A method and system for inferring power distribution system node survivability expectations

Abstract

The present application belongs to but is not limited to the technical field of power grid control, and particularly relates to a power distribution system node survival expectation deduction method and system, comprising: S1, acquiring a power source node set, a load node set, a line set and a line reliability probability set in a power distribution system; S2, establishing a non-directional probability graph based on the power source node set, the load node set, the line set and the line reliability probability set, and calculating a preliminary load node survival expectation; and S3, combining the preliminary load node survival expectation with a pre-acquired maintenance process node survival expectation to generate a final power distribution system node survival expectation. The present application establishes a non-directional probability graph, directly calculates a load node survival expectation under a non-coincidence edge condition, and under a condition that there are coinciding edges in the path in the non-directional probability graph, simplifies through deletion-shrinking until the preliminary load node survival expectation can be directly decomposed and calculated.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power grid control, and particularly relates to a method and system for deriving node survival expectation of a power distribution system. BACKGROUND

[0002] To evaluate the resilience level of a power distribution system and the effect of various resilience improvement measures, resilience evaluation is an indispensable part of resilience research of the power distribution system. Before a disaster occurs, the damage of extreme events to the power distribution system is not clear, and the system resilience level under different fault scenarios may have significant differences. Therefore, how to consider the influence of disaster uncertainty in resilience evaluation is a major challenge in related research on resilience evaluation.

[0003] Most existing researches use pre-disaster resilience evaluation methods based on scenario sampling. This kind of method simulates the disaster scenarios by using simulation method, generates system disaster scenarios based on device outage probability, calculates the resilience recovery and resilience level of each disaster scenario, and evaluates the system resilience level under the disaster. However, with the emergence of resilience evaluation requirements of the power distribution system considering medium and long-term disaster risks, the system cannot only consider a single disaster when performing resilience evaluation. The scenario-based resilience evaluation method needs to sample a large number of disaster scenarios to obtain relatively accurate results. When the number of disasters involved increases significantly, the calculation amount of the scenario-based method will increase significantly, making it difficult to implement resilience evaluation considering medium and long-term disaster risks.

[0004] Therefore, it is necessary to establish a more efficient resilience evaluation method to reasonably simplify the resilience evaluation process and reduce the calculation requirements of resilience evaluation considering medium and long-term disaster risks.

[0005] In view of the above analysis, the existing technical problems to be solved urgently in the prior art are: with the emergence of resilience evaluation requirements of the power distribution system considering medium and long-term disaster risks, the system cannot only consider a single disaster when performing resilience evaluation. The scenario-based resilience evaluation method needs to sample a large number of disaster scenarios to obtain relatively accurate results. When the number of disasters involved increases significantly, the calculation amount of the scenario-based method will increase significantly, making it difficult to implement resilience evaluation considering medium and long-term disaster risks. SUMMARY

[0006] In view of the problems existing in the prior art, the present application provides a method and system for deriving node survival expectation of a power distribution system,

[0007] The present application is implemented as follows: a method for deriving node survival expectation of a power distribution system, comprising:

[0008] S1, obtaining a power source node set, a load node set, a line set and a line reliability probability set in the power distribution system;

[0009] S2, establishing a undirected probabilistic graph based on the power node set, load node set, line set and line reliability probability set, and calculating preliminary load node survival expectation;

[0010] S3, combining the preliminary load node survival expectation with the pre-acquired maintenance process node survival expectation to generate final power distribution system node survival expectation.

[0011] Further, the step of establishing a undirected probabilistic graph based on the power node set, load node set, line set and line reliability probability set, and calculating preliminary load node survival expectation comprises:

[0012] In the case of no overlapping edges in the undirected probabilistic graph, modeling the power distribution system as a undirected probabilistic graph

[0013]

[0014] wherein and represent the power node set and the load node set, respectively, represents the line set, represents the line reliability probability set.

[0015] The step of calculating the preliminary load node survival expectation can be represented as and satisfies the following conditions:

[0016]

[0017]

[0018] wherein, e i is a random variable representing the power supply state of node i, e i = 1 represents that the node has power or is reliable, and L ij is a random variable representing the working state of line i-j.

[0019] Further, the step of establishing a undirected probabilistic graph based on the power node set, load node set, line set and line reliability probability set, and calculating preliminary load node survival expectation further comprises:

[0020] In the case of overlapping edges in the path in the undirected probabilistic graph, simplifying by deletion-shrinking until the preliminary load node survival expectation can be directly decomposed.

[0021] Further, the step of simplifying by deletion-shrinking in the case of overlapping edges in the path in the undirected probabilistic graph until the preliminary load node survival expectation can be directly decomposed comprises:

[0022] In the case that there are coincident edges in the path of the undirected probability graph, the undirected probability graph is put into the element set;

[0023] In the case that there are elements in the element set that cannot be directly calculated, the elements that cannot be directly calculated are deleted and contracted to generate a new subgraph, which is put into the element set to replace the original undirected probability graph, until all elements in the element set can be directly calculated;

[0024] In the case that all elements in the element set can be directly calculated, an equation group is established for each element by setting a formula;

[0025] Based on the equation group, the preliminary load node survival expectation is calculated.

[0026] Further, the pre-acquired maintenance process node survival expectation comprises the following steps:

[0027] Based on the undirected probability graph, a power supply node and a load node are selected;

[0028] The shortest path between the power supply node and the load node is calculated, and the line weight on the shortest path is increased;

[0029] In the case that the traversal of all load nodes and power supply nodes is completed, the lines are sorted according to the weight from large to small to complete the line importance sorting.

[0030] Further, the combination of the preliminary load node survival expectation and the pre-acquired maintenance process node survival expectation generates a final power distribution system node survival expectation, which specifically comprises the following steps:

[0031] According to the line importance sorting, the line maintenance priority is determined;

[0032] The node survival expectation of the maintenance process is calculated through the line maintenance priority, extreme disaster information, line priority and maintenance resource information;

[0033] The node survival expectation of the maintenance process is combined with the preliminary load node survival expectation to generate a final power distribution system node survival expectation.

[0034] Another object of the present application is to provide a probability graph-based power distribution system node survival expectation deduction system based on a probability graph-based power distribution system node survival expectation deduction method, which comprises the following steps:

[0035] A data acquisition module is configured to acquire a power supply node set, a load node set, a line set and a line reliability probability set in a power distribution system;

[0036] The undirected probabilistic graph establishment module is configured to establish an undirected probabilistic graph based on the power supply node set, the load node set, the line set, and the line reliability probability set, and to calculate a preliminary load node survival expectation.

[0037] The final node survival expectation calculation module is configured to combine the preliminary load node survival expectation with a pre-acquired maintenance process node survival expectation, and to generate a final power distribution system node survival expectation.

[0038] Another object of the present application is to provide a computer device comprising a memory and a processor, the memory storing a computer program, the computer program being executed by the processor to cause the processor to perform the steps of the probabilistic graph-based power distribution system node survival expectation derivation method.

[0039] Another object of the present application is to provide a computer-readable storage medium storing a computer program, the computer program being executed by a processor to cause the processor to perform the steps of the probabilistic graph-based power distribution system node survival expectation derivation method.

[0040] Another object of the present application is to provide an information data processing terminal comprising the probabilistic graph-based power distribution system node survival expectation derivation system.

[0041] In combination with the above technical solutions and solved technical problems, the technical solution to be protected by the present application has the following advantages and positive effects:

[0042] The present application provides a probabilistic graph-based power distribution system node survival expectation derivation method and system, which establishes an undirected probabilistic graph, directly calculates a load node survival expectation in the case of no overlapping edges, and in the case of overlapping edges in the undirected probabilistic graph, simplifies through deletion and contraction until a preliminary load node survival expectation can be directly decomposed and calculated. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 FIG. 1 is a flowchart of the probabilistic graph-based power distribution system node survival expectation derivation method provided by the present application.

[0044] Figure 2 FIG. 4 is a probabilistic graph solvable condition diagram of a four-node system provided by an embodiment of the present application.

[0045] Figure 3is a four-node system decomposition schematic provided by an embodiment of the present application.

[0046] Figure 4 is a probability graph decomposition flowchart provided by an embodiment of the present application.

[0047] Figure 5 is a line importance ranking flowchart provided by an embodiment of the present application.

[0048] Figure 6 is a module connection schematic of a power distribution system node survival expectation derivation system based on a probability graph provided by an embodiment of the present application.

[0049] Figure 7 is a structural schematic of an electronic device provided by an embodiment of the present application.

[0050] In the figure: 110, data acquisition module; 120, undirected probability graph establishment module; 130, final node survival expectation calculation module; 710, processor; 720, communication interface; 730, memory; 740, communication bus. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0052] The present application is described below in combination with Figure 1 A power distribution system node survival expectation derivation method based on a probability graph is described, which comprises:

[0053] Step S1, acquiring a power supply node set, a load node set, a line set and a line reliability probability set in a power distribution system;

[0054] In the present application, probability inference is a method of reasoning the probability of one or more random variables according to known distribution and correlation between variables. In the resilience evaluation of a power distribution system, it can be considered that solving the node survival expectation of the power distribution system is a probability inference problem, and the condition for the survival of a load node is defined as the existence of a power supply path between the load node and the system power supply. In the solving process, the grid line reliability probability is a known quantity, and the survival probability of each load node is an unknown quantity.

[0055] For such network probabilistic inference problems, Monte Carlo simulation is usually adopted to perform probabilistic inference. However, such methods are inefficient for large-scale networks, and researchers have developed several sampling methods to improve sampling efficiency and accuracy, such as stratified sampling, rejection sampling and random walk. However, the resilience study focuses on extreme disaster scenarios, which have the characteristics of high loss and low probability, and often require a large number of samples to obtain reasonable results.

[0056] As an alternative to simulation methods, analytical methods can calculate the exact or approximate value of the node outage probability. One commonly used analytical algorithm is to model the distribution network using a probabilistic graphical model (PGM) and infer the node state. The probabilistic graphical model is a general framework that can use complex network topology characteristics and mutual influence relationships to achieve probabilistic inference, which uses a graph to represent the joint probability distribution or marginal probability distribution of random variables in the model.

[0057] Step S2, based on the power supply node set, load node set, line set and line reliability probability set, a undirected probability graph is established, and the preliminary load node survival expectation is calculated;

[0058] In the present application, the probability graph modeling is based on the following related conditions: line failure is an independent random event; as long as there is a power supply path between the load node and the power supply node, the load node is considered reliable / powered.

[0059] Among them, assumption 2 simplifies the actual problem to a certain extent. In the actual system, even if there is a power supply path between the load node and the power supply node, the load node may not be powered due to lack of reactive support / insufficient active capacity, etc.

[0060] Specifically, based on the above assumptions, the distribution system can be modeled as an undirected probability graph Among them and represent the set of power supply nodes and load nodes respectively, represents the set of system lines, represents the set of line reliability probabilities.

[0061] Then the calculation of the load node survival expectation can be represented as and satisfies the following conditions:

[0062]

[0063] Among them, e i is a random variable representing the power supply state of node i, e i = 1 indicates that the node is powered / reliable. L ijTo express the random variable of the working state of the line i-j, formula (1) shows that it is subject to a Bernoulli distribution with a line reliability probability r ij is a parameter. In this problem, the reliability probability of each line is a known quantity, and satisfies r ij ji , and formula (2) shows that the reliability probability of the power supply node is 1.

[0064] In an actual system, there may be a case where the reliability probability of the power supply node is not 1, such as a distributed power supply being affected by weather and being faulty, a main network power supply being insufficient, and the like. At this time, this case can be converted to satisfy formula (2), and the specific method is as follows: the power supply node is converted into a load node, and a virtual power supply node and a virtual line connecting the node and the virtual power supply are added, and the reliability probability of the virtual line is set as the reliability probability of the power supply node.

[0065] In the case where there are coinciding edges in the path in the undirected probability graph, simplification is performed by deletion-shrinking until the survival expectation of the preliminary load node can be directly decomposed and solved;

[0066] Specifically, in the case where there are coinciding edges in the path in the undirected probability graph, the undirected probability graph is put into an element set;

[0067] In the case where there are elements in the element set that cannot be directly calculated, the elements that cannot be directly calculated are deleted and shrunk to produce a new subgraph and replace the original undirected probability graph in the element set until all elements in the element set can be directly calculated.

[0068] In the present application, when there is no coinciding edge (line) between the paths between the same load node and the power supply node in a system, the survival expectation of the load node in the system can be solved by the reliability probability of each path through a series-parallel connection. When there are coinciding edges in the path, the reliability probability of different paths is not an independent event and cannot be directly solved. To analyze whether the system topology can be directly solved, the load node subgraph after the power supply node is removed can be analyzed. If the load subgraph is tree-shaped / radiation-shaped, the power supply path of each node does not have a repeated edge at this time, and the system topology can be directly solved. Referring to Figure 2 , a simple four-node system solvable condition diagram is shown. When the system meets the above conditions, the node survival expectation can be obtained by separately calculating the probability of each power supply path, but when the system is large, the calculation complexity of calculating the power supply path for each node is very large. The probability graph method of the present application can avoid the calculation of finding the power supply path.

[0069] For any load node i, there must be a neighboring node to supply power to it when it has power. Therefore, the probability x j→i that node i is powered by the neighboring node j is:

[0070] ​

[0071] where Ω j denotes the set of adjacent nodes of node j, Ω j denotes the set of adjacent nodes of node j excluding node i. When , x j→i = 0.

[0072] For any load node i, its outage probability is the probability that all its adjacent nodes are simultaneously unable to supply power to it:

[0073]

[0074] Correspondingly, its survival probability is:

[0075]

[0076] At this time, a nonlinear equation set consisting of 2b+n equations and 2b+n variables is formed by equations (3), (4) and (5) together, where b is the number of lines and n is the number of nodes. This nonlinear equation set can be solved by the Newton-Raphson method.

[0077] For system topologies that cannot be directly calculated, the correct result is obtained by decomposing into combinations of topologies that can be directly decomposed. Although the power distribution system usually maintains radial operation in daily operation, the system topology may also contain transfer lines and the like, so that the power supply paths of the actual system overlap. For overlapping lines in the power supply path, the delete-shrink method can be used for simplification.

[0078] For overlapping line i-j, it is denoted as line l ij , which is intact with a probability of r ij and damaged with a probability of 1-r ij . At this time, the original probability graph G can be decomposed to shrink line i-j with a probability of r ij , that is, to merge nodes i and j to form subgraph G / l ij ; or to delete line i-j with a probability of 1-r ij , to form subgraph G\l ij . Referring to Figure 3 , a decomposition diagram of a four-node system is shown.

[0079] The calculation result after one decomposition can be calculated as follows:

[0080]

[0081] where g(·) denotes the calculation result of the corresponding graph, and Pr(·) denotes the occurrence probability of the corresponding graph.

[0082] For the system topology with multiple coincident lines that need to be decomposed, the support tree can be calculated for the load subgraph, and the lines not in the support tree are processed one by one according to the delete-shrink method. The subgraph generated after the decomposition of the system topology that needs to be decomposed may still not be directly solvable, and can be recursively decomposed according to the delete-shrink method until the last subgraph can be directly solved. The calculation process is as shown in Figure 4

[0083] At this time, the original probability graph G is decomposed into a set of subgraphs Ω G At this time, the reliable probability of node i is:

[0084]

[0085] Where Pr(e i | G k ) represents the reliable probability of node i in the subgraph G k .

[0086] Through the above process, the reliable probability of the load node in the system can be calculated under the condition of given system line reliability.

[0087] In the present application, the pre-acquired maintenance process node survival expectation includes:

[0088] Selecting a power node and a load node based on an undirected probability graph;

[0089] Calculating the shortest path between the power node and the load node, and increasing the line weight on the shortest path;

[0090] In the case where the traversal of all load nodes and power nodes is completed, the lines are sorted in descending order of weight to complete the line importance sorting.

[0091] Specifically, when the line reliability of the multi-period power distribution system under the given extreme disaster is given, the change of the node survival expectation of the system affected by the extreme disaster can be obtained by time period. The line reliability of the power distribution system in different time periods can be calculated according to the extreme disaster information. Since the damage caused by the extreme disaster to the system can be described in the form of probability, the probability graph method is suitable for the calculation of the disaster loss of the multi-period power distribution system. In the study of the resilience of the power distribution system, the maintenance process is an important means to restore the function level of the system. However, unlike the influence of the extreme disaster on the system, the maintenance resources of the power distribution system are usually discrete and limited, and it is difficult to describe them in the form of probability.

[0092] Therefore, a maintenance process calculation method suitable for the probability graph calculation method is needed, which can combine the probability graph calculation method to obtain a complete simulation of the disaster and recovery process of the power distribution system.

[0093] ​For simplicity, the maintenance process calculation of the present application is based on the following assumptions: the maintenance time of each damaged line in the system is the same; the moving time of maintenance personnel in the system is ignored; the maintenance work starts immediately when the line fails; and when multiple failures occur at the same time, the maintenance is performed according to the importance of the lines from high to low. Some assumptions are different from the actual maintenance process, which may introduce some errors. However, since the present work focuses on pre-disaster resilience assessment, and the present application is ultimately applied to the consideration of long-term disaster risk of the system, it is considered that the errors introduced by the above assumptions are acceptable.

[0094] In order to perform maintenance process calculation, it is necessary to first calculate the importance of each line in the system. Here, a relatively simple line importance ranking method is given, which is referred to Figure 5 .

[0095] The above method can give the maintenance priority of the system lines according to the importance of the lines in the power supply path. The calculation method of line priority can also be replaced by other methods, which does not affect the subsequent maintenance process calculation.

[0096] Step S3, combining the preliminary load node survival expectation with the pre-acquired maintenance process node survival expectation to generate the final power distribution system node survival expectation.

[0097] Specifically, it includes determining the line maintenance priority according to the line importance ranking;

[0098] calculating the node survival expectation of the maintenance process through the line maintenance priority, extreme disaster information, line priority, and maintenance resource information;

[0099] combining the node survival expectation of the maintenance process with the preliminary load node survival expectation to generate the final power distribution system node survival expectation.

[0100] In the present application, for the node survival expectation calculation considering the maintenance process, extreme disaster information, line priority, and maintenance resource information are needed. The maintenance resource information includes: the total number of maintenance teams, the average maintenance time of the lines. The specific calculation process is shown in the algorithm of Table 1.

[0101] In the algorithm process of Table 1, the queue q repair records the maintenance effect that has been arranged but has not yet taken effect in each period, the array records the number of available maintenance teams in each period, and the array{△r ij} records the total of the current maintenance effect that has not yet taken effect.

[0102] In the actual maintenance process, a maintenance team will be arranged at t~t+t repairA specific damaged line is repaired within a fixed time period. In the probabilistic graph calculation, since the degree of damage to each line is represented by probabilities, the number of repair teams assigned to a particular line in the algorithm in Table 1 represents the expected number of repair teams assigned to that line. For example, if the reliability probability of a line in time period t is 0.9, then when repair resources are sufficient, 0.1 repair teams are assigned to it, ensuring that the number of repair teams from t to t+t is within the expected range. repair The remaining number of available repair teams decreases by 0.1 during the -1 time period, as shown in steps 4-12. The array {△rij} records the sum of currently ineffective repair effects, preventing the reliability probability of a line repair from exceeding 1, as shown in step 6. When the disaster begins (t=0), the repair team starts repairing the line and stores the repair effect in q. repair In the middle, the repair effect will be t repair It will take effect after a certain time, as shown in steps 13-16.

[0103] Table 1

[0104]

[0105] This invention provides a method for extrapolating the survival expectation of distribution system nodes based on probabilistic graphs. By establishing an undirected probabilistic graph, the survival expectation of load nodes without overlapping edges is directly obtained. When overlapping edges exist in the path of the undirected probabilistic graph, the method simplifies the process by deletion and contraction until the initial survival expectation of load nodes can be directly decomposed and obtained. Based on the initial survival expectation of load nodes and the pre-acquired survival expectation of maintenance process nodes, the final survival expectation of distribution system nodes is generated. Compared with traditional methods, scenario sampling is no longer required, simplifying the resilience assessment process, improving computational efficiency, and reducing the difficulty of resilience assessment of distribution system nodes considering medium- and long-term disaster risks.

[0106] refer to Figure 6 The present invention also discloses a prediction system for the expected survival of nodes in a power distribution system based on a probabilistic graph, the system comprising:

[0107] Data acquisition module 110 is used to acquire the set of power supply nodes, load nodes, lines, and line reliability probability in the power distribution system;

[0108] The undirected probability graph establishment module 120 is used to establish an undirected probability graph based on the power node set, load node set, line set and line reliability probability set, and to obtain the preliminary load node survival expectation.

[0109] The final node survival expectation calculation module 130 is used to combine the preliminary load node survival expectation with the pre-acquired maintenance process node survival expectation to generate the final power distribution system node survival expectation.

[0110] wherein, based on the power node set, the load node set, the line set and the line reliability probability set, a undirected probabilistic graph is established, and a preliminary load node survival expectation is calculated, comprising:

[0111] In the case of no overlapping edges in the undirected probabilistic graph, the power distribution system is modeled as an undirected probabilistic graph

[0112] wherein and represent the power node set and the load node set, respectively, represents the line set, and r represents the line reliability probability set;

[0113] The calculation of the preliminary load node survival expectation can be represented as and satisfies the following conditions:

[0114]

[0115] wherein, e i is a random variable representing the power supply state of node i, e i = 1 represents that the node has power or is reliable, and L ij is a random variable representing the working state of line i-j.

[0116] Based on the power node set, the load node set, the line set and the line reliability probability set, the undirected probabilistic graph is established, and the preliminary load node survival expectation is calculated, further comprising:

[0117] In the case of overlapping edges in the path of the undirected probabilistic graph, the simplification is performed by deletion-shrinking until the preliminary load node survival expectation can be directly decomposed and calculated.

[0118] In the case of overlapping edges in the path of the undirected probabilistic graph, the simplification is performed by deletion-shrinking until the preliminary load node survival expectation can be directly decomposed and calculated, comprising:

[0119] In the case of overlapping edges in the path of the undirected probabilistic graph, the undirected probabilistic graph is put into an element set;

[0120] In the case of elements in the element set that cannot be directly calculated, the elements that cannot be directly calculated are deleted and shrunk to produce a new subgraph to replace the original undirected probabilistic graph in the element set until all elements in the element set can be directly calculated;

[0121] In the case of all elements in the element set being able to be directly calculated, an equation set is established for each element by setting a formula;

[0122] Solving based on the equation set, the preliminary load node survival expectation is calculated.

[0123] The pre-acquired maintenance process node survival expectation comprises:

[0124] The power supply node and the load node are selected based on the undirected probability graph.

[0125] The shortest path between the power supply node and the load node is calculated, and the line weight is increased on the shortest path.

[0126] In the case of traversal of all load nodes and power supply nodes, the lines are sorted according to the weight from large to small, and the line importance sorting is completed.

[0127] Based on the preliminary load node survival expectation and the pre-acquired maintenance process node survival expectation, the final power distribution system node survival expectation is generated, and specifically comprises:

[0128] The line maintenance priority is determined according to the line importance sorting.

[0129] The node survival expectation of the maintenance process is calculated through the line maintenance priority, extreme disaster information, line priority, and maintenance resource information.

[0130] The node survival expectation of the maintenance process is combined with the preliminary load node survival expectation to generate the final power distribution system node survival expectation.

[0131] Based on the present application, a power distribution system node survival expectation deduction system based on a probability graph is provided, which directly calculates the load node survival expectation under the condition of no overlapping edges by establishing an undirected probability graph, and under the condition of overlapping edges in the undirected probability graph, simplifies through deletion-shrinking until the preliminary load node survival expectation can be directly decomposed and solved. Based on the preliminary load node survival expectation and the pre-acquired maintenance process node survival expectation, the final power distribution system node survival expectation is generated. Compared with the traditional method, scene sampling is no longer needed, the resilience evaluation process is simplified, the calculation efficiency is improved, and the difficulty of considering the resilience evaluation of the power distribution system node in the long-term disaster risk is reduced.

[0132] Figure 7 An example of an entity structure diagram of an electronic device is shown as Figure 7As shown, the electronic device can include a processor 710, a communications interface 720, a memory 730, and a communications bus 740, wherein the processor 710, the communications interface 720, and the memory 730 complete mutual communication through the communications bus 740. The processor 710 can invoke a logic instruction in the memory 730 to execute a deduction method of a power distribution system node survival expectation based on a probability graph, the method including: acquiring a power source node set, a load node set, a line set, and a line reliability probability set in a power distribution system; establishing a undirected probability graph based on the power source node set, the load node set, the line set, and the line reliability probability set, and calculating a preliminary load node survival expectation; and combining the preliminary load node survival expectation with a pre-acquired maintenance process node survival expectation to generate a final power distribution system node survival expectation.

[0133] In addition, the logic instruction in the memory 730 described above can be implemented in the form of a software functional unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0134] The application embodiment of the present application provides a computer device, the computer device includes a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the method.

[0135] The application embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to make the processor execute the steps of the method.

[0136] The application embodiment of the present application provides an information data processing terminal, and the information data processing terminal includes a system.

[0137] It should be noted that embodiments of the present application can be realized by hardware, software, or a combination of software and hardware. The hardware portion can be realized by a special logic; the software portion can be stored in a memory and executed by a proper instruction execution system, such as a microprocessor or a specially designed hardware. A person of ordinary skill in the art can understand that the above-mentioned apparatus and method can be realized by computer executable instructions and / or included in processor control codes, such as a carrier medium, such as a magnetic disk, CD or DVD-ROM, a programmable memory, such as a read-only memory (firmware), or a data carrier, such as an optical or electronic signal carrier. The apparatus of the present application and its modules can be realized by a hardware circuit, such as a very large scale integrated circuit or a gate array, a semiconductor, such as a logic chip, a transistor, or a programmable hardware device, such as a field programmable gate array, a programmable logic device, or the like, by software executed by various types of processors, or by a combination of the above-mentioned hardware circuit and software, such as firmware.

[0138] The above description is merely a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any modification, equivalent replacement, and improvement within the technical range disclosed by the present application, and within the spirit and principle of the present application, should be covered within the protection scope of the present application.

Claims

1. A method for extrapolating the expected survival rate of nodes in a power distribution system, characterized in that, include: S1, obtain the set of power supply nodes, load nodes, lines, and line reliability probabilities in the power distribution system; S2, Based on the set of power nodes, the set of load nodes, the set of lines, and the set of line reliability probabilities, establish an undirected probability graph and calculate the preliminary expected survival of load nodes; S3, Based on the initial load node survival expectation and the pre-acquired maintenance process node survival expectation, the final power distribution system node survival expectation is generated; The step of establishing an undirected probability graph based on the power node set, load node set, line set, and line reliability probability set to calculate the preliminary load node survival expectation includes: In an undirected probabilistic graph with no overlapping edges, the power distribution system can be modeled as an undirected probabilistic graph. ; in and These represent the sets of power supply nodes and load nodes, respectively. Represents a set of routes. A set representing the probability of line reliability; The initial load node survival expectation can be expressed as: And satisfy the following conditions: in, Let be a random variable representing the power supply state of node i. This indicates that the node is powered or reliable. Let be a random variable representing the working state of line ij; The step of establishing an undirected probability graph based on the power node set, load node set, line set, and line reliability probability set to calculate the preliminary load node survival expectation also includes: In the case where there are overlapping edges in the path of the undirected probability graph, simplification is performed by deletion-shrinkage until the initial load node survival expectation can be directly decomposed and obtained. The process of combining the preliminary load node survival expectation with the pre-acquired maintenance process node survival expectation to generate the final distribution system node survival expectation specifically includes: Determine the maintenance priority of the lines based on their importance; The expected survival of nodes in the maintenance process is calculated using the line maintenance priority, extreme disaster information, line priority, and maintenance resource information. The node survival expectation during the maintenance process is combined with the initial load node survival expectation to generate the final distribution system node survival expectation.

2. The method for extrapolating the expected survival of nodes in a power distribution system as described in claim 1, characterized in that, In the case where there are overlapping edges in the path of the undirected probability graph, simplification is performed by deletion-shrinkage until the initial load node survival expectation can be directly decomposed and calculated, including: If there are overlapping edges in the paths of the undirected probability graph, the undirected probability graph is placed into the element set. If there are elements in the set of elements that cannot be directly calculated, the elements that cannot be directly calculated are deleted or shrunk, and a new subgraph is generated and placed into the set of elements to replace the original undirected probability graph, until all elements in the combination of elements can be directly calculated. If all elements in the set of elements can be directly calculated, a system of equations is established for each element by setting a formula. Solve the system of equations to calculate the initial expected survival of load nodes.

3. The method for extrapolating the expected survival of nodes in a power distribution system as described in claim 1, characterized in that, The pre-acquired maintenance process node survival expectation includes: Selecting power and load nodes based on undirected probabilistic graphs; Calculate the shortest path between the power node and the load node, and increase the line weight on the shortest path; After traversing all load nodes and power nodes, sort the lines according to their weights from largest to smallest to complete the line importance ranking.

4. A system for extrapolating the expected survival of nodes in a power distribution system using the method described in any one of claims 1 to 3, characterized in that, include: The data acquisition module is used to acquire the set of power supply nodes, load nodes, lines, and line reliability probabilities in the power distribution system. The undirected probability graph establishment module is used to establish an undirected probability graph based on the power node set, load node set, line set, and line reliability probability set, and to obtain the preliminary load node survival expectation. The final node survival expectation calculation module is used to combine the preliminary load node survival expectation with the pre-acquired maintenance process node survival expectation to generate the final distribution system node survival expectation.

5. A computer device comprising a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the deduction method for the expected survival of a power distribution system node as described in any one of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the deduction method for the expected survival of nodes in a power distribution system as described in any one of claims 1 to 3.

7. An information data processing terminal, comprising the power distribution system node survival expectation simulation system as described in claim 4.

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