Power transmission line risk assessment method and system fusing structure reliability and analytic hierarchy process

Abstract

The invention provides a power transmission line risk assessment method and system integrating structure reliability and analytic hierarchy process, and belongs to the technical field of power system safety and disaster prevention. The overall risk level of the power transmission line section in a specific disaster scene is determined through data preparation and evaluation system construction, standard tower type mechanical property calibration, parameterized load effect calculation, mechanical failure probability calculation, electrical risk index evaluation, heterogeneous index normalization fusion and comprehensive risk index aggregation. According to the method, accurate and efficient component-level reliability evaluation can be carried out on a large-scale line, key shortages in an evaluation system are solved, the evaluation system is more complete and closer to engineering practice, the scientificity and rationality of a final evaluation result are ensured, and the evaluation efficiency is improved. And decision support is provided for differentiated operation and maintenance, precise investment and disaster prevention and reduction of the power grid.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system safety and disaster prevention, and specifically relates to a power transmission line risk assessment method and system fusing structural reliability and analytic hierarchy process. BACKGROUND

[0002] Power transmission lines are exposed to the natural environment all year round and are extremely vulnerable to extreme weather disasters such as strong winds. Strong winds are the main inducement for mechanical damage and electrical failure of the lines.

[0003] Existing power transmission line risk assessment methods usually adopt analytic hierarchy process (AHP) or fuzzy analytic hierarchy process (FAHP) to construct an evaluation model through expert scoring. Although this kind of method has a clear system, it has the following technical bottlenecks: It is difficult to quantize the bottom-level indicators. For key mechanical failure modes such as tower instability and conductor fracture, the existing evaluation system often uses qualitative or semi-quantitative description, and lacks accurate quantization based on probability theory. Although some studies have introduced structural reliability theory, its application usually relies on independent and complex finite element analysis (FEA) for each foundation tower and each working condition, which results in a huge amount of calculation, making the evaluation method not universal and difficult to be widely promoted.

[0004] The electrical risk assessment is not accurate. For electrical failure modes such as insulator windage flashover, it is almost impossible to directly calculate the failure probability in engineering practice, because the parameters involved in the limit state equation need to be accurately obtained through complex high-voltage tests, and the data is seriously missing. This makes the existing evaluation system have a short board in the evaluation of electrical safety.

[0005] It is difficult to fuse heterogeneous indicators. Even if the mechanical failure probability can be calculated, its value is usually very small, while other indicators such as expert scores and design margin ratios are distributed in different intervals. How to scientifically compare and weight these "heterogeneous indicators" with different properties, dimensions and orders of magnitude in a unified FAHP framework is a technical problem that existing methods have not effectively solved.

[0006] Therefore, there is an urgent need for a new power transmission line risk assessment method that can accurately quantify the failure risk of key components, has high calculation efficiency and universality, and can scientifically fuse multiple heterogeneous indicators. SUMMARY

[0007] In order to overcome the shortcomings of the prior art, the application provides a power transmission line risk assessment method and system fusing structural reliability and analytic hierarchy process, which combines the use of refined finite element analysis, windage risk index and logarithmic risk mapping, not only realizes accurate and efficient component-level reliability assessment of large-scale lines, but also fundamentally solves the problem of scientific fusion of heterogeneous indicators.

[0008] To solve the above technical problems, the technical solution adopted by the present application is: A power transmission line risk assessment method combining structural reliability and analytic hierarchy process, comprising: Step 1: data preparation and evaluation system construction, obtaining line design parameters from design drawings, completion data or equipment account, obtaining geographic environment data from geographic information system or environmental monitoring department, setting disaster scenario data based on historical extreme value or weather forecast; Build a four-layer hierarchical evaluation system framework required for subsequent quantitative calculation; Step 2: standard tower type mechanical property calibration, based on the line design parameters of step 1, establish a load-effect influence coefficient database for the tower; Step 3: parameterized load effect calculation, for the specific line section to be evaluated, based on the line design parameters and geographic environment data obtained in step 1, calculate the standard value of the total external load; According to the load-effect influence coefficient of the corresponding tower type in step 2, the standard value of the load effect of the tower leg main material is obtained through algebraic operation; Step 4: Calculate the mechanical failure probability, according to the load-effect influence coefficient database of step 2 and the standard value of the total external load and the standard value of the load effect of the tower leg main material of step 3, establish a function function based on the resistance and load effect standard value for the tower and the conductor; According to the load effect statistical parameters and material resistance statistical parameters of step 3, the structural reliability algorithm is used to calculate the failure probability of each component; Step 5: Electrical risk index evaluation, for the insulator windage flashover risk, based on statics and geometry analysis, calculate the windage risk index; Step 6: Heterogeneous index normalization fusion, according to the minimum failure probability of each component value obtained in step 4, a preset logarithmic risk mapping function is used to nonlinearly map it to a unified, dimensionless risk scale interval; For the risk index obtained in step 5 and other evaluation indexes, a piecewise linear function is used to map them to the same risk scale interval; Step 7: Comprehensive risk index aggregation, under the unified fuzzy analytic hierarchy process framework, all the bottom layer indexes after normalization are weighted and summed, and are aggregated layer by layer upwards according to the four-layer hierarchical evaluation system framework built in step 1, to obtain the comprehensive risk index.

[0009] Further, in step 1: The line design parameters include voltage level, tower geometric size, outer diameter, and rated tensile strength; The geographic environment data includes geographic coordinates of the line path, altitudes of various sections along the line, topographic features, and ground roughness categories.

[0010] Further, in step 2, the process of establishing the load-effect influence coefficient database for the tower includes: for commonly used standard tower types of the power grid, through one-off refined finite element analysis, a unit external load is applied to the tower body, the internal force response of the main material of the tower leg is calculated and extracted, thereby establishing a load-effect influence coefficient database describing the transmission relationship between the external load and the internal load effect of the tower.

[0011] Further, the unit external load is applied to each phase conductor hanging point and the ground wire support vertex of the tower body model, and a unit load is sequentially applied along three orthogonal directions of the global coordinate system, in addition, a distributed load with a total lateral force of a unit load is applied to the entire tower.

[0012] Further, in step 3, the standard value of the total external load includes a wind load standard value acting on the tower and a wind load standard value acting on the conductor and ground wire, the wind load standard value acting on the tower W t The calculation formula is: Wherein, W0 is the basic wind pressure (N / m 2 ), calculated by the following formula: Wherein, v0 is the basic wind speed (m / s); μ z is the wind pressure height variation coefficient, determined according to the ground roughness category and height; μ s is the body shape coefficient of the tower, determined according to the section form of the tower component by table lookup; β z is the gust factor, determined according to the ground roughness category and height by table lookup A s is the calculation wind receiving area (m 2 ) of the tower component; The wind load standard value W c (N) acting on the conductor and ground wire is calculated by the following formula: Wherein, μ sc is the body shape coefficient of the conductor and ground wire; β c is the conductor wind load adjustment coefficient; d is the outer diameter (m) of the conductor and ground wire; L p is the wind speed span (m) of the line; θ is the angle (°) between the wind direction and the axis of the conductor.

[0013] Further, in step 5, the method for calculating the wind deflection risk index is: Calculate the total horizontal load HT and total vertical load V T : where F wc is the wind load acting on the conductor, F wi is the wind load acting on the insulator string, L w is the wind barrier (m), L wt is the gravity barrier (m), w c is the total vertical load per unit length of the conductor, W i is the weight of the insulator string; Calculate the wind deflection angle alpha: Calculate the minimum air gap D min : Geometric calculation according to the tower geometry, the length of the insulator string L ins and the wind deflection angle alpha: Determine the specification-required gap D req : According to the line voltage level, consult the national standard to determine the minimum safe air gap; Calculate the wind deflection risk index: 。

[0014] Further, the other evaluation indexes in step 6 include: the total score THS of the terrain risk factor according to the rule table and the total score MHS of the microclimate risk factor according to the rule table.

[0015] A power line risk assessment system fusing structural reliability and analytic hierarchy process, comprising: a data acquisition and input module for acquiring and structurally storing all relevant information of a line to be evaluated, including line account data, environmental meteorological data and expert knowledge; a database module for storing material parameter library, load parameter library and load-effect influence coefficient library; a calculation engine module, which is embedded with a mechanical reliability calculation unit, an electrical risk index calculation unit and a heterogeneous index fusion and aggregation unit, wherein the mechanical reliability calculation unit calculates the failure probability based on the load effect standard value; the electrical risk index calculation unit is used for quantifying the failure probability of wind deflection flashover; and the heterogeneous index fusion and aggregation unit is used for calculating a single, quantitative comprehensive risk index representing the overall risk level of the line; an output module: generating a diagnostic report, a risk map and a decision support suggestion based on the comprehensive risk index generated by the calculation engine module.

[0016] Compared with the prior art, the beneficial effects of the present application are as follows: 1) High-efficiency mechanical reliability evaluation by "one-time calibration, parameterized application": a one-time refined finite element analysis is performed for the standard tower type to establish a "load-effect influence coefficient database"; in subsequent evaluation, algebraic operation is performed by calling the database to replace repetitive modeling, thereby realizing accurate and efficient component-level reliability evaluation of large-scale lines; 2) Quantification of electrical risk by constructing a deterministic "windage swing risk index (WSRI)": to solve the engineering problem that windage flashover probability is difficult to calculate directly, the invention creatively uses a calculable deterministic index WSRI based on physical mechanism and engineering specifications as a proxy index of electrical risk, which solves the key short board in the evaluation system and makes the evaluation system more complete and closer to engineering practice; 3) Scientific integration of heterogeneous indicators by "logarithmic risk mapping": to solve the fundamental problem that the failure probability with a very small numerical value is difficult to compare and weight with other risk indicators in the FAHP framework, the invention proposes a logarithmic mapping function, which preserves the order of magnitude difference while unifying all indicators to the same risk scale interval, fundamentally solving the problem of scientific integration of heterogeneous indicators and ensuring the scientificity and rationality of the final evaluation result; 4) System and automation of the evaluation system: complex evaluation theory is transformed into clear and automated system workflow, and the evaluation result not only gives the overall risk level, but also clearly traces back to the specific risk item, providing decision support for differentiated operation, precise investment and disaster prevention and mitigation of power grids. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The overall flowchart of the risk evaluation method provided by the embodiment one of the invention is shown in the figure; Figure 2 The schematic diagram of the finite element model of the standard tower type provided by the embodiment one of the invention is shown in the figure; Figure 3 The schematic diagram of the pre-computation calibration step of the "load-effect influence coefficient" provided by the embodiment one of the invention is shown in the figure; Figure 4 The calculation flowchart of the windage swing risk index (WSRI) provided by the embodiment one of the invention is shown in the figure; Figure 5 The functional architecture block diagram of the risk evaluation system provided by the embodiment two of the invention is shown in the figure. DETAILED DESCRIPTION

[0018] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work belong to the protection scope of the present application.

[0019] Embodiment one Referring to Figures 1-4 The embodiment provides a power transmission line risk assessment method fusing structural reliability and analytic hierarchy process, and specifically comprises the following steps. I. Data preparation and evaluation system construction, obtain the line design parameters from the design drawings, completion data or equipment account book, obtain the geographic environment data from the geographic information system or environmental monitoring department, set the disaster scenario data based on historical extreme value or weather forecast; Build a four-layer hierarchical evaluation system framework required for subsequent quantitative calculation.

[0020] This step is the starting point of the evaluation work, and the purpose is to provide all the necessary input information and structured framework for subsequent quantitative calculation.

[0021] Firstly, all the related data information of the line to be evaluated is obtained and structuredly stored.

[0022] These information is divided into three categories: Line design parameters: this is the inherent physical property of the line, which is obtained from design drawings, completion data or equipment account book; specifically as shown in Table 1.

[0023] Geographic environment data: this is the static environmental characteristics of the location where the line is located, which can be obtained from the geographic information system (GIS) and the environmental monitoring department. Specifically, it includes the geographic coordinates of the line path, the elevation (m) of each section along the line, the topographic features, and the ground roughness category.

[0024] Disaster scenario data: this is the specific disaster condition for evaluation, which can be set based on historical extreme value or weather forecast. For example, define "gale disaster scenario" as: design return period 50 years, 10-minute average wind speed 35 m / s.

[0025] Table 1 Line design parameter table Then, a unified four-layer hierarchical evaluation index system is constructed in the system, which is used as the logical framework for subsequent calculation and aggregation. The structure and function of the system are shown in Table 2.

[0026] Table 2 Evaluation system of gale disaster power transmission line II. Standard tower type mechanical property calibration, based on the line design parameters in step I, to establish a load-effect influence coefficient database for the tower.

[0027] This step is the core of the universality of the present application, which is a one-time, offline research and development work, and its purpose is to establish a load-effect influence coefficient database for the tower, so as to completely get rid of the dependence on finite element analysis in subsequent evaluation.

[0028] 1. Selection and modeling of standard tower type: Select a standard tower type widely used in the power grid as the calibration object, and take the ZBV302-39 type straight tower commonly used in 550 kV transmission lines as an example. The data of this tower are also recorded in the line design parameters. A high-precision three-dimensional finite element model is established by using commercial finite element analysis software (such as ANSYS, ABAQUS, etc.), as shown in Figure 2 During modeling, in order to accurately simulate the mechanical behavior of the tower, the most suitable element type is used for different components: for the main material bearing compression-bending combined effect, three-dimensional beam element is used; for the inclined material and auxiliary material mainly bearing axial force, three-dimensional rod element can be used to improve efficiency. According to the design drawing, the model is given accurate material properties and cross-sectional geometric parameters, and fixed constraints are applied to the tower leg to simulate the actual support conditions.

[0029] 2. Unit load application: as shown in Figure 3 A unit load is applied to each phase conductor hanging point and ground wire support vertex of the model along the three orthogonal directions of the global coordinate system in turn, in addition, a distributed load with a total force of a unit load is applied to the lateral of the entire tower. The application of each unit load constitutes an independent analysis case.

[0030] 3. Influence coefficient calculation and extraction: for each unit load case described above, run linear static analysis to calculate the internal force response of the tower leg main material under each unit load case, which is used to calculate the load-effect influence coefficient. The load-effect influence coefficient is defined as: Wherein, S response is the internal force response value of the component, F unit is the unit load value applied.

[0031] 4. Database establishment: all the calculated influence coefficients are stored in a structured manner according to the index mode of "tower code-load action point-load direction-component number-internal force type", forming the load-effect influence coefficient database of the standard tower. This database completely and digitally encapsulates the linear mechanical transmission characteristics of the tower.

[0032] Three, parameterized load effect calculation: based on the line design parameters and geographical environment data obtained in step one, the standard value of the total external load is calculated for the specific line section to be evaluated; According to the load-effect influence coefficient of the corresponding tower type in step two, the standard value of the load effect of the tower leg main material is obtained through algebraic operation.

[0033] This step calculates the load effect under different working conditions for the specific tower to be evaluated without the need for finite element analysis.

[0034] 1. External load standard value calculation: according to the specific line parameters input in the first step, the total external load standard value acting on the tower is calculated according to the formula in the national standard "Overhead Transmission Line Load Specification" (GB50545-2010 or DL / T5551-2018).

[0035] Standard wind load acting on the tower Value W t The calculation formula is: Where, W 0 is the basic wind pressure (N / m 2 ), which is calculated by the following formula: Where, v 0 is the basic wind speed (m / s); μ z is the wind pressure height variation coefficient, which is determined according to the ground roughness category and height by table lookup; μ s is the body shape coefficient of the tower, which is determined according to the cross-sectional form of the tower component by table lookup; β z is the gust factor, which is determined according to the ground roughness category and height by table lookup A s is the calculated wind area of the tower component (m 2 ).

[0036] Standard wind load acting on the ground wire W c The calculation formula is: Where,μ sc Body coefficient of the ground wire; β c Adjustment coefficient of wind load of the ground wire; d Outside diameter of the ground wire (m); L p Wind speed span of the line (m); θ Angle between the wind direction and the axis of the ground wire (°).

[0037] 2. Component calculation of the standard value of external load: the total standard value of external load acting on the tower is calculated in the previous step, wherein the standard value of wind load acting on the ground wire needs to be decomposed into horizontal, vertical and longitudinal components acting on the tower-ground wire hanging point.

[0038] The horizontal and longitudinal components only come from the wind load, and it is considered that each tower bears half of the wind load of the ground wire within the sum of the lengths of two adjacent spans. Then, the wind direction angle is used for vector decomposition. The vertical component only comes from the self-weight of the ground wire, i.e. the vertical component is equal to the self-weight of the ground wire.

[0039] 3. Calculation of the standard value of load effect: the database established in the second step is called, and the standard value of load effect of the main material of the tower leg is calculated through algebraic multiplication. S k : wherein, W k,i is the standard value of the nth external load, i C i is the corresponding influence coefficient of the load on the target component obtained from the database.

[0040] Four, calculation of mechanical failure probability: based on the load-effect influence coefficient database in step two, the standard value of the total external load and the standard value of the load effect of the main material of the tower leg in step three, a function function based on the resistance and the standard value of the load effect is established for the tower and the ground wire; According to the statistical parameters of the load effect in step three and the statistical parameters of the material resistance, the failure probability of each component is calculated by using the structural reliability algorithm.

[0041] This step is based on the results of the third step to perform probability analysis.

[0042] 1. Establish the limit state equation: Tower compression and bending component: wherein, f y is the yield strength of steel; N and M ​The calculation method for the axial force and bending moment of the main tower leg members is included in "a" below. N k , M k In the calculation of ) steps; ϕ This is the axial compression stability coefficient; A The cross-sectional area of ​​the component. W x It is the net section modulus.

[0043] Wire breakage: in, T max The rated tensile strength of the conductor; T load This represents the maximum working tension of the conductor.

[0044] 2. Probability modeling of random variables: a) Standard values ​​of internal forces in the main material of the tower leg ( N k , M k The calculation of the required internal forces (axial force N and bending moment M) is performed by calling the influence coefficient database established in the second step. in, D k The standard value of the internal force representing the main material of the tower leg (i.e. N k or M k ); W k,i It is calculated based on the third step and acts on the first. i The standard value of the external load at each hanging point is: Wt and W c Component force; C i This is the influence coefficient corresponding to the external load, retrieved from the database. Through this algebraic calculation, the standard values ​​of the internal forces of all tower leg main materials can be obtained.

[0045] b) Standard value of maximum working tension ( T load,k Calculation of the required maximum working tension of the conductor: T load,k Instead of using the influence coefficient method, this method precisely calculates the load per unit length of conductor by solving the classic catenary state equations used in power transmission engineering. First, the vertical load (self-weight) on the conductor is calculated. w selfand horizontal load (wind load) w wind and vector synthesis, the total load per unit length w total , under the condition of known span, height difference and w total , the maximum working tension of the conductor under this working condition is calculated by iterative solution of the catenary equation, which is T load,k .

[0046] c) f y and T max The standard value is determined: f y_k The minimum yield strength standard value of the tower steel can be determined by consulting the product standard or manufacturer's guarantee value of the rated tensile strength. T max_k The minimum yield strength standard value of the tower steel can be determined by consulting the product standard or manufacturer's guarantee value of the rated tensile strength.

[0047] d) The probability model parameters of the key random variables: give all the basic variables in the above function function a probability model. The distribution type, mean coefficient and variation coefficient are determined based on existing research literature and stored in the database module of the system as configurable parameters. As shown in Table 3.

[0048] Table 3 Statistical parameters of load and resistance random variables N k , M k 、T load,k composed of permanent load effect and variable load effect, wherein N k , M k The permanent load standard value of step two is determined by the finite element modeling in step two, under the condition of only applying all permanent loads without variable loads, the permanent load standard value of the tower main material under the condition of only applying all permanent loads without variable loads is determined N G and M G . T load,k The permanent load standard value of step two is determined by the finite element modeling in step two, under the condition of only applying all permanent loads without variable loads, the permanent load standard value of the tower main material under the condition of only applying all permanent loads without variable loads is determined N k , M k 、Tload,k ) minus the permanent load standard value.

[0049] Combining the individual resistance standard values R k and the load effect standard value S k , the mean value thereof can be calculated μ S and the standard deviation σ S : Through the above steps, all variables are defined as random variables with complete probability characteristics.

[0050] 3. Calculate the failure probability: after obtaining the probability model of each basic random variable, the structural reliability algorithm known in the art, such as the first-order second-moment method (FORM), also known as the JC method, is used to calculate the failure probability P f . The calculation process includes transforming non-normal random variables to standard normal space, searching for the most likely failure point through an iterative algorithm such as the HLRF algorithm, and finally calculating the reliability index β and the failure probability P f = Φ(- β ) and other standard steps. The innovation of the present application does not lie in the algorithm itself, but in providing the load effect statistical parameters obtained through the aforementioned steps as input for the algorithm.

[0051] Five, electrical risk index evaluation, for the windage flashover risk of insulators, based on statics and geometry analysis, the windage risk index is calculated.

[0052] This step is performed by the electrical risk index calculation unit, which quantifies the failure probability of windage flashover.

[0053] 1. Calculate the windage risk index (WSRI): as shown in Figure 4 , the calculation is based on deterministic mechanical and geometric analysis.

[0054] a) Calculate the wind load: according to the disaster wind speed v and the line parameters input in the first step, the wind load acting on the conductor and the insulator string is calculated according to the national standard GB50545-2010 F wc and F wi .

[0055] b) Calculate total horizontal and vertical loads: Perform static analysis to obtain the total horizontal load acting on the suspension point of the insulator string H T and total vertical load V T .

[0056] where, L w is the wind barrier (m), L wt is the gravity barrier (m), w c is the total vertical load per unit length of conductor, W i is the weight of the insulator string.

[0057] c) Calculate the wind deflection angle a: d) Calculate the minimum air gap D min : Geometric calculation according to the tower geometry, insulator string length L ins and wind deflection angle a: e) Determine the regulatory required gap D req : According to the line voltage level, consult GB50545-2010 to determine the minimum safe air gap.

[0058] f) Calculate the WSRI: Six, heterogeneous index normalization fusion, according to the failure probability of each component value obtained in step four, a preset logarithmic risk mapping function is used to nonlinearly map it to a unified, dimensionless risk scale interval; For the risk index obtained in step five and other evaluation indexes, a piecewise linear function is used to map them to the same risk scale interval.

[0059] 1. Line crossing complex terrain This index is used to quantify the additional risk brought about by the particularity of the terrain where the line is located (such as wind gap, ridge), which is usually difficult to reflect through conventional macro meteorological data. Score the terrain risk factor, upgrade the qualitative "expert scoring" to a set of structured, semi-quantitative scoring criteria to improve the objectivity and repeatability of the evaluation. The scoring rules table is shown in Table 4.

[0060] Table 4 Topographic Risk Factor Scoring Table The evaluation experts or system score the tower location based on the GIS information of the route, refer to the rule table, and sum the scores to obtain the total terrain risk factor score (THS).

[0061] 2. Micrometeorological environment This indicator aims to capture severe environmental risks within local areas of the railway corridor that differ significantly from regional macro-meteorological data due to terrain, landforms, or special meteorological phenomena. Similar to terrain risk assessment, a structured scoring rule table is used, as shown in Table 5.

[0062] Table 5 Micrometeorological Risk Factor Scoring Table The assessors, based on historical disaster data and on-site investigation experience, scored and summed the results according to the rule table to obtain the total score of micrometeorological risk factor, MHS.

[0063] VII. Aggregation of Comprehensive Risk Index: Under the unified framework of fuzzy hierarchical analysis, all normalized underlying indicators are weighted and summed, and aggregated layer by layer according to the four-level hierarchical evaluation system framework built in step one to obtain the comprehensive risk index.

[0064] This step aims to calculate a single, quantitative comprehensive risk index that represents the overall risk level of the route by systematically weighting and aggregating all the normalized underlying indicator values ​​obtained in the previous steps that are within a unified risk scaling range.

[0065] 1. Determining the weight vector (application of FAHP) The purpose of this step is to determine the relative importance weight of each element in the four-level hierarchical evaluation index system constructed in the first step, relative to the upper-level objectives.

[0066] a) Constructing a fuzzy judgment matrix: For each layer in the evaluation system except the target layer, multiple experts in the field are invited to conduct pairwise importance comparisons of all evaluation elements within that layer. Triangular fuzzy numbers (...) are used... l, m, u To characterize their relative importance, we need to address the ambiguity of expert judgment. Among these, l The minimum possible value as determined by experts; m The representative expert believes this is the most likely value; u This represents the maximum possible value of the expert's judgment. The expert's linguistic variable judgment is converted into the corresponding triangular fuzzy number mapping rule, as shown in Table 6.

[0067] Table 6. Triangular Fuzzy Number Mapping Rules To synthesize the opinions of all experts, their proposed TFNs need to be aggregated. If there are K experts, their opinions on the indicator... i and j The comparison results are as follows: ,in The aggregated TFN The fuzzy geometric mean method is usually used for calculation. Fill all the aggregated TFNs into an n×n matrix. In this context, the fuzzy judgment matrix constitutes the level.

[0068] b) Calculate and defuzzify the weights: Using the fuzzy extended analysis method, calculate the weights for each fuzzy judgment matrix. First, calculate the fuzzy composite value. For an n-order fuzzy judgment matrix... The first in i The nth object, i.e., the nth i Each evaluation indicator, its fuzzy composite value Defined as: in, It is a triangular fuzzy number element in the matrix.

[0069] After obtaining the fuzzy composite value, the probability degree of the fuzzy value is then calculated. For two fuzzy composite values... and , probability In triangular fuzzy numbers, the calculation formula is: This value represents the fuzzy number. Greater than or equal to fuzzy number The degree of credibility is determined by defining the probability as the weight value of the indicator: This results in a non-normalized weight vector. W′ = (w 1 , w 2 , ..., w n ) T Finally, the vector is normalized to obtain the final accurate weight vector.

[0070] c) Formation of the weight database: The calculated weight vectors of all levels are stored in a structured manner to form the weight database of the evaluation system.

[0071] 2. Normalization of heterogeneous indices Before the aggregation calculation, the evaluation results of all underlying indicators must be normalized to fall into the uniform, dimensionless risk scale [0, 1] interval, and the higher the score represents the higher the risk.

[0072] a) Normalization of failure probability: In risk aggregation calculation, a key technical challenge is how to fuse heterogeneous indicators with completely different natures, for example, the failure probability with a very small numerical value, such as 10 -6 , and the risk index with a value range between 0 and 1. If a simple linear normalization method is used, the failure probability will be compressed from 10 -6 to 10 -5 (the risk increases tenfold) into a negligible small change in the normalized interval, which will lead to the risk of low-probability, high-consequence events being severely underestimated in the final aggregation result. The logarithmic risk mapping function proposed in this invention is designed to solve this fundamental problem. By converting the probability value to its logarithmic scale, the function can effectively maintain the order-of-magnitude difference between different probability values, ensuring that significant risk changes in failure probability indicators are properly reflected in the final comprehensive risk index, making the aggregation result more scientific and reasonable. The logarithmic risk mapping function is as follows: is the target failure probability defined by the standard. According to the "Unified Standard for Reliability Design of Building Structures" (GB50068-2018), for structures with a safety level of one, the target reliability index should not be less than 3.7, corresponding to a target failure probability . An acceptable or extremely low risk failure probability lower limit is set to 10 -7 .

[0073] b) Normalization of risk index (WSRI): A piecewise linear function is used for mapping: c) Normalization of other indicators: Linear proportional mapping is used for both terrain risk factor and microclimate risk factor normalization: 3. Bottom-up aggregation calculation of comprehensive risk index Using the fuzzy comprehensive evaluation model of weighted summation, the normalized indicator values obtained in step 7 are calculated layer by layer from the indicator layer (3rd layer) upwards using the weights determined in step 7.

[0074] a) Calculate the risk score of the 2nd layer (subsystem layer): For any subsystemSC j its risk score S SCj the normalized risk values of all the indicator layers under it V Ii the weights corresponding to it w Ii The weighted sum is: b) Calculate the risk score of the first layer (system layer): For any one system C k its risk score S Ck the risk scores of all the subsystem layers under it S SCj the weights corresponding to it w SCj The weighted sum is: c) Calculate the final comprehensive risk index of the 0th layer (target layer): the final comprehensive risk index G the risk scores of all the system layers under it S Ck the weights corresponding to it w Ck The weighted sum is: This comprehensive risk index G is a single quantitative value between 0 and 1, which determines the overall risk level of the transmission line section under a specific disaster scenario, as shown in the following table.

[0075] Table 7 Risk classification Example two Referring to Figure 5 On the basis of the above technical solutions, the application further provides a power line risk assessment system integrating structural reliability and analytic hierarchy process, which can be implemented by software and / or hardware and specifically configured in an electronic device, comprising: a data acquisition and input module for acquiring and structuring all relevant information of the line to be evaluated, including line account data, environmental and meteorological data, and expert knowledge; a database module for storing material parameter library, load parameter library, and load-effect influence coefficient library; a calculation engine module embedded with a mechanical reliability calculation unit, an electrical risk index calculation unit, and a heterogeneous index fusion and aggregation unit.

[0076] The mechanical reliability calculation unit calculates the failure probability based on the load effect standard value; the electrical risk index calculation unit is used for quantifying the failure probability of windage flashover; and the heterogeneous index fusion and aggregation unit is used for calculating a single, quantitative comprehensive risk index representing the overall risk level of the line by systematically weighting and aggregating all the normalized bottom-layer index values in the uniform risk scale interval obtained in the previous steps.

[0077] The output module generates a diagnostic report, a risk map and a decision support suggestion based on the comprehensive risk index generated by the calculation engine module.

[0078] Finally, it should be pointed out that the above embodiments are only preferred embodiments of the present application, and are only used to illustrate the technical solutions of the present application but not to limit the present application. Any modification, improvement or other modification or equivalent replacement of the technical solutions of the present application made by those skilled in the art, as long as it does not deviate from the spirit and scope of the technical solutions of the present application, shall be included in the protection scope of the present application.

Claims

1. A method for risk assessment of power transmission lines integrating structural reliability and hierarchical analysis, characterized in that, include: Step 1: Data preparation and evaluation system construction. Obtain line design parameters from design drawings, as-built documents or equipment ledgers, obtain geographic environment data from geographic information systems or environmental monitoring departments, and set disaster scenario data based on historical extreme values ​​or weather forecasts. Establish a four-tiered evaluation framework required for subsequent quantitative calculations; Step 2: Standard tower mechanical property calibration, and establish a load-effect influence coefficient database for towers based on the line design parameters in Step 1; Step 3: Parametric load effect calculation. For the specific line segment to be evaluated, calculate the standard value of the total external load based on the line design parameters and geographical environment data obtained in Step 1. Based on the load-effect influence coefficient of the corresponding tower type in step 2, the standard value of the load effect of the main material of the tower leg is obtained through algebraic calculation. Step 4: Calculate the probability of mechanical failure. Based on the load-effect influence coefficient database from Step 2 and the standard value of the total external load and the standard value of the load effect of the tower leg main material from Step 3, establish a functional function for the tower and conductor based on the standard values ​​of resistance and load effect. Based on the statistical parameters of load effects and material resistance obtained in step 3, the failure probability of each component is calculated using a structural reliability algorithm. Step 5: Electrical risk indexation assessment. For the risk of insulator wind deflection flashover, calculate the wind deflection risk index based on static and geometric analysis. Step 6: Heterogeneous index normalization and fusion. Based on the extremely small failure probability of each component obtained in Step 4, a preset logarithmic risk mapping function is used to nonlinearly map them to a unified, dimensionless risk scaling interval. For the risk index and other assessment indicators obtained in step 5, a piecewise linear function is used to map them to the same risk scaling interval; Step 7: Aggregate the comprehensive risk index. Under the unified framework of fuzzy hierarchical analysis, perform weighted summation on all normalized bottom-level indicators, and aggregate them layer by layer upward according to the four-level hierarchical evaluation system framework built in Step 1 to obtain the comprehensive risk index.

2. The transmission line risk assessment method integrating structural reliability and hierarchical analysis according to claim 1, characterized in that, In step 1: The line design parameters include voltage level, tower geometry, outer diameter, and rated tensile strength; The geographic environment data includes the geographic coordinates of the route, the altitude of each section along the route, topographic features, and ground roughness categories.

3. The transmission line risk assessment method integrating structural reliability and hierarchical analysis according to claim 1, characterized in that, Step 2 involves establishing a load-effect influence coefficient database for towers, which includes: applying a unit external load to the tower body through a one-time refined finite element analysis for commonly used standard tower types in power grids, calculating and extracting the internal force response of the main material of the tower legs, thereby establishing a load-effect influence coefficient database that describes the relationship between the external load and the internal load effect transmission of the tower type.

4. The transmission line risk assessment method integrating structural reliability and hierarchical analysis according to claim 3, characterized in that: The unit external load is applied to each phase conductor suspension point and ground wire support vertex of the tower model, and a unit load is applied sequentially along the three orthogonal directions of the global coordinate system. In addition, a distributed load with a total force of one unit load is applied to the lateral side of the entire tower.

5. The transmission line risk assessment method integrating structural reliability and hierarchical analysis according to claim 1, characterized in that, In step 3, the standard value of the total external load includes the standard value of the wind load acting on the tower and the standard value of the wind load acting on the conductor and ground wire. The standard value of the wind load acting on the tower, W t The formula for calculating (N) is: Where W0 is the basic wind pressure (N / m³). 2 ), calculated by the following formula: Where v0 is the basic wind speed (m / s); μ z The wind pressure height variation coefficient is determined by referring to a table based on the ground roughness category and height; μ s β is the shape factor of the tower, determined by referring to a table based on the cross-sectional shape of the tower components; z The gust coefficient, A, is determined by referring to a table based on the ground roughness category and height. s Calculated wind-receiving area (m²) for tower components 2 ); Standard value of wind load W acting on the conductor c The formula for calculating (N) is: Where, μ sc β is the shape factor of the conductor / ground wire; c d is the wind load adjustment factor for the conductor; d is the outer diameter of the conductor (m); L p θ represents the wind speed span of the line (m); θ is the angle between the wind direction and the conductor axis (°).

6. The transmission line risk assessment method integrating structural reliability and hierarchical analysis according to claim 1, characterized in that, In step 5, the method for calculating the risk index is as follows: Calculate the total horizontal load H acting on the suspension point of the insulator string. T and total vertical load V T : Among them, F wc For the wind load acting on the conductor, F wi For the wind load acting on the insulator string, L w For windshield (m), L wt For gravity setting (m), w c W is the total vertical load per unit length of the conductor. i This refers to the weight of the insulator string. Calculate the wind deflection angle α: Calculate the minimum air gap D min Based on the tower geometry and insulator string length L ins Geometric calculation of the wind deflection angle α: Determine the required clearance D according to specifications req Determine the minimum safe air gap based on the line voltage level and consult national standards. Calculate the risk index: 。 7. The transmission line risk assessment method integrating structural reliability and hierarchical analysis according to claim 1, characterized in that, Other assessment metrics in step 6 include: the total topographic risk factor score (THS) calculated by scoring and summing according to the rule table, and the total micrometeorological risk factor score (MHS) calculated and summing according to the rule table.

8. A transmission line risk assessment system integrating structural reliability and hierarchical analysis, used to implement the transmission line risk assessment method integrating structural reliability and hierarchical analysis as described in any one of claims 1-7, characterized in that, include: The data acquisition and input module is used to acquire and structure all relevant information about the route to be evaluated, including route ledger data, environmental and meteorological data, and expert knowledge. The database module is used to store material parameter libraries, load parameter libraries, and load-effect influence coefficient libraries; The calculation engine module embeds a mechanical reliability calculation unit, an electrical risk index calculation unit, and a heterogeneous index fusion and aggregation unit. The mechanical reliability calculation unit calculates the failure probability based on the standard value of load effect. The electrical risk index calculation unit is used to quantify the failure probability of wind-induced flashover; the heterogeneous index fusion and aggregation unit is used to calculate a single, quantitative comprehensive risk index that characterizes the overall risk level of the line. Output module: Based on the comprehensive risk index generated by the computing engine module, it generates diagnostic reports, risk maps, and decision support suggestions.

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