Power distribution line failure probability calculation method and apparatus, terminal device, and storage medium

Abstract

Disclosed in the present invention are a power distribution line failure probability calculation method and apparatus, a terminal device, and a storage medium. The method comprises: calculating a foundation failure probability, a conductor breakage probability, and a concrete cracking probability by means of physical models of time-dependent degradation of a utility pole, said models comprising models of foundation loosening, conductor tension variations, and concrete stress variations; further calculating a comprehensive failure probability of the utility pole; and finally calculating a power distribution line failure probability on the basis of the comprehensive failure probabilities of all utility poles. The present invention truly reflects changes in the performance of the utility poles over time, and considers the impact of performance degradation of the foundation, conductor and concrete components on the failure probability, thereby improving the accuracy of failure probability evaluation of power system components.

Description

Methods, devices, terminal equipment and storage media for calculating the probability of power line failure Technical Field

[0001] This invention relates to the field of power distribution network risk assessment technology, and in particular to a method, apparatus, terminal equipment and storage medium for calculating the failure probability of power distribution lines. Background Technology

[0002] Currently, the static vulnerability curve method is mainly used for typhoon risk analysis of power distribution lines. This method constructs vulnerability curves for relevant components of the distribution line based on historical data, assesses the failure probability of components at different wind speeds, and then further analyzes the failure probability of the entire distribution line based on the failure probability of the components. However, because the static vulnerability curve method cannot reflect the performance degradation of power system components such as foundations, conductors, and concrete parts over time, the component failure probabilities assessed by this method are inaccurate, leading to inaccurate results in the distribution line failure probability analysis. Summary of the Invention

[0003] This invention provides a method, apparatus, terminal equipment, and storage medium for calculating the failure probability of power distribution lines, in order to solve the technical problem of inaccurate failure probability analysis in existing technologies.

[0004] To address the aforementioned technical problems, embodiments of the present invention provide a method for calculating the failure probability of power distribution lines, including:

[0005] Obtain the foundation service life, conductor service life, concrete service life, and failure probability caused by wind load for each pole;

[0006] For each utility pole, based on the foundation's service life and the established foundation loosening model, the foundation stiffness variation curve of the pole over time is calculated within the foundation's service life. Based on the foundation stiffness variation curve, the foundation failure probability of the pole is calculated. Based on the conductor's service life and the established conductor tension increase model, the conductor tension variation curve of the pole over time is calculated. Based on the conductor tension variation curve, the conductor breakage probability of the pole is calculated. Based on the concrete's service life and the established concrete stress model, the concrete stress variation curve of the pole over time is calculated. Based on the concrete stress variation curve, the concrete cracking probability of the pole is calculated. Based on the foundation failure probability, conductor breakage probability, concrete cracking probability, and failure probability caused by wind load, the comprehensive failure probability of the pole is calculated. The foundation loosening model characterizes the change in foundation stiffness over time; the conductor tension increase model characterizes the change in conductor tension over time; and the concrete stress model characterizes the change in concrete stress over time.

[0007] Calculate the failure probability of the power distribution line based on the combined failure probability of all poles.

[0008] As a preferred embodiment, the construction process of the basic loosening model includes:

[0009] The initial stiffness of the pole's foundation, the rate of foundation loosening, the index of foundation loosening process, the seasonal fluctuation amplitude of foundation stiffness, and the seasonal cycle are obtained; wherein, the index of inductive foundation loosening process is used to characterize the nonlinear characteristics of the foundation loosening process.

[0010] Based on the initial stiffness of the foundation, the loosening rate of the foundation, the loosening process index of the foundation, and the seasonal fluctuation amplitude and period of the foundation stiffness, a foundation loosening model for the pole is constructed.

[0011] The step of calculating the foundation failure probability of the pole based on the foundation stiffness variation curve includes:

[0012] Obtain the critical stiffness of the pole's foundation;

[0013] Based on the foundation stiffness variation curve and the foundation critical stiffness, calculate the first failure probability that the pole's foundation stiffness does not exceed the foundation critical stiffness during the foundation's service life.

[0014] The base failure probability is calculated based on the first failure probability.

[0015] The formula for the basic loosening model is as follows:

[0016] In the formula, K f (t) represents the foundation stiffness; K(0) represents the initial stiffness of the foundation; μ represents the foundation loosening rate; β represents the foundation loosening process exponent; The value represents the seasonal fluctuation amplitude of the foundation stiffness; T represents the seasonal period; t represents the time.

[0017] The formula for calculating the basic failure probability is:

[0018] In the formula, P f,base K represents the basic failure probability; f,critical P(K) represents the critical stiffness of the foundation. f (t)≤K f,critical This represents the first failure probability that the foundation stiffness does not exceed the critical stiffness of the foundation during its service life. represents the soil hardening coefficient; S represents the site category parameter.

[0019] As a preferred embodiment, the construction process of the conductor tension increase model includes:

[0020] The initial tension of the conductor, the maximum tension increment, the rate of increase of the conductor tension, the coefficient of periodic change of the conductor tension, the frequency of periodic change of the conductor tension, and the long-term drift coefficient of the conductor tension are obtained for the pole; wherein, the long-term drift coefficient of the conductor tension is used to characterize the trend of slow increase of the conductor tension over a long period of time.

[0021] Based on the initial tension of the conductor, the maximum tension increment of the conductor, the rate of increase of the conductor tension, the coefficient of the periodic change of the conductor tension, the frequency of the periodic change of the conductor tension, and the long-term drift coefficient of the conductor tension, a model for the increase of conductor tension on the pole is constructed.

[0022] Based on the conductor tension variation curve, the probability of conductor breakage on the pole is calculated, including:

[0023] Obtain the critical tension of the conductor on the pole;

[0024] Calculate the probability of conductor breakage based on the conductor tension variation curve and the conductor critical tension;

[0025] The formula for the conductor tension increase model is: T(t)=T(0)+ΔT·(1-e -vt )·[1+γ·sin(ωt)+κ·ln(t+1)];

[0026] In the formula, T(t) represents the conductor tension; T(0) represents the initial conductor tension; ΔT represents the maximum conductor tension increment; v represents the conductor tension increase rate; γ represents the conductor tension periodic change amplitude coefficient; ω represents the conductor tension periodic change frequency; κ represents the conductor tension long-term drift coefficient; and t represents time.

[0027] The formula for calculating the probability of conductor breakage is: P f,wire =P(T(t)>T critical );

[0028] In the formula, P f,wire T represents the probability of wire breakage. critical This indicates the critical tension of the conductor.

[0029] As a preferred embodiment, the process of constructing the concrete stress model includes:

[0030] The inner diameter and outer diameter of the pole are obtained at each moment during the service time of the concrete; the wind-receiving area, surface wind speed, and pole height of the pole are obtained.

[0031] Based on the inner and outer diameters of the pole at each moment during the concrete service life, the inner diameter function and outer diameter function of the pole are determined through mathematical fitting; wherein, the inner diameter function is used to characterize the change of the inner diameter of the pole over time; and the outer diameter function is used to characterize the change of the outer diameter of the pole over time.

[0032] The wind load on the pole is calculated based on the wind-receiving area, surface wind speed, and failure probability caused by wind load.

[0033] Based on the wind load and pole height, calculate the wind load bending moment at the conductor of the pole;

[0034] The concrete stress model is constructed based on the wind load bending moment at the conductor, the inner diameter function of the pole, and the outer diameter function of the pole.

[0035] The step of calculating the probability of concrete cracking of the pole based on the concrete stress variation curve includes:

[0036] Obtain the concrete cross-sectional width, concrete cross-sectional height, concrete compressive strength, and concrete design service life of the pole;

[0037] Calculate the wind load bending moment at the concrete section of the pole based on the concrete section width, concrete section height, concrete compressive strength, concrete design service life, and concrete service life.

[0038] Calculate the critical stress of the concrete of the pole based on the wind load bending moment at the concrete.

[0039] Based on the concrete stress variation curve and the concrete critical stress, calculate the second probability that the concrete stress of the pole exceeds the concrete critical stress during the concrete service life.

[0040] Calculate the concrete cracking probability based on the second probability;

[0041] The formula for calculating the wind load is as follows:

[0042] In the formula, F wind Indicates wind load; P wind Indicates the probability of failure due to wind load; A represents the wind-receiving area; V represents the surface wind speed; C d R represents the drag coefficient; e Represents the Reynolds number; n represents the Reynolds number exponent; F tree This represents the failure probability caused by the additional force generated by the falling tree; η represents the wind speed amplification factor; V0 represents the reference wind speed.

[0043] The formula for calculating the wind load bending moment at the conductor is: Mwind =F wind ·H / 2+M vortex +M gust ;

[0044] In the formula, M wind H represents the bending moment of the wind load at the conductor; M represents the height of the pole; vortex M represents the additional bending moment caused by vortex-induced vibration; gust This represents the additional bending moment caused by the gust effect;

[0045] The formula for the concrete stress model is:

[0046] In the formula, σ(t) represents the concrete stress; d(t) represents the inner diameter function of the pole; and D(t) represents the outer diameter function of the pole.

[0047] The formula for calculating the wind load bending moment at the concrete is:

[0048] In the formula, M capacity f represents the bending moment of concrete under wind load; c b represents the compressive strength of the concrete; h represents the width of the concrete section; t′ represents the height of the concrete section; t′ represents the number of years of service life of the concrete; t d Design service life of concrete; χ represents the long-term load effect coefficient; ζ represents the degradation index;

[0049] The formula for calculating the critical stress of concrete is:

[0050] In the formula, σ critical (t) represents the critical stress of concrete;

[0051] The formula for calculating the probability of concrete cracking is:

[0052] In the formula, represents the probability of concrete cracking; P(σ(t)>σ critical (t) represents the second probability that the concrete stress exceeds the critical stress of the concrete during the service life of the concrete; The coefficient represents the corrosion impact factor; C represents the chloride ion content.

[0053] As a preferred embodiment, before obtaining the surface wind speed of the pole, the following is also included:

[0054] Obtain the distance of the power pole from the center of the typhoon, the maximum surface wind speed, the radius of the maximum surface wind speed, and the azimuth of the surface wind speed;

[0055] The surface wind speed is calculated based on the distance of the power pole from the typhoon center, the maximum surface wind speed, the radius of the maximum surface wind speed, and the azimuth of the surface wind speed.

[0056] The formula for calculating the surface wind speed is as follows:

[0057] In the formula, V s V represents surface wind speed; ms R represents the maximum surface wind speed. s The radius of the maximum surface wind speed is indicated by r; r represents the distance of the power pole from the center of the typhoon; θ represents the azimuth angle of the surface wind speed; B s denoted by Holland parameter for surface; x represents scale parameter; ε represents asymmetry coefficient; F z This represents the vertical profile function.

[0058] As a preferred embodiment, the formula for calculating the overall failure probability is:

[0059] In the formula, P f P represents the overall failure probability; f,wind represents the failure probability caused by wind load; w1 represents the weighting coefficient of the failure probability caused by wind load; P f,base w2 represents the base failure probability; w2 represents the weighting coefficient of the base failure probability; P f,wire w3 represents the probability of wire breakage; w3 represents the weighting coefficient of the probability of wire breakage; P f,crack w4 represents the probability of concrete cracking; w4 represents the weighting coefficient of the probability of concrete cracking.

[0060] As a preferred embodiment, the calculation of the power distribution line failure probability based on the combined failure probability of all poles includes:

[0061] Obtain the system importance coefficient, spatial correlation coefficient with other poles, and temporal correlation coefficient with other poles for each pole;

[0062] The failure probability of the power distribution line is calculated based on the comprehensive failure probability, system importance coefficient, spatial correlation coefficient, and temporal correlation coefficient of all poles.

[0063] The formula for calculating the failure probability of the power distribution line is as follows:

[0064] In the formula, FR represents the probability of power line failure; ρ s,i ρ represents the spatial correlation coefficient between the i-th pole and other poles; t,i P represents the time correlation coefficient between the i-th pole and other poles; f,i θ represents the overall failure probability of the i-th pole; iThis represents the system importance coefficient of the i-th pole.

[0065] Based on the above embodiments, another embodiment of the present invention provides a power distribution line failure probability calculation device, including: a data acquisition module, a pole comprehensive failure probability calculation module, and a power distribution line failure probability calculation module;

[0066] The data acquisition module is used to acquire the foundation service life, conductor service life, concrete service life, and failure probability caused by wind load for each pole.

[0067] The comprehensive failure probability calculation module for the utility pole is used to calculate, for each pole, the following: First, based on the foundation's service life and a constructed foundation loosening model, the foundation stiffness variation curve over time within the foundation's service life; second, based on the foundation stiffness variation curve, the foundation failure probability of the pole; third, based on the conductor's service life and a constructed conductor tension increase model, the conductor tension variation curve over time within the conductor's service life; fourth, based on the conductor tension variation curve, the conductor breakage probability of the pole; fifth, based on the concrete's service life and a constructed concrete stress model, the concrete stress variation curve over time within the concrete's service life; sixth, based on the concrete stress variation curve, the concrete cracking probability of the pole; and finally, based on the foundation failure probability, conductor breakage probability, concrete cracking probability, and failure probability caused by wind load, the comprehensive failure probability of the pole. The foundation loosening model characterizes the change in foundation stiffness over time; the conductor tension increase model characterizes the change in conductor tension over time; and the concrete stress model characterizes the change in concrete stress over time.

[0068] The power distribution line failure probability calculation module is used to calculate the power distribution line failure probability based on the comprehensive failure probability of all poles.

[0069] Based on the above embodiments, another embodiment of the present invention provides a terminal device, the terminal device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the power distribution line failure probability calculation method described in the above embodiments of the invention.

[0070] Based on the above embodiments, another embodiment of the present invention provides a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the power distribution line failure probability calculation method described in the above embodiments of the invention.

[0071] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0072] This invention uses a physical model of the pole's degradation process over time, including foundation loosening, conductor tension changes, and concrete stress changes, to calculate the probability of foundation failure, conductor breakage, and concrete cracking. It further calculates the overall failure probability of the pole and finally, based on the overall failure probability of all poles, calculates the failure probability of the distribution line. This invention more realistically reflects the performance changes of poles over time. By considering the impact of performance degradation in the foundation, conductors, and concrete on the failure probability, it improves the accuracy of power system component failure probability assessment. Attached Figure Description

[0073] Figure 1 is a flowchart illustrating a method for calculating the failure probability of a power distribution line according to an embodiment of the present invention;

[0074] Figure 2 is a schematic diagram of a power distribution line failure probability calculation device provided in an embodiment of the present invention. Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] Example 1

[0077] Please refer to Figure 1, which illustrates a method for calculating the failure probability of a power distribution line according to an embodiment of the present invention, including:

[0078] S1. Obtain the foundation service life, conductor service life, concrete service life, and failure probability caused by wind load for each pole.

[0079] It should be noted that "foundation" refers to the pole foundation; "concrete" refers to the concrete portion of the pole; "foundation service life" refers to the time the pole foundation has been in use; "conductor service life" refers to the time the conductors on the pole have been in use; and "concrete service life" refers to the time the concrete portion of the pole has been in use. The failure probability caused by wind load is pre-calculated. This invention primarily focuses on considering the performance degradation of the foundation, conductors, and concrete portion over time when calculating the failure probability of power distribution lines.

[0080] S2. For each pole, based on the foundation service life and the constructed foundation loosening model, calculate the foundation stiffness variation curve of the pole over time within the foundation service life; calculate the foundation failure probability of the pole based on the foundation stiffness variation curve; calculate the conductor tension variation curve of the pole over time within the conductor service life based on the conductor service life and the constructed conductor tension increase model; calculate the conductor breakage probability of the pole based on the conductor tension variation curve; calculate the concrete stress variation curve of the pole over time within the concrete service life based on the concrete service life and the constructed concrete stress model; calculate the concrete cracking probability of the pole based on the concrete stress variation curve; calculate the comprehensive failure probability of the pole based on the foundation failure probability, conductor breakage probability, concrete cracking probability, and failure probability caused by wind load; wherein, the foundation loosening model is used to characterize the change law of foundation stiffness over time; the conductor tension increase model is used to characterize the change law of conductor tension over time; and the concrete stress model is used to characterize the change law of concrete stress over time.

[0081] In step S2, this invention introduces a foundation loosening model to calculate the foundation stiffness variation curve over the foundation's service life, considering the impact of foundation loosening over time on the failure probability of the power distribution line; it also introduces a conductor tension increase model to calculate the conductor tension variation curve over the conductor's service life, considering the impact of increased conductor tension over time on the failure probability of the power distribution line; and a concrete stress model to calculate the concrete stress variation curve over the concrete's service life, considering the impact of stress changes in the concrete over time on the failure probability of the power distribution line. The foundation stiffness variation curve reflects the foundation stiffness data over the foundation's service life, and the foundation failure probability is calculated based on this curve. The conductor tension variation curve reflects the conductor tension data over the conductor's service life, and the conductor breakage probability is calculated based on this curve. The concrete stress variation curve reflects the concrete stress data over the concrete's service life, and the concrete cracking probability is calculated based on this curve. Then, based on the foundation failure probability, conductor breakage probability, concrete cracking probability, and failure probability caused by wind load, the overall failure probability of the pole is calculated.

[0082] In a preferred embodiment, the process of constructing the basic loosening model includes:

[0083] The initial stiffness of the pole's foundation, the rate of foundation loosening, the index of foundation loosening process, the seasonal fluctuation amplitude of foundation stiffness, and the seasonal cycle are obtained; wherein, the index of inductive foundation loosening process is used to characterize the nonlinear characteristics of the foundation loosening process.

[0084] Based on the initial stiffness of the foundation, the loosening rate of the foundation, the loosening process index of the foundation, and the seasonal fluctuation amplitude and period of the foundation stiffness, a foundation loosening model for the pole is constructed.

[0085] The step of calculating the foundation failure probability of the pole based on the foundation stiffness variation curve includes:

[0086] Obtain the critical stiffness of the pole's foundation;

[0087] Based on the foundation stiffness variation curve and the foundation critical stiffness, calculate the first failure probability that the pole's foundation stiffness does not exceed the foundation critical stiffness during the foundation's service life.

[0088] The base failure probability is calculated based on the first failure probability.

[0089] The formula for the basic loosening model is as follows:

[0090] In the formula, K f (t) represents the foundation stiffness; K(0) represents the initial foundation stiffness; μ represents the foundation loosening rate; β represents the foundation loosening process index; φ represents the seasonal fluctuation amplitude of foundation stiffness; T represents the seasonal period; t represents the time.

[0091] The formula for calculating the basic failure probability is:

[0092] In the formula, P f,base K represents the basic failure probability; f,critical P(K) represents the critical stiffness of the foundation. f (t)≤K f,critical This represents the first failure probability that the foundation stiffness does not exceed the critical stiffness of the foundation during its service life. represents the soil hardening coefficient; S represents the site category parameter.

[0093] It should be noted that the foundation loosening rate μ is a parameter representing the rate of foundation loosening, with a typical value range of 0.005-0.02 / year. The foundation loosening process index β is an index describing the nonlinear characteristics of the foundation loosening process, generally taking a value of 0.9-1.1. Both μ and β can be obtained in advance through fitting on-site test data. The initial foundation stiffness K(0) is the stiffness of the foundation before it begins to be used.

[0094] The soil hardening coefficient is influenced by soil type, soil moisture content, and soil compaction. Soil types include sandy soil, clay soil, and silty soil. The soil hardening coefficient for sandy soil ranges from 1.0 to 1.2, for clay soil from 0.8 to 1.0, and for silty soil from 1.2 to 1.5; these are all baseline values ​​for the soil hardening coefficient. When the soil moisture content is saturated, the soil hardening coefficient increases by 15% to 30% of the baseline value; when the soil moisture content is dry, the soil hardening coefficient decreases by 10% to 20%. When the soil is dense, the soil hardening coefficient decreases by 10% to 20%; when the soil is loose, the soil hardening coefficient increases by 20% to 40% of the baseline value.

[0095] The rules for determining the site category and its corresponding parameter values ​​are as follows:

[0096] (1) The soil type is rock or hard soil foundation, the terrain is flat and open, the soil layer thickness is less than 5 meters, which is the first type of site, and the site category parameter value is 1.0.

[0097] (2) The soil type is dense sandy soil or gravelly soil, the terrain is relatively flat, and the soil layer thickness is between 5 meters and 15 meters. It is a second type of site, and the site category parameter is 1.2.

[0098] (3) The soil type is medium-dense sandy soil or clay soil, the terrain is undulating, the soil layer thickness is between 15 meters and 30 meters, which is a third type of site, and the site category parameter is 1.4.

[0099] (4) The soil type is loose soil or soft soil, the terrain is complex, and the soil thickness is greater than 30 meters. It is a Class IV site, and the site category parameter value is 1.6.

[0100] In a preferred embodiment, the process of constructing the conductor tension increase model includes:

[0101] The initial tension of the conductor, the maximum tension increment, the rate of increase of the conductor tension, the coefficient of periodic change of the conductor tension, the frequency of periodic change of the conductor tension, and the long-term drift coefficient of the conductor tension are obtained for the pole; wherein, the long-term drift coefficient of the conductor tension is used to characterize the trend of slow increase of the conductor tension over a long period of time.

[0102] Based on the initial tension of the conductor, the maximum tension increment of the conductor, the rate of increase of the conductor tension, the coefficient of the periodic change of the conductor tension, the frequency of the periodic change of the conductor tension, and the long-term drift coefficient of the conductor tension, a model for the increase of conductor tension on the pole is constructed.

[0103] Based on the conductor tension variation curve, the probability of conductor breakage on the pole is calculated, including:

[0104] Obtain the critical tension of the conductor on the pole;

[0105] Calculate the probability of conductor breakage based on the conductor tension variation curve and the conductor critical tension;

[0106] The formula for the conductor tension increase model is: T(t)=T(0)+ΔT·(1-e -vt )·[1+γ·sin(ωt)+κ·ln(t+1)];

[0107] In the formula, T(t) represents the conductor tension; T(0) represents the initial conductor tension; ΔT represents the maximum conductor tension increment; v represents the conductor tension increase rate; γ represents the conductor tension periodic change amplitude coefficient; ω represents the conductor tension periodic change frequency; κ represents the conductor tension long-term drift coefficient; and t represents time.

[0108] The formula for calculating the probability of conductor breakage is: P f,wire =P(T(t)>T critical );

[0109] In the formula, P f,wire T represents the probability of wire breakage. critical This indicates the critical tension of the conductor.

[0110] It should be noted that the maximum increment ΔT of the conductor represents the maximum possible increase in conductor tension, typically ranging from 10% to 30% of the initial conductor tension. The conductor tension increase rate v describes the rate at which conductor tension increases over time, generally taken as 0.1-0.5 / hour. The conductor tension periodicity variation amplitude coefficient γ describes the amplitude of periodic tension changes, reflecting the degree of tension fluctuation caused by seasonal temperature changes or load cycles, generally taken as 0.05-0.2. The conductor tension periodicity variation frequency ω describes the frequency of periodic conductor tension changes, generally taken as 0.1-1 rad / hour. The conductor tension long-term drift coefficient κ describes the long-term slow increase trend of conductor tension, generally taken as 0.01-0.1 kN / ln(hour). ΔT, v, γ, ω, and κ can all be estimated in advance using historical typhoon event data. T(0) The initial conductor tension is the tension of the conductor before it is put into use.

[0111] In a preferred embodiment, the process of constructing the concrete stress model includes:

[0112] The inner diameter and outer diameter of the pole are obtained at each moment during the service time of the concrete; the wind-receiving area, surface wind speed, and pole height of the pole are obtained.

[0113] Based on the inner and outer diameters of the pole at each moment during the concrete service life, the inner diameter function and outer diameter function of the pole are determined through mathematical fitting; wherein, the inner diameter function is used to characterize the change of the inner diameter of the pole over time; and the outer diameter function is used to characterize the change of the outer diameter of the pole over time.

[0114] The wind load on the pole is calculated based on the wind-receiving area, surface wind speed, and failure probability caused by wind load.

[0115] Based on the wind load and pole height, calculate the wind load bending moment at the conductor of the pole;

[0116] The concrete stress model is constructed based on the wind load bending moment at the conductor, the inner diameter function of the pole, and the outer diameter function of the pole.

[0117] The step of calculating the probability of concrete cracking of the pole based on the concrete stress variation curve includes:

[0118] Obtain the concrete cross-sectional width, concrete cross-sectional height, concrete compressive strength, and concrete design service life of the pole;

[0119] Calculate the wind load bending moment at the concrete section of the pole based on the concrete section width, concrete section height, concrete compressive strength, concrete design service life, and concrete service life.

[0120] Calculate the critical stress of the concrete of the pole based on the wind load bending moment at the concrete.

[0121] Based on the concrete stress variation curve and the concrete critical stress, calculate the second probability that the concrete stress of the pole exceeds the concrete critical stress during the concrete service life.

[0122] Calculate the concrete cracking probability based on the second probability;

[0123] The formula for calculating the wind load is as follows:

[0124] In the formula, F wind Indicates wind load; P wind Indicates the probability of failure due to wind load; A represents the wind-receiving area; V represents the surface wind speed; C d R represents the drag coefficient; e Represents the Reynolds number; n represents the Reynolds number exponent; P tree This represents the failure probability caused by the additional force generated by the falling tree; η represents the wind speed amplification factor; V0 represents the reference wind speed.

[0125] The formula for calculating the wind load bending moment at the conductor is: Mwind =F wind ·H / 2+M vortex +M gust ;

[0126] In the formula, M wind H represents the bending moment of the wind load at the conductor; M represents the height of the pole; vortex M represents the additional bending moment caused by vortex-induced vibration; gust This represents the additional bending moment caused by the gust effect;

[0127] The formula for the concrete stress model is:

[0128] In the formula, σ(t) represents the concrete stress; d(t) represents the inner diameter function of the pole; and D(t) represents the outer diameter function of the pole.

[0129] The formula for calculating the wind load bending moment at the concrete is:

[0130] In the formula, M capacity f represents the bending moment of concrete under wind load; c b represents the compressive strength of the concrete; h represents the width of the concrete section; t' represents the height of the concrete section; t' represents the number of years of service life of the concrete; t d Design service life of concrete; χ represents the long-term load effect coefficient; ζ represents the degradation index;

[0131] The formula for calculating the critical stress of concrete is:

[0132] In the formula, σ critical (t) represents the critical stress of concrete;

[0133] The formula for calculating the probability of concrete cracking is:

[0134] In the formula, represents the probability of concrete cracking; P(σ(t)>σ critical (t) represents the second probability that the concrete stress exceeds the critical stress of the concrete during the service life of the concrete; The coefficient represents the corrosion impact factor; C represents the chloride ion content.

[0135] It should be noted that the degradation of the concrete portion of the pole over time is mainly manifested in the increase of the pole's inner diameter and the decrease of its outer diameter. This invention uses mathematical fitting to determine the functions of the pole's inner diameter and outer diameter over time during the concrete's service life. Then, based on the pole's inner diameter function, outer diameter function, and the calculated wind load bending moment at the conductor, a concrete stress model is constructed to characterize the time-dependent variation of concrete stress.

[0136] Since the concrete will face the risk of cracking if the wind load bending moment at the conductor exceeds the wind load bending moment at the concrete, this invention uses the concrete stress calculated by the wind load bending moment at the concrete as the critical stress of the concrete, and uses whether the concrete stress corresponding to the wind load bending moment at the conductor exceeds the critical stress of the concrete as the judgment condition for probability calculation.

[0137] The probability of failure P caused by the additional force generated by the falling tree. tree This typically represents the frequency or probability of tree falls affecting power lines, reflecting the likelihood of power system failure due to tree falls within a specific area. The probability of failure due to wind load, P, is also relevant. wind This represents the probability of a power line failure directly caused by strong winds; it is a dimensionless quantity with a value range between 0 and 1. The additional bending moment M caused by vortex-induced vibration... vortex and the resulting additional bending moment M gust M is a pre-calculated quantity. vortex M is calculated using fluid dynamics models and structural dynamics analysis. gust Estimation is achieved through wind load models and structural response analysis.

[0138] Corrosion Influence Coefficient This reflects the impact of corrosion on the strength and durability of concrete under different environments, particularly in marine environments. Values ​​range from 0.015 to 0.025 MPa / (kg / m²). 3 ); Industrial environment Values ​​range from 0.010 to 0.020 MPa / (kg / m²). 3 ); Under normal circumstances Values ​​range from 0.005 to 0.015 MPa / (kg / m²). 3 ).

[0139] In a preferred embodiment, before obtaining the surface wind speed of the pole, the method further includes:

[0140] Obtain the distance of the power pole from the center of the typhoon, the maximum surface wind speed, the radius of the maximum surface wind speed, and the azimuth of the surface wind speed;

[0141] The surface wind speed is calculated based on the distance of the power pole from the typhoon center, the maximum surface wind speed, the radius of the maximum surface wind speed, and the azimuth of the surface wind speed.

[0142] The formula for calculating the surface wind speed is as follows:

[0143] In the formula, V s V represents surface wind speed; ms R represents the maximum surface wind speed. sThe radius of the maximum surface wind speed is indicated by r; r represents the distance of the power pole from the center of the typhoon; θ represents the azimuth angle of the surface wind speed; B s denoted by Holland parameter for surface; x represents scale parameter; ε represents asymmetry coefficient; F z This represents the vertical profile function.

[0144] In this embodiment, a typhoon model is provided for wind load input in the above calculation process.

[0145] It should be noted that this invention uses an improved surface Holland parameter B. s :

[0146] In the formula, g s This represents the attenuation factor from gradient wind to surface wind; This represents the intensity correlation coefficient.

[0147] This invention also uses an improved scale parameter x:

[0148] In the formula, x min δ represents the minimum value of x; δ represents the attenuation coefficient.

[0149] This invention introduces an improved Holland model to describe typhoon wind fields, taking into account the asymmetry and vertical structure of the wind field, thereby improving the accuracy of wind field simulation.

[0150] It should also be noted that the parameters in the typhoon model (such as V) ms R s (etc.) There is uncertainty, so adjustments need to be made based on real-time observation data. As time changes, new V values ​​need to be extracted in real time based on meteorological forecast data. ms and R s The values ​​of these parameters are determined. A particle filter algorithm is used to process the observed data, continuously updating the probability distribution of these parameters. The updated parameter distribution is then fed back to the typhoon model to improve the accuracy of wind field simulation.

[0151] An improved particle filter (PF) algorithm is used for Bayesian inference to update the posterior distribution of system parameters:

[0152] (1) Initialization: Sample N particles from the prior distribution and assign them initial weights;

[0153] (2) Importance Sampling: The weight of each particle is calculated using an improved importance function.

[0154] in, Let be the weight of the i-th particle at time k. Let be the likelihood function, q(·) be the importance function, and π(·) be the π / 2 ...

[0155] It is the a priori transfer function;

[0156] (3) Adaptive resampling: Resampling is performed adaptively based on the effective number of particles.

[0157] If N eff ≤N threshold If so, resampling will be performed;

[0158] (4) Regularization: Estimate the kernel density of the resampled particles to increase particle diversity;

[0159] Where h is the bandwidth parameter and K(·) is the kernel function;

[0160] (5) Movement: The improved Markov chain Monte Carlo (MCMC) method is used to move particles to improve sampling efficiency;

[0161] (6) Parameter estimation: Estimate the posterior distribution of parameters based on the weighted average of the particles:

[0162] The posterior filter density is approximately:

[0163] Among them, K h This is the kernel density estimation function.

[0164] Particle filtering requires multiple runs of the physical model, resulting in high computational costs in large-scale systems. The Gaussian process surrogate model establishes an approximate relationship between input and output through a finite number of physical model calculations. During the iteration process of particle filtering, the surrogate model replaces part of the physical model calculations, significantly improving computational efficiency. The Gaussian process (GP) algorithm establishes the surrogate model:

[0165] (1) Choosing the covariance function: Use the combined kernel function: k(x, x′)=k SE (x, x′) + k RQ (x, x′) + k P (x, x′);

[0166] Where, k SE The kernel function is a quadratic exponential function, k RQ Let k be a rational quadratic kernel function. P It is a periodic kernel function;

[0167] Where, σ f Let l be the signal variance. jLet M be the length scale parameter, M be the metric matrix, α be the shape parameter, and p be the period. x, x′: two input vectors. j and x′ j Represents the j-th component of the input vectors x and x′. The length scale l controls the smoothness or rate of change of the function. N x This represents the dimension of the input vector, i.e., the number of features or variables;

[0168] (2) Training sample generation: Training samples are generated using the Latin hypercube sampling method;

[0169] (3) Hyperparameter optimization: The hyperparameters of the kernel function are optimized using the Bayesian optimization method.

[0170] (4) Prediction: The response of the pole is predicted using the trained GP model.

[0171] Gaussian process models may include multiple input variables, such as pole diameter, material strength, and environmental factors. This invention uses an improved automatic correlation determination method to calculate the correlation of each variable, selects the variable with the strongest correlation, simplifies the Gaussian process model, and further improves computational efficiency and model interpretability. The specific method for selecting highly correlated predictor variables using the improved automatic correlation determination method is as follows:

[0172] (1) Calculate the adaptive length scale parameter for each input variable.

[0173] Where l0 is the reference length scale, ρ j These are adaptive coefficients;

[0174] (2) Normalize the reciprocal of the length scale.

[0175] (4) Use an improved threshold selection method to select highly correlated variables:

[0176] If r j If the value is greater than or equal to τ·max(r), then choose variable j.

[0177] Where τ is the adaptive threshold coefficient; r j This typically represents the correlation length scale or feature length. j This is the correlation length scale of the j-th input variable, reflecting the range of influence of the input variable on the output. A larger r... j The value indicates how much the input variable affects the output over a larger range. A smaller value for r... j The value indicates that the effect of the input variable is more localized.

[0178] In a preferred embodiment, the formula for calculating the overall failure probability is:

[0179] In the formula, P f P represents the overall failure probability; f,wind represents the failure probability caused by wind load; w1 represents the weighting coefficient of the failure probability caused by wind load; P f,base w2 represents the base failure probability; w2 represents the weighting coefficient of the base failure probability; P f,wire w3 represents the probability of wire breakage; w3 represents the weighting coefficient of the probability of wire breakage; P f,crack w4 represents the probability of concrete cracking; w4 represents the weighting coefficient of the probability of concrete cracking.

[0180] S3. Calculate the failure probability of the power distribution line based on the combined failure probability of all poles.

[0181] In step S3, the overall failure probability of all poles in the power distribution line is calculated using the above method, and then the failure probability of the power distribution line is calculated based on the overall failure probability of all poles.

[0182] In a preferred embodiment, calculating the failure probability of the power distribution line based on the combined failure probability of all poles includes:

[0183] Obtain the system importance coefficient, spatial correlation coefficient with other poles, and temporal correlation coefficient with other poles for each pole;

[0184] The failure probability of the power distribution line is calculated based on the comprehensive failure probability, system importance coefficient, spatial correlation coefficient, and temporal correlation coefficient of all poles.

[0185] The formula for calculating the failure probability of the power distribution line is as follows:

[0186] In the formula, FR represents the probability of power line failure; ρ s,i ρ represents the spatial correlation coefficient between the i-th pole and other poles; t,i P represents the time correlation coefficient between the i-th pole and other poles; f,i θ represents the overall failure probability of the i-th pole; i This represents the system importance coefficient of the i-th pole.

[0187] It should be noted that this invention establishes a physical model that considers the degradation process of power poles over time, including foundation loosening, changes in conductor tension, and changes in concrete stress. This model more realistically reflects the performance changes of power poles, thereby improving the accuracy of power system component failure probability assessment and enabling more accurate typhoon risk analysis.

[0188] Example 2

[0189] Please refer to Figure 2, which is a structural schematic diagram of a power distribution line failure probability calculation device provided in an embodiment of the present invention, including: a data acquisition module, a pole comprehensive failure probability calculation module, and a power distribution line failure probability calculation module;

[0190] The data acquisition module is used to acquire the foundation service life, conductor service life, concrete service life, and failure probability caused by wind load for each pole.

[0191] The comprehensive failure probability calculation module for the utility pole is used to calculate, for each pole, the following: First, based on the foundation's service life and a constructed foundation loosening model, the foundation stiffness variation curve over time within the foundation's service life; second, based on the foundation stiffness variation curve, the foundation failure probability of the pole; third, based on the conductor's service life and a constructed conductor tension increase model, the conductor tension variation curve over time within the conductor's service life; fourth, based on the conductor tension variation curve, the conductor breakage probability of the pole; fifth, based on the concrete's service life and a constructed concrete stress model, the concrete stress variation curve over time within the concrete's service life; sixth, based on the concrete stress variation curve, the concrete cracking probability of the pole; and finally, based on the foundation failure probability, conductor breakage probability, concrete cracking probability, and failure probability caused by wind load, the comprehensive failure probability of the pole. The foundation loosening model characterizes the change in foundation stiffness over time; the conductor tension increase model characterizes the change in conductor tension over time; and the concrete stress model characterizes the change in concrete stress over time.

[0192] The power distribution line failure probability calculation module is used to calculate the power distribution line failure probability based on the comprehensive failure probability of all poles.

[0193] Example 3

[0194] Accordingly, this invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power distribution line failure probability calculation method described in the above-described embodiments of the invention.

[0195] Example 4

[0196] Accordingly, embodiments of the present invention provide a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the power distribution line failure probability calculation method described in the above embodiments of the invention.

[0197] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0198] Those skilled in the art will clearly understand that, for convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0199] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0200] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the device, connecting various parts of the device via various interfaces and lines.

[0201] The memory can be used to store the computer program. The processor implements various functions of the device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0202] The storage medium is a storage medium in which the computer program is stored. When executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0203] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for calculating the failure probability of a power distribution line, characterized in that, include: Obtain the foundation service life, conductor service life, concrete service life, and failure probability caused by wind load for each pole; For each pole, based on the foundation service life and the constructed foundation loosening model, calculate the foundation stiffness variation curve of the pole over time within the foundation service life; and calculate the foundation failure probability of the pole based on the foundation stiffness variation curve. Based on the conductor service life and the constructed conductor tension increase model, the conductor tension variation curve of the pole over time is calculated within the conductor service life. Based on the conductor tension variation curve, the conductor breakage probability of the pole is calculated. Based on the concrete service life and the constructed concrete stress model, the concrete stress variation curve of the pole over time is calculated within the concrete service life. Based on the concrete stress variation curve, the concrete cracking probability of the pole is calculated. Based on the foundation failure probability, conductor breakage probability, concrete cracking probability, and failure probability caused by wind load, the comprehensive failure probability of the pole is calculated. The foundation loosening model is used to characterize the change in foundation stiffness over time; the conductor tension increase model is used to characterize the change in conductor tension over time; and the concrete stress model is used to characterize the change in concrete stress over time. Calculate the failure probability of the power distribution line based on the combined failure probability of all poles.

2. The method for calculating the failure probability of power distribution lines as described in claim 1, characterized in that, The construction process of the basic loosening model includes: The initial stiffness of the pole's foundation, the rate of foundation loosening, the index of foundation loosening process, the seasonal fluctuation amplitude of foundation stiffness, and the seasonal cycle are obtained; wherein, the index of inductive foundation loosening process is used to characterize the nonlinear characteristics of the foundation loosening process. Based on the initial stiffness of the foundation, the loosening rate of the foundation, the loosening process index of the foundation, and the seasonal fluctuation amplitude and period of the foundation stiffness, a foundation loosening model for the pole is constructed. The step of calculating the foundation failure probability of the pole based on the foundation stiffness variation curve includes: Obtain the critical stiffness of the pole's foundation; Based on the foundation stiffness variation curve and the foundation critical stiffness, calculate the first failure probability that the pole's foundation stiffness does not exceed the foundation critical stiffness during the foundation's service life. The base failure probability is calculated based on the first failure probability. The formula for the basic loosening model is as follows: In the formula, K f (t) represents the foundation stiffness; K(0) represents the initial stiffness of the foundation; μ represents the foundation loosening rate; β represents the foundation loosening process exponent; The value represents the seasonal fluctuation amplitude of the foundation stiffness; T represents the seasonal period; t represents the time. The formula for calculating the basic failure probability is: In the formula, P f,base K represents the basic failure probability; f,critical P(K) represents the critical stiffness of the foundation. f (t)≤K f,critical This represents the first failure probability that the foundation stiffness does not exceed the critical stiffness of the foundation during its service life. represents the soil hardening coefficient; S represents the site category parameter.

3. The method for calculating the failure probability of power distribution lines as described in claim 1, characterized in that, The construction process of the conductor tension increase model includes: The initial tension of the conductor, the maximum tension increment, the rate of increase of the conductor tension, the coefficient of periodic change of the conductor tension, the frequency of periodic change of the conductor tension, and the long-term drift coefficient of the conductor tension are obtained for the pole; wherein, the long-term drift coefficient of the conductor tension is used to characterize the trend of slow increase of the conductor tension over a long period of time. Based on the initial tension of the conductor, the maximum tension increment of the conductor, the rate of increase of the conductor tension, the coefficient of the periodic change of the conductor tension, the frequency of the periodic change of the conductor tension, and the long-term drift coefficient of the conductor tension, a model for the increase of conductor tension on the pole is constructed. Based on the conductor tension variation curve, the probability of conductor breakage on the pole is calculated, including: Obtain the critical tension of the conductor on the pole; Calculate the probability of conductor breakage based on the conductor tension variation curve and the conductor critical tension; The formula for the conductor tension increase model is as follows: T(t)=T(0)+ΔT·(1-e -vt )·[1+γ·sin(ωt)+κ·ln(t+1)]; In the formula, T(t) represents the conductor tension; T(0) represents the initial conductor tension; ΔT represents the maximum conductor tension increment; v represents the conductor tension increase rate; γ represents the conductor tension periodic change amplitude coefficient; ω represents the conductor tension periodic change frequency; κ represents the conductor tension long-term drift coefficient; and t represents time. The formula for calculating the probability of wire breakage is: P f,wire =P(T(t)>T critical ); In the formula, P f,wire T represents the probability of wire breakage. critical This indicates the critical tension of the conductor.

4. The method for calculating the failure probability of power distribution lines as described in claim 1, characterized in that, The process of constructing the concrete stress model includes: The inner diameter and outer diameter of the pole are obtained at each moment during the service time of the concrete; the wind-receiving area, surface wind speed, and pole height of the pole are obtained. Based on the inner and outer diameters of the pole at each moment during the concrete service life, the inner diameter function and outer diameter function of the pole are determined through mathematical fitting; wherein, the inner diameter function is used to characterize the change of the inner diameter of the pole over time; and the outer diameter function is used to characterize the change of the outer diameter of the pole over time. The wind load on the pole is calculated based on the wind-receiving area, surface wind speed, and failure probability caused by wind load. Based on the wind load and pole height, calculate the wind load bending moment at the conductor of the pole; The concrete stress model is constructed based on the wind load bending moment at the conductor, the inner diameter function of the pole, and the outer diameter function of the pole. The step of calculating the probability of concrete cracking of the pole based on the concrete stress variation curve includes: Obtain the concrete cross-sectional width, concrete cross-sectional height, concrete compressive strength, and concrete design service life of the pole; Calculate the wind load bending moment at the concrete section of the pole based on the concrete section width, concrete section height, concrete compressive strength, concrete design service life, and concrete service life. Calculate the critical stress of the concrete of the pole based on the wind load bending moment at the concrete. Based on the concrete stress variation curve and the concrete critical stress, calculate the second probability that the concrete stress of the pole exceeds the concrete critical stress during the concrete service life. Calculate the concrete cracking probability based on the second probability; The formula for calculating the wind load is as follows: In the formula, F wind Indicates wind load; P wind Indicates the probability of failure due to wind load; A represents the wind-receiving area; V represents the surface wind speed; C d R represents the drag coefficient; e Represents the Reynolds number; n represents the Reynolds number exponent; F tree This represents the failure probability caused by the additional force generated by the falling tree; η represents the wind speed amplification factor; V0 represents the reference wind speed. The formula for calculating the bending moment of wind load at the conductor is: M wind =F wind ·H / 2+M vortex +M gust ; In the formula, M wind H represents the bending moment of the wind load at the conductor; M represents the height of the pole; vortex M represents the additional bending moment caused by vortex-induced vibration; gust This represents the additional bending moment caused by the gust effect; The formula for the concrete stress model is: In the formula, σ(t) represents the concrete stress; d(t) represents the inner diameter function of the pole; and D(t) represents the outer diameter function of the pole. The formula for calculating the wind load bending moment at the concrete is: In the formula, M capacity f represents the bending moment of concrete under wind load; c b represents the compressive strength of the concrete; h represents the width of the concrete section; t' represents the height of the concrete section; t' represents the number of years of service life of the concrete; t d Design service life of concrete; χ represents the long-term load effect coefficient; ζ represents the degradation index; The formula for calculating the critical stress of concrete is: In the formula, σ critical (t) represents the critical stress of concrete; The formula for calculating the probability of concrete cracking is: In the formula, represents the probability of concrete cracking; P(σ(t)>σ critical (t) represents the second probability that the concrete stress exceeds the critical stress of the concrete during the service life of the concrete; The coefficient represents the corrosion impact factor; C represents the chloride ion content.

5. The method for calculating the failure probability of power distribution lines as described in claim 4, characterized in that, Before obtaining the surface wind speed of the utility pole, the following steps are also included: Obtain the distance of the power pole from the center of the typhoon, the maximum surface wind speed, the radius of the maximum surface wind speed, and the azimuth of the surface wind speed; The surface wind speed is calculated based on the distance of the power pole from the typhoon center, the maximum surface wind speed, the radius of the maximum surface wind speed, and the azimuth of the surface wind speed. The formula for calculating the surface wind speed is as follows: In the formula, V s V represents surface wind speed; ms R represents the maximum surface wind speed. s The radius of the maximum surface wind speed is indicated by r; r represents the distance of the power pole from the center of the typhoon; θ represents the azimuth angle of the surface wind speed; B s denoted by Holland parameter for surface; x represents scale parameter; ε represents asymmetry coefficient; F z This represents the vertical profile function.

6. The method for calculating the failure probability of power distribution lines as described in claim 1, characterized in that, The formula for calculating the overall failure probability is: In the formula, P f P represents the overall failure probability; f,wind represents the failure probability caused by wind load; w1 represents the weighting coefficient of the failure probability caused by wind load; P f,base w2 represents the base failure probability; w2 represents the weighting coefficient of the base failure probability; P f,wire w3 represents the probability of wire breakage; w3 represents the weighting coefficient of the probability of wire breakage; P f,crack w4 represents the probability of concrete cracking; w4 represents the weighting coefficient of the probability of concrete cracking.

7. The method for calculating the failure probability of power distribution lines as described in claim 1, characterized in that, The calculation of the power distribution line failure probability based on the combined failure probability of all poles includes: Obtain the system importance coefficient, spatial correlation coefficient with other poles, and temporal correlation coefficient with other poles for each pole; The failure probability of the power distribution line is calculated based on the comprehensive failure probability, system importance coefficient, spatial correlation coefficient, and temporal correlation coefficient of all poles. The formula for calculating the failure probability of the power distribution line is as follows: In the formula, FR represents the probability of power line failure; ρ s,i ρ represents the spatial correlation coefficient between the i-th pole and other poles; t,i P represents the time correlation coefficient between the i-th pole and other poles; f,i θ represents the overall failure probability of the i-th pole; i This represents the system importance coefficient of the i-th pole.

8. A device for calculating the failure probability of a power distribution line, characterized in that, include: Data acquisition module, pole comprehensive failure probability calculation module, and power distribution line failure probability calculation module; The data acquisition module is used to acquire the foundation service life, conductor service life, concrete service life, and failure probability caused by wind load for each pole. The pole comprehensive failure probability calculation module is used to calculate, for each pole, the foundation stiffness variation curve of the pole over time within the foundation service life, based on the foundation service life and the constructed foundation loosening model; and to calculate the foundation failure probability of the pole based on the foundation stiffness variation curve. Based on the conductor service life and the constructed conductor tension increase model, the conductor tension variation curve of the pole over time is calculated within the conductor service life. Based on the conductor tension variation curve, the conductor breakage probability of the pole is calculated. Based on the concrete service life and the constructed concrete stress model, the concrete stress variation curve of the pole over time is calculated within the concrete service life. Based on the concrete stress variation curve, the concrete cracking probability of the pole is calculated. Based on the foundation failure probability, conductor breakage probability, concrete cracking probability, and failure probability caused by wind load, the comprehensive failure probability of the pole is calculated. The foundation loosening model is used to characterize the change in foundation stiffness over time; the conductor tension increase model is used to characterize the change in conductor tension over time; and the concrete stress model is used to characterize the change in concrete stress over time. The power distribution line failure probability calculation module is used to calculate the power distribution line failure probability based on the comprehensive failure probability of all poles.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the power distribution line failure probability calculation method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to execute the power distribution line failure probability calculation method as described in any one of claims 1 to 7.

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