Methods, devices and equipment for analyzing the probability of wind-induced flashover failure in transmission lines

Abstract

This invention relates to the field of power technology and provides a method, apparatus, and equipment for analyzing the probability of wind-induced flashover failure in transmission lines. The method includes: acquiring meteorological data, including wind speed and rainfall intensity; establishing a marginal probability distribution function for wind speed and a marginal probability distribution function for rainfall intensity; establishing a combined wind and rain probability density function using a Copula function based on the wind speed and rainfall intensity marginal probability distribution functions; establishing a transmission line model and performing wind deflection response analysis on the model using different combinations of wind and rain loads, and establishing a vulnerability function based on the wind deflection response results; and determining the wind-induced flashover failure probability parameters based on the combined wind and rain probability density function and the vulnerability function. This invention can accurately calculate the probability of wind-induced flashover failure in transmission lines.

Description

Technical Field

[0001] This invention belongs to the field of power technology, and in particular relates to a method, device and equipment for analyzing the probability of wind-induced flashover failure of transmission lines. Background Technology

[0002] Severe convective weather, a sudden and destructive extreme meteorological phenomenon, is often accompanied by complex weather processes such as short-duration heavy rainfall, thunderstorms, strong winds, and hail, posing a serious threat to the safe and stable operation of power transmission lines. Under severe convective weather, power transmission lines are prone to wind-induced flashover failure. Specifically, wind-induced flashover failure refers to the phenomenon where, under the strong wind load generated by severe convective weather, the gap between the conductor and the transmission tower or surrounding obstacles is too small, causing a breakdown discharge, resulting in line tripping, power outages, and other problems, seriously affecting the safe operation of the power grid.

[0003] In recent years, influenced by global climate change, the frequency and intensity of extreme severe convective weather have shown an upward trend, significantly increasing the risk of transmission lines experiencing wind-induced flashover failures. Against this backdrop, establishing a scientifically sound analytical model to accurately calculate the probability of wind-induced flashover failures of transmission lines and clarify the risk levels of lines under different meteorological conditions provides theoretical support and data basis for power grid operation and maintenance departments to formulate targeted prevention and control measures. This has significant theoretical and engineering application value for reducing the incidence of wind-induced flashover accidents, minimizing power outage losses, and ensuring the safe and stable operation of the power grid. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, apparatus and equipment for analyzing the probability of wind-induced flashover failure of transmission lines, so as to accurately calculate the probability of wind-induced flashover failure of transmission lines.

[0005] A first aspect of this invention provides a method for analyzing the probability of wind-induced flashover failure in transmission lines, comprising: Acquire meteorological data, including wind speed and rainfall intensity; Establish a marginal probability distribution function for wind speed based on the wind speed, and establish a marginal probability distribution function for rainfall intensity based on the rainfall intensity; Based on the marginal probability distribution function of wind speed and the marginal probability distribution function of rainfall intensity, a combined wind and rain probability density function is established using the Copula function; A transmission line model is established, and wind deflection response analysis is performed on the transmission line model through different combinations of wind and rain loads. Based on the wind deflection response results, a vulnerability function is established. Based on the combined wind and rain probability density function and the vulnerability function, the wind deflection flashover failure probability parameter is determined.

[0006] In one possible implementation, establishing a marginal probability distribution function for wind speed based on the wind speed and establishing a marginal probability distribution function for rainfall intensity based on the rainfall intensity include: By fitting the wind speed with different types of marginal probability distribution functions, the marginal probability distribution function with the smallest fitting error is selected to establish the marginal probability distribution function of wind speed. By fitting different types of marginal probability distribution functions to the rainfall intensity, the marginal probability distribution function with the smallest fitting error is selected to establish the marginal probability distribution function of rainfall intensity.

[0007] In one possible implementation, the different types of marginal probability distribution functions include: gamma distribution, Weibull distribution, log-normal distribution, Rayleigh distribution, and generalized extreme value distribution.

[0008] In one possible implementation, establishing the combined wind and rain probability density function using a Copula function based on the wind speed marginal probability distribution function and the rainfall intensity marginal probability distribution function includes: Based on the marginal probability distribution function of wind speed and the marginal probability distribution function of rainfall intensity, different types of Copula functions are established respectively; The goodness of fit of each Copula function is evaluated, and the Copula function with the best goodness of fit is selected as the joint probability density function of wind and rain.

[0009] In one possible implementation, evaluating the goodness of fit of each Copula function includes: calculating the goodness of fit of each Copula function based on the Akaike criterion value, the Bayes criterion value, and the root mean square error of each Copula function.

[0010] In one possible implementation, establishing the vulnerability function based on the wind deflection response results includes: performing regression analysis on the wind deflection response results using the Seyedi seismic vulnerability surface method to obtain the vulnerability function.

[0011] In one possible implementation, determining the wind-induced flashover failure probability parameter based on the combined wind-rain probability density function and the vulnerability function includes: according to Determine the probability density of wind-induced failure in transmission lines; according to Determine the cumulative failure probability of wind-induced deflection in transmission lines; in, The probability density of wind-induced failure; The cumulative failure probability due to wind deflection; Let wind and rain be the combined probability density function; It is a vulnerable function;v Wind speed; u This refers to the intensity of rainfall.

[0012] In one possible implementation, the different combinations of wind and rain loads are obtained in the following way: Within a preset wind speed range, multiple reference wind speeds are determined according to a first preset interval, and a first preset number of wind speed samples are extracted within a preset neighborhood of each reference wind speed. Within a preset rainfall intensity range, multiple benchmark rainfall intensities are determined according to a second preset interval, and within a preset neighborhood of each benchmark rainfall intensity, a second preset number of rainfall intensity samples are extracted. By performing Cartesian product operations, the first preset number of wind speed samples and the second preset number of rainfall intensity samples are fully combined to obtain different combinations of wind and rain loads.

[0013] A second aspect of the present invention provides a power transmission line wind-induced flashover failure probability analysis device, comprising: The acquisition module is used to acquire meteorological data, including wind speed and rainfall intensity. The processing module is used to establish a marginal probability distribution function for wind speed based on the wind speed and a marginal probability distribution function for rainfall intensity based on the rainfall intensity. A module is established to establish a combined wind and rain probability density function using the Copula function based on the marginal probability distribution function of wind speed and the marginal probability distribution function of rainfall intensity. The analysis module is used to establish a transmission line model and perform wind deflection response analysis on the transmission line model through different combinations of wind and rain loads, and establish a vulnerability function based on the wind deflection response results. The determination module is used to determine the wind deflection flashover failure probability parameter based on the combined wind and rain probability density function and the vulnerability function.

[0014] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect or any implementation thereof.

[0015] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: This invention, through the acquisition of meteorological data, establishes a marginal probability distribution function for wind speed and a marginal probability distribution function for rainfall intensity, thereby quantifying the probability of occurrence under different wind speeds and rainfall intensities. Based on these two types of marginal probability distribution functions, a combined wind and rain probability density function is established using a Copula function, which can accurately capture the linkage characteristics of the coordinated changes in wind speed and rainfall intensity under severe convective weather, quantifying the probability of occurrence under different wind and rain combinations. A transmission line model is established, and wind deflection response analysis is conducted under different wind and rain load combinations, establishing a vulnerability function. This allows for a systematic analysis of the wind deflection response patterns of transmission lines under different wind and rain conditions, quantifying the structural failure probability corresponding to different wind deflection responses. Finally, based on the combined wind and rain probability density function and the vulnerability function, the wind deflection flashover failure probability parameters are determined, achieving quantitative assessment and accurate prediction of the wind deflection flashover risk of transmission lines. This invention improves the accuracy and scientific rigor of wind deflection flashover risk assessment for transmission lines, providing practical technical support for engineering practice. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating the implementation process of the transmission line wind-induced flashover failure probability analysis method provided in this embodiment of the invention; Figure 2 This is a detailed flowchart illustrating the probability analysis method for wind-induced flashover failure of transmission lines provided in this embodiment of the invention. Figure 3 This is a schematic diagram of wind speed fitting provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of rainfall intensity fitting provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the transmission line wind-induced flashover failure probability analysis device provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0019] To illustrate the technical solution described in this invention, specific embodiments are described below.

[0020] Figure 1 This is a schematic diagram illustrating the implementation process of the transmission line wind-induced flashover failure probability analysis method provided in this embodiment of the invention. See also... Figure 1 As shown, it includes: Step S101: Obtain meteorological data, including wind speed and rainfall intensity.

[0021] For transmission lines that need to be evaluated, wind speed and rainfall intensity data at key geographical locations along the entire transmission line are obtained by combining the actual laying range and route distribution with methods such as actual measurement at meteorological observation stations, meteorological data retrieval, and regional meteorological numerical simulation.

[0022] Step S102: Establish the marginal probability distribution function of wind speed based on wind speed, and establish the marginal probability distribution function of rainfall intensity based on rainfall intensity.

[0023] Statistical methods are used to study the probability patterns of wind speed and rainfall intensity. Wind speed and rainfall intensity data are analyzed and fitted separately, resulting in two independent probability distribution curves. These two distributions are called marginal probability distribution functions. The marginal probability distribution function reflects the probability of a particular wind / rainfall intensity occurring. Commonly used marginal probability distribution functions include: gamma distribution, Weibull distribution, log-normal distribution, Rayleigh distribution, and generalized extreme value distribution.

[0024] Step S103: Based on the marginal probability distribution function of wind speed and the marginal probability distribution function of rainfall intensity, establish the joint probability density function of wind and rain using the Copula function.

[0025] The Copula function is a tool specifically designed to describe the correlation between multiple variables, capturing the degree of association between wind speed and rainfall intensity. By combining the established marginal probability distribution functions of wind speed and rainfall intensity with a calibrated Copula function, the joint probability density function of wind and rain can be derived. Common Copula functions include Gaussian Copula and t-Copula.

[0026] Step S104: Establish a transmission line model and perform wind deflection response analysis on the transmission line model through different combinations of wind and rain loads. Establish a vulnerability function based on the wind deflection response results.

[0027] First, based on the actual transmission line's design parameters and structural characteristics (such as conductor type, tower height, insulator string length, conductor sag, etc.), a precise numerical model of the transmission line needs to be constructed using professional simulation software. The model must realistically reproduce the line's mechanical properties and geometry, covering core components such as towers, conductors, and insulator strings, while also considering the line's constraints (such as tower fixing methods and conductor suspension configurations), providing a reliable model foundation for subsequent wind deflection response analysis.

[0028] Wind and rain are the main natural loads on transmission lines, and their combined effect can exacerbate wind-induced deformation of the lines. Various wind and rain load combinations that closely resemble real-world scenarios are designed, such as "light breeze + light rain," "strong wind + moderate rain," and "extreme strong wind + torrential rain." These different wind and rain load combinations are then loaded one by one into the established transmission line model to simulate the external forces borne by the lines under different meteorological scenarios.

[0029] After applying wind and rain loads, the model is subjected to mechanical analysis using simulation software. The core task is to calculate the wind deflection response parameters of the transmission line under different wind and rain combinations, mainly including key indicators such as the wind deflection angle. The vulnerability function is obtained by performing regression analysis on the wind deflection angle.

[0030] Step S105: Determine the wind-induced flashover failure probability parameters based on the combined wind and rain probability density function and the vulnerability function.

[0031] This invention, through the acquisition of meteorological data, establishes a marginal probability distribution function for wind speed and a marginal probability distribution function for rainfall intensity, thereby quantifying the probability of occurrence under different wind speeds and rainfall intensities. Based on these two types of marginal probability distribution functions, a combined wind and rain probability density function is established using a Copula function, which can accurately capture the linkage characteristics of the coordinated changes in wind speed and rainfall intensity under severe convective weather, quantifying the probability of occurrence under different wind and rain combinations. A transmission line model is established, and wind deflection response analysis is conducted under different wind and rain load combinations, establishing a vulnerability function. This allows for a systematic analysis of the wind deflection response patterns of transmission lines under different wind and rain conditions, quantifying the structural failure probability corresponding to different wind deflection responses. Finally, based on the combined wind and rain probability density function and the vulnerability function, the wind deflection flashover failure probability parameters are determined, achieving quantitative assessment and accurate prediction of the wind deflection flashover risk of transmission lines. This invention improves the accuracy and scientific rigor of wind deflection flashover risk assessment for transmission lines, providing practical technical support for engineering practice.

[0032] Figure 2This is a detailed flowchart illustrating the probability analysis method for wind-induced flashover failure of transmission lines provided in this embodiment of the invention. See also... Figure 2 As shown, it includes: (1) Meteorological data collection and analysis.

[0033] For detailed implementation instructions for this step, please refer to [link / reference]. Figure 1 The description of step S101 in the embodiment will not be repeated here.

[0034] (2) Selection of marginal probability distribution functions for wind speed and rainfall intensity.

[0035] For example, five commonly used marginal probability distribution functions (gamma distribution, Weibull distribution, log-normal distribution, Rayleigh distribution, and generalized extreme value distribution) were selected to fit meteorological data on wind speed and rainfall intensity in a region prone to severe convective weather. The results are shown in [link to results]. Figure 3 and Figure 4 As shown. Based on the minimum mean square error, the generalized extreme value distribution is selected to simulate the wind speed distribution of severe convective weather, and the Weibull distribution is selected to simulate the rainfall intensity distribution of severe convective weather.

[0036] The formula for the marginal probability distribution of wind speed is:

[0037]

[0038] The formula for the marginal probability distribution of rainfall intensity is:

[0039]

[0040] In the formula, The cumulative distribution function is... Let be the probability density function. v For wind speed, u This refers to the intensity of rainfall.

[0041] (3) Select the Copula function to establish the combined probability density function of wind and rain.

[0042] In this embodiment, different types of Copula functions are established based on the marginal probability distribution function of wind speed and the marginal probability distribution function of rainfall intensity. By evaluating the goodness of fit of each Copula function (such as Gaussian Copula, t-Copula, etc.), the Copula function with the best goodness of fit is selected to establish the combined wind and rain probability density function.

[0043] The goodness of fit for each Copula function can be calculated using its Akaike criterion value, Bayes criterion value, and root mean square error (RMSE). Smaller Akaike and Bayes criterion values ​​indicate a better fit; the RMSE measures the deviation between the Copula function's fit and the actual data, with a smaller value indicating less deviation. For example, the goodness of fit can be a weighted sum of the Akaike criterion value, Bayes criterion value, and RMSE.

[0044] For example, the t-Copula function is selected to establish the joint probability density function of wind and rain:

[0045] In the formula, Let be the probability density function. yes t The cumulative distribution function of the distribution is the Gamma function. It is a degree of freedom parameter that controls the strength of tail dependence.

[0046] (4) Determine all wind and rain combination samples to generate wind and rain loads.

[0047] In this embodiment, within a preset wind speed range, multiple reference wind speeds are determined according to a first preset interval, and a first preset number of wind speed samples are extracted within a preset neighborhood of each reference wind speed; within a preset rainfall intensity range, multiple reference rainfall intensities are determined according to a second preset interval, and a second preset number of rainfall intensity samples are extracted within a preset neighborhood of each reference rainfall intensity; through Cartesian product operation, the first preset number of wind speed samples and the second preset number of rainfall intensity samples are fully combined to obtain different wind and rain load combinations.

[0048] For example, wind speed (v) has 6 levels, varying from 10 m / s to 35 m / s in a step of 5 m / s; rainfall intensity (u) has 6 levels, varying from 30 mm / h to 180 mm / h in a step of 30 mm / h. Then, wind speed intervals are divided into neighborhoods of ±2.5 m / s from the baseline value, with 4 samples randomly selected from each interval; rainfall intensity intervals are divided into neighborhoods of ±15 mm / h from the baseline value, with 3 samples randomly selected from each interval. The Cartesian product of the wind speed and rainfall intensity samples yields multiple independent operating conditions.

[0049] (5) Wind deflection response analysis.

[0050] These different combinations of wind and rain loads are applied one by one to the established transmission line model to simulate the external forces borne by the line under different weather scenarios. The model is then subjected to mechanical analysis using simulation software to calculate the wind deflection response parameters of the transmission line under different wind and rain combinations.

[0051] (6) The vulnerability function is obtained by regression analysis.

[0052] A numerical model of the transmission tower-line-string system was established. The Seyedi seismic vulnerability surface establishment method was adopted. The wind deflection angle calculation results of the transmission line were used as the core analysis object. Through regression analysis, a vulnerability function describing the relationship between wind deflection response and structural failure probability was obtained.

[0053] For example, the Seyedi seismic vulnerable surface formula is:

[0054] In the formula, v For wind speed, u For rainfall intensity, ln( v ) and ln( u The regression coefficients for ) were 0.95 and 0.10, respectively.

[0055] (7) Calculate the probability of wind deflection failure under the combined action of strong convective wind and rain.

[0056] The specific formula is as follows:

[0057]

[0058] In the formula, The probability density of wind-induced failure of transmission lines under severe convective weather; The cumulative failure probability of transmission lines due to wind deflection under severe convective weather; Let wind and rain be the combined probability density function; It is a two-dimensional vulnerability function of transmission lines under wind and rain loads.

[0059] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0060] Figure 5 This is a schematic diagram of the structure of the transmission line wind-induced flashover failure probability analysis device provided in an embodiment of the present invention. Figure 5 As shown, the transmission line wind-induced flashover failure probability analysis device 5 includes: Module 51 is used to acquire meteorological data, including wind speed and rainfall intensity. Processing module 52 is used to establish a marginal probability distribution function of wind speed based on wind speed and a marginal probability distribution function of rainfall intensity based on rainfall intensity. Module 53 is established to establish a combined wind and rain probability density function based on the marginal probability distribution function of wind speed and the marginal probability distribution function of rainfall intensity using the Copula function; Analysis module 54 is used to establish a transmission line model and perform wind deflection response analysis on the transmission line model through different combinations of wind and rain loads, and establish a vulnerability function based on the wind deflection response results; Module 55 is used to determine the wind-induced flashover failure probability parameters based on the combined wind and rain probability density function and the vulnerability function.

[0061] In one possible implementation, processing module 52 is used for: By fitting wind speed to different types of marginal probability distribution functions, the marginal probability distribution function with the smallest fitting error is selected to establish the marginal probability distribution function of wind speed. By fitting different types of marginal probability distribution functions to rainfall intensity, the marginal probability distribution function with the smallest fitting error is selected to establish the marginal probability distribution function of rainfall intensity.

[0062] In one possible implementation, different types of marginal probability distribution functions include: gamma distribution, Weibull distribution, log-normal distribution, Rayleigh distribution, and generalized extreme value distribution.

[0063] In one possible implementation, module 53 is used for: Based on the marginal probability distribution function of wind speed and the marginal probability distribution function of rainfall intensity, different types of Copula functions are established respectively; The goodness of fit of each Copula function is evaluated, and the Copula function with the best goodness of fit is selected as the joint probability density function of wind and rain.

[0064] In one possible implementation, module 53 is used to: calculate the goodness of fit of each Copula function based on the Akaike criterion value, Bayes criterion value, and root mean square error of each Copula function.

[0065] In one possible implementation, a vulnerability function is established based on the wind deflection response results, including: performing regression analysis on the wind deflection response results using the Seyedi seismic vulnerable surface method to obtain the vulnerability function.

[0066] In one possible implementation, the analysis module 54 is used for: according to Determine the probability density of wind-induced failure in transmission lines; according to Determine the cumulative failure probability of wind-induced deflection in transmission lines; in, The probability density of wind-induced failure; The cumulative failure probability due to wind deflection; Let wind and rain be the combined probability density function; It is a vulnerable function; v Wind speed; u This refers to the intensity of rainfall.

[0067] In one possible implementation, different combinations of wind and rain loads are obtained through the following method: Within a preset wind speed range, multiple reference wind speeds are determined according to a first preset interval, and a first preset number of wind speed samples are extracted within a preset neighborhood of each reference wind speed. Within a preset rainfall intensity range, multiple benchmark rainfall intensities are determined according to a second preset interval, and within a preset neighborhood of each benchmark rainfall intensity, a second preset number of rainfall intensity samples are extracted. By performing Cartesian product operations, a first preset number of wind speed samples and a second preset number of rainfall intensity samples are fully combined to obtain different combinations of wind and rain loads.

[0068] This invention, through the acquisition of meteorological data, establishes a marginal probability distribution function for wind speed and a marginal probability distribution function for rainfall intensity, thereby quantifying the probability of occurrence under different wind speeds and rainfall intensities. Based on these two types of marginal probability distribution functions, a combined wind and rain probability density function is established using a Copula function, which can accurately capture the linkage characteristics of the coordinated changes in wind speed and rainfall intensity under severe convective weather, quantifying the probability of occurrence under different wind and rain combinations. A transmission line model is established, and wind deflection response analysis is conducted under different wind and rain load combinations, establishing a vulnerability function. This allows for a systematic analysis of the wind deflection response patterns of transmission lines under different wind and rain conditions, quantifying the structural failure probability corresponding to different wind deflection responses. Finally, based on the combined wind and rain probability density function and the vulnerability function, the wind deflection flashover failure probability parameters are determined, achieving quantitative assessment and accurate prediction of the wind deflection flashover risk of transmission lines. This invention improves the accuracy and scientific rigor of wind deflection flashover risk assessment for transmission lines, providing practical technical support for engineering practice.

[0069] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 6 As shown, the electronic device 6 of this embodiment includes a processor 60 and a memory 61. The memory 61 stores a computer program 62. When the processor 60 executes the computer program 62, it implements the steps in the various method embodiments described above. Alternatively, when the processor 60 executes the computer program 62, it implements the functions of each module in the various device embodiments described above.

[0070] For example, computer program 62 may be divided into one or more modules / units, which are stored in memory 61 and executed by processor 60 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 62 in electronic device 6.

[0071] Electronic device 6 may include, but is not limited to, processor 60 and memory 61. Those skilled in the art will understand that... Figure 6 This is merely an example of electronic device 6 and does not constitute a limitation on electronic device 6. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 6 may also include input / output devices, network access devices, buses, etc.

[0072] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0073] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0074] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for analyzing the probability of wind-induced flashover failure in transmission lines, characterized in that, include: Acquire meteorological data, including wind speed and rainfall intensity; Establish a marginal probability distribution function for wind speed based on the wind speed, and establish a marginal probability distribution function for rainfall intensity based on the rainfall intensity; Based on the marginal probability distribution function of wind speed and the marginal probability distribution function of rainfall intensity, a combined wind and rain probability density function is established using the Copula function; A transmission line model is established, and the wind deflection response of the transmission line model is analyzed through different combinations of wind and rain loads. A vulnerability function is established based on the wind deflection response results. Based on the combined wind and rain probability density function and the vulnerability function, the wind deflection flashover failure probability parameter is determined.

2. The method for analyzing the probability of wind-induced flashover failure in transmission lines as described in claim 1, characterized in that, The step of establishing a marginal probability distribution function for wind speed based on the wind speed and establishing a marginal probability distribution function for rainfall intensity based on the rainfall intensity includes: By fitting the wind speed with different types of marginal probability distribution functions, the marginal probability distribution function with the smallest fitting error is selected to establish the marginal probability distribution function of wind speed. By fitting different types of marginal probability distribution functions to the rainfall intensity, the marginal probability distribution function with the smallest fitting error is selected to establish the marginal probability distribution function of rainfall intensity.

3. The method for analyzing the probability of wind-induced flashover failure in transmission lines as described in claim 2, characterized in that, The different types of marginal probability distribution functions include: gamma distribution, Weibull distribution, log-normal distribution, Rayleigh distribution, and generalized extreme value distribution.

4. The method for analyzing the probability of wind-induced flashover failure in transmission lines as described in claim 1, characterized in that, The step of establishing a combined wind and rain probability density function using a Copula function based on the marginal probability distribution function of wind speed and the marginal probability distribution function of rainfall intensity includes: Based on the marginal probability distribution function of wind speed and the marginal probability distribution function of rainfall intensity, different types of Copula functions are established respectively; The goodness of fit of each Copula function is evaluated, and the Copula function with the best goodness of fit is selected as the joint probability density function of wind and rain.

5. The method for analyzing the probability of wind-induced flashover failure in transmission lines as described in claim 4, characterized in that, The evaluation of the goodness of fit of each Copula function includes: calculating the goodness of fit of each Copula function based on the Akaike criterion value, Bayes criterion value, and root mean square error of each Copula function.

6. The method for analyzing the probability of wind-induced flashover failure in transmission lines as described in claim 1, characterized in that, The step of establishing the vulnerability function based on the wind deflection response results includes: performing regression analysis on the wind deflection response results using the Seyedi seismic vulnerability surface method to obtain the vulnerability function.

7. The method for analyzing the probability of wind-induced flashover failure in transmission lines as described in claim 1, characterized in that, The determination of wind-induced flashover failure probability parameters based on the combined wind and rain probability density function and the vulnerability function includes: according to Determine the probability density of wind-induced failure in transmission lines; according to Determine the cumulative failure probability of wind-induced deflection in transmission lines; in, This represents the probability density of wind-induced failure. The cumulative failure probability due to wind deflection; Let wind and rain be the combined probability density function; It is a vulnerable function; v Wind speed; u This refers to the intensity of rainfall.

8. The method for analyzing the probability of wind-induced flashover failure in transmission lines as described in claim 1, characterized in that, The different combinations of wind and rain loads are obtained through the following methods: Within a preset wind speed range, multiple reference wind speeds are determined according to a first preset interval, and a first preset number of wind speed samples are extracted within a preset neighborhood of each reference wind speed. Within a preset rainfall intensity range, multiple benchmark rainfall intensities are determined according to a second preset interval, and within a preset neighborhood of each benchmark rainfall intensity, a second preset number of rainfall intensity samples are extracted. By performing Cartesian product operations, the first preset number of wind speed samples and the second preset number of rainfall intensity samples are fully combined to obtain different combinations of wind and rain loads.

9. A device for analyzing the probability of wind-induced flashover failure in transmission lines, characterized in that, include: The acquisition module is used to acquire meteorological data, including wind speed and rainfall intensity. The processing module is used to establish a marginal probability distribution function for wind speed based on the wind speed and a marginal probability distribution function for rainfall intensity based on the rainfall intensity. A module is established to establish a combined wind and rain probability density function using the Copula function based on the marginal probability distribution function of wind speed and the marginal probability distribution function of rainfall intensity. The analysis module is used to establish a transmission line model and perform wind deflection response analysis on the transmission line model through different combinations of wind and rain loads, and establish a vulnerability function based on the wind deflection response results. The determination module is used to determine the wind deflection flashover failure probability parameter based on the combined wind and rain probability density function and the vulnerability function.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 8.

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