Future maximum wind speed prediction method and device, equipment and storage medium

Abstract

The invention discloses a future maximum wind speed prediction method and device, equipment and a storage medium. The method comprises the following steps: acquiring actual measurement data of at least one preset anemometer tower in a first historical period and related data of meteorological reanalysis data in a second historical period; according to a data rule of related data of meteorological reanalysis data in a second historical period and actually measured data of the first historical period, a plurality of speculated maximum wind speeds of the at least one preset anemometer tower in the second historical period are speculated through a matrix time sequence method; and analyzing the predicted maximum wind speed in a third time period in the future through any one or more of a national standard method, an independent storm method and an EWTSII method. According to the technical scheme, the data sample of the future maximum wind speed of at least one anemometer tower is obtained based on the correlation between the long-term ERA5 meteorological reanalysis data and the short-term measured data, and the future predicted maximum wind speed is speculated.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method, device, equipment and storage medium for predicting future maximum wind speed. Background Art

[0002] The maximum wind speed in the area where the wind farm is located once in 50 years is a key indicator for determining the ultimate load of the wind turbine, and is also one of the key indicators for selecting the turbine in the development of wind power projects. This maximum wind speed will directly affect the future operation of the wind farm, especially in terms of safe operation, construction costs and power generation benefits. The calculation of the maximum wind speed usually involves the following two issues:

[0003] (1) The calculation of the ultimate wind speed requires a certain amount of large wind speed samples. However, due to the limitations of project development costs and progress, the initial wind measurement period is often only 1-2 years, and the sample size is insufficient.

[0004] (2) The calculation method of the extreme wind speed commonly used in the industry often refers to the Type I extreme value probability distribution method in the "National Wind Energy Resource Evaluation Technical Regulations": first estimate the maximum wind speed at a height of 10 meters at a meteorological station with long-term data once in 50 years, and then estimate the maximum wind speed at different heights in the wind farm once in 50 years based on the correlation between the meteorological station and the wind farm wind speed. However, since the meteorological station is generally far away from the wind farm and the terrain and topography of the two are quite different, the correlation is generally poor.

[0005] How to properly solve the above problems has become an urgent issue to be solved in the industry. Summary of the invention

[0006] The present invention provides a method, device, equipment and storage medium for predicting the future maximum wind speed, which are used to obtain data samples of the future maximum wind speed of at least one wind measuring tower based on the correlation between long-term ERA5 meteorological reanalysis data and short-term measured data, and to infer the future predicted maximum wind speed, and further obtain the predicted maximum wind speed of the target unit time according to the wind speed coefficient between different time granularities.

[0007] According to a first aspect of the present invention, a method for predicting a future maximum wind speed is provided, the method for predicting a future maximum wind speed comprising:

[0008] Obtaining measured data of a first historical period and relevant data of meteorological reanalysis data of at least one preset wind measurement tower in a second historical period, wherein the duration of the second historical period is longer than the duration of the first historical period;

[0009] According to the data rules of the relevant data of the meteorological reanalysis data in the second historical period and the measured data of the first historical period, a plurality of estimated maximum wind speeds of the at least one preset wind measuring tower in the second historical period are estimated by using a matrix time series method;

[0010] According to the multiple estimated maximum wind speeds of the preset at least one wind tower in the second historical period, the predicted maximum wind speed in the future third period is analyzed by any one or more of the national standard method, the independent storm method and the EWTSII method.

[0011] In one embodiment, the predicted maximum wind speed in the future third period is analyzed based on the multiple estimated maximum wind speeds of the preset at least one wind tower in the second historical period by any one or more of the national standard method, the independent storm method and the EWTSII method, including:

[0012] According to the extreme value type I probability distribution in "NBT 31147-2018 Technical Specification for Measurement and Assessment of Wind Energy Resources in Wind Farm Engineering", multiple predicted wind speeds in the third time period in the future are fitted;

[0013] The fitting result is corrected according to the preset location parameters and scale parameters of at least one wind tower to obtain the predicted maximum wind speed in the future third time period.

[0014] In one embodiment, according to the multiple estimated maximum wind speeds of the preset at least one wind tower in the second historical period, the predicted maximum wind speed in the future third period is analyzed by any one or more of the national standard method, the independent storm method and the EWTSII method, further comprising:

[0015] According to the preset empirical distribution function of independent ordered maximum value samples, the extreme value type I Gumbel distribution is transformed into a linear function of regression period and regression level;

[0016] The predicted maximum wind speed in the third period in the future is calculated using the value of the regression period.

[0017] In one embodiment, it further includes:

[0018] According to the EWTSII method, the predicted maximum wind speed in the third period in the future is calculated as follows:

[0019]

[0020] Among them, Vmax is the predicted maximum wind speed in the third period in the future, Vave is the average annual wind speed, Γ is a constant, k is the Weibull coefficient, Tr is the regression period, and n is the number of samples.

[0021] In one embodiment, it further includes:

[0022] Compare the values ​​of the predicted maximum wind speed in the third future period calculated by the national standard method, the independent storm method and the EWTSII method;

[0023] The predicted maximum wind speed with the largest value is selected as the predicted maximum wind speed in the third future time period.

[0024] In one embodiment, it further includes:

[0025] Based on the data of different time granularities, the wind speed coefficient for converting the maximum wind speed of the first unit time into the maximum wind speed of the second unit time is calculated;

[0026] The predicted maximum wind speed in the third future period is converted by the wind speed coefficient to obtain the predicted maximum wind speed in the third future period of target unit time;

[0027] According to the preset air density of at least one wind tower, a predicted maximum wind speed within a future third time period of a target unit time under the standard air density is calculated.

[0028] According to a second aspect of the present invention, there is provided a device for predicting future maximum wind speed, comprising:

[0029] An acquisition module, used to acquire measured data of a first historical period and relevant data of meteorological reanalysis data of at least one preset wind measurement tower in a second historical period, wherein the duration of the second historical period is longer than the duration of the first historical period;

[0030] An inference module, used to infer multiple inferred maximum wind speeds of the at least one preset wind tower in the second historical period by using a matrix time series method according to the data rules of the relevant data of the meteorological reanalysis data in the second historical period and the measured data of the first historical period;

[0031] The analysis module is used to analyze the predicted maximum wind speed in the future third period according to the multiple inferred maximum wind speeds of the preset at least one wind measuring tower in the second historical period through any one or more of the national standard method, the independent storm method and the EWTSII method.

[0032] In one embodiment, the acquisition module, the inference module, the analysis module and the conversion module are controlled to execute any one of the above-mentioned methods for predicting the future maximum wind speed.

[0033] According to a third aspect of the present invention, there is provided an electronic device, the electronic device comprising: a communication interface, a processor, and a memory;

[0034] The memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, any of the above-mentioned methods for predicting the future maximum wind speed is implemented.

[0035] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having computer program instructions stored thereon, which implement any of the above-mentioned methods for predicting future maximum wind speeds when executed by a computer (e.g., a processor in a computer).

[0036] In summary, the present invention provides a method and device for predicting the maximum wind speed in the future, the method comprising: obtaining measured data of the first historical period of at least one preset wind tower and relevant data of meteorological reanalysis data in the second historical period, the duration of the second historical period being greater than the duration of the first historical period; inferring multiple inferred maximum wind speeds of the preset at least one wind tower in the second historical period through the matrix time series method according to the data regularity of the relevant data of the meteorological reanalysis data in the second historical period and the measured data of the first historical period; analyzing the predicted maximum wind speed in the third period in the future according to the multiple inferred maximum wind speeds of the preset at least one wind tower in the second historical period through any one or more of the national standard method, the independent storm method and the EWTSII method. The technical solution of the present application is based on the correlation between long-term ERA5 meteorological reanalysis data and short-term measured data, obtains data samples of the future maximum wind speed of at least one wind tower, and infers the future predicted maximum wind speed, and further obtains the predicted maximum wind speed of the target unit time according to the wind speed coefficient between different time granularities.

[0037] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0038] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1A flowchart of a method for predicting future maximum wind speed provided by an embodiment of the present invention;

[0041] Figure 2 A flowchart of another method for predicting future maximum wind speed provided by an embodiment of the present invention;

[0042] Figure 3 A flowchart of another method for predicting future maximum wind speed provided by an embodiment of the present invention;

[0043] Figure 4 A structural diagram of a device for predicting future maximum wind speed provided by an embodiment of the present invention;

[0044] Figure 5 A structural diagram of an electronic device provided by an embodiment of the present invention;

[0045] Figure 6 A data diagram of the independent storming method provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0047] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0048] like Figure 1 As shown, the present invention provides a method for predicting the maximum wind speed in the future, and the method for predicting the maximum wind speed in the future includes:

[0049] In step S11, measured data of a first historical period and relevant data of meteorological reanalysis data of at least one preset wind tower in a second historical period are obtained, and the duration of the second historical period is longer than the duration of the first historical period;

[0050] In step S12, based on the data regularity of the relevant data of the meteorological reanalysis data in the second historical period and the measured data of the first historical period, a plurality of estimated maximum wind speeds of the at least one preset wind tower in the second historical period are estimated by using a matrix time series method;

[0051] In step S13, based on the multiple estimated maximum wind speeds of the preset at least one wind tower in the second historical period, the predicted maximum wind speed in the future third period is analyzed by any one or more of the national standard method, the independent storm method and the EWTSII method.

[0052] In one embodiment, based on the acquisition of historical wind speed data, the method of inferring future wind speed, and the prediction of future maximum wind speed by different analysis methods. Obtain the measured data of at least one wind tower in the first historical period, and the time granularity is 10 minutes. The data acquisition instrument generally collects data once every 3 seconds, so the sample size of 10 minutes is 200 in total. The variables of the previous measured data at each moment include the 10-minute wind speed average value Vave (the average value of 200 samples), the 10-minute wind speed standard deviation Sd (the standard deviation of 200 samples), the 10-minute wind speed maximum value Vmax (the maximum value of 200 samples), and the 10-minute wind speed minimum value Vmin (the minimum value of 200 samples). That is, each moment Vi contains 4 parameters Vave(i), Sd(i), Vmax(i), and Vmin(i), where i=1, 2, 3...n. According to GB / T 18710-2002 "Wind Energy Resource Assessment Method for Wind Farms" and NB / T 31147-2018 "Technical Specifications for Measurement and Assessment of Wind Energy Resources in Wind Farm Projects", and combined with actual conditions, the 10-minute data series is inspected and cleaned. The monthly changes in average wind speed are counted, and the missing data in the windy month are focused on, because the data in the windy month is the sample for subsequent calculations. The 10-minute average data series after cleaning is smoothed to obtain the hourly average wind speed series:

[0053] V 小时平均(j) =[(V 10min平均(i) +V 10min平均(i+1) +……+V 10min平均(i+5) ] / 6

[0054] Where j = 1, 2, 3 ... n / 6. According to the relevant requirements of IEC61400, the maximum wind speed and the extreme wind speed once in 50 years refer to the results of 10-minute average and 3-second average respectively.

[0055] At the same time, the meteorological reanalysis data ERA5 of the second historical period is obtained, and the duration of the second historical period is longer than that of the first historical period. Meteorological reanalysis data is a meteorological data with temporal continuity generated by combining and processing numerical weather forecasts and observational data. ERA5 is the fifth generation of global atmospheric reanalysis data set produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), which provides global climate information from January 1950 to the present. ERA5 combines model data with observational data from all over the world to form a comprehensive and consistent data set, replacing its predecessor ERA-Interim reanalysis. The characteristics of the ERA5 data set include high accuracy (0.25°*0.25°) and time resolution (hourly), and high altitude (100m). It covers a 30-kilometer grid on the earth and uses 137 altitude layers to resolve the atmosphere from the surface to 80 kilometers. ERA5 is updated daily with a delay of about 5 days, and the data is provided in NetCDF format. ERA5 reanalysis data includes a variety of meteorological field data, such as temperature, humidity, wind speed, precipitation, etc., as well as meteorological diagnostic variables, such as vertical velocity, ground pressure, cloud cover, etc. These data can be used for research in many fields such as global climate change, weather forecast, air quality, and weather disasters. For example, in the field of climate change, the ERA5 dataset can be used to study global climate change trends and extreme climate events; in the field of weather forecast, it can improve the accuracy and timeliness of forecasts; in the field of air quality, it can be used to study the transmission and diffusion laws of atmospheric pollutants; in the field of weather disasters, it can be used to predict and monitor natural disasters. In the field of wind resource assessment, ERA5 is the most widely used meteorological reanalysis data. Download and collect the 100m height ERA5 wind speed and wind direction data series at representative grid points near the site area. The ERA5 data must have a time period that is the same as the measured data.

[0056] Through the matrix time series method (MTS), based on the measured data of the first historical period and the laws of the meteorological reanalysis data of the second historical period, multiple maximum wind speeds in the second historical period are inferred, such as the long-term (nearly 20 or 30 years) and hourly sequence data at the location of the wind tower. Use historical data and meteorological models to make reasonable inferences about past data and construct an inferred wind speed sequence.

[0057] The maximum wind speed in the future third period is predicted by using any one or more of the national standard method, the independent storm method or the EWTSII method and the multiple maximum wind speeds in the second historical period that are inferred.

[0058] National standard method: Based on the "Technical Specification for Wind Energy Resource Measurement and Evaluation in Wind Farm Projects (NBT 31147-2018)", according to the extreme value type I probability distribution, that is, the Gumbel distribution, the future wind speed is fitted. The probability density function of the extreme value type I Gumbel distribution is as follows:

[0059]

[0060] Among them, Vmax is the sample of the annual hourly average maximum value, and u and α are the location parameter and scale parameter of the extreme value type I probability distribution.

[0061] According to the location parameter and scale parameter of the anemometer tower, the fitting result is corrected to obtain a more accurate prediction result of the future maximum wind speed. The formula group is as follows:

[0062]

[0063]

[0064]

[0065]

[0066] The coefficients c1 and c2 in the above formula group are matrix estimation parameters, obtained by the general empirical method, as shown in Table 1 below:

[0067] Table 1

[0068] n <![CDATA[c1]]> <![CDATA[c2]]> n <![CDATA[c1]]> <![CDATA[c2]]> 10 0.94970 0.49520 60 1.17465 0.55208 15 1.02057 0.51820 70 1.18536 0.55477 20 1.06283 0.52355 80 1.19385 0.55688 25 1.09145 0.53086 90 1.20073 0.55860 30 1.11238 0.53622 100 1.20649 0.56002 35 1.12847 0.54034 250 1.24292 0.56878 40 1.14132 0.54362 500 1.25880 0.57240 45 1.15185 0.54630 1000 1.26851 0.57450 50 1.16066 0.54853 100000 1.28255 0.57722

[0069] Independent storm method: Through the empirical distribution function of independent ordered maximum value samples, the extreme value type I Gumbel distribution is transformed into a linear function of return period and return level. The return period refers to the average interval time for the wind speed event to occur repeatedly, and the return level refers to the intensity level of the wind speed. Through the numerical calculation of the return period, the predicted maximum wind speed in the third future period is deduced. It is achieved through the following steps

[0070] S01: Let 1 - F(x) = p, 1 / p is the return period, and Vmax is the return level under different p.

[0071] S02: From the probability density function of the extreme value type I Gumbel distribution, we can get Vmax = u - ln[-ln(1 - p)] / α, where u and α are undetermined parameters. In this way, the extreme value type I Gumbel distribution is transformed into a linear function between the return level Vmax and the return period.

[0072] S03: Suppose there are n independent ordered maximum value samples, X1 < X2 < X3 < X4 < …… < Xn. According to the empirical distribution function, as follows:

[0073] F(x)=I / (n+1), where x(i) <x<x(i+1)

[0074] S04: In this way, based on the above empirical distribution function, a series of points [-ln[-ln(1-p)], Vmax] can be obtained, and then u and α can be fitted.

[0075] S05: Finally, let p = 0.02 (the regression period is 50 years) to obtain the 50-year average hourly maximum wind speed.

[0076] EWTSII method: Calculate the predicted maximum wind speed in the third period in the future based on the EWTSII method. The formula is as follows:

[0077]

[0078] Among them, Vmax is the predicted maximum wind speed in the third period in the future, Vave is the average annual wind speed, Γ is a constant, k is the Weibull coefficient, which is used to describe the statistical distribution characteristics of wind speed, Tr is the regression period, and n is the number of samples. The EWTSII method has high requirements for the k value. It is generally believed that the results are not representative when the k value is lower than 1.77.

[0079] The technical solution in this embodiment is based on the correlation between long-term ERA5 meteorological reanalysis data and short-term measured data, obtains data samples of the future maximum wind speed of at least one wind measuring tower, and infers the future predicted maximum wind speed, and further obtains the predicted maximum wind speed per target unit time based on the wind speed coefficient between different time granularities.

[0080] In one embodiment, Figure 2 As shown, the following steps S21-S22 are also included:

[0081] In step S21, the values ​​of the predicted maximum wind speed in the third future period calculated by the national standard method, the independent storm method and the EWTSII method are compared;

[0082] In step S22, the predicted maximum wind speed with the largest value is selected as the predicted maximum wind speed in the future third time period.

[0083] In one embodiment, by comparing the wind speed prediction values ​​calculated by different methods, the maximum prediction result is selected as the final prediction value. The national standard method, the independent storm method, and the EWTSII method are used to predict the maximum wind speed in the third period in the future. Each method has its own unique calculation model and assumptions, and provides different prediction results.

[0084] The national standard method uses the calculation method of the national standard to conduct statistical analysis of the data and combines it with the extreme value theory to predict the possible maximum wind speed in the future. The independent storm method is based on the independence of single storms. It assumes that the extreme wind speed occurs in independent storm events. By analyzing the extreme values ​​of historical storms, the maximum wind speed in the future is inferred. The EWTSII method predicts future wind speed extremes based on theoretical models and mathematical formulas (such as Weibull distribution) in specific wind energy fields, taking into account the shape and scale parameters of the wind speed distribution.

[0085] By comparing the calculation results of the national standard method, the independent storm method and the EWTSII method, the wind speed with the largest value is selected as the final prediction value. Due to differences in assumptions, data processing methods and model parameters among different methods, the prediction results may be different. After comparing the multiple prediction results, the predicted maximum wind speed with the largest value is selected as the predicted maximum wind speed for the third period in the future. This ensures that when predicting future wind speed extremes, the most conservative and safe solution is adopted to cope with possible extreme wind speed conditions in the future, thereby ensuring the robustness and safety of related projects or engineering.

[0086] In one embodiment, Figure 3 As shown, the following step S31 is also included:

[0087] In step S31, a wind speed coefficient for converting the maximum wind speed of a first unit time into the maximum wind speed of a second unit time is calculated based on data of different time granularities;

[0088] In step S32, the predicted maximum wind speed in the third future time period is converted by the wind speed coefficient to obtain the predicted maximum wind speed in the third future time period of the target unit time;

[0089] In step S33, the predicted maximum wind speed in a future third period of target unit time under standard air density is calculated according to the preset air density of at least one wind tower.

[0090] In one embodiment, wind speed data of different time granularities are converted, and the future predicted maximum wind speed at a target unit time is obtained by adjusting the wind speed coefficient and the air density.

[0091] Different time granularities refer to the time intervals used to record or measure wind speed. For example, the first unit of time may be the average wind speed data within 1 hour, while the second unit of time may be the average wind speed data within 10 minutes or 3 seconds.

[0092] By calculating the maximum wind speed at different time granularities, the conversion coefficient from the first unit time to the second unit time is obtained. The wind speed coefficient reflects the change of wind speed on different time scales.

[0093] The wind speed coefficient is calculated to convert the predicted maximum wind speed in the third period of the future to obtain the wind speed forecast value in the target unit time, ensuring that the wind speed forecast can be consistent with the actual required time scale.

[0094] In actual environments, air density may vary due to temperature, pressure or altitude. By using the preset wind tower air density data, the wind speed forecast value in the target unit time is converted into the wind speed under the standard air density. In order to make the wind speed forecast value more universally applicable, it is convenient to compare wind energy evaluation or wind speed forecast in various environments.

[0095] By comparing wind speed data at different time scales, the conversion coefficient is obtained for wind speed conversion at different unit times. The maximum wind speed in the third period in the future is converted into the predicted wind speed value under the target unit time using the wind speed coefficient. By comparing the actual air density of the wind tower with the standard air density, the predicted wind speed in the target time period is adjusted to make it applicable under standard conditions.

[0096] For example, the wind speed coefficients between different time granularities are calculated based on the measured data with a unit time of 10 minutes. Based on the industry's conventional standard of once in 50 years, all subsequent data are based on the standard of once in 50 years. The unit time of the data obtained through ERA5 is 1 hour, so the unit time of the subsequent predicted maximum wind speed in the next 50 years is also 1 hour. However, in the wind power industry, the unit time of the maximum wind speed of concern is 10 minutes and 3 seconds. The predicted maximum wind speed with a unit time of 10 minutes is usually called the maximum wind speed, and the predicted maximum wind speed with a unit time of 3 seconds is usually called the maximum wind speed, which are subsequently referred to as V and V respectively. 10m Indicates the maximum wind speed, V 3s Indicates the maximum wind speed, V 1H Indicates the predicted maximum wind speed.

[0097] Obtain the wind speed coefficient and calculate the average wind speed V in hours based on the known measured data with a unit time of 10 minutes. 小时平均 , the formula is as follows:

[0098] V 小时平均 =[(V 10m平均(i) +V 10m平均(i+1) +……+V 10m(平均i+5) )] / 6

[0099] Among them, V 10m平均(i) Indicates the average wind speed during the first 10 minutes, and so on.

[0100] Calculate the maximum wind speed V in hours 小时最大 , the formula is as follows:

[0101] V 小时最大 =MAX[(V 10m最大(i) , V 10m最大(i+1) , ..., V 10m最大(i+5) )]

[0102] Among them, MAX is the operation of taking the maximum value, V 10m最大(i) It represents the maximum wind speed that occurred in the ten minutes, which is equivalent to V 小时最大 Yes V 3s (maximum wind speed).

[0103] The above V 小时平均 and V 小时最大 We need to obtain n groups of data, where n>=6.

[0104] Then, the wind speed coefficient T for 1 hour and 3 seconds is 3s-1h , you can pass V 小时最大 and V 小时平均 The ratio of is obtained, that is, T 3s-1h =V 小时最大 / V 小时平均 .

[0105] Similarly, the wind speed coefficient T for 1 hour and 10 minutes is 10m-1h , you can pass V 小时最大 and V 10m平均(i) The ratio of is obtained, that is, T 10m-1h =V 10m平均(i) / V 小时平均 .

[0106] The n groups of data are divided into 小时平均 Arrange according to the numerical size.

[0107] Count each V 小时平均 T 3s-1h The mean, maximum, and sample size of .

[0108] Count each V 小时平均 T 10m-1h The mean, maximum, and sample size of .

[0109] Set a V 小时平均 As the threshold, only the T values ​​above the threshold are counted. 3s-1h The mean, maximum value and sample size of the sample. Select the T with the largest value of the maximum value. 3s-1h as the wind speed factor.

[0110] Set a V 小时平均 As the threshold, only the T values ​​above the threshold are counted. 10m-1h The mean, maximum value and sample size of the sample. Select the T with the largest value of the maximum value. 10m-1h as the wind speed factor.

[0111] Then, in the previous embodiment, the predicted maximum wind speed V can be calculated. 1H , and then according to the wind speed coefficient T obtained above 3s-1h , we can get the maximum wind speed V 3s =V 1H *T 3s-1h .

[0112] Similarly, according to the wind speed coefficient T obtained above 10m-1h , we can get the maximum wind speed V 10m =V 1H *T 10m-1h .

[0113] In addition, the air density ρ around the wind tower is calculated, and V under standard air density is further calculated. 3sB =V 3s *(ρ / 1.225) 0.5 , and V 10mB =V 10m *(ρ / 1.225) 0.5 .

[0114] Preferred specific embodiments

[0115] 1. Wind data collection

[0116] A certain wind tower is numbered 2378#. The wind measurement period is a full year in 2015. The data completeness rate is 99%, and the time granularity is 10 minutes. After verification, the data during the strong wind period are not missing.

[0117] 2. ERA5 Data Collection

[0118] Download ERA5 meteorological reanalysis data near the wind tower numbered 2378#. The data time length is 1997 / 1 / 1-2016 / 12 / 31, a total of 20 years of long-term reference data.

[0119] 3. Extended wind data

[0120] The MTS method was used to conduct long-term correlation analysis on the wind tower data based on the ERA5 long-term data, and the data series in hours from 1997 / 1 to 2016 / 12 at the wind tower were inferred.

[0121] A maximum value is taken every year, with a total of 20 data samples, as shown in Table 2 below.

[0122] Table 2

[0123] years 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Maximum wind speed (m / s) 29.808 25.506 25.776 27.909 26.042 32.85 30.183 25.224 25.763 23.939 years 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Maximum wind speed (m / s) 29.385 26.332 26.107 30.489 25.804 31.251 30.021 28.737 28.476 27.075

[0124] 4. Calculation of the maximum wind speed in 50 years per hour

[0125] 4.1 National standard method: the calculated result is 35.4m / s

[0126] (1) The sample size is 20, c1 and c2 are 1.06283 and 0.52355 respectively.

[0127] (2) The position and shape parameters u and α are 26.4 m / s and 0.42219 respectively.

[0128] (3) The maximum wind speed once in 50 years is calculated to be 35.6 m / s.

[0129] 4.2 Independent storm method: The calculated result is 36.7m / s. The specific data is as follows: Figure 6 shown.

[0130] 4.3EWTSII method: The k value is 1.6, and the calculated result is 43m / s. It is generally believed that the k value is lower than 1.77, and the result is not representative, so this result is considered to be unrepresentative.

[0131] Taking the maximum value among the above three methods, the maximum wind speed once in 50 years at the location of the wind tower in hours is 36.7m / s.

[0132] 5. Calculation of maximum wind speed and extreme wind speed once in 50 years

[0133] 5.1 Statistical calculation of wind speed coefficient

[0134] Based on the measured data, the wind speed coefficients for unit time between 1 hour and 10 minutes and between 1 hour and 3 seconds in different wind speed ranges are statistically analyzed, as shown in Table 3 below:

[0135] Table 3

[0136]

[0137] The threshold is 22 m / s, and the wind speed coefficient for the unit time of 1 hour and 10 minutes is 1.09, and the wind speed coefficient between 1 hour and 3 seconds is 1.36.

[0138] 5.2 Statistical calculation of extreme wind speed

[0139] Calculate the air density around the wind tower and further calculate the data under standard air density.

[0140] The maximum wind speed once in 50 years: 40.0m / s (36.7m / s*1.09) under local air density and 37.9m / s under standard conditions.

[0141] The maximum wind speed once in 50 years: 49.9m / s (36.7m / s*1.36) under local air density and 47.3m / s under standard conditions.

[0142] In one embodiment, Figure 4 FIG. 1 is a block diagram of a device for predicting the maximum future wind speed according to an exemplary embodiment. Figure 4 As shown, the prediction device for the future maximum wind speed includes an acquisition module 41, a prediction module 42 and an analysis module 43.

[0143] The acquisition module 41 is used to acquire the measured data of the first historical period and the relevant data of the meteorological reanalysis data of at least one preset wind measurement tower in the second historical period, wherein the duration of the second historical period is longer than the duration of the first historical period;

[0144] The inference module 42 is used to infer multiple inferred maximum wind speeds of the at least one preset wind tower in the second historical period by using a matrix time series method according to the data rules of the relevant data of the meteorological reanalysis data in the second historical period and the measured data of the first historical period;

[0145] The analysis module 43 is used to analyze the predicted maximum wind speed in the future third period according to the multiple inferred maximum wind speeds of the preset at least one wind measuring tower in the second historical period through any one or more of the national standard method, the independent storm method and the EWTSII method.

[0146] The acquisition module 41, the estimation module 42 and the analysis module 43 included in the block diagram of the device for predicting the future maximum wind speed are controlled to execute the method for predicting the future maximum wind speed described in any of the above embodiments.

[0147] like Figure 5 As shown, the present invention provides an electronic device 500, the electronic device comprising: a communication interface, a processor 501, and a memory 502;

[0148] Among them, the memory 502 is used to store program instructions. When the program instructions are executed by the processor 501 that is communicatively connected to the memory 502 through the communication interface, the program instructions obtain the measured data of the first historical period of at least one preset wind measuring tower and the relevant data of the meteorological reanalysis data in the second historical period, and the duration of the second historical period is greater than the duration of the first historical period; according to the data rules of the relevant data of the meteorological reanalysis data in the second historical period and the measured data of the first historical period, a plurality of estimated maximum wind speeds of the preset at least one wind measuring tower in the second historical period are inferred through the matrix time series method; according to the plurality of estimated maximum wind speeds of the preset at least one wind measuring tower in the second historical period, the predicted maximum wind speed in the third time period in the future is analyzed through any one or more of the national standard method, the independent storm method and the EWTSII method.

[0149] The present invention provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, measured data of a first historical period of at least one preset wind measuring tower and relevant data of meteorological reanalysis data in a second historical period are obtained, and the duration of the second historical period is longer than the duration of the first historical period; according to the data regularity of the relevant data of the meteorological reanalysis data in the second historical period and the measured data of the first historical period, a plurality of inferred maximum wind speeds of the preset at least one wind measuring tower in the second historical period are inferred through a matrix time series method; according to the plurality of inferred maximum wind speeds of the preset at least one wind measuring tower in the second historical period, any one or more of a national standard method, an independent storm method and an EWTSII method are used to analyze the predicted maximum wind speed in a third time period in the future.

[0150] It should be understood that the specific features, operations and details described hereinabove about the method of the present invention may also be similarly applied to the device and system of the present invention, or, vice versa. In addition, each step of the method of the present invention described above may be performed by the corresponding parts or units of the device or system of the present invention.

[0151] It should be understood that each module / unit of the device of the present invention can be implemented in whole or in part by software, hardware, firmware or a combination thereof. Each module / unit can be embedded in the processor of the computer device in the form of hardware or firmware or independent of the processor, or can be stored in the memory of the computer device in the form of software for the processor to call to perform the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0152] In one embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores computer instructions executable by the processor, and the computer instructions instruct the processor to execute each step of the method of the embodiment of the present invention when executed by the processor. The computer device can be a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities in a broad sense. In one embodiment, the computer device may include a processor, a memory, a network interface, a communication interface, etc. connected through a system bus. The processor of the computer device can be used to provide necessary computing, processing and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. An operating system, a computer program, etc. may be stored in or on the non-volatile storage medium. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the computer device can be used to connect and communicate with external devices through a network. The steps of the method of the present invention are executed by the processor.

[0153] The present invention may be implemented as a computer-readable storage medium having a computer program stored thereon, which causes the steps of the method of an embodiment of the present invention to be executed when executed by a processor. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be performed by one or more computer devices or processors, and one or more other method steps / operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step / operation, or perform two or more method steps / operations.

[0154] It can be understood by a person skilled in the art that the method steps of the present invention can be completed by instructing related hardware such as a computer device or a processor through a computer program, and the computer program can be stored in a non-temporary computer-readable storage medium, and the steps of the present invention are executed when the computer program is executed. Depending on the circumstances, any reference to memory, storage, database or other media in this article may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (based on the correlation between ERA5 meteorological reanalysis data and short-term measured data, obtain the data samples of the strong wind period and hourly average of the site area, and calculate the wind speed coefficient between different time granularities according to the measured data, and finally obtain a more reliable 50-year maximum wind speed and maximum wind speed PROM at the wind tower), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0155] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the maximum wind speed in the future, characterized in that: include: Obtaining measured data of a first historical period and relevant data of meteorological reanalysis data of at least one preset wind measurement tower in a second historical period, wherein the duration of the second historical period is longer than the duration of the first historical period; According to the data rules of the relevant data of the meteorological reanalysis data in the second historical period and the measured data of the first historical period, a plurality of estimated maximum wind speeds of the at least one preset wind measuring tower in the second historical period are estimated by using a matrix time series method; According to the multiple estimated maximum wind speeds of the at least one preset wind tower in the second historical period, the predicted maximum wind speed in the future third period is analyzed by any one or more of the national standard method, the independent storm method and the EWTSI I method; The predicted maximum wind speed in the third future time period is converted into the predicted maximum wind speed in the target unit time in the third future time period through the wind speed coefficient of the time granularity.

2. The method for predicting the future maximum wind speed according to claim 1, characterized in that: The method of analyzing the predicted maximum wind speed in the future third period according to the multiple estimated maximum wind speeds of the at least one preset wind tower in the second historical period by any one or more of the national standard method, the independent storm method and the EWTSI I method includes: According to the extreme value type I probability distribution in "NBT 31147-2018 Technical Specification for Measurement and Assessment of Wind Energy Resources in Wind Farm Engineering", multiple predicted wind speeds in the third time period in the future are fitted; The fitting result is corrected according to the preset location parameters and scale parameters of at least one wind tower to obtain the predicted maximum wind speed in the future third time period.

3. The method for predicting the future maximum wind speed according to claim 1, characterized in that: According to the multiple estimated maximum wind speeds of the at least one preset wind tower in the second historical period, the predicted maximum wind speed in the future third period is analyzed by any one or more of the national standard method, the independent storm method and the EWTSI I method, further comprising: According to the preset empirical distribution function of independent ordered maximum value samples, the extreme value type I Gumbel distribution is transformed into a linear function of regression period and regression level; The predicted maximum wind speed in the third period in the future is calculated using the value of the regression period.

4. The method for predicting the future maximum wind speed according to claim 1, characterized in that: The step of analyzing the predicted maximum wind speed in the future third period according to the multiple estimated maximum wind speeds of the at least one preset wind tower in the second historical period by any one or more of the national standard method, the independent storm method and the EWTSI I method further includes: According to the EWTSI I method, the predicted maximum wind speed in the third period in the future is calculated as follows: Among them, Vmax is the predicted maximum wind speed in the third period in the future, Vave is the average annual wind speed, Γ is a constant, k value is the Weibul l coefficient, Tr is the regression period, and n is the number of samples.

5. The method for predicting the future maximum wind speed according to claims 2-4, characterized in that: Also includes: Compare the values ​​of the predicted maximum wind speed in the third future period calculated by the national standard method, the independent storm method and the EWTSI I method; The predicted maximum wind speed with the largest value is selected as the predicted maximum wind speed in the third future time period.

6. The method for predicting the future maximum wind speed according to claim 5, characterized in that: Also includes: Based on the data of different time granularities, the wind speed coefficient for converting the maximum wind speed of the first unit time into the maximum wind speed of the second unit time is calculated; The predicted maximum wind speed in the third future period is converted by the wind speed coefficient to obtain the predicted maximum wind speed in the third future period of target unit time; According to the preset air density of at least one wind tower, a predicted maximum wind speed within a future third time period of a target unit time under the standard air density is calculated.

7. A device for predicting future maximum wind speed, characterized in that: include: An acquisition module, used to acquire measured data of a first historical period and relevant data of meteorological reanalysis data of at least one preset wind measurement tower in a second historical period, wherein the duration of the second historical period is longer than the duration of the first historical period; An inference module, used to infer multiple inferred maximum wind speeds of the at least one preset wind tower in the second historical period by using a matrix time series method according to the data rules of the relevant data of the meteorological reanalysis data in the second historical period and the measured data of the first historical period; The analysis module is used to analyze the predicted maximum wind speed in the future third period according to the multiple estimated maximum wind speeds of the preset at least one wind measuring tower in the second historical period through any one or more of the national standard method, the independent storm method and the EWTSI I method.

8. The device for predicting future maximum wind speed according to claim 7, characterized in that: The acquisition module, the inference module, the analysis module and the conversion module are controlled to execute the method for predicting the future maximum wind speed according to any one of claims 1-6.

9. An electronic device, characterized in that: include: Communication interface, processor, memory; Wherein, the memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, the electronic device implements the method for predicting the future maximum wind speed as described in any one of claims 1 to 6.

10. A computer-readable storage medium having program instructions stored thereon, characterized in that: When the program instructions are executed by a computer, the computer implements the method for predicting future maximum wind speed according to any one of claims 1 to 6.

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