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

Abstract

The invention discloses a distribution line failure probability calculation method and device, terminal equipment and a storage medium, and the method comprises the steps: calculating a foundation failure probability, a lead fracture probability and a concrete cracking probability according to a physical model of a degradation process of an electric pole along with time, including foundation looseness, lead tension change and concrete stress change; further calculating the comprehensive failure probability of the electric poles, and finally calculating the failure probability of the distribution line according to the comprehensive failure probability of all the electric poles. According to the method, the change of the performance of the electric pole along with time is truly reflected, and the accuracy of power system component failure probability evaluation is improved by considering the influence of the performance degradation of the foundation, the wire and the concrete part on the failure probability.

Description

Technical Field

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

[0002] At present, the typhoon risk analysis of power distribution lines mainly adopts the static fragility curve method, which constructs the fragility curve of components related to distribution lines based on historical data, evaluates the failure probability of components at different wind speeds, and further analyzes the failure probability of the entire distribution line based on the failure probability of components. However, since the static fragility curve method cannot reflect the performance degradation of power system components such as foundations, conductors and concrete parts over time, the component failure probability evaluated by this method is inaccurate, which in turn leads to inaccurate results in the failure probability analysis of distribution lines. Summary of the invention

[0003] The present invention provides a method, an apparatus, a terminal device and a 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, according to 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; according to the foundation stiffness change curve, calculate the foundation failure probability of the pole; according to the conductor usage time and the constructed conductor tension increase model, calculate the conductor tension change curve of the pole that changes with time during the conductor usage time; according to the conductor tension change curve, calculate the conductor breakage probability of the pole; according to the concrete usage time and the constructed concrete stress model, calculate the concrete stress change curve of the pole that changes with time during the concrete usage time; according to the concrete stress change curve, calculate the concrete cracking probability of the pole; according to the foundation failure probability, conductor breakage probability, concrete cracking probability and failure probability caused by wind load, calculate the comprehensive failure probability of the pole; 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; 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 foundation initial stiffness, foundation loosening rate and foundation loosening process index, foundation stiffness seasonal fluctuation amplitude and seasonal cycle of the pole; 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, 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] According to the foundation stiffness variation curve and the foundation critical stiffness, calculating the first failure probability of the pole within the service life of the foundation that the foundation stiffness does not exceed 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] In the formula, 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 within the service life of the foundation; 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 speed 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 long-term slow increase of the conductor tension;

[0023] Constructing a conductor tension increase model of the pole according to the conductor initial tension, the conductor maximum tension increment, the conductor tension increase speed, the conductor tension periodic change amplitude coefficient, the conductor tension periodic change 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 of 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 construction process of the concrete stress model includes:

[0034] Obtain the inner diameter and outer diameter of the pole at each moment during the use time of the concrete; obtain the wind-exposed area, surface wind speed and pole height of the pole;

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

[0036] Calculating the wind load of the pole according to 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 bending moment of the wind load at the conductor, the inner diameter function of the pole and the outer diameter function of the pole;

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

[0040] Obtaining the concrete section width, concrete 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 according to the concrete section width, concrete section height, concrete compressive strength, concrete design service life and 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, according to 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] In the formula, 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 Represents the drag coefficient; R e represents the Reynolds number; n represents the Reynolds number index; F tree represents the failure probability caused by the additional force generated by the falling of trees; η represents the wind speed amplification factor; V0 represents the reference wind speed;

[0048] The calculation formula of 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 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] In the formula, σ critical (t) represents the critical stress of concrete;

[0060] The calculation formula of 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 is also included:

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

[0065] Calculating the surface wind speed according to the distance between the 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] Wherein, the calculation formula of the surface wind speed is:

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

[0075] The calculation formula of 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 ith pole and other poles; ρ t,i represents the time correlation coefficient between 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] On the basis of the above-mentioned 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, the foundation stiffness change curve of the pole that changes with time during the foundation use time according to the foundation use time and the constructed foundation loosening model; calculate the foundation failure probability of the pole according to the foundation stiffness change curve; calculate the wire tension change curve of the pole that changes with time during the wire use time according to the wire use time and the constructed wire tension increase model; calculate the wire breakage probability of the pole according to the wire tension change curve; calculate the concrete stress change curve of the pole that changes with time during the concrete use time according to the concrete use time and the constructed 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, the wire 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 wire tension increase model is used to characterize the change law of wire 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, and when the processor executes the computer program, the method for calculating the failure probability of a distribution line described in the above embodiments of the invention is implemented.

[0083] Based on the above embodiments, 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 invention embodiment.

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

[0085] The present invention calculates the probability of foundation failure, the probability of conductor breakage, and the probability of concrete cracking based on the physical model of the degradation process of the pole over time, including foundation loosening, conductor tension changes, and concrete stress changes, and further calculates the comprehensive failure probability of the pole, and finally calculates the failure probability of the distribution line based on the comprehensive failure probability of all poles. The present invention more truly reflects the change of the performance of the pole over time, and improves the accuracy of the failure probability assessment of the power system components by considering the impact of the performance degradation of the foundation, conductor and concrete parts on the failure probability. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0087] Figure 2 It is a structural schematic diagram of a distribution line failure probability calculation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0088] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0089] Embodiment 1

[0090] Please refer to Figure 1 , a method for calculating the failure probability of a distribution line provided by an 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 the foundation refers to the pole foundation; concrete refers to the concrete part of the pole; the foundation service life refers to the time when the pole foundation is put into use; the conductor service life refers to the time when the pole conductor is put into use; the concrete service life refers to the time when the concrete part is put into use. The failure probability caused by wind load is calculated in advance. The present invention mainly focuses on considering the impact of performance degradation of the foundation, conductor and concrete part over time when calculating the failure probability of the distribution line.

[0093] S2. For each pole, according to 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 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 usage time and the constructed 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 introduces a foundation loosening model, calculates the foundation stiffness change curve within the foundation use time, and considers the influence of looseness caused by foundation change over time on the failure probability of distribution line; introduces a conductor tension increase model, calculates the conductor tension change curve within the conductor use time, and considers the influence of tension increase caused by conductor change over time on the failure probability of distribution line; introduces a concrete stress model, calculates the concrete stress change curve within the concrete use time, and considers the influence of stress change caused by concrete part change over time on the failure probability of distribution line. The foundation stiffness change curve reflects the foundation stiffness data within the foundation use time, and the foundation failure probability is calculated according to the foundation stiffness change curve. The conductor tension change curve reflects the conductor tension data within the conductor use time, and the conductor fracture probability is calculated according to the conductor tension change curve. The concrete stress change curve reflects the concrete stress data within the concrete use time, and the concrete cracking probability is calculated according to the concrete stress change curve. Then, according to the foundation failure probability, the conductor fracture probability, the concrete cracking probability and the failure probability caused by wind load, the comprehensive failure probability of the pole is calculated.

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

[0096] Obtaining the foundation initial stiffness, foundation loosening rate and foundation loosening process index, foundation stiffness seasonal fluctuation amplitude and seasonal cycle of the pole; 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] According to the foundation stiffness variation curve and the foundation critical stiffness, calculating the first failure probability of the pole within the service life of the foundation that the foundation stiffness does not exceed 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] In the formula, 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 within the service life of the foundation; represents the soil hardening coefficient; S represents the site category parameter.

[0108] It should be noted that the foundation loosening rate μ is a rate parameter indicating foundation loosening, and the typical value range is: 0.005-0.02 / year. The foundation loosening process index β is an index describing the nonlinear characteristics of the foundation loosening process, and the value is generally 0.9-1.1. Both μ and β can be obtained in advance by fitting the on-site test data. The initial foundation stiffness K(0) is the stiffness before the foundation is used.

[0109] The soil hardening coefficient is jointly affected by soil type, soil water content and soil compaction state. Soil types include sandy soil, clay soil and silty soil. The soil hardening coefficient of sandy soil is 1.0-1.2, the soil hardening coefficient of clay soil is 0.8-1.0, and the soil hardening coefficient of silty soil is 1.2-1.5, all of which are the benchmark values ​​of the soil hardening coefficient. When the soil water content is saturated, the soil hardening coefficient increases by 15%-30% of the benchmark value; when the soil water content is dry, the soil hardening coefficient decreases by 10%-20%. When the soil compaction state is dense, the soil hardening coefficient decreases by 10%-20%; when the soil compaction state is loose, the soil hardening coefficient increases by 20%-40% of the benchmark value.

[0110] The site categories and their corresponding parameter value rules 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. It 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, the terrain is undulating, the soil layer thickness is between 15 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. It 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 speed 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 long-term slow increase of the conductor tension;

[0117] Constructing a conductor tension increase model of the pole according to the conductor initial tension, the conductor maximum tension increment, the conductor tension increase speed, the conductor tension periodic change amplitude coefficient, the conductor tension periodic change 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 of 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 ΔT of the conductor represents the maximum value of the possible increase in the conductor tension, and the typical value range is 10%-30% of the initial conductor tension. The conductor tension increase rate v is used to describe the rate at which the conductor tension increases over time, and is generally taken as 0.1-0.5 / hour. The conductor tension periodic change amplitude coefficient γ is used to describe the amplitude of the periodic change of tension, 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 change frequency ω is used to describe the frequency of periodic changes in conductor tension, and is generally taken as 0.1-1rad / hour. The conductor tension long-term drift coefficient κ is used to describe the trend of the long-term slow increase in conductor tension, and is generally taken as 0.01-0.1kN / ln (hour). ΔT, v, γ, ω and κ can all be estimated in advance through historical typhoon event data. T(0) The initial conductor tension is the tension of the conductor before it is used.

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

[0129] Obtain the inner diameter and outer diameter of the pole at each moment during the use time of the concrete; obtain the wind-exposed area, surface wind speed and pole height of the pole;

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

[0131] Calculating the wind load of the pole according to 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 bending moment of the wind load at the conductor, the inner diameter function of the pole and the outer diameter function of the pole;

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

[0135] Obtaining the concrete section width, concrete 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 according to the concrete section width, concrete section height, concrete compressive strength, concrete design service life and 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, according to 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] In the formula, 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 Represents the drag coefficient; R e represents the Reynolds number; n represents the Reynolds number index; P tree represents the failure probability caused by the additional force generated by the falling of trees; η represents the wind speed amplification factor; V0 represents the reference wind speed;

[0143] The calculation formula of 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 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] In the formula, σ critical (t) represents the critical stress of concrete;

[0155] The calculation formula of 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 part of the pole over time is mainly manifested in the increase of the inner diameter of the pole and the decrease of the outer diameter of the pole. The present invention determines the function of the change of the inner diameter of the pole over time and the function of the change of the outer diameter of the pole over time during the service life of the concrete by a mathematical fitting method, and then constructs a concrete stress model for characterizing the law of change of concrete stress over time based on the inner diameter function of the pole, the outer diameter function of the pole and the calculated wind load bending moment at the conductor.

[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] The probability of failure due to the additional force generated by the falling tree is P tree It usually indicates the frequency or probability of tree collapse affecting power lines, reflecting the possibility of power system failure caused by tree collapse 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 the pre-calculated quantity, M vortex Through the calculation of fluid mechanics model and structural dynamics analysis, M gust Estimated through wind load modeling and structural response analysis.

[0161] Corrosion influence factor 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 ); industrial environment 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 maximum surface wind speed, the radius of the maximum surface wind speed, and the azimuth of the surface wind speed;

[0164] Calculating the surface wind speed according to the distance between the 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] Wherein, the calculation formula of the surface wind speed is:

[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 center of the typhoon; 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 an improved surface Holland parameter B s :

[0170]

[0171] In the formula, g s represents the attenuation factor of the gradient wind to the surface wind; represents the intensity correlation coefficient.

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

[0173]

[0174] In the formula, x min represents the minimum value of x; δ represents the attenuation coefficient.

[0175] The present invention introduces an improved Holland model to describe a 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] The 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 an improved importance function:

[0180]

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

[0182] (3) Adaptive resampling: Adaptively resample according to the number of valid particles:

[0183]

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

[0185] (4) Regularization: perform 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 running the physical model multiple times, which has high computational cost in large-scale systems. The Gaussian process proxy model establishes an approximate relationship between input and output through a finite number of physical model calculations. In the iterative process of particle filtering, the proxy model is used to replace part of the physical model calculation, greatly improving the computational efficiency. 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 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, environmental factors, etc. The present invention uses an improved automatic correlation determination method to calculate the correlation of each variable, select the variable with the strongest correlation, simplify the Gaussian process model, and further improve the calculation efficiency and model interpretability. The specific method of using the improved automatic correlation determination method to select the prediction variable with strong correlation 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 smaller value indicates that the input variable affects the output over 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 of 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 according to 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] Calculate the failure probability of the distribution line according to the comprehensive failure probability of all poles, the system importance coefficient, the spatial correlation coefficient and the time correlation coefficient;

[0219] The calculation formula of 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 ith pole and other poles; ρ t,i represents the time correlation coefficient between 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] Embodiment 2

[0224] Please refer to Figure 2, is a schematic diagram of the structure 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, the foundation stiffness change curve of the pole that changes with time during the foundation use time according to the foundation use time and the constructed foundation loosening model; calculate the foundation failure probability of the pole according to the foundation stiffness change curve; calculate the wire tension change curve of the pole that changes with time during the wire use time according to the wire use time and the constructed wire tension increase model; calculate the wire breakage probability of the pole according to the wire tension change curve; calculate the concrete stress change curve of the pole that changes with time during the concrete use time according to the concrete use time and the constructed 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, the wire 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 wire tension increase model is used to characterize the change law of wire 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] Embodiment 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, and when the processor executes the computer program, it implements the method for calculating the failure probability of a distribution line described in the above-mentioned embodiment of the invention.

[0230] Embodiment 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 schematic, 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 on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying 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 may understand and implement it without paying any creative effort.

[0233] Those skilled in the art can 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, 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), or 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, and various interfaces and lines are used to connect various parts of the entire device.

[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, the steps of each method embodiment described above can be implemented. Among them, 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, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, 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, etc. 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 a person skilled in the art can make several improvements and modifications without departing from the principle 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 foundation service life, conductor service life, concrete service life and failure probability caused by wind load for each pole; For each pole, according to 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; according to the foundation stiffness change curve, calculate the foundation failure probability of the pole; 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; the concrete stress model is used to characterize the change law of the concrete stress over time; The failure probability of the distribution line is calculated based on the comprehensive failure probability of all poles.

2. The method for calculating the failure probability of a power distribution line according to claim 1, characterized in that: The construction process of the basic loose model includes: Obtaining the foundation initial stiffness, foundation loosening rate and foundation loosening process index, foundation stiffness seasonal fluctuation amplitude and seasonal cycle of the pole; wherein the inductive 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, 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; According to the foundation stiffness variation curve and the foundation critical stiffness, calculating the first failure probability of the pole within the service life of the foundation that the foundation stiffness does not exceed the foundation critical stiffness; Calculating the basic failure probability according to the first failure probability; The formula of the foundation loosening model is: In the formula, 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; The calculation formula of the basic failure probability is: 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 within the service life of the foundation; represents the soil hardening coefficient; S represents the site category parameter.

3. The method for calculating the failure probability of a power distribution line according to claim 1, characterized in that: 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 speed 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 long-term slow increase of the conductor tension; Constructing a conductor tension increase model of the pole according to the conductor initial tension, the conductor maximum tension increment, the conductor tension increase speed, the conductor tension periodic change amplitude coefficient, the conductor tension periodic change 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: T(t)=T(0)+ΔT·(1-e -vt )·[1+γ·sin(ωt)+κ·ln(t+1)]; 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; k represents the conductor tension long-term drift coefficient; t represents the time; The calculation formula of the wire breakage probability is: P f,wire =P(T(t)>T critical ); Where P f,wire represents the probability of wire breakage; T critical Indicates the critical tension of the wire.

4. The method for calculating the failure probability of a power distribution line according to claim 1, characterized in that: The construction process of the concrete stress model includes: Obtain the inner diameter and outer diameter of the pole at each moment during the use time of the concrete; obtain the wind-exposed area, surface wind speed and pole height of the pole; According to the inner diameter and outer diameter of the pole at each moment during the service life of the concrete, the inner diameter function and outer diameter function of the pole are determined by mathematical fitting; wherein the inner diameter function of the pole is used to characterize the change law of the inner diameter of the pole over time; the outer diameter function of the pole is used to characterize the change law of the outer diameter of the pole over time; Calculating the wind load of the pole according to 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 bending moment of the wind load at the conductor, the inner diameter function of the pole and the outer diameter function of the pole; Calculating the concrete cracking probability of the pole according to the concrete stress variation curve includes: Obtaining the concrete section width, concrete 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 according to the concrete section width, concrete section height, concrete compressive strength, concrete design service life and 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, according to 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: In the formula, 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 Represents the drag coefficient; R e represents the Reynolds number; n represents the Reynolds number index; F tree represents the failure probability caused by the additional force generated by the falling of trees; η represents the wind speed amplification factor; V0 represents the reference wind speed; The calculation formula of the wind load bending moment at the conductor is: M wind =F wind ·H / 2+M vortex +M gust ; 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; The formula of the concrete stress model is: Where, σ(t) represents 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; The calculation formula for the wind load bending moment at the concrete is: 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; The calculation formula of the critical stress of concrete is: In the formula, σ critical (t) represents the critical stress of concrete; The calculation formula of the concrete cracking probability is: 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.

5. The method for calculating the failure probability of a power distribution line according to claim 4, characterized in that: Before obtaining the surface wind speed of the pole, it also includes: Obtain the distance between the power pole and the typhoon center, the maximum surface wind speed, the radius of the maximum surface wind speed, and the azimuth of the surface wind speed; Calculating the surface wind speed according to the distance between the pole and the typhoon center, the surface maximum wind speed, the surface maximum wind speed radius and the azimuth of the surface wind speed; Wherein, the calculation formula of the surface wind speed is: 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 center of the typhoon; 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.

6. The method for calculating the failure probability of a power distribution line according to claim 1, characterized in that: The calculation formula of the comprehensive failure probability is: 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.

7. The method for calculating the failure probability of a power distribution line according to claim 1, characterized in that: 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; Calculate the failure probability of the distribution line according to the comprehensive failure probability of all poles, the system importance coefficient, the spatial correlation coefficient and the time correlation coefficient; The calculation formula of the failure probability of the distribution line is: Where FR represents the failure probability of the distribution line; ρ s,i represents the spatial correlation coefficient between the ith pole and other poles; ρ t,i represents the time correlation coefficient between 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.

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 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 electric pole comprehensive failure probability calculation module is used to calculate, for each electric pole, a foundation stiffness change curve of the electric pole that changes with time during the foundation use time according to the foundation use time and the constructed foundation loosening model; and calculate the foundation failure probability of the electric pole according to 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; 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.

9. 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 7 is implemented.

10. 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 7.

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