Wind disaster vulnerability assessment method for branched towers considering material parameter variation

Abstract

The present invention discloses a method for assessing the vulnerability of a commutating branch tower to wind disasters that takes into account the variation of material parameters. When calculating the wind-induced response of a commutating branch tower, a sampling operation is used to consider the variation patterns of various structural material parameters, and an intelligent prediction model is used to learn the relationship between wind speed and the mean and standard deviation of structural demand indicators. Finally, the exceedance probability of the structure under a series of wind speed values ​​is predicted to form a comprehensive wind disaster vulnerability result that takes into account the variation of multiple structural material parameters. The method of the present invention provides a more accurate exceedance probability assessment based on the existing wind disaster vulnerability assessment of transmission towers, taking into account more uncertain risks. The present invention considers the uncertainty of multiple types of variation parameters through random sampling, reducing the amount of calculation. By using an intelligent prediction model, the quantitative relationship between wind speed and exceedance probability, as well as the relationship between wind speed and multiple types of material parameters and exceedance probability are obtained. Under a specified wind speed, the exceedance probability can be calculated more quickly through the intelligent prediction model, and vulnerability curves or surfaces under multiple wind speeds can be obtained, greatly improving the analysis efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind resistance assessment of transmission towers, and in particular relates to a method for assessing the wind disaster vulnerability of a commutating branch tower taking into account material parameter variations. Background Art

[0002] With climate change, the frequency of extreme weather events, including super typhoons and hurricanes, is increasing. Transmission towers are critical supporting structures for power transmission, and any damage directly impacts the safety of transmission lines and the continuity of power supply. In strong winds, transmission towers experience significantly increased wind loads. Especially during extreme weather events like typhoons and hurricanes, the structures are susceptible to damage such as fatigue, bending, and even collapse. Therefore, wind resistance research to ensure the stability and wind resistance of transmission towers under various wind conditions is crucial for ensuring the safe operation of power grids, helping to reduce the probability of disasters and enhance the resilience of power systems. Existing wind resistance studies for transmission towers primarily focus on structural reinforcement and wind load dynamics, with limited research on the vulnerability of transmission towers to wind damage. While this vulnerability assessment primarily considers wind load uncertainty, structural material properties significantly influence the wind-induced response of transmission towers, which still significantly impacts probabilistic vulnerability assessments. Using extensive wind load dynamic time-history analysis is one solution to account for material parameter variation. However, when considering a large number of material parameters over a wide range, computational efficiency is low, significantly impacting assessment efficiency. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for assessing the wind disaster vulnerability of a commutating branch tower considering material parameter variation, so as to predict the wind disaster vulnerability of a commutating branch transmission tower considering material performance variation using a small amount of calculation data, and to predict the failure exceedance probability of the structure under the influence of multiple types of uncertainties through an intelligent prediction model.

[0004] The present invention provides a method for assessing the wind disaster vulnerability of a reversing branch tower taking into account material parameter variation, comprising the following steps:

[0005] S1. Obtaining material parameters of the branch tower components that cause parameter variation;

[0006] S2. Randomly sample and combine the material parameters obtained in step S1 to obtain a material parameter data set;

[0007] S3. Select a wind load time history, randomly select a wind speed value and a sample combination from the material parameter data set obtained in step S2 to obtain a sample data set;

[0008] S4. Establish a finite element model of the reversing branch tower;

[0009] S5. Perform wind load response analysis based on the sample data set obtained in step S3 and the finite element model of the commutating branch tower obtained in step S4 to obtain the demand index and the corresponding capacity value, and calculate the mean and standard deviation of the wind speed value corresponding to the demand index, as well as the mean and standard deviation of the wind speed value and several material parameters corresponding to the demand index;

[0010] S6. For a single wind speed value input, based on the intelligent regression model, fit the mean relationship between the wind speed value and the wind speed value obtained in step S5 corresponding to the demand index, as well as the standard deviation relationship between the wind speed value and the wind speed value obtained in step S5 corresponding to the demand index, to obtain a single-input regression model;

[0011] S7. Based on the wind speed value and several material parameter inputs, fitting the mean relationship between the wind speed value and the wind speed value obtained in step S5 and the corresponding demand indicators of the several material parameters, and fitting the standard deviation relationship between the wind speed value and the wind speed value obtained in step S5 and the corresponding demand indicators of the several material parameters, to obtain a multi-input regression model;

[0012] S8. Given several wind speed values, the single-input regression model obtained in step S6 is used to obtain the mean and standard deviation of the corresponding demand index. The exceedance probability based on the demand index capacity value is calculated based on the mean and standard deviation. The corresponding relationship between wind speed value and exceedance probability is established to obtain the wind disaster vulnerability curve of the reversing branch tower;

[0013] S9. Given several wind speed values ​​and corresponding multiple material parameters, the multi-input regression model obtained in step S6 is used to obtain the mean and standard deviation of the corresponding demand index. The exceedance probability based on the demand index capacity value is then calculated based on the mean and standard deviation. A corresponding relationship between wind speed values, material parameters, and exceedance probability is established to obtain a wind disaster vulnerability surface for the reversing branch tower.

[0014] S10. Perform actual wind disaster vulnerability assessment of the reversing branch tower based on the single-input regression model, multi-input regression model, reversing branch tower wind disaster vulnerability curve, and reversing branch tower wind disaster vulnerability surface obtained in steps S6 to S9.

[0015] In step S1, the materials of the commutation branch tower components include steel structure materials and insulator materials; material parameters of different materials of the same type are identified according to different parameters;

[0016] The obtained material parameters are parameters of the switching branch tower components that result from parameter variations due to instability in the production process; the number of types of material parameters obtained is K; and the distribution of the material parameters is normal distribution or lognormal distribution;

[0017] The normal distribution model is expressed using the following formula:

[0018]

[0019] Among them, a i is the i-th material parameter; μ ai is the mean value of the i-th material parameter; σ ai is the standard deviation of the i-th material parameter; f(a i ) is the material parameter a under the normal distribution model i probability;

[0020] The lognormal distribution model is expressed using the following formula:

[0021]

[0022] In step S2, Latin hypercube sampling or random sampling is used to select M material parameter combinations as a material parameter data set based on the distribution of the material parameters; each combination includes K material parameters.

[0023] In step S3, N groups of wind load time histories are selected; the randomness of wind speed satisfies a uniform distribution in the preset site, M wind speed values ​​are selected by random sampling, and are randomly combined with the M material parameter combinations in the material parameter data set described in step S2 to obtain a sample data set;

[0024] The uniform distribution model is expressed using the following formula:

[0025]

[0026] Where W is the wind speed value; a is the lower limit of the wind speed range; b is the upper limit of the wind speed range; and f(W) is the probability that the wind speed is W under the uniform distribution model.

[0027] In step S4, the finite element model of the reversing branch tower simultaneously considers the crossarm forces in different directions and the dynamic effects of the reversing ground conductor; within the error requirement range, the reversing ground conductor is simplified to a net force and equivalently loaded on the crossarms in different directions;

[0028] The demand index D and capacity value T0 of the reversing branch tower structure are specified. The demand index D is determined by selecting the maximum value of the horizontal 360-degree displacement of the top of the reversing branch tower. The capacity value T0 is determined by the entire tower rotation angle limit and the actual tower height.

[0029] In step S5, each of the M sample combinations in the sample data set is combined with the N wind load time histories, and a wind load response analysis is performed based on the finite element model of the reversing branch tower obtained in step S4 to calculate the demand index D i and the corresponding capacity value T i Among them, D i is the demand index corresponding to the i-th wind load time history; T iis the capacity value corresponding to the i-th wind load time history; the wind load response analysis adopts the dynamic time history analysis;

[0030] If all K material parameters are considered, the means μ i ,..., D N} of the N demand indicators {D1, D2,..., D j and the standard deviations σ j obtained under a single sample combination are calculated, where j is the serial number corresponding to the wind speed value, and 0 < j ≤ M; all M wind speed values are traversed to obtain the means and standard deviations of the M demand indicators;

[0031] If L material parameters are considered, where L < K, the material parameters not considered in each sample in the sample dataset are removed, and the means μ i ',..., D N '} of the N demand indicators {D1', D2',..., D j ' and the standard deviations σ j ' obtained under a single sample combination are calculated, where j is the serial number corresponding to the wind speed value, and 0 < j ≤ M; all M wind speed values are traversed to obtain the means and standard deviations of the M demand indicators considering partial material parameters.

[0032] In step S8, according to the normal distribution, the exceedance probability P corresponding to the wind speed value based on the demand indicator capacity T0 is calculated to obtain the M exceedance probabilities of the corresponding wind speed values, and the following formula is used for calculation:

[0033]

[0034] where x is the wind speed integration variable, ranging from T0 to positive infinity; P j is the exceedance probability corresponding to the j-th wind speed value;

[0035] According to the wind speed value and the corresponding exceedance probability, the change of the exceedance probability with the wind speed is fitted using the lognormal distribution to obtain the wind disaster vulnerability curve of the commutation branch tower, which is expressed by the following formula:

[0036]

[0037] where P(W) is the exceedance probability when the wind speed value is W; σ is the logarithmic standard deviation of the wind speed W distribution; μ is the logarithmic mean of the wind speed W distribution.

[0038] In step S9, according to the normal distribution, the exceedance probability P corresponding to the wind speed value and partial material parameters is calculated to obtain the M exceedance probabilities corresponding to the wind speed value and partial material parameters, and the following formula is used for calculation:

[0039]

[0040] where x is the integral variable of wind speed, ranging from T0 to positive infinity; P j ' is the exceedance probability corresponding to the j-th wind speed value and partial material parameters;

[0041] For the overall variation law of the exceedance probability with wind speed and material parameters, there is no general fitting function, and the wind disaster vulnerability surface of the commutation branch tower is directly used for expression.

[0042] Step S10 specifically includes the following steps:

[0043] Confirm the number l of material parameters to be considered. When l = K, first input the current actual wind speed value into the single-input regression model obtained in step S6 to obtain the mean and standard deviation of the demand index corresponding to the wind speed, and then combine the wind speed value and input it into the wind disaster vulnerability curve of the commutation branch tower obtained in step S8 to obtain the predicted exceedance probability, and complete the wind disaster vulnerability assessment of the commutation branch tower;

[0044] When l < K, input the current actual wind speed value and the material parameters to be considered into the multi-input regression model obtained in step S7 to obtain the mean and standard deviation of the demand index corresponding to the wind speed value and partial material parameters, and then combine the actual wind speed value and input it into the wind disaster vulnerability surface of the commutation branch tower obtained in step S9 to obtain the predicted exceedance probability, and complete the wind disaster vulnerability assessment of the commutation branch tower.

[0045] The present invention discloses a method for evaluating the wind disaster vulnerability of a commutation branch tower considering the variation of material parameters, which considers the variation laws of various structural material parameters, provides a more accurate exceedance probability assessment based on the existing wind disaster vulnerability assessment of transmission towers, and considers more uncertainty risks. At the same time, the method of the present invention considers the uncertainty of multiple types of variation parameters through random sampling, and the calculation amount is sharply reduced. By using an intelligent prediction model, the quantitative relationship between wind speed and exceedance probability, and the relationship between wind speed, multiple types of material parameters and exceedance probability are obtained. At a specified wind speed, the exceedance probability can be calculated faster through the intelligent prediction model, and the vulnerability curve or surface at multiple wind speeds can be obtained, greatly reducing the calculation cost and improving the analysis efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a schematic flow chart of the method of the present invention;

[0047] Figure 2 is the commutation branch tower in the embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention provides a method for evaluating the wind disaster vulnerability of a commutation branch tower considering the variation of material parameters, and its schematic flow chart is as Figure 1 shown, and the commutation branch tower is as Figure 2As shown, the following steps are included:

[0049] S1. Obtaining material parameters of the branch tower components that cause parameter variation;

[0050] The materials of the commutation branch tower components include steel structure materials and insulator materials; the material parameters of different materials of the same type are determined according to different parameters;

[0051] The obtained material parameters are parameters of the switching branch tower components that result from parameter variations due to instability in the production process; the number of types of material parameters obtained is K; and the distribution of the material parameters is normal distribution or lognormal distribution;

[0052] The normal distribution model is expressed using the following formula:

[0053]

[0054] Among them, a i is the i-th material parameter; μ ai is the mean value of the i-th material parameter; σ ai is the standard deviation of the i-th material parameter; f(a i ) is the material parameter a under the normal distribution model i probability;

[0055] The lognormal distribution model is expressed using the following formula:

[0056]

[0057] S2. Based on the material parameters obtained in step S1, random sampling and combination are performed to obtain a material parameter data set, specifically:

[0058] According to the distribution of material parameters, Latin hypercube sampling or random sampling is used to select M material parameter combinations as the material parameter data set; each combination includes K material parameters.

[0059] S3. Select a wind load time history, randomly extract wind speed values ​​and sample combinations from the material parameter data set obtained in step S2, and obtain a sample data set, specifically:

[0060] Select N groups of wind load time histories; the randomness of wind speed satisfies a uniform distribution in the preset site, select M wind speed values ​​by random sampling, and randomly combine them with the M material parameter combinations in the material parameter data set described in step S2 to obtain a sample data set;

[0061] The uniform distribution model is expressed using the following formula:

[0062]

[0063] Among them, W is the wind speed value; a is the lower limit of the wind speed range; b is the upper limit of the wind speed range; f(W) is the probability that the wind speed is W under the uniform distribution model.

[0064] S4. Establish a finite element model of the commutation branch tower;

[0065] The finite element model of the commutation branch tower simultaneously considers the cross-arm forces in different directions and the dynamic effects of the commutation guide wire; within the error requirement range, the commutation guide wire is simplified to a net force and equivalently loaded onto the cross-arms in different directions;

[0066] Specify the required index D and the capacity value T0 of the commutation branch tower structure. The required index D is determined by selecting the maximum value in the 360-degree direction of the displacement at the top of the commutation branch tower, and the capacity value T0 is determined by the tower rotation limit and the actual tower height.

[0067] S5. Perform wind load response analysis based on the sample data set obtained in step S3 and the finite element model of the commutation branch tower obtained in step S4, obtain the required index and the corresponding capacity value, and calculate the mean and standard deviation of the required index corresponding to the wind speed value, as well as the mean and standard deviation of the required index corresponding to the wind speed value and several material parameters, specifically:

[0068] Combine each of the M sample combinations in the sample data set with N wind load time histories, and perform wind load response analysis based on the finite element model of the commutation branch tower obtained in step S4 to calculate the required index D i and the corresponding capacity value T i ; among them, D i is the required index corresponding to the i-th wind load time history; T i is the capacity value corresponding to the i-th wind load time history; the wind load response analysis uses dynamic time history analysis;

[0069] If all K material parameters are considered, calculate the mean μ i and the standard deviation σ N of the N required indices {D1, D2,..., D j ,..., D j} obtained under a single sample combination, where j is the serial number of the corresponding wind speed value, and 0 < j ≤ M; traverse all M wind speed values to obtain M means and standard deviations of the required indices;

[0070] If L material parameters are considered, where L < K, remove the material parameters not considered in each sample in the sample data set, and calculate the mean μ i ' and the standard deviation σ N ' of the N required indices {D1', D2',..., D j ',..., D j', j is the serial number of the corresponding wind speed value, and 0<j≤M; traverse all M wind speed values ​​to obtain the mean and standard deviation of M demand indicators considering some material parameters.

[0071] S6. For a single wind speed value input, based on the intelligent regression model, fit the mean relationship between the wind speed value and the wind speed value obtained in step S5 corresponding to the demand index, as well as the standard deviation relationship between the wind speed value and the wind speed value obtained in step S5 corresponding to the demand index, to obtain a single-input regression model;

[0072] S7. Based on the wind speed value and several material parameter inputs, fitting the mean relationship between the wind speed value and the wind speed value obtained in step S5 and the corresponding demand indicators of the several material parameters, and fitting the standard deviation relationship between the wind speed value and the wind speed value obtained in step S5 and the corresponding demand indicators of the several material parameters, to obtain a multi-input regression model;

[0073] S8. Given several wind speed values, the single-input regression model obtained in step S6 is used to obtain the mean and standard deviation of the corresponding demand index. The exceedance probability based on the demand index capacity value is then calculated based on the mean and standard deviation. A corresponding relationship between wind speed value and exceedance probability is established to obtain the wind disaster vulnerability curve of the reversing branch tower, specifically:

[0074] According to the normal distribution, the exceedance probability P of the corresponding wind speed value based on the demand indicator capacity T0 is calculated, and M exceedance probabilities corresponding to the wind speed value are obtained. The calculation is performed using the following formula:

[0075]

[0076] Where x is the wind speed integral variable, ranging from T0 to positive infinity; P j is the exceedance probability corresponding to the j-th wind speed value;

[0077] According to the wind speed value and the exceedance probability corresponding to the wind speed value, the log-normal distribution is used to fit the change of exceedance probability with wind speed to obtain the wind disaster vulnerability curve of the reversing branch tower, which is expressed using the following formula:

[0078]

[0079] Where P(W) is the exceedance probability when the wind speed value is W; σ is the logarithmic standard deviation of the wind speed W distribution; μ is the logarithmic mean of the wind speed W distribution.

[0080] S9. Given several wind speed values ​​and corresponding multiple material parameters, the multi-input regression model obtained in step S6 is used to obtain the mean and standard deviation of the corresponding demand index. The exceedance probability based on the demand index capacity value is then calculated based on the mean and standard deviation. A corresponding relationship between wind speed values, material parameters, and exceedance probability is established to obtain the wind disaster vulnerability surface of the reversing branch tower, specifically:

[0081] According to the normal distribution, the exceedance probability P of the corresponding wind speed value and some material parameters is calculated, and M exceedance probabilities of the corresponding wind speed value and some material parameters are obtained. The calculation is performed using the following formula:

[0082]

[0083] Where x is the wind speed integral variable, ranging from T0 to positive infinity; P j ' is the exceedance probability corresponding to the j-th wind speed value and some material parameters;

[0084] There is no general fitting function for the overall variation of exceedance probability with wind speed and material parameters, so the wind disaster vulnerability surface of the reversing branch tower is directly used to express it.

[0085] S10. Based on the single-input regression model, multi-input regression model, reversing branch tower wind disaster vulnerability curve, and reversing branch tower wind disaster vulnerability surface obtained in steps S6 to S9, actual reversing branch tower wind disaster vulnerability assessment is performed, specifically comprising the following steps:

[0086] Confirm the number of material parameters l that need to be considered. When l=K, first input the current actual wind speed value into the single-input regression model obtained in step S6 to obtain the mean and standard deviation of the demand index corresponding to the wind speed. Then, combine the wind speed value with the wind disaster vulnerability curve of the reversing branch tower obtained in step S8 to obtain the predicted exceedance probability, and complete the wind disaster vulnerability assessment of the reversing branch tower;

[0087] When l

Claims

1. A method for assessing the vulnerability of a branch tower to wind disasters considering material parameter variation, characterized in that: The following steps are involved: S1. Obtaining material parameters of the branch tower components that cause parameter variation; S2. Randomly sample and combine the material parameters obtained in step S1 to obtain a material parameter data set; S3. Select a wind load time history, randomly select a wind speed value and a sample combination from the material parameter data set obtained in step S2 to obtain a sample data set; S4. Establish a finite element model of the reversing branch tower; S5. Perform wind load response analysis based on the sample data set obtained in step S3 and the finite element model of the commutating branch tower obtained in step S4 to obtain the demand index and the corresponding capacity value, and calculate the mean and standard deviation of the wind speed value corresponding to the demand index, as well as the mean and standard deviation of the wind speed value and several material parameters corresponding to the demand index; S6. For a single wind speed value input, based on the intelligent regression model, fit the mean relationship between the wind speed value and the wind speed value obtained in step S5 corresponding to the demand index, as well as the standard deviation relationship between the wind speed value and the wind speed value obtained in step S5 corresponding to the demand index, to obtain a single-input regression model; S7. Based on the wind speed value and several material parameter inputs, fitting the mean relationship between the wind speed value and the wind speed value obtained in step S5 and the corresponding demand indicators of the several material parameters, and fitting the standard deviation relationship between the wind speed value and the wind speed value obtained in step S5 and the corresponding demand indicators of the several material parameters, to obtain a multi-input regression model; S8. Given several wind speed values, the single-input regression model obtained in step S6 is used to obtain the mean and standard deviation of the corresponding demand index. The exceedance probability based on the demand index capacity value is calculated based on the mean and standard deviation. The corresponding relationship between wind speed value and exceedance probability is established to obtain the wind disaster vulnerability curve of the reversing branch tower; S9. Given several wind speed values ​​and corresponding multiple material parameters, the multi-input regression model obtained in step S6 is used to obtain the mean and standard deviation of the corresponding demand index. The exceedance probability based on the demand index capacity value is then calculated based on the mean and standard deviation. A corresponding relationship between wind speed values, material parameters, and exceedance probability is established to obtain a wind disaster vulnerability surface for the reversing branch tower. S10. Perform actual wind disaster vulnerability assessment of the reversing branch tower based on the single-input regression model, multi-input regression model, reversing branch tower wind disaster vulnerability curve, and reversing branch tower wind disaster vulnerability surface obtained in steps S6 to S9.

2. The wind disaster vulnerability assessment method for a branch tower considering material parameter variation according to claim 1 is characterized in that: In step S1, the materials of the commutation branch tower components include steel structure materials and insulator materials; material parameters of different materials of the same type are identified according to different parameters; The obtained material parameters are parameters of the switching branch tower components that result from parameter variations due to instability in the production process; the number of types of material parameters obtained is K; and the distribution of the material parameters is normal distribution or lognormal distribution; The normal distribution model is expressed using the following formula: Among them, a i is the i-th material parameter; μ ai is the mean value of the i-th material parameter; σ ai is the standard deviation of the i-th material parameter; f(a i ) is the material parameter a under the normal distribution model i probability; The lognormal distribution model is expressed using the following formula:

3. The wind disaster vulnerability assessment method for a reversing branch tower considering material parameter variation according to claim 2 is characterized in that: In step S2, Latin hypercube sampling or random sampling is used to select M material parameter combinations as a material parameter data set based on the distribution of the material parameters; each combination includes K material parameters.

4. The method for wind disaster vulnerability assessment of a branch tower considering material parameter variation according to claim 3 is characterized in that: In step S3, N groups of wind load time histories are selected; the randomness of the wind speed satisfies a uniform distribution at the preset site. M wind speed values are selected by random sampling and randomly combined with M material parameter combinations in the material parameter dataset described in step S2 to obtain a sample dataset. The uniform distribution model is expressed by the following formula: where W is the wind speed value; a is the lower limit of the wind speed range; b is the upper limit of the wind speed range; f(W) is the probability that the wind speed is W under the uniform distribution model.

5. The method for wind disaster vulnerability assessment of a branch tower considering material parameter variation according to claim 4, characterized in that: In step S4, the finite element model of the commutation branch tower simultaneously considers the acting forces on the cross arms in different directions and the dynamic effects of the commutation guide wires; within the error requirement range, the commutation guide wires are simplified to net acting forces and equivalently loaded onto the cross arms in different directions. The required index D and the capacity value T0 of the commutation branch tower structure are specified. The required index D is determined by selecting the maximum value in the 360-degree horizontal direction of the displacement at the top of the commutation branch tower, and the capacity value T0 is determined by the limit value of the whole tower rotation angle and the actual tower height.

6. The method for wind disaster vulnerability assessment of a reversing branch tower considering material parameter variation according to claim 5, characterized in that: In step S5, each of the M sample combinations in the sample data set is combined with the N wind load time histories, and a wind load response analysis is performed based on the finite element model of the reversing branch tower obtained in step S4 to calculate the demand index D i and the corresponding capacity value T i ; Among them, D i is the demand index corresponding to the i-th wind load time history; T i is the capacity value corresponding to the i-th wind load time history; wind load response analysis adopts dynamic time history analysis; If all K material parameters are considered, the N demand indices {D1, D2, ..., D i ,...,D N The mean μ j and standard deviation σ j , j is the serial number of the corresponding wind speed value, and 0<j≤M; traverse all M wind speed values ​​to obtain the mean and standard deviation of M demand indicators; If L material parameters are considered, where L < K, the unconsidered material parameters in each sample of the sample dataset are removed, and the means μ i ' and standard deviations σ N ' of the N demand indicators {D1', D2',..., D j ',..., D j '} obtained under a single sample combination are calculated. j is the serial number of the corresponding wind speed value, and 0 < j ≤ M; all M wind speed values are traversed to obtain the means and standard deviations of the demand indicators considering partial material parameters for M times.

7. The method for wind disaster vulnerability assessment of a branch tower considering material parameter variation according to claim 6, characterized in that: In step S8, according to the normal distribution, the exceedance probability P corresponding to the wind speed value based on the capacity T0 of the required index is calculated, and M exceedance probabilities corresponding to the wind speed values are obtained. The calculation is performed using the following formula: Where x is the wind speed integral variable, ranging from T0 to positive infinity; P j is the exceedance probability corresponding to the j-th wind speed value; According to the wind speed value and the corresponding exceedance probability, the change of the exceedance probability with the wind speed is fitted using the lognormal distribution to obtain the wind disaster vulnerability curve of the commutation branch tower, which is expressed by the following formula: where P(W) is the exceedance probability when the wind speed value is W; σ is the logarithmic standard deviation of the wind speed W distribution; μ is the logarithmic mean of the wind speed W distribution.

8. The method for wind disaster vulnerability assessment of a branch tower considering material parameter variation according to claim 7, characterized in that: According to the normal distribution, the exceedance probability P corresponding to the wind speed value and some material parameters is calculated, and M exceedance probabilities corresponding to the wind speed value and some material parameters are obtained. The calculation is performed using the following formula: Where x is the wind speed integral variable, ranging from T0 to positive infinity; P j ' is the exceedance probability corresponding to the j-th wind speed value and some material parameters; For the overall change law of the exceedance probability with the wind speed and material parameters, there is no general fitting function, and the wind disaster vulnerability surface of the commutation branch tower is directly used for expression.

9. The method for wind disaster vulnerability assessment of a reversing branch tower considering material parameter variation according to claim 8, characterized in that: Step S10 specifically includes the following steps: Confirm the number l of material parameters to be considered. When l = K, first input the current actual wind speed value into the single-input regression model obtained in step S6 to obtain the mean value and standard deviation of the required index corresponding to the wind speed, and then combine it with the wind speed value and input it into the wind disaster vulnerability curve of the commutation branch tower obtained in step S8 to obtain the predicted exceedance probability, and complete the wind disaster vulnerability assessment of the commutation branch tower. When l < K, input the current actual wind speed value and the material parameters to be considered into the multi-input regression model obtained in step S7 to obtain the mean value and standard deviation of the required index corresponding to the wind speed value and some material parameters, and then combine it with the actual wind speed value and input it into the wind disaster vulnerability surface of the commutation branch tower obtained in step S9 to obtain the predicted exceedance probability, and complete the wind disaster vulnerability assessment of the commutation branch tower.

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