Ice disaster line fault analysis method and device and storage medium

Abstract

The invention discloses an ice disaster line fault analysis method and device and a storage medium, and the method comprises the steps: determining the ice disaster occurrence data of a to-be-analyzed line, and determining the ice disaster probability distribution according to the ice disaster occurrence data; wherein the ice disaster occurrence data comprises ice disaster occurrence frequency, ice disaster duration and ice disaster intensity; and according to the ice disaster probability distribution, the ice disaster intensity and a preset index type ice disaster line fault model, determining the line fault occurrence rate of the to-be-analyzed line. According to the method, various influence factors such as the ice disaster occurrence frequency, the ice disaster duration and the ice disaster intensity are comprehensively considered, the ice disaster probability distribution of the to-be-analyzed line is determined, the line fault occurrence rate of the to-be-analyzed line is determined in combination with the preset index type ice disaster line fault model, the ice disaster fault processing capacity and the ice disaster fault recovery capacity of a power grid are considered, and the fault analysis accuracy of the to-be-analyzed line is improved. Therefore, the comprehensiveness and accuracy of ice disaster line fault analysis can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster analysis, and in particular to a method, device and storage medium for analyzing ice disaster line faults. Background Art

[0002] Ice disasters are one of the main natural disasters faced by power systems, especially in areas with changeable climates. Ice disasters may cause power line failures, including fractures and tower collapses. In severe cases, they may cause large-scale power outages, which have a significant impact on economic and social operations. Therefore, analyzing the faults of ice-damaged lines is an important task to ensure the stable operation of the power system and the safety of power supply.

[0003] Existing methods for analyzing ice disaster line faults usually mainly perform fault analysis based on ice disaster thickness, without fully considering the factors affecting ice disaster line faults, resulting in low accuracy of ice disaster line fault analysis. Summary of the invention

[0004] The present invention provides an ice disaster line fault analysis method, device and storage medium to solve the technical problem that the existing ice disaster line fault analysis method does not fully consider the factors affecting the ice disaster line fault, resulting in low accuracy of ice disaster line fault analysis.

[0005] The present invention provides a method for analyzing ice disaster line faults, comprising:

[0006] Determine ice disaster occurrence data of the line to be analyzed, and determine ice disaster probability distribution according to the ice disaster occurrence data; wherein the ice disaster occurrence data includes ice disaster occurrence frequency, ice disaster duration and ice disaster intensity;

[0007] The line failure occurrence rate of the line to be analyzed is determined according to the ice disaster probability distribution, the ice disaster intensity and a preset exponential ice disaster line failure model.

[0008] Furthermore, the step of determining ice disaster occurrence data of the line to be analyzed includes:

[0009] Determine the frequency of occurrence of the ice disaster based on historical ice disaster occurrence data and Poisson distribution;

[0010] Determining the duration of the ice disaster based on a normal distribution;

[0011] The intensity of the ice disaster is determined based on the effective ice cover growth time and the rainfall per unit time.

[0012] Furthermore, determining the intensity of the ice disaster according to the effective ice cover growth time and the rainfall per unit time includes:

[0013] The ice thickness is calculated according to the following formula:

[0014]

[0015] Among them, N is the effective ice growth time, P is the rainfall per unit time, ρ i is the density of ice, ρ w is the density of water, v is the wind speed, and W is the liquid water content of precipitation;

[0016] The intensity of the ice disaster is determined according to the ice cover thickness.

[0017] Further, determining the line failure occurrence rate of the line to be analyzed according to the ice disaster probability distribution, the ice disaster intensity and a preset exponential ice disaster line failure model includes:

[0018] Dividing the line to be analyzed into a plurality of line segments;

[0019] Inputting the probability distribution and the ice disaster intensity into the preset exponential ice disaster line fault model, and outputting the fault occurrence rate of each line section;

[0020] Determining a non-failure rate of each of the line sections according to the failure rate of each of the line sections;

[0021] The failure occurrence rate of the line to be analyzed is determined according to the non-failure occurrence rate of each line segment.

[0022] Furthermore, the probability distribution and the ice disaster intensity are input into the preset exponential ice disaster line fault model, and the fault occurrence rate of each line section is output, including:

[0023] The calculation formula for the failure rate of each line section is as follows:

[0024]

[0025] Among them, p is the failure rate of each line section, m is the ice disaster intensity, and M is the ice disaster intensity comparison value.

[0026] Furthermore, determining the failure occurrence rate of the line to be analyzed according to the non-failure occurrence rate of each line segment includes:

[0027] The calculation formula of the failure rate of the line to be analyzed is as follows:

[0028]

[0029] Among them, p ij is the failure rate of the line to be analyzed, is the failure-free rate of each of the line sections.

[0030] Furthermore, after the probability distribution and the ice disaster intensity are input into the preset exponential ice disaster line fault model and the fault occurrence rate of each line section is output, the method further includes:

[0031] Randomly assigning a value to each of the line segments to obtain a random state value, and when the random state value is less than the failure rate of the current line segment, determining that a failure occurs in the current line segment;

[0032] A line fault set is generated according to the faults occurring in each line segment.

[0033] Furthermore, after generating a line fault set according to the faults occurring in each line segment, the method further includes:

[0034] Constructing a fault repair model according to the line fault set, wherein the objective function of the fault repair model is an objective function of minimum load loss;

[0035] Based on preset constraints, the fault repair model is solved to obtain a fault repair strategy, wherein the preset constraints include fault propagation constraints, network topology constraints, operation flow constraints, maintenance resource constraints and emergency repair team constraints.

[0036] The present invention also provides an ice disaster line fault analysis device, comprising:

[0037] An ice disaster probability distribution determination module is used to determine ice disaster occurrence data of the line to be analyzed, and determine the ice disaster probability distribution according to the ice disaster occurrence data; wherein the ice disaster occurrence data includes ice disaster occurrence frequency, ice disaster duration and ice disaster intensity;

[0038] The line fault occurrence rate determination module is used to determine the line fault occurrence rate of the line to be analyzed according to the ice disaster probability distribution, the ice disaster intensity and a preset exponential ice disaster line fault model.

[0039] The present invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned ice disaster line fault analysis method.

[0040] The embodiment of the present invention comprehensively considers multiple influencing factors such as ice disaster frequency, ice disaster duration and ice disaster intensity, determines the ice disaster probability distribution of the line to be analyzed, and combines a preset exponential ice disaster line fault model to determine the line failure rate of the line to be analyzed, and considers the processing and recovery capabilities of the power grid for ice disaster failures, thereby effectively improving the comprehensiveness and accuracy of ice disaster line failure analysis.

[0041] Furthermore, the embodiment of the present invention divides the line to be analyzed into several line segments, and applies an exponential ice disaster line fault model to each line segment for fault analysis, so as to more accurately predict the failure risk of each line segment, and comprehensively considers the ice disaster intensity and probability distribution, which can effectively improve the accuracy of ice disaster line fault analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of a method for analyzing ice disaster line faults provided by an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram comparing the load loss time before and after the disaster prevention resource configuration provided by an embodiment of the present invention;

[0044] Figure 3 It is a structural schematic diagram of an ice disaster line fault analysis device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0047] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0048] See also Figure 1 The present invention provides a method for analyzing ice disaster line faults, comprising:

[0049] S1. Determine ice disaster occurrence data of the line to be analyzed, and determine ice disaster probability distribution according to the ice disaster occurrence data; wherein the ice disaster occurrence data includes ice disaster occurrence frequency, ice disaster duration and ice disaster intensity;

[0050] In the embodiment of the present invention, probability modeling of disaster scenarios can be performed based on three parameters: ice disaster frequency, ice disaster duration, and ice disaster intensity, to determine the probability distribution of ice disasters.

[0051] S2. Determine the line failure rate of the line to be analyzed based on the ice disaster probability distribution, ice disaster intensity and a preset exponential ice disaster line failure model.

[0052] The embodiment of the present invention determines the ice disaster probability distribution of the line to be analyzed according to the ice disaster frequency, ice disaster duration and ice disaster intensity, and determines the line failure rate of the line to be analyzed in combination with a preset exponential ice disaster line failure model, and comprehensively considers multiple fault influencing factors such as ice disaster frequency, ice disaster duration and ice disaster intensity, thereby effectively improving the comprehensiveness and accuracy of ice disaster line failure analysis.

[0053] In one embodiment, step S1, determining ice disaster occurrence data of the line to be analyzed, includes:

[0054] S11. Determine the frequency of ice disasters based on historical ice disaster data and Poisson distribution;

[0055] In the embodiment of the present invention, the frequency of ice disasters can be determined according to the following formula:

[0056]

[0057] Among them, λ is the historical ice disaster data.

[0058] S12. Determine the duration of ice disaster based on normal distribution;

[0059] In the embodiment of the present invention, the duration of the ice disaster can be determined according to the following formula:

[0060]

[0061] S13. Determine the intensity of the ice disaster based on the effective ice cover growth time and the rainfall per unit time.

[0062] In the embodiment of the present invention, the effective ice cover growth time is the effective time calculated by converting the ice cover thickness into an increase with freezing rain, a maintenance time with snowfall, and a melt time with rainfall.

[0063] In one embodiment, S13, determining the intensity of the ice disaster according to the effective ice cover growth time and the rainfall per unit time, includes:

[0064] S131. Calculate the ice thickness according to the following formula:

[0065]

[0066] Among them, N is the effective ice growth time, P is the rainfall per unit time, ρ i is the density of ice, ρ w is the density of water, v is the wind speed, W is the liquid water content of precipitation, W can be 0.067P 0.846 ;

[0067] S132. Determine the intensity of the ice disaster according to the thickness of the ice cover.

[0068] In the embodiment of the present invention, the greater the ice thickness, the greater the intensity of the ice disaster, and the more serious the impact on the power line. The embodiment of the present invention can determine the relationship between the intensity of the ice disaster and the ice thickness by analyzing historical ice disaster data, and determine the intensity of the ice disaster in combination with the currently calculated ice thickness.

[0069] In one embodiment, S2, determining the line failure occurrence rate of the line to be analyzed according to the ice disaster probability distribution, the ice disaster intensity and a preset exponential ice disaster line failure model, includes:

[0070] S21, dividing the line to be analyzed into a number of line segments;

[0071] S22, inputting the probability distribution and ice disaster intensity into a preset exponential ice disaster line fault model, and outputting the fault occurrence rate of each line section;

[0072] In the embodiment of the present invention, according to the preset exponential ice disaster line fault analysis model, and the determined ice disaster probability distribution and ice disaster intensity, the vulnerability analysis of the line to be analyzed is performed, and the fault occurrence rate of each line section can be output.

[0073] The calculation formula for the failure rate of each line section is as follows:

[0074]

[0075] Among them, p is the fault occurrence rate of each section of the line, m is the ice disaster intensity, and M is the ice disaster intensity comparison value. If m is less than M, it is determined that no fault will occur. When m is greater than five times M, it is determined that the threshold is exceeded and the line is damaged.

[0076] S23, determining the non-failure rate of each line section according to the failure rate of each line section;

[0077] S24. Determine the failure occurrence rate of the line to be analyzed based on the failure-free rate of each line segment.

[0078] In the embodiment of the present invention, the calculation formula of the fault occurrence rate of the line to be analyzed is as follows:

[0079]

[0080] Among them, p ij is the failure rate of the line to be analyzed, is the failure-free rate of each line segment.

[0081] The embodiment of the present invention divides the line to be analyzed into several line segments, and applies the exponential ice disaster line fault model to each line segment for fault analysis, so as to more accurately predict the failure risk of each line segment, and comprehensively considers the ice disaster intensity and probability distribution, which can effectively improve the accuracy of ice disaster line fault analysis.

[0082] In one embodiment, after the probability distribution and ice disaster intensity are input into a preset exponential ice disaster line fault model in step S2 and the fault occurrence rate of each line section is output, the method further includes:

[0083] S3, randomly assigning a value to each line segment to obtain a random state value, and when the random state value is less than the failure rate of the current line segment, determining that the current line segment has a failure;

[0084] S4. Generate a line fault set according to the faults occurring in each line segment.

[0085] In one embodiment, after generating a line fault set according to the faults occurring in each line segment in step S4, the method further includes:

[0086] S5. construct a fault repair model according to the line fault set, wherein the objective function of the fault repair model is an objective function of minimum load loss;

[0087] In the embodiment of the present invention, the fault repair strategy of the repair team for the fault can be considered as a vehicle path constraint problem. The fault repair model determined based on the vehicle path constraint problem has an objective function with the minimum load loss as the goal:

[0088]

[0089] Among them, A T is a binary matrix, representing the matrix of load loss status of all nodes in each stage of the distribution network; P L represents the load of each node; TR represents the time to repair the corresponding faulty node; N Fault Indicates the number of faulty nodes in the fault set.

[0090] In the embodiment of the present invention, each time a fault is repaired, the fault set is reduced, and the distribution network can be reconstructed. The reconstruction plan decision process is regarded as solving the problem of the objective function, and its goal is also to minimize the load loss.

[0091]

[0092] Among them, P R Indicates the load recovery amount after the network architecture is reconstructed. In the solution process, the constraints can be written as MILP problems, and commercial solvers can be used for fast solution or intelligent algorithm programs such as simulated annealing algorithm, particle swarm algorithm, genetic algorithm, etc. can be used for optimization solution.

[0093] S6. Based on preset constraints, solve the fault repair model to obtain a fault repair strategy, wherein the preset constraints include fault propagation constraints, network topology constraints, operation flow constraints, maintenance resource constraints, and emergency repair team constraints.

[0094] In the embodiment of the present invention, the fault propagation constraint is: if the line at both ends of the node fails and there is no switch isolation fault, the node is considered to have lost the load; if the nodes at both ends of the line fail and there is no switch isolation fault, the line is considered to have failed; if there is no normally operating line between the node and the source node to form a path, the node is considered to have lost the load. The network topology constraint is: when the network is reconstructed, the new network needs to meet the radial topology. The operation flow constraint is: the reconstructed distribution network needs to meet the operation flow needs within the allowable range, and the operation flow can be constrained by the relationship between voltage, current and power. The maintenance resource constraint is: a fault point should be supplemented with enough materials to meet the needs, but not more than the cargo volume of a vehicle, and consider the time required for the material vehicle to depart from the warehouse, unload after arriving at the fault site, return to the warehouse for replenishment, and then go to the next planned location. The repair team constraint is: the repair targets of each repair team do not overlap, and each fault point will only be repaired once. After the repair team departs, it will not return to the warehouse or the repaired fault point, and will go to the next fault point in turn according to the plan. The time it takes for the fault point to be repaired depends on the larger value of the time it takes for the materials to be sufficient at the point and the time it takes for the rescue team to arrive at the scene plus the time required for repair.

[0095] In one embodiment, disaster prevention resource configuration may also be performed based on the ice disaster line fault analysis results, including:

[0096] S10, determine the power grid system to be configured, obtain its geographical environment information, obtain its geographical environment information, statistical information on disaster occurrence, information on past disasters in the region, location settings of substations, line information, node load and radiation user information, statistical average data information on travel time for each station and line, statistics on repair resources required for repairing power grid equipment failures and information on average repair time, etc.;

[0097] S20. Determine the types and scale of elastic resources expected to be invested, such as the number of vehicles and labor for the repair team, the types and capacity of energy storage technology, the number and location of intelligent control switches, etc. Select key elasticity indicators corresponding to the corresponding input resources, such as the ability to resist and adapt during disasters, and the ability to coordinate and recover quickly after disasters.

[0098] S30. According to the estimated investment and construction situation in step S20, the investment and construction cost is identified and calculated using the life cycle cost (LCC) of assets, equipment and labor.

[0099] In the embodiment of the present invention, the full life cycle cost confirmation of asset equipment refers to the sum of all expenses required to be spent on the asset during its entire life cycle. The cost categories of each stage in the life cycle of power grid asset equipment are confirmed, including the following factors from the purchase (construction) of equipment (facilities) to the decommissioning (dismantling) and commissioning: initial investment cost CI, technical transformation cost CR, operation and maintenance cost CM, fault handling cost CF and decommissioning cost CD.

[0100] The confirmation of the full life cycle cost of labor refers to the analysis of the cost of labor factors in the production system and the confirmation of the following costs: employment costs CH, including recruitment costs, additional production costs, and training costs; operating costs CO, including wages and management expenses; environmental costs CE, including absenteeism costs, medical expenses, reinstatement costs, and disability pensions.

[0101] In an embodiment of the present invention, the average annual life cycle cost can be calculated:

[0102]

[0103] Where, L labor and L vehicle are the employee's employment time and the vehicle's total service time, respectively.

[0104] S40, conduct resilience assessment on the main body of the target power grid and calculate the resilience performance index of the system. The resilience assessment model needs to consider the models of the following links in sequence, namely, the generation of extreme disaster scenarios, the vulnerability analysis of power grid components, and the response and recovery process of the power system. Finally, the resilience index of the corresponding system is calculated based on the key resilience characteristics of the main body of the target power grid in response to the simulated disaster when resilience enhancement resources are added;

[0105] S401. Conditional probability modeling of key parameters selected for extreme disaster scenarios. For extreme disasters, such as typhoons, rainstorms, ice disasters, etc., we first need to analyze and extract key factors that can describe the characteristics of disaster scenarios, usually including the frequency of disasters, duration, scope of impact, dynamics during disasters, and degree of disaster. The selection of key parameters will vary depending on the disaster. The degree of disaster can usually be obtained by using various disaster scenario models proposed by scholars in the field of disaster science, and the data required for input can be obtained from local meteorological stations and meteorological observation departments.

[0106] S402. Analyze the damage mechanism of extreme disasters to power grid components, i.e., vulnerability analysis; select appropriate component failure probability models based on historical failure conditions while considering failure uncertainty, and characterize the failure modes of power system components while considering the independence and correlation of components. Component vulnerability analysis links disaster scenarios with component failures, and generates random numbers that symbolize component status through standard uniform distribution and compares them with failure rates to obtain a set of faults that represent the degree of power grid loss.

[0107] S403. Model the power system response and power system recovery. The power system response is to use the margin left for risks in the planning stage, such as disconnected interconnection lines and distributed generation under normal conditions, to help the power grid to reconstruct the network in a timely manner when the original power grid is damaged; the power system recovery process is to simulate the orderly repair of damaged key facilities by dispatching emergency repair teams after the disaster, considering that the power grid components affected by the disaster generally cannot recover by themselves. Both of the above are important aspects of improving resilience.

[0108] S404: Obtain system recovery data of a simulation, and then, through the simulation process of iterative steps S402 and S403, statistically analyze the selected elasticity index.

[0109] S50, compare the obtained investment and construction cost with the elasticity level obtained in step S40, analyze the elasticity improvement level of the system, and conduct a comprehensive analysis of the investment and construction benefit level based on the sensitivity of elasticity benefits and multi-standard decision-making methods to help grid planning and decision-making.

[0110] In the embodiment of the present invention, the elastic function and cost economy are not in the same dimension, so the ratio method is used to reflect the benefit measurement between the function improvement obtained by the investment and the cost expenditure. That is:

[0111]

[0112] Among them, B CBA is the investment and construction benefit, ΔR is the improvement in the elasticity level obtained by resource investment and construction, and C is the cost expenditure, which can be obtained from S2.

[0113] Based on the investment and construction benefits obtained from the above formula, a more comprehensive analysis of decision-making related content is conducted.

[0114] In the above example, the effect of increasing the number of repair teams as an investment means on improving the resilience of the system is analyzed. Assuming that the employees are on the job for 3 years and the vehicles are in service for 8 years, the full life cycle cost analysis is shown in the following table.

[0115] Table 1: Life cycle cost calculation of relevant factors when investing in emergency repair team resources

[0116]

[0117] The embodiment of the present invention selects a PG&E69 node distribution network with a tie line for testing, and considers the active load of each network node, which is listed in Table 2 below.

[0118] Table 2: Active loads at each bus node of PG&E 69-node distribution network

[0119]

[0120]

[0121] Assume that the distribution network company plans to build 6 emergency repair teams, with 4 emergency repair teams as the reference. The difference in recovery speed between the two in a disaster will allow the team with more emergency repair teams to restore power more quickly and have more room to arrange emergency repair tasks. Figure 2 The load loss time index of the increased number of construction teams (b) and the reference team group (a) was counted. The load loss of the system after the disaster was simulated for 1000 virtual years. After iteration and statistics, the annual average indexes of the two groups were P and load,1 =272.53MW·h (6 repair teams) and P load,2 =261.43MW·h (4 repair teams).

[0122] Substituting into the benefit formula, we can get the investment and construction benefit:

[0123]

[0124] In summary, based on the calculated investment and construction benefits, optimal allocation of disaster prevention resources can be achieved.

[0125] The embodiment of the present invention considers assets, equipment and labor to perform cost-benefit analysis, which can effectively improve the comprehensiveness of cost analysis.

[0126] Furthermore, the embodiments of the present invention can establish grid resilience indicators that meet the needs from multiple dimensions and multiple states through resilience assessment, which helps to flexibly select effective resilience indicators when facing different grid entities, user characteristics and geographical environments, thereby effectively improving the flexibility of disaster prevention configuration.

[0127] The implementation of the embodiments of the present invention has the following beneficial effects:

[0128] The present invention comprehensively considers multiple influencing factors such as the frequency, duration and intensity of ice disasters, determines the probability distribution of ice disasters for the line to be analyzed, and combines a preset exponential ice disaster line fault model to determine the line failure rate of the line to be analyzed. The processing and recovery capabilities of the power grid for ice disaster failures are considered, thereby effectively improving the comprehensiveness and accuracy of ice disaster line fault analysis.

[0129] Furthermore, the embodiment of the present invention divides the line to be analyzed into several line segments, and applies an exponential ice disaster line fault model to each line segment for fault analysis, so as to more accurately predict the failure risk of each line segment, and comprehensively considers the ice disaster intensity and probability distribution, which can effectively improve the accuracy of ice disaster line fault analysis.

[0130] See also Figure 3 Based on the same inventive concept as the above embodiment, the present invention also provides an ice disaster line fault analysis device, comprising:

[0131] The ice disaster probability distribution determination module 10 is used to determine the ice disaster occurrence data of the line to be analyzed, and determine the ice disaster probability distribution according to the ice disaster occurrence data; wherein the ice disaster occurrence data includes the ice disaster occurrence frequency, ice disaster duration and ice disaster intensity;

[0132] The line fault occurrence rate determination module 20 is used to determine the line fault occurrence rate of the line to be analyzed according to the ice disaster probability distribution, the ice disaster intensity and the preset exponential ice disaster line fault model.

[0133] In one embodiment, determining ice disaster occurrence data of the line to be analyzed includes:

[0134] Determine the frequency of ice disasters based on historical ice disaster data and Poisson distribution;

[0135] Determine the duration of ice disaster based on normal distribution;

[0136] The intensity of the ice disaster is determined based on the effective ice cover growth time and the rainfall per unit time.

[0137] In one embodiment, determining the intensity of ice disaster according to the effective ice cover growth time and the rainfall per unit time includes:

[0138] The ice thickness is calculated according to the following formula:

[0139]

[0140] Among them, N is the effective ice growth time, P is the rainfall per unit time, ρ i is the density of ice, ρ w is the density of water, v is the wind speed, and W is the liquid water content of precipitation;

[0141] The intensity of the ice disaster is determined based on the thickness of the ice cover.

[0142] In one embodiment, the line failure occurrence rate of the line to be analyzed is determined according to the ice disaster probability distribution, the ice disaster intensity and a preset exponential ice disaster line failure model, including:

[0143] Divide the line to be analyzed into several line segments;

[0144] Input the probability distribution and ice disaster intensity into the preset exponential ice disaster line failure model, and output the failure occurrence rate of each line section;

[0145] According to the failure occurrence rate of each line section, determine the failure-free rate of each line section;

[0146] The failure rate of the line to be analyzed is determined based on the non-failure rate of each line section.

[0147] In one embodiment, the probability distribution and ice disaster intensity are input into a preset exponential ice disaster line fault model, and the fault occurrence rate of each line section is output, including:

[0148] The calculation formula for the failure rate of each line section is as follows:

[0149]

[0150] Among them, p is the failure rate of each line section, m is the ice disaster intensity, and M is the ice disaster intensity comparison value.

[0151] In one embodiment, determining the failure occurrence rate of the line to be analyzed according to the non-failure occurrence rate of each line segment includes:

[0152] The calculation formula for the fault occurrence rate of the line to be analyzed is as follows:

[0153]

[0154] Among them, p ij is the failure rate of the line to be analyzed, is the failure-free rate of each line segment.

[0155] In one embodiment, after the probability distribution and ice disaster intensity are input into a preset exponential ice disaster line fault model and the fault occurrence rate of each line section is output, the method further includes:

[0156] A random state value is obtained by randomly assigning a value to each line segment. When the random state value is less than the failure rate of the current line segment, it is determined that the current line segment has a failure.

[0157] A line fault set is generated according to the faults occurring in each line segment.

[0158] In one embodiment, after generating a line fault set according to the faults occurring in each line segment, the method further includes:

[0159] A fault repair model is constructed according to the line fault set, and the objective function of the fault repair model is the objective function of minimum load loss;

[0160] Based on the preset constraints, the fault repair model is solved to obtain the fault repair strategy, where the preset constraints include fault propagation constraints, network topology constraints, operation flow constraints, maintenance resource constraints and emergency repair team constraints.

[0161] Accordingly, an embodiment of the present invention further provides a terminal device, comprising: 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 ice disaster line fault analysis method of any one of the above embodiments is implemented.

[0162] The terminal device of this embodiment includes: a processor, a memory, and a computer program and computer instructions stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above embodiment 1 is implemented, for example Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above device embodiment are implemented, such as the line fault occurrence rate determination module 20.

[0163] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in a memory and executed by a processor to complete the present invention. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in a terminal device. For example, the line fault occurrence rate determination module 20 is used to determine the line fault occurrence rate of the line to be analyzed based on the ice disaster probability distribution, the ice disaster intensity, and a preset exponential ice disaster line fault model.

[0164] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the terminal device may also include an input / output device, a network access device, a bus, etc.

[0165] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0166] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the terminal device by running or executing the computer programs and / or modules 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 terminal, 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 (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0167] Among them, if the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. 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. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, 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. 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, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0168] Correspondingly, an embodiment of the present invention further provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the function recovery execution method of any one of the above embodiments.

[0169] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for analyzing line faults caused by ice disasters, characterized in that: include: Determine ice disaster occurrence data of the line to be analyzed, and determine ice disaster probability distribution according to the ice disaster occurrence data; wherein the ice disaster occurrence data includes ice disaster occurrence frequency, ice disaster duration and ice disaster intensity; The line failure occurrence rate of the line to be analyzed is determined according to the ice disaster probability distribution, the ice disaster intensity and a preset exponential ice disaster line failure model.

2. The ice disaster line fault analysis method according to claim 1, characterized in that: The step of determining ice disaster occurrence data of the line to be analyzed includes: Determine the frequency of occurrence of the ice disaster based on historical ice disaster occurrence data and Poisson distribution; Determining the duration of the ice disaster based on a normal distribution; The intensity of the ice disaster is determined based on the effective ice cover growth time and the rainfall per unit time.

3. The ice disaster line fault analysis method according to claim 2, characterized in that: Determining the intensity of the ice disaster according to the effective ice cover growth time and the rainfall per unit time includes: The ice thickness is calculated according to the following formula: Among them, N is the effective ice growth time, P is the rainfall per unit time, ρ i is the density of ice, ρ w is the density of water, v is the wind speed, and W is the liquid water content of precipitation; The intensity of the ice disaster is determined according to the ice cover thickness.

4. The ice disaster line fault analysis method according to claim 1, characterized in that: The determining the line failure occurrence rate of the line to be analyzed according to the ice disaster probability distribution, the ice disaster intensity and a preset exponential ice disaster line failure model includes: Dividing the line to be analyzed into a plurality of line segments; Inputting the probability distribution and the ice disaster intensity into the preset exponential ice disaster line fault model, and outputting the fault occurrence rate of each line section; Determining a non-failure rate of each of the line sections according to the failure rate of each of the line sections; The failure occurrence rate of the line to be analyzed is determined according to the non-failure occurrence rate of each line segment.

5. The ice disaster line fault analysis method according to claim 4, characterized in that: Inputting the probability distribution and the ice disaster intensity into the preset exponential ice disaster line fault model, and outputting the fault occurrence rate of each line section, including: The calculation formula for the failure rate of each line section is as follows: Among them, p is the failure rate of each line section, m is the ice disaster intensity, and M is the ice disaster intensity comparison value.

6. The ice disaster line fault analysis method according to claim 4, characterized in that: Determining the failure occurrence rate of the line to be analyzed according to the non-failure occurrence rate of each line segment includes: The calculation formula of the failure rate of the line to be analyzed is as follows: Among them, p ij is the failure rate of the line to be analyzed, is the failure-free rate of each of the line sections.

7. The ice disaster line fault analysis method according to claim 4, characterized in that: After the probability distribution and the ice disaster intensity are input into the preset exponential ice disaster line fault model and the fault occurrence rate of each line section is output, the method further includes: Randomly assigning a value to each of the line segments to obtain a random state value, and when the random state value is less than the failure rate of the current line segment, determining that a failure occurs in the current line segment; A line fault set is generated according to the faults occurring in each line segment.

8. The ice disaster line fault analysis method according to claim 7, characterized in that: After generating a line fault set according to the faults occurring in each line section, the method further includes: Constructing a fault repair model according to the line fault set, wherein the objective function of the fault repair model is an objective function of minimum load loss; Based on preset constraints, the fault repair model is solved to obtain a fault repair strategy, wherein the preset constraints include fault propagation constraints, network topology constraints, operation flow constraints, maintenance resource constraints and emergency repair team constraints.

9. An ice disaster line fault analysis device, characterized in that: include: An ice disaster probability distribution determination module is used to determine ice disaster occurrence data of the line to be analyzed, and determine the ice disaster probability distribution according to the ice disaster occurrence data; wherein the ice disaster occurrence data includes ice disaster occurrence frequency, ice disaster duration and ice disaster intensity; The line fault occurrence rate determination module is used to determine the line fault occurrence rate of the line to be analyzed according to the ice disaster probability distribution, the ice disaster intensity and a preset exponential ice disaster line fault model.

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, the device where the computer-readable storage medium is located is controlled to execute the ice disaster line fault analysis method according to any one of claims 1 to 8.

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