Method and system for deducing survival expectation of nodes of power distribution system

Abstract

The invention 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, and the method comprises the steps: S1, obtaining a power supply node set, a load node set, a line set and a line reliability probability set in a power distribution system; s2, establishing an undirected probability graph based on the power supply node set, the load node set, the line set and the line reliability probability set, and solving a preliminary load node survival expectation; and S3, combining the initial load node survival expectation with a pre-obtained maintenance process node survival expectation to generate a final power distribution system node survival expectation. According to the method, the undirected probability graph is established, the survival expectation of the load node under the condition of no coincident edge is directly solved, and under the condition that the coincident edge exists in the path in the undirected probability graph, simplification is carried out in a deletion-contraction mode until the initial survival expectation of the load node can be solved through direct decomposition.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of power grid control technology, and in particular relates to a method and system for deducing node survival expectations of a power distribution system. Background Art

[0002] In order to evaluate the resilience level of the distribution system and the effectiveness of various resilience improvement measures, resilience assessment is an indispensable part of the research on the resilience of the distribution system. Before a disaster occurs, the damage caused by extreme events to the distribution system is still unclear, and the resilience level of the system may vary significantly under different fault scenarios. Therefore, how to consider the impact of disaster uncertainty in resilience assessment is a major challenge in research related to resilience assessment.

[0003] Most existing studies use pre-disaster resilience assessment methods based on scenario sampling. This type of method uses simulation methods to simulate disaster scenarios, generates system disaster scenarios based on the probability of equipment outage, calculates resilience recovery and resilience levels for each disaster scenario, and evaluates the system resilience level under the disaster. However, with the emergence of the need for resilience assessment of distribution systems considering medium- and long-term disaster risks, it is not possible to consider only a single disaster when assessing the resilience of the system. Scenario-based resilience assessment methods require more sampling of disaster scenarios to obtain relatively accurate results. When the number of disasters involved increases significantly, the amount of calculation of scenario-based methods will increase significantly, making it difficult to achieve resilience assessment considering medium- and long-term disaster risks.

[0004] To this end, it is necessary to establish a more efficient resilience assessment method, reasonably simplify the resilience assessment process, and reduce the computational requirements when conducting resilience assessments that take into account medium- and long-term disaster risks.

[0005] In view of the above analysis, the technical problems that need to be urgently solved in the existing technology are: with the emergence of the demand for resilience assessment of distribution systems considering medium- and long-term disaster risks, it is not enough to consider only a single disaster when conducting resilience assessment of the system. The scenario-based resilience assessment method requires more sampling of disaster-affected scenes 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 achieve resilience assessment considering medium- and long-term disaster risks. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides a method and system for deducing the survival expectation of nodes in a power distribution system.

[0007] The present invention is implemented as follows: a method for deducing the survival expectation of a distribution system node, 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 an undirected probability graph based on the power node set, load node set, line set and line reliability probability set, and obtaining a preliminary load node survival expectation;

[0010] S3, generating a final distribution system node survival expectation based on combining the preliminary load node survival expectation with the pre-acquired maintenance process node survival expectation.

[0011] Further, the establishing of an undirected probability graph based on the power node set, the load node set, the line set and the line reliability probability set to obtain a preliminary load node survival expectation includes:

[0012] Modeling the distribution system as an undirected probabilistic graph without overlapping edges

[0013]

[0014] in and Represent the collection of power nodes and load nodes respectively, Represents a collection of lines. represents the set of line reliability probabilities;

[0015] The survival expectation of the calculated initial load node can be expressed as And meet the following conditions:

[0016]

[0017]

[0018] Among them, e i is a random variable representing the power supply status of node i, e i =1 indicates that the node has power or is reliable, L ij is a random variable representing the working state of line ij.

[0019] Furthermore, the establishing of an undirected probability graph based on the power node set, the load node set, the line set and the line reliability probability set to obtain a preliminary load node survival expectation also includes:

[0020] When there are overlapping edges in the undirected probability graph, the path is simplified by deletion-contraction until the initial load node survival expectation can be directly decomposed and obtained.

[0021] Furthermore, when there are overlapping edges in the path of the undirected probability graph, simplification is performed by deletion-contraction until the initial load node survival expectation can be directly decomposed and obtained, including:

[0022] When there are overlapping edges in the paths in the undirected probability graph, the undirected probability graph is put into the element set;

[0023] When 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 and put it into the element set to replace the original undirected probability graph until all elements in the element combination can be directly calculated;

[0024] When all elements in the element set can be directly calculated, a system of equations is established for each element by setting a formula;

[0025] The equation group is solved to calculate the preliminary load node survival expectation.

[0026] Furthermore, the pre-acquired maintenance process node survival expectation includes:

[0027] Select power nodes and load nodes based on undirected probability graph;

[0028] Calculating the shortest path between the power source node and the load node, and increasing the line weight on the shortest path;

[0029] When all load nodes and power supply nodes are traversed, the lines are sorted from large to small according to their weight to complete the line importance sorting.

[0030] Further, the generating of the final distribution system node survival expectation based on the preliminary load node survival expectation and the pre-acquired maintenance process node survival expectation specifically includes:

[0031] Determine the priority of line maintenance according to the importance of the lines;

[0032] Calculate the node survival expectation of the maintenance process through the line maintenance priority and 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 the final distribution system node survival expectation.

[0034] Another object of the present invention is to provide a method for deducing the survival expectation of a distribution system node based on a probability graph and a system for deducing the survival expectation of a distribution system node based on a probability graph, comprising:

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

[0036] An undirected probability graph building module is used to build an undirected probability graph based on the power node set, load node set, line set and line reliability probability set to obtain preliminary load node survival expectation;

[0037] The final node survival expectation calculation module is used to generate the final distribution system node survival expectation based on the combination of the preliminary load node survival expectation and the pre-acquired maintenance process node survival expectation.

[0038] Another object of the present invention is to provide a computer device, the computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for deducing the survival expectation of distribution system nodes based on probability graphs.

[0039] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for deducing the survival expectation of distribution system nodes based on probability graphs.

[0040] Another object of the present invention is to provide an information data processing terminal, which includes the above-mentioned probability graph-based distribution system node survival expectation deduction system.

[0041] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0042] The present invention provides a method and system for deducing the survival expectation of distribution system nodes based on a probability graph. By establishing an undirected probability graph, the survival expectation of load nodes in the case of no overlapping edges is directly obtained. When there are overlapping edges in the path of the undirected probability graph, simplification is performed by deletion-contraction until the preliminary load node survival expectation can be directly decomposed and obtained; based on the combination of the preliminary load node survival expectation and the pre-acquired maintenance process node survival expectation, the final distribution system node survival expectation is generated. Compared with the traditional method, scene sampling is no longer required, the resilience assessment process is simplified, the calculation efficiency is improved, and the difficulty of resilience assessment of distribution system nodes considering medium- and long-term disaster risks is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of a method for deducing the survival expectation of nodes in a power distribution system based on a probability graph provided in an embodiment of the present invention.

[0044] Figure 2 It is a schematic diagram of the conditions under which a probability graph of a four-node system can be solved provided by an embodiment of the present invention.

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

[0046] Figure 4 It is a probability graph decomposition flow chart provided by an embodiment of the present invention.

[0047] Figure 5 This is a flow chart of line importance sorting provided by an embodiment of the present invention.

[0048] Figure 6 It is a schematic diagram of module connections of a system for deducing the survival expectation of power distribution system nodes based on probability graphs provided in an embodiment of the present invention.

[0049] Figure 7 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[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 solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0052] Combine the following Figure 1 A method for deducing the survival expectation of a power distribution system node based on a probability graph of the present invention is described, comprising:

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

[0054] In the present invention, probabilistic inference is a method of inferring the probability of one or more random variables taking values ​​based on known distributions and correlations between variables. In the resilience assessment of the distribution system, it can be considered that solving the node survival expectation of the distribution system is a probabilistic inference problem, and the condition for the survival of the load node is defined as the existence of a power supply path between the load node and the system power supply. In the solution process, the reliability probability of the power grid line is a known quantity, and the survival probability of each load node is an unknown quantity.

[0055] For this type of network probability inference problem, Monte Carlo simulation is usually used for probability inference. However, this method has low sampling efficiency for large-scale networks. Researchers have developed several sampling methods to improve sampling efficiency and sampling accuracy, such as stratified sampling, rejection sampling, and random walk. However, resilience research focuses on extreme disaster scenarios, which have the characteristics of high losses and low probabilities, 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 node outage probability. A commonly used analytical algorithm is to use probabilistic graphical models (PGMs) to model distribution networks and infer node states. Probabilistic graphical models are a general framework that can use complex network topology features and mutual influence relationships to achieve probabilistic inference. They use graphs to represent the joint probability distribution or marginal probability distribution of random variables in the model.

[0057] Step S2, establishing an undirected probability graph based on the power node set, load node set, line set and line reliability probability set, and obtaining a preliminary load node survival expectation;

[0058] In the present invention, 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 to be 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 node, the load node may not be powered due to lack of reactive support / insufficient active capacity.

[0060] Specifically, based on the above assumptions, the power distribution system can be modeled as an undirected probability graph in and Represent the collection of power nodes and load nodes respectively, Represents a collection of system lines, Represents the set of line reliability probabilities.

[0061] Then the survival expectation of the computing load node can be expressed as And meet the following conditions:

[0062]

[0063] Among them, e i is a random variable representing the power supply status of node i, e i =1 indicates the node has power / reliable. ijis a random variable representing the working state of line ij. Formula (1) shows that it obeys the line reliability probability r ij is the Bernoulli distribution of the parameters. In this problem, the reliability probability of each line is a known quantity and satisfies r ij =r ji , Formula (2) indicates that the reliability probability of the power node is 1.

[0064] In actual systems, there may be situations where the reliability probability of a power source node is not 1, such as a failure of a distributed power source due to weather conditions or insufficient power in the main grid. In this case, this situation can be transformed into a situation that satisfies equation (2). The specific method is as follows: transform the power source node into a load node, add a virtual power source node and a virtual line connecting the node and the virtual power source, and set the reliability probability of the virtual line to the reliability probability of the power source node.

[0065] When there are overlapping edges in the undirected probability graph, the graph is simplified by deletion-contraction until the initial load node survival expectation can be directly decomposed and obtained.

[0066] Specifically, it includes: when there are overlapping edges in the paths in the undirected probability graph, the undirected probability graph is put into the element set;

[0067] When there are elements in the element set that cannot be directly calculated, the elements that cannot be directly calculated are deleted and shrunk to generate a new subgraph and put it into the element set to replace the original undirected probability graph until all elements in the element combination can be directly calculated.

[0068] In the present invention, when there are no overlapping edges (lines) in the paths between the same load node and the power node in a system, the survival expectation of the load nodes in the system can be solved by analyzing the reliability probabilities of each path in a series-parallel manner. When there are overlapping edges in the paths, the reliability probabilities of different paths are not independent events and cannot be solved directly. In order to analyze whether the system topology can be solved directly, the load node subgraph after the power node is removed can be analyzed. If the load subgraph is tree-like / radial, there are no repeated edges in the power supply path of each node, and the system topology can be solved directly. Reference Figure 2 , shows a simple diagram of the solvability conditions of a four-node system. When the system meets the above conditions, the node survival expectation can be obtained by calculating the probability of each power supply path separately, 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 invention can avoid the calculation of finding the power supply path.

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

[0070]

[0071] Among them, Ω j represents the set of adjacent nodes of node j, Ω j \i represents the set of adjacent nodes excluding node i and node j. When x j→i =0.

[0072] For any load node i, the probability of power failure is the probability that its adjacent nodes cannot supply power to it at the same time:

[0073]

[0074] Accordingly, the survival probability is:

[0075]

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

[0077] For system topologies that cannot be directly calculated, the correct results can be obtained by decomposing them into combinations of topologies that can be directly decomposed. Although the distribution system usually maintains radial operation in daily operation, the system topology may also contain transfer lines, etc., which may cause the power supply paths of the actual system to overlap. For overlapping lines in the power supply path, the deletion-contraction method can be used to simplify them.

[0078] For the coincident line ij, record it as line l ij , which is r ij The probability of being intact is 1-r ij At this point, the original probability graph G can be decomposed into r ij The probability of shrinking line ij is that nodes i and j are merged to form a subgraph G / l ij ; 1-r ij The probability of deleting line ij to form a subgraph G\l ij . refer to Figure 3 , shows a decomposed schematic of a four-node system.

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

[0080]

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

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

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

[0084]

[0085] Where Pr(e i =1|G k ) indicates that node i is in the subgraph G k The probability of reliability.

[0086] Through the above process, the reliability probability of the load nodes in the system can be calculated given the reliability of the system lines.

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

[0088] Select power nodes and load nodes based on undirected probability graph;

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

[0090] When all load nodes and power supply nodes are traversed, the lines are sorted from large to small according to their weight to complete the line importance sorting.

[0091] Specifically, when the reliability of the distribution system lines in multiple periods under the influence of extreme disasters is given, the expected changes in the survival of nodes in the system affected by extreme disasters can be obtained period by period. Among them, the reliability of the distribution system lines in different periods can be calculated based on the extreme disaster information. Since the damage caused by extreme disasters to the system can be characterized in a probabilistic form, the probability graph method is suitable for the calculation of the disaster losses of the distribution system in multiple periods. In the study of the resilience of the distribution system, the maintenance process is an important means to restore the system functional level. However, unlike the impact of extreme disasters on the system, the maintenance resources of the distribution system are usually discrete and limited, and it is difficult to characterize them in a probabilistic form.

[0092] Therefore, a maintenance process calculation method suitable for the probability graph calculation method is needed. Combined with the probability graph calculation method, a complete simulation of the distribution system disaster and recovery process can be obtained.

[0093] For the sake of simplicity, the maintenance process calculation of the present invention is based on the following assumptions: the maintenance time of each damaged line in the system is the same; the movement time of the maintenance personnel in the system is ignored; the maintenance work starts immediately when the line fails; when multiple faults occur at the same time, the lines are repaired from high to low importance. Some assumptions are different from the actual maintenance process, which will introduce certain errors. However, since this work focuses on pre-disaster resilience assessment, and the present invention is ultimately applied to the resilience assessment of the system considering medium- and long-term disaster risks, it is believed that the errors introduced by the above assumptions are acceptable.

[0094] In order to calculate the maintenance process, we first need to calculate the importance of each line in the system. Here is a relatively simple method for ranking the importance of lines. Figure 5 .

[0095] The above method can give the maintenance priority of the system line according to the importance of the line in the power supply path. The calculation method of the line priority can also be replaced by other methods without affecting the subsequent maintenance process calculation.

[0096] Step S3: generating a final distribution system node survival expectation based on combining the preliminary load node survival expectation with the pre-acquired maintenance process node survival expectation.

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

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

[0099] The node survival expectation of the maintenance process is combined with the preliminary load node survival expectation to generate the final distribution system node survival expectation.

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

[0101] In the algorithm flow of Table 1, queue q repair Records the maintenance results that have been scheduled but not yet implemented in each period, array Records the number of available maintenance teams in each period, array {△r ij}Records the total amount of maintenance effects that are not currently in effect.

[0102] In the actual maintenance process, a maintenance team will be arranged at t~t+t repairDuring the calculation of the probability graph, since the degree of damage of each line is expressed in the form of probability, the number of maintenance teams assigned to a line in the algorithm of Table 1 is the expectation of the maintenance team assigned to the line. For example, if the reliability probability of a line in time period t is 0.9, when the maintenance resources are sufficient, 0.1 maintenance teams are assigned to it, and t~t+t repair The number of available maintenance teams remaining in the -1 time period is reduced by 0.1, as shown in steps 4 to 12. The array {△rij} records the sum of the maintenance effects that are not currently effective, which prevents the reliability probability of a line maintenance from exceeding 1 after it takes effect, as shown in step 6. When the disaster begins, i.e., t=0, the maintenance team starts to repair the line and stores the maintenance effect in q repair In the process, the maintenance effect will be repair The time will come into effect after that, as shown in steps 13 to 16.

[0103] Table 1

[0104]

[0105] Based on the present invention, a method for deducing the survival expectation of distribution system nodes based on probability graph is provided. By establishing an undirected probability graph, the survival expectation of load nodes in the case of no overlapping edges is directly obtained. When there are overlapping edges in the path of the undirected probability graph, simplification is performed by deletion-contraction until the preliminary load node survival expectation can be directly decomposed and obtained; based on the combination of the preliminary load node survival expectation and the pre-acquired maintenance process node survival expectation, the final distribution system node survival expectation is generated. Compared with the traditional method, scenario sampling is no longer required, the resilience assessment process is simplified, the calculation efficiency is improved, and the difficulty of resilience assessment of distribution system nodes considering medium- and long-term disaster risks is reduced.

[0106] refer to Figure 6 The present invention also discloses a distribution system node survival expectation deduction system based on probability graph, the system comprising:

[0107] The data acquisition module 110 is used to acquire a power source node set, a load node set, a line set, and a line reliability probability set in the power distribution system;

[0108] An undirected probability graph building module 120 is used to build an undirected probability graph based on the power node set, the load node set, the line set and the line reliability probability set to obtain a preliminary load node survival expectation;

[0109] The final node survival expectation calculation module 130 is used to generate a final distribution system node survival expectation based on the combination of the preliminary load node survival expectation and the pre-acquired maintenance process node survival expectation.

[0110] Wherein, an undirected probability graph is established based on the power node set, load node set, line set and line reliability probability set to obtain the initial load node survival expectation, including:

[0111] Modeling the distribution system as an undirected probabilistic graph without overlapping edges

[0112] in and Represent the collection of power nodes and load nodes respectively, represents the set of lines, r represents the set of line reliability probabilities;

[0113] The survival expectation of the calculated initial load node can be expressed as And meet the following conditions:

[0114]

[0115] Among them, e i is a random variable representing the power supply status of node i, e i =1 indicates that the node has power or is reliable, L ij is a random variable representing the working state of line ij.

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

[0117] When there are overlapping edges in the undirected probability graph, the paths are simplified by deletion-contraction until the initial load node survival expectation can be directly decomposed and obtained.

[0118] When there are overlapping edges in the path of the undirected probability graph, it is simplified by deletion-contraction until the initial load node survival expectation can be directly decomposed and obtained, including:

[0119] When there are overlapping edges in the paths in the undirected probability graph, the undirected probability graph is put into the element set;

[0120] When 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 and put it into the element set to replace the original undirected probability graph until all elements in the element combination can be directly calculated;

[0121] When all elements in the element set can be directly calculated, a system of equations is established for each element by setting a formula;

[0122] The equation group is solved to calculate the preliminary load node survival expectation.

[0123] The pre-acquired maintenance process node survival expectations previously included:

[0124] Select power nodes and load nodes based on undirected probability graph;

[0125] Calculating the shortest path between the power source node and the load node, and increasing the line weight on the shortest path;

[0126] When all load nodes and power supply nodes are traversed, the lines are sorted from large to small according to their weight to complete the line importance sorting.

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

[0128] Determine the priority of line maintenance according to the importance of the lines;

[0129] Calculating the node survival expectation of the maintenance process through the line maintenance priority and 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 distribution system node survival expectation.

[0131] Based on the present invention, a system for deducing the survival expectation of distribution system nodes based on probability graphs is provided. By establishing an undirected probability graph, the survival expectation of load nodes in the case of no overlapping edges is directly obtained. When there are overlapping edges in the path of the undirected probability graph, simplification is performed by deletion-contraction until the preliminary load node survival expectation can be directly decomposed and obtained; based on the combination of the preliminary load node survival expectation and the pre-acquired maintenance process node survival expectation, the final distribution system node survival expectation is generated. Compared with the traditional method, scenario sampling is no longer required, the resilience assessment process is simplified, the calculation efficiency is improved, and the difficulty of resilience assessment of distribution system nodes considering medium- and long-term disaster risks is reduced.

[0132] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730 and a communication bus 740, wherein the processor 710, the communication interface 720 and the memory 730 communicate with each other through the communication bus 740. The processor 710 may call the logic instructions in the memory 730 to execute the deduction method of the distribution system node survival expectation based on the probability graph, the method comprising: obtaining a power supply node set, a load node set, a line set and a line reliability probability set in the distribution system; establishing an undirected probability graph based on the power supply node set, the load node set, the line set and the line reliability probability set, and obtaining a preliminary load node survival expectation; combining the preliminary load node survival expectation with the pre-acquired maintenance process node survival expectation to generate a final distribution system node survival expectation.

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

[0134] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the method.

[0135] An application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method.

[0136] An application embodiment of the present invention provides an information data processing terminal, which includes a system.

[0137] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a 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. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0138] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for deducing the survival expectation of nodes in a power distribution system, characterized in that: include: 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; S2, establishing an undirected probability graph based on the power node set, load node set, line set and line reliability probability set, and obtaining a preliminary load node survival expectation; S3, generating a final distribution system node survival expectation based on combining the preliminary load node survival expectation with the pre-acquired maintenance process node survival expectation.

2. A method for deducing the survival expectation of nodes in a power distribution system, characterized in that: The step of establishing an undirected probability graph based on the power node set, the load node set, the line set, and the line reliability probability set to obtain a preliminary load node survival expectation includes: Modeling the distribution system as an undirected probabilistic graph without overlapping edges in and Represent the collection of power nodes and load nodes respectively, Represents a collection of lines. represents the set of line reliability probabilities; The survival expectation of the calculated initial load node can be expressed as And meet the following conditions: Among them, e i is a random variable representing the power supply status of node i, e i =1 indicates that the node has power or is reliable, L ij is a random variable representing the working state of line ij.

3. A method for deducing the survival expectation of nodes in a power distribution system, characterized in that: The step of establishing an undirected probability graph based on the power node set, the load node set, the line set, and the line reliability probability set to obtain a preliminary load node survival expectation further includes: When there are overlapping edges in the path of the undirected probability graph, it is simplified by deletion-contraction until the initial load node survival expectation can be directly decomposed and obtained.

4. A method for deducing the survival expectation of nodes in a power distribution system, characterized in that: When there are overlapping edges in the path of the undirected probability graph, the path is simplified by deletion-contraction until the initial load node survival expectation can be directly decomposed and obtained, including: When there are overlapping edges in the paths in the undirected probability graph, the undirected probability graph is put into the element set; When 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 and put it into the element set to replace the original undirected probability graph until all elements in the element combination can be directly calculated; When all elements in the element set can be directly calculated, a system of equations is established for each element by setting a formula; The equation group is solved to calculate the preliminary load node survival expectation.

5. A method for deducing the survival expectation of nodes in a power distribution system, characterized in that: The pre-acquired maintenance process node survival expectation includes: Select power nodes and load nodes based on undirected probability graph; Calculating the shortest path between the power source node and the load node, and increasing the line weight on the shortest path; When all load nodes and power supply nodes are traversed, the lines are sorted from large to small according to their weight to complete the line importance sorting.

6. A method for deducing the survival expectation of nodes in a power distribution system, characterized in that: The generating of the final distribution system node survival expectation based on the preliminary load node survival expectation and the pre-acquired maintenance process node survival expectation specifically includes: Determine the priority of line maintenance according to the importance of the lines; Calculate the node survival expectation of the maintenance process through the line maintenance priority and extreme disaster information, line priority and maintenance resource information; The node survival expectation of the maintenance process is combined with the preliminary load node survival expectation to generate the final distribution system node survival expectation.

7. A system for deducing the survival expectation of a distribution system node based on a probability graph, using the method for deducing the survival expectation of a distribution system node based on a probability graph according to any one of claims 1 to 6, characterized in that: include: A data acquisition module, used to acquire a power source node set, a load node set, a line set, and a line reliability probability set in a power distribution system; An undirected probability graph building module is used to build an undirected probability graph based on the power node set, load node set, line set and line reliability probability set to obtain preliminary load node survival expectation; The final node survival expectation calculation module is used to generate the final distribution system node survival expectation based on the combination of the preliminary load node survival expectation and the pre-acquired maintenance process node survival expectation.

8. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for deducing the survival expectation of distribution system nodes based on a probability graph as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the method for deducing the survival expectation of distribution system nodes based on a probability graph as described in any one of claims 1 to 6.

10. An information data processing terminal, comprising the probability graph-based distribution system node survival expectation deduction system according to claim 7.

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