Wind measurement data correction method and device, electronic equipment and medium
By acquiring short-term and long-term data from wind measurement equipment, performing matrix processing and transformation relationship calculations, the problems of wind frequency distribution and interannual differences in wind direction were solved, achieving high-precision wind farm power generation assessment and improving the accuracy and reliability of wind power projects.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-24
- Publication Date
- 2026-04-07
AI Technical Summary
Existing wind measurement data correction methods cannot avoid data distortion caused by interannual differences in wind frequency distribution and wind direction, which affects the accuracy of wind power project power generation assessment.
By acquiring short-term and long-term wind data from anemometers, extracting a complete year's reference dataset, performing matrix processing and Weibull distribution fitting, calculating transformation relationships, and performing normalization and zeroing processes, a high-precision wind frequency matrix is obtained, achieving two-dimensional feature correction of wind speed and direction.
It significantly reduces the impact of interannual variations, improves the accuracy and reliability of long-term wind farm power generation assessment, and makes the correction results more realistically reflect long-term wind conditions.
Smart Images
Figure CN121808333A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of wind measurement data processing technology, and in particular to a wind measurement data correction method, apparatus, electronic device, and medium. Background Technology
[0002] The processing of wind measurement data is crucial in wind resource assessment. The accuracy of the processed data directly affects the accuracy of the wind power project's power generation assessment, thus influencing investment decisions. Since most wind measurement data is short-term (one year), while wind turbines operate for over 20 years, and short-term data is subject to randomness and varies depending on wind year (good or bad), it cannot be directly used as long-term data. Therefore, methods for correcting short-term data to long-term data are essential, and minimizing correction errors is key. Currently, common methods for correcting wind measurement data include proportional correction, sector-based correction, and interpolation correction.
[0003] Current methods only focus on wind speed correction, but the interannual differences in wind frequency distribution and wind direction can affect the accuracy of wind power project assessments, and it is difficult to avoid the influence of these factors in existing methods. For example, proportional correction methods may lead to abnormal wind frequency distribution, sector-based correction methods may lead to abnormal correction ratios, and interpolation correction methods are affected by the correlation between mesoscale simulation data and measured data, which may cause data distortion. Summary of the Invention
[0004] This invention provides a wind measurement data correction method, device, electronic device, and medium to address the shortcomings of existing wind measurement data correction methods that are unable to avoid data distortion caused by interannual differences in wind frequency distribution and wind direction. It achieves high-precision data correction that comprehensively considers the dual-dimensional characteristics of wind speed and wind direction, effectively preserves the wind frequency distribution pattern, and significantly reduces the impact of interannual variations. This improves the accuracy and reliability of long-term wind farm power generation assessment, making the correction results more realistically reflect long-term wind conditions and directly applicable to the power generation calculation of wind power projects.
[0005] This invention provides a wind measurement data correction method, including: Acquire wind measurement data from a wind measuring device, wherein the wind measurement data is measurement data for a first time period containing a time series, and acquire wind data for a second time period at the location of the wind measuring device; Extract data from one or more complete years with the highest data integrity rate from the measurement data of the first time period as the first reference dataset; Extract data from the wind data of the second time period that are the same as the data period of the first reference dataset and use it as the second reference dataset; Based on the data period of the first reference dataset, the second reference dataset is extended forward and backward to obtain multiple third reference datasets for the whole year; The first reference dataset, the second reference dataset, and the third reference dataset are respectively matrixed to obtain the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix. Based on the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix, the correction result of the wind measurement data is determined.
[0006] In one possible implementation, the method further includes: Calculate the first transformation relationship between the second wind frequency matrix and the third wind frequency matrix; The first transformation relationship is applied to the first wind frequency matrix and normalized to obtain the fourth wind frequency matrix; Based on the first wind frequency matrix, the second wind frequency matrix, the third wind frequency matrix, and the fourth wind frequency matrix, determine whether the average wind speed of the fourth wind frequency matrix is valid; If the average wind speed is determined to be valid, the matrix data of the fourth wind frequency matrix is used as the corrected wind measurement data.
[0007] In one possible implementation, the method further includes: Each reference dataset contains wind speed and wind direction data; Based on the preset total number of wind speed intervals and the preset number of wind direction partitions, the wind speed data in each reference dataset is divided into wind speed intervals and wind direction partitions respectively. Based on the wind frequency distribution data in each wind speed interval and wind direction interval corresponding to the first reference dataset, the second reference dataset, and the third reference dataset, a first initial wind frequency matrix, a second initial wind frequency matrix, and a third initial wind frequency matrix are generated. The data in the first initial wind frequency matrix, the second initial wind frequency matrix, and the third initial wind frequency matrix are verified. When the verification is successful, the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix are obtained.
[0008] In one possible implementation, the method further includes: For each wind direction partition of the first initial wind frequency matrix, the second initial wind frequency matrix, and the third initial wind frequency matrix, a Weibull distribution curve is fitted respectively; Calculate the correlation coefficient between each column of data in each initial wind frequency matrix and the Weibull distribution curve; When the correlation coefficient corresponding to a certain column of data is greater than or equal to the correlation threshold, the data in that column is deemed valid. When the effective data in the initial wind frequency matrix reaches a preset proportion, the initial wind frequency matrix is deemed to have passed the test. If the effective data does not reach the preset ratio, the total number of wind speed intervals and the number of wind direction zones are reset and iterative calculations are performed until the first initial wind frequency matrix, the second initial wind frequency matrix, and the third initial wind frequency matrix pass the verification, and the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix are obtained.
[0009] In one possible implementation, the method further includes: The correlation between each corresponding wind direction zone in the second and third wind frequency matrices is solved by using the column-by-column correspondence method. The correlation is applied to each column of the second wind frequency matrix to obtain the transformed second wind frequency matrix, and it is determined whether the numerical distribution of the transformed second wind frequency matrix is valid. If the numerical distribution of the transformed second wind frequency matrix is determined to be valid, the correlation is used as the first transformation relationship between the second wind frequency matrix and the third wind frequency matrix.
[0010] In one possible implementation, the method further includes: Applying the first transformation relationship to each column of the first wind frequency matrix yields the transformed first matrix; The distorted data in the first matrix caused by the zero value of the first wind frequency matrix is zeroed out to obtain the second matrix; The second matrix is normalized to obtain the fourth wind frequency matrix.
[0011] In one possible implementation, the method further includes: Calculate the average wind speed corresponding to the first wind frequency matrix, the second wind frequency matrix, the third wind frequency matrix, and the fourth wind frequency matrix, respectively; The proportional error is calculated based on the average wind speed corresponding to each wind frequency matrix. When the proportional error is less than the error threshold, the average wind speed of the fourth wind frequency matrix is determined to be valid.
[0012] The present invention also provides a wind measurement data correction device, comprising the following modules: The acquisition module is used to acquire wind measurement data measured by the wind measuring device, wherein the wind measurement data is measurement data of a first time period containing a time series, and to acquire wind data of a second time period at the location of the wind measuring device. The extraction module is used to extract data from one or more complete years with the highest data integrity rate from the measurement data of the first time period as a first reference dataset; extract data from the wind data of the second time period that are the same as the data period of the first reference dataset as a second reference dataset; and extend the second reference dataset forward and backward based on the data period of the first reference dataset to obtain multiple third reference datasets for complete years. The processing module is used to perform matrix processing on the first reference dataset, the second reference dataset, and the third reference dataset respectively to obtain the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix; The correction module is used to determine the correction result of the wind measurement data based on the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix.
[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the wind measurement data correction method as described above.
[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the wind measurement data correction method as described above.
[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the wind measurement data correction method as described above.
[0016] The wind measurement data correction method, apparatus, electronic device, and medium provided by this invention acquire wind measurement data measured by a wind measurement device, wherein the wind measurement data is measurement data of a first time period containing a time series, and acquires wind data of a second time period at the location of the wind measurement device; extracts data of one or more complete years with the highest data integrity rate from the measurement data of the first time period as a first reference dataset; extracts data of the same data period as the first reference dataset from the wind data of the second time period as a second reference dataset; extends the second reference dataset forward and backward based on the data period of the first reference dataset to obtain a third reference dataset of multiple complete years; performs matrix processing on the first reference dataset, the second reference dataset, and the third reference dataset respectively to obtain a first wind frequency matrix, a second wind frequency matrix, and a third wind frequency matrix; and determines the correction result of the wind measurement data based on the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix. Compared to existing wind measurement data correction methods, which are prone to data distortion due to interannual differences in wind frequency distribution and wind direction, this solution achieves high-precision data correction that comprehensively considers both wind speed and wind direction characteristics. It effectively preserves the wind frequency distribution pattern and significantly reduces the impact of interannual variations, thereby improving the accuracy and reliability of long-term wind farm power generation assessment. The correction results more realistically reflect long-term wind conditions and can be directly applied to the power generation calculation of wind power projects. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0018] Figure 1 This is a flowchart illustrating the wind measurement data correction method provided by the present invention.
[0019] Figure 2 This is a schematic diagram of the wind measurement data correction device provided by the present invention.
[0020] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation
[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0022] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.
[0023] Figure 1 This is a flowchart illustrating the wind measurement data correction method provided by the present invention, as shown below. Figure 1 As shown, the method includes the following: S11. Obtain wind measurement data measured by the wind measuring device, wherein the wind measurement data is measurement data of a first time period containing a time series, and obtain wind data of a second time period at the location of the wind measuring device.
[0024] Acquire time-series measurement data A1 (first time period measurement data) containing wind speed and wind direction from the wind measurement equipment data. This first time period measurement data is short-term data.
[0025] Obtain long-term wind data L (second time period wind data) at the location of the wind measurement equipment. L can be mesoscale data or meteorological station observation data. It must include wind speed and wind direction columns, and the data duration must be relatively long (exceeding the planned operation year of the wind farm where the wind measurement data is located).
[0026] S12. Extract data from one or more complete years with the highest data integrity rate from the measurement data of the first time period as a first reference dataset. Extract data from the wind data of the second time period that are the same as the data period of the first reference dataset as a second reference dataset. Based on the data period of the first reference dataset, extend the second reference dataset forward and backward to obtain multiple third reference datasets for complete years.
[0027] The data from one or more complete years with the highest data completeness rate are extracted from the measurement data of the first time period as the first reference dataset; the data from the wind data of the second time period with the same data period as the first reference dataset are extracted as the second reference dataset; based on the data period of the first reference dataset, the second reference dataset is extended forward and backward to obtain multiple third reference datasets for complete years.
[0028] Specifically, the data is processed as follows: if the duration of A exceeds one year, data from one or more complete years with the highest data integrity rate is extracted and used as the first reference dataset A; if the duration of A is less than one year, data L is used to interpolate to one year using correlation relationships and used as the first reference dataset A.
[0029] Extract the second reference dataset B, which contains wind speed and wind direction columns, from L and the first reference dataset A within the same time period.
[0030] Extract a third reference dataset C from L, which contains the time of the first reference dataset A and extends backwards / forwards for a total time period of y years (generally the year the wind turbine is running), and includes columns for wind speed and wind direction.
[0031] S13. Perform matrix processing on the first reference dataset, the second reference dataset, and the third reference dataset to obtain the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix.
[0032] Based on the preset total number of wind speed intervals and wind direction partitions, the wind speed data in each reference dataset is divided into wind speed intervals and wind direction partitions respectively; based on the wind frequency distribution data in each wind speed interval and wind direction interval corresponding to the first reference dataset, the second reference dataset, and the third reference dataset respectively, the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix are generated.
[0033] Specifically, the maximum wind speed in the reference datasets A, B, and C is calculated and rounded up to obtain the maximum wind speed Vmax. The total number of wind speed intervals I (usually set to 1 to 10 times Vmax + 1) and the number of wind direction zones J are set. Common values for J are 8, 12, 16, 24, and 36, which means dividing the 360° circle into 8, 12, and 16 zones, respectively, and so on.
[0034] The wind speed segment is divided into I intervals. The wind speed in the In-th interval is between (n-1)*Vmax / I-Vmax / 2I (the value is 0 when the value is less than 0) and nVmax / I+Vmax / 2I.
[0035] The wind direction is divided into J equal intervals. The wind direction value in the Jn-th interval is between (n-1)*360 / J-180 / J and n*360 / J+180 / J. When the value of (n-1)*360 / J-180 / J is less than 0, the interval value is (360+(n-1)*360 / J-180 / J, 360)&(0, n*360 / J+180 / J).
[0036] The frequency of events occurring in the i-th wind speed interval and the j-th wind direction interval in the target data time series is used as the value Nij in the i-th row and j-th column of the matrix. A matrix with a data volume of I×J is generated in this way, which is the wind frequency matrix of wind data in different sectors. Matrix MA is created for the first reference dataset A, matrix MB is created for the second reference dataset B, and matrix MC is created for the third reference dataset C.
[0037] Furthermore, a Weibull distribution fitting test is performed on each matrix: a Weibull distribution curve is fitted to each wind direction zone of the first, second, and third wind frequency matrices; the correlation coefficient between the actual data distribution and the Weibull distribution curve is calculated; when the correlation coefficient is greater than or equal to the correlation threshold, the data is deemed valid; when the valid data reaches a preset proportion, the first, second, and third wind frequency matrices are deemed to have passed the test; if the valid data does not reach the preset proportion, the total number of wind speed intervals and the number of wind direction zones are reset and iterative calculations are performed until the first, second, and third wind frequency matrices pass the test.
[0038] Specifically, the rationality of the wind frequency distribution in each column of the wind frequency matrix MA, MB, and MC, i.e., each sector, is examined.
[0039] Fit the Weibull distribution curve of each sector of the target data and obtain the sector-specific fit degree Rj (i.e., the correlation between the actual data distribution and the fitted Weibull distribution data R2).
[0040] If Rj ≥ 0.6, the result is considered valid; if Rj < 0.6, it is considered invalid. The validity rate of the J Rj values for each target data point must reach over 80%. If this is not met, the total number of wind speed intervals and wind direction zones are reset, and iterative calculations are performed. The I and J values are then reset. If the data meets the requirements, the next step is performed. If the data still does not meet the requirements after more than 3 iterations, it is considered special wind condition data, and the result with the highest compliance rate is used as the output for this step.
[0041] S14. Based on the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix, determine the correction result of the wind measurement data.
[0042] Calculate the first transformation relationship between the second and third wind frequency matrices; apply the first transformation relationship to the first wind frequency matrix and perform normalization to obtain the fourth wind frequency matrix; based on the first, second, third, and fourth wind frequency matrices, determine whether the average wind speed of the fourth wind frequency matrix is valid; if the average wind speed is determined to be valid, use the matrix data of the fourth wind frequency matrix as the corrected wind measurement data.
[0043] Specifically, the column-by-column correspondence method is used to solve the correlation between each corresponding wind direction zone in the second and third wind frequency matrices respectively; the correlation is applied to each column of the second wind frequency matrix to obtain the transformed second wind frequency matrix, and the rationality of the numerical distribution of the transformed second wind frequency matrix is checked; if the check passes, the correlation is used as the first transformation relationship between the second and third wind frequency matrices.
[0044] The first transformation relationship is applied to each column of the first wind frequency matrix to obtain the transformed first matrix; the distorted data caused by the zero values of the first wind frequency matrix in the first matrix is normalized to obtain the second matrix; the second matrix is normalized to obtain the fourth wind frequency matrix.
[0045] Specifically, to solve the transformation relationship from matrix MB to matrix MC, the column-by-column correspondence method is used: solve the correlation Xkj of each corresponding column NBj and NCj of MB and MC respectively, and apply J Xkj to the corresponding column NBj to obtain matrix MB1.
[0046] Examine the values in MB1. If a column contains data <0, adjust the method for solving the correlation and return to the steps for solving the transformation relationship from matrix MB to matrix MC.
[0047] If the sum of all values of MB1 is checked, and the difference between the summation result and the control value of 100 exceeds 2%, the method for solving the correlation relationship should be adjusted, and the process should return to the steps of solving the transformation relationship from matrix MB to matrix MC.
[0048] By solving the sum of the sums of each row and the sum of the multipliers of the median of the wind speed intervals in the row, we can check the difference in average wind speed between MB1 and MC. If the difference is greater than 2%, we return to the steps of solving the transformation relationship between matrix MB and matrix MC.
[0049] Furthermore, the transformation relationship Xkj output above is applied to each column NAj of MA to obtain the transformed matrix MD1 (first matrix); the data in MD1 is numerically checked, and the distortion of the transformation result of the MD1 data group caused by the transformation of the value 0 in MA is checked. If there are a large number of duplicate data at the position corresponding to the MA data 0 in the same column, it is considered that the data in this part has transformation distortion, and the distorted data is normalized to 0. MD1 is processed by normalizing the distorted data to 0 to obtain matrix MD2 (second matrix).
[0050] Normalize MD2: The columns are processed separately, and the sums of the values of NAj, NBj, and ND2j in each column of MA, MB, and MD2 are calculated as FAj, FBj, and FD2j.
[0051] By comparing the numerical distribution of FBj and FD2j in each column of MB and MC, the values are divided into several regions based on their concentration and dispersion. Each region contains several Fj values. The sum of Fj in each region is calculated. The regional ratio relationship between the matrices MB and MC is applied to MD2 and the FD2j values in each column are normalized.
[0052] The ratio of the normalized FD2j value to the original FD2j value is applied to each column of MD2 to obtain the final output normalized matrix MD (fourth wind frequency matrix).
[0053] Furthermore, the rationality of the average wind speed in the fourth wind frequency matrix is examined.
[0054] Calculate the average wind speed corresponding to the first, second, third, and fourth wind frequency matrices respectively; calculate the proportional error based on the average wind speed corresponding to each wind frequency matrix; when the proportional error is less than the error threshold, determine that the average wind speed of the fourth wind frequency matrix is reasonable.
[0055] Specifically, the sum of the sum of each row of the matrix is calculated by multiplying the product of the largest value of the wind speed interval in that row, to obtain the average wind speeds VA, VB, VC, and VD corresponding to MA, MB, MC, and MD, respectively.
[0056] Calculate the proportional error using Formula 1: △=VD / (VA*VC / VB)-1 (Formula 1) If Δ≤1%, the result is considered reasonable; if Δ>1%, return to the steps of solving the transformation relationship from matrix MB to matrix MC.
[0057] Furthermore, assuming the average wind speed is valid, the matrix data of the fourth wind frequency matrix is used as the corrected wind measurement data.
[0058] After the verification is passed, the final matrix MD is the corrected matrix data of wind speed segments by sector that can represent the long-term wind conditions at the wind measurement tower. It can be used as input data for wind power project power generation calculation.
[0059] Taking a certain wind measurement data as an example, the steps for long-term correction of the wind measurement data according to the wind measurement data correction method provided by the present invention are as follows: 1. Data collection: Extract the time series data, wind speed at 90m height, and wind direction at 90m height from the target meteorological tower; download the ERA5 data from the meteorological tower.
[0060] 2. Data time period selection: Data from the wind measurement tower from January 1, 2017 to December 31, 2017 were selected as reference data A; Select the wind speed and wind direction time series data from January 1, 2017 to December 31, 2017 in ERA5 data, and denote it as data B; Select wind speed and wind direction time series data from the period from May 1, 2016 to April 30, 2021 in ERA5 data, and denote it as data C.
[0061] 3. Data processing: The maximum wind speed obtained from data A, B, and C is 17.11. Rounding up, we get Vmax = 18. We set the total number of wind speed intervals to 19 and the total number of wind direction intervals to 16. The wind speed is divided into 19 intervals, namely 0~0.5, 0.5~1.5, 1.5~2.5...17.5~18.5; The matrices MA, MB, and MC are obtained as shown in Tables 1, 2, and 3 below: Table 1 Matrix MA Table 2 Matrix MB Table 3 Matrix MC The Weibull fit of each sector of the data matrix was tested. The calculated effective rates were 87.5% for MA, 93.75% for MB, and 93.75% for MC, which passed the test.
[0062] 4. Find the transformation relationship: To solve for the relationship between each sector column of matrices MB and MC, we first consider using a linear relationship. The slope and intercept values of the relationship for each sector are shown in Table 4 below: Table 4 Applying the transformation to MB yields MB1. Upon inspection, column 14 of MB1 contains negative values. After adjusting the slope and intercept values, the results are shown in Table 5 below: Table 5 The sum of the MB1 values is 100.2, which is less than 2% different from 100; the calculated average wind speed is 4.04, which is 0.2% different from the calculated MC wind speed of 4.05, thus meeting the requirements.
[0063] 5. Calculate the corrected data matrix: Applying Table 5 to matrix MA, we obtain MD1 as shown in Table 6 below: Table 6 The MD2 results obtained by zeroing out the MD1 distorted data are shown in Table 7 below: Table 7 The MD results obtained after MD2 normalization are shown in Table 8 below: Table 8 6. Result verification: The calculated proportional error Δ = 0.7%, which is less than 1%, so the result is reasonable.
[0064] The wind measurement data correction method provided by this invention involves acquiring wind measurement data from a wind measurement device, wherein the wind measurement data is measurement data for a first time period containing a time series, and acquiring wind data for a second time period at the location of the wind measurement device; extracting data from one or more complete years with the highest data integrity rate from the measurement data for the first time period as a first reference dataset; extracting data from the wind data for the second time period that has the same data period as the first reference dataset as a second reference dataset; extending the second reference dataset forward and backward based on the data period of the first reference dataset to obtain multiple third reference datasets for complete years; performing matrix processing on the first reference dataset, the second reference dataset, and the third reference dataset respectively to obtain a first wind frequency matrix, a second wind frequency matrix, and a third wind frequency matrix; and determining the correction result of the wind measurement data based on the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix. Compared to existing wind measurement data correction methods, which are prone to data distortion due to interannual differences in wind frequency distribution and wind direction, this method achieves high-precision data correction that comprehensively considers both wind speed and wind direction characteristics. It effectively preserves the wind frequency distribution pattern and significantly reduces the impact of interannual variations, thereby improving the accuracy and reliability of long-term wind farm power generation assessment. The correction results more realistically reflect long-term wind conditions and can be directly applied to the power generation calculation of wind power projects.
[0065] The wind measurement data correction device provided by the present invention is described below. The wind measurement data correction device described below and the wind measurement data correction method described above can be referred to in correspondence.
[0066] Figure 2 This is a schematic diagram of the structure of the wind measurement data correction device provided by the present invention, specifically including: The acquisition module 201 is used to acquire wind measurement data measured by the wind measuring device, wherein the wind measurement data is measurement data of a first time period containing a time series, and to acquire wind data of a second time period at the location of the wind measuring device. The extraction module 202 is used to extract data from one or more complete years with the highest data integrity rate from the measurement data of the first time period as a first reference dataset; extract data from the wind data of the second time period that are the same as the data period of the first reference dataset as a second reference dataset; and extend the second reference dataset forward and backward based on the data period of the first reference dataset to obtain multiple third reference datasets for complete years. Processing module 203 is used to perform matrix processing on the first reference dataset, the second reference dataset, and the third reference dataset respectively to obtain the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix; The correction module 204 is used to determine the correction result of the wind measurement data based on the first wind frequency matrix, the second wind frequency matrix and the third wind frequency matrix.
[0067] In one possible implementation, the processing module 203 is further configured to divide the wind speed data in each reference dataset into wind speed intervals and wind direction partitions based on a preset total number of wind speed intervals and a preset number of wind direction partitions; generate a first initial wind frequency matrix, a second initial wind frequency matrix, and a third initial wind frequency matrix based on the wind frequency distribution data in each wind speed interval and wind direction interval corresponding to the first reference dataset, the second reference dataset, and the third reference dataset; and verify the data in the first initial wind frequency matrix, the second initial wind frequency matrix, and the third initial wind frequency matrix, and obtain the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix when the verification passes.
[0068] In one possible implementation, the processing module 203 is further configured to: fit a Weibull distribution curve to each wind direction partition of the first initial wind frequency matrix, the second initial wind frequency matrix, and the third initial wind frequency matrix; calculate the correlation coefficient between each column of data in each initial wind frequency matrix and the Weibull distribution curve; determine that the column of data is valid when the correlation coefficient corresponding to a certain column of data is greater than or equal to a correlation threshold; determine that the initial wind frequency matrix passes the test when the valid data in the initial wind frequency matrix reaches a preset proportion; if the valid data does not reach the preset proportion, reset the total number of wind speed intervals and the number of wind direction partitions and perform iterative calculations until the first initial wind frequency matrix, the second initial wind frequency matrix, and the third initial wind frequency matrix pass the test, thereby obtaining the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix.
[0069] In one possible implementation, the correction module 204 is further configured to calculate a first transformation relationship between the second wind frequency matrix and the third wind frequency matrix; apply the first transformation relationship to the first wind frequency matrix and perform normalization processing to obtain a fourth wind frequency matrix; based on the first wind frequency matrix, the second wind frequency matrix, the third wind frequency matrix and the fourth wind frequency matrix, determine whether the average wind speed of the fourth wind frequency matrix is valid; if the average wind speed is determined to be valid, use the matrix data of the fourth wind frequency matrix as the corrected wind measurement data.
[0070] In one possible implementation, the correction module 204 is further configured to use a column-by-column correspondence solution method to solve the correlation relationship of each corresponding wind direction zone in the second wind frequency matrix and the third wind frequency matrix respectively; apply the correlation relationship to each column of the second wind frequency matrix to obtain the transformed second wind frequency matrix, and determine whether the numerical distribution of the transformed second wind frequency matrix is valid; if it is determined that the numerical distribution of the transformed second wind frequency matrix is valid, use the correlation relationship as the first transformation relationship between the second wind frequency matrix and the third wind frequency matrix.
[0071] In one possible implementation, the correction module 204 is further configured to apply the first transformation relationship to each column of the first wind frequency matrix to obtain a transformed first matrix; perform zeroing processing on the distorted data in the first matrix caused by the zero value of the first wind frequency matrix to obtain a second matrix; and perform normalization processing on the second matrix to obtain a fourth wind frequency matrix.
[0072] In one possible implementation, the correction module 204 is further configured to calculate the average wind speeds corresponding to the first wind frequency matrix, the second wind frequency matrix, the third wind frequency matrix, and the fourth wind frequency matrix respectively; calculate the proportional error based on the average wind speeds corresponding to each wind frequency matrix; and determine that the average wind speed of the fourth wind frequency matrix is valid when the proportional error is less than the error threshold.
[0073] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communications bus 340, wherein the processor 310, the communications interface 320, and the memory 330 communicate with each other through the communications bus 340. The processor 310 can call logic instructions in the memory 330 to execute a wind measurement data correction method. This method includes: acquiring wind measurement data measured by a wind measuring device, wherein the wind measurement data is measurement data for a first time period containing a time series, and acquiring wind data for a second time period at the location of the wind measuring device; extracting data from the first time period measurement data that has the highest data integrity rate for one or more complete years as a first reference dataset; extracting data from the second time period wind data that has the same data period as the first reference dataset as a second reference dataset; extending the second reference dataset forward and backward based on the data period of the first reference dataset to obtain multiple third reference datasets for complete years; performing matrix processing on the first, second, and third reference datasets respectively to obtain a first wind frequency matrix, a second wind frequency matrix, and a third wind frequency matrix; and determining the correction result of the wind measurement data based on the first, second, and third wind frequency matrices.
[0074] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0075] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the wind measurement data correction method provided by the above methods. The method includes: acquiring wind measurement data measured by a wind measuring device, wherein the wind measurement data is measurement data of a first time period containing a time series, and acquiring wind data of a second time period at the location of the wind measuring device; extracting data of one or more complete years with the highest data integrity rate from the measurement data of the first time period as a first reference dataset; extracting data of the same data period as the first reference dataset from the wind data of the second time period as a second reference dataset; extending the second reference dataset forward and backward based on the data period of the first reference dataset to obtain a third reference dataset of multiple complete years; performing matrix processing on the first reference dataset, the second reference dataset, and the third reference dataset respectively to obtain a first wind frequency matrix, a second wind frequency matrix, and a third wind frequency matrix; and determining the correction result of the wind measurement data based on the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix.
[0076] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a wind measurement data correction method provided by the above methods. The method includes: acquiring wind measurement data measured by a wind measuring device, wherein the wind measurement data is measurement data for a first time period containing a time series, and acquiring wind data for a second time period at the location of the wind measuring device; extracting data from the first time period measurement data for one or more complete years with the highest data integrity rate as a first reference dataset; extracting data from the second time period wind data for the same data period as the first reference dataset as a second reference dataset; extending the second reference dataset forward and backward based on the data period of the first reference dataset to obtain multiple third reference datasets for complete years; performing matrix processing on the first reference dataset, the second reference dataset, and the third reference dataset respectively to obtain a first wind frequency matrix, a second wind frequency matrix, and a third wind frequency matrix; and determining the correction result of the wind measurement data based on the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix.
[0077] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0078] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for correcting wind measurement data, characterized in that, include: Acquire wind measurement data from a wind measuring device, wherein the wind measurement data is measurement data for a first time period containing a time series, and acquire wind data for a second time period at the location of the wind measuring device; Extract data from one or more complete years with the highest data integrity rate from the measurement data of the first time period as the first reference dataset; Extract data from the wind data of the second time period that are the same as the data period of the first reference dataset and use it as the second reference dataset; Based on the data period of the first reference dataset, the second reference dataset is extended forward and backward to obtain multiple third reference datasets for the whole year; The first reference dataset, the second reference dataset, and the third reference dataset are respectively matrixed to obtain the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix. Based on the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix, the correction result of the wind measurement data is determined.
2. The method according to claim 1, characterized in that, The step of determining the correction result of the wind measurement data based on the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix includes: Calculate the first transformation relationship between the second wind frequency matrix and the third wind frequency matrix; The first transformation relationship is applied to the first wind frequency matrix and normalized to obtain the fourth wind frequency matrix; Based on the first wind frequency matrix, the second wind frequency matrix, the third wind frequency matrix, and the fourth wind frequency matrix, determine whether the average wind speed of the fourth wind frequency matrix is valid; If the average wind speed is determined to be valid, the matrix data of the fourth wind frequency matrix is used as the corrected wind measurement data.
3. The method according to claim 2, characterized in that, Each reference dataset contains wind speed and wind direction data; The step of performing matrix-based processing on the first reference dataset, the second reference dataset, and the third reference dataset to obtain the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix includes: Based on the preset total number of wind speed intervals and the preset number of wind direction partitions, the wind speed data in each reference dataset is divided into wind speed intervals and wind direction partitions respectively. Based on the wind frequency distribution data in each wind speed interval and wind direction interval corresponding to the first reference dataset, the second reference dataset, and the third reference dataset, a first initial wind frequency matrix, a second initial wind frequency matrix, and a third initial wind frequency matrix are generated. The data in the first initial wind frequency matrix, the second initial wind frequency matrix, and the third initial wind frequency matrix are verified. When the verification is successful, the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix are obtained.
4. The method according to claim 3, characterized in that, The step of verifying the data in the first, second, and third initial wind frequency matrices, and obtaining the first, second, and third wind frequency matrices when the verification passes, includes: For each wind direction partition of the first initial wind frequency matrix, the second initial wind frequency matrix, and the third initial wind frequency matrix, a Weibull distribution curve is fitted respectively; Calculate the correlation coefficient between each column of data in each initial wind frequency matrix and the Weibull distribution curve; When the correlation coefficient corresponding to a certain column of data is greater than or equal to the correlation threshold, the data in that column is deemed valid. When the effective data in the initial wind frequency matrix reaches a preset proportion, the initial wind frequency matrix is deemed to have passed the test. If the effective data does not reach the preset ratio, the total number of wind speed intervals and the number of wind direction zones are reset and iterative calculations are performed until the first initial wind frequency matrix, the second initial wind frequency matrix, and the third initial wind frequency matrix pass the verification, and the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix are obtained.
5. The method according to claim 4, characterized in that, The calculation of the first transformation relationship between the second wind frequency matrix and the third wind frequency matrix includes: The correlation between each corresponding wind direction zone in the second and third wind frequency matrices is solved by using the column-by-column correspondence method. The correlation is applied to each column of the second wind frequency matrix to obtain the transformed second wind frequency matrix, and it is determined whether the numerical distribution of the transformed second wind frequency matrix is valid. If the numerical distribution of the transformed second wind frequency matrix is determined to be valid, the correlation is used as the first transformation relationship between the second wind frequency matrix and the third wind frequency matrix.
6. The method according to claim 5, characterized in that, The step of applying the first transformation relationship to the first wind frequency matrix and performing normalization processing to obtain the fourth wind frequency matrix includes: Applying the first transformation relationship to each column of the first wind frequency matrix yields the transformed first matrix; The distorted data in the first matrix caused by the zero value of the first wind frequency matrix is zeroed out to obtain the second matrix; The second matrix is normalized to obtain the fourth wind frequency matrix.
7. The method according to claim 6, characterized in that, The step of determining whether the average wind speed of the fourth wind frequency matrix is valid based on the first wind frequency matrix, the second wind frequency matrix, the third wind frequency matrix, and the fourth wind frequency matrix includes: Calculate the average wind speed corresponding to the first wind frequency matrix, the second wind frequency matrix, the third wind frequency matrix, and the fourth wind frequency matrix, respectively; The proportional error is calculated based on the average wind speed corresponding to each wind frequency matrix. When the proportional error is less than the error threshold, the average wind speed of the fourth wind frequency matrix is determined to be valid.
8. A wind measurement data correction device, characterized in that, include: The acquisition module is used to acquire wind measurement data measured by the wind measuring device, wherein the wind measurement data is measurement data of a first time period containing a time series, and to acquire wind data of a second time period at the location of the wind measuring device. The extraction module is used to extract data from the measurement data of the first time period, specifically one or more complete years with the highest data integrity rate, as a first reference dataset. Extract data from the wind data of the second time period that are the same as the data period of the first reference dataset and use it as the second reference dataset; Based on the data period of the first reference dataset, the second reference dataset is extended forward and backward to obtain multiple third reference datasets for the whole year; The processing module is used to perform matrix processing on the first reference dataset, the second reference dataset, and the third reference dataset respectively to obtain the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix; The correction module is used to determine the correction result of the wind measurement data based on the first wind frequency matrix, the second wind frequency matrix, and the third wind frequency matrix.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the wind measurement data correction method as described in any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the wind measurement data correction method as described in any one of claims 1 to 7.