed series Smaller than The probability of is any number; Number of strong winds exceeding the threshold each year m is a random variable and obeys Poisson distributed: ； In the formula, The meaning is that the number of occurrences is k The probability of k is any non-negative integer, λ 1 is the average number of times exceeding the threshold occurs each year, ; exist R The CCP λ 1 R A wind exceeding the threshold corresponds to R The annual return period of strong winds is: ； in The frequency corresponding to the design wind speed of the GPD model.

2. The design wind speed analysis method for areas without measured wind speed data according to claim 1 is characterized in that: Design wind speed V R The approximate calculation formula is as follows: ； in, λ = n / (12 T ), n is the number of samples exceeding the threshold sequence, and T is the set number of years.

3. The design wind speed analysis method for areas without measured wind speed data according to claim 1 is characterized in that: Design wind speed V R The specific method for determining the design wind speed in areas without measured wind speed data for a specific project area is as follows: use KS The goodness-of-fit test method tests whether the threshold-exceeding sequence conforms to the generalized Pareto distribution model. 2 The test method is to check whether the number of occurrences of exceeding the threshold value each month complies with the following λ For parameters Poisson Distribution, determine the design wind speed in areas without measured wind speed data in specific project areas based on the set confidence level.

4. The design wind speed analysis method for areas without measured wind speed data according to claim 1 is characterized in that: The method for simulating and generating the average wind speed data of the set time interval based on the acquired wind speed data is as follows: Based on the measured wind speed data and combined with the ERA5 reanalysis data, the optimal parameterization scheme is obtained through simulation and verification of the mesoscale WRF numerical model, and the average wind speed data for the set time interval is simulated and generated.

5. The design wind speed analysis method for areas without measured wind speed data according to claim 1 is characterized in that: The time interval average wind speed data is set to use the 10-minute average wind speed data.

6. A design wind speed analysis device for areas without measured wind speed data, characterized in that: include: Simulation data generation module, super-threshold sequence determination module, super-threshold sequence fitting module, design wind speed calculation module and rationality verification module; The simulation data generation module is used to obtain wind speed data by actual measurement in a specific project area, and to simulate and generate a set time-distance average wind speed data sample at a set time interval for a set number of years and days based on the obtained wind speed data; The over-threshold sequence determination module is used to select the daily maximum sample from the set time-distance average wind speed data samples obtained by the simulation data generation module to form the daily maximum set time-distance average wind speed sequence ; Select wind speed samples greater than a preset threshold from the daily maximum set time-distance average wind speed sequence to form an over-threshold sequence; The super-threshold sequence fitting module is used to adopt a generalized Pareto Distribution model fitting super-threshold sequence determination module determines the super-threshold sequence and determines the scale parameter σ ; The design wind speed calculation module is used to calculate the wind speed based on the scale parameters. σ and the average number of samples per month for the super-threshold series λ Get the return period as R Design wind speed for the year V R ; The rationality verification module is used to verify the design wind speed V R The rationality of the value of is analyzed to determine the design wind speed in the specific project area where there is no measured wind speed data; Among them, the super-threshold sequence According to GPD distribution: ； In the formula, is the threshold value, is the scale parameter and is always positive. , is the distribution function of GPD, is the super-threshold average wind speed series Smaller than The probability of is any number; Number of strong winds exceeding the threshold each year m is a random variable and obeys Poisson distributed: ； In the formula, The meaning is that the number of occurrences is k The probability of k is any non-negative integer, λ 1 is the average number of times exceeding the threshold occurs each year, ; exist R The CCP λ 1 R A wind exceeding the threshold corresponds to R The annual return period of strong winds is: ； in The frequency corresponding to the design wind speed of the GPD model.

7. The design wind speed analysis device for areas without measured wind speed data according to claim 6, characterized in that: The design wind speed calculation module determines the design wind speed V R The approximate calculation formula is as follows: ； in, λ = n / (12 T ), n is the number of samples exceeding the threshold sequence, and T is the number of years.

8. The design wind speed analysis device for areas without measured wind speed data according to claim 6, characterized in that: The rationality verification module is used KS The goodness-of-fit test method tests whether the threshold-exceeding sequence conforms to the generalized Pareto distribution model. 2 The test method is to check whether the number of occurrences of exceeding the threshold value each month complies with the following λ For parameters Poisson Distribution, determine the design wind speed in areas without measured wind speed data in specific project areas based on the set confidence level.

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