Overhead conductor safety assessment method and device based on Monte Carlo simulation, terminal equipment and storage medium

Abstract

The invention discloses an overhead conductor safety assessment method and device based on Monte Carlo simulation, terminal equipment and a storage medium, and relates to the field of material testing, and the method comprises the steps: obtaining the actual operation load of an overhead conductor, and determining the failure probability through combining the breaking force distribution of the overhead conductor; when the failure probability exceeds a preset risk threshold value, evaluating as a high-risk state; wherein the breaking force distribution of the overhead conductor is determined through Monte Carlo simulation: extracting parameters of a corresponding sub-conductor according to the model and specification of the conductor, randomly selecting parameters from the parameter distribution of the sub-conductor to generate a parameter set, calculating the total breaking force corresponding to the parameter set, and repeating the Monte Carlo simulation process to obtain the breaking force distribution of the overhead conductor. And when the simulation frequency exceeds a first preset frequency or the statistical characteristics of the total breaking force converge, generating the breaking force distribution of the overhead conductor according to the total breaking force corresponding to all the parameter sets. By implementing the method, the problem of dependence on a large number of destructive tests in the prior art can be solved, and the engineering application cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of material testing, in particular to a method and device for safety evaluation of overhead conductor based on Monte Carlo simulation, a terminal device and a storage medium. BACKGROUND

[0002] As an important part of the power system, overhead transmission line conductors bear the key task of long-distance and large-capacity power transmission, and their safe and stable operation is directly related to the reliability and power supply quality of the power grid. With the growth of power demand and the development of power transmission technology, the types of overhead transmission line conductors are increasingly diverse, including steel-cored aluminum stranded wire (ACSR), all-aluminum conductor (AAC), aluminum alloy conductor (AAAC), steel-cored aluminum alloy stranded wire (AACSR), aluminum-clad steel-cored aluminum stranded wire (ACSR / AW) and other types. In the past engineering practice, the deterministic rated strength is generally used to evaluate the tensile performance and safety margin of various overhead conductors. This method calculates the rated strength through a simple linear superposition formula, that is, the algebraic sum of the breaking force or stress at a specific elongation of conductors of different materials, but it ignores the dispersion caused by material properties and manufacturing processes.

[0003] Therefore, the reliability evaluation method based on probability statistics is adopted in the prior art to build a probability distribution model of the tensile strength of the conductor, and the model is analyzed with the actual load in operation to calculate the failure probability, so as to more scientifically evaluate the safety of the conductor.

[0004] However, due to the increasing diversity of overhead transmission line conductors, it is necessary to conduct a large number of destructive tensile tests to establish a high-confidence tensile strength probability distribution model for each different type of conductor, which greatly limits the efficient application and promotion of the probability reliability method in engineering practice. SUMMARY

[0005] The embodiments of the present application provide a method and device for safety evaluation of overhead conductor based on Monte Carlo simulation, a terminal device and a storage medium, which can solve the problem that the prior art relies on a large number of destructive tests and is difficult to adapt to the diversified needs of conductors, thereby reducing the cost of engineering application.

[0006] An embodiment of the present application provides a method for safety evaluation of overhead conductor based on Monte Carlo simulation, comprising:

[0007] obtaining the actual operating load of the overhead conductor in the application scenario;

[0008] determining the failure probability according to the actual operating load of the overhead conductor and the breaking force distribution of the overhead conductor;

[0009] In a case where the failure probability exceeds a preset risk threshold corresponding to the application scenario, determining that the safety evaluation result of the overhead conductor is a high-risk state, and replacing the overhead conductor;

[0010] The breaking force distribution of the overhead conductor is determined in the following manner:

[0011] The model specification of the overhead conductor is obtained;

[0012] According to the model specification of the overhead conductor, the diameter distribution, the breaking stress distribution, the stranding coefficient, and the material correction parameter of the corresponding sub-conductor are extracted from a preset database;

[0013] The Monte Carlo simulation process is repeated to obtain the breaking force distribution of the overhead conductor;

[0014] The Monte Carlo simulation process includes: for each sub-conductor, a diameter parameter and a breaking stress are randomly selected from the diameter distribution and the breaking stress distribution of the sub-conductor to obtain a sample set of the sub-conductor, and all sample sets of the sub-conductors, the stranding coefficient, and the material correction parameter are taken as a parameter set;

[0015] According to the parameter set, the corresponding total breaking force is calculated;

[0016] In a case where the number of Monte Carlo simulations exceeds a first preset number, or the mean relative change rate and the standard deviation relative change rate of the total breaking force are both converged, the breaking force distribution of the overhead conductor is generated according to the total breaking force corresponding to all parameter sets.

[0017] Further, according to the parameter set, the corresponding total breaking force is calculated, including:

[0018] According to the parameter set, the corresponding total breaking force is calculated by the following formula:

[0019]

[0020]

[0021] wherein F total represents the total breaking force of the overhead conductor, i represents the index of the sub-conductor in the overhead conductor, n represents the total number of sub-conductors in the overhead conductor, F i,eff represents the effective contribution force of the i-th sub-conductor, σ i represents the breaking stress of the i-th sub-conductor, d i represents the diameter of the i-th sub-conductor, k i represents the stranding coefficient of the i-th sub-conductor, and η i represents the material correction coefficient of the i-th sub-conductor.

[0022] Further, the mean relative change rate and the standard deviation relative change rate of the total breaking force are determined in the following manner:

[0023] A plurality of second preset numbers are set, and each second preset number is less than the first preset number;

[0024] For each second preset number, when the Monte Carlo simulation number reaches the second preset number, the mean relative change rate and the standard deviation relative change rate of the total breaking force are calculated according to the total breaking force corresponding to each parameter set obtained in the simulation process.

[0025] Further, after the Monte Carlo simulation process is repeated to obtain the breaking force distribution of the overhead conductor, the method further comprises:

[0026] Obtaining a theoretical normal distribution function of the overhead conductor;

[0027] According to the breaking force distribution of the overhead conductor and the theoretical normal distribution function, a distribution maximum deviation is calculated;

[0028] It is judged whether the distribution maximum deviation is less than a preset threshold value, if yes, the breaking force distribution of the overhead conductor is marked as credible, otherwise, the breaking force distribution of the overhead conductor is marked as uncredible, and the preset database is updated.

[0029] Further, after the Monte Carlo simulation process is repeated to obtain the breaking force distribution of the overhead conductor, the method further comprises:

[0030] Obtaining an experimental rated strength of the overhead conductor;

[0031] According to the breaking force distribution of the overhead conductor, a mean value of the breaking force distribution of the overhead conductor is determined;

[0032] According to the mean value of the breaking force distribution of the overhead conductor and the experimental rated strength, a mean deviation is calculated;

[0033] It is judged whether the mean deviation is less than a preset deviation value, if yes, the breaking force distribution of the overhead conductor is marked as credible, otherwise, the breaking force distribution of the overhead conductor is marked as uncredible, and the preset database is updated.

[0034] Further, after the Monte Carlo simulation process is repeated to obtain the breaking force distribution of the overhead conductor, the method further comprises:

[0035] Selecting a plurality of parameter sets from all the parameter sets generated in the Monte Carlo simulation process as target parameter sets;

[0036] For each type of parameter in each target parameter set, each type of parameter in the target parameter set is subjected to perturbation processing according to a preset perturbation ratio to obtain a perturbed parameter set;

[0037] According to the perturbed parameter set, a corresponding total breaking force is calculated;

[0038] According to the total fracture force corresponding to the disturbance parameter set and the total fracture force corresponding to the target parameter set, a sensitivity index is calculated;

[0039] For each type of parameter, the sensitivity indexes corresponding to the same type of parameter in all target parameter sets are summed and averaged to obtain the sensitivity index of each type of parameter.

[0040] According to the sensitivity index of each type of parameter, the parameters of all categories are sorted according to sensitivity.

[0041] According to the sensitivity ranking, the target parameter category is determined, and the target parameter category in the overhead conductor is detected regularly.

[0042] On the basis of the above method embodiment, the present application correspondingly provides a device embodiment, comprising: an actual load acquisition module, a failure probability calculation module, and an overhead conductor safety evaluation module.

[0043] The actual load acquisition module is configured to acquire the actual operating load of the overhead conductor in the application scenario.

[0044] The failure probability calculation module is configured to determine the failure probability according to the actual operating load of the overhead conductor and the fracture force distribution of the overhead conductor. The fracture force distribution of the overhead conductor is determined by the following method: acquiring the model specification of the overhead conductor; extracting the diameter distribution, fracture stress distribution, stranding coefficient, and material correction parameter of the corresponding sub-conductor from the preset database according to the model specification of the overhead conductor; repeating the Monte Carlo simulation process to obtain the fracture force distribution of the overhead conductor; the Monte Carlo simulation process includes: for each sub-conductor, randomly selecting a diameter parameter and a fracture stress from the diameter distribution and the fracture stress distribution of the sub-conductor to obtain a sample set of the sub-conductor, and taking all sample sets of the sub-conductors, the stranding coefficient, and the material correction parameter as a parameter set; calculating the corresponding total fracture force according to the parameter set; in the case that the number of Monte Carlo simulations exceeds the first preset number, or the mean relative change rate and the standard deviation relative change rate of the total fracture force are both convergent, generating the fracture force distribution of the overhead conductor according to the total fracture force corresponding to all parameter sets.

[0045] The overhead conductor safety evaluation module is configured to determine that the safety evaluation result of the overhead conductor is in a high-risk state and replace the overhead conductor in the case that the failure probability exceeds the preset risk threshold corresponding to the application scenario.

[0046] Further, the mean relative change rate and the standard deviation relative change rate of the total fracture force are determined by the following method:

[0047] A plurality of second preset numbers are set, and each second preset number is less than the first preset number.

[0048] For each second preset number, when the number of Monte Carlo simulations reaches the second preset number, the mean relative change rate and the standard deviation relative change rate of the total breaking force are calculated according to the total breaking force corresponding to each parameter set obtained in the simulation process.

[0049] On the basis of the above-mentioned method embodiment, the application correspondingly provides a terminal device embodiment, which comprises 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 steps of the overhead conductor safety evaluation method based on Monte Carlo simulation are implemented.

[0050] On the basis of the above-mentioned method embodiment, the application correspondingly provides a computer readable storage medium embodiment, which comprises a stored computer program, and when the computer program runs, the device where the computer readable storage medium is located is controlled to execute the steps of the overhead conductor safety evaluation method based on Monte Carlo simulation.

[0051] Compared with the prior art, the beneficial effects of the present application embodiment are as follows:

[0052] The application obtains the actual running load of the overhead conductor in the application scene, determines the failure probability according to the actual running load of the overhead conductor and the fracture force distribution of the overhead conductor, determines the safety evaluation result of the overhead conductor as a high-risk state in the case that the failure probability exceeds the preset risk threshold corresponding to the application scene, and replaces the overhead conductor. Wherein, the fracture force distribution of the overhead conductor is determined by the following method: first, the model specification of the overhead conductor is obtained as the index for subsequent parameter extraction, then the diameter distribution, fracture stress distribution, stranding coefficient and material correction parameter of the corresponding sub-conductor are extracted from the preset database according to the model specification of the overhead conductor, the overhead conductor is divided into a combination of each sub-conductor, and the existing sub-conductor performance data in the database is used, so that destructive testing of the current conductor is not required, which greatly reduces the time and cost consumption of evaluation, and solves the limitation of the traditional method relying on a large number of physical tests; subsequently, a diameter parameter and a fracture stress are randomly selected from the diameter distribution and the fracture stress distribution of the sub-conductor to obtain a sampling set of the sub-conductor, and all sampling sets of the sub-conductor, the stranding coefficient and the material correction parameter are taken as a parameter set, the uncertainty of the sub-conductor in manufacturing process, material performance and the like is converted into a large number of specific parameter combinations, all kinds of possible parameter matching scenes are comprehensively covered, the corresponding total fracture force is calculated according to the parameter set, and finally, in the case that the Monte Carlo simulation frequency exceeds the first preset frequency, or the mean value relative change rate and the standard deviation relative change rate of the total fracture force are all convergent, it is considered that a sufficient number of sample statistics have been obtained, or the generated fracture force distribution has sufficiently approximated the true probability characteristics. At this time, the fracture force distribution of the overhead conductor is generated according to the total fracture force corresponding to all parameter sets, and the probability transfer and fusion from the single sub-conductor detection data to the overall conductor strength are realized.

[0053] In summary, the application randomly samples and combines the sub-conductor parameters through Monte Carlo simulation, so that the strength probability model of each type of conductor can be efficiently constructed without a large number of destructive tests, thereby solving the problem that the existing overhead conductor safety evaluation method relies on a large number of destructive tests and is difficult to adapt to the diversified demand of conductors, and reducing the engineering application cost. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 is a flowchart of the overhead conductor safety evaluation method based on Monte Carlo simulation provided by an embodiment of the application;

[0055] Figure 2 is a theoretical normal distribution function diagram of the steel-cored aluminum stranded conductor provided by an embodiment of the application;

[0056] Figure 3 is a structural schematic diagram of the overhead conductor safety evaluation device based on Monte Carlo simulation provided by an embodiment of the application. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0058] In the description of the present application, it should be understood that the terms "first", "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features.

[0059] As shown in Figure 1 In order to solve the problem that the prior art relies on a large number of destructive tests and is difficult to adapt to the diversified demand of conductor types, an embodiment of the present application provides an overhead conductor safety evaluation method based on Monte Carlo simulation, which at least includes the following steps:

[0060] Step S1: obtaining the actual operating load of the overhead conductor in the application scene;

[0061] For step S1, the actual operating load of the overhead conductor in the application scene is obtained through the tension sensor deployed on the overhead conductor. When the conductor is subjected to tension, the sensor produces a slight deformation, causing the internal electrical properties to change. The tension sensor converts this physical deformation into a measurable electrical signal. Through the data acquisition system (DAQ) for amplification, filtering and digitization processing, the tension value is finally converted, so as to obtain the axial tension of the overhead conductor in the real operating environment.

[0062] It should be noted that different application scenes have significant differences in the demand for overhead conductor reliability. For example, in the hot and humid tropical climate scene, the overhead conductor material is prone to mechanical property degradation due to thermal aging acceleration. At this time, the failure risk needs to be strictly controlled. In the ordinary plain scene with moderate climate and stable load, the operating conditions of the overhead conductor are relatively loose, and the application scene is used for subsequent preset risk threshold setting.

[0063] In the embodiment, the types of overhead conductors include at least five categories: the first category is pure aluminum (Al) conductor, mainly used for all-aluminum conductor (AAC), made of electrolytic aluminum, with a purity of usually more than 99.5%, having good electrical conductivity but relatively low mechanical strength, with a fracture stress of 160-180 MPa. The performance of this type of conductor is mainly affected by the purity of raw materials and processing technology, and it is the most basic conductive material. The second category is aluminum alloy (AA) conductor, used for aluminum alloy conductor (AAC) and steel-cored aluminum alloy stranded wire (AACSR), with the addition of alloy elements such as silicon and magnesium to improve strength, with a fracture stress of 180-320 MPa, and the performance varies greatly according to different alloy series. The third category is carbon steel (St) conductor, used for the core of composite conductor, mainly bearing mechanical support, using high-carbon steel wire and surface zinc plating for corrosion protection, with a stress of 1200-1700 MPa at 1% elongation, which is the main source of strength of composite conductor. The fourth category is aluminum-clad steel (ACS) conductor, which uses a special process to clad aluminum on the outer surface of the steel core, with the composite structure of outer aluminum and inner steel, which has both electrical conductivity and mechanical strength, and the performance parameters need to consider the composite effect of the two materials. The fifth category is special alloy (SA) conductor, including copper-clad steel, stainless steel and other special materials, mainly used in special environments or special performance requirements.

[0064] Step S2: determining the failure probability according to the actual operating load of the overhead conductor and the fracture force distribution of the overhead conductor; wherein the fracture force distribution of the overhead conductor is determined by: obtaining the model specification of the overhead conductor; extracting the diameter distribution, fracture stress distribution, stranding coefficient and material correction parameter of the corresponding sub-conductor from the preset database according to the model specification of the overhead conductor; repeating the Monte Carlo simulation process to obtain the fracture force distribution of the overhead conductor; the Monte Carlo simulation process includes: for each sub-conductor, randomly selecting a diameter parameter and a fracture stress from the diameter distribution and the fracture stress distribution of the sub-conductor to obtain a sample set of the sub-conductor, and taking all sample sets of the sub-conductor, the stranding coefficient and the material correction parameter as a parameter set; calculating the corresponding total fracture force according to the parameter set; in the case that the number of Monte Carlo simulations exceeds the first preset number, or the mean value and the standard deviation of the total fracture force are both convergent, generating the fracture force distribution of the overhead conductor according to the total fracture force corresponding to all parameter sets;

[0065] For step S2, in the engineering application of overhead conductor, due to factors such as manufacturing process or material defects, the breaking force of the overhead conductor presents an approximate normal distribution, which reflects the consistency fluctuation of the mechanical properties of the overhead conductor in batch production. The traditional method relies on a large number of physical tensile experiments to obtain the breaking force distribution of the overhead conductor, which requires destructive testing on a large number of conductor samples. This process not only consumes a lot of time, but also faces high material cost and experimental equipment investment. Moreover, with the development of modern power transmission technology, the types of overhead conductors are significantly rich, from traditional steel core aluminum stranded wire to carbon fiber composite core conductor, aluminum alloy conductor and other new material conductors. If the traditional experimental method is followed, hundreds of tensile tests need to be carried out independently for each type of conductor, which seriously restricts the innovation efficiency of power transmission line engineering design.

[0066] Therefore, the present application decomposes the overhead conductor through Monte Carlo simulation, randomly generates a parameter set containing the parameters of each sub-conductor and the global correction parameters through the diameter distribution, breaking stress distribution, stranding coefficient and material correction parameters of the sub-conductor, calculates the total breaking force of the whole overhead conductor through the mechanical model using the parameter set, ensures the robustness of the statistical result when the number of Monte Carlo simulation repetitions exceeds the first preset number, and finally fits the breaking force distribution of the overhead conductor by summarizing the total breaking force corresponding to all parameter sets.

[0067] This method replaces the traditional destructive experiment with numerical simulation, does not need to rely on a large number of physical samples, not only greatly reduces the cost and shortens the acquisition period of the breaking force distribution, but also can flexibly adapt to overhead conductors of different types and specifications, effectively solving the problem of difficult and efficient acquisition of breaking force distribution in the rapid engineering application of new conductors.

[0068] After obtaining the breaking force distribution of the overhead conductor, the actual operating load is taken as the stress input to calculate the probability that the actual operating load exceeds the breaking strength of the overhead conductor, which is determined by the following formula:

[0069]

[0070] Wherein, P f represents the failure probability, f(F) represents the breaking force distribution of the overhead conductor, F represents the breaking force of the overhead conductor, and L represents the actual operating load of the overhead conductor.

[0071] Next, the generation process of the breaking force distribution of the overhead conductor of the present application will be described in detail:

[0072] The generation process of the breaking force distribution of the overhead conductor of the present application includes the following steps:

[0073] Step S201: Obtain the type and specification of the overhead conductor;

[0074] For step S201, in an embodiment of the present application, the model specification of the overhead conductor can be determined by manual configuration. For example, if all sub-conductor materials in the overhead conductor are Al, it is determined that the overhead conductor is an all-aluminum conductor (AAC).

[0075] In another embodiment of the present application, in order to ensure the accuracy of parameter setting and avoid human configuration errors, the actual outer diameter D (mm) of the overhead conductor is obtained, reflecting the overall size of the conductor; the diameter set {di} (mm) of the sub-conductors in the overhead conductor, i = 1, 2, …, n, describes the geometric characteristics of each sub-conductor; the material set {Mi} represents the material type of each sub-conductor; and the stranding structure S, such as "1+6+12+18", represents the arrangement of 1 center, 6 first layer, 12 second layer, and 18 third layer. These parameters comprehensively describe the structural characteristics of the overhead conductor and provide sufficient information for accurate identification.

[0076] According to the material type of each sub-conductor, the type of the overhead conductor is preliminarily determined, and the judgment logic is as follows: when all sub-conductor materials are Al, the overhead conductor is determined to be an all-aluminum conductor (AAC); when all sub-conductor materials are AA, the overhead conductor is determined to be an aluminum alloy conductor (AAC); when Al and St materials exist simultaneously, the overhead conductor is determined to be a steel-cored aluminum stranded conductor (ACSR); when AA and St materials exist simultaneously, the overhead conductor is determined to be a steel-cored aluminum alloy stranded conductor (AACSR); and when ACS material exists, it is determined to be an aluminum-clad steel conductor (ACSR / AW). This material combination-based identification method is simple and effective, and can cover the main types of overhead conductors.

[0077] Next, according to the preliminary determination of the type of the overhead conductor, the corresponding theoretical outer diameter, the theoretical diameter of the sub-conductor, and the stranding structure are obtained from the preset database. The deviation between the theoretical outer diameter and the actual outer diameter, as well as the deviation between the theoretical diameter of the sub-conductor and the actual diameter, are calculated, and the theoretical stranding structure is compared with the actual stranding structure to test the rationality of the identification result. When the stranding structure is different or the deviation exceeds 5%, an abnormal processing procedure is triggered, and manual intervention is required. This automatic identification method of the overhead conductor type can effectively reduce the human workload in the identification of mainstream overhead conductors, significantly improve the identification efficiency and accuracy, and reduce the errors that may occur in manual judgment, making the overhead conductor type identification work more efficient and reliable.

[0078] Step S202: According to the model specification of the overhead conductor, the corresponding diameter distribution, fracture stress distribution, stranding coefficient, and material correction parameter of the sub-conductor are extracted from the preset database;

[0079] For step S202, a unified performance parameter description model is established for each type of sub-conductor to ensure that sub-conductors of different materials can be analyzed and simulated under the same framework:

[0080] P = {d, σ b , k, η+;

[0081] where d represents the diameter of the sub-conductor (mm), which is the most basic geometric parameter, directly affecting the cross-sectional area and current-carrying capacity of the sub-conductor, and is also the main random variable in probability analysis; σ b represents the breaking stress of the sub-conductor (MPa), representing the ultimate strength of the material, which is the core parameter for strength calculation, and its probability distribution directly determines the randomness of the sub-conductor strength; k represents the stranding coefficient of the sub-conductor, which is determined according to the conductor structure, and η represents the material correction coefficient, which is determined according to the material type.

[0082] All overhead conductors can be decomposed into the smallest structure of sub-conductor, the diameter of each sub-conductor determines its carrying area, and the stress distribution reflects its stress state, both of which jointly affect the contribution of each sub-conductor to the overall breaking force, therefore, the breaking force distribution of the entire overhead conductor can be determined by the diameter distribution and stress distribution of the sub-conductor. It should be noted that for the same type of overhead conductor, since its structure (such as stranding method, layer number and arrangement rule) and material (such as metal type and alloy composition) are fixed, the stranding coefficient and material correction coefficient of the sub-conductor contained in different single overhead conductors of the same type are fixed.

[0083] For the diameter distribution of the sub-conductor, through the statistics of a large amount of production data, the diameter distribution of the sub-conductor presents the characteristics of truncated normal distribution, which can accurately reflect the quality control effect in the manufacturing process:

[0084]

[0085] where f d (d) represents the diameter distribution of the sub-conductor, represents the standard normal density function, reflecting the randomness of the manufacturing process, Φ(·) represents the standard normal distribution function, used to calculate the truncated probability, μ d represents the standard diameter value specified by the design, σ d represents the standard deviation of the diameter distribution of the sub-conductor, reflecting the dispersion degree of the manufacturing process, d l and d u represent the truncation points, corresponding to the minimum allowable diameter and maximum allowable diameter specified by the quality standard respectively.

[0086] The determination of the standard deviation adopts a statistical method based on the tolerance range:

[0087]

[0088] where T drepresents the diameter tolerance, κ represents the confidence coefficient. Different materials use different confidence coefficients, for example: aluminum wire κ = 1.96 (corresponding to 95% confidence), aluminum alloy wire κ = 2.326 (corresponding to 98% confidence), steel wire κ = 1.645 (corresponding to 90% confidence). This differentiated treatment reflects the differences in manufacturing precision and quality control of different materials. Aluminum alloy wire is more difficult to process and has more stringent quality control.

[0089] For the fracture stress distribution of the sub-conductor, through statistical analysis of a large number of test data of different materials, the fracture stress distribution model of each material sub-conductor is established:

[0090] (1) The fracture stress distribution of aluminum conductor is: wherein, μ b, Al = 160-180 MPa, the specific value is determined by the purity of aluminum material and processing technology. In normal distribution, the standard deviation is an absolute dispersion index, reflecting the absolute amplitude of data deviating from the mean. However, when comparing data sets with different means, such as comparing the fracture stresses of sub-conductors of different materials, it is difficult to intuitively determine which has greater dispersion by using only the standard deviation. Therefore, by comparing the ratio of the mean and the standard deviation of the fracture stress distribution, the coefficient of variation C v is calculated, which quantifies the relative level of data dispersion. The coefficient of variation C v of the fracture stress distribution of aluminum conductor is 0.05-0.08, indicating that the standard deviation is about 5%-8% of the mean. As the most basic conductive material, the performance of aluminum conductor is relatively stable, but due to the influence of raw material purity and drawing process, there is still some dispersion.

[0091] (2) The fracture stress distribution of aluminum alloy conductor is: wherein, μ b,AA = 180-320 MPa, the range is large because the performance of different alloy series differs significantly, 1350 series is about 180-220 MPa, 6201 series can reach 280-320 MPa, and the calculated coefficient of variation C v = 0.04-0.07. Aluminum alloy conductor has relatively small dispersion due to relatively strict alloy composition control, but there is a significant difference between different alloy series.

[0092] (3) Since the steel core in the composite conductor mainly bears the supporting role, it usually does not directly bear the fracture load, but provides support force at a specific elongation rate. Therefore, the stress distribution of steel conductor is specially treated, and the stress distribution at 1% elongation rate is established: wherein, μ 1％,St = 1200-1700 MPa, depending on the steel grade and surface treatment method, and the calculated coefficient of variation C v = 0.03-0.06. Steel performance is relatively stable, with a small coefficient of variation.

[0093] It should be noted that the introduction of truncation processing simulates the quality control process in actual production, and sub-conductors exceeding the tolerance range will be rejected. This processing makes the distribution model closer to the engineering practice and improves the credibility of the simulation results.

[0094] wherein the truncation degree of each parameter distribution of the sub-conductor is calculated using a standardization method: α L = Φ -1 (ε L ), α U = Φ -1 (1-ε U ), wherein α L and α U represent the truncation points in the parameter distribution, ε L and ε U represent the exclusion probabilities, and Φ -1 (·) represents the inverse function of the standard normal distribution. The setting of the exclusion probability reflects the quality requirements of different application levels, for example, 0.1% (99.8% pass rate) for the extra-high voltage level, 0.5% (99.0% pass rate) for the ultra-high voltage level, 1.0% (98.0% pass rate) for the high voltage level, and 2.0% (96.0% pass rate) for the conventional level. This grading process reflects the differences in quality requirements for sub-conductors at different voltage levels, with the highest quality control level required for extra-high voltage sub-conductors and relatively relaxed requirements for conventional sub-conductors. By adjusting the truncation parameters, performance distributions under different quality control levels can be simulated to provide accurate evaluation results for different application scenarios.

[0095] For the stranding coefficient of the sub-conductor, since the sub-conductor is arranged in a spiral shape in the conductor, its axial load bearing capacity will decrease due to the geometric angle, and sub-conductors of different layers are subjected to different degrees of constraint, which need to be handled separately.

[0096] The stranding coefficient is corrected using a geometric correction method:

[0097] k i = k 0,i × cos 2 (α i );

[0098] wherein k 0,i represents the reference stranding coefficient, reflecting the constraint degree and load distribution characteristics of sub-conductors of different layers, α i represents the stranding angle, i.e., the angle between the sub-conductor and the conductor axis, which affects its ability to bear axial load, and cos 2 (α iThe item reflects the influence of geometric angle on axial load transmission, which is a correction relationship based on mechanical analysis. In this embodiment, the determination of the reference stranding coefficient is based on a large amount of engineering experience and test data: the center line k0=1.0, because it is located at the center of the conductor and has no stranding angle; the first layer k0=0.985, the constraint is smaller; the second layer k0=0.96, the constraint is increased; the third layer k0=0.94, the outermost layer has the smallest constraint but the largest stranding angle. This layered processing reflects the difference in load transmission of sub-conductors at different positions.

[0099] For the material correction coefficient of the sub-conductor, the interaction effect between different materials is reflected, and different materials have synergistic or antagonistic effects in the composite conductor. If the sub-conductor is a pure aluminum conductor, η=1.000; if the sub-conductor is aluminum alloy 1350, the introduction of alloying elements will change the mechanical synergistic effect, so the coefficient is slightly lower than that of pure aluminum, η=0.995; if the sub-conductor is aluminum alloy 6201, the difference between steel and aluminum materials is greater, the synergistic effect is reduced more obviously, and the coefficient is lower, η=0.990; if the sub-conductor is a steel wire, η=0.985.

[0100] The diameter distribution, fracture stress distribution, stranding coefficient and material correction parameter of all types of sub-conductors described above are determined in advance and stored in a preset database. In actual application, according to the model specification of the overhead conductor, the diameter distribution, fracture stress distribution, stranding coefficient and material correction parameter of the corresponding sub-conductor are automatically configured from the preset database. Taking steel-cored aluminum stranded conductor (ACSR) as an example, the sub-conductor material of this overhead conductor is composed of 1 steel conductor and 6 aluminum conductors, and the fracture stress distribution, fracture stress distribution, stranding coefficient and material correction parameter of the aluminum conductor and the steel conductor are obtained.

[0101] Step S203: repeating the Monte Carlo simulation process to obtain the fracture force distribution of the overhead conductor;

[0102] For step S203, after obtaining the diameter distribution and fracture stress distribution of the sub-conductor, the Monte Carlo simulation process is repeated, that is, the probability distribution of the overall conductor strength is calculated through a large number of random sampling, realizing the probability transmission from the random performance of a single sub-conductor to the strength distribution of the overall conductor, and solving the calculation problem of the probability analysis of a complex system.

[0103] Specifically, the Monte Carlo simulation process includes the following steps:

[0104] Step S2031: for each sub-conductor, randomly selecting a diameter parameter and a fracture stress from the diameter distribution and the fracture stress distribution of the sub-conductor to obtain a sampling set of the sub-conductor, and taking the sampling set of all sub-conductors, the stranding coefficient and the material correction parameter as a parameter set;

[0105] For step S2031, for each sub-conductor, a diameter parameter is randomly selected from its diameter distribution, and a fracture stress value is randomly selected from the fracture stress distribution, so that the sampling data of a single sub-conductor is obtained. After the sampling of all sub-conductors is completed, the sampling results of all sub-conductors are combined with the predetermined stranding coefficient and material correction parameter to generate a set of key parameters of the sub-conductors, that is, a parameter set. Through the random generation of the parameter set, the subsequent analysis and simulation of the performance of the overhead conductor can be used, and the diversity and randomness of the data are increased, which conforms to the characteristics of the parameters in the actual project.

[0106] Step S2032: calculating the corresponding total fracture force according to the parameter set.

[0107] For step S2032, the general fracture force calculation model is the core calculation formula of the Monte Carlo simulation algorithm of the present application, and the purpose is to establish a mathematical relationship from the performance of a single sub-conductor to the strength of the whole conductor. The model needs to consider the contribution of sub-conductors of different materials, the stranding geometric effect and the interaction between materials to ensure the accuracy and universality of the calculation results.

[0108] In a preferred embodiment, calculating the corresponding total fracture force according to the parameter set comprises:

[0109] According to the parameter set, the corresponding total fracture force is calculated by the following formula:

[0110]

[0111] wherein Ftotal represents the total fracture force of the overhead conductor, i represents the index of the sub-conductor in the overhead conductor, n represents the total number of sub-conductors in the overhead conductor, F total represents the effective contribution force of the i-th sub-conductor, σ i,eff represents the fracture stress of the i-th sub-conductor, d i represents the diameter of the i-th sub-conductor, k i represents the stranding coefficient of the i-th sub-conductor, and η i represents the material correction coefficient of the i-th sub-conductor. i

[0112] This general formula considers three main factors for the formation of the strength of the overhead conductor. First, the intrinsic strength contribution of each sub-conductor, which is determined by the material performance and geometric size. Second, the stranding geometric effect, which needs to be corrected because the sub-conductors are arranged in a spiral shape and affect the axial load. Finally, the interaction effect between materials, which has a synergistic or antagonistic effect in the composite conductor.

[0113] ​Step S2033: in the case that the number of Monte Carlo simulations exceeds the first preset number, or the mean relative variation rate and the standard deviation relative variation rate of the total breaking force are both converged, generating the breaking force distribution of the overhead conductor according to the total breaking force corresponding to all parameter sets;

[0114] For step S2033, it is judged whether the number of repetitions of the current Monte Carlo simulation process exceeds the first preset number. If yes, it is indicated that enough groups of parameter sets generated by random combination of sub-conductor parameters have been accumulated. The total breaking force corresponding to all parameter sets is summarized or statistically analyzed. Based on the distribution characteristics of these data, the probability distribution of the breaking force of the overhead conductor is fitted, that is, the breaking force distribution of the overhead conductor is obtained, so as to reflect the appearance probability of the breaking force of the overhead conductor under different values, and provide a basis for evaluating the mechanical performance and reliability of the conductor. Otherwise, it is indicated that the number of currently generated parameter sets is still insufficient to support reliable fitting. The number of Monte Carlo simulations is incremented by 1, and the Monte Carlo simulation process is repeated.

[0115] It should be noted that the number of repetitions at the initial time is set to 1, and in the embodiment, the first preset number N0 is set to 100000.

[0116] In a preferred embodiment, the mean relative variation rate and the standard deviation relative variation rate of the total breaking force are determined by the following method:

[0117] A plurality of second preset numbers are set, and each second preset number is less than the first preset number.

[0118] For each second preset number, in the case that the number of Monte Carlo simulations reaches the second preset number, the mean relative variation rate and the standard deviation relative variation rate of the total breaking force are calculated according to the total breaking force corresponding to each parameter set obtained in the simulation process.

[0119] In an embodiment of the application, in the case that the number of repetitions of the current Monte Carlo simulation process does not exceed the first preset number, it is judged whether the mean relative variation rate and the standard deviation relative variation rate of the total breaking force are both converged. The mean relative variation rate and the standard deviation relative variation rate of the total breaking force are calculated by the following method:

[0120] First, a plurality of second preset numbers are set, and each second preset number is less than the first preset number. In the embodiment, the plurality of second preset numbers N τ are {100, 200, 300, …, 1000+}. The number interval ΔN of each second preset number is 100, which means that the simulation repetition number interval of adjacent two stage verifications is 100. When each second preset number is reached, the mean relative variation rate and the standard deviation relative variation rate of the total breaking force are calculated by the following formula:

[0121]

[0122] wherein, δμ represents a mean relative change rate, used to measure the relative change degree of the mean of the total breaking force in the interval ΔN of different simulation repetition numbers, μ(N τ +ΔN) represents the mean of the total breaking force when the simulation repetition number reaches N τ +ΔN, μ(N τ ) represents the mean of the total breaking force when the simulation repetition number reaches N τ , δσ represents a standard deviation relative change rate, used to measure the relative change degree of the standard deviation of the total breaking force in the interval ΔN of different simulation repetition numbers, σ(N τ +ΔN) represents the standard deviation of the total breaking force when the simulation repetition number reaches N τ +ΔN, σ(N τ ) represents the standard deviation of the total breaking force when the simulation repetition number reaches N τ .

[0123] The convergence condition is set as δμ<0.001 and δσ<0.002 for 5 times in succession, and in the case that the mean relative change rate and the standard deviation relative change rate of the total breaking force converge, it is considered that the mean and the standard deviation of the total breaking force tend to be stable with the increase of the simulation repetition number, the simulation result no longer changes obviously with the increase of the number, and the result precision cannot be effectively improved by continuing to repeat the simulation, so the repetition of the Monte Carlo simulation process is stopped, and subsequent work such as the construction of the breaking force distribution and the safety evaluation can be carried out based on the currently converged simulation result.

[0124] Since the fixed simulation number is adopted, it is difficult to balance the precision and the efficiency, and the application automatically adjusts the calculation amount according to the complexity of the specific problem through adaptive convergence. For a conductor with simple structure and small parameter discreteness, fewer simulation numbers can achieve convergence; for a conductor with complex structure and large parameter discreteness, the simulation number is automatically increased to ensure the precision. Meanwhile, the maximum simulation number limit (namely, N0 is set to 100000) is set to avoid long calculation time.

[0125] In a preferred embodiment, after the repetition of the Monte Carlo simulation process to obtain the breaking force distribution of the overhead conductor, the method further comprises:

[0126] obtaining a theoretical normal distribution function of the overhead conductor;

[0127] calculating a maximum deviation of the distribution according to the breaking force distribution of the overhead conductor and the theoretical normal distribution function;

[0128] judging whether the maximum deviation of the distribution is less than a preset threshold value, if yes, marking the breaking force distribution of the overhead conductor as credible, otherwise, marking the breaking force distribution of the overhead conductor as incredible, and updating a preset database.

[0129] In one embodiment of the present invention, the fracture force distribution of overhead conductors is statistically verified. Specifically, firstly, the theoretical normal distribution function of the overhead conductors is obtained through actual experimental data, such as... Figure 2 The figure shows the theoretical normal distribution function of aluminum-steel cored wire. The breaking force distribution of aluminum-steel cored wire approximately follows a normal distribution. The normality is verified using the Kolmogorov-Smirnov test, a non-parametric test method that can effectively verify whether the sample comes from the specified theoretical distribution. The formula for calculating the maximum distribution deviation is:

[0130]

[0131] Where f(F) represents the breaking force distribution of the overhead conductor. Let μ represent the theoretical normal distribution function of the overhead conductor, and let μ and σ represent the mean and standard deviation of the theoretical normal distribution function. This represents the maximum deviation in the distribution, reflecting the maximum deviation between the fracture force distribution of the overhead conductor and the theoretical normal distribution function. When the maximum deviation in the distribution... When the value is less than the preset critical value, it indicates that the fracture force distribution obtained from the simulation conforms to the expected law of the theoretical normal distribution. The normality assumption is accepted, and the fracture force distribution of the overhead conductor is marked as reliable. Otherwise, it indicates that the fracture force distribution does not conform to the theoretical distribution characteristics, and it is marked as unreliable. Simultaneously, the preset database is updated. Specifically, new diameter distribution, fracture stress distribution, stranding coefficient, and material correction parameters of the sub-conductors are obtained through experimental or production testing. The accuracy of the new data must be ensured. Subsequently, the new parameters replace the corresponding original parameters in the preset database one by one, completing the iterative update of these parameters in the preset database.

[0132] It should be noted that, in addition to the KS test, the Anderson-Darling test and the Jarque-Bera test can also be used as supplementary validations. The Anderson-Darling test is more sensitive to the tails of the distribution and can better detect tail bias; the Jarque-Bera test, based on skewness and kurtosis, can detect the asymmetry and sharpness of the distribution. The combined use of multiple tests improves the reliability and comprehensiveness of statistical validation.

[0133] In a preferred embodiment, after repeating the Monte Carlo simulation process to obtain the fracture force distribution of the overhead conductor, the method further includes:

[0134] Obtain the experimental rated strength of overhead conductors;

[0135] Based on the breaking force distribution of the overhead conductor, determine the mean value of the breaking force distribution of the overhead conductor;

[0136] According to the mean value of the fracture force distribution of the overhead conductor and the experimental rated strength, a mean deviation is calculated;

[0137] It is judged whether the mean deviation is less than a preset deviation value. If yes, the fracture force distribution of the overhead conductor is marked as credible, otherwise, the fracture force distribution of the overhead conductor is marked as incredible, and the preset database is updated.

[0138] In an embodiment of the present application, in addition to statistical verification, engineering verification can also be performed on the fracture force distribution of the overhead conductor, and the simulation result is compared with the existing engineering standard and actual test data. Specifically, the mean value of the theoretical normal distribution function is taken as the experimental rated strength F rated , and the mean value μ sim of the fracture force distribution of the overhead conductor is obtained, and the mean deviation is calculated by the following formula:

[0139]

[0140] Wherein, δ represents the mean deviation, F rated represents the experimental rated strength, and μ sim represents the mean value of the fracture force distribution of the overhead conductor. In this embodiment, the acceptance criterion is set as δ is 0.05, that is, the deviation is less than 5%, which means that the fitting degree of the simulation result and the actual test data meets the accuracy requirement, and the fracture force distribution of the overhead conductor is marked as credible. If not, it means that the simulation result deviates too much from the actual engineering index, and it is marked as incredible. Similarly, the preset database is updated by obtaining new parameters of the sub-conductor through experimental detection or production detection.

[0141] It should be noted that the above statistical verification and engineering verification can be used alone or in combination. The statistical verification focuses on the statistical rationality of the distribution form, and the engineering verification focuses on the fitting degree of the numerical value and the actual index. From the two dimensions of statistical law and engineering practice, the fracture force distribution of the overhead conductor obtained by the Monte Carlo simulation is comprehensively verified to ensure that the simulation result is correct in statistical logic and reliable in actual application, and has the applicability required by the engineering scene.

[0142] In a preferred embodiment, after the fracture force distribution of the overhead conductor is obtained by repeating the Monte Carlo simulation process, the method further comprises:

[0143] Selecting a plurality of parameter sets from all parameter sets generated by the Monte Carlo simulation process as target parameter sets;

[0144] For each type of parameter in each target parameter set, each type of parameter in the target parameter set is disturbed according to a preset disturbance ratio to obtain a disturbed parameter set;

[0145] According to the disturbed parameter set, the corresponding total fracture force is calculated;

[0146] According to the total breaking force corresponding to the perturbation parameter set and the total breaking force corresponding to the target parameter set, a sensitivity index is calculated;

[0147] For each type of parameter, the sensitivity indexes corresponding to the same type of parameter in all target parameter sets are summed and averaged to obtain the sensitivity index of each type of parameter;

[0148] According to the sensitivity index of each type of parameter, the parameters of all types are sorted according to sensitivity;

[0149] According to the sensitivity sorting, the target parameter category is determined, and the target parameter category in the overhead conductor is regularly detected.

[0150] In an embodiment of the present application, the influence degree of each input parameter on the simulation result is analyzed, and the key control parameter is identified to provide guidance for parameter measurement accuracy requirement and uncertainty control. Specifically, first, select a number of parameter sets from all parameter sets generated by the Monte Carlo simulation process as target parameter sets; then, for each type of parameter in each target parameter set, such as diameter, breaking stress, stranding coefficient and material correction coefficient, the parameters of this type are perturbed according to a preset perturbation ratio to obtain a perturbation parameter set, the perturbation ratio is usually ±1% or ±5% small perturbation, and then according to the perturbation parameter set, the corresponding total breaking force is calculated, and then through the total breaking force corresponding to the perturbation parameter set and the total breaking force corresponding to the target parameter set, the sensitivity index is calculated by using the following formula:

[0151]

[0152] Wherein, S i represents the sensitivity index, μ + and μ - represent the total breaking force after parameter perturbation ±θ%, θ represents the preset perturbation ratio, μ0 represents the reference total breaking force, i.e. the total breaking force corresponding to the target parameter set, and S i The greater the value is, the more sensitive the constraint parameter is.

[0153] Since the sensitivity index calculated by a single target parameter set has randomness and fluctuation, the sensitivity calculated by a target parameter set cannot accurately reflect the true sensitivity of the parameter. Therefore, the sensitivity indexes corresponding to the same type of parameters in all target parameter sets are summed and averaged to obtain the sensitivity index of each type of parameter. Finally, according to the sensitivity index of each type of parameter, the sensitivity of all types of parameters is sorted to identify the key control parameters. In this embodiment, the fracture stress parameter has the highest sensitivity, the diameter parameter is second, and the twisting coefficient is relatively low. Based on the sorting result, the highest sensitive parameter category is selected as the target parameter, such as the fracture stress parameter of the sub-conductor. The target parameter has the most significant impact on the fracture stress distribution of the overhead conductor and directly determines the reliability of the tensile limit of the overhead conductor. Once the parameter of this type abnormally fluctuates, such as the decrease in fracture stress caused by material aging, it is easy to cause conductor fracture and even power transmission accidents. Therefore, the target parameter is included in the regular detection of the overhead conductor, and the deviation of the actual state from the design threshold is monitored periodically to capture the abnormal trend of the parameter in time.

[0154] Step S3: In the case where the failure probability exceeds the preset risk threshold corresponding to the application scenario, determining that the safety evaluation result of the overhead conductor is in a high-risk state, and replacing the overhead conductor.

[0155] For step S3, first, set the corresponding preset risk threshold based on different application scenarios. For example, the urban power distribution network is related to the daily electricity use of residents, and the preset risk threshold is set to 5% of the failure probability, which is determined as high risk; the high-voltage transmission trunk line affects industrial and regional power supply, and the preset risk threshold is set to 3%; the extra-high voltage cross-regional power transmission project is crucial to energy strategy transportation, and the preset risk threshold is set to 1%.

[0156] After calculating the failure probability of the overhead conductor through step S2, it is determined whether it exceeds the preset risk threshold corresponding to the application scenario. If yes, it means that the possibility of power supply interruption and other problems caused by conductor failure in this scenario is too high, and the safety evaluation result of the overhead conductor is determined to be in a high-risk state. In order to avoid serious consequences such as large-area power failure, equipment damage, and even influence on social stability caused by failure, the overhead conductor needs to be replaced in time to ensure the safe and stable operation of the power system. Otherwise, it means that the possibility of serious problems caused by conductor failure in this scenario is within an acceptable range, and the safety evaluation result of the overhead conductor is determined to be in a low-risk or acceptable risk state. The current use state of the overhead conductor can be maintained, and the performance change of the overhead conductor is continuously monitored to ensure the continuous safe and stable operation of the power system.

[0157] The application generates multiple parameter sets randomly based on the diameter distribution, fracture stress distribution and other parameters of the sub-conductor, combined with the stranding coefficient and material correction parameters, and then calculates the total fracture force, and builds the fracture force distribution of the whole overhead conductor after the number of repetitions meets the requirements. Compared with the traditional method which relies on a large number of physical experiments, this method greatly reduces the experimental cost, avoids separate testing of different types of conductors, and only needs unified sub-conductor testing data, the comprehensive cost is reduced by more than 85%, the evaluation period is shortened from several months to several days, and the engineering design efficiency is significantly improved.

[0158] As shown in the above method embodiment, corresponding device embodiments are provided; Figure 3

[0159] An embodiment of the application provides an overhead conductor safety evaluation device based on Monte Carlo simulation, comprising an actual load acquisition module, a failure probability calculation module and an overhead conductor safety evaluation module.

[0160] The actual load acquisition module is used to acquire the actual operating load of the overhead conductor in an application scenario.

[0161] The failure probability calculation module is used to determine the failure probability according to the actual operating load of the overhead conductor and the fracture force distribution of the overhead conductor; wherein the fracture force distribution of the overhead conductor is determined by the following method: acquiring the model specification of the overhead conductor; extracting the diameter distribution, fracture stress distribution, stranding coefficient and material correction parameter of the corresponding sub-conductor from the preset database according to the model specification of the overhead conductor; repeating the Monte Carlo simulation process to obtain the fracture force distribution of the overhead conductor; the Monte Carlo simulation process comprises: for each sub-conductor, randomly selecting a diameter parameter and a fracture stress from the diameter distribution and the fracture stress distribution of the sub-conductor to obtain a sampling set of the sub-conductor, and taking all the sampling sets of the sub-conductors, the stranding coefficient and the material correction parameter as a parameter set; calculating the corresponding total fracture force according to the parameter set; in the case that the number of Monte Carlo simulations exceeds the first preset number, or the mean relative change rate and the standard deviation relative change rate of the total fracture force are both convergent, generating the fracture force distribution of the overhead conductor according to the total fracture force corresponding to all the parameter sets.

[0162] The overhead conductor safety evaluation module is used to determine that the safety evaluation result of the overhead conductor is in a high-risk state and replace the overhead conductor in the case that the failure probability exceeds the preset risk threshold corresponding to the application scenario.

[0163] In a preferred embodiment, the mean relative change rate and the standard deviation relative change rate of the total fracture force are determined by the following method:

[0164] A plurality of second preset numbers are set, and each second preset number is less than the first preset number. ​

[0165] For each second preset number, in a case that the number of Monte Carlo simulations reaches the second preset number, a mean relative change rate and a standard deviation relative change rate of the total breaking force are calculated according to the total breaking force corresponding to each parameter set obtained in the simulation process.

[0166] It can be understood that the above device embodiments correspond to the method embodiments of the present application, and can implement the overhead conductor safety evaluation method based on Monte Carlo simulation provided by any one of the above method embodiments.

[0167] It should be noted that the device embodiments described above are only illustrative, and part or all of the modules can be selected to achieve the purpose of the present embodiment. In addition, in the device embodiments provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement without creative labor.

[0168] On the basis of the above-mentioned embodiment of the overhead conductor safety evaluation method based on Monte Carlo simulation, another embodiment of the present application provides a terminal device, which comprises 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, the overhead conductor safety evaluation method based on Monte Carlo simulation of any one embodiment of the present application is realized.

[0169] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the terminal device.

[0170] The terminal device can be a desktop computer, a notebook computer, a palm computer and a cloud server, etc. The terminal device can include, but is not limited to, a processor and a memory.

[0171] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting various parts of the terminal device via various interfaces and lines.

[0172] Based on the above-described method embodiments, another embodiment is provided: another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the overhead conductor safety assessment method based on Monte Carlo simulation as described in any of the above-described method embodiments of the present invention.

[0173] The modules / units integrated into the overhead conductor safety assessment device / terminal equipment based on Monte Carlo simulation, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0174] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for safety assessment of overhead power lines based on Monte Carlo simulation, characterized in that, include: Obtain the actual operating load of overhead conductors in the application scenario; The failure probability is determined based on the actual operating load of the overhead conductor and the fracture force distribution of the overhead conductor. If the failure probability exceeds the preset risk threshold corresponding to the application scenario, the safety assessment result of the overhead conductor is determined to be a high-risk state, and the overhead conductor is replaced. The distribution of the breaking force of the overhead conductor is determined in the following way: Obtain the model and specifications of the overhead conductor; Based on the model and specifications of the overhead conductor, the diameter distribution, fracture stress distribution, stranding factor and material correction parameters of the corresponding sub-conductors are extracted from the preset database. The Monte Carlo simulation process was repeated to obtain the fracture force distribution of the overhead conductor; The Monte Carlo simulation process includes: for each sub-conductor, randomly selecting a diameter parameter and a fracture stress from the diameter distribution and fracture stress distribution of the sub-conductor to obtain a sampling set of the sub-conductor, and taking the sampling set of all sub-conductors, stranding coefficient and material correction parameter as a parameter set; Calculate the corresponding total fracture force based on the parameter set; If the number of Monte Carlo simulations exceeds the first preset number, or if the relative rate of change of the mean and the relative rate of change of the standard deviation of the total fracture force converge, the fracture force distribution of the overhead conductor is generated based on the total fracture force corresponding to all parameter sets.

2. The method for safety assessment of overhead power lines based on Monte Carlo simulation according to claim 1, characterized in that, Based on the parameter set, the corresponding total fracture force is calculated, including: Based on the parameter set, the corresponding total fracture force is calculated using the following formula: Among them, F total F represents the total breaking force of the overhead conductor, i represents the index of the sub-conductor in the overhead conductor, n represents the total number of sub-conductors in the overhead conductor, and F i,eff σ represents the effective contribution force of the i-th sub-conductor. i d represents the breaking stress of the i-th sub-conductor. i Let k represent the diameter of the i-th sub-wire. i η represents the stranding factor of the i-th sub-conductor. i This represents the material correction factor for the i-th sub-wire.

3. The method for safety assessment of overhead power lines based on Monte Carlo simulation according to claim 1, characterized in that, The mean relative rate of change and the standard deviation relative rate of change of the total fracture force were determined in the following ways: Set several second preset counts, and each second preset count is less than the first preset count; For each second preset number of simulations, when the number of Monte Carlo simulations reaches the second preset number of simulations, the relative rate of change of the mean and the relative rate of change of the standard deviation of the total fracture force are calculated based on the total fracture force corresponding to each parameter set obtained during the simulation.

4. The method for safety assessment of overhead power lines based on Monte Carlo simulation according to claim 1, characterized in that, After repeating the Monte Carlo simulation process to obtain the fracture force distribution of the overhead conductor, the following steps are also included: Obtain the theoretical normal distribution function of overhead conductors; Based on the fracture force distribution of overhead conductors and the theoretical normal distribution function, the maximum distribution deviation is calculated. If the maximum deviation of the distribution is less than a preset critical value, the breaking force distribution of the overhead conductor is marked as reliable; otherwise, the breaking force distribution of the overhead conductor is marked as unreliable, and the preset database is updated.

5. The method for safety assessment of overhead power lines based on Monte Carlo simulation according to claim 1, characterized in that, After repeating the Monte Carlo simulation process to obtain the fracture force distribution of the overhead conductor, the following steps are also included: Obtain the experimental rated strength of overhead conductors; Based on the breaking force distribution of the overhead conductor, determine the mean value of the breaking force distribution of the overhead conductor; The mean deviation is calculated based on the mean value of the breaking force distribution of the overhead conductor and the experimental rated strength. If the mean deviation is less than a preset deviation value, the breaking force distribution of the overhead conductor is marked as reliable; otherwise, the breaking force distribution of the overhead conductor is marked as unreliable, and the preset database is updated.

6. The method for safety assessment of overhead power lines based on Monte Carlo simulation according to claim 1, characterized in that, After repeating the Monte Carlo simulation process to obtain the fracture force distribution of the overhead conductor, the following steps are also included: Select several parameter sets from the parameter sets generated by all Monte Carlo simulation processes as the target parameter sets; For each type of parameter in each target parameter set, perturbation processing is performed on each type of parameter in the target parameter set according to a preset perturbation ratio to obtain a perturbation parameter set; Calculate the corresponding total fracture force based on the set of disturbance parameters; The sensitivity index is calculated based on the total fracture force corresponding to the disturbance parameter set and the total fracture force corresponding to the target parameter set. For each type of parameter, the sensitivity index of all target parameters in the same type of parameter is summed and averaged to obtain the sensitivity index of each type of parameter. Based on the sensitivity index of each type of parameter, the parameters of all categories are ranked by sensitivity. Based on sensitivity ranking, target parameter categories are determined, and target parameter categories in overhead conductors are periodically detected.

7. A safety assessment device for overhead power lines based on Monte Carlo simulation, characterized in that, include: The module includes an actual load acquisition module, a failure probability calculation module, and an overhead conductor safety assessment module. The actual load acquisition module is used to acquire the actual operating load of the overhead conductor in the application scenario; The failure probability calculation module is used to determine the failure probability based on the actual operating load of the overhead conductor and the fracture force distribution of the overhead conductor. The fracture force distribution of the overhead conductor is determined as follows: obtaining the model and specifications of the overhead conductor; extracting the diameter distribution, fracture stress distribution, stranding factor, and material correction parameters of the corresponding sub-conductors from a preset database based on the model and specifications of the overhead conductor; repeating the Monte Carlo simulation process to obtain the fracture force distribution of the overhead conductor; the Monte Carlo simulation process includes: for each sub-conductor, randomly selecting a diameter parameter and a fracture stress from the diameter distribution and fracture stress distribution of the sub-conductor to obtain a sampling set of the sub-conductor, and using the sampling sets, stranding factors, and material correction parameters of all sub-conductors as a parameter set; calculating the corresponding total fracture force based on the parameter set; and generating the fracture force distribution of the overhead conductor based on the total fracture force corresponding to all parameter sets when the number of Monte Carlo simulations exceeds a first preset number, or when the relative change rate of the mean and the relative change rate of the standard deviation of the total fracture force converge. The overhead conductor safety assessment module is used to determine that the safety assessment result of the overhead conductor is in a high-risk state when the failure probability exceeds the preset risk threshold corresponding to the application scenario, and to replace the overhead conductor.

8. The overhead conductor safety assessment device based on Monte Carlo simulation according to claim 7, characterized in that, The mean relative rate of change and the standard deviation relative rate of change of the total fracture force were determined in the following ways: Set several second preset counts, and each second preset count is less than the first preset count; For each second preset number of simulations, when the number of Monte Carlo simulations reaches the second preset number of simulations, the relative rate of change of the mean and the relative rate of change of the standard deviation of the total fracture force are calculated based on the total fracture force corresponding to each parameter set obtained during the simulation.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the overhead conductor safety assessment method based on Monte Carlo simulation as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the overhead conductor safety assessment method based on Monte Carlo simulation as described in any one of claims 1-6.

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