A method for evaluating failure probability of power transmission line considering ice wind coupling

Abstract

The application provides a power transmission line failure probability evaluation method considering ice and wind coupling, and relates to the technical field of power transmission line risk evaluation. The method firstly establishes an ice and wind combined disaster probability model, mainly considering the wind speed and wind direction correlation; then performs vulnerability analysis on the power transmission line, and constructs a vulnerability function model of the power transmission line under the coupling action of ice and wind load; finally, an evaluation model of the failure probability of the power transmission line is constructed according to the ice and wind combined disaster probability model and the vulnerability model. Compared with the prior art, in the field of ice and wind risk evaluation of power transmission towers, the ice and wind combined probability distribution is combined with the vulnerability analysis of the power transmission line, the wind speed and wind direction correlation is considered for the first time, the analysis of the power transmission line under the dynamic action is performed, and the failure probability of the power transmission line under the coupling action of ice and wind can be more accurately and reasonably evaluated.

Description

Technical Field

[0001] This invention relates to the field of transmission line risk assessment technology, and in particular to a method for assessing the failure probability of transmission lines considering ice and wind coupling. Background Technology

[0002] my country is one of the countries most frequently affected by icing disasters. Icing disasters cause widespread power outages, grid disconnections, and line shutdowns, impacting customer electricity supply and resulting in significant economic losses. Therefore, icing of transmission lines is a major threat to their safe operation. Meanwhile, the impact of wind loads cannot be ignored. Icing loads and wind loads on transmission lines are correlated; they are not independent probabilistic events. Ignoring this correlation will affect the accuracy of probability calculations. Therefore, it is essential to conduct research on the failure probability of transmission lines under the coupled effects of ice and wind.

[0003] However, current research on transmission line damage under the combined effects of ice and wind does not consider the correlation between wind speed and wind direction, neglecting the probability distribution of wind direction. Research on the vulnerability of transmission lines under ice and wind loads is also relatively lacking. Therefore, constructing a probability distribution model for combined ice and wind disasters, analyzing the vulnerability of transmission lines under ice and wind coupled loads, and proposing an assessment model for the failure probability of transmission lines have become hot topics in the field of transmission line risk assessment.

[0004] Icing observation in my country started relatively late, and although many regions have relatively complete meteorological data, they lack icing records. Currently, in traditional design methods, when icing observation data is lacking, the design ice thickness is determined by surveying the icing of nearby existing lines and operational experience. This method has certain limitations in terms of the universality and accuracy of the design. However, based on the relationship between meteorological data and icing, several icing prediction models have emerged. Common icing prediction models include the Imai model, the Makkonen model, and the Jones model. Among them, the Imai model ignores the influence of precipitation and air humidity, and the icing load results are often underestimated when the temperature is near 0℃, while the assumption of moisture growth is difficult to hold when the temperature drops to 5℃. The Makkonen model requires many parameters, such as the freezing coefficient and the capture coefficient, which are difficult to obtain accurately. It is mostly used for more detailed icing mechanism studies, and it is very difficult to calculate long-term cumulative icing. Although the Jones model ignores the influence of conductor diameter on icing thickness, the meteorological data parameters used in the model are easy to obtain and have high accuracy. Therefore, the Jones model has better universality in obtaining icing data during structural design.

[0005] Furthermore, traditional analysis methods typically design structures based on the most unfavorable load combinations to ensure that the difference between the target reliability and calculated values ​​under the serviceability limit state and ultimate limit state meets tolerance requirements. However, existing research shows that structural design is not solely controlled by the most unfavorable loads; other, smaller load combinations often influence the design as well. Therefore, considering the coupling effects of multiple disasters is crucial for transmission lines. Currently, various stochastic load combination models based on disaster probabilities are commonly used, including the Ferry-Borges model, the load encounter combination method, and the Copula function joint probability model. Among these, the Ferry-Borges model not only ignores the correlation between loads but also requires careful selection of reasonable time periods; the load encounter combination method has strict requirements on load duration and is less applicable to load combinations with significantly different durations. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method for assessing the failure probability of transmission lines considering ice and wind coupling, which is in response to the shortcomings of the prior art. This method can assess the failure probability of transmission lines under ice and wind coupled loads within the design life cycle.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] A method for assessing the failure probability of transmission lines considering ice-wind coupling includes the following steps:

[0009] S1: Establish a probability model for combined ice and wind disasters. The specific method is as follows:

[0010] S11: Collect relevant meteorological data and build a meteorological data super station;

[0011] S12: Statistically analyze the ice thickness and extreme wind speed during each icing event; the ice thickness is referred to as ice thickness.

[0012] S13: Establish single-hazard probability distribution models for ice thickness, wind speed, and wind direction based on extreme value distribution and mixed von Mises distribution, respectively; and based on the coefficient of determination R... 2 The optimal single-hazard probability distribution model is selected based on the root mean square error (RMSE).

[0013] S14: Construct a Copula joint probability distribution model for ice thickness-wind speed and wind speed-wind direction based on the optimal single disaster distribution probability model;

[0014] S15: Select the optimal joint disaster probability distribution model of ice thickness-wind speed and wind speed-wind direction by using the minimum AIC information criterion and the minimum BIC information criterion;

[0015] S2: Analyze the vulnerability of transmission lines. The specific methods are as follows:

[0016] S21: Establish a finite element model of the uncertain transmission tower-line system, and consider the influence of initial defects in the transmission tower;

[0017] S22: Nonlinear static pushover analysis of the transmission tower-line system under ice and wind load coupling; using the maximum tower top displacement as the ultimate performance index of the transmission tower, the critical collapse criterion of the transmission tower is determined;

[0018] S23: Determine the ice thickness and wind speed range, randomly sample the ice and wind loads, and construct random ice and wind load combinations; apply the random ice and wind load combinations to the transmission tower-line system to obtain the engineering demand parameters under different ice and wind load combinations, and obtain the engineering demand parameter model through regression analysis.

[0019] S24: Construct a vulnerability function model for transmission lines based on the critical collapse criterion for transmission towers and the engineering requirement parameter model, and obtain the critical collapse curve of the transmission towers;

[0020] S3: Based on the ice-wind combined disaster probability model and the transmission line vulnerability function model, construct a failure probability assessment model for transmission lines under ice-wind coupled loads.

[0021] Furthermore, in step S11, the meteorological data of the meteorological data superstation is obtained through measured data from actual meteorological stations or through cumulative icing data calculated using the Jones icing prediction model.

[0022] When there is no icing data in the actual meteorological station's measured data, the Jones icing prediction model is used to obtain the corresponding icing data, as shown in the following expression:

[0023]

[0024] In the formula, ρ i The density of ice is expressed as 0.9 g / cm³. 3 ρ0 represents the raindrop density, taken as 1 g / cm³. 3 V j Wind speed is expressed in m / s; P j Indicates precipitation intensity, in mm / h; W j The liquid water content is expressed in g / m³. 3 Its value is 0.067P. j 0.846 .

[0025] Furthermore, the extreme value distributions in step S13 include three types: extreme value type I distribution, namely the Gumbel distribution; extreme value type II distribution, namely the Frechet distribution; and extreme value type III distribution, namely the Weibull distribution. The expressions for the three extreme value distributions include the cumulative probability distribution function (CDF) expression and the probability density function (PDF) expression.

[0026] The CDF expression for the Gumbel distribution is:

[0027]

[0028] The PDF expression for the Gumbel distribution is:

[0029]

[0030] Where a, b, and c are the position parameter, scale parameter, and shape parameter, respectively, and are determined by regression analysis.

[0031] The CDF expression for the Frechet distribution is:

[0032]

[0033] The PDF expression for the Frechet distribution is:

[0034]

[0035] The CDF expression for the Weibull distribution is:

[0036]

[0037] The PDF expression for the Weibull distribution is:

[0038]

[0039] The expression for the mixed von Mises distribution used is as follows:

[0040]

[0041] In the formula, N t Let be the order of the mixed von Mises distribution. Expressed as wind direction angle, ω i ω is the weighting coefficient, 0 ≤ ω i ≤1 and ∑ω i =1; μ i Let μ be the location parameter of the mixed von Mises distribution, 0 ≤ μ i ≤2π; k i k is the scale parameter. i ≥0; I0(k)i ) is a zero-order modified Bessel equation of the first kind.

[0042] Furthermore, the criterion for selecting the optimal single disaster probability distribution model in step S13 is R. 2 It has the largest RMSE and the smallest RMSE.

[0043] Furthermore, the Copula joint probability distribution model in step S14 is as follows:

[0044] GH Copula function:

[0045] C(u,v;θ)=exp{-[(-lnu) θ +(-lnv) θ ] 1 / θ},θ∈[1,∞)

[0046]

[0047] Clayton Copula function:

[0048] C(u,v;θ)=(u -θ +v -θ -1) -1 / θ ,θ∈(0,∞)

[0049]

[0050] Frank Copula function:

[0051]

[0052]

[0053] AMH Copula function:

[0054]

[0055]

[0056] Where θ is a parameter of the two-dimensional Archimedes Copula function, and its value is determined according to the Kendall rank correlation coefficient τ, which is the ratio of the difference between the same-order logarithms and the different-order logarithms to their sum.

[0057] Furthermore, in step S21, the uncertainties in the transmission tower-line system finite element model are considered in terms of elastic modulus, yield strength, leg thickness, leg width, Poisson's ratio, and density; the uniform mode method is used to consider the influence of initial defects in the transmission tower.

[0058] Furthermore, in step S23, the random ice wind load combination applied takes into account the influence of pulsating wind and performs dynamic analysis.

[0059] The model expression for the engineering requirement parameters is as follows:

[0060] ln(EDP) = mln(IM1) + nln(IM2) + k

[0061] Wherein, EDP represents engineering requirement parameters; IM1 represents ice thickness in mm; and IM2 represents wind speed in m / s.

[0062] Furthermore, the vulnerability function model expression in step S24 is as follows:

[0063]

[0064]

[0065] Where Φ represents the standard normal distribution, LS represents the ultimate strength index; β EDP|IM This represents the log-normal standard deviation of the EDP.

[0066] Furthermore, the failure probability model expression for the transmission line under ice and wind coupled load constructed in step S3 is as follows:

[0067]

[0068]

[0069] In the formula, and These represent the failure probability density and cumulative failure probability, respectively; im1 and im2 represent the intensity indices of ice thickness and wind speed. Indicates the wind direction angle The combined vulnerability function of ice and wind loads at that time; Let it be expressed as the joint probability density function of ice thickness and wind speed; Indicates in im 21 -im 22 The probability of a wind direction occurring within a given wind speed range is obtained by integrating the joint density function of wind speed and wind direction.

[0070] The beneficial effects of adopting the above technical solution are as follows: The transmission line failure probability assessment method considering ice-wind coupling provided by this invention has good universality. Regardless of whether there is actual icing observation data, as long as there is relevant meteorological data, risk analysis of transmission lines under ice-wind load coupling can be carried out; it focuses on considering the correlation between wind speed and wind direction and the probability distribution of wind direction, takes into account the uncertainty of transmission tower structural parameters, and considers the dynamic influence of pulsating wind, resulting in more accurate and reasonable results; by combining the probability distribution of ice-wind combined disasters with structural vulnerability analysis, a transmission line failure probability assessment model is constructed, which can intuitively obtain the failure probability of transmission lines under different specific design loads. Attached Figure Description

[0071] Figure 1 A flowchart of a transmission line failure probability assessment method considering ice-wind coupling provided in an embodiment of the present invention;

[0072] Figure 2 This is a diagram illustrating the optimal single disaster probability distribution model for ice thickness provided in an embodiment of the present invention.

[0073] Figure 3 This is a diagram of the optimal single disaster probability distribution model for wind speed A provided in an embodiment of the present invention;

[0074] Figure 4 This is a diagram of the optimal single disaster probability distribution model for wind speed B provided in an embodiment of the present invention;

[0075] Figure 5 This is a diagram illustrating the optimal single-hazard probability distribution model for wind direction provided in an embodiment of the present invention.

[0076] Figure 6 This is a diagram of the optimal joint disaster probability distribution model of ice thickness and wind speed provided in an embodiment of the present invention.

[0077] Figure 7 This is a diagram of the optimal joint disaster probability distribution model of wind speed and wind direction provided in an embodiment of the present invention.

[0078] Figure 8 The random sampling results of Poisson's ratio provided in the embodiments of the present invention;

[0079] Figure 9 This is a finite element model diagram of the transmission tower-line system provided in an embodiment of the present invention;

[0080] Figure 10 A wind direction diagram provided for an embodiment of the present invention;

[0081] Figure 11 The tower top displacement curve diagram under ice and wind load coupling at 90° wind direction is provided for the embodiments of the present invention.

[0082] Figure 12 A critical collapse criterion diagram for a transmission tower with a 90° wind direction is provided for an embodiment of the present invention;

[0083] Figure 13 This is a time-history curve of wind speed at a simulated point on a transmission tower provided in an embodiment of the present invention;

[0084] Figure 14 This is a diagram of random ice and wind load combinations provided in an embodiment of the present invention;

[0085] Figure 15 This is a model diagram of engineering requirements parameters for a 90° wind direction provided in an embodiment of the present invention;

[0086] Figure 16 A diagram illustrating the 90° wind direction vulnerability function provided in this embodiment of the invention;

[0087] Figure 17 The diagram shows a transmission line failure probability assessment model that considers the correlation between ice thickness and wind speed and wind speed and wind direction, provided for embodiments of the present invention.

[0088] In the diagram: 1. Observation point at the top of the tower; 2. Tower body; 3. Crossbeam. Detailed Implementation

[0089] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0090] like Figure 1 As shown, the method of this embodiment is described below.

[0091] S1: The establishment of the ice-wind joint disaster probability model includes the following sub-steps:

[0092] S11: Collect relevant meteorological data by obtaining measured data from actual meteorological stations or by using cumulative icing data calculated using the Jones icing prediction model, and construct a meteorological data superstation;

[0093] In this embodiment, the meteorological data comes from the official website of the National Oceanic and Aeronautical Administration (NOAA), selecting meteorological data from Chenzhou, Yongzhou, and Shaoyang cities in southern Hunan Province between 1973 and 2020. The NOAA website provides wind direction data from 0° to 360°. Because the NOAA website has significant gaps in its freezing rain records, the Jones icing model is used to calculate the icing amount of freezing rain, establishing a meteorological data superstation.

[0094] The Jones icing prediction model expression is as follows:

[0095]

[0096] In the formula, ρ i The density of ice is expressed as 0.9 g / cm³. 3 ρ0 represents the raindrop density, taken as 1 g / cm³. 3 V j Wind speed is expressed in m / s; P j Indicates precipitation intensity, in mm / h; W j The liquid water content is expressed in g / m³. 3 Its value is 0.067P. j 0.846 .

[0097] S12: Statistical analysis of ice thickness (hereinafter referred to as "ice thickness") and extreme wind speed during each icing event;

[0098] In this embodiment, the ice thickness and extreme wind speed during each icing event are statistically analyzed. For an icing event that occurs at different weather stations within a week, it is considered a related event. To prevent underestimating the probability of thick icing, only the maximum ice thickness is statistically analyzed once. The maximum wind speed during each icing event is the extreme wind speed A.

[0099] In this embodiment, wind speed and direction are affected by factors such as climate and season. However, the number of icing events is limited. It is insufficient to describe the wind speed-wind direction relationship in this region by only statistically analyzing the wind speed and wind direction data during the icing period. Therefore, in order to expand the data sample size, the daily maximum wind speed for each wind direction in the month in which each icing event occurs is statistically analyzed as the wind speed extreme value B, with the wind direction ranging from 0° to 360°.

[0100] S13: Establish single-hazard probability distribution models for ice thickness, wind speed, and wind direction based on three extreme value distributions and a mixed von Mises distribution; and based on the coefficient of determination R... 2 The optimal single-hazard probability distribution model is selected based on the root mean square error (RMSE).

[0101] The three extreme value distributions are the Gumbel distribution (Type I), the Frechet distribution (Type II), and the Weibull distribution (Type III), and their expressions are shown in Table 1.

[0102] Table 1. Expressions for three extreme value distributions

[0103]

[0104] Where CDF is the cumulative probability distribution function; PDF is the probability density function; a, b, and c are the position parameter, scale parameter, and shape parameter, respectively, which are determined by regression analysis.

[0105] The expression for the mixed von Mises distribution is as follows:

[0106]

[0107] In the formula, N t Let be the order of the mixed von Mises distribution. Expressed as wind direction angle, ω i ω is the weighting coefficient, 0 ≤ ω i ≤1 and ∑ω i =1; μ i Let μ be the location parameter of the mixed von Mises distribution, 0 ≤ μ i ≤2π; k i k is the scale parameter. i ≥0; I0(k) i ) is a zero-order modified Bessel equation of the first kind.

[0108] In this embodiment, the extreme values ​​of ice thickness (Gumbel), wind speed (Frechet), and ice thickness (Weibull) are fitted using the Gumbel, Frechet, and Weibull distributions. By comparing R... 2 According to RMSE, the optimal single-hazard probability model for the extreme value type III (Weibull) distribution with ice thickness and wind speed A is obtained, and the fitted curve is as follows: Figure 2 and Figure 3 As shown.

[0109] In this embodiment, the extreme wind speed B is fitted using the Gumbel, Frechet, and Weibull distributions (Type I, Type II, and Type III); the wind direction probability is fitted using a mixed von Mises distribution. By comparing R... 2 According to RMSE, the extreme value type III (Weibull) distribution is the optimal single hazard probability model for wind speed B, and the fourth-order mixed von Mises distribution is the optimal single hazard probability model for wind direction. The fitted curves are as follows: Figure 4 and Figure 5 As shown.

[0110] S14: Construct a Copula joint disaster probability distribution model based on the optimal single disaster distribution probability model, which includes ice thickness-wind speed and wind speed-wind direction.

[0111] The expression for the Copula joint probability distribution model is shown in Table 2.

[0112] Table 2 Expression of Copula Joint Probability Distribution Model

[0113]

[0114] Where θ is a parameter of the two-dimensional Archimedes Copula function, the value of which can be determined by the Kendall rank correlation coefficient τ, the value of which is the ratio of the difference between the same-order logarithms and the different-order logarithms to their sum.

[0115] In this embodiment, the Kendall correlation coefficient calculated based on the extreme ice thickness and extreme wind speed A data is 0.2571. Substituting these into the four Copula functions mentioned above, a joint ice thickness-wind speed disaster probability distribution model is constructed. The Kendall correlation coefficient calculated based on the extreme wind speed B and wind direction data is -0.2077.

[0116] S15: Select the optimal joint disaster probability distribution model of ice thickness-wind speed and wind speed-wind direction by using the minimum AIC information criterion and the minimum BIC information criterion.

[0117] In this embodiment, based on the minimum AIC information criterion and the minimum BIC information criterion, Clayton Copula is determined to be the optimal joint disaster probability distribution model. Figure 6 As shown.

[0118] In this embodiment, for the joint distribution model of wind speed and wind direction, the GH Copula and Clayton Copula functions can only describe the joint distribution of positive correlations between variables, and the AMH Copula function can only describe the joint distribution of weaker correlations, but the Frank Copula has no such limitations. Therefore, the Frank Copula is the optimal joint disaster probability distribution model of wind speed and wind direction, as shown below. Figure 7 As shown.

[0119] S2: Vulnerability analysis of transmission lines, including the following sub-steps:

[0120] S21: Establish a finite element model of the uncertain transmission tower-line system and consider the influence of initial defects in the transmission tower.

[0121] In this embodiment, the uncertainties of the steel's elastic modulus, yield strength, flange thickness, flange width, Poisson's ratio, and density were considered. Latin hypercube sampling (LHS) was used to sample the parameters, resulting in 30 random combinations of transmission tower physical parameters. Figure 8 The sampling results for Poisson's ratio are given.

[0122] In this embodiment, the large-scale commercial software ANSYS is used to establish a finite element model of the transmission tower-line system, such as... Figure 9 As shown, the influence of initial defects in the transmission tower is considered using the consistent mode method.

[0123] S22: Nonlinear static pushover analysis of the transmission tower-line system under ice and wind load coupling; the maximum tower top displacement is used as the ultimate performance index of the transmission tower to determine the critical collapse criterion of the transmission tower.

[0124] In this embodiment, four wind directions—0°, 45°, 60°, and 90°—are selected. A wind direction diagram is shown below. Figure 10 As shown, static pushover analysis of wind loads was performed on the transmission tower-line system under different ice thicknesses. The ice thicknesses were 5 mm, 10 mm, 15 mm, 20 mm, and 25 mm.

[0125] In this embodiment, when the wind direction angle is 90°, the tower top displacement curve of the transmission tower under the ice-wind coupled load is as follows: Figure 11 As shown. Because the ultimate displacement of the transmission tower top varies significantly under different ice and wind load couplings, using a fixed value as the ultimate performance index of the transmission tower is unreasonable. Therefore, the critical collapse criterion of the transmission tower is determined by fitting the data, as shown below. Figure 12 As shown.

[0126] S23: Determine the ice thickness and wind speed range, randomly sample the ice and wind loads, and construct random ice and wind load combinations; apply the random ice and wind load combinations to the transmission tower-line system to obtain the engineering demand parameters under different load combinations, and obtain the engineering demand parameter model through regression analysis.

[0127] The applied random ice wind load combination takes into account the influence of pulsating wind and performs dynamic analysis.

[0128] The engineering requirement parameter model expression is as follows:

[0129] ln(EDP) = mln(IM1) + nln(IM2) + k

[0130] In the formula, EDP represents the engineering requirement parameters; IM1 represents the ice thickness in mm; and IM2 represents the wind speed in m / s.

[0131] In this embodiment, the ice thickness is selected within the range of 0-25mm. According to statistics from the meteorological superstation, the maximum wind speed recorded during the ice accumulation period in this region from 1973 to 2020 was 24m / s; therefore, the wind speed selection range is 0-24m / s. Using a MATLAB program, random sampling and combination were performed, resulting in 40 sets of random ice-wind load combinations, such as... Figure 13 As shown.

[0132] In this embodiment, the Davenport spectrum is used for the fluctuating wind speed spectrum. The total wind speed time history curve including the fluctuating wind speed is generated using the harmonic superposition method. The duration of the fluctuating wind is 300 s, the time interval is 0.2 s, the cutoff frequency is 5 Hz, and the ground roughness is 0.15. When the basic wind speed at a height of 10 m is taken as 15 m / s, the wind speed time history curves at some simulated points in the transmission tower system are as follows: Figure 14 As shown.

[0133] In this embodiment, the engineering requirement parameters under different load combinations are obtained through regression analysis. The engineering requirement parameter model for a 90° wind direction is as follows: Figure 15 As shown.

[0134] S24: Construct a vulnerability function model for transmission lines based on the critical collapse criterion for transmission towers and the engineering requirement parameter model, and obtain the critical collapse curve of the transmission tower.

[0135] The vulnerability function model expression in step S24 is as follows:

[0136]

[0137]

[0138] In the formula, Φ represents the standard normal distribution, and LS (limit state) represents the limit strength index. β EDP|IM This represents the log-normal standard deviation of the EDP.

[0139] In this embodiment, under a 90° wind direction, a vulnerability function model for the transmission line is constructed based on the critical collapse criterion for the transmission tower and the engineering requirement parameter model, such as... Figure 16 As shown.

[0140] S3. Based on the ice-wind combined disaster probability model and the transmission line vulnerability function model, a failure probability assessment model for transmission lines under ice-wind coupled loads is constructed.

[0141] The failure probability model expression for the transmission line under ice and wind coupled load, constructed in step S3, is as follows:

[0142]

[0143]

[0144] In the formula, and These represent the failure probability density and cumulative failure probability, respectively; im1 and im2 represent the intensity indices of ice thickness and wind speed. Indicates the wind direction angle The combined vulnerability function of ice and wind loads at that time; Let it be expressed as the joint probability density function of ice thickness and wind speed; Indicates in im 21 -im 22 The probability of a wind direction occurring within a given wind speed range is obtained by integrating the joint density function of wind speed and wind direction.

[0145] In this embodiment, under a 90° wind direction, the transmission line failure probability model considering the correlation between ice thickness and wind speed, and wind speed and wind direction is as follows: Figure 17 As shown.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A method for assessing the failure probability of transmission lines considering ice-wind coupling, characterized in that: The method includes the following steps: S1: Establish a probability model for combined ice and wind disasters. The specific method is as follows: S11: Collect relevant meteorological data and build a meteorological data super station; S12: Statistically analyze the ice thickness and extreme wind speed during each icing event; the ice thickness is referred to as ice thickness. S13: Establish single-hazard probability distribution models for ice thickness, wind speed, and wind direction based on extreme value distribution and mixed von Mises distribution, respectively; and based on the coefficient of determination R... 2 The optimal single-hazard probability distribution model is selected based on the root mean square error (RMSE). The extreme value distributions include three types: Type I extreme value distribution, namely the Gumbel distribution; Type II extreme value distribution, namely the Frechet distribution; and Type III extreme value distribution, namely the Weibull distribution. The expressions for the three extreme value distributions include the cumulative probability distribution function (CDF) expression and the probability density function (PDF) expression. The CDF expression for the Gumbel distribution is: The PDF expression for the Gumbel distribution is: Where a, b, and c are the position parameter, scale parameter, and shape parameter, respectively, and are determined by regression analysis. The CDF expression for the Frechet distribution is: The PDF expression for the Frechet distribution is: ； The CDF expression for the Weibull distribution is: The PDF expression for the Weibull distribution is: ； The expression for the mixed von Mises distribution used is as follows: In the formula, N t Let be the order of the mixed von Mises distribution. Expressed as wind direction angle, These are the weighting coefficients. and ; For the location parameters of the mixed von Mises distribution, ; k i For scale parameters, ; I 0( k i () is a zero-order modified Bessel equation of the first kind; S14: Construct a Copula joint probability distribution model for ice thickness-wind speed and wind speed-wind direction based on the optimal single disaster distribution probability model; S15: Select the optimal joint disaster probability distribution model of ice thickness-wind speed and wind speed-wind direction by using the minimum AIC information criterion and the minimum BIC information criterion; S2: Analyze the vulnerability of transmission lines. The specific methods are as follows: S21: Establish a finite element model of the uncertain transmission tower-line system, and consider the influence of initial defects in the transmission tower; S22: Nonlinear static pushover analysis of the transmission tower-line system under ice and wind load coupling; using the maximum tower top displacement as the ultimate performance index of the transmission tower, the critical collapse criterion of the transmission tower is determined; S23: Determine the ice thickness and wind speed range, randomly sample the ice and wind loads, and construct random ice and wind load combinations; apply the random ice and wind load combinations to the transmission tower-line system to obtain the engineering demand parameters under different ice and wind load combinations, and obtain the engineering demand parameter model through regression analysis. S24: Construct a vulnerability function model for transmission lines based on the critical collapse criterion for transmission towers and the engineering requirement parameter model, and obtain the critical collapse curve of the transmission towers; S3: Based on the ice-wind combined disaster probability model and the transmission line vulnerability function model, construct a failure probability assessment model for transmission lines under ice-wind coupled loads.

2. The method for assessing the failure probability of transmission lines considering ice and wind coupling according to claim 1, characterized in that: In step S11, the meteorological data of the meteorological data superstation is obtained through actual measured data from actual meteorological stations or through cumulative icing data calculated using the Jones icing prediction model. When there is no icing data in the actual meteorological station's measured data, the Jones icing prediction model is used to obtain the corresponding icing data, as shown in the following expression: In the formula, ρ i The density of ice is expressed as 0.9 g / cm³. ρ 0 represents the density of raindrops, taken as 1 g / cm³; V j Wind speed is expressed in m / s. P j This indicates the intensity of precipitation, expressed in mm / h. W j This indicates the liquid water content, expressed in g / m³, with a value of 0.

067. P j 0.846 .

3. The method for assessing the failure probability of transmission lines considering ice and wind coupling according to claim 1, characterized in that: The criterion for selecting the optimal single disaster probability distribution model in step S13 is that R² is maximized and RMSE is minimized.

4. The method for assessing the failure probability of transmission lines considering ice and wind coupling according to claim 1, characterized in that: The Copula joint probability distribution model in step S14 is as follows: GH Copula function: Clayton Copula function: Frank Copula function: AMH Copula function: in, θ The parameters of the two-dimensional Archimedean Copula function are determined by the Kendall rank correlation coefficient. Sure, The value is the ratio of the difference between the same-order logarithms and the different-order logarithms to their sum.

5. The method for assessing the failure probability of transmission lines considering ice and wind coupling according to claim 1, characterized in that: In step S21, the uncertainties in the finite element model of the transmission tower-line system are considered in terms of elastic modulus, yield strength, leg thickness, leg width, Poisson's ratio, and density; the uniform modal method is used to consider the influence of initial defects in the transmission tower.

6. The method for assessing the failure probability of transmission lines considering ice and wind coupling according to claim 1, characterized in that: In step S23, the random ice wind load combination applied takes into account the influence of pulsating wind and performs dynamic analysis. The model expression for the engineering requirement parameters is as follows: in, EDP Indicates the parameters required for the project; IM 1 indicates ice thickness, in mm; IM 2 indicates wind speed, in m / s.

7. The method for assessing the failure probability of transmission lines considering ice and wind coupling according to claim 6, characterized in that: The vulnerability function model expression in step S24 is as follows: in, Represents the standard normal distribution. LS Indicates the ultimate strength index; β EDP IM Representative engineering requirement parameters EDP The log-normal standard deviation.

8. The method for assessing the failure probability of transmission lines considering ice and wind coupling according to claim 1, characterized in that: The failure probability model expression for the transmission line under ice and wind coupled load constructed in step S3 is as follows: In the formula, and These represent the failure probability density and the cumulative failure probability, respectively. im 1 and im 2 represents the intensity index of ice thickness and wind speed; Indicates the wind direction angle The combined vulnerability function of ice and wind loads at that time; Let it be expressed as the joint probability density function of ice thickness and wind speed; Indicates in im 21 -im 22 The probability of a wind direction occurring within a given wind speed range is obtained by integrating the joint density function of wind speed and wind direction.

Images