An extreme wind quantitative diagnosis method and system for an ultra-high voltage transmission line

Abstract

The application belongs to the technical field of power system automation, and particularly relates to a kind of extreme wind quantitative diagnosis method and system for ultra-high voltage transmission line, comprising: obtaining reanalysis meteorological data and terrain height data of transmission line corridor, generating double-layer wind speed sequence and air temperature and pressure sequence;Calculate power law index and surface roughness and calculate stability sequence;Extrapolate to obtain conductor height average wind speed sequence;Calculate turbulence intensity and dynamic gust factor, generate hourly gust speed sequence;Perform threshold method analysis and block extreme value method analysis, determine each return period extreme wind speed and confidence interval, and correct according to terrain acceleration factor.The technical scheme of the application realizes dynamic diagnosis and uncertainty quantification of conductor height extreme wind speed, and improves the accuracy and reliability of extreme wind evaluation results.

Description

Technical Field

[0001] This invention belongs to the field of power system automation technology, specifically relating to a quantitative diagnostic method and system for extreme winds in ultra-high voltage transmission lines. Background Technology

[0002] Ultra-high voltage (UHV) transmission lines span wide areas and have tall towers, with their corridors potentially traversing diverse terrains such as plains, hills, ridges, and valleys. Sudden, extreme winds can generate significant wind loads on conductors, insulator strings, and tower structures, potentially leading to conductor galloping, hardware damage, line breaks, or tower collapse. Therefore, during the planning, design, wind resistance testing, and operation and maintenance of transmission lines, it is necessary to assess the extreme wind speeds and their return periods at conductor heights based on long-term meteorological data of the transmission line corridor.

[0003] Current extreme wind speed assessments typically rely on near-surface wind speed data, using preset wind profile parameters to convert wind speeds to target heights, and estimating short-term gust speeds using empirical gust coefficients. However, because atmospheric stratification and near-surface turbulence conditions along the transmission line corridor vary with time, season, and weather events, fixed wind profile parameters and gust coefficients are difficult to consistently adapt to different meteorological conditions, potentially leading to errors in the estimation of average wind speed and instantaneous gust speeds along the conductor. For transmission line corridors lacking long-term measured data, when using reanalysis data for assessments, these biases can accumulate over long time series and affect subsequent extreme value statistics.

[0004] Therefore, there is an urgent need for a quantitative diagnostic method for extreme winds on ultra-high voltage transmission lines to improve the accuracy and reliability of extreme wind speed assessment of conductor height. Summary of the Invention

[0005] This invention provides a quantitative diagnostic method for extreme winds on ultra-high voltage transmission lines. By employing dual-layer wind speed parameter back-calculation, stability-driven gust calculation, dual-track extreme value analysis, and terrain correction, it solves the problems of insufficient adaptability of target height wind speed estimation, large deviation in gust characterization, and insufficient reliability of extreme value results in existing technologies.

[0006] In a first aspect, the present invention provides a quantitative diagnostic method for extreme winds affecting ultra-high voltage transmission lines, the method comprising: Acquire hourly reanalysis meteorological data and terrain elevation data from power transmission lines, and generate a two-layer wind speed sequence and temperature and pressure sequence based on the hourly reanalysis meteorological data; The power law exponent and surface roughness are calculated hourly based on the dual-layer wind speed sequence. The overall atmospheric stability parameters are calculated hourly based on the dual-layer wind speed components and the air temperature and pressure sequence, and a stability sequence composed of the overall atmospheric stability parameters at each time point is generated. Based on the power law exponent or the surface roughness, the reference height wind speed is extrapolated to the traverse height to generate a traverse height average wind speed sequence. The stability-corrected turbulence intensity is calculated based on the stability sequence and the surface roughness. The dynamic gust factor is determined based on the turbulence intensity. The hourly gust velocity sequence at the conductor height is generated by combining the average wind speed sequence at the conductor height. Extreme event samples exceeding the threshold and annual maximum value samples are extracted from the hourly gust speed sequence at the conductor height. Extreme value analysis using both the over-threshold method and the block extreme value method is performed, and extreme wind speeds for each return period are generated based on the results of these two extreme value analyses. Uncertainty quantification is performed on the extreme event samples exceeding the threshold and the annual maximum value samples to generate confidence intervals corresponding to the extreme wind speeds for each return period. A terrain acceleration factor is determined based on the terrain height data, and the extreme wind speeds for each return period and their corresponding confidence intervals are corrected to generate terrain-corrected extreme wind speeds and confidence intervals for each return period.

[0007] Using the above scheme, the present invention provides a quantitative diagnostic method for extreme winds on ultra-high voltage transmission lines. This method constructs a dual-layer wind speed sequence and a temperature and pressure sequence by reanalyzing meteorological data, achieving a unified data foundation for wind profile parameters and atmospheric stability calculations. It calculates the hourly average wind speed at conductor height by inversely calculating the power-law exponent and surface roughness and extrapolating the wind speed at reference height. Through the correlation processing of stability sequences, turbulence intensity, and dynamic gust factors, it dynamically constructs the hourly gust speed sequence at conductor height. Furthermore, through two types of extreme value sample analysis, uncertainty quantification, and terrain correction, it achieves quantitative output of extreme wind speeds and confidence intervals for each return period, improving the accuracy and reliability of the diagnostic results for extreme winds on ultra-high voltage transmission lines.

[0008] In some embodiments of the present invention, the step of generating a two-layer wind speed sequence and a temperature and pressure sequence based on hourly reanalysis meteorological data includes: The zonal and meridional wind speed components corresponding to two reference heights are extracted from the hourly reanalysis meteorological data, and wind speed vectors are synthesized to generate the two-layer wind speed sequence. Near-surface air temperature and surface air pressure are extracted to generate the air temperature and pressure sequence; The terrain potential is converted into geometric height, and the coordinates of the two-layer wind speed sequence, the temperature and pressure sequence, and the geometric height are unified and quality controlled to generate the terrain height data.

[0009] In some embodiments of the present invention, generating the conductor height average wind speed sequence includes: Based on the wind speed ratio corresponding to the two reference heights in the dual-layer wind speed sequence, the power law exponent and the surface roughness are calculated hourly. When the wind speed ratio is not greater than the preset wind speed ratio threshold, the corresponding time is determined to be an abnormal time of the headwind profile, and the surface roughness corresponding to the abnormal headwind profile time is replaced with climatological surface roughness. Based on the stability sequence, a power law or logarithmic law is selected, and the wind speed at the reference height is extrapolated to the conductor height based on the corresponding power law exponent or surface roughness to generate the average wind speed sequence at the conductor height.

[0010] In some embodiments of the present invention, the step of calculating the overall atmospheric stability parameters and generating a stability sequence includes: The potential temperature corresponding to the two reference heights is determined based on the air temperature and pressure sequence, and the interlayer potential temperature difference is determined based on the potential temperature corresponding to the two reference heights. The wind shear between the two reference heights is determined based on the two-layer wind speed components, and the overall atmospheric stability parameters are calculated based on the interlayer potential temperature difference, the interlayer height difference, and the wind shear. Based on the overall atmospheric stability parameters, the atmospheric stability at each time point is divided into strongly unstable, weakly unstable, near-neutral, weakly stable, and strongly stable, thus generating the stability sequence.

[0011] In some embodiments of the present invention, generating the hourly gust velocity sequence of the conductor height includes: The stability correction function and the Moning-Obukhoff length are determined based on the stability sequence. The stability-corrected turbulence intensity is calculated based on the stability correction function, the Monin-Obukhov length, and the surface roughness. The dynamic gust factor is determined based on the turbulence intensity, peak factor, and turbulence energy spectrum integral factor corresponding to the stability correction. Based on the dynamic gust factor and the average wind speed sequence at the conductor height, an hourly gust speed sequence at the conductor height is generated.

[0012] In some embodiments of the present invention, the step of performing super-threshold extreme value analysis and block extreme value analysis respectively, and generating extreme wind speeds for each return period based on the results of the two extreme value analyses, includes: An initial threshold is determined based on a preset quantile of the hourly gust speed sequence of the conductor height. The initial threshold is adjusted based on the change in average remaining lifetime. Gust speeds exceeding the adjusted threshold are de-clustered to generate the extreme event samples exceeding the threshold. The extreme event samples exceeding the threshold are fitted with a generalized Pareto distribution to generate extreme value analysis results using the threshold method. The maximum value is extracted from the hourly gust speed sequence of the conductor height according to a preset time block to generate the annual maximum value sample. The annual maximum value sample is fitted with a generalized extreme value distribution to generate the block extreme value method extreme value analysis result. The extreme wind speeds for each return period are generated based on the extreme value analysis results of the over-threshold method and the extreme value analysis results of the block extreme value method, and the two extreme value analysis results are compared to generate consistency test results. The consistency test result is generated by comparing the differences between the first type of return period extreme value and the second type of return period extreme value.

[0013] In some embodiments of the present invention, the uncertainty quantization of the over-threshold extreme event samples and the annual maximum value samples includes: The extreme event samples exceeding the threshold and the annual maximum value samples are resampled with replacement to generate multiple sets of resampled extreme value samples. Refit the corresponding extreme value distribution for each group of resampled extreme value samples, and calculate the extreme wind speed corresponding to each return period; Confidence intervals are determined based on multiple extreme wind speeds corresponding to each return period, and confidence intervals corresponding to the extreme wind speeds of each return period are generated.

[0014] In some embodiments of the present invention, the generation of extreme wind speeds for each return period based on the results of two extreme value analyses includes: Based on the over-threshold extreme event samples and the annual maximum value samples, fit multiple candidate extreme value distributions to generate the return period extreme values ​​corresponding to each candidate extreme value distribution; The model weights are determined based on the information criterion values ​​corresponding to the distributions of each candidate extreme value. The return period extreme values ​​corresponding to each candidate extreme value distribution are weighted according to the model weights to generate the extreme wind speeds for each return period.

[0015] In some embodiments of the present invention, the generation of extreme wind speeds and confidence intervals for each return period after terrain correction includes: The terrain slope and terrain type at the corresponding location of the transmission line corridor are determined based on the terrain elevation data. The terrain acceleration factor is determined based on the terrain slope, and when the terrain type is a canyon, the canyon acceleration coefficient is determined based on the canyon width-to-depth ratio. Based on the terrain acceleration factor and the canyon acceleration coefficient, the extreme wind speeds and confidence intervals for each return period are corrected to generate the terrain-corrected extreme wind speeds and confidence intervals for each return period.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention uses hourly back-calculation of the power-law exponent and surface roughness from a dual-layer wind speed sequence, combined with temperature, air pressure, and wind shear to calculate the stability sequence, enabling the conversion of conductor height wind speed to adapt to different atmospheric conditions at different times. It constructs an hourly gust speed sequence using stability-corrected turbulence intensity and dynamic gust factors, allowing the input for extreme wind speed analysis to reflect the characteristics of gust changes with atmospheric stability. Furthermore, through dual-track analysis of over-threshold extreme event samples and annual maximum value samples, resampling confidence intervals, weighted distribution of multiple candidate extreme values, and terrain acceleration correction, it generates extreme wind speed results for each return period that include terrain influence and uncertainty range, improving the adaptability, statistical reliability, and engineering application value of extreme wind diagnosis for UHV transmission lines.

[0017] Secondly, the present invention provides a quantitative diagnostic system for extreme high winds in ultra-high voltage transmission lines, comprising: The data preprocessing module is used to acquire reanalysis meteorological data and terrain elevation data of the transmission line corridor, and generate a two-layer wind speed sequence and temperature and pressure sequence based on the hourly reanalysis meteorological data; The parameter calculation module is used to calculate the power law exponent and surface roughness based on the dual-layer wind speed sequence, calculate the overall atmospheric stability parameters based on the dual-layer wind speed components and the air temperature and pressure sequence, and generate a stability sequence composed of the overall atmospheric stability parameters at each time point. The average wind speed generation module is used to generate a series of average wind speeds at the conductor height by extrapolating the reference height wind speed based on the power law exponent or the surface roughness. The dynamic gust generation module is used to calculate turbulence intensity and dynamic gust factor based on the stability sequence and the surface roughness, and to generate an hourly gust speed sequence by combining the average wind speed sequence at the conductor height. The extreme value diagnosis and correction module extracts over-threshold extreme event samples and annual maximum value samples from the hourly gust speed sequence of the conductor height, and performs over-threshold extreme value analysis and block extreme value analysis respectively, generating extreme wind speeds for each return period based on the results of the two extreme value analyses; it quantifies the uncertainty of the over-threshold extreme event samples and the annual maximum value samples to generate confidence intervals corresponding to the extreme wind speeds for each return period; it determines the terrain acceleration factor based on the terrain height data, and corrects the extreme wind speeds for each return period and their corresponding confidence intervals to generate terrain-corrected extreme wind speeds for each return period and their confidence intervals.

[0018] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the method for quantitative diagnosis of extreme winds for ultra-high voltage transmission lines as described in any embodiment of the first aspect.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for quantitative diagnosis of extreme winds for ultra-high voltage transmission lines as described in any embodiment of the first aspect.

[0020] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the text, or may be learned by practice of the invention. The objects and other advantages of the invention will become apparent from the description and the accompanying drawings.

[0021] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0022] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] In the attached diagram: Figure 1 This is a flowchart illustrating a quantitative diagnostic method for extreme winds in ultra-high voltage transmission lines, provided by an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of extreme wind analysis for a certain tower in an ultra-high voltage transmission line provided in an embodiment of the present invention, wherein... Figure 2 (a) shows the hourly gust speed sequence at conductor height and the distribution of extreme events exceeding the threshold. Figure 2 (b) is the mean remaining life plot. Figure 2 (c) is a probability-probability test plot of the fit to the generalized Pareto distribution. Figure 2 (d) is a reproduction of the horizontal curve.

[0025] Figure 3 A schematic diagram of an extreme high wind quantitative diagnostic system for ultra-high voltage transmission lines provided in this embodiment of the invention. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0027] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0028] Figure 1 This is a flowchart illustrating a quantitative diagnostic method for extreme winds in ultra-high voltage transmission lines, provided by an embodiment of the present invention.

[0029] Example 1, such as Figure 1 As shown, this invention provides a quantitative diagnostic method for extreme high winds in ultra-high voltage transmission lines, the method comprising the following steps: S1. Obtain reanalysis meteorological data and terrain elevation data of the transmission line corridor, and generate a two-layer wind speed sequence and temperature and pressure sequence based on the hourly reanalysis meteorological data; S2. Calculate the power law exponent and surface roughness based on the dual-layer wind speed sequence, calculate the overall atmospheric stability parameters based on the dual-layer wind speed components and the air temperature and pressure sequence, and generate a stability sequence composed of the overall atmospheric stability parameters at each time point. S3. Based on the power law exponent or the surface roughness, extrapolate the reference height wind speed to generate a conductor height average wind speed sequence. S4. Calculate the turbulence intensity and dynamic gust factor based on the stability sequence and the surface roughness, and generate an hourly gust speed sequence by combining the average wind speed sequence at the conductor height. S5. Extract extreme event samples and annual maximum value samples from the hourly gust speed sequence at the conductor height, and perform extreme value analysis using the over-threshold method and the block extreme value method respectively. Generate extreme wind speeds for each return period based on the results of the two extreme value analyses. Quantify the uncertainty of the extreme event samples and the annual maximum value samples to generate confidence intervals corresponding to the extreme wind speeds for each return period. Determine the terrain acceleration factor based on the terrain height data, and correct the extreme wind speeds for each return period and their corresponding confidence intervals to generate terrain-corrected extreme wind speeds for each return period and their confidence intervals.

[0030] Using the above scheme, the present invention provides a quantitative diagnostic method for extreme winds on ultra-high voltage transmission lines. This method generates a dual-layer wind speed sequence and a temperature and pressure sequence from hourly reanalysis meteorological data, enabling continuous calculation of wind profile parameters, atmospheric stability, and gust parameters under the same time reference. Through joint processing of power-law exponents, surface roughness, and overall atmospheric stability parameters, the average wind speed above the conductor and dynamic gust speed can be adjusted according to atmospheric conditions. Furthermore, through two types of extreme value sample analysis, confidence interval estimation, and terrain acceleration correction, the final result simultaneously characterizes the return period level, statistical uncertainty, and the influence of the transmission line corridor terrain, improving the completeness and reliability of the quantitative diagnostic results for extreme winds.

[0031] Specifically, step S1 is used to form a unified data foundation required for subsequent wind profile parameter calculation, stability calculation, and terrain correction. Hourly reanalysis meteorological data covering the transmission line corridor is acquired, from which the zonal and meridional wind speed components, near-surface air temperature, surface air pressure, topographic potential, and sea-level air pressure at the first and second reference heights are read. The first and second reference heights can be 10m and 100m respectively, and the near-surface air temperature can be 2m air temperature. For each grid point and each time interval, the zonal and meridional wind speed components at the same reference height are vector-synthesized. The scalar wind speed at the first reference height is calculated according to equation (1): (1) In the formula, Scalar wind speed representing the first reference altitude; Represents the zonal wind speed component at the first reference altitude; The meridional wind speed component at the first reference altitude is represented by the scalar wind speed component at the second reference altitude, which is expressed in the same manner. and The wind speeds are synthesized and then arranged in chronological order to form a two-layer wind speed sequence.

[0032] Topography according to Converted to terrain height data, where, Indicates terrain elevation. This represents standard gravitational acceleration. Furthermore, time alignment, spatial coordinate unification, missing value checks, and outlier checks are performed on the dual-layer wind speed series, air temperature and pressure series, and terrain height data. Time intervals with missing wind speed components or air pressures not meeting physical ranges are marked as invalid. For data with inconsistent spatial coordinates, resampling is performed according to the transmission line corridor grid to ensure that different variables correspond hourly at the same grid point.

[0033] Specifically, step S2 includes hourly back-calculation of wind profile parameters and hourly calculation of overall atmospheric stability parameters. First, the power-law exponent is back-calculated based on the wind speed ratio and height ratio corresponding to the two reference heights in the two-layer wind speed sequence. The power-law exponent is calculated according to equation (2): (2) In the formula, Indicates time The corresponding power-law exponent; Indicates time Second reference height scalar wind speed; Indicates time The first reference height scalar wind speed. Calculated hourly. This can avoid using the same power law exponent throughout the entire historical period.

[0034] Furthermore, based on the logarithmic law relationship between the first and second reference heights for wind speed, the surface roughness is calculated hourly. When the wind speed is higher than When the wind speed ratio is not greater than the preset threshold, it indicates that the upper-layer wind speed is not higher than the lower-layer wind speed, and the corresponding time period can be marked as an abnormal headwind profile time period. The preset threshold for the wind speed ratio can be 1. For abnormal headwind profile times periods, the climatological surface roughness corresponding to the same grid point, the same season, or the same terrain type can be used as a backup value to maintain the continuity of subsequent wind speed extrapolation and turbulence intensity calculation.

[0035] The overall atmospheric stability parameter is used to characterize the thermodynamic stability and dynamic shear state between two atmospheric layers. In this embodiment, the overall Richardson number is used as the overall atmospheric stability parameter, which is calculated according to equation (3): (3) In the formula, Indicates time The overall Richardson number; Represents gravitational acceleration; Indicates the average potential temperature of the reference layer; This represents the potential temperature difference between the second reference altitude potential temperature and the near-surface potential temperature; This indicates the height difference between two wind speed reference heights; This represents the difference in zonal wind speed components between two reference altitudes; This represents the difference in the meridional wind speed component between two reference altitudes.

[0036] Near-surface potential temperature can be calculated using the Poisson relationship based on near-surface air temperature and surface air pressure. Since reanalysis meteorological data may not directly provide the temperature at the second reference altitude, this embodiment estimates the temperature at the second reference altitude based on the near-surface air temperature and the standard atmospheric temperature lapse rate. For example, when the first reference altitude is 10m, the second reference altitude is 100m, and the near-surface air temperature altitude is 2m, we can take... Based on this, the potential temperature at the second reference altitude and the interlayer potential temperature difference are calculated.

[0037] The stability category for each time interval is determined based on the overall Richardson number. For example, when... It is determined to be strongly unstable when It is determined to be weakly unstable when When determined to be near neutral, It is determined to be weakly stable when The time period is determined to be strongly stable. The overall Richardson number and stability category at each time point are arranged in chronological order to generate a stability sequence.

[0038] Specifically, in step S3, the wind speed at the reference height is extrapolated to the conductor height. Based on the stability sequence, either a power law or a logarithmic law can be chosen as the extrapolation method for the wind speed at the corresponding time interval. For example, under near-neutral conditions, a logarithmic law can be used based on surface roughness; under non-near-neutral conditions, a power law can be used based on the power law exponent. When using a power law, the average wind speed at the conductor height can be expressed as... ,in, Indicates time Average wind speed at conductor height, This indicates the target conductor height. For ±800kV UHVDC transmission lines, the target conductor height can be set to 65m based on the tower structure and sag condition, or it can be configured according to the actual conductor height of the line under diagnosis. Wind speed extrapolation is performed for all valid time intervals to generate an average wind speed sequence based on conductor height.

[0039] Specifically, in step S4, the stability-corrected turbulence intensity and dynamic gust factor are calculated based on atmospheric stability. First, the Moning-Obukhov length is determined based on the stability sequence. and stability correction function The Mönning-Obukhoff length can be approximated by the global Richardson number, for example, it can be expressed as... ,in, This represents the reference altitude. Under unstable conditions, the stability correction function can adopt the Buntinger-Dyer relationship; under stable conditions, it can be taken as... Under near-neutral conditions, the stability correction function approaches zero.

[0040] The longitudinal turbulence intensity is calculated according to equation (4): (4) In the formula, Indicates time At conductor height Longitudinal turbulence intensity at the location; Indicates time Surface roughness; Represents the stability correction function; Indicates time The Monin-Obukhoff length. Under near-neutral conditions, equation (4) can degenerate into .

[0041] The dynamic gust factor was calculated using the Durst model, and the calculation relationship is shown in equation (5): (5) In the formula, Indicates time At conductor height Dynamic gust factor at the location; Indicates the peak factor; Indicates the longitudinal turbulence intensity; Indicates the average time interval The corresponding turbulent energy spectrum integral factor. For a 3-second gust, the peak factor... A value of 3.5 can be used. Since the stability and turbulence intensity differ at each time point, the dynamic gust factor varies with time.

[0042] The hourly gust speed at the conductor height is calculated according to formula (6): (6) In the formula, Indicates time At conductor height The speed of the gusts at that location; Indicates the average wind speed at the height of the conductor; This represents the dynamic gust factor determined by the conductor height and the corresponding overall atmospheric stability parameters. The gust velocities at each grid point and at each time point are arranged chronologically to generate an hourly gust velocity sequence. Simultaneously, the average wind speed at the conductor height, power-law exponent, surface roughness, overall Richardson number, and dynamic gust factor can be saved to form an hourly state vector, which serves as input for subsequent extreme value diagnosis.

[0043] Specifically, step S5 includes extreme value sample extraction, dual-track extreme value analysis, uncertainty quantification, multi-model weighting, and terrain correction.

[0044] For the block extremum method, the maximum value is extracted from the hourly gust velocity sequence year by year, and the annual maximum value is calculated according to equation (7): (7) In the formula, Indicates the first The maximum annual gust speed; Indicates the first The set of time periods corresponding to each year; Indicates time The gust speed at the conductor height was determined. The annual maximum gust speed for each year was used to construct a sample of annual maximum values. The GEV distribution was fitted using the L-moment method to generate block extremum analysis results.

[0045] For the thresholding method, the 90th to 95th percentile of the hourly gust velocity sequence can be used as the initial threshold, and the threshold can be adjusted according to the mean remaining lifetime curve and the stability of the distribution parameters. Run-length de-clustering is performed on gust velocities exceeding the adjusted threshold. The separation window between adjacent extreme events can be 72 hours. Representative peak values ​​are retained for each cluster event, generating independent samples of thresholding extreme events. The L-moment method is used to fit the GPD, and calculations are based on the annual average number of thresholding events. Extreme wind speeds that occur once a year. The annual return period extreme wind speed is calculated according to formula (8): (8) In the formula, express Extreme wind speeds that occur once a year; This indicates the threshold used in the over-threshold method; The scale parameter representing GPD; Indicates the shape parameters of the GPD; This indicates the average number of events exceeding the threshold per year. The return period is indicated. The results of the over-threshold method and the block extreme value method can respectively give the extreme wind speeds corresponding to return periods of 30 years, 50 years, 100 years, 200 years or 250 years.

[0046] The relative differences between the results of two extreme value analyses under the same return period are compared, and a consistency indicator is generated. For example, if the relative difference is less than 10%, it can be marked as consistent; if the relative difference is greater than 15%, it can be marked as needing to be checked, and the data quality, threshold selection, declustering window, and distribution tail type are reviewed. The consistency indicator is associated with the corresponding extreme wind speed of the return period and is used to indicate the stability of the results.

[0047] The extreme event samples exceeding the threshold and the annual maximum value samples are resampled with replacement. After each resampling, the corresponding extreme value distribution is refitted and the extreme wind speed for each return period is calculated. After repeating this process a preset number of times, the confidence interval is determined according to the quantiles of the resampled extreme wind speeds in the same return period. For example, the number of resampling times can be 2000, the confidence level can be 95%, and the corresponding confidence interval can be determined by the 2.5% quantile and the 97.5% quantile.

[0048] To reduce the impact of choosing a single extreme value distribution on the results, a set of candidate extreme value distributions, including GEV, GPD, Gumbel, and Weibull distributions, can be used to fit the corresponding extreme value samples, and the model weights can be determined according to the Akaike Information Criterion. The model weights of the candidate extreme value distributions are calculated according to equation (9): (9) In the formula, Indicates the first Model weights for each candidate extreme value distribution; Indicates the first The difference between the information criterion value and the minimum information criterion value of each candidate extreme value distribution; This represents the index of the candidate extreme value distribution. The extreme values ​​corresponding to the same return period for each candidate extreme value distribution are weighted according to the model weights to obtain the extreme wind speeds for each return period after multi-model integration.

[0049] The terrain slope of each grid point along the route corridor is calculated based on terrain elevation data to identify terrain types such as mountain peaks, ridges, slopes, or valleys. For hilly or ridge terrain, the terrain acceleration factor can be calculated according to equation (10): (10) In the formula, Indicates the terrain acceleration factor; This represents the terrain correction factor related to topographic features and wind direction. Indicates the relative height of a hill or ridge; This indicates the width of a hill or ridge. For canyon terrain, it can also be determined by the width-to-depth ratio. Determine the canyon acceleration coefficient The extreme wind speeds and their confidence intervals for each return period are synchronously corrected using terrain acceleration factors and canyon acceleration coefficients, ultimately generating terrain-corrected extreme wind speeds and their confidence intervals for each return period.

[0050] By placing the raw data preprocessing in step S1, the wind profile parameter back calculation and overall atmospheric stability calculation in step S2, the wind speed extrapolation in step S3, the turbulence intensity, dynamic gust factor and hourly gust sequence construction in step S4, and the dual-track extreme value analysis, uncertainty quantification, multi-model weighting and terrain correction in step S5, the technical contents are naturally connected in the order of input data, hourly physical quantity calculation, extreme value statistics and result correction.

[0051] Example 2 provides a specific application of the above-mentioned quantitative diagnostic method for extreme winds on ultra-high voltage (UHV) transmission lines in a ±800kV UHVDC transmission line corridor. Specifically, it involves acquiring 30 consecutive years of hourly reanalysis meteorological data covering the corridor, with a grid spatial resolution of 0.25°×0.25°, a first reference height of 10m and a second reference height of 100m, and a conductor target height of 65m.

[0052] For each grid point, hourly composited two-layer wind speeds were performed, and the power-law exponent and surface roughness were calculated inversely. The overall Richardson number and stability category were then determined, and the average wind speed at a 65m traverse height was generated. Subsequently, based on stability-corrected turbulence intensity and dynamic gust factor, hourly gust velocity sequences for each grid point over a continuous 30-year period were obtained. .

[0053] In the overthreshold method analysis, the 90th percentile of the hourly gust velocity sequence was used as the initial threshold. The threshold range was confirmed based on the mean remaining life curve, and de-clustering was performed using a 72-hour separation window, resulting in approximately 600 independent extreme event samples. GPD fitting was performed on these independent extreme event samples, and the distribution parameters were estimated using the L-moment method to calculate the 50-year return period 3-second gust velocity and its 95% confidence interval. In the block extreme value method analysis, 30 annual maximum values ​​were extracted from the 30-year hourly gust velocity sequence and fitted with the GEV distribution. Under the same return period, the difference between the block extreme value method results and the overthreshold method results was approximately 6% (satisfying the consistency verification (<10%)), and the corresponding consistency indicator was passed.

[0054] 2000 resampling cycles with replacement were performed on the extreme value samples, resulting in a 95% confidence interval for the 50-year return period extreme wind speed, which is approximately ±12% relative to the point estimate. For grid points located on ridge terrain, terrain slope and terrain acceleration factor were calculated based on terrain height data. When the slope is approximately 0.15, the terrain correction margin is approximately 8%. The extreme wind speeds and their upper and lower limits of confidence intervals for each return period were simultaneously corrected to form the final diagnostic results for the corresponding tower locations. This embodiment outputs the extreme wind speeds and confidence intervals for each return period, providing a quantitative basis for differentiated wind protection design of power lines.

[0055] Figure 2The diagram shows the time series of over-threshold events, the mean remaining lifetime curve, the GPD fitting probability plot, and the return period curve for a specific tower in this example. These graphs allow for the examination of threshold selection, distribution fitting, and return period extrapolation. Diagnostic results for multiple towers can be output as shown in Table 1.

[0056] Figure 2 (a) is a wind speed sequence and distribution map of events exceeding the threshold, which is used to represent the hourly gust speed sequence of the target tower conductor height and extreme events exceeding the preset threshold. It can intuitively reflect the distribution of extreme gust events in the historical time series.

[0057] Figure 2 (b) is the average remaining lifetime plot, which is used to represent the changes in average remaining lifetime corresponding to different candidate thresholds, and can help determine the threshold used in the extreme value analysis of the overthreshold method.

[0058] Figure 2 (c) is a generalized Pareto distribution fitting test diagram, which is used to represent the correspondence between the empirical cumulative probability of the overthreshold extreme event sample and the theoretical cumulative probability of the generalized Pareto distribution, and can test the fitting effect of the generalized Pareto distribution on the overthreshold extreme event sample.

[0059] Figure 2 (d) is a return curve diagram, which is used to represent the extreme wind speed and its confidence interval corresponding to different return periods, and can determine the extreme wind speed level of the target tower under different return periods.

[0060] Table 1. Statistical Analysis of Extreme Wind Values

[0061] Using the scheme in this embodiment, the extreme wind speeds and corresponding confidence intervals for 50-year, 100-year, and 250-year return periods after terrain correction can be output for different tower locations along the line corridor. The similarity between the two extreme value analysis results is reflected by the consistency indicator, thereby providing a quantitative basis for tower wind resistance verification and differentiated wind protection for the line.

[0062] Compared with the prior art, the beneficial effects of the present invention are as follows: In the first aspect, the present invention utilizes a two-layer wind speed sequence to inversely calculate the power law exponent and surface roughness hourly, and extrapolates the reference height wind speed to the conductor height. This avoids using a fixed power law exponent or fixed surface roughness to uniformly process different atmospheric conditions, reduces the influence of empirical assumptions about wind profile parameters on the calculation results of wind speed at conductor height, and improves the accuracy of the average wind speed sequence at conductor height.

[0063] Secondly, this invention calculates the overall atmospheric stability parameters based on the two-layer wind speed components and the air temperature and pressure sequence, and calculates the stability-corrected turbulence intensity by combining the stability sequence and surface roughness. Based on the turbulence intensity, a dynamic gust factor is determined, so that the gust factor can change with the atmospheric stability and turbulence state hourly. This reduces the overestimation or underestimation of extreme instantaneous wind speeds caused by a fixed gust factor, and improves the consistency between the hourly gust speed sequence at the conductor height and the actual atmospheric physical state.

[0064] Thirdly, this invention extracts extreme event samples and annual maximum value samples from the hourly gust speed sequence of conductor height, and performs extreme value analysis using the over-threshold method and the block extreme value method respectively. This improves the utilization rate of extreme wind speed samples and uses the results of the two extreme value analyses to cross-validate the extreme wind speeds in each return period, reducing the tail extrapolation bias caused by the single extreme value analysis method and improving the reliability of the diagnostic results of extreme wind speeds in each return period.

[0065] Fourthly, this invention quantifies the uncertainty of extreme event samples and annual maximum value samples to obtain the confidence intervals corresponding to extreme wind speeds in each return period. It can also combine the weighted processing of multiple candidate extreme value distributions to reduce the impact of extreme value distribution selection and parameter estimation on the diagnostic results, so that the output results can simultaneously reflect the extreme wind speed estimate and its uncertainty range, providing a quantitative basis for wind-resistant design and disaster risk assessment of UHV transmission lines.

[0066] Fifthly, this invention determines the terrain acceleration factor based on terrain height data and performs terrain correction on the extreme wind speeds and confidence intervals for each return period. It incorporates the amplification effect of local terrain such as mountain peaks, ridges, or canyons on wind speed into the diagnostic process, generating terrain-corrected extreme wind speeds and confidence intervals for each return period. This forms a complete technical link from hourly reanalysis of meteorological data, dynamic gust calculation of conductor height, extreme value analysis and uncertainty quantification to the output of terrain correction results, improving the accuracy, reliability, and engineering applicability of quantitative diagnosis of extreme winds in UHV transmission lines.

[0067] Example 3, as Figure 3 As shown, the present invention provides a quantitative diagnostic system for extreme winds on ultra-high voltage transmission lines. The system includes a data preprocessing module, a parameter calculation module, an average wind speed generation module, a dynamic gust generation module, and an extreme value diagnosis and correction module.

[0068] The data preprocessing module is used to read the reanalysis meteorological data of the transmission line corridor, complete the wind speed component vector synthesis, temperature and pressure extraction, topographic potential transformation, coordinate unification and quality control, and output the two-layer wind speed sequence, temperature and pressure sequence and topographic height data.

[0069] The parameter calculation module is used to inversely calculate the power-law exponent and surface roughness hourly based on the two-layer wind speed sequence, calculate the overall atmospheric stability parameters based on the two-layer wind speed components, air temperature, and air pressure, and output the stability sequence in chronological order. The parameter calculation module can also mark the times of upwind profile anomalies and call up the reserve values ​​of climatological surface roughness.

[0070] The average wind speed generation module is used to call a power-law extrapolation path or a logarithmic-law extrapolation path to extrapolate the reference height wind speed to the corresponding conductor height, generating an average wind speed sequence for that conductor height. The conductor height can be configured according to tower type, conductor sag, and the location to be diagnosed.

[0071] The dynamic gust generation module is used to calculate the stability-corrected turbulence intensity and dynamic gust factor based on the stability sequence and surface roughness. It combines the dynamic gust factor with the average wind speed sequence at the guide height to generate an hourly gust speed sequence.

[0072] The extreme value diagnosis and correction module is used to extract over-threshold extreme event samples and annual maximum value samples from the hourly gust speed sequence of the conductor height, and perform over-threshold extreme value analysis and block extreme value analysis respectively, generating extreme wind speeds for each return period based on the results of the two extreme value analyses; quantifying the uncertainty of the over-threshold extreme event samples and the annual maximum value samples to generate confidence intervals corresponding to the extreme wind speeds for each return period; determining the terrain acceleration factor based on the terrain height data, and correcting the extreme wind speeds for each return period and their corresponding confidence intervals to generate terrain-corrected extreme wind speeds for each return period and their confidence intervals.

[0073] Each module can be deployed on the same computing node, or it can be implemented collaboratively by data storage nodes, computing nodes, and result display nodes. System outputs may include tower number, average wind speed at conductor height, historical maximum gust speed, extreme wind speeds for each return period, upper and lower limits of confidence interval, consistency indicator, terrain type, and terrain correction factor.

[0074] This invention improves the accuracy of estimating the average wind speed at the guide height by extrapolating the wind speed from the reference height to the guide height based on hourly back-calculation of the power-law exponent and surface roughness using a two-layer wind speed model combined with atmospheric stability. This reduces systematic errors introduced by fixed wind profile parameters. Furthermore, by establishing the correlation between atmospheric stability, surface roughness, turbulence intensity, and dynamic gust factors, an hourly gust speed sequence at the guide height is generated, avoiding overestimation or underestimation caused by using fixed gust factors and improving the physical consistency of instantaneous extreme wind speed characterization. Finally, by performing overthreshold extreme value analysis and block extreme value analysis on overthreshold extreme event samples and annual maximum value samples, respectively, this invention improves the accuracy of estimating the average wind speed at the guide height. The extreme value analysis method, combined with the quantification of resampling uncertainty and the weighted processing of multiple candidate extreme value distributions, improves the utilization efficiency of extreme event samples, reduces the uncertainty introduced by tail extrapolation and single distribution selection, and obtains extreme wind speeds with confidence intervals for each return period. Furthermore, by determining the terrain acceleration factor based on terrain height data, terrain correction is performed on the extreme wind speeds and confidence intervals for each return period, forming a complete diagnostic link from hourly reanalysis meteorological data to the terrain-corrected extreme wind speeds and confidence intervals for each return period, thereby improving the accuracy, reliability, and engineering applicability of the quantitative diagnostic results for extreme gales on UHV transmission lines.

[0075] Example 4: The present invention also provides a quantitative diagnostic device for extreme winds on ultra-high voltage transmission lines. The device includes a computer device, which includes a processor and a memory. The memory stores computer instructions. When the computer instructions are executed, the device implements the quantitative diagnostic method for extreme winds on ultra-high voltage transmission lines.

[0076] Example 5, as Figure 4 As shown, the present invention also provides an electronic device 100 for implementing a quantitative diagnostic method for extreme winds on ultra-high voltage transmission lines.

[0077] The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0078] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the quantitative diagnosis method for extreme winds of ultra-high voltage transmission lines as described in the first aspect of the present invention by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0079] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0080] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0081] The memory 101 in the electronic device 100 stores multiple instructions to implement a quantitative diagnostic method for extreme winds on ultra-high voltage transmission lines, and the processor 102 can execute multiple instructions to achieve the following: Acquire reanalysis meteorological data and topographic elevation data of the transmission line corridor, extract dual-layer wind speed components, temperature and air pressure, and generate dual-layer wind speed sequence and temperature and air pressure sequence; Based on the power-law exponent and surface roughness calculated from the dual-layer wind speed sequence, the overall atmospheric stability parameters are calculated based on the dual-layer wind speed components and the air temperature and pressure sequence, and a stability sequence is generated. Based on the power law exponent or the surface roughness, extrapolate the reference height wind speed to generate a conductor height average wind speed sequence. Based on the stability sequence and the surface roughness, the turbulence intensity and dynamic gust factor are calculated, and the hourly gust speed sequence is generated by combining the average wind speed sequence at the conductor height. Extreme event samples exceeding the threshold and annual maximum value samples are extracted from the hourly gust speed sequence at the conductor height. Extreme value analysis using both the over-threshold method and the block extreme value method is performed, and extreme wind speeds for each return period are generated based on the results of these two extreme value analyses. Uncertainty quantification is performed on the extreme event samples exceeding the threshold and the annual maximum value samples to generate confidence intervals corresponding to the extreme wind speeds for each return period. A terrain acceleration factor is determined based on the terrain height data, and the extreme wind speeds for each return period and their corresponding confidence intervals are corrected to generate terrain-corrected extreme wind speeds and confidence intervals for each return period.

[0082] Example 6: If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0083] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A quantitative diagnostic method for extreme high winds in ultra-high voltage transmission lines, characterized in that, The method includes: Acquire hourly reanalysis meteorological data and terrain elevation data from transmission lines, and generate a two-layer wind speed sequence and temperature and pressure sequence based on the hourly reanalysis meteorological data; The power law exponent and surface roughness are calculated hourly based on the dual-layer wind speed sequence. The overall atmospheric stability parameters are calculated hourly based on the dual-layer wind speed components and the air temperature and pressure sequence, and a stability sequence composed of the overall atmospheric stability parameters at each time point is generated. Based on the power law exponent or the surface roughness, the reference height wind speed is extrapolated to the traverse height to generate a traverse height average wind speed sequence. The stability-corrected turbulence intensity is calculated based on the stability sequence and the surface roughness. The dynamic gust factor is determined based on the turbulence intensity. The hourly gust velocity sequence at the conductor height is generated by combining the average wind speed sequence at the conductor height. Extreme event samples exceeding the threshold and annual maximum value samples are extracted from the hourly gust speed sequence at the conductor height. Extreme value analysis using both the over-threshold method and the block extreme value method is performed, and extreme wind speeds for each return period are generated based on the results of these two extreme value analyses. Uncertainty quantification is performed on the extreme event samples exceeding the threshold and the annual maximum value samples to generate confidence intervals corresponding to the extreme wind speeds for each return period. A terrain acceleration factor is determined based on the terrain height data, and the extreme wind speeds for each return period and their corresponding confidence intervals are corrected to generate terrain-corrected extreme wind speeds and confidence intervals for each return period.

2. The quantitative diagnostic method for extreme strong winds on ultra-high voltage transmission lines according to claim 1, characterized in that, The generation of a two-layer wind speed sequence and a temperature and pressure sequence based on hourly reanalysis meteorological data includes: The zonal and meridional wind speed components corresponding to two reference heights are extracted from the hourly reanalysis meteorological data, and wind speed vectors are synthesized to generate the two-layer wind speed sequence. Near-surface air temperature and surface air pressure are extracted to generate the air temperature and pressure sequence; The terrain potential is converted into geometric height, and the coordinates of the two-layer wind speed sequence, the temperature and pressure sequence and the geometric height are unified and quality controlled to generate the terrain height data.

3. The quantitative diagnostic method for extreme strong winds on ultra-high voltage transmission lines according to claim 1, characterized in that, The generated average wind speed sequence at conductor height includes: Based on the wind speed ratio corresponding to the two reference heights in the dual-layer wind speed sequence, the power law exponent and the surface roughness are calculated hourly. When the wind speed ratio is not greater than the preset wind speed ratio threshold, the corresponding time is determined to be the time of abnormal headwind profile, and the surface roughness corresponding to the time of abnormal headwind profile is replaced with climatological surface roughness. Based on the stability sequence, a power law or logarithmic law is selected, and the wind speed at the reference height is extrapolated to the conductor height based on the corresponding power law exponent or surface roughness to generate the average wind speed sequence at the conductor height.

4. The quantitative diagnostic method for extreme strong winds on ultra-high voltage transmission lines according to claim 1, characterized in that, The calculation of overall atmospheric stability parameters and the generation of a stability sequence include: Based on the air temperature and pressure sequence, determine the potential temperature corresponding to the two reference altitudes, and determine the interlayer potential temperature difference based on the potential temperature corresponding to the two reference altitudes. The wind shear between the two reference heights is determined based on the two-layer wind speed components, and the overall atmospheric stability parameters are calculated based on the interlayer potential temperature difference, the interlayer height difference, and the wind shear. Based on the overall atmospheric stability parameters, the atmospheric stability at each time point is divided into strongly unstable, weakly unstable, near-neutral, weakly stable, and strongly stable, thus generating the stability sequence.

5. The quantitative diagnostic method for extreme strong winds on ultra-high voltage transmission lines according to claim 1, characterized in that, The generated hourly gust speed sequence for conductor height includes: The stability correction function and the Moning-Obukhoff length are determined based on the stability sequence. The stability-corrected turbulence intensity is calculated based on the stability correction function, the Monin-Obukhov length, and the surface roughness. The dynamic gust factor is determined based on the turbulence intensity, peak factor, and turbulence energy spectrum integral factor corresponding to the stability correction. Based on the dynamic gust factor and the average wind speed sequence at the conductor height, an hourly gust speed sequence at the conductor height is generated.

6. The quantitative diagnostic method for extreme strong winds on ultra-high voltage transmission lines according to claim 1, characterized in that, The process involves performing extreme value analysis using both the over-threshold method and the block extreme value method, and generating extreme wind speeds for each return period based on the results of these two extreme value analyses. An initial threshold is determined based on a preset quantile of the hourly gust speed sequence of the conductor height. The initial threshold is adjusted based on the change in average remaining lifetime. Gust speeds exceeding the adjusted threshold are de-clustered to generate the extreme event samples exceeding the threshold. The extreme event samples exceeding the threshold are fitted with a generalized Pareto distribution to generate extreme value analysis results using the threshold method. The maximum value is extracted from the hourly gust speed sequence of the conductor height according to a preset time block to generate the annual maximum value sample. The annual maximum value sample is fitted with a generalized extreme value distribution to generate the block extreme value method extreme value analysis result. Based on the extreme value analysis results of the over-threshold method and the extreme value analysis results of the block extreme value method, the extreme wind speeds for each return period are generated, and the two extreme value analysis results are compared to generate consistency test results. The consistency test result is generated by comparing the differences between the first type of return period extreme value and the second type of return period extreme value.

7. The quantitative diagnostic method for extreme strong winds on ultra-high voltage transmission lines according to claim 1, characterized in that, The uncertainty quantification of the extreme event samples exceeding the threshold and the annual maximum value samples includes: The extreme event samples exceeding the threshold and the annual maximum value samples are resampled with replacement to generate multiple sets of resampled extreme value samples. Refit the corresponding extreme value distribution for each group of resampled extreme value samples, and calculate the extreme wind speed corresponding to each return period; Confidence intervals are determined based on multiple extreme wind speeds corresponding to each return period, and confidence intervals corresponding to the extreme wind speeds of each return period are generated.

8. The quantitative diagnostic method for extreme winds on ultra-high voltage transmission lines according to claim 1, characterized in that, The generation of extreme wind speeds for each return period based on the results of two extreme value analyses includes: Based on the over-threshold extreme event samples and the annual maximum value samples, fit multiple candidate extreme value distributions to generate the return period extreme values ​​corresponding to each candidate extreme value distribution; The model weights are determined based on the information criterion values ​​corresponding to the distributions of each candidate extreme value. The return period extreme values ​​corresponding to each candidate extreme value distribution are weighted according to the model weights to generate the extreme wind speeds for each return period.

9. The quantitative diagnostic method for extreme strong winds on ultra-high voltage transmission lines according to claim 1, characterized in that, The generated terrain-corrected extreme wind speeds and confidence intervals for each return period include: The terrain slope and terrain type at the corresponding location of the transmission line corridor are determined based on the terrain elevation data. The terrain acceleration factor is determined based on the terrain slope, and when the terrain type is a canyon, the canyon acceleration coefficient is determined based on the canyon width-to-depth ratio. Based on the terrain acceleration factor and the canyon acceleration coefficient, the extreme wind speeds and confidence intervals for each return period are corrected to generate the terrain-corrected extreme wind speeds and confidence intervals for each return period.

10. A quantitative diagnostic system for extreme high winds in ultra-high voltage transmission lines, characterized in that, include: The data preprocessing module is used to acquire hourly reanalysis meteorological data and terrain elevation data of the transmission line corridor, and generate a two-layer wind speed sequence and temperature and pressure sequence based on the hourly reanalysis meteorological data; The wind profile and stability calculation module is used to calculate the power law exponent and surface roughness hourly based on the double-layer wind speed sequence, calculate the overall atmospheric stability parameters based on the double-layer wind speed components and the air temperature and pressure sequence, and generate a stability sequence composed of the overall atmospheric stability parameters at each time point. The conductor height wind speed generation module is used to extrapolate the reference height wind speed to the conductor height based on the power law exponent or the surface roughness, and generate a conductor height average wind speed sequence. The dynamic gust calculation module is used to calculate the stability-corrected turbulence intensity based on the stability sequence and the surface roughness, determine the dynamic gust factor based on the turbulence intensity, and generate an hourly gust velocity sequence at the conductor height by combining the average wind speed sequence at the conductor height. The extreme value diagnosis and correction module is used to extract samples of extreme events exceeding the threshold and samples of the annual maximum value from the hourly gust speed sequence of the conductor height, perform extreme value analysis by the over-threshold method and extreme value analysis by the block extreme value method respectively, generate two types of return period extreme values ​​and perform consistency checks. Uncertainty quantification is performed on the extreme event samples exceeding the threshold and the annual maximum value samples to generate extreme wind speeds and confidence intervals for each return period; Based on the terrain height data, a terrain acceleration factor is determined, and the extreme wind speeds and confidence intervals for each return period are corrected to generate terrain-corrected extreme wind speeds and confidence intervals for each return period.

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