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

Abstract

The application discloses a kind of power distribution line failure probability calculation method, device, terminal equipment and storage medium, wherein method includes: by according to the physical model of electric pole degradation process with time, including basic loosening, wire tension change and concrete stress change, the basic failure probability, wire fracture probability, concrete cracking probability are calculated, the comprehensive failure probability of the electric pole is further calculated, finally according to the comprehensive failure probability of all electric poles, the failure probability of power distribution line is calculated.The application truly reflects the change of the performance of electric pole with time, improves the accuracy of power system component failure probability evaluation by considering the influence of the performance degradation of foundation, wire and concrete part on failure probability.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network risk assessment, and in particular to a method, apparatus, terminal device and storage medium for calculating the failure probability of a distribution line. Background Art

[0002] Currently, typhoon risk analysis for power distribution lines primarily relies on the static vulnerability curve method. This method constructs vulnerability curves for distribution line components based on historical data, assesses component failure probabilities at different wind speeds, and then uses these component failure probabilities to analyze the failure probability of the entire distribution line. However, because the static vulnerability curve method fails to reflect the time-varying performance degradation of power system components such as foundations, conductors, and concrete, the component failure probabilities estimated using this method are inaccurate, leading to inaccurate analysis of distribution line failure probabilities. Summary of the Invention

[0003] The present invention provides a method, apparatus, terminal device and storage medium for calculating the failure probability of a distribution line, so as to solve the technical problem of inaccurate failure probability analysis in the prior art.

[0004] In order to solve the above technical problems, an embodiment of the present invention provides a method for calculating the failure probability of a distribution line, comprising:

[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 pole, based on the foundation usage time and the constructed foundation loosening model, calculate the foundation stiffness change curve of the pole that changes with time during the foundation usage time; calculate the foundation failure probability of the pole based on the foundation stiffness change curve; calculate the conductor tension change curve of the pole that changes with time during the conductor usage time based on the constructed conductor tension increase model; calculate the conductor breakage probability of the pole based on the conductor tension change curve; calculate the concrete stress change curve of the pole that changes with time during the concrete usage time based on the concrete stress model; calculate the concrete cracking probability of the pole based on the concrete stress change 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;

[0007] The failure probability of the distribution line is calculated based on the comprehensive failure probability of all poles.

[0008] As a preferred solution, the process of constructing the foundation loosening model includes:

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

[0010] Constructing a foundation loosening model of the pole according to the foundation initial stiffness, foundation loosening rate and foundation loosening process index, and foundation stiffness seasonal fluctuation amplitude and period;

[0011] Calculating the foundation failure probability of the pole according to the foundation stiffness change curve includes:

[0012] Obtaining the critical foundation stiffness of the pole;

[0013] Calculating the first failure probability of the pole having a foundation stiffness that does not exceed the foundation critical stiffness within the service life of the foundation according to the foundation stiffness change curve and the foundation critical stiffness;

[0014] Calculating the basic failure probability according to the first failure probability;

[0015] The formula of the foundation loosening model is:

[0016]

[0017] Where K f (t) represents foundation stiffness; K(0) represents initial foundation stiffness; μ represents foundation loosening rate; β represents foundation loosening process index; represents the seasonal fluctuation amplitude of foundation stiffness; T represents the seasonal cycle; t represents the time;

[0018] The calculation formula of the basic failure probability is:

[0019]

[0020] Where, P f,base represents the basic failure probability; K f,critical represents the critical stiffness of the foundation; P(K f (t)≤K f,critical ) represents the first failure probability that the foundation stiffness does not exceed the critical stiffness of the foundation during the foundation service life; represents the soil hardening coefficient; S represents the site category parameter.

[0021] As a preferred solution, the process of constructing the wire tension increase model includes:

[0022] Obtaining the initial tension of the conductor of the pole, the maximum tension increment of the conductor, the rate of increase of the conductor tension, the amplitude 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; wherein the long-term drift coefficient of the conductor tension is used to characterize the trend of the conductor tension slowly increasing over a long period of time;

[0023] Constructing a conductor tension increase model for the pole according to the conductor initial tension, the conductor maximum tension increment, the conductor tension increase speed, the conductor tension periodic variation amplitude coefficient, the conductor tension periodic variation frequency, and the conductor tension long-term drift coefficient;

[0024] Calculating the conductor breakage probability of the pole according to the conductor tension variation curve includes:

[0025] Obtaining the critical tension of the conductor of the pole;

[0026] Calculating the wire breakage probability according to the wire tension variation curve and the wire critical tension;

[0027] The formula of the wire tension increase model is:

[0028] T(t)=T(0)+ΔT·(1-e -vt )·[1+γ·sin(ωt)+κ·ln(t+1)];

[0029] Where, 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; t represents the time;

[0030] The calculation formula for the wire breakage probability is:

[0031] P f,wire =P(T(t)>T critical );

[0032] Where, P f,wire represents the probability of wire breakage; T critical Indicates the critical tension of the wire.

[0033] As a preferred solution, the process of constructing the concrete stress model includes:

[0034] Obtaining the inner diameter and outer diameter of the pole at each moment during the service life of the concrete; obtaining the wind-exposed area, surface wind speed, and pole height of the pole;

[0035] Determine, by mathematical fitting, the inner diameter function and outer diameter function of the pole according to the inner diameter and outer diameter of the pole at each moment during the service life of the concrete; wherein the inner diameter function is used to characterize the change pattern of the inner diameter of the pole over time; and the outer diameter function is used to characterize the change pattern of the outer diameter of the pole over time;

[0036] Calculating the wind load on the pole based on the wind-exposed area, surface wind speed, and failure probability caused by the wind load;

[0037] Calculating the wind load bending moment at the conductor of the pole according to the wind load and the height of the pole;

[0038] Constructing the concrete stress model according to the wind load bending moment at the conductor, the pole inner diameter function, and the pole outer diameter function;

[0039] Calculating the concrete cracking probability of the pole according to the concrete stress change curve includes:

[0040] Obtaining the concrete cross-section width, concrete cross-section height, concrete compressive strength, and concrete design service life of the pole;

[0041] Calculate the wind load bending moment at the concrete of the pole based on the concrete section width, concrete section height, concrete compressive strength, concrete design service life and the concrete service life;

[0042] Calculating the critical stress of the concrete of the pole according to the bending moment of the wind load at the concrete;

[0043] Calculating, based on the concrete stress variation curve and the concrete critical stress, a second probability that the concrete stress of the pole exceeds the concrete critical stress within the concrete service life;

[0044] Calculating the concrete cracking probability according to the second probability;

[0045] The calculation formula of the wind load is:

[0046]

[0047] Where, F wind represents wind load; P wind represents the failure probability caused by wind load; A represents the wind-exposed area; V represents the surface wind speed; C d Indicates the resistance coefficient; R e Reynolds number; n represents the Reynolds number index; F tree 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;

[0048] The calculation formula for the wind load bending moment at the conductor is:

[0049] M wind =F wind H / 2+M vortex +M gust ;

[0050] Where M wind Indicates the bending moment of wind load at the conductor; H indicates the height of the pole; M vortex represents the additional bending moment caused by vortex-induced vibration; M gust represents the additional bending moment caused by gust effect;

[0051] The formula of the concrete stress model is:

[0052]

[0053] Where, σ(t) represents the concrete stress; d(t) represents the function of the inner diameter of the pole; D(t) represents the function of the outer diameter of the pole;

[0054] The calculation formula for the wind load bending moment at the concrete is:

[0055]

[0056] Where M capacity represents the concrete wind load bending moment; f c represents the compressive strength of concrete; b represents the width of the concrete section; h represents the height of the concrete section; t′ represents the service life of the concrete; t d The design service life of concrete; χ represents the long-term load effect coefficient; ζ represents the degradation index;

[0057] The calculation formula of the critical stress of concrete is:

[0058]

[0059] Where, σ critical (t) represents the critical stress of concrete;

[0060] The calculation formula for the concrete cracking probability is:

[0061]

[0062] Where, 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; represents the corrosion influence coefficient; C represents the chloride ion content.

[0063] As a preferred solution, before obtaining the surface wind speed of the pole, the following steps are also included:

[0064] Obtain the distance between the power pole and the typhoon center, the surface maximum wind speed, the surface maximum wind speed radius, and the azimuth of the surface wind speed;

[0065] Calculating the surface wind speed based on the distance between the power pole and the typhoon center, the surface maximum wind speed, the surface maximum wind speed radius, and the azimuth of the surface wind speed;

[0066] The surface wind speed is calculated as follows:

[0067]

[0068] Where V s Indicates surface wind speed; V ms Indicates the maximum surface wind speed; R s represents the radius of maximum wind speed on the surface; r represents the distance between the pole and the typhoon center; θ represents the azimuth of the surface wind speed; B s represents the surface Holland parameter; x represents the scale parameter; ε represents the asymmetry coefficient; F z represents the vertical profile function.

[0069] As a preferred solution, the calculation formula for the comprehensive failure probability is:

[0070]

[0071] Where, P f represents the comprehensive failure probability; P f,wind represents the failure probability caused by wind load; w1 represents the weight coefficient of the failure probability caused by wind load; P f,base represents the basic failure probability; w2 represents the weight coefficient of the basic failure probability; P f,wire represents the probability of wire breakage; w3 represents the weight coefficient of the probability of wire breakage; P f,crack represents the probability of concrete cracking; w4 represents the weight coefficient of the probability of concrete cracking.

[0072] As a preferred solution, the calculation of the failure probability of the distribution line based on the comprehensive failure probability of all poles includes:

[0073] Obtain the system importance coefficient of each pole, the spatial correlation coefficient with other poles, and the temporal correlation coefficient with other poles;

[0074] Calculating the failure probability of the distribution line according to the comprehensive failure probability, system importance coefficient, spatial correlation coefficient and time correlation coefficient of all poles;

[0075] The calculation formula for the failure probability of the distribution line is:

[0076]

[0077] Where FR represents the failure probability of the distribution line; ρ s,i represents the spatial correlation coefficient between the i-th pole and other poles; ρ t,i represents the time correlation coefficient of the ith pole and other poles; P f,i represents the comprehensive failure probability of the i-th pole; θ i represents the system importance coefficient of the i-th pole.

[0078] Based on the above embodiment, another embodiment of the present invention provides a distribution line failure probability calculation device, comprising: a data acquisition module, a pole comprehensive failure probability calculation module and a distribution line failure probability calculation module;

[0079] The data acquisition module is used to obtain the foundation usage time, conductor usage time, concrete usage time and failure probability caused by wind load of each pole;

[0080] The pole comprehensive failure probability calculation module is used to calculate, for each pole, a foundation stiffness change curve of the pole that changes with time during the foundation usage time based on the foundation usage time and the constructed foundation loosening model; calculate the foundation failure probability of the pole based on the foundation stiffness change curve; calculate the conductor tension change curve of the pole that changes with time during the conductor usage time based on the conductor usage time and the constructed conductor tension increase model; calculate the conductor breakage probability of the pole based on the conductor tension change curve; calculate the concrete stress change curve of the pole that changes with time during the concrete usage time based on the concrete usage time and the constructed concrete stress model; calculate the concrete cracking probability of the pole based on the concrete stress change curve; calculate the comprehensive failure probability of the pole based on the foundation failure probability, the conductor breakage probability, the concrete cracking probability and the 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] The distribution line failure probability calculation module is used to calculate the distribution line failure probability based on the comprehensive failure probability of all poles.

[0082] Based on the above embodiments, another embodiment of the present 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 distribution line failure probability calculation method described in the above invention embodiment.

[0083] Based on the above embodiment, another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the distribution line failure probability calculation method described in the above embodiment of the invention.

[0084] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0085] Based on a physical model of the pole's degradation over time, including foundation loosening, conductor tension changes, and concrete stress variations, this method calculates the probability of foundation failure, conductor breakage, and concrete cracking. It then calculates the combined failure probability of the pole itself. Ultimately, based on the combined failure probability of all poles, the distribution line failure probability is calculated. This method more accurately reflects how pole performance changes over time. By considering the impact of performance degradation of the foundation, conductors, and concrete on failure probability, it improves the accuracy of failure probability assessments for power system components. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 This is a flow chart of a method for calculating the failure probability of a distribution line provided by one embodiment of the present invention;

[0087] Figure 2 The figure is a schematic structural diagram of a device for calculating the failure probability of a power distribution line provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0088] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0089] Example 1

[0090] Please refer to Figure 1 , a method for calculating the failure probability of a distribution line provided in one embodiment of the present invention, comprising:

[0091] S1. Obtain the foundation service life, conductor service life, concrete service life, and failure probability caused by wind load for each pole.

[0092] It should be noted that "foundation" refers to the pole foundation; "concrete" refers to the concrete portion of the pole; "foundation life" refers to the time the pole foundation has been in service; "conductor life" refers to the time the pole conductors have been in service; and "concrete life" refers to the time the concrete portion has been in service. The failure probability due to wind load is pre-calculated. This invention primarily focuses on considering the impact of time-dependent performance degradation of the foundation, conductors, and concrete when calculating the failure probability of distribution lines.

[0093] S2. For each pole, calculate the foundation stiffness change curve of the pole that changes with time during the foundation usage time according to the foundation usage time and the constructed foundation loosening model; calculate the foundation failure probability of the pole according to the foundation stiffness change curve; calculate the conductor tension change curve of the pole that changes with time during the conductor usage time according to the constructed conductor tension increase model; calculate the conductor breakage probability of the pole according to the conductor tension change curve; calculate the concrete stress change curve of the pole that changes with time during the concrete usage time according to the concrete stress model; calculate the concrete cracking probability of the pole according to the concrete stress change curve; calculate the comprehensive failure probability of the pole according to 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.

[0094] In step S2, the present invention incorporates a foundation loosening model to calculate the foundation stiffness variation curve over the foundation's lifetime, accounting for the impact of foundation loosening over time on the distribution line failure probability. A conductor tension increase model is also introduced to calculate the conductor tension variation curve over the conductor's lifetime, accounting for the impact of conductor tension increase over time on the distribution line failure probability. A concrete stress model is also introduced to calculate the concrete stress variation curve over the concrete's lifetime, accounting for the impact of stress changes in the concrete over time on the distribution line failure probability. The foundation stiffness variation curve reflects foundation stiffness data over the foundation's lifetime, and the foundation failure probability is calculated based on the foundation stiffness variation curve. The conductor tension variation curve reflects conductor tension data over the conductor's lifetime, and the conductor fracture probability is calculated based on the conductor tension variation curve. The concrete stress variation curve reflects concrete stress data over the concrete's lifetime, and the concrete cracking probability is calculated based on the concrete stress variation curve. The combined failure probability of the pole is then calculated based on the foundation failure probability, conductor fracture probability, concrete cracking probability, and failure probability due to wind load.

[0095] In a preferred embodiment, the process of constructing the basic loosening model includes:

[0096] Obtaining the initial stiffness of the pole foundation, the foundation loosening rate and foundation loosening process index, the foundation stiffness seasonal fluctuation amplitude and the seasonal cycle; wherein the inductive foundation loosening process index is used to characterize the nonlinear characteristics of the foundation loosening process;

[0097] Constructing a foundation loosening model of the pole according to the foundation initial stiffness, foundation loosening rate and foundation loosening process index, and foundation stiffness seasonal fluctuation amplitude and period;

[0098] Calculating the foundation failure probability of the pole according to the foundation stiffness change curve includes:

[0099] Obtaining the critical foundation stiffness of the pole;

[0100] Calculating the first failure probability of the pole having a foundation stiffness that does not exceed the foundation critical stiffness within the service life of the foundation according to the foundation stiffness change curve and the foundation critical stiffness;

[0101] Calculating the basic failure probability according to the first failure probability;

[0102] The formula of the foundation loosening model is:

[0103]

[0104] Where K f(t) represents foundation stiffness; K(0) represents initial foundation stiffness; μ represents foundation loosening rate; β represents foundation loosening process index; represents the seasonal fluctuation amplitude of foundation stiffness; T represents the seasonal cycle; t represents the time;

[0105] The calculation formula of the basic failure probability is:

[0106]

[0107] Where, P f,base represents the basic failure probability; K f,critical represents the critical stiffness of the foundation; P(K f (t)≤K f,critical ) represents the first failure probability that the foundation stiffness does not exceed the critical stiffness of the foundation during the foundation service life; represents the soil hardening coefficient; S represents the site category parameter.

[0108] It should be noted that the foundation loosening rate μ is a parameter that indicates the rate of foundation loosening, with a typical value range of 0.005-0.02 / year. The foundation loosening process index β describes the nonlinear characteristics of the foundation loosening process, with a typical value of 0.9-1.1. Both μ and β can be obtained in advance by fitting field test data. The initial foundation stiffness K(0) is the stiffness of the foundation before it is put into use.

[0109] The soil hardening coefficient is influenced by soil type, soil moisture content, and soil compaction. Soil types include sandy, clayey, and silty soils. The soil hardening coefficient for sandy soils ranges from 1.0 to 1.2, for clayey soils from 0.8 to 1.0, and for silty soils from 1.2 to 1.5. These are baseline values. 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 compaction is dense, the soil hardening coefficient decreases by 10% to 20%. When the soil compaction is loose, the soil hardening coefficient increases by 20% to 40% of the baseline value.

[0110] The rules for site categories and their corresponding parameter values ​​are as follows:

[0111] (1) The soil type is rock or hard soil foundation, the terrain is flat and open, and the soil layer thickness is less than 5 meters. This is a Class I site, and the site category parameter value is 1.0;

[0112] (2) The soil type is dense sand or gravel soil, the terrain is relatively flat, the soil layer thickness is between 5 meters and 15 meters, which is the second type of site, and the site category parameter value is 1.2;

[0113] (3) The soil type is medium-dense sand or clay soil, the terrain is undulating, the soil layer thickness is between 15 meters and 30 meters, which is the third type of site, and the site category parameter value is 1.4;

[0114] (4) The soil type is loose soil or soft soil, the terrain is complex, and the soil thickness is greater than 30 meters. This is a Class IV site, and the site category parameter value is 1.6.

[0115] In a preferred embodiment, the process of constructing the wire tension increase model includes:

[0116] Obtaining the initial tension of the conductor of the pole, the maximum tension increment of the conductor, the rate of increase of the conductor tension, the amplitude 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; wherein the long-term drift coefficient of the conductor tension is used to characterize the trend of the conductor tension slowly increasing over a long period of time;

[0117] Constructing a conductor tension increase model for the pole according to the conductor initial tension, the conductor maximum tension increment, the conductor tension increase speed, the conductor tension periodic variation amplitude coefficient, the conductor tension periodic variation frequency, and the conductor tension long-term drift coefficient;

[0118] Calculating the conductor breakage probability of the pole according to the conductor tension variation curve includes:

[0119] Obtaining the critical tension of the conductor of the pole;

[0120] Calculating the wire breakage probability according to the wire tension variation curve and the wire critical tension;

[0121] The formula of the wire tension increase model is:

[0122] T(t)=T(0)+ΔT·(1-e -vt )·[1+γ·sin(ωt)+κ·ln(t+1)];

[0123] Where, 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; t represents the time;

[0124] The calculation formula for the wire breakage probability is:

[0125] P f,wire =P(T(t)>T critical );

[0126] Where, Pf,wire represents the probability of wire breakage; T critical Indicates the critical tension of the wire.

[0127] It should be noted that the maximum increase in conductor tension, ΔT, represents the maximum value that conductor tension may increase, with a typical value range of 10%-30% of the initial conductor tension. The conductor tension increase rate, v, is used to describe the rate at which conductor tension increases over time, and is generally taken as 0.1-0.5 / hour. The conductor tension periodic variation amplitude coefficient, γ, is used to describe the amplitude of periodic tension changes, reflecting the degree of tension fluctuation caused by seasonal temperature changes or load cycles, and is generally taken as 0.05-0.2. The conductor tension periodic variation frequency, ω, is used to describe the frequency of periodic conductor tension changes, and is generally taken as 0.1-1 rad / hour. The conductor tension long-term drift coefficient, κ, is used to describe the trend of slow long-term increase in conductor tension, and is 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.

[0128] In a preferred embodiment, the process of constructing the concrete stress model includes:

[0129] Obtaining the inner diameter and outer diameter of the pole at each moment during the service life of the concrete; obtaining the wind-exposed area, surface wind speed, and pole height of the pole;

[0130] Determine, by mathematical fitting, the inner diameter function and outer diameter function of the pole according to the inner diameter and outer diameter of the pole at each moment during the service life of the concrete; wherein the inner diameter function is used to characterize the change pattern of the inner diameter of the pole over time; and the outer diameter function is used to characterize the change pattern of the outer diameter of the pole over time;

[0131] Calculating the wind load on the pole based on the wind-exposed area, surface wind speed, and failure probability caused by the wind load;

[0132] Calculating the wind load bending moment at the conductor of the pole according to the wind load and the height of the pole;

[0133] Constructing the concrete stress model according to the wind load bending moment at the conductor, the pole inner diameter function, and the pole outer diameter function;

[0134] Calculating the concrete cracking probability of the pole according to the concrete stress change curve includes:

[0135] Obtaining the concrete cross-section width, concrete cross-section height, concrete compressive strength, and concrete design service life of the pole;

[0136] Calculate the wind load bending moment at the concrete of the pole based on the concrete section width, concrete section height, concrete compressive strength, concrete design service life and the concrete service life;

[0137] Calculating the critical stress of the concrete of the pole according to the bending moment of the wind load at the concrete;

[0138] Calculating, based on the concrete stress variation curve and the concrete critical stress, a second probability that the concrete stress of the pole exceeds the concrete critical stress within the concrete service life;

[0139] Calculating the concrete cracking probability according to the second probability;

[0140] The calculation formula of the wind load is:

[0141]

[0142] Where, F wind represents wind load; P wind represents the failure probability caused by wind load; A represents the wind-exposed area; V represents the surface wind speed; C d Indicates the resistance coefficient; R e Reynolds number; n represents the Reynolds number index; P tree 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;

[0143] The calculation formula for the wind load bending moment at the conductor is:

[0144] M wind =F wind H / 2+M vortex +M gust ;

[0145] Where M wind Indicates the bending moment of wind load at the conductor; H indicates the height of the pole; M vortex represents the additional bending moment caused by vortex-induced vibration; M gust represents the additional bending moment caused by gust effect;

[0146] The formula of the concrete stress model is:

[0147]

[0148] Where, σ(t) represents the concrete stress; d(t) represents the function of the inner diameter of the pole; D(t) represents the function of the outer diameter of the pole;

[0149] The calculation formula for the wind load bending moment at the concrete is:

[0150]

[0151] Where M capacity represents the concrete wind load bending moment; f c represents the compressive strength of concrete; b represents the width of the concrete section; h represents the height of the concrete section; t′ represents the service life of the concrete; t d The design service life of concrete; χ represents the long-term load effect coefficient; ζ represents the degradation index;

[0152] The calculation formula of the critical stress of concrete is:

[0153]

[0154] Where, σ critical (t) represents the critical stress of concrete;

[0155] The calculation formula for the concrete cracking probability is:

[0156]

[0157] Where, 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; represents the corrosion influence coefficient; C represents the chloride ion content.

[0158] It should be noted that the degradation of the concrete portion of a pole over time is primarily manifested by an increase in the inner diameter and a decrease in the outer diameter. This paper uses mathematical fitting to determine the time-varying functions of the pole's inner and outer diameters over the concrete's lifetime. Based on these functions and the calculated wind load bending moment at the conductor, a concrete stress model is constructed to characterize the temporal variation of concrete stress.

[0159] If the wind load bending moment at the conductor exceeds the wind load bending moment at the concrete, the concrete will face the risk of cracking. Therefore, the present 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.

[0160] Failure probability P caused by additional force generated by tree falling tree It usually indicates the frequency or probability of tree fall affecting power lines, reflecting the possibility of power system failure caused by tree fall in a specific area. windIt represents the probability of power line failure caused by strong wind. It is a dimensionless quantity with a value range between 0 and 1. The additional bending moment M caused by vortex-induced vibration vortex The additional bending moment M caused by gust is a pre-calculated quantity, M vortex By calculating the fluid mechanics model and structural dynamics analysis, M gust Estimated through wind load modeling and structural response analysis.

[0161] Corrosion influence coefficient It reflects the influence of corrosion on the strength and durability of concrete in different environments. The value is 0.015-0.025MPa / (kg / m 3 ); in industrial environments The value is 0.010-0.020MPa / (kg / m 3 ); Under normal circumstances The value is 0.005-0.015MPa / (kg / m 3 ).

[0162] In a preferred embodiment, before obtaining the surface wind speed of the pole, the method further includes:

[0163] Obtain the distance between the power pole and the typhoon center, the surface maximum wind speed, the surface maximum wind speed radius, and the azimuth of the surface wind speed;

[0164] Calculating the surface wind speed based on the distance between the power pole and the typhoon center, the surface maximum wind speed, the surface maximum wind speed radius, and the azimuth of the surface wind speed;

[0165] The surface wind speed is calculated as follows:

[0166]

[0167] Where V s Indicates surface wind speed; V ms Indicates the maximum surface wind speed; R s represents the radius of maximum wind speed on the surface; r represents the distance between the pole and the typhoon center; θ represents the azimuth of the surface wind speed; B s represents the surface Holland parameter; x represents the scale parameter; ε represents the asymmetry coefficient; F z represents the vertical profile function.

[0168] In this embodiment, a typhoon model is provided for wind load input in the above calculation process.

[0169] It should be noted that the present invention uses the improved surface Holland parameter B s :

[0170]

[0171] Where g s represents the attenuation factor from gradient wind to surface wind; represents the intensity correlation coefficient.

[0172] The present invention also uses an improved scale parameter x:

[0173]

[0174] Where x min represents the minimum value of x; δ represents the attenuation coefficient.

[0175] The present invention introduces an improved Holland model to describe the typhoon wind field, takes into account the asymmetry and vertical structure of the wind field, and improves the accuracy of wind field simulation.

[0176] It should also be noted that the parameters in the typhoon model (such as V ms 、R s There are uncertainties in the V, etc., which need to be adjusted according to real-time observation data. As time changes, new V is extracted in real time according to weather forecast data. ms and R s The particle filter algorithm is used to process the observed data and continuously update the probability distribution of these parameters. The updated parameter distribution is fed back to the typhoon model to improve the accuracy of wind field simulation.

[0177] An improved particle filter (PF) algorithm is used for Bayesian inference to update the posterior distribution of system parameters:

[0178] (1) Initialization: Sample N particles from the prior distribution and assign initial weights;

[0179] (2) Importance sampling: The weight of each particle is calculated using the improved importance function:

[0180]

[0181] in, is the weight of the i-th particle at time k, is the likelihood function, q(·) is the importance function, and π(·) is the prior transfer function;

[0182] (3) Adaptive resampling: Adaptive resampling based on the number of valid particles:

[0183]

[0184] If N eff ≤N threshold, then resampling is performed;

[0185] (4) Regularization: performing kernel density estimation on the resampled particles to increase particle diversity;

[0186]

[0187] Where h is the bandwidth parameter and K(·) is the kernel function;

[0188] (5) Movement: Use the improved Markov chain Monte Carlo (MCMC) method to move particles to improve sampling efficiency;

[0189] (6) Parameter estimation: Estimate the posterior distribution of parameters based on the weighted average of particles:

[0190] The posterior filter density is approximated as:

[0191]

[0192] Among them, K h is the kernel density estimation function.

[0193] Particle filtering requires multiple runs of the physical model, which is computationally expensive in large-scale systems. The Gaussian process proxy model establishes an approximate relationship between input and output by performing a finite number of physical model calculations. During the particle filter iteration process, the proxy model is used to replace some physical model calculations, significantly improving computational efficiency. The Gaussian process (GP) algorithm establishes the proxy model:

[0194] (1) Select the covariance function: Use the combined kernel function:

[0195] k(x, x′)=k SE (x, x′)+k RQ (x, x′)+k P (x, x′);

[0196] Among them, k SE is the square exponential kernel function, k RQ is a rational quadratic kernel function, k P is a periodic kernel function;

[0197]

[0198] Among them, σ f is the signal variance, l j is the length scale parameter, M is the metric matrix, α is the shape parameter, and p is the period. x, x′: two input vectors. j and x′ j Represents the jth component of the input vectors x and x′. The length scale l controls the smoothness or rate of change of the function. N xRepresents the dimension of the input vector, that is, the number of features or variables;

[0199] (2) Training sample generation: Use Latin hypercube sampling method to generate training samples;

[0200] (3) Hyperparameter optimization: Bayesian optimization method is used to optimize the hyperparameters of the kernel function

[0201] (4) Prediction: Use the trained GP model to predict the response of the pole.

[0202] The Gaussian process model may contain multiple input variables, such as pole diameter, material strength, and environmental factors. This paper uses an improved automatic correlation determination method to calculate the correlation of each variable, select the variables with the strongest correlation, simplify the Gaussian process model, and further improve computational efficiency and model interpretability. The specific method for selecting predictor variables with strong correlation using the improved automatic correlation determination method is as follows:

[0203] (1) Calculate the adaptive length scale parameter for each input variable.

[0204]

[0205] Where l0 is the reference length scale, ρ j is the adaptive coefficient;

[0206] (2) Normalize the inverse of the length scale.

[0207]

[0208] (4) Use the improved threshold selection method to select variables with strong correlation:

[0209] If r j >τ·max(r), then select variable j;

[0210] Among them, τ is the adaptive threshold coefficient; r j Usually represents the correlation length scale or characteristic length. j is the correlation length scale of the jth input variable, reflecting the influence of the input variable on the output. j A larger value indicates that the input variable affects the output in a larger range. j A value of indicates that the effect of that input variable is more localized.

[0211] In a preferred embodiment, the calculation formula for the comprehensive failure probability is:

[0212]

[0213] Where, P f represents the comprehensive failure probability; Pf,wind represents the failure probability caused by wind load; w1 represents the weight coefficient of the failure probability caused by wind load; P f,base represents the basic failure probability; w2 represents the weight coefficient of the basic failure probability; P f,wire represents the probability of wire breakage; w3 represents the weight coefficient of the probability of wire breakage; P f,crack represents the probability of concrete cracking; w4 represents the weight coefficient of the probability of concrete cracking.

[0214] S3. Calculate the failure probability of the distribution line based on the comprehensive failure probability of all poles.

[0215] In step S3, the comprehensive failure probability of all poles in the distribution line is calculated by the above method, and then the failure probability of the distribution line is calculated based on the comprehensive failure probability of all poles.

[0216] In a preferred embodiment, the calculation of the failure probability of the distribution line based on the comprehensive failure probability of all poles includes:

[0217] Obtain the system importance coefficient of each pole, the spatial correlation coefficient with other poles, and the temporal correlation coefficient with other poles;

[0218] Calculating the failure probability of the distribution line according to the comprehensive failure probability, system importance coefficient, spatial correlation coefficient and time correlation coefficient of all poles;

[0219] The calculation formula for the failure probability of the distribution line is:

[0220]

[0221] Where FR represents the failure probability of the distribution line; ρ s,i represents the spatial correlation coefficient between the i-th pole and other poles; ρ t,i represents the time correlation coefficient of the ith pole and other poles; P f,i represents the comprehensive failure probability of the i-th pole; θ i represents the system importance coefficient of the i-th pole.

[0222] It should be noted that the present invention establishes a physical model that takes into account the degradation process of electric poles over time, including foundation loosening, changes in conductor tension and concrete stress, which more realistically reflects the performance changes of electric poles, thereby improving the accuracy of failure probability assessment of power system components and enabling more accurate typhoon risk analysis.

[0223] Example 2

[0224] Please refer to Figure 2, is a schematic structural diagram of a distribution line failure probability calculation device provided by an embodiment of the present invention, comprising: a data acquisition module, a pole comprehensive failure probability calculation module, and a distribution line failure probability calculation module;

[0225] The data acquisition module is used to obtain the foundation usage time, conductor usage time, concrete usage time and failure probability caused by wind load of each pole;

[0226] The pole comprehensive failure probability calculation module is used to calculate, for each pole, a foundation stiffness change curve of the pole that changes with time during the foundation usage time based on the foundation usage time and the constructed foundation loosening model; calculate the foundation failure probability of the pole based on the foundation stiffness change curve; calculate the conductor tension change curve of the pole that changes with time during the conductor usage time based on the conductor usage time and the constructed conductor tension increase model; calculate the conductor breakage probability of the pole based on the conductor tension change curve; calculate the concrete stress change curve of the pole that changes with time during the concrete usage time based on the concrete usage time and the constructed concrete stress model; calculate the concrete cracking probability of the pole based on the concrete stress change curve; calculate the comprehensive failure probability of the pole based on the foundation failure probability, the conductor breakage probability, the concrete cracking probability and the 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.

[0227] The distribution line failure probability calculation module is used to calculate the distribution line failure probability based on the comprehensive failure probability of all poles.

[0228] Example 3

[0229] Accordingly, an embodiment of the present 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 distribution line failure probability calculation method described in the above-mentioned embodiment of the invention.

[0230] Example 4

[0231] Accordingly, an embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the distribution line failure probability calculation method described in the above-mentioned embodiment of the invention.

[0232] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive work.

[0233] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0234] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0235] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the device, connecting various parts of the entire device using various interfaces and lines.

[0236] The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0237] The storage medium is a storage medium, and the computer program is stored in the storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned method embodiments. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased 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 electric carrier signals and telecommunication signals.

[0238] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. 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 distribution line, characterized in that: include: Obtain the service life of the foundation, conductor, concrete and failure probability caused by wind load for each pole; For each pole, based on the foundation usage time and the constructed foundation loosening model, calculate the foundation stiffness change curve of the pole over time during the foundation usage time; calculate the foundation failure probability of the pole based on the foundation stiffness change curve; According to the service life of the conductor and the constructed conductor tension increase model, the conductor tension change curve of the pole that changes with time during the service life of the conductor is calculated; according to the conductor tension change curve, the conductor breakage probability of the pole is calculated; according to the service life of the concrete and the constructed concrete stress model, the concrete stress change curve of the pole that changes with time during the service life of the concrete is calculated; according to the concrete stress change curve, the concrete cracking probability of the pole is calculated; according to the foundation failure probability, the conductor breakage probability, the concrete cracking probability and the failure probability caused by wind load, the comprehensive failure probability of the pole is calculated; wherein, the foundation loosening model is used to characterize the change law of the foundation stiffness over time; the conductor tension increase model is used to characterize the change law of the conductor tension over time; and the concrete stress model is used to characterize the change law of the concrete stress over time; Calculate the failure probability of distribution lines based on the comprehensive failure probability of all poles; The process of constructing the basic loosening model includes: Obtaining the initial stiffness of the pole foundation, the foundation loosening rate and foundation loosening process index, the seasonal fluctuation amplitude of the foundation stiffness and the seasonal cycle; wherein the pole foundation loosening process index is used to characterize the nonlinear characteristics of the foundation loosening process; Constructing a foundation loosening model of the pole according to the foundation initial stiffness, foundation loosening rate and foundation loosening process index, and foundation stiffness seasonal fluctuation amplitude and period; Calculating the foundation failure probability of the pole according to the foundation stiffness change curve includes: Obtaining the critical foundation stiffness of the pole; Calculating the first failure probability of the pole having a foundation stiffness that does not exceed the foundation critical stiffness within the service life of the foundation according to the foundation stiffness change curve and the foundation critical stiffness; Calculating the basic failure probability according to the first failure probability; The formula of the foundation loosening model is: ； Where, represents the foundation stiffness; represents the initial stiffness of the foundation; Indicates the rate of foundation loosening; Indicates the foundation loosening process index; Indicates the seasonal fluctuation amplitude of foundation stiffness; Represents seasonal cycles; Indicates the moment; The calculation formula of the basic failure probability is: ； Where, represents the basic failure probability; represents the critical stiffness of the foundation; It represents the first failure probability that the foundation stiffness does not exceed the critical stiffness of the foundation during the service life of the foundation; represents the soil hardening coefficient; Represents the site category parameters.

2. The method for calculating the failure probability of a power distribution line according to claim 1, wherein: The construction process of the wire tension increase model includes: Obtaining the initial tension of the conductor of the pole, the maximum tension increment of the conductor, the rate of increase of the conductor tension, the amplitude 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; wherein the long-term drift coefficient of the conductor tension is used to characterize the trend of the conductor tension slowly increasing over a long period of time; Constructing a conductor tension increase model for the pole according to the conductor initial tension, the conductor maximum tension increment, the conductor tension increase speed, the conductor tension periodic variation amplitude coefficient, the conductor tension periodic variation frequency, and the conductor tension long-term drift coefficient; Calculating the conductor breakage probability of the pole according to the conductor tension variation curve includes: Obtaining the critical tension of the conductor of the pole; Calculating the wire breakage probability according to the wire tension variation curve and the wire critical tension; The formula of the wire tension increase model is: ； Where, Indicates the conductor tension; Indicates the initial tension of the wire; Indicates the maximum tension increment of the conductor; Indicates the rate of increase of wire tension; Indicates the amplitude coefficient of periodic variation of conductor tension; Indicates the frequency of periodic changes in conductor tension; Indicates the long-term drift coefficient of conductor tension; Indicates the moment; The calculation formula for the wire breakage probability is: ； Where, Indicates the probability of wire breakage; Indicates the critical tension of the wire.

3. The method for calculating the failure probability of a power distribution line according to claim 1, wherein: The construction process of the concrete stress model includes: Obtaining the inner diameter and outer diameter of the pole at each moment during the service life of the concrete; obtaining the wind-exposed area, surface wind speed, and pole height of the pole; Determine, by mathematical fitting, the inner diameter function and outer diameter function of the pole according to the inner diameter and outer diameter of the pole at each moment during the service life of the concrete; wherein the inner diameter function is used to characterize the change pattern of the inner diameter of the pole over time; and the outer diameter function is used to characterize the change pattern of the outer diameter of the pole over time; Calculating the wind load on the pole based on the wind-exposed area, surface wind speed, and failure probability caused by the wind load; Calculating the wind load bending moment at the conductor of the pole according to the wind load and the height of the pole; Constructing the concrete stress model according to the wind load bending moment at the conductor, the pole inner diameter function, and the pole outer diameter function; Calculating the concrete cracking probability of the pole according to the concrete stress change curve includes: Obtaining the concrete cross-section width, concrete cross-section height, concrete compressive strength, and concrete design service life of the pole; Calculate the wind load bending moment at the concrete of the pole based on the concrete section width, concrete section height, concrete compressive strength, concrete design service life and the concrete service life; Calculating the critical stress of the concrete of the pole according to the bending moment of the wind load at the concrete; Calculating, based on the concrete stress variation curve and the concrete critical stress, a second probability that the concrete stress of the pole exceeds the concrete critical stress within the concrete service life; Calculating the concrete cracking probability according to the second probability; The calculation formula of the wind load is: ； Where, represents wind load; represents the failure probability caused by wind load; Indicates the area exposed to wind; represents the surface wind speed; represents the drag coefficient; represents the Reynolds number; represents the Reynolds number index; represents the probability of failure due to the additional force generated by the falling tree; represents the wind speed amplification factor; Indicates the reference wind speed; The calculation formula for the wind load bending moment at the conductor is: ； Where, Indicates the bending moment of wind load at the conductor; Indicates the height of the pole; represents the additional bending moment caused by vortex-induced vibration; represents the additional bending moment caused by gust effect; The formula of the concrete stress model is: ； Where, represents the concrete stress; represents the function of the inner diameter of the pole; represents the function of the outer diameter of the pole; The calculation formula for the wind load bending moment at the concrete is: ； Where, represents the concrete wind load bending moment; Indicates the compressive strength of concrete; Indicates the width of the concrete section; Indicates the height of the concrete section; Indicates the service life of concrete; Design service life of concrete; represents the long-term load effect coefficient; represents the degradation index; The calculation formula of the critical stress of concrete is: ； Where, represents the critical stress of concrete; The calculation formula for the concrete cracking probability is: ； Where, represents the probability of concrete cracking; A second probability representing that the concrete stress exceeds the critical stress of the concrete during the service life of the concrete; represents the corrosion influence coefficient; Indicates the chloride ion content.

4. The method for calculating the failure probability of a power distribution line according to claim 3, wherein: Before obtaining the surface wind speed of the pole, it also includes: Obtain the distance between the power pole and the typhoon center, the surface maximum wind speed, the surface maximum wind speed radius, and the azimuth of the surface wind speed; Calculating the surface wind speed based on the distance between the power pole and the typhoon center, the surface maximum wind speed, the surface maximum wind speed radius, and the azimuth of the surface wind speed; The surface wind speed is calculated as follows: ； Where, represents the surface wind speed; Indicates the maximum surface wind speed; Indicates the radius of maximum wind speed on the surface; Indicates the distance between the power pole and the center of the typhoon; The azimuth angle representing the surface wind speed; Represents the surface Holland parameter; represents the scale parameter; represents the asymmetric coefficient; represents the vertical profile function.

5. The method for calculating the failure probability of a power distribution line according to claim 1, wherein: The calculation formula of the comprehensive failure probability is: ； Where, represents the comprehensive failure probability; represents the failure probability caused by wind load; The weight coefficient representing the failure probability caused by wind load; represents the basic failure probability; The weight coefficient representing the basic failure probability; Indicates the probability of wire breakage; The weight coefficient representing the probability of wire breakage; represents the probability of concrete cracking; The weight coefficient representing the probability of concrete cracking.

6. The method for calculating the failure probability of a power distribution line according to claim 1, wherein: The calculation of the failure probability of the distribution line based on the comprehensive failure probability of all poles includes: Obtain the system importance coefficient of each pole, the spatial correlation coefficient with other poles, and the temporal correlation coefficient with other poles; Calculating the failure probability of the distribution line according to the comprehensive failure probability, system importance coefficient, spatial correlation coefficient and time correlation coefficient of all poles; The calculation formula for the failure probability of the distribution line is: ； Where, represents the probability of failure of the distribution line; Indicates the The spatial correlation coefficient between the pole and other poles; Indicates the Time correlation coefficient between the pole and other poles; Indicates the The comprehensive failure probability of each pole; Indicates the The system importance coefficient of each pole.

7. A device for calculating the failure probability of a distribution line, characterized in that: include: Data acquisition module, pole comprehensive failure probability calculation module and distribution line failure probability calculation module; The data acquisition module is used to obtain the foundation usage time, conductor usage time, concrete usage time and failure probability caused by wind load of each pole; The pole comprehensive failure probability calculation module is used to calculate, for each pole, a foundation stiffness change curve of the pole that changes over time during the foundation usage period based on the foundation usage period and the constructed foundation loosening model; and calculate the foundation failure probability of the pole based on the foundation stiffness change curve; According to the service life of the conductor and the constructed conductor tension increase model, the conductor tension change curve of the pole that changes with time during the service life of the conductor is calculated; according to the conductor tension change curve, the conductor breakage probability of the pole is calculated; according to the service life of the concrete and the constructed concrete stress model, the concrete stress change curve of the pole that changes with time during the service life of the concrete is calculated; according to the concrete stress change curve, the concrete cracking probability of the pole is calculated; according to the foundation failure probability, the conductor breakage probability, the concrete cracking probability and the failure probability caused by wind load, the comprehensive failure probability of the pole is calculated; wherein, the foundation loosening model is used to characterize the change law of the foundation stiffness over time; the conductor tension increase model is used to characterize the change law of the conductor tension over time; and the concrete stress model is used to characterize the change law of the concrete stress over time; The distribution line failure probability calculation module is used to calculate the distribution line failure probability based on the comprehensive failure probability of all poles; The process of constructing the basic loosening model includes: Obtaining the initial stiffness of the pole foundation, the foundation loosening rate and foundation loosening process index, the seasonal fluctuation amplitude of the foundation stiffness and the seasonal cycle; wherein the pole foundation loosening process index is used to characterize the nonlinear characteristics of the foundation loosening process; Constructing a foundation loosening model of the pole according to the foundation initial stiffness, foundation loosening rate and foundation loosening process index, and foundation stiffness seasonal fluctuation amplitude and period; Calculating the foundation failure probability of the pole according to the foundation stiffness change curve includes: Obtaining the critical foundation stiffness of the pole; Calculating the first failure probability of the pole having a foundation stiffness that does not exceed the foundation critical stiffness within the service life of the foundation according to the foundation stiffness change curve and the foundation critical stiffness; Calculating the basic failure probability according to the first failure probability; The formula of the foundation loosening model is: ； Where, represents the foundation stiffness; represents the initial stiffness of the foundation; Indicates the rate of foundation loosening; Indicates the foundation loosening process index; Indicates the seasonal fluctuation amplitude of foundation stiffness; Represents seasonal cycles; Indicates the moment; The calculation formula of the basic failure probability is: ； Where, represents the basic failure probability; represents the critical stiffness of the foundation; It represents the first failure probability that the foundation stiffness does not exceed the critical stiffness of the foundation during the foundation service life; represents the soil hardening coefficient; Represents the site category parameters.

8. A terminal device, characterized in that: The method comprises 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, the method for calculating the failure probability of a distribution line according to any one of claims 1 to 6 is implemented.

9. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute the method for calculating the failure probability of a distribution line according to any one of claims 1 to 6.

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