Interpolation and correction method and system for heterogeneous wind resource data

Abstract

The invention belongs to the field of wind resource data processing, and provides a heterogeneous wind resource data interpolation and correction method and system. The heterogeneous wind resource data interpolation and correction method comprises the following steps: acquiring wind resource data and mesoscale weather data of a target anemometer tower and an adjacent anemometer tower; performing same-tower interpolation on the wind resource data of different heights at the current moment according to the wind resource data of the available height at the set moment of the target anemometer tower; judging whether the wind direction or / and the wind speed in the wind resource data of the target anemometer tower after the same-tower interpolation is / are missing or not, and further judging whether different-tower interpolation is carried out or not to obtain representative wind resource data capable of representing the long-term average level of the wind power plant; judging whether the representative wind resource data has a tower shadow effect or not, and if not, obtaining final interpolated and corrected wind resource data of the target anemometer tower; and otherwise, carrying out tower shadow correction on the representative wind resource data, and taking a result after tower shadow correction as final interpolated and corrected wind resource data of the target anemometer tower.

Description

Technical Field

[0001] The present invention belongs to the field of wind resource data processing, and in particular relates to an interpolation and correction method and system for heterogeneous wind resource data. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] At present, wind resource data is scattered in different sources, and integrated and intensive management is difficult, which increases the complexity of data maintenance. In addition, the interpolation and correction of wind resource data are mostly processed manually, which makes it impossible to automatically identify and fill in missing, unreasonable, frozen, invalid, tower shadow effect and other data based on multi-source heterogeneous data, and it is impossible to guarantee the reliability of wind resource data quality. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for interpolating and correcting heterogeneous wind resource data, which can improve the efficiency of wind resource data processing and realize the integration and intensive management of multi-source data.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: A first aspect of the present invention provides a method for interpolating and correcting heterogeneous wind resource data.

[0006] A method for interpolating and correcting heterogeneous wind resource data, comprising: Obtain effective wind resource data and mesoscale weather data of the target wind tower and its neighboring wind towers; According to the effective wind resource data of the available height at the target wind tower at the set time, the effective wind resource data at different heights at the current time are interpolated on the same tower; Determine whether the wind direction and / or wind speed in the wind resource data of the target wind tower after same-tower interpolation are missing. If so, interpolate the wind direction and / or wind speed with the effective wind resource data of the neighboring wind towers, and then use the mesoscale weather data to correct the wind resource data after the interpolation with the other tower to the representative wind resource data; otherwise, directly use the mesoscale weather data to correct the wind resource data after the same-tower interpolation to the representative wind resource data; Determine whether the representative wind resource data has a tower shadow effect. If not, obtain the final interpolated and corrected target wind tower wind resource data; otherwise, perform tower shadow correction on the representative wind resource data, and use the tower shadow corrected result as the final interpolated and corrected target wind tower wind resource data.

[0007] As an implementation manner, the wind resource data at different heights interpolated on the same tower include wind speed data, wind direction data and temperature data.

[0008] As an implementation method, the process of interpolating wind speed data at different heights at the current moment on the same tower is as follows: Determine whether there is at least one valid wind speed at the target height at a certain moment. If so, calculate the average of these valid wind speeds as the wind speed value of the target height at that moment; If there is no effective wind speed at the target height at a certain moment, determine whether there is an effective wind speed other than the target height at that moment; When there are effective wind speeds at at least two heights other than the target height at that moment, the Pearson correlation coefficient between the target height and the channels with effective wind speeds at that moment is calculated respectively, and the effective wind speeds of a set number of channels with the highest correlation are selected for same-tower interpolation of the wind speed data of the target height at that moment; when interpolating at the same tower, the wind shear coefficient at that moment is calculated using the selected effective wind speed, and the wind shear profile is calculated based on the wind shear coefficient at that moment, thereby inferring the wind speed data of the target height at that moment; When there is an effective wind speed at a height other than the target height at that moment, the wind shear profile is calculated using the average wind shear coefficient observed in the known wind resource data set at similar times and dates in the day, and then the wind speed data at the target height at that moment is extrapolated.

[0009] As an implementation method, the process of interpolating the wind direction data / temperature data at different heights at the current moment on the same tower is as follows: Determine the amount of wind direction data / temperature data at the target height at a certain moment; When the number of wind direction data / temperature data at the target height at a certain moment is at least two, the average of these wind direction data / temperature data is calculated as the wind direction data / temperature data at the target height at that moment; When there is only one wind direction data / temperature data at the target height at a certain moment, the wind direction data / temperature data at the target height at that moment are inferred by using the average wind direction deflection / temperature gradient observed at similar times and days in a known wind resource data set.

[0010] As an implementation method, the process of interpolating wind direction between towers based on wind resource data of adjacent wind towers is as follows: The adjacent wind tower is used as a reference wind tower, and the wind direction data of the target wind tower is divided into different sectors according to the wind direction data of the reference wind tower; Calculate the change from the reference wind direction to the target wind direction at each moment in each sector, and calculate the average offset of each sector; The interpolation value at the corresponding moment can be obtained by adding the sector average offset to the reference wind direction.

[0011] As an implementation method, the process of interpolating wind speed from different towers based on wind resource data from adjacent wind towers is as follows: Divide the wind speed data of the target wind tower into several sectors according to the interpolated wind direction data of the target wind tower; The neighboring wind tower is used as the reference wind tower, and the relationship between the wind speed data of the target wind tower and the reference wind speed of the reference wind tower is fitted sector by sector. Based on the fitted wind speed relationship of each sector, the wind speed data in each sector of the target wind tower is interpolated.

[0012] As an implementation method, a representative year correction method is used to correct representative wind resource data that can characterize the long-term average level of the wind farm; Or when the representative wind resource data of the target wind measurement tower have two wind measurement channels at the same height, it is determined that the representative wind resource data have a tower shadow effect.

[0013] In one or more embodiments, a method for interpolating and correcting heterogeneous wind resource data is provided, comprising: A heterogeneous effective data acquisition module is used to obtain effective wind resource data and mesoscale weather data of the target wind tower and its neighboring wind towers; A wind resource data same-tower interpolation module is used to perform same-tower interpolation on effective wind resource data at different heights at the current moment according to the effective wind resource data at the available height at the target wind tower setting moment; The representative wind resource data correction module is used to determine whether the wind direction and / or wind speed in the wind resource data of the target wind tower after same-tower interpolation are missing. If so, the wind direction and / or wind speed are interpolated from different towers according to the effective wind resource data of the neighboring wind towers, and then the wind resource data after the interpolation from different towers are corrected to the representative wind resource data using the mesoscale weather data; otherwise, the wind resource data after the same-tower interpolation are directly corrected to the representative wind resource data using the mesoscale weather data; The tower shadow effect judgment module is used to judge whether the representative wind resource data has a tower shadow effect. If not, the final interpolated and corrected target wind resource data of the wind tower is obtained; otherwise, the representative wind resource data is subjected to tower shadow correction, and the tower shadow corrected result is used as the final interpolated and corrected target wind resource data of the wind tower.

[0014] A third aspect of the present invention provides a computer-readable storage medium.

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the above-mentioned method for interpolating and correcting heterogeneous wind resource data.

[0016] A fourth aspect of the present invention provides an electronic device.

[0017] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the above-mentioned method for interpolating and correcting heterogeneous wind resource data are implemented.

[0018] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention utilizes the effective wind resource data and mesoscale weather data of the target wind tower and its neighboring wind towers, performs same-tower interpolation based on the effective wind resource data of the available height at the set time of the target wind tower, and then performs cross-tower interpolation of the wind direction and / or wind speed based on the effective wind resource data of the neighboring wind towers, and uses the mesoscale weather data to correct it into representative wind resource data. Finally, the final interpolated and corrected wind resource data of the target wind tower is obtained through tower shadow correction, which simplifies the data processing process, improves data consistency and accuracy, adapts to the diversity of heterogeneous data sources, and is more flexible and universal.

[0019] (2) The present invention realizes intelligent processing of heterogeneous wind resource data through intelligent identification and interpolation methods, and can automatically identify and fill in missing, unreasonable, frozen, invalid, tower shadow effect and other data, thereby improving the integrity and availability of data, saving users a lot of manual processing time, and ensuring the reliability of data quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0021] Figure 1 is a flow chart of a method for interpolating and correcting heterogeneous wind resource data according to an embodiment of the present invention; Figure 2 is a schematic diagram of the structure of the interpolation and correction system for heterogeneous wind resource data according to an embodiment of the present invention; Figure 3 is a schematic diagram of an electronic device according to an embodiment of the present invention; Figure 4 is a flow chart of interpolating wind speed on the same tower according to an embodiment of the present invention; Figure 5 is a flow chart of interpolation temperature / wind direction in the same tower according to an embodiment of the present invention; Figure 6 This is a flow chart of different tower interpolation according to an embodiment of the present invention; Figure 7 is a representative year correction flow chart of an embodiment of the present invention; Figure 8 is a flow chart of the annual corrected wind speed based on the national standard method according to an embodiment of the present invention; Fig. 9 This is a flowchart of tower shadow area correction in an embodiment of the present invention; Fig.10 It is a flowchart of tower shadow correction using the maximum value method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0023] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0024] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0025] Figure 1 is a flow chart of a method for interpolating and correcting heterogeneous wind resource data in an embodiment of the present invention, such as Figure 1 The interpolation and correction method of heterogeneous wind resource data in the embodiment shown may include: S101, obtaining effective wind resource data and mesoscale weather data of a target wind tower and its neighboring wind towers; S102, performing same-tower interpolation of effective wind resource data at different heights at the current moment according to the effective wind resource data at the available height of the target wind tower at the set moment; S103, determining whether the wind direction and / or wind speed in the wind resource data of the target wind tower after same-tower interpolation are missing, if so, interpolating the wind direction and / or wind speed with different towers according to the effective wind resource data of the neighboring wind towers, and then correcting the wind resource data with different towers interpolated to representative wind resource data using mesoscale weather data; otherwise, directly correcting the wind resource data with same-tower interpolation to representative wind resource data using mesoscale weather data; S104, determining whether the representative wind resource data has a tower shadow effect, if not, obtaining the final interpolated and corrected target wind resource data of the wind tower; otherwise, performing tower shadow correction on the representative wind resource data, and using the tower shadow corrected result as the final interpolated and corrected target wind resource data of the wind tower.

[0026] This embodiment uses the effective wind resource data and mesoscale weather data of the target wind tower and its neighboring wind towers, and performs same-tower interpolation according to the effective wind resource data of the available height at the set time of the target wind tower, and then performs cross-tower interpolation of the wind direction and / or wind speed according to the effective wind resource data of the neighboring wind towers, and uses the mesoscale weather data to correct it into representative wind resource data. Finally, the final interpolated and corrected target wind tower wind resource data is obtained through tower shadow correction, which simplifies the data processing process, improves data consistency and accuracy, adapts to the diversity of heterogeneous data sources, and is more flexible and universal.

[0027] In step S101, the weather system is divided into planetary scale, weather scale, intermediate scale, mesoscale and microscale according to the size of the horizontal scale and time scale. The horizontal scale of the mesoscale is 10 to 200-300 kilometers, and the time scale is 1-10 hours. When long-term station information is lacking in reality, mesoscale simulated wind resource reanalysis data is needed as a reference. The reanalysis products currently used include: European Center for Medium-Term Numerical Weather Forecasts reanalysis data-ERA5, National Aeronautics and Space Administration (NASA) Modern Retrospective Analysis Research and Application Reanalysis Data-MERRA2.

[0028] In step S101, before obtaining the effective wind resource data of the target wind tower and its neighboring wind towers, the wind resource data is also tested and screened: the wind resource data of the target wind tower is tested for data integrity and validity, and unreasonable and missing data in the wind tower data are screened out; including data reasonable range test, reasonable correlation test and reasonable change trend test.

[0029] The reasonable range test of data is carried out on the average values ​​of wind speed, wind direction, temperature and air pressure to determine whether they are within a reasonable range.

[0030] The test of reasonable correlation of data is to determine whether the difference between the hourly average values ​​of wind speed or wind direction at different heights is within a reasonable range.

[0031] Test the reasonable change trend of data to determine whether the interpolation of the average value of wind speed and direction in the last hour and the average value in the previous hour is within a reasonable range.

[0032] By screening the data integrity and validity, low-quality and invalid data can be screened out, and the remaining data are valid data.

[0033] In step S102, wind resource data at different heights interpolated on the same tower include wind speed data, wind direction data and temperature data.

[0034] In step S102, if Figure 4 As shown in the figure, the process of interpolating wind speed data at different heights at the current moment on the same tower is: Determine whether there is at least one valid wind speed at the target height at a certain moment. If so, calculate the average of these valid wind speeds as the wind speed value of the target height at that moment; If there is no effective wind speed at the target height at a certain moment, determine whether there is an effective wind speed other than the target height at that moment; When there are effective wind speeds at at least two heights other than the target height at that moment, the Pearson correlation coefficient between the target height and the channels with effective wind speeds at that moment is calculated respectively, and the effective wind speeds of a set number of channels with the highest correlation are selected for same-tower interpolation of the wind speed data of the target height at that moment; when interpolating at the same tower, the wind shear coefficient at that moment is calculated using the selected effective wind speed, and the wind shear profile is calculated based on the wind shear coefficient at that moment, thereby inferring the wind speed data of the target height at that moment; When there is an effective wind speed at a height other than the target height at that moment, the wind shear profile is calculated using the average wind shear coefficient observed in the known wind resource data set at similar times and dates in the day, and then the wind speed data at the target height at that moment is extrapolated.

[0035] The change of wind speed with height satisfies the following relationship:

[0036] is the wind speed at height zm, is a constant, is the wind shear coefficient. Taking the logarithm of both sides of the above equation, we get:

[0037] This formula can be regarded as and The linear equation between , using the least squares method to solve the wind shear coefficient and constant b.

[0038] In step S102, if Figure 5 As shown in the figure, the process of interpolating the wind direction data / temperature data at different heights at the current moment on the same tower is: Determine the amount of wind direction data / temperature data at the target height at a certain moment; When the number of wind direction data / temperature data at the target height at a certain moment is at least two, the average of these wind direction data / temperature data is calculated as the wind direction data / temperature data at the target height at that moment; When there is only one wind direction data / temperature data at the target height at a certain moment, the wind direction data / temperature data at the target height at that moment are inferred by using the average wind direction deflection / temperature gradient observed at similar times and days in a known wind resource data set.

[0039] In step S103, interpolation between towers is to interpolate the wind direction / wind speed of the target tower to be interpolated using the data of neighboring towers or mesoscale data. When interpolation between towers is performed, the wind direction is interpolated first, and then the wind speed is interpolated.

[0040] When interpolating the wind direction, the wind direction offset method is used. For the wind direction, it is considered that within a certain area, it satisfies: D=d+c; where D is the average wind direction of the target data (i.e. the data to be interpolated), d is the average wind direction of the reference data (i.e. the "different tower" data used as a reference during interpolation), and c is the wind direction deviation of the target data and the reference data during the same period.

[0041] like Figure 6 As shown in the figure, the process of interpolating wind direction from different towers based on the wind resource data of adjacent wind towers is as follows: Take the neighboring wind tower as the reference wind tower, and divide the wind direction data of the target wind tower into different sectors according to the wind direction data of the reference wind tower; when dividing the sectors, you first need to specify the number of sectors to be divided, which is generally 16 sectors. When set to 16 sectors, 360° is evenly divided into 16 parts, and the span of each sector is 22.5°. The first sector is 348.75-11.25 degrees, that is, it extends 11.25 degrees to the left and right from 0 degrees. The second sector is 22.5 degrees, extending 11.25 degrees to the left and right, with a range of 11.25-33.75 degrees, and so on. For the target wind direction data, the corresponding sector can be determined based on the wind direction value of the reference wind tower at the corresponding moment; Calculate the change from the reference wind direction to the target wind direction at each moment in each sector, and calculate the average offset c of each sector; The interpolation value at the corresponding moment can be obtained by adding the sector average offset c to the reference wind direction.

[0042] In step S103, when interpolating the wind speed, the MCP (Measure-Correlate-Predict) method is used. The correlation between the reference wind speed and the target wind speed is established, usually using: U = aV + b; U is the wind speed at the specified height of the target wind tower; V is the wind speed at the corresponding height of the reference data; a is the slope of the linear correlation formula obtained based on the data of the target data and the reference data in the same time period; b is the intercept of the linear correlation formula obtained based on the data of the target data and the reference data in the same time period.

[0043] The process of interpolating wind speed from different towers based on the wind resource data of neighboring wind towers is as follows: Divide the wind speed data of the target wind tower into several sectors according to the interpolated wind direction data of the target wind tower; The neighboring wind tower is used as the reference wind tower, and the relationship between the wind speed data of the target wind tower and the reference wind speed of the reference wind tower is fitted sector by sector. Based on the fitted wind speed relationship of each sector, the wind speed data in each sector of the target wind tower is interpolated.

[0044] The core of wind speed interpolation is to construct a mathematical relationship between the wind speed of the target wind tower and the wind speed of the reference wind tower, and then use this relationship to predict the missing or invalid wind speed data of the target wind tower through the wind speed data of the reference wind tower. In wind speed interpolation, different fitting methods can be used to construct this relationship: Linear least squares method: This is the most basic fitting method, assuming that there is a linear relationship y=ax+b between the target wind speed y and the reference wind speed x. By calculating the linear correlation coefficient a and intercept b, the reference wind speed x is used to predict the target wind speed y.

[0045] Overall least squares method: When considering that there may be measurement errors in both the reference wind speed and the target wind speed, the overall least squares method can better fit the relationship between the two by simultaneously minimizing the errors in the lateral and longitudinal directions, thereby reducing the fitting error caused by measurement errors.

[0046] Variance ratio-based method: The variance ratio method determines the slope a based on the variance of the reference wind speed and the target wind speed, using the ratio of the standard deviations To describe the fluctuation relationship between the two, it is suitable for data with strong volatility.

[0047] Weibull fitting: If the wind speed data follows the Weibull distribution, the nonlinear relationship can be constructed by fitting the Weibull distribution parameters k and c. , thereby more accurately capturing the probability distribution characteristics of wind speed.

[0048] Wind speed classification method: classify the target wind speed and reference wind speed by wind speed, and perform different linear fittings respectively. For example, use the non-intercept fitting y=ax for the low wind speed interval, and use the intercept fitting y=ax+b for the high wind speed interval, so that the relationship between different wind speed intervals can be more accurately characterized.

[0049] For example, wind speed classification: Firstly, the reference wind speed and the target wind speed are arranged in ascending order, and a scatter plot is drawn after the arrangement; Select a threshold value, which is the smaller of 4 m / s and half of the average value of the long sequence reference data; The threshold is selected by comparing 4 m / s with half of the average value of the reference data and taking the smaller value as the threshold.

[0050] If the average value of the reference wind speed is greater, use 4 m / s; if it is smaller, use half of the average value.

[0051] This threshold is used to distinguish the parts with low and high wind speeds, so that different regression methods can be applied.

[0052] For the part greater than the threshold, fit according to y=mx+b; For the part smaller than the threshold, fit according to y=mx.

[0053] Wind speed interval regression: Divide the wind speed data into multiple intervals according to the wind speed range, calculate the average value of the target wind speed and the reference wind speed in each interval, use these average points for piecewise regression, and construct a piecewise curve to capture the changing trend within different wind speed ranges.

[0054] For example, regression in wind speed intervals: Divide the x-axis of the target wind speed and reference wind speed into N equal-sized segments (the number of segments will be input, and the interval size is determined based on the number of segments); Calculate the average target wind speed for each segment; Assign a point to each partition, the x value is the midpoint of the segment, and the y value is the average target wind speed in the segment; connect these points to get a segmented curve (the starting point is (0, 0)); If the amount of data in the interval is less than 5 points, the overall least squares method is used to fit the average wind speed of the predicted partition.

[0055] By dividing the wind speed into multiple intervals and calculating the average wind speed value in each interval, the segmented change curve of the target wind speed is obtained. This method is particularly suitable for dealing with complex wind speed change patterns, especially when the wind speed change is not completely linear, segmented regression can better capture local changes.

[0056] Linear Support Vector Machine (SVM): SVM establishes an optimized linear or nonlinear relationship between the reference wind speed and the target wind speed by finding an optimal separation plane, which is particularly suitable for processing noise and outliers in the data.

[0057] The role of these fitting methods in wind speed interpolation is to determine the wind speed relationship between the reference wind tower and the target wind tower based on historical data, and then use the reference wind speed to predict the missing values ​​or outliers of the target wind speed to ensure the integrity and reliability of the wind speed data.

[0058] In the application of wind speed interpolation, different fitting methods can be flexibly selected according to the specific data characteristics and interpolation requirements to improve the prediction accuracy. For example, the linear least squares method is simple and easy to use, and is suitable for wind speed data with obvious linear relationships. However, when the wind speed fluctuates greatly, the overall least squares method will be more effective because it can simultaneously consider the measurement errors of the target wind speed and the reference wind speed. For situations where the wind speed distribution is close to the Weibull distribution, the use of Weibull fitting can better reflect the probability characteristics of the wind speed, and is especially suitable for the fitting and prediction of long-term wind speed data.

[0059] In the interpolation of multiple wind speed intervals, wind speed classification and wind speed interval regression are common strategies. They fit the data according to different wind speed intervals to avoid the accumulation of errors in low wind speed intervals and high wind speed intervals that may be caused by a single fitting method. For example, when the wind speed is low, directly using simple linear regression may lead to large errors. At this time, regression by interval can effectively capture the wind speed characteristics of each interval.

[0060] In step S103, a representative year correction method is used to correct representative wind resource data that can characterize the long-term average level of the wind farm.

[0061] When making corrections to the representative year, the difference method adds the use of slope and correlation coefficient to determine whether to make corrections to the sector, and no corrections are made to sectors with weak correlation.

[0062] Intelligent correction of wind measurement data: Automatic correction of wind measurement data is realized, and correction for representative years can be performed in a variety of ways. Calculated charts and tables are automatically output, and manual intervention is supported.

[0063] like Figure 7 The correction process is as follows: The mesoscale data of the four points closest to the wind tower are selected from the wind resource map of the database, and the correlation coefficient with the target data is calculated. The data of the point with the highest correlation is automatically selected as the reference data, or the mesoscale data file can be manually uploaded as the reference data; then the mesoscale data is used to correct the wind direction and wind speed of the target data.

[0064] When correcting the wind direction of the target data, the method used is consistent with the interpolation of wind direction of different towers.

[0065] When correcting the wind speed of the target data, three methods are provided: The first method is the interpolation method of wind speed between different towers.

[0066] The second method is as follows Figure 8The method provided in the national standard shown is: the wind speed correlation curves of each wind direction quadrant between the wind field station and the long-term station in the corresponding year. The wind speed correlation curves of 16 wind field stations and long-term stations are obtained. For each wind speed correlation curve, the annual average wind speed of the long-term station for many years and the annual average wind speed of the long-term station during the same period as the wind field station observation are marked on the horizontal axis, and then the two wind speed values ​​of the corresponding wind field station are found on the vertical axis, and the algebraic interpolation of these two wind speed values ​​is calculated (a total of 16 algebraic differences). Each wind speed in each wind direction quadrant of the wind field station data is added with the corresponding wind speed algebraic difference to obtain the corrected wind speed and direction data of the wind field station. The process is: Based on the reference wind direction data, the wind speed data is divided into several sectors; In each sector, the target data is segmented into intervals of 0.1 m / s, and the average value of the reference wind speed corresponding to each wind speed interval of the target data is calculated; For the target wind speed interval in the previous step, take the average wind speed at the beginning of the interval as the new dependent variable, take the average reference wind speed corresponding to the target wind speed interval as the new independent variable, and perform sector fitting to obtain the relationship between the new independent variable and the dependent variable; In each sector, based on the relationship between the new independent variable and the dependent variable obtained in the previous step, calculate y1 corresponding to the average tower wind speed and y2 corresponding to the average reference wind speed; Sector-by-sector calculation ; Corrected tower wind speed = actual tower wind speed + .

[0067] The third method is the difference method, and its main process is: The wind direction based on the target data divides the wind speed into several sectors; Fit each sector according to U=aV+b, and calculate the correlation coefficient R between the reference wind speed and the target wind speed in each sector; Based on the value of the correlation coefficient R and the slope a, it is determined whether to correct the sector; For the sectors that need to be corrected, calculate the difference Δx between the average value of the reference data and the average value of the long sequence reference data, and the correction amount of each sector is aΔx. For the sectors that need to be corrected, use the target data + aΔx to get the corrected representative data.

[0068] These three wind speed correction methods have different focuses and applicable scenarios.

[0069] The core of the MCP method is to establish a linear correlation between the reference wind speed and the target wind speed, and use the existing reference wind data to fill in the missing data of the target wind tower. This method is suitable for use when the distance between wind towers is close or the terrain and meteorological conditions are similar. Because it is based on the correlation between the reference and target wind speeds in the same period, it can effectively fill in the missing data, but the accuracy of long time series interpolation may be reduced. Applicable scenarios: interpolation of missing wind speed data in short time series, and the geographical environment and meteorological conditions of the reference station and the target station are similar.

[0070] The characteristic of the national standard method is that the wind speed of each wind field station and long-term station is divided into sectors, and the wind speed correlation curve is fitted for each wind direction quadrant. The wind speed of each sector is corrected by calculating the algebraic difference. This method refines the difference in wind speed under different wind directions and can better cope with scenes with complex terrain and unstable wind direction. The advantages are: the wind speed difference in each wind direction quadrant is carefully processed, which can significantly improve the wind speed correction accuracy under complex terrain; when there is data at the long-term station, it can better reflect the wind speed relationship between the wind field station and the long-term station. Applicable scenarios: It is suitable for areas with complex terrain and unstable wind direction. When there is sufficient data from the long-term station, the wind speed difference can be refined.

[0071] The difference method is based on the linear fitting relationship between the target wind speed and the reference wind speed, and determines whether correction is needed based on the correlation coefficient and slope. For sectors that need correction, the target data is adjusted using the average difference between the reference data and the long sequence reference data. This method corrects the target wind speed through linear fitting, and performs differentiated processing on each sector to avoid deviations caused by unified correction. The advantages are: judging whether correction is needed by correlation coefficient and slope to avoid over-correction; the calculation is relatively simple, but at the same time it can handle differences in different wind directions. Applicable scenarios: scenarios where the wind direction is relatively stable and the correlation between the reference station and the target station is strong, corrections can be made quickly.

[0072] Among them, short-term wind speed data is missing: If the target wind tower data is missing for a short time, the MCP method is more appropriate because it can quickly interpolate through reference data.

[0073] Complex terrain or unstable wind direction: In areas with complex terrain and large changes in wind direction, the national standard method can better reflect the differences in wind speed in all directions due to its fine sector division and wind speed-related curve fitting.

[0074] Long-term wind speed correction: If the representative year correction is to be made to the long-term data, the difference method can effectively adjust it according to the long series reference data, and determine whether correction is needed through the correlation coefficient to avoid error accumulation.

[0075] In actual wind speed interpolation and correction, the overall correction amount of the three methods can be comprehensively evaluated, and the method with the smallest overall correction amount can be selected as the final interpolation and correction solution. The specific steps are as follows: Calculate the overall correction of the three methods respectively: First, the target wind speed is interpolated and corrected using the MCP method, national standard method and difference method to obtain the corrected results for each sector.

[0076] All sector correction values ​​of each method are accumulated to calculate the overall correction of the three methods, that is, the sum of the corrections of all sectors.

[0077] Calculate the weighted correction of each method. The correction Δy of each sector is weighted according to the amount of data in the sector. The weighted formula is as follows:

[0078] is the correction amount for each sector is the number of data points in the sector.

[0079] The overall performance of the MCP method, national standard method and difference method are compared through the weighted average correction amount.

[0080] The method with the smallest weighted correction amount is selected as the final wind speed interpolation and correction scheme.

[0081] By comparing the overall correction amounts of the three methods, the method with the smallest correction amount is selected.

[0082] After selecting the method with the smallest correction amount, this method is used to perform wind speed interpolation and correction on the entire data set to obtain the final target wind speed data.

[0083] This method of minimizing the overall correction can effectively balance the correction accuracy of each sector by selecting the optimal interpolation scheme from a global perspective, ensuring that the results are more accurate overall. Compared with only comparing the correction amount of a single sector, this method can avoid the inaccuracy caused by local optimality and ensure that the interpolated data is consistent and representative over a large range.

[0084] In step S104, when there are two wind measurement channels at the same height of the representative wind resource data of the target wind measurement tower, it is determined that the representative wind resource data has a tower shadow effect.

[0085] If there are two wind measurement channels at the same height of the target wind tower, the wind speed behind the tower will decrease due to the blocking effect of the tower. This phenomenon is called the tower shadow effect. At this time, the tower shadow correction method is needed to correct the wind speed data at this height.

[0086] The "channel" of a wind tower usually refers to the collection channels of various sensors or measuring equipment installed on the tower. These channels are used to transmit meteorological data such as wind speed, wind direction, temperature, etc. collected from different heights and directions. A wind tower is generally equipped with multiple sensors, and each sensor transmits data to the data acquisition system through a different channel for recording and analysis. A wind tower may be equipped with anemometers or wind vanes at different heights (such as 10 meters, 30 meters, 50 meters, etc.), and each sensor corresponds to a channel. For example, a wind speed sensor at a height of 10 meters may correspond to the first channel, and a sensor at a height of 50 meters may correspond to another channel.

[0087] Smart tower shadow correction: When two wind speed sensors are installed at the same height, a tower shadow effect will occur, which requires tower shadow correction. The system provides three methods for tower shadow correction: The first one is Fig. 9 The shown correction is based on the tower shadow area. The main process is: The two wind speed channels at the same height are divided into several sectors according to the wind direction; Calculate the ratio of the wind speed values ​​of channels A and B at the same time in each sector; For each sector, the median of the ratio within the sector is calculated; For the values ​​obtained in the previous step, take the central angle of the sector where the maximum value is located, and take 15 degrees to the left and right, and regard it as the area where channel B is affected by the tower shadow. The wind speed channel B value in this area is corrected by the corresponding channel A value; similarly, take the sector where the minimum value is located, select the corresponding range, and regard it as the area where channel A is affected by the tower shadow.

[0088] This method can more finely consider the impact of different wind directions on wind speed, especially when clarifying the area where wind speed channel A or B is affected by the tower shadow. By dividing the wind direction sector, selecting the area affected by the tower shadow and correcting the corresponding wind speed channel, the actual situation of local wind speed can be better reflected. It can correct specific sectors in a targeted manner to avoid the wind speed data of the entire area being disturbed by the tower shadow, and has high local accuracy.

[0089] This method relies on clear division of wind direction and tower shadow area, and is suitable for situations where the wind speed is significantly affected by the local tower shadow and can be accurately identified. It may be difficult to apply when the tower shadow effect cannot be clearly identified.

[0090] The second method is as follows Fig.10 The maximum value method shown is that at each moment, the larger value of channels A and B is selected as the value at that moment.

[0091] Taking the larger wind speed value in channels A and B can effectively avoid the impact of tower shadow on the data.

[0092] This method is simple and direct. By selecting a larger wind speed value, it avoids the phenomenon of wind speed reduction caused by the tower shadow and ensures that the selected wind speed is closer to the actual value.

[0093] Ignoring the situation where channels A and B may be affected by the tower shadow at the same time may lead to misjudgment of some extreme situations.

[0094] The third method is to directly select the channel with the larger average value among the two wind speed channels.

[0095] This method is suitable for long-term wind speed data analysis. If a certain wind speed channel is less affected by the tower shadow and the measured wind speed is more accurate within a period of time, the channel with a larger average value can be selected as the representative of the time period.

[0096] Through long-term wind speed comparison, channels that are less affected by tower shadows can be selected. This is suitable for long-term correction of the entire time series and can effectively reduce the interference of tower shadows on long-term data.

[0097] Statistical analysis that relies on long-term data may not be applicable to situations with significant changes in the short term, nor can it handle differences in tower shadow effects caused by changes in wind direction.

[0098] If the tower shadow is more obvious in wind speed channels A and B and the tower shadow area can be accurately divided, the correction method based on the tower shadow area is preferred. This method can correct the specific area affected by the tower shadow with higher accuracy.

[0099] If it is difficult to clearly identify the area affected by the tower shadow and the wind speed fluctuates greatly, it is recommended to use the maximum value method. This method is simple and can avoid the impact of the tower shadow on the wind speed reduction, and is suitable for real-time correction.

[0100] Figure 2 is a schematic diagram of the structure of a heterogeneous wind resource data interpolation and correction system in an embodiment of the present invention. Figure 1 The interpolation and correction methods of heterogeneous wind resource data correspond to Figure 2 As shown, the interpolation and correction system for heterogeneous wind resource data in this embodiment may include: A heterogeneous effective data acquisition module 201 is used to acquire effective wind resource data and mesoscale weather data of a target wind tower and its neighboring wind towers; A wind resource data same-tower interpolation module 202 is used to perform same-tower interpolation on effective wind resource data at different heights at the current moment according to effective wind resource data at the available height at the target wind tower setting moment; The representative wind resource data correction module 203 is used to determine whether the wind direction and / or wind speed in the wind resource data of the target wind measuring tower after the same-tower interpolation are missing. If so, the wind direction and / or wind speed are interpolated from different towers according to the effective wind resource data of the neighboring wind measuring towers, and then the wind resource data after the interpolation from different towers are corrected to the representative wind resource data using the mesoscale weather data; otherwise, the wind resource data after the same-tower interpolation are directly corrected to the representative wind resource data using the mesoscale weather data; The tower shadow effect judgment module 204 is used to judge whether the representative wind resource data has a tower shadow effect. If not, the final interpolated and corrected target wind resource data of the wind tower is obtained; otherwise, the representative wind resource data is subjected to tower shadow correction, and the tower shadow corrected result is used as the final interpolated and corrected target wind resource data of the wind tower.

[0101] It should be noted here that Figure 2 The various modules in the interpolation and correction system of heterogeneous wind resource data in Figure 1 The various steps in the interpolation and correction method for heterogeneous wind resource data in correspond one to one, and the specific implementation process is the same, which will not be repeated here.

[0102] Reference Figure 3 , a schematic diagram of an electronic device is given. It should be noted that, Figure 3 The electronic device 300 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0103] like Figure 3 As shown, the electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303. In the RAM 303, various programs and data required for system operation are also stored. The central processing unit 301, the ROM 302 and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0104] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a local area network (LAN) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage section 308 as needed.

[0105] When the central processing unit 301 in the electronic device of this embodiment executes the program, the following is achieved: Figure 1 The steps in the method for interpolation and correction of heterogeneous wind resource data are shown.

[0106] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, the computer program including a computer program for executing Figure 1 In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit 301, various functions defined in the apparatus of the present application are executed.

[0107] in, Figure 1 The computer program instructions corresponding to the method shown may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0108] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0109] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for interpolation and correction of heterogeneous wind resource data, characterized in that: include: Obtain effective wind resource data and mesoscale weather data of the target wind tower and its neighboring wind towers; According to the effective wind resource data of the available height at the target wind tower at the set time, the effective wind resource data at different heights at the current time are interpolated on the same tower; Determine whether the wind direction and / or wind speed in the wind resource data of the target wind tower after same-tower interpolation are missing. If so, interpolate the wind direction and / or wind speed with the effective wind resource data of the neighboring wind towers, and then use the mesoscale weather data to correct the wind resource data after the interpolation with the other tower to the representative wind resource data; otherwise, directly use the mesoscale weather data to correct the wind resource data after the same-tower interpolation to the representative wind resource data; Determine whether the representative wind resource data has a tower shadow effect. If not, obtain the final interpolated and corrected target wind tower wind resource data; otherwise, perform tower shadow correction on the representative wind resource data, and use the tower shadow corrected result as the final interpolated and corrected target wind tower wind resource data.

2. The method for interpolating and correcting heterogeneous wind resource data according to claim 1, characterized in that: The wind resource data at different heights interpolated on the same tower include wind speed data, wind direction data and temperature data.

3. The method for interpolating and correcting heterogeneous wind resource data according to claim 2, characterized in that: The process of interpolating wind speed data at different heights on the same tower is as follows: Determine whether there is at least one valid wind speed at the target height at a certain moment. If so, calculate the average of these valid wind speeds as the wind speed value of the target height at that moment; If there is no effective wind speed at the target height at a certain moment, determine whether there is an effective wind speed other than the target height at that moment; When there are effective wind speeds at at least two heights other than the target height at that moment, the Pearson correlation coefficient between the target height and the channels with effective wind speeds at that moment is calculated respectively, and the effective wind speeds of a set number of channels with the highest correlation are selected for same-tower interpolation of the wind speed data of the target height at that moment; when interpolating at the same tower, the wind shear coefficient at that moment is calculated using the selected effective wind speed, and the wind shear profile is calculated based on the wind shear coefficient at that moment, thereby inferring the wind speed data of the target height at that moment; When there is an effective wind speed at a height other than the target height at that moment, the wind shear profile is calculated using the average wind shear coefficient observed in the known wind resource data set at similar times and dates in the day, and then the wind speed data at the target height at that moment is extrapolated.

4. The method for interpolating and correcting heterogeneous wind resource data according to claim 2, characterized in that: The process of interpolating wind direction data / temperature data at different heights on the same tower is as follows: Determine the amount of wind direction data / temperature data at the target height at a certain moment; When the number of wind direction data / temperature data at the target height at a certain moment is at least two, the average of these wind direction data / temperature data is calculated as the wind direction data / temperature data at the target height at that moment; When there is only one wind direction data / temperature data at the target height at a certain moment, the wind direction data / temperature data at the target height at that moment are inferred by using the average wind direction deflection / temperature gradient observed at similar times and days in a known wind resource data set.

5. The method for interpolating and correcting heterogeneous wind resource data according to claim 1, characterized in that: The process of interpolating wind direction based on wind resource data from adjacent wind towers is as follows: The adjacent wind tower is used as a reference wind tower, and the wind direction data of the target wind tower is divided into different sectors according to the wind direction data of the reference wind tower; Calculate the change from the reference wind direction to the target wind direction at each moment in each sector, and calculate the average offset of each sector; The interpolation value at the corresponding moment can be obtained by adding the sector average offset to the reference wind direction.

6. The method for interpolating and correcting heterogeneous wind resource data according to claim 1, characterized in that: The process of interpolating wind speed from different towers based on the wind resource data of neighboring wind towers is as follows: Divide the wind speed data of the target wind tower into several sectors according to the interpolated wind direction data of the target wind tower; The neighboring wind tower is used as the reference wind tower, and the relationship between the wind speed data of the target wind tower and the reference wind speed of the reference wind tower is fitted sector by sector. Based on the fitted wind speed relationship of each sector, the wind speed data in each sector of the target wind tower is interpolated.

7. The method for interpolating and correcting heterogeneous wind resource data according to claim 1, characterized in that: The representative year correction method is used to correct the representative wind resource data that can characterize the long-term average level of wind farms; Or when the representative wind resource data of the target wind measurement tower have two wind measurement channels at the same height, it is determined that the representative wind resource data have a tower shadow effect.

8. A system for interpolation and correction of heterogeneous wind resource data, characterized in that: include: A heterogeneous effective data acquisition module is used to obtain effective wind resource data and mesoscale weather data of the target wind tower and its neighboring wind towers; A wind resource data same-tower interpolation module is used to perform same-tower interpolation on effective wind resource data at different heights at the current moment according to the effective wind resource data at the available height at the target wind tower setting moment; The representative wind resource data correction module is used to determine whether the wind direction and / or wind speed in the wind resource data of the target wind tower after same-tower interpolation are missing. If so, the wind direction and / or wind speed are interpolated from different towers according to the effective wind resource data of the neighboring wind towers, and then the wind resource data after the interpolation from different towers are corrected to the representative wind resource data using the mesoscale weather data; otherwise, the wind resource data after the same-tower interpolation are directly corrected to the representative wind resource data using the mesoscale weather data; The tower shadow effect judgment module is used to judge whether the representative wind resource data has a tower shadow effect. If not, the final interpolated and corrected target wind resource data of the wind tower is obtained; otherwise, the representative wind resource data is subjected to tower shadow correction, and the tower shadow corrected result is used as the final interpolated and corrected target wind resource data of the wind tower.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for interpolating and correcting heterogeneous wind resource data as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the method for interpolating and correcting heterogeneous wind resource data according to any one of claims 1 to 7 are implemented.

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