Environmental turbulence intensity standard deviation vacancy value interpolation method and system based on wind parameter binning-dynamic fitting

Abstract

The invention discloses an environmental turbulence intensity standard deviation vacancy value interpolation method and system based on wind parameter binning-dynamic fitting, and belongs to the technical field of wind resource assessment. The objective of the invention is to solve the problems that the current wind measurement data sample is limited, the sample size of the turbulence intensity standard deviation matrix at the high wind speed section is insufficient, and the deviation of the current vacancy value calibration method is large, thereby causing the calculation error of the effective turbulence intensity of the machine. According to the invention, based on a statistical framework of wind speed and wind direction joint warehouse division, a parameter adaptive double-exponential function fitting algorithm is adopted, and an environmental turbulence intensity standard deviation matrix vacancy value interpolation method which better accords with mathematical rules is constructed. According to the method, wind parameter warehouse division statistics and function dynamic fitting are innovatively combined, turbulence intensity standard deviation matrix vacancy value filling and optimization are achieved, the accuracy of effective turbulence intensity calculation is improved, a practical and effective new method is provided for wind resource data preprocessing, and the method has important practical significance in development and utilization of wind energy resources.

Description

Technical Field

[0001] This application belongs to the field of wind resource assessment technology, and in particular relates to a method and system for interpolating missing values ​​of environmental turbulence intensity standard deviation based on wind parameter compartment-dynamic fitting. Background Art

[0002] The domestic wind power industry is currently experiencing rapid development, with increasingly fierce market and resource competition. More and more wind power development projects are shifting from plains to mountainous and hilly areas. Compared to open and flat terrain, rugged underlying surface conditions create complex wind speeds and directions within the boundary layer of the wind farm area, intensifying airflow disturbances. Wind turbines are typically subject to greater turbulence, posing higher requirements for turbine safety risk assessment. The effective turbulence intensity at the turbine location is a key and important reference wind parameter for assessing wind turbine loads, directly affecting the calculated load results. Therefore, accurately assessing the effective turbulence intensity at wind farm turbine locations has become a core issue of great concern and urgent need for resolution across the entire industry.

[0003] In wind farm resource assessment, measured wind parameter data is crucial input data, as its environmental turbulence intensity and standard deviation directly determine the effective turbulence intensity at the turbine site. However, due to limitations such as cost and land acquisition, the sample size of wind measurement data from wind towers and radar is usually limited, resulting in insufficient sample size for the measured environmental turbulence intensity and turbulence intensity standard deviation matrix in high wind speed ranges, necessitating interpolation of missing values. Chinese invention patent CN117785848A discloses a method and device for calculating effective turbulence under wind turbine sector management, proposing a method to use an improved form of the Normal Turbulence Model (NTM) as the target fitting function to complete the missing data in the turbulence matrix. This method is one of the most commonly used methods for filling the turbulence intensity matrix from wind measurement data; however, the standard deviation of turbulence intensity does not have the same physical meaning as turbulence intensity, reflecting the fluctuation characteristics of turbulence intensity. Therefore, this method cannot effectively perform missing value interpolation and may even lead to systematic deviations in the interpolated environmental turbulence intensity standard deviation data, affecting the reliability of subsequent calculation results.

[0004] In summary, current methods for interpolating missing values ​​of the standard deviation of turbulence intensity in wind measurement data have limitations in wind farm resource assessment and can further lead to errors in the calculation of effective turbulence intensity at turbine sites. Therefore, there is an urgent need to develop a more mathematically sound method for interpolating missing values ​​of the environmental turbulence intensity standard deviation matrix to improve the accuracy of effective turbulence calculation at turbine sites. This invention proposes a method for interpolating missing values ​​of the environmental turbulence intensity standard deviation based on wind parameter compartmentalization and dynamic fitting. This method is more mathematically sound, captures the multi-stage fluctuation characteristics of the turbulence intensity standard deviation, and effectively interpolates missing values ​​of the environmental turbulence intensity standard deviation matrix, thereby improving the calculation accuracy of effective turbulence intensity at turbine sites. Summary of the Invention

[0005] The purpose of this application is to overcome the problems of the prior art. This application discloses a method and system for interpolating missing values ​​of the standard deviation of environmental turbulence intensity based on wind parameter compartmenting and dynamic fitting. It adopts a wind speed-wind direction joint compartmenting statistical framework and is equipped with a parameter adaptive double exponential function fitting algorithm to construct a sector-based adaptive regression model that better conforms to the variation law of the standard deviation of turbulence intensity. This realizes the effective interpolation of missing values ​​of the standard deviation matrix of environmental turbulence intensity and improves the calculation accuracy of the effective turbulence intensity of the machine position.

[0006] The objective of this application is achieved through the following technical solution: A method for interpolating missing values ​​of the standard deviation of environmental turbulence intensity based on wind parameter compartmentalization and dynamic fitting, the method comprising: S1: Obtain wind speed, wind speed standard deviation, and wind direction time series data from wind farm meteorological towers or radars, and verify the validity of the data; S2: Develop a partitioning strategy, including partitioning boundaries and intervals for wind speed and wind direction, and partition time series data for wind speed and wind speed standard deviation. S3: Based on the IQR algorithm, outlier detection is performed on the wind parameter data in each compartment according to the set minimum sample size requirement and the control of outlier detection sensitivity. S4: After removing outliers, calculate the effective average wind speed and the standard deviation of wind speed standard deviation in each compartment unit, and then calculate the turbulence intensity standard deviation matrix divided by different wind speed segments and different wind direction sectors. S5: Based on the standard deviation matrix of turbulence intensity of wind measurement data, dynamic fitting of effective data for each sector is performed. The median value of each wind speed interval is the independent variable, the standard deviation of turbulence intensity is the dependent variable, and the double exponential function is the target model. An adaptive regression model for each sector is constructed to fill in the missing values ​​of each sector.

[0007] According to a preferred embodiment, step S1 further includes: Identify and remove invalid data caused by abnormal equipment status and environmental interference, and perform integrity verification, statistical distribution test and physical threshold check; The abnormal equipment status segment includes data generated when the tower collapses / lowers due to sensor failure or mechanical malfunction. The environmental interference segment includes data generated by icing and preset extreme weather conditions.

[0008] According to a preferred embodiment, step S2 includes: discretizing the time series data using an equidistant grading strategy, dividing the wind speed interval into 1 m / s intervals, dividing the wind direction interval into 16 azimuth wind direction angles, and performing multi-dimensional joint compartmenting on the processed wind parameter time series data.

[0009] According to a preferred embodiment, step S3 includes: S31: Based on the data obtained from the S2 warehouse statistics, the IQR algorithm is used for secondary filtering of outliers; S32: Set the minimum sample size requirement and outlier detection sensitivity k, and only detect compartment units whose valid data meet the minimum sample size requirement; S33. Perform box-by-box inspection on the sub-compartment units that meet the conditions according to the settings in S32, mark and remove abnormal values ​​that exceed the upper and lower boundaries.

[0010] According to a preferred embodiment, step S31, the outlier secondary filtering process using the IQR algorithm, includes:

[0011]

[0012] In the formula, outlier represents outliers, Q1 is the first quartile, Q3 is the third quartile, IQR is the interquartile range representing the span of the middle 50% of the data, and k is the outlier detection sensitivity. In step S32, the minimum sample size is set to be greater than 10, and the outlier detection sensitivity k is set to 1.5 for flat terrain and 3 for other terrains.

[0013] According to a preferred embodiment, in step S4, based on the compartmentalized data after cleaning in S3, the average wind speed and standard deviation of turbulence intensity for each compartmentalized unit are calculated, resulting in a matrix form as shown in the table below:

[0014] In the table, Dir represents different wind direction sectors, V represents different wind speed segments at a certain height, and the data to be filled in the table are the values ​​of the measured wind parameters for different wind direction sectors at different wind speed segments.

[0015] According to a preferred embodiment, in step S4, the calculation of the standard deviation of turbulence intensity for each compartment unit follows the definition of IEC 61400-1 standard, and its mathematical representation is the ratio of the standard deviation of wind speed to the average wind speed. The calculation formula is as follows:

[0016] In the formula, The standard deviation of turbulence intensity for each compartment unit. and These are the standard deviation and average wind speed of each compartment unit, respectively. By calculating the wind parameter matrix of these two variables, the standard deviation matrix of environmental turbulence intensity can be obtained.

[0017] According to a preferred embodiment, step S5 includes: S51. Establish a criterion for the validity of the compartment unit, set a minimum sample size threshold, remove boxes in each sector with fewer than the preset value of valid samples in the compartment unit, and remove isolated compartments, so as to maintain the spatial continuity of the wind field while ensuring the statistical significance of the samples. S52. Set the minimum number of fitting points and fill in missing data only for sectors that meet the criteria; S53. Perform dynamic fitting of effective data for each sector, using the median value of each wind speed interval as the independent variable, the standard deviation of turbulence intensity as the dependent variable, and the double exponential function as the target model to construct an adaptive regression model for each sector and fill in the missing values ​​of each sector. S54. The obtained optimal sector adaptive interpolation function system is used to interpolate the missing values ​​of the standard deviation of turbulence intensity in each sector wind speed segment. Finally, the missing data of sectors that do not meet the fitting conditions are filled by averaging the values ​​of adjacent sectors.

[0018] According to a preferred embodiment, in step S51, the minimum sample size threshold is greater than or equal to 10; in step S52, the minimum number of fitting points in the qualified sector is not less than 3.

[0019] According to a preferred embodiment, in step S53, The mathematical expression for the target model is as follows:

[0020] In the formula, The standard deviation of the simulated turbulence intensity, Let a, b, c, and d be the median values ​​of each wind speed interval, and a, b, c, and d be the parameters to be fitted, with a>0, b>0, c≥0, and d>0, to ensure that the function is a monotonically decreasing positive function. The minimum mean square error of the standard deviation of the measured-simulated turbulence intensity at the fitting point is used as the verification criterion:

[0021] Based on the minimum mean square error (MSEmin), the objective function parameter values ​​for each sector are determined, and a sector-adaptive dynamic interpolation function system is established.

[0022] On the other hand, this application also discloses: An interpolation system for missing values ​​of environmental turbulence intensity standard deviation based on wind parameter compartmentalization and dynamic fitting, wherein the environmental turbulence intensity standard deviation missing value interpolation system uses the aforementioned method to interpolate missing values ​​of environmental turbulence intensity standard deviation; The missing value interpolation system for the standard deviation of environmental turbulence intensity includes: The wind parameter data preprocessing module is used to read and process wind speed, wind speed standard deviation, and wind direction time series data from wind farm anemometer towers or radars, clean up invalid data, and verify data validity. The wind parameter data partitioning module is used to formulate partitioning strategies, including partitioning boundaries and intervals for wind speed and wind direction, and to partition the processed wind parameter time series data. The compartment unit outlier detection module uses the IQR algorithm to detect outliers in the wind parameter data of each compartment unit, clean the data, and ensure the validity of the data in each compartment unit, based on the set control outlier detection sensitivity and minimum sample size requirements. The statistical calculation module performs sub-compartment statistical calculations, calculates the effective average wind speed and the standard deviation of wind speed standard deviation within each sub-compartment unit, and then calculates the turbulence intensity standard deviation matrix divided by different wind speed segments and different wind direction sectors. The sector-by-sector dynamic fitting module is used to fill in the missing values ​​of the turbulence intensity standard deviation matrix. Based on the calculated environmental turbulence intensity standard deviation matrix, the module uses the median of each wind speed interval in each sector as the independent variable, the environmental turbulence intensity standard deviation as the dependent variable, and the double exponential function as the target model to construct an adaptive regression model for each sector. This model is then used to dynamically fit the effective data for each sector and fill in the missing values ​​for each sector.

[0023] The aforementioned main solution and its various further alternative solutions can be freely combined to form multiple solutions, all of which are solutions that can be adopted and are claimed in this application. Those skilled in the art, after understanding the solution of this application, will realize that there are many combinations based on the prior art and common general knowledge, all of which are technical solutions to be protected in this application, and will not be exhaustively listed here.

[0024] The beneficial effects of this application are: This application is based on a statistical framework of wind speed and wind direction combined into compartments, and adopts a parameter-adaptive double exponential function fitting algorithm, which can accurately capture the multi-stage dynamics of turbulence intensity standard deviation. By combining wind parameter compartment statistics and function dynamic fitting, a method for interpolating missing values ​​of the turbulence intensity standard deviation matrix that is more in line with mathematical laws is constructed.

[0025] This application optimizes the filling of missing values ​​in the standard deviation matrix of turbulence intensity, improves the accuracy of effective turbulence intensity calculation, provides practical and effective methodological support for wind resource data preprocessing, and has important practical significance for the development and utilization of wind energy resources. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a method for interpolating missing values ​​of environmental turbulence intensity standard deviation based on wind parameter compartmenting and dynamic fitting, provided in an embodiment of this application. Figure 2 This is a technical roadmap for a method for interpolating missing values ​​of environmental turbulence intensity standard deviation based on wind parameter compartmenting and dynamic fitting, provided in an embodiment of this application. Detailed Implementation

[0027] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0028] To facilitate understanding of the overall technical solution, the relevant concepts of each original variable and intermediate variable are explained.

[0029] Turbulence intensity: In aerodynamics, turbulence refers to short-term fluctuations in wind speed. Turbulence intensity is a standard for measuring the degree of wind speed fluctuation, expressed as the ratio of the standard deviation of wind speed to the average wind speed, and is a dimensionless quantity. A higher turbulence intensity indicates greater instability in the wind flow.

[0030] Turbulence intensity standard deviation: A parameter that quantifies the fluctuation characteristics of turbulence intensity in the form of standard deviation.

[0031] Turbulence matrix: Using wind speed as column labels and wind direction as row labels, it presents the turbulence parameters corresponding to different wind speed ranges and different wind direction combinations in matrix form.

[0032] Environmental turbulence: Turbulence generated by the combined effects of local topography, geomorphology, and atmospheric environment. Environmental turbulence at a wind tower specifically refers to the turbulence actually detected at the wind tower location; the intensity of environmental turbulence at the tower location usually refers to turbulence that is not disturbed or affected by wakes generated by other wind turbines or obstacles, and its turbulence intensity is usually calculated using CFD directional calculations and calibration of the measured environmental turbulence intensity at the wind tower.

[0033] Effective turbulence intensity at wind turbine location: The effective turbulence intensity experienced by wind turbines in a wind farm consists of two parts: the ambient turbulence intensity and the additional turbulence intensity generated by the wake between wind turbine units. It is an important reference wind parameter for assessing the load on wind turbine units. The calculation of effective turbulence intensity at wind turbine location involves the ambient turbulence intensity, the standard deviation of the ambient turbulence intensity, and the additional turbulence.

[0034] Example 1 refer to Figure 1 and Figure 2 As shown in the figure, this embodiment discloses a method for interpolating missing values ​​of the standard deviation of environmental turbulence intensity based on wind parameter compartmentalization and dynamic fitting. The method provided in this embodiment innovatively combines wind parameter compartmentalization statistics and function dynamic fitting, which solves the problem that the current wind measurement data sample is limited, the sample size of the measured environmental turbulence intensity standard deviation matrix in the high wind speed range is insufficient, and the current missing value interpolation method has a large deviation, which leads to the calculation error of the effective turbulence intensity of the machine position.

[0035] The method for interpolating missing values ​​of environmental turbulence intensity standard deviation in this embodiment includes the following steps.

[0036] Step S1: Obtain wind speed, wind speed standard deviation, and wind direction time series data from wind farm meteorological towers or radar, and verify the validity of the data.

[0037] Preferably, step S1 specifically includes: acquiring wind vector time series data (wind speed, wind direction) and turbulence characteristic parameters (10-minute wind speed standard deviation) from the wind farm's wind measurement tower or radar, identifying and removing invalid data caused by abnormal equipment status segments (sensor failure, tower collapse / reduction due to mechanical failure) and environmental interference segments (icing, extreme weather), and performing integrity verification, statistical distribution verification, and physical threshold verification.

[0038] Step S2: Develop a data categorization strategy, using equidistant grading, and categorize the time series data into categorized data at 1 m / s wind speed intervals and 22.5° wind direction intervals.

[0039] Preferably, step S2 includes: discretizing the time series data using an equidistant grading strategy, dividing the wind speed interval into 1 m / s intervals, dividing the wind direction intervals into 16 azimuth wind direction angles (N, NNE, NE, etc.), and performing multi-dimensional joint compartmenting on the processed wind parameter time series data to achieve data discretization and enhance data stability.

[0040] Step S3: Based on the IQR algorithm, outlier detection is performed in separate compartments according to the set control sensitivity and minimum sample size requirements, which ensures the effectiveness of data cleaning while maintaining the original data distribution characteristics.

[0041] Preferably, step S3 includes: S31. Based on the data obtained from the S2 warehouse statistics, the IQR algorithm is used for secondary filtering of outliers. The specific formula is as follows:

[0042]

[0043] In the formula, outlier represents outliers, Q1 is the first quartile, Q3 is the third quartile, IQR is the interquartile range representing the span of the middle 50% of the data, and k is the outlier detection sensitivity. S32. Set the minimum sample size requirement (must be greater than 10) and the outlier detection sensitivity k (it is recommended to set 1.5 for flat terrain and 3 for complex terrain), and only detect compartments with valid data that meet the minimum sample size requirement; S33. Perform box-by-box inspection on the sub-compartment units that meet the conditions according to the settings in S32, mark and remove abnormal values ​​that exceed the upper and lower boundaries.

[0044] Step S4: After data cleaning is completed, calculate the standard deviation matrix of turbulence intensity.

[0045] Preferably, step S4 includes: based on the compartmentalized data after cleaning in S3, calculating the average wind speed and standard deviation of turbulence intensity for each compartmentalized unit, resulting in a matrix form as shown in the table below:

[0046] In the table, Dir represents different wind direction sectors, V represents different wind speed segments at a certain height, and the data in the table are the values ​​of measured wind parameters for different wind direction sectors at different wind speed segments.

[0047] The calculation of the standard deviation of turbulence intensity for each compartment strictly follows the definition in IEC 61400-1, and its mathematical representation is the ratio of the standard deviation of wind speed to the average wind speed. The calculation formula is as follows:

[0048] In the formula, The standard deviation of turbulence intensity for each compartment unit. and These represent the standard deviation and average wind speed of each compartment unit, respectively. By calculating the wind parameter matrices of these two variables, the standard deviation matrix of the environmental turbulence intensity can be further obtained.

[0049] Step S5: Based on the standard deviation matrix of turbulence intensity of wind measurement data, perform dynamic fitting of effective data for each sector. With the median of wind speed interval as the independent variable, the standard deviation of turbulence intensity as the dependent variable, and the double exponential function as the target model, construct an adaptive regression model for each sector, fill in the missing values ​​of each sector, and obtain the complete standard deviation matrix of environmental turbulence intensity.

[0050] Preferably, step S5 specifically includes: S51. Establish a criterion for the validity of the compartmentalized units, set a minimum sample size threshold (usually not less than 10), remove boxes with too few valid samples in each sector, and remove isolated compartments (compartments in adjacent wind speed segments are empty), so as to maintain the spatial continuity of the wind field while ensuring the statistical significance of the samples. S52. Set the minimum number of fitted points, and fill in missing data only for qualified sectors (minimum of 3 fitted points); S53. Perform dynamic fitting of effective data for each sector, using the median of each wind speed interval as the independent variable, the standard deviation of turbulence intensity as the dependent variable, and a double exponential function as the target model to construct an adaptive regression model for each sector, filling in the missing values ​​for each sector. The mathematical formula of the target model is as follows:

[0051] In the formula, The standard deviation of the simulated turbulence intensity, The x-value represents the median of each wind speed interval (e.g., x = 2.5 m / s for the 2-3 m / s wind speed interval). a, b, c, and d are the parameters to be fitted (a > 0, b > 0, c ≥ 0, d > 0, to ensure the function is a monotonically decreasing positive function). The double exponential function can capture the characteristics of a rapid and sharp decline in the early stage and a slow and stable decline in the later stage of the turbulence intensity standard deviation. It can also bridge data fluctuations in the transition zone, significantly improving the physical rationality and predictive accuracy of the fitted model.

[0052] The minimum mean square error of the standard deviation of the measured-simulated turbulence intensity at the fitting point is used as the verification criterion:

[0053] Based on the minimum mean square error (MSEmin), the objective function parameter values ​​of each sector are determined, and a dynamic interpolation function system for sector adaptation is established. S54. The obtained optimal sector adaptive interpolation function system is applied to interpolate the missing values ​​of the standard deviation of turbulence intensity in each sector's wind speed range. Finally, the missing data of sectors that do not meet the fitting conditions are filled by averaging the values ​​of adjacent sectors.

[0054] This application's method is based on a statistical framework that combines wind speed and direction, employing a parameter-adaptive double exponential function fitting algorithm to accurately capture the multi-stage dynamics of turbulence intensity standard deviation. By combining wind parameter compartmentalization statistics with function dynamic fitting, a method for interpolating missing values ​​in the turbulence intensity standard deviation matrix that better conforms to mathematical laws is constructed. This invention optimizes the filling of missing values ​​in the turbulence intensity standard deviation matrix, improves the accuracy of effective turbulence intensity calculation, provides practical and effective methodological support for wind resource data preprocessing, and has significant practical implications for the development and utilization of wind energy resources.

[0055] Example 2 This embodiment discloses an environmental turbulence intensity standard deviation missing value interpolation system based on wind parameter compartment-dynamic fitting. The environmental turbulence intensity standard deviation missing value interpolation system uses the method described in Embodiment 1 to interpolate the environmental turbulence intensity standard deviation missing value.

[0056] The environmental turbulence intensity standard deviation missing value interpolation system in this embodiment includes: The wind parameter data preprocessing module is used to read and process wind speed, wind speed standard deviation, and wind direction time series data from wind farm anemometer towers or radars, clean up invalid data, and verify data validity.

[0057] The wind parameter data partitioning module is used to formulate partitioning strategies, including partitioning boundaries and intervals for wind speed and wind direction, and to partition the processed wind parameter time series data.

[0058] The outlier detection module for each compartment unit uses the IQR algorithm to detect outliers in the wind parameter data within each compartment unit, cleans the data, and ensures the validity of the data in each compartment unit, based on the set control sensitivity and minimum sample size requirements for outlier detection.

[0059] The statistical calculation module performs sub-compartmental statistical calculations, calculates the effective average wind speed and the standard deviation of the wind speed standard deviation within each sub-compartment, and then calculates the turbulence intensity standard deviation matrix divided by different wind speed segments and different wind direction sectors.

[0060] The sector-by-sector dynamic fitting module is used to fill in the missing values ​​of the turbulence intensity standard deviation matrix. Based on the calculated environmental turbulence intensity standard deviation matrix, the module uses the median of each wind speed interval in each sector as the independent variable, the environmental turbulence intensity standard deviation as the dependent variable, and the double exponential function as the target model to construct an adaptive regression model for each sector. This model is then used to dynamically fit the effective data for each sector and fill in the missing values ​​for each sector.

[0061] Specific application examples A complete annual wind parameter time series was selected at the hub height of a wind farm's meteorological tower. The operational steps in Example 1 were executed, and the method described in this patent was compared with the method using an improved form of NTM as the target fitting function to complete the missing data in the environmental turbulence intensity standard deviation matrix. The latter exhibited a significant systematic positive bias. According to the method for calculating the effective turbulence intensity at the wind farm site, this positive anomaly in the standard deviation of environmental turbulence intensity leads to an overestimation of the effective turbulence intensity at the wind farm site.

[0062] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for interpolating missing values ​​of environmental turbulence intensity standard deviation based on wind parameter compartmentalization and dynamic fitting, characterized in that, The method for interpolating missing values ​​of the standard deviation of environmental turbulence intensity includes: S1: Obtain wind speed, wind speed standard deviation, and wind direction time series data from wind farm meteorological towers or radars, and verify the validity of the data; S2: Develop a partitioning strategy, including partitioning boundaries and intervals for wind speed and wind direction, and partition time series data for wind speed and wind speed standard deviation. S3: Based on the IQR algorithm, outlier detection is performed on the wind parameter data in each compartment according to the set minimum sample size requirement and the control of outlier detection sensitivity. S4: After removing outliers, calculate the effective average wind speed and the standard deviation of wind speed standard deviation in each compartment unit, and then calculate the turbulence intensity standard deviation matrix divided by different wind speed segments and different wind direction sectors. S5: Based on the standard deviation matrix of turbulence intensity of wind measurement data, dynamic fitting of effective data for each sector is performed. The median value of each wind speed interval is the independent variable, the standard deviation of turbulence intensity is the dependent variable, and the double exponential function is the target model. An adaptive regression model for each sector is constructed to fill in the missing values ​​of each sector.

2. The method for interpolating missing values ​​of environmental turbulence intensity standard deviation as described in claim 1, characterized in that, Step S1 also includes: Identify and remove invalid data caused by abnormal equipment status and environmental interference, and perform integrity verification, statistical distribution test and physical threshold check; The abnormal equipment status segment includes data generated when the tower collapses / lowers due to sensor failure or mechanical malfunction. The environmental interference segment includes data generated by icing and preset extreme weather conditions.

3. The method for interpolating missing values ​​of environmental turbulence intensity standard deviation as described in claim 2, characterized in that, Step S2 includes: discretizing the time series data using an equidistant grading strategy, dividing the wind speed interval into 1 m / s intervals, dividing the wind direction interval into 16 azimuth wind direction angles, and performing multi-dimensional joint compartmenting on the processed wind parameter time series data.

4. The method for interpolating missing values ​​of environmental turbulence intensity standard deviation as described in claim 1 or 3, characterized in that, Step S3 includes: S31: Based on the data obtained from the S2 warehouse statistics, the IQR algorithm is used for secondary filtering of outliers; S32: Set the minimum sample size requirement and outlier detection sensitivity k, and only detect compartment units whose valid data meet the minimum sample size requirement; S33. Perform box-by-box inspection on the sub-compartment units that meet the conditions according to the settings in S32, mark and remove abnormal values ​​that exceed the upper and lower boundaries.

5. The method for interpolating missing values ​​of environmental turbulence intensity standard deviation as described in claim 4, characterized in that, In step S31, the outlier secondary filtering process using the IQR algorithm includes: In the formula, outlier represents outliers, Q1 is the first quartile, Q3 is the third quartile, IQR is the interquartile range representing the span of the middle 50% of the data, and k is the outlier detection sensitivity. In step S32, the minimum sample size is set to be greater than 10, and the outlier detection sensitivity k is set to 1.5 for flat terrain and 3 for other terrains.

6. The method for interpolating missing values ​​of environmental turbulence intensity standard deviation as described in claim 4, characterized in that, In step S4, based on the compartmentalized data after cleaning in S3, the average wind speed and standard deviation of turbulence intensity for each compartmentalized unit are calculated, resulting in the matrix form shown in the table below: In the table, Dir represents different wind direction sectors, V represents different wind speed segments at a certain height, and the data to be filled in the table are the values ​​of the measured wind parameters for different wind direction sectors at different wind speed segments.

7. The method for interpolating missing values ​​of environmental turbulence intensity standard deviation as described in claim 6, characterized in that, In step S4, the calculation of the standard deviation of turbulence intensity for each compartment unit follows the definition of IEC 61400-1 standard. Its mathematical representation is the ratio of the standard deviation of wind speed to the average wind speed. The calculation formula is as follows: In the formula, The standard deviation of turbulence intensity for each compartment unit. and These are the standard deviation and average wind speed of each compartment unit, respectively. By calculating the wind parameter matrix of these two variables, the standard deviation matrix of environmental turbulence intensity can be obtained.

8. The method for interpolating missing values ​​of environmental turbulence intensity standard deviation as described in claim 7, characterized in that, Step S5 includes: S51. Establish a criterion for the validity of the compartment unit, set a minimum sample size threshold, remove boxes in each sector with fewer than the preset value of valid samples in the compartment unit, and remove isolated compartments, so as to maintain the spatial continuity of the wind field while ensuring the statistical significance of the samples. S52. Set the minimum number of fitting points and fill in missing data only for sectors that meet the criteria; S53. Perform dynamic fitting of effective data for each sector, using the median value of each wind speed interval as the independent variable, the standard deviation of turbulence intensity as the dependent variable, and the double exponential function as the target model to construct an adaptive regression model for each sector and fill in the missing values ​​of each sector. S54. The obtained optimal sector adaptive interpolation function system is used to interpolate the missing values ​​of the standard deviation of turbulence intensity in each sector wind speed segment. Finally, the missing data of sectors that do not meet the fitting conditions are filled by averaging the values ​​of adjacent sectors.

9. The method for interpolating missing values ​​of environmental turbulence intensity standard deviation as described in claim 8, characterized in that, In step S53, The mathematical expression for the target model is as follows: In the formula, The standard deviation of the simulated turbulence intensity, Let a, b, c, and d be the median values ​​of each wind speed interval, and a, b, c, and d be the parameters to be fitted, with a>0, b>0, c≥0, and d>0, to ensure that the function is a monotonically decreasing positive function. The minimum mean square error of the standard deviation of the measured-simulated turbulence intensity at the fitting point is used as the verification criterion: Based on the minimum mean square error (MSEmin), the objective function parameter values ​​for each sector are determined, and a sector-adaptive dynamic interpolation function system is established.

10. A system for interpolating missing values ​​of environmental turbulence intensity standard deviation based on wind parameter compartmentalization and dynamic fitting, characterized in that, The missing value interpolation system for the standard deviation of environmental turbulence intensity is performed by the method described in any one of claims 1 to 9; The missing value interpolation system for the standard deviation of environmental turbulence intensity includes: The wind parameter data preprocessing module is used to read and process wind speed, wind speed standard deviation, and wind direction time series data from wind farm anemometer towers or radars, clean up invalid data, and verify data validity. The wind parameter data partitioning module is used to formulate partitioning strategies, including partitioning boundaries and intervals for wind speed and wind direction, and to partition the processed wind parameter time series data. The compartment unit outlier detection module uses the IQR algorithm to detect outliers in the wind parameter data of each compartment unit, clean the data, and ensure the validity of the data in each compartment unit, based on the set control outlier detection sensitivity and minimum sample size requirements. The statistical calculation module performs sub-compartment statistical calculations, calculates the effective average wind speed and the standard deviation of wind speed standard deviation within each sub-compartment unit, and then calculates the turbulence intensity standard deviation matrix divided by different wind speed segments and different wind direction sectors. The sector-by-sector dynamic fitting module is used to fill in the missing values ​​of the turbulence intensity standard deviation matrix. Based on the calculated environmental turbulence intensity standard deviation matrix, the module uses the median of each wind speed interval in each sector as the independent variable, the environmental turbulence intensity standard deviation as the dependent variable, and the double exponential function as the target model to construct an adaptive regression model for each sector. This model is then used to dynamically fit the effective data for each sector and fill in the missing values ​​for each sector.

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