Power distribution network fault risk assessment method and device based on historical typhoon disaster chain, terminal equipment and computer readable storage medium

Abstract

The invention discloses a power distribution network fault risk assessment method and device based on a historical typhoon disaster chain, terminal equipment and a computer readable storage medium, and the method comprises the steps: obtaining the historical typhoon disaster chain of all power distribution network equipment in a monitoring region in a preset time period, and constructing a power distribution network typhoon disaster directed network, calculating the comprehensive importance of each node and the vulnerability of each connection edge in the network; for each historical typhoon disaster chain, respectively counting the occurrence probability of each disaster event and the probability that any two disaster events appear together, and calculating the occurrence probability of a connecting edge; calculating the risk value of each disaster chain according to the occurrence probability of the connecting edge, the comprehensive importance of each node and the vulnerability of each connecting edge; and determining the fault risk degree of the power distribution network equipment in the monitoring area according to the risk value of each disaster chain. By implementing the method, the influence of various typhoon disaster events can be comprehensively considered, and a scientific basis is provided for formulating disaster emergency response and recovery strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of chain disaster assessment, and in particular to a distribution network failure risk assessment method, device, terminal equipment and computer-readable storage medium based on a historical typhoon disaster chain. Background Art

[0002] Power infrastructure, especially the power grid network, has become one of the key systems most vulnerable to meteorological disasters and broader natural disasters due to its widely distributed and highly interconnected nature.

[0003] At present, there are many studies on power grid fault assessment, and many scholars are committed to exploring risk identification and quantitative analysis in the context of meteorological disasters based on the internal structure of the power grid. In particular, with the rapid development of artificial intelligence technology, complex network theory in the field of machine learning has been introduced into the power grid risk assessment framework to simulate and analyze the vulnerability of the power grid structure after meteorological disasters. These studies attempt to accurately capture the fault propagation mode by constructing a complex network model of the power grid, providing a scientific basis for the formulation of disaster emergency response and recovery strategies.

[0004] Although these studies have some innovations in methodology, they are still mainly limited to the scope of static analysis, that is, they only consider the risk status of the power grid structure in a certain fixed state. The risk prediction under this fixed state can usually only provide the probability of one or a limited number of disaster situations, because it does not take into account the diversity and uncertainty of meteorological disasters in the actual environment. For example, it may only predict the failure probability of the power grid under a specific wind speed and rainfall, without considering the changes in the typhoon path, the fluctuations in intensity, and the impact of possible secondary disasters (such as floods, landslides, etc.) on the power grid, which leads to limitations and one-sidedness in the analysis results. Summary of the invention

[0005] The embodiments of the present invention provide a distribution network failure risk assessment method, apparatus, terminal equipment and computer-readable storage medium based on a historical typhoon disaster chain, which can comprehensively consider the impact of various typhoon disaster events and provide a scientific basis for the formulation of disaster emergency response and recovery strategies.

[0006] An embodiment of the present invention provides a method for evaluating the risk of distribution network failure based on a historical typhoon disaster chain, comprising:

[0007] Obtain the historical typhoon disaster chain of all distribution network equipment in the monitoring area within the preset period;

[0008] According to the historical typhoon disaster chain, a typhoon disaster directed network of the distribution network is constructed;

[0009] According to the typhoon disaster directed network of the distribution network, the comprehensive importance of each node and the vulnerability of each connecting edge in the typhoon disaster directed network of the distribution network are calculated;

[0010] For each historical typhoon disaster chain, the probability of occurrence of each disaster event and the probability of any two disaster events occurring together are counted, and the probability of occurrence of the connecting edges is calculated;

[0011] The risk value of each disaster chain is calculated based on the occurrence probability of the connection edge, the comprehensive importance of each node, and the vulnerability of each connection edge;

[0012] According to the risk value of each disaster chain, the failure risk degree of the distribution network equipment in the monitoring area is determined.

[0013] Furthermore, according to the typhoon disaster directed network of the distribution network, the comprehensive importance of each node and the vulnerability of each connection edge in the typhoon disaster directed network of the distribution network are calculated, including:

[0014] According to the typhoon disaster directed network of the distribution network, the node characteristics of each node in the network and the connection edge characteristics of each connection edge are calculated; the node characteristics include: degree centrality, betweenness centrality, closeness centrality and clustering coefficient; the connection edge characteristics include: network average path length, number of betweenness edges and network connectivity;

[0015] Calculate the comprehensive importance of each node according to the node characteristics of each node;

[0016] The vulnerability of each connection edge is calculated according to the connection edge characteristics of each connection edge.

[0017] Furthermore, according to the risk value of each disaster chain, the failure risk degree of the distribution network equipment in the monitoring area is determined, including:

[0018] According to the risk value of each disaster chain, the overall risk value of the monitoring area is calculated by weighted average;

[0019] Compare the overall risk value with the preset threshold;

[0020] If the overall risk value does not exceed the first preset threshold, it is determined that the failure risk level of the distribution network equipment in the monitoring area is low risk;

[0021] If the overall risk value exceeds the first preset threshold value and the overall risk value does not exceed the second preset threshold value, it is determined that the failure risk level of the distribution network equipment in the monitoring area is medium risk;

[0022] If the overall risk value exceeds the second preset threshold, it is determined that the failure risk level of the distribution network equipment in the monitoring area is high risk.

[0023] Furthermore, after determining the failure risk level of the distribution network equipment in the monitoring area, it also includes:

[0024] Generate a fault handling plan for distribution network equipment based on the fault risk level of the distribution network equipment in the monitoring area.

[0025] Furthermore, according to the failure risk level of the distribution network equipment in the monitoring area, a failure handling plan for the distribution network equipment is generated, including:

[0026] If the fault risk level of the distribution network equipment in the monitoring area is low risk, a first fault handling plan for the distribution network equipment is generated; the first fault handling plan specifically includes: performing inspections according to a first inspection cycle and a preset inspection route of the distribution network equipment;

[0027] If the fault risk level of the distribution network equipment in the monitoring area is medium risk, a second fault handling plan for the distribution network equipment is generated; the second fault handling plan is specifically: inspection is performed according to the second inspection cycle and the preset inspection route of the distribution network equipment, and emergency materials are deployed according to the preset emergency material deployment plan; wherein the second inspection cycle is shorter than the first inspection cycle;

[0028] If the fault risk level of the distribution network equipment in the monitoring area is high risk, a third fault handling plan for the distribution network equipment is generated; the third fault handling plan is specifically: isolating the distribution network equipment according to a preset fault isolation plan.

[0029] Furthermore, the distribution network failure risk assessment method based on the historical typhoon disaster chain also includes:

[0030] The typhoon disaster directed network of the distribution network, the risk values ​​of each disaster chain, and the failure risk degree of the distribution network equipment in the monitoring area are sent to the visualization device, so that the visualization device converts the typhoon disaster directed network of the distribution network, the risk values ​​of each disaster chain, and the failure risk degree of the distribution network equipment in the monitoring area into a visualization chart.

[0031] Based on the above method embodiment, the present invention provides a corresponding device embodiment, including: a historical typhoon disaster chain acquisition module, a directed network construction module, a parameter calculation module, an edge probability calculation module, a risk value calculation module, and a fault risk degree determination module;

[0032] A historical typhoon disaster chain acquisition module is used to obtain the historical typhoon disaster chain of all distribution network equipment in the monitoring area within a preset period of time;

[0033] Directed network construction module, used to construct a typhoon disaster directed network for distribution network based on historical typhoon disaster chains;

[0034] A parameter calculation module is used to calculate the comprehensive importance of each node and the vulnerability of each connection edge in the typhoon disaster directed network of the distribution network according to the typhoon disaster directed network of the distribution network;

[0035] The edge probability calculation module is used to count the occurrence probability of each disaster event and the probability of any two disaster events occurring together for each historical typhoon disaster chain, and calculate the occurrence probability of the connecting edges;

[0036] The risk value calculation module is used to calculate the risk value of each disaster chain based on the occurrence probability of the connection edge, the comprehensive importance of each node, and the vulnerability of each connection edge;

[0037] The fault risk level determination module is used to determine the fault risk level of the distribution network equipment in the monitoring area according to the risk value of each disaster chain.

[0038] Furthermore, the distribution network fault risk assessment device based on the historical typhoon disaster chain also includes: a fault handling plan generation module;

[0039] The fault handling plan generation module is used to generate a fault handling plan for distribution network equipment according to the fault risk level of the distribution network equipment in the monitoring area.

[0040] Based on the above-mentioned method embodiment, the present invention provides a corresponding terminal device embodiment, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the steps of the distribution network failure risk assessment method based on the historical typhoon disaster chain as described in the present invention are implemented.

[0041] Based on the above-mentioned method embodiment, the present invention provides a corresponding computer-readable storage medium embodiment, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to execute the steps of the distribution network failure risk assessment method based on the historical typhoon disaster chain as described in the present invention.

[0042] Compared with the prior art, the beneficial effects of the embodiment of this solution are:

[0043] The present invention constructs a typhoon disaster directed network for distribution network by acquiring historical typhoon disaster chains of all distribution network equipment in a monitoring area within a preset time period. The nodes in the network represent distribution network equipment or disaster events, and the edges represent the causal relationship or influence relationship between disaster events. Then, the comprehensive importance of each node and the vulnerability of each connecting edge in the typhoon disaster directed network for distribution network are calculated, the importance of each disaster event to the distribution network equipment and the impact between disaster events are quantified, and the occurrence probability of each disaster event in each historical typhoon disaster chain and the probability of any two disaster events occurring together are counted respectively, and the occurrence probability of the connecting edge is calculated. The risk value of each disaster chain is calculated according to the occurrence probability of the connecting edge, the comprehensive importance of each node and the vulnerability of each connecting edge, and the risk values ​​of various disaster situations can be obtained, which comprehensively considers the impact of various disaster events. Finally, according to the risk value of each disaster chain, the failure risk degree of the distribution network equipment in the monitoring area is determined.

[0044] In summary, the present invention solves the limitations of traditional risk assessment methods by constructing a typhoon disaster directed network for distribution network and calculating relevant parameters, achieves more accurate risk assessment and more comprehensive fault handling plan generation, provides a scientific basis and effective guidance for fault handling of distribution network equipment, and enhances the resilience and reliability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flow chart of a method for evaluating the risk of distribution network failure based on a historical typhoon disaster chain provided by an embodiment of the present invention;

[0046] Figure 2 It is a structural schematic diagram of a distribution network fault risk assessment device based on a historical typhoon disaster chain provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features.

[0049] like Figure 1 As shown, an embodiment of the present invention provides a distribution network failure risk assessment method based on a historical typhoon disaster chain, the method comprising at least the following steps:

[0050] Step S1: Obtain the historical typhoon disaster chain of all distribution network equipment in the monitoring area within a preset period;

[0051] For step S1, in order to conduct a typhoon failure risk assessment on the distribution network equipment in the monitoring area, the typhoon disaster data of all distribution network equipment in the monitoring area within a preset period is obtained from the relevant departments or power companies to generate a historical typhoon disaster chain. The historical typhoon disaster chain records the damage, power outage or other disasters suffered by the distribution network equipment in each typhoon event, for example, one of the historical typhoon disaster chains of distribution network equipment: typhoon-heavy rain-damage to transformer equipment. The monitoring area can be a city or a specific geographical area.

[0052] Step S2: construct a typhoon disaster directed network of the distribution network based on the historical typhoon disaster chain;

[0053] For step S2, according to the historical typhoon disaster chain, a typhoon disaster directed network of the distribution network can be constructed, wherein the directed network uses typhoon disaster events as nodes. In this embodiment, according to the historical typhoon disaster chain of the distribution network equipment in the monitoring area, 14 typhoon disaster network nodes are obtained (as shown in Table 1). At the same time, the correlation between disasters is used as an edge, that is, if a typhoon event causes the occurrence or aggravation of another typhoon event, an edge is established between the two events to indicate the propagation path and impact relationship of the disaster.

[0054] Table 1 Typhoon disaster network node number table

[0055]

[0056] Step S3: According to the typhoon disaster directed network of the distribution network, the comprehensive importance of each node and the vulnerability of each connection edge in the typhoon disaster directed network of the distribution network are calculated;

[0057] In a preferred embodiment, according to the typhoon disaster directed network of the distribution network, the comprehensive importance of each node and the vulnerability of each connection edge in the typhoon disaster directed network of the distribution network are calculated, including:

[0058] According to the typhoon disaster directed network of the distribution network, the node characteristics of each node in the network and the connection edge characteristics of each connection edge are calculated; the node characteristics include: degree centrality, betweenness centrality, closeness centrality and clustering coefficient; the connection edge characteristics include: network average path length, number of betweenness edges and network connectivity;

[0059] Calculate the comprehensive importance of each node according to the node characteristics of each node;

[0060] The vulnerability of each connection edge is calculated according to the connection edge characteristics of each connection edge.

[0061] For step S3, according to the typhoon disaster directed network of the distribution network constructed in step S2, the node characteristics of each node in the directed network and the connection edge characteristics of each connection edge are calculated, wherein the node characteristics include the following four indicators: degree centrality, betweenness centrality, closeness centrality and clustering coefficient; wherein:

[0062] Degree Centrality (DC i ) is an indicator to measure the importance of nodes in complex networks. It is based on the degree of the node (Degree, D i ), that is, the number of edges directly connected to the node. In the typhoon disaster directed network of the distribution network, the degree of the node can be further divided into out-degree and in-degree. The out-degree refers to the potential impact of a disaster event node on other secondary disasters, while the in-degree reflects the degree to which the node is affected by other events. The higher the degree value of the node, the closer its connection with other nodes in the network. Therefore, degree centrality (DC i ) can intuitively reflect the importance and influence of nodes in the network structure. A higher DC i The value shows that the corresponding disaster node plays a more critical role in the typhoon disaster directed network and has a greater influence. i The calculation formula is as follows:

[0063]

[0064] Among them, D i represents the degree of the node, and N represents the total number of nodes in the network.

[0065] Betweenness Centrality (BC i ) is an indicator to measure the importance of nodes in a network. It is based on the concept of the shortest path, which is the path with the least number of edges connecting any two nodes in the network. In the context of the typhoon disaster directed network, the shortest path represents the disaster from node V i Propagate to node V j The most direct and rapid way to find the best solution. The betweenness centrality (BC) of a node i ) refers to all the shortest paths passing through the node V i This indicator reveals the influence of disaster events as intermediary points in the network on other disaster events, that is, the strength of its role as a connection point in the disaster evolution network. Betweenness centrality (BC i ) value is higher, indicating that the node V i The more significant the influence of a node on other nodes in the disaster network, the more critical its role in disaster risk propagation. i The calculation formula is as follows:

[0066]

[0067] Among them, n jk Represents node V j and node V k The number of shortest paths between Represents the connection node V j and node V k The shortest path through V i The number of shortest paths.

[0068] Closeness Centrality (CC i ) is an indicator to measure the proximity of a node to other nodes in the network. It reflects the node V i The shortest path length to all other nodes in the typhoon disaster directed network. CC i A higher value means that the node V i The shorter the average distance to other nodes, the closer the node is to the center in the network geometry. Such nodes are more likely to quickly affect other disaster events during the disaster propagation process. i The calculation formula is as follows:

[0069]

[0070] Among them, d ij Represents node V i To Node V j The shortest path length.

[0071] Clustering Coefficient (C i ) is an indicator for evaluating the degree of interconnection between neighboring nodes in a network. It calculates the i The probability of a connection between two directly connected nodes is also measured. In the analysis of the typhoon disaster directed network, a higher clustering coefficient indicates that the node V i The closer the connection with other disaster event nodes is, the more dense the clustering network is formed between disaster events. i The calculation formula is as follows:

[0072]

[0073] Among them, S i Represents node V i The number of associated adjacent nodes, n represents the number of nodes with adjacent nodes.

[0074] According to the degree centrality DC calculated above i, Betweenness Centrality BC i , close centrality CC i And the clustering coefficient C i , using grey correlation analysis technology to evaluate the comprehensive importance of nodes. Grey correlation analysis is particularly suitable for correlation evaluation in small sample situations because of its relatively small amount of calculation. Its core principle is to determine the correlation coefficient by setting a reference sequence and a comparison sequence according to the geometric similarity between the sequence curves. In an embodiment of the present invention, the maximum value of each node index is selected to construct a reference sequence, and then the correlation degree between each node index sequence and this reference sequence is calculated. The higher the correlation degree, the greater the importance of the node. The specific calculation formula of the correlation coefficient is as follows:

[0075]

[0076] Among them, ε ij represents the correlation coefficient of the jth index of the comparison sequence and the reference sequence, ρ is 0.5, Δ min and Δ max Respectively represent the minimum absolute difference and maximum absolute difference between the comparison sequence and the reference sequence in the jth item, r i It represents the grey correlation degree, which is also the comprehensive importance of the node.

[0077] Next, the connection edge characteristics of each connection edge in the typhoon disaster directed network of the distribution network include the following three indicators: the average path length of the network, the number of intermediate edges and the network connectivity, among which:

[0078] The average path length (L) of the network refers to the arithmetic mean of the shortest path lengths between any two nodes in the network. In the analysis of typhoon disaster directed networks, this indicator can reflect the speed of the spread of typhoon disaster events. When a specific connection edge r is removed from the network, the structure and average path length of the network may change. If the removal of an edge r causes the average path length to increase, this indicates that the removal of the edge can slow down the spread of typhoon disaster events, indicating that this edge plays a key role in the network and has a higher disaster risk. Therefore, by comparing the impact of removing different edges on the average path length of the network, the importance of each edge in the disaster propagation process can be evaluated. The calculation formula for L is as follows:

[0079]

[0080] Among them, d ij Represents node V i and node V j The shortest path length between them.

[0081] Bridgeness,B r) refers to the number of shortest paths through an edge r in the network. This indicator can reveal the scope of influence of the edge in the network. In the context of the typhoon disaster directed network, a connecting edge with a higher edge betweenness means that it involves a larger number of shortest paths, so it has a wider impact on the evolution path of typhoon disasters and a more significant effect on the disaster propagation process. In short, the size of the edge betweenness can measure the criticality of an edge in the disaster network and the degree of influence it may have on the propagation of disaster events. r The calculation formula is as follows:

[0082]

[0083] in, Represents node V j and node V k The number of connecting edges r in all shortest paths between them.

[0084] Network connectivity (Connectivity, H) measures the proportion of interconnected nodes in the network. This indicator can reflect the connectivity and stability of the network. In the typhoon disaster directed network, if the removal of a certain connection edge causes the connectivity H to decrease, it indicates that the edge plays an important role in maintaining the connectivity of the network, and its removal may have an impact on the development of subsequent disaster nodes. This situation shows that the connection edge is a relatively fragile link in the process of disaster evolution, and has a significant impact on the overall stability and connectivity of the network. Therefore, by analyzing the impact of removing different connection edges on the connectivity H, the key vulnerable edges in the network can be identified, providing important reference information for disaster management. The calculation formula of H is as follows:

[0085]

[0086] Among them, N i Indicates the number of connected nodes in the network after removing a certain connection edge.

[0087] According to the above calculation, the average path length L and the number of edges B of the network r and network connectivity H, calculate the vulnerability of each connection edge. It should be noted that the vulnerability index (Vulnerability Index, V r) aims to quantify the criticality of a certain connection edge in the network during the evolution of disasters. When a connection edge is removed, the impact it has on the topological structure of the network can be used as a basis for assessing the risk of the edge in disaster propagation. Therefore, analyzing the vulnerability of changes in network connection edges to the topological structure of the directed network to typhoon disasters is helpful in assessing the risk value of the connection edges of typhoon disaster events. The present invention combines three indicators, namely, the average path length of the network, the edge betweenness, and the network connectivity, to construct a vulnerability index for the connection edges of the directed network for typhoon disasters. Among them, the average path length and the edge betweenness of the network are positively correlated with the vulnerability of the connection edge, while the connectivity is negatively correlated with the vulnerability of the connection edge. The vulnerability V of the connection edge r r It can be calculated by the following formula:

[0088]

[0089] Among them, B r represents the edge betweenness of the connecting edge r, L r and H r They represent the average path length and connectivity of the network after removing the connecting edge r.

[0090] Step S4: For each historical typhoon disaster chain, the probability of occurrence of each disaster event and the probability of any two disaster events occurring together are counted, and the probability of occurrence of the connecting edge is calculated;

[0091] For step S4, first, for each disaster event, count its frequency of occurrence in the historical typhoon disaster chain, denoted as C i Then, statistics C i The frequency of occurrence of the disaster sub-node is denoted by C j The probability of occurrence of the disaster event connection edge is defined as the possibility that disaster event i triggers disaster event j, which can also be regarded as the probability of the two events i and j occurring simultaneously, that is, their co-occurrence probability. According to the probability of occurrence of the connection edge, the information on the correlation and propagation path of the typhoon disaster event can be obtained. The calculation formula of the probability of occurrence of the connection edge is as follows:

[0092]

[0093] Among them, P ij represents the probability of connecting edges, C ij Represents the disaster parent node V i and disaster subnode V j The frequency of co-occurrence, C i Represents the disaster parent node V i The frequency of occurrence, C j Represents the disaster child node V j Frequency of occurrence.

[0094] Step S5: Calculate the risk value of each disaster chain according to the occurrence probability of the connection edge, the comprehensive importance of each node, and the vulnerability of each connection edge;

[0095] In step S5, for a complete disaster chain, according to the comprehensive importance of each node and the vulnerability of each connecting edge calculated in step S3, and the occurrence probability of the connecting edge calculated in step S4, the risk value of each disaster chain is calculated by the following risk assessment model:

[0096]

[0097] Among them, R represents the risk value of the disaster chain, P i,i+1 Indicates disaster event V i Caused disaster event V i+1 The probability of the connection edge occurring, Q i Indicates disaster event V i The comprehensive importance of typhoon disaster in the directed network i Value, V i,i+1 Indicates disaster event V i To disaster event V i+1 The fragility of the connecting edge.

[0098] Step S6: Determine the failure risk level of the distribution network equipment in the monitoring area according to the risk value of each disaster chain.

[0099] In a preferred embodiment, determining the failure risk level of the distribution network equipment in the monitoring area according to the risk value of each disaster chain includes:

[0100] According to the risk value of each disaster chain, the overall risk value of the monitoring area is calculated by weighted average;

[0101] Compare the overall risk value with the preset threshold;

[0102] If the overall risk value does not exceed the first preset threshold, it is determined that the failure risk level of the distribution network equipment in the monitoring area is low risk;

[0103] If the overall risk value exceeds the first preset threshold value and the overall risk value does not exceed the second preset threshold value, it is determined that the failure risk level of the distribution network equipment in the monitoring area is medium risk;

[0104] If the overall risk value exceeds the second preset threshold, it is determined that the failure risk level of the distribution network equipment in the monitoring area is high risk.

[0105] For step S6, after calculating the risk values ​​of all disaster chains of all distribution network equipment in the monitoring area through step S5, first, it is necessary to determine the weight of each distribution network equipment, that is, its importance or contribution in the entire monitoring area. The weight can be evaluated and determined based on the importance, load conditions, failure rate and other factors of the distribution network equipment. More important or contributing equipment can be given a higher weight. For the risk value of each disaster chain, it can be multiplied by the weight of the corresponding distribution network equipment to obtain a weighted risk value, which can be regarded as the contribution of each disaster chain to the overall risk. Finally, all weighted risk values ​​are added and then divided by the sum of the weights of all distribution network equipment to obtain the overall risk value of the monitoring area. In this way, the risk value of each disaster chain and the weight of the corresponding equipment can be comprehensively considered to obtain an overall evaluation result.

[0106] Next, the overall risk value is compared with the preset threshold value. According to the comparison result between the overall risk value and the preset threshold value, the failure risk degree of the distribution network equipment in the monitoring area can be evaluated and classified. Specifically, if the overall risk value does not exceed the first preset threshold value, it means that the failure risk degree of the distribution network equipment in the monitoring area is low, which means that the risk level of each disaster chain in the distribution network is relatively controllable as a whole, and it is unlikely that a major failure or disaster will occur; if the overall risk value exceeds the first preset threshold value, but does not exceed the second preset threshold value, it means that the failure risk degree of the distribution network equipment in the monitoring area is medium, which means that the risk of some disaster chains may increase, and further attention and management are needed to prevent possible failures and disasters; if the overall risk value exceeds the second preset threshold value, it means that the failure risk degree of the distribution network equipment in the monitoring area is high, which means that the risks of multiple disaster chains are high, and there may be serious failure and disaster risks, and urgent measures need to be taken to reduce the risk and ensure the reliability and safety of power supply.

[0107] In a preferred embodiment, after determining the failure risk level of the distribution network equipment in the monitoring area, the method further includes:

[0108] Generate a fault handling plan for distribution network equipment based on the fault risk level of the distribution network equipment in the monitoring area.

[0109] In a preferred embodiment, a fault handling plan for distribution network equipment is generated according to the fault risk level of the distribution network equipment in the monitoring area, including:

[0110] If the fault risk level of the distribution network equipment in the monitoring area is low risk, a first fault handling plan for the distribution network equipment is generated; the first fault handling plan specifically includes: performing inspections according to a first inspection cycle and a preset inspection route of the distribution network equipment;

[0111] If the fault risk level of the distribution network equipment in the monitoring area is medium risk, a second fault handling plan for the distribution network equipment is generated; the second fault handling plan is specifically: inspection is performed according to the second inspection cycle and the preset inspection route of the distribution network equipment, and emergency materials are deployed according to the preset emergency material deployment plan; wherein the second inspection cycle is shorter than the first inspection cycle;

[0112] If the fault risk level of the distribution network equipment in the monitoring area is high risk, a third fault handling plan for the distribution network equipment is generated; the third fault handling plan is specifically: isolating the distribution network equipment according to a preset fault isolation plan.

[0113] In one embodiment of the present invention, after determining the fault risk level of the distribution network equipment in the monitoring area through step S6, a corresponding fault handling plan is formulated according to the fault risk level of the distribution network equipment in the monitoring area, aiming to take different measures according to the severity of the risk to ensure the stable operation and safety of the power grid. The following is a detailed explanation of three different fault risk levels and their corresponding handling plans:

[0114] When the risk of distribution network equipment failure in the monitoring area is assessed as low risk, it means that the operating status of the equipment can remain relatively stable when a typhoon comes, and the possibility of failure is low. The generated fault handling plan focuses on the regular inspection arrangement of power grid equipment, including inspection of distribution network equipment according to the established first inspection cycle (relatively long time interval) and preset inspection route, requiring operation and maintenance personnel to focus on inspecting equipment parts that are easily damaged in historical typhoon disasters, such as tower foundations, overhead lines, etc., to ensure that the equipment is in normal operation. This inspection method aims to maintain regular monitoring of equipment status, promptly discover and deal with possible minor problems, and prevent small problems from evolving into big problems.

[0115] When the fault risk is assessed as medium risk, it means that the equipment may have certain hidden faults when the typhoon comes, but it has not yet reached an emergency state. The fault handling plan generated at this time requires inspections to be carried out according to the second inspection cycle, which is shorter than the first inspection cycle, so as to check the equipment status more frequently and promptly discover and deal with potential problems. In addition, the plan also stipulates that emergency materials should be deployed according to the preset emergency material deployment plan, and the types and quantities of emergency power generation equipment, repair tools and spare parts should be clearly reserved, and the storage location and transportation method of these emergency materials should be planned, so that the required resources can be quickly deployed when a fault occurs, providing a solid guarantee for the rapid restoration of power supply. At the same time, the fault handling plan also arranges some emergency repair personnel to be on standby, ready to respond to faults at any time, so that they can arrive at the scene in the shortest time, quickly carry out repair work, and minimize the impact of the fault on the operation of the power grid.

[0116] When the fault risk is assessed as high risk, it means that the equipment will have serious hidden dangers of failure when the typhoon comes, and failure may occur at any time and affect the stable operation of the power grid. In this case, the generated fault handling plan requires immediate isolation of the distribution network equipment according to the preset fault isolation plan, and clarifies which equipment needs to be isolated first to prevent the spread of the fault when the fault occurs, and which lines need to be switched to ensure the power supply of key areas. At the same time, all emergency repair personnel are urgently summoned to start a comprehensive repair process plan, including the order of repair, the collaborative arrangement of different professionals, etc., and keep in close contact with the meteorological department to obtain the latest dynamic information of the typhoon in a timely manner, so as to flexibly adjust the plan according to the actual situation.

[0117] In a preferred embodiment, the distribution network failure risk assessment method based on the historical typhoon disaster chain further includes:

[0118] The typhoon disaster directed network of the distribution network, the risk values ​​of each disaster chain, and the failure risk degree of the distribution network equipment in the monitoring area are sent to the visualization device, so that the visualization device converts the typhoon disaster directed network of the distribution network, the risk values ​​of each disaster chain, and the failure risk degree of the distribution network equipment in the monitoring area into a visualization chart.

[0119] In one embodiment of the present invention, the typhoon disaster directed network of the distribution network generated during the fault risk assessment process, the risk value of each disaster chain, and the fault risk degree of the distribution network equipment in the monitoring area are packaged into a data packet, and the data packet is sent to a visualization device using an appropriate data transmission protocol (such as TCP / IP). The visualization device may be a high-performance computer equipped with special visualization software. Then, the visualization software uses these data to generate a visualization chart. For example, a node-link graph can be used to represent the typhoon disaster directed network of the distribution network, in which the nodes represent different disaster chains or devices, and the links represent the association relationship between them. Through the directed edges in the typhoon disaster directed network of the distribution network, it is possible to clearly see how historical typhoon disasters spread to other typhoon events, thereby understanding the propagation path and diffusion impact of disasters in the distribution network.

[0120] For the risk value of each disaster chain, a bar chart or pie chart can be used to represent its size.

[0121] For the degree of failure risk of distribution network equipment in the monitoring area, a heat map or scatter plot can be used to represent the spatial risk distribution of the equipment.

[0122] By observing the visual charts, distribution network management departments can quickly identify which disaster chains have higher risks and which areas have higher risks of distribution network equipment failure, thereby making more scientific and reasonable decisions, which will help reduce typhoon damage to the distribution network and ensure the stability and security of power supply.

[0123] like Figure 2 As shown, based on the above method embodiment, a corresponding device embodiment is provided;

[0124] An embodiment of the present invention provides a distribution network fault risk assessment device based on a historical typhoon disaster chain, comprising: a historical typhoon disaster chain acquisition module, a directed network construction module, a parameter calculation module, an edge probability calculation module, a risk value calculation module, and a fault risk degree determination module;

[0125] A historical typhoon disaster chain acquisition module is used to obtain the historical typhoon disaster chain of all distribution network equipment in the monitoring area within a preset period of time;

[0126] Directed network construction module, used to construct a typhoon disaster directed network for distribution network based on historical typhoon disaster chains;

[0127] A parameter calculation module is used to calculate the comprehensive importance of each node and the vulnerability of each connection edge in the typhoon disaster directed network of the distribution network according to the typhoon disaster directed network of the distribution network;

[0128] The edge probability calculation module is used to count the occurrence probability of each disaster event and the probability of any two disaster events occurring together for each historical typhoon disaster chain, and calculate the occurrence probability of the connecting edges;

[0129] The risk value calculation module is used to calculate the risk value of each disaster chain based on the occurrence probability of the connection edge, the comprehensive importance of each node, and the vulnerability of each connection edge;

[0130] The fault risk level determination module is used to determine the fault risk level of the distribution network equipment in the monitoring area according to the risk value of each disaster chain.

[0131] In a preferred embodiment, the distribution network fault risk assessment device based on the historical typhoon disaster chain further includes: a fault handling plan generation module;

[0132] The fault handling plan generation module is used to generate a fault handling plan for distribution network equipment according to the fault risk level of the distribution network equipment in the monitoring area.

[0133] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present invention, and can implement the distribution network failure risk assessment method based on the historical typhoon disaster chain provided by any one of the above-mentioned method item embodiments of the present invention.

[0134] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art may understand and implement the present invention without creative work.

[0135] Based on the above-mentioned embodiment of the distribution network failure risk assessment method based on historical typhoon disaster chains, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the distribution network failure risk assessment method based on historical typhoon disaster chains according to any embodiment of the present invention is implemented.

[0136] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0137] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0138] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0139] Based on the above method embodiment, another embodiment is provided: another embodiment of the present invention provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network failure risk assessment method based on historical typhoon disaster chains described in any one of the above method embodiments of the present invention.

[0140] Wherein, the module / unit integrated in the distribution network fault risk assessment device / terminal device based on the historical typhoon disaster chain, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0141] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A distribution network failure risk assessment method based on historical typhoon disaster chains, characterized in that: include: Obtain the historical typhoon disaster chain of all distribution network equipment in the monitoring area within the preset period; According to the historical typhoon disaster chain, a typhoon disaster directed network of the distribution network is constructed; According to the typhoon disaster directed network of the distribution network, the comprehensive importance of each node and the vulnerability of each connection edge in the typhoon disaster directed network of the distribution network are calculated; For each historical typhoon disaster chain, the probability of occurrence of each disaster event and the probability of any two disaster events occurring together are counted, and the probability of occurrence of the connecting edges is calculated; Calculate the risk value of each disaster chain according to the occurrence probability of the connection edge, the comprehensive importance of each node and the vulnerability of each connection edge; According to the risk values ​​of each disaster chain, the failure risk degree of the distribution network equipment in the monitoring area is determined.

2. The distribution network failure risk assessment method based on historical typhoon disaster chains according to claim 1 is characterized in that: According to the typhoon disaster directed network of the distribution network, the comprehensive importance of each node and the vulnerability of each connection edge in the typhoon disaster directed network of the distribution network are calculated, including: According to the typhoon disaster directed network of the distribution network, the node characteristics of each node in the network and the connection edge characteristics of each connection edge are calculated; the node characteristics include: degree centrality, betweenness centrality, closeness centrality and clustering coefficient; the connection edge characteristics include: average path length of the network, number of betweenness edges and network connectivity; Calculating the comprehensive importance of each node according to the node characteristics of each node; The vulnerability of each connection edge is calculated according to the connection edge characteristics of each connection edge.

3. The distribution network failure risk assessment method based on historical typhoon disaster chains according to claim 1 is characterized in that: According to the risk values ​​of each disaster chain, the failure risk level of the distribution network equipment in the monitoring area is determined, including: According to the risk values ​​of each disaster chain, the overall risk value of the monitoring area is calculated by weighted average; comparing the overall risk value with a preset threshold; If the overall risk value does not exceed the first preset threshold, it is determined that the failure risk level of the distribution network equipment in the monitoring area is low risk; If the overall risk value exceeds the first preset threshold value and the overall risk value does not exceed the second preset threshold value, it is determined that the failure risk level of the distribution network equipment in the monitoring area is medium risk; If the overall risk value exceeds a second preset threshold, it is determined that the failure risk level of the distribution network equipment in the monitoring area is high risk.

4. The distribution network failure risk assessment method based on historical typhoon disaster chains according to claim 3 is characterized in that: After determining the failure risk level of the distribution network equipment in the monitoring area, it also includes: Generate a fault handling plan for distribution network equipment based on the fault risk level of the distribution network equipment in the monitoring area.

5. The distribution network failure risk assessment method based on historical typhoon disaster chains according to claim 4 is characterized in that: According to the fault risk level of the distribution network equipment in the monitoring area, a fault handling plan for the distribution network equipment is generated, including: If the fault risk level of the distribution network equipment in the monitoring area is low risk, a first fault handling plan for the distribution network equipment is generated; the first fault handling plan specifically comprises: performing inspections according to a first inspection cycle and a preset inspection route of the distribution network equipment; If the fault risk level of the distribution network equipment in the monitoring area is medium risk, a second fault handling plan for the distribution network equipment is generated; the second fault handling plan is specifically: inspection is performed according to the second inspection cycle and the preset inspection route of the distribution network equipment, and emergency materials are deployed according to the preset emergency material deployment plan; wherein the second inspection cycle is shorter than the first inspection cycle; If the fault risk level of the distribution network equipment in the monitoring area is high risk, a third fault handling plan for the distribution network equipment is generated; the third fault handling plan specifically comprises: isolating the distribution network equipment according to a preset fault isolation plan.

6. The distribution network failure risk assessment method based on historical typhoon disaster chains according to claim 5 is characterized in that: Also includes: The typhoon disaster directed network of the distribution network, the risk values ​​of each disaster chain, and the failure risk degree of the distribution network equipment in the monitoring area are sent to the visualization device, so that the visualization device converts the typhoon disaster directed network of the distribution network, the risk values ​​of each disaster chain, and the failure risk degree of the distribution network equipment in the monitoring area into a visualization chart.

7. A distribution network fault risk assessment device based on historical typhoon disaster chains, characterized in that: include: Historical typhoon disaster chain acquisition module, directed network construction module, parameter calculation module, edge probability calculation module, risk value calculation module, and fault risk degree determination module; The historical typhoon disaster chain acquisition module is used to acquire the historical typhoon disaster chain of all distribution network equipment in the monitoring area within a preset period of time; The directed network construction module is used to construct a typhoon disaster directed network for the distribution network according to the historical typhoon disaster chain; The parameter calculation module is used to calculate the comprehensive importance of each node and the vulnerability of each connection edge in the typhoon disaster directed network of the distribution network according to the typhoon disaster directed network of the distribution network; The edge probability calculation module is used to count the occurrence probability of each disaster event and the probability of any two disaster events occurring together for each historical typhoon disaster chain, and calculate the occurrence probability of the connecting edge; The risk value calculation module is used to calculate the risk value of each disaster chain according to the occurrence probability of the connection edge, the comprehensive importance of each node and the vulnerability of each connection edge; The fault risk level determination module is used to determine the fault risk level of the distribution network equipment in the monitoring area according to the risk value of each disaster chain.

8. The distribution network failure risk assessment device based on historical typhoon disaster chains according to claim 7 is characterized in that: Also includes: Fault handling plan generation module; The fault handling plan generation module is used to generate a fault handling plan for distribution network equipment according to the fault risk level of the distribution network equipment in the monitoring area.

9. A terminal device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the distribution network failure risk assessment method based on the historical typhoon disaster chain as described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that: include: A stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network failure risk assessment method based on the historical typhoon disaster chain as described in any one of claims 1-6.

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