Power Grid Dynamic Resilience Assessment Method, Device, Equipment, and Storage Medium

Abstract

The present application provides a method, apparatus, device, and storage medium for dynamically evaluating the resilience of a power grid. The method includes: splicing a Bayesian network model for rainstorm disasters, a Bayesian network model for flood disasters, and a Bayesian network model for waterlogging disasters to obtain a spatio-temporal evolution Bayesian network model for rainstorm-flood-waterlogging disasters; obtaining an evolution scenario of power grid line damage and a recovery plan under the power grid damage evolution scenario according to the spatio-temporal evolution Bayesian network model for rainstorm-flood-waterlogging disasters and the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and waterlogging disasters respectively; and performing a dynamic resilience evaluation of the power grid based on the evolution scenario of power grid line damage and the recovery plan under the power grid damage evolution scenario, providing a theoretical support for improving the resilience of the power grid and reducing the impact of disasters.

Description

Technical Field

[0001] This application relates to the technical field of power systems, and in particular, to a method, device, equipment, and storage medium for dynamically evaluating the resilience of a power network. Background Art

[0002] As the artery of modern social operation, the importance of the power network is self-evident. It undertakes multiple functions such as residential electricity consumption, enterprise production and operation, and urban infrastructure operation, and is a key force supporting social and economic development. However, it cannot be ignored that when a rainstorm disaster strikes, it may trigger secondary disasters such as floods and waterlogging. These disasters not only directly threaten the lives and property safety of the people, but also cause serious secondary impacts on the power network. As the starting point of the disaster chain, the heavy rainfall of the rainstorm not only directly impacts the power grid facilities, but may also trigger subsequent flood disasters. Once a flood forms, its huge impact force and inundation range will cause the power grid facilities to face devastating damage, especially the substations and transmission lines in low-lying areas. And waterlogging, as the continuation of the disaster chain, its long-term water accumulation not only affects the normal operation of equipment, but may also trigger electrical failures, further exacerbating the damage to the power grid.

[0003] As an indicator characterizing the ability of the power network to maintain normal operation and quickly recover under extreme events, the resilience of the power network has become a hot research direction. However, in actual operation scenarios, the spatio-temporal evolution scenarios of the rainstorm disaster chain are complex, making the power grid often in a passive state when dealing with it, and it is difficult to effectively prevent and reduce the losses caused by disasters. Existing resilience assessment methods often only consider single-disaster impact scenarios, and it is difficult to effectively characterize the resilience indicators of the power network under the "rainstorm - flood - waterlogging" disaster chain. There is an urgent need for an assessment framework that can characterize the spatio-temporal evolution relationship of the "rainstorm - flood - waterlogging" disaster chain and the dynamic resilience indicators of the power network to improve the ability of the power network to resist chain disaster impacts. Summary of the Invention

[0004] To solve one of the above technical defects, this application provides a method, device, equipment, and storage medium for dynamically evaluating the resilience of a power network.

[0005] In the first aspect of this application, a method for dynamically evaluating the resilience of a power network is provided, and the method includes:

[0006] Collect the topological structure of the power network and the geographical distribution of the transmission lines;

[0007] Based on the topological structure of the power network and the geographical distribution of the transmission lines, divide the power network into regions;

[0008] Based on the spatial factors between adjacent regions after division and the time factors within the regions, splice the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters to obtain the spatio-temporal evolution Bayesian network model of rainstorm-flood-waterlogging disasters; among them, the Bayesian network model of rainstorm disasters takes the characteristic indicators of rainstorm disasters as output nodes, and the characteristic indicator of rainstorm disasters is the 24-hour precipitation; the Bayesian network model of flood disasters takes the disaster-forming environmental factors of flood disasters as input nodes and the characteristic indicators of flood disasters as output nodes. The disaster-forming environmental factors of flood disasters are 24-hour precipitation, normalized difference vegetation index, basin area ratio, land use type, and impervious surface ratio; the characteristic indicators of flood disasters are peak flow and flood volume; the Bayesian network model of waterlogging disasters takes the disaster-forming environmental factors of waterlogging disasters as input nodes and the characteristic indicators of waterlogging disasters as output nodes. The disaster-forming environmental factors of waterlogging disasters are 24-hour precipitation, flood volume, land use type, terrain wetness index, normalized difference vegetation index, and impervious surface ratio; the characteristic indicator of waterlogging disasters is the water depth;

[0009] According to the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters, evaluate the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and waterlogging disasters respectively;

[0010] According to the spatio-temporal evolution Bayesian network model of rainstorm-flood-waterlogging disasters and the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and waterlogging disasters respectively, obtain the damage evolution scenarios of power network lines and the recovery plans under the power network damage evolution scenarios; among them, the recovery plan under the power network damage evolution scenario is determined according to the load supply importance of transmission lines;

[0011] Based on the damage evolution scenarios of power network lines and the recovery plans under the power network damage evolution scenarios, conduct dynamic resilience assessment of the power network.

[0012] In the second aspect of this application, a device for dynamic resilience assessment of a power network is provided. The device includes:

[0013] A collection module for collecting the topological structure of the power network and the geographical distribution of transmission lines;

[0014] A division module for dividing the power network based on the topological structure of the power network and the geographical distribution of transmission lines collected by the collection module;

[0015] The first construction module is used to splice the Bayesian network model for rainstorm disasters, the Bayesian network model for flood disasters, and the Bayesian network model for waterlogging disasters based on the spatial factors between adjacent regions divided by the division module and the temporal factors within the regions, so as to obtain the spatio-temporal evolution Bayesian network model for rainstorm-flood-waterlogging disasters; among them, the Bayesian network model for rainstorm disasters takes the characteristic indicators of rainstorm disasters as output nodes, and the characteristic indicators of rainstorm disasters are the 24-hour precipitation; the Bayesian network model for flood disasters takes the disaster-forming environmental factors of flood disasters as input nodes and the characteristic indicators of flood disasters as output nodes, and the disaster-forming environmental factors of flood disasters are the 24-hour precipitation, normalized difference vegetation index, basin area ratio, land use type, and impervious surface ratio; the characteristic indicators of flood disasters are peak flow and flood volume; the Bayesian network model for waterlogging disasters takes the disaster-forming environmental factors of waterlogging disasters as input nodes and the characteristic indicators of waterlogging disasters as output nodes, and the disaster-forming environmental factors of waterlogging disasters are the 24-hour precipitation, flood volume, land use type, terrain wetness index, normalized difference vegetation index, and impervious surface ratio; the characteristic indicator of waterlogging disasters is the waterlogging depth;

[0016] The first evaluation module is used to evaluate the probability of damage to power facilities under rainstorm disasters, flood disasters, and waterlogging disasters respectively according to the Bayesian network model for rainstorm disasters, the Bayesian network model for flood disasters, and the Bayesian network model for waterlogging disasters;

[0017] The second construction module is used to obtain the damage evolution scenario of the power network line and the recovery plan under the power network damage evolution scenario according to the spatio-temporal evolution Bayesian network model for rainstorm-flood-waterlogging disasters constructed by the first construction module and the probabilities of damage to power facilities under rainstorm disasters, flood disasters, and waterlogging disasters evaluated by the first evaluation module; among them, the recovery plan under the power network damage evolution scenario is determined according to the importance of load supply of the transmission line;

[0018] The second evaluation module is used to conduct dynamic resilience evaluation of the power network based on the damage evolution scenario of the power network line constructed by the second construction module and the recovery plan under the power network damage evolution scenario.

[0019] In the third aspect of the present application, an electronic device is provided, including:

[0020] A memory;

[0021] A processor; and

[0022] A computer program;

[0023] Wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the method described in the first aspect above.

[0024] In the fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored; the computer program is executed by a processor to implement the method described in the first aspect above.

[0025] The present application provides a method, device, equipment, and storage medium for evaluating the dynamic resilience of a power grid. The method includes: splicing a Bayesian network model for rainstorm disasters, a Bayesian network model for flood disasters, and a Bayesian network model for waterlogging disasters to obtain a spatio-temporal evolution Bayesian network model for rainstorm-flood-waterlogging disasters; obtaining the damage evolution scenarios of power grid lines and the recovery plans under the power grid damage evolution scenarios according to the spatio-temporal evolution Bayesian network model for rainstorm-flood-waterlogging disasters and the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and waterlogging disasters respectively; and performing dynamic resilience evaluation of the power grid based on the damage evolution scenarios of power grid lines and the recovery plans under the power grid damage evolution scenarios, providing a theoretical support for improving the resilience of the power grid and reducing the impact of disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0027] Figure 1 It is a schematic flowchart of a method for evaluating the dynamic resilience of a power grid provided by an embodiment of the present application;

[0028] Figure 2 It is a schematic diagram of a power grid provided by an embodiment of the present application;

[0029] Figure 3 It is a schematic diagram of a power grid area division scheme provided by an embodiment of the present application;

[0030] Figure 4 It is a schematic diagram of a Bayesian network model for rainstorm disasters provided by an embodiment of the present application;

[0031] Figure 5 It is a schematic diagram of a Bayesian network model for flood disasters provided by an embodiment of the present application;

[0032] Figure 6 It is a schematic diagram of a Bayesian network model for waterlogging disasters provided by an embodiment of the present application;

[0033] Figure 7 It is a schematic diagram of a Bayesian network model for rainstorm-flood-waterlogging chain disasters provided by an embodiment of the present application;

[0034] Figure 8Schematic diagram of a time evolution model for rainstorm-flood-internal flooding disasters provided by an embodiment of the present application;

[0035] Figure 9 Schematic diagram of a spatio-temporal evolution model for rainstorm-flood-internal flooding disasters provided by an embodiment of the present application;

[0036] Figure 10 Schematic diagram of a spatio-temporal evolution Bayesian network model for rainstorm-flood-internal flooding disasters provided by an embodiment of the present application;

[0037] Figure 11 Schematic diagram of the framework of a spatio-temporal evolution Bayesian network model for rainstorm-flood-internal flooding disasters provided by an embodiment of the present application;

[0038] Figure 12 Schematic diagram of the evolution probability distribution of the peak flood flow index in Area A provided by an embodiment of the present application;

[0039] Figure 13 Schematic diagram of the evolution probability distribution of the flood volume index in Area A provided by an embodiment of the present application;

[0040] Figure 14 Schematic diagram of the evolution probability distribution of the waterlogging depth index in Area A provided by an embodiment of the present application;

[0041] Figure 15 Schematic diagram of the evolution probability distribution of the peak flood flow index in Area B provided by an embodiment of the present application;

[0042] Figure 16 Schematic diagram of the evolution probability distribution of the flood volume index in Area B provided by an embodiment of the present application;

[0043] Figure 17 Schematic diagram of the evolution probability distribution of the waterlogging depth index in Area B provided by an embodiment of the present application;

[0044] Figure 18 Schematic diagram of the evolution probability distribution of partial line damage provided by an embodiment of the present application;

[0045] Figure 19 Schematic diagram of a dynamic resilience assessment model for the power grid under rainstorm-flood-internal flooding disasters provided by an embodiment of the present application;

[0046] Figure 20 Schematic diagram of the evolution scenario of the power grid load supply rate in an embodiment of the present invention;

[0047] Figure 21 Schematic diagram of the structure of a power grid dynamic resilience assessment device provided by an embodiment of the present application;

[0048] Figure 22A schematic structural diagram of an electronic device provided by an embodiment of the present application. Specific implementation manners

[0049] In order to make the technical solutions and advantages in the embodiments of the present application clearer and more understandable, the following further describes the exemplary embodiments of the present application in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than an exhaustive list of all embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0050] In the process of implementing the present application, the inventors found that the resilience of the power grid, as an indicator characterizing the ability of the power grid to maintain normal operation and quickly recover under extreme events, has become a hot research direction. However, in actual operation scenarios, the spatio-temporal evolution scenarios of the rainstorm disaster chain are complex, making the power grid often in a passive state when dealing with it, and it is difficult to effectively prevent and reduce the losses caused by disasters. Existing resilience assessment methods often only consider single-disaster impact scenarios and are difficult to effectively characterize the resilience indicators of the power grid under the "rainstorm-flood-internal waterlogging" disaster chain. There is an urgent need for an assessment framework that can characterize the spatio-temporal evolution relationship of the "rainstorm-flood-internal waterlogging" disaster chain and the dynamic resilience indicators of the power grid to improve the ability of the power grid to resist chain disaster impacts.

[0051] In view of the above problems, an embodiment of the present application provides a method, device, equipment, and storage medium for assessing the dynamic resilience of a power grid. The method includes: splicing a Bayesian network model for rainstorm disasters, a Bayesian network model for flood disasters, and a Bayesian network model for internal waterlogging disasters to obtain a spatio-temporal evolution Bayesian network model for rainstorm-flood-internal waterlogging disasters; according to the spatio-temporal evolution Bayesian network model for rainstorm-flood-internal waterlogging disasters and the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and internal waterlogging disasters respectively, obtaining the evolution scenario of power grid line damage and the recovery plan under the power grid damage evolution scenario; based on the evolution scenario of power grid line damage and the recovery plan under the power grid damage evolution scenario, conducting an assessment of the dynamic resilience of the power grid, providing a theoretical support for improving the resilience of the power grid and reducing the impact of disasters.

[0052] See Figure 1 , this embodiment provides a method for assessing the dynamic resilience of a power grid. The implementation process of this method is as follows:

[0053] 101, collect the topological structure of the power grid and the geographical distribution of transmission lines.

[0054] In step 101, the topological structure and geographical location of the power network are collected to determine the geographical locations and connection modes of the power generation nodes, power transformation nodes, power transmission nodes, power distribution nodes, and user nodes, thereby obtaining the topological structure of the power network. The geographical distribution of the power transmission and distribution lines is also determined.

[0055] Taking the improved IEEE 33-node example as an illustration, the power network is as Figure 2 shown. Node 0 is the power plant, and the remaining nodes are all load nodes. Assuming that during the disaster evolution process, the nodes will not be damaged, and the node parameters and line parameters are the same as those of the standard IEEE 33-node model.

[0056] 102. Based on the topological structure of the power network and the geographical distribution of the power transmission lines, the power network is divided into regions.

[0057] In step 102, the area where the power network is located is divided according to the administrative division. As Figure 3 shown, it is divided into 2 regions in total. During the disaster evolution process, the disaster characteristic indicators within each region are the same.

[0058] 103. Based on the spatial factors between adjacent regions after division and the time factors within the regions, the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters are spliced to obtain the spatio-temporal evolution Bayesian network model of rainstorm-flood-waterlogging disasters.

[0059] The specific implementation process of step 103 is as follows:

[0060] 103-1. Construct the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters.

[0061] When performing step 103-1, the disaster-forming environment factors and disaster characteristic indicators of the three disasters of rainstorm, flood, and waterlogging are respectively extracted, the Bayesian network node types and division methods are determined, and single-disaster Bayesian network models are established, namely the Bayesian network model of rainstorm disasters, the Bayesian network model of flood disasters, and the Bayesian network model of waterlogging disasters.

[0062] For example, the disaster-forming environment factors and disaster characteristic indicators of the three disasters of rainstorm, flood, and waterlogging shown in Table 1 are extracted.

[0063] Table 1

[0064]

[0065] Among them, the 24-hour precipitation node is a discrete node, and its values include: below 49.9 mm (non-rainstorm), 50.0 - 99.9 mm (rainstorm), 100.0 - 249.9 mm (heavy rainstorm), and above 250.0 mm (extreme rainstorm).

[0066] The normalized difference vegetation index node is a discrete node, and its values include: [-1, -0.35) (low coverage), [-0.35, 0.35) (medium coverage), and [0.35, 1] (high coverage).

[0067] The basin area proportion node is a discrete node, and its values include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, and 80% - 100%.

[0068] The land use type node is a discrete node, and its values include: forest, shrub, construction land, grassland, permanent ice and snow, water body, farmland, and wasteland.

[0069] The impervious surface proportion node is a discrete node, and its values include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, and 80% - 100%.

[0070] The flood volume node is a discrete node, and its values include: below 10 million cubic meters, 10 - 30 million cubic meters, 30 - 50 million cubic meters, and above 50 million cubic meters.

[0071] The terrain wetness index node is a discrete node, and its values include: 0 - 10, 10 - 20, and 20 - 30.

[0072] In this embodiment, precipitation is used to characterize rainstorm disasters. Since the existing weather forecasting technology is relatively mature, the precipitation data predicted by the meteorological bureau is considered as the evolution basis of the rainstorm disaster intensity, that is, only the "precipitation" node is included in the rainstorm disaster Bayesian network. For flood and waterlogging disasters, the disaster-forming environmental factors and disaster characteristic indicators together constitute the nodes of their Bayesian network model.

[0073] 1. Determine the correlation between the disaster-forming environmental factors and disaster characteristic indicators of rainstorm disasters, and establish a rainstorm disaster Bayesian network model. Among them, the rainstorm disaster Bayesian network model uses the characteristic indicators of rainstorm disasters as output nodes, and the characteristic indicator of rainstorm disasters is the 24-hour precipitation. Table 2 shows the node types and value ranges in the rainstorm disaster Bayesian network model. Figure 4 shows the rainstorm disaster Bayesian network model.

[0074] Table 2

[0075]

[0076] The Bayesian network model of flood disasters takes the disaster-forming environmental factors of flood disasters as input nodes and the characteristic indicators of flood disasters as output nodes. The disaster-forming environmental factors of flood disasters are 24-hour precipitation, normalized vegetation index, proportion of basin area, land use type, and proportion of impervious surface. The characteristic indicators of flood disasters are peak flood discharge and flood volume. Table 3 shows the node types and value ranges in the Bayesian network model of flood disasters. Figure 5 shows the Bayesian network model of flood disasters.

[0077] Table 3

[0078]

[0079] The Bayesian network model of waterlogging disasters takes the disaster-forming environmental factors of waterlogging disasters as input nodes and the characteristic indicators of waterlogging disasters as output nodes. The disaster-forming environmental factors of waterlogging disasters are 24-hour precipitation, flood volume, land use type, terrain humidity index, normalized vegetation index, and proportion of impervious surface. The characteristic indicator of waterlogging disasters is waterlogging depth. Table 4 shows the node types and value ranges in the Bayesian network model of waterlogging disasters. Figure 6 shows the Bayesian network model of waterlogging disasters.

[0080] Table 4

[0081]

[0082] 103-2. Based on the spatial factors between adjacent regions after division and the time factors within the regions, splice the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters to obtain the spatio-temporal evolution Bayesian network model of rainstorm-flood-waterlogging disasters.

[0083] The implementation process in step 103-2 is as follows:

[0084] 1. Through the operation of merging common variables, splice the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters into a rainstorm-flood-waterlogging chain disaster Bayesian network model. The rainstorm-flood-waterlogging chain disaster Bayesian network model is as Figure 7 shown.

[0085] Among them, there are two types of common variables. The first type of common variable is that the output variable in a certain disaster model (such as Bayesian network A) is the same as the input variable in another certain disaster model (such as Bayesian network B); the second type of common variable is the same part among the input variables of different disaster models.

[0086] Through the operation of merging common variables, the single-disaster Bayesian network models can be merged to form a composite chain-generated disaster Bayesian network model.

[0087] For the first type of common variable, the specific merging operation is as follows: delete the common variable in Bayesian network A, and the parent nodes of the deleted common variable point to the common variable in Bayesian network B; for the second type of common variable, the merging operation is as follows: for the same variable, only keep one node, and its connection relationships with all other nodes remain unchanged.

[0088] 2. Introduce the time factor within the divided regions to construct a time evolution model for rainstorm-flood-internal waterlogging disasters.

[0089] For the evolution situation of the rainstorm-flood-internal waterlogging disaster chain on the time scale, a dynamic Bayesian network is used for characterization. Introduce the time factor within the divided regions, and establish time slices for characterization at different moments respectively. Model the influence relationships between different disasters within the slices, and model the evolution situation of the same disaster between the slices.

[0090] For example, the evolution process satisfies the first-order Markov property, and the intra-slice connection relationships and inter-slice connection relationships do not change with time. Finally, establish Figure 8 the shown time evolution model for rainstorm-flood-internal waterlogging disasters.

[0091] 3. Introduce the spatial factor between adjacent divided regions to construct a spatio-temporal evolution Bayesian network model for rainstorm-flood-internal waterlogging disasters.

[0092] Based on the time evolution model for rainstorm-flood-internal waterlogging disasters, introduce the spatial factor between adjacent divided regions, and establish a spatio-temporal evolution model for rainstorm-flood-internal waterlogging disasters for different regions. As Figure 9 shown, determine the connection relationships of the sub-models in each region, and characterize the intra-region time evolution relationship and the multi-region time evolution relationship, then the construction of the spatio-temporal evolution Bayesian network model for rainstorm-flood-internal waterlogging disasters can be completed.

[0093] Among them, the intra-region time evolution relationship describes the time evolution relationship of the disaster chain within regions A and B, and the cross-region time evolution relationship describes the mutual influence relationship in time of the occurrence of disasters between regions A and B.

[0094] Figure 8 The shown model describes the spatio-temporal evolution situation of the rainstorm-flood-internal waterlogging disaster chain between regions A and B. In this embodiment, the spatial influence relationship of the disaster chain is considered as the spatial evolution of the flood disaster within each region, and a schematic diagram of the spatio-temporal evolution Bayesian network model for rainstorm-flood-internal waterlogging disasters is established by establishing the connection edges of the flood disaster output nodes between different regions. As Figure 10 shown, the architecture of the spatio-temporal evolution Bayesian network model for rainstorm-flood-internal waterlogging disasters is as Figure 11As shown, the same-region evolution relationship and cross-region time evolution relationship of the Bayesian network model for the spatio-temporal evolution of rainstorm-flood-internal waterlogging disasters are the same as those Figure 10 shown in

[0095] 104. According to the Bayesian network model for rainstorm disasters, the Bayesian network model for flood disasters, and the Bayesian network model for internal waterlogging disasters, evaluate the probability of damage to power facilities under rainstorm disasters, flood disasters, and internal waterlogging disasters respectively.

[0096] In step 104, according to the Bayesian network model for rainstorm disasters, the Bayesian network model for flood disasters, and the Bayesian network model for internal waterlogging disasters, analyze the mapping relationship between rainstorm, flood, internal waterlogging disaster indicators and the operating state of transmission lines, and construct a probability model for damage to power facilities under rainstorm disasters, a probability model for damage to power facilities under flood disasters, and a probability model for damage to power facilities under internal waterlogging disasters, and then obtain the probability models for damage to power facilities under various disasters. That is, evaluate the probability of damage to power facilities under rainstorm disasters according to the probability model for damage to power facilities under rainstorm disasters , evaluate the probability of damage to power facilities under flood disasters according to the probability model for damage to power facilities under flood disasters , and evaluate the probability of damage to power facilities under internal waterlogging disasters according to the probability model for damage to power facilities under internal waterlogging disasters .

[0097] 1. For rainstorm disasters

[0098] The probability model for damage to power facilities under rainstorm disasters is , then evaluate the probability of damage to power facilities under rainstorm disasters .

[0099] Among them, is the characteristic index value of rainstorm disasters obtained according to the Bayesian network model for rainstorm disasters, is the maximum value of the characteristic index of rainstorm disasters that power facilities can withstand.

[0100] Since the characteristic index of rainstorm disasters is the 24-hour precipitation, therefore is the 24-hour precipitation of rainstorm disasters obtained according to the Bayesian network model for rainstorm disasters, is the maximum value of the 24-hour precipitation of rainstorm disasters that power facilities can withstand.

[0101] For example, if the power facility is a 10-km long transmission line, then the probability of damage to the 10-km long transmission line under rainstorm disasters .

[0102] Among them, is the characteristic index value of the rainstorm disaster for a 10-km transmission line obtained from the rainstorm disaster Bayesian network model. is the maximum value of the characteristic index of the rainstorm disaster that the power facilities can withstand for a 10-km transmission line.

[0103] In specific implementation, it can be 360 mm (precipitation in 24 hours).

[0104] 2. For flood disasters

[0105] The probability model of power facilities being damaged under flood disasters is , then evaluate the probability of power facilities being damaged under flood disasters .

[0106] Among them, is the probability of power facilities being damaged corresponding to the peak flood flow under flood disasters, is the probability of power facilities being damaged corresponding to the flood volume under flood disasters, is the weight coefficient of the peak flood flow under flood disasters, .

[0107] , is the peak flood flow index value under flood disasters obtained from the flood disaster Bayesian network model, is the maximum value of the peak flood flow index that the power facilities can withstand under flood disasters, is the flood volume index value under flood disasters obtained from the flood disaster Bayesian network model, is the maximum value of the flood volume index that the power facilities can withstand under flood disasters.

[0108] In specific implementation, it can be 1500 cubic meters per second (peak flood flow), it can be 50 million cubic meters (flood volume), .

[0109] 3. For waterlogging disasters

[0110] The probability model of being damaged under waterlogging disasters is , then evaluate the probability of power facilities being damaged under waterlogging disasters .

[0111] Among them, is the characteristic index value of waterlogging disasters obtained from the waterlogging disaster Bayesian network model, is the maximum value of the characteristic index of waterlogging disasters that the power facilities can withstand.

[0112] Since the characteristic index of the waterlogging disaster is the water accumulation depth, therefore is the water accumulation depth of the waterlogging disaster obtained according to the Bayesian network model of the waterlogging disaster is the maximum value of the water accumulation depth of the waterlogging disaster that the power facilities can withstand.

[0113] For example, is 60 cm (water accumulation depth).

[0114] 105. According to the spatio-temporal evolution Bayesian network model of the rainstorm-flood-waterlogging disaster and the probabilities of the power facilities being damaged under the rainstorm disaster, flood disaster, and waterlogging disaster respectively, obtain the damage evolution scenario of the power network lines and the recovery plan under the power network damage evolution scenario.

[0115] The implementation process of step 105 is as follows:

[0116] 105-1. According to the spatio-temporal evolution Bayesian network model of the rainstorm-flood-waterlogging disaster and the probabilities of the power facilities being damaged under the rainstorm disaster, flood disaster, and waterlogging disaster respectively, determine the probability distribution of the disaster evolution scenario.

[0117] For example, the initial operating state of the power network is that all lines and nodes are operating normally. Taking 6 hours as the time unit for disaster evolution analysis, the relevant input node data of the two regions and the precipitation data within the next 60 hours are shown in Table 5.

[0118] Table 5

[0119]

[0120] In Table 5, the values "1-4" in "Precipitation" respectively represent the corresponding state intervals in the Bayesian network corresponding to the actual data, corresponding to "below 12.49 mm", "12.50~24.99 mm", "25.00~62.49 mm", and "above 62.50 mm" respectively. Each numerical sequence represents the evolution of the precipitation in the two regions within the next 60 hours.

[0121] Input the data in Table 5 into the spatio-temporal evolution Bayesian network model of the rainstorm-flood-waterlogging disaster, and use the junction tree algorithm for inference to obtain the disaster evolution situation of each region, as Figures 12 to 17 shown.

[0122] 105-2. According to the probability distribution of the disaster evolution scenario, obtain the damage evolution scenario of the power network lines.

[0123] In step 105-2, according to the probability distribution of the disaster evolution scenario, the Monte Carlo sampling method is used to obtain several spatio-temporal evolution scenarios of the disaster chain. For each spatio-temporal evolution scenario of the disaster chain, combined with the probability model of the damage of power facilities under each disaster, the Monte Carlo sampling method is used to obtain several damage evolution scenarios of the power grid.

[0124] For example, Figure 18 shows the damage probability of some lines. For different damage probability scenarios of transmission lines, the Monte Carlo sampling method is used to generate 100 groups of failure scenarios respectively under different scenarios. One group of failure scenarios is shown in Table 6.

[0125] Among them, "1" indicates that the line is operating normally, and "0" indicates that the line has failed.

[0126] Table 6

[0127]

[0128] 105-3. Develop a recovery plan for the damage evolution scenario of the power grid.

[0129] Among them, the recovery plan for the damage evolution scenario of the power grid is determined according to the importance of load supply of the transmission line. That is, based on the importance of load supply, the importance of the line is determined, the load supply capacity of each line in the power grid is evaluated, the importance of load supply of the transmission line is determined, and the repair is carried out in the order of descending importance of load supply, so as to develop a recovery plan for the damage evolution scenario of the power grid.

[0130] Among them, the importance of load supply of the transmission line is the decrease value of the load supply rate of the power grid when only this line is damaged in the power grid. That is, the importance of load supply of any transmission line , .

[0131] Among them, is the set of all power generation nodes, substation nodes, transmission nodes, distribution nodes and user nodes in the power grid, , , and are node identifiers, is the transmission line composed of nodes and , is the transmission line composed of nodes and .

[0132] is the importance of load supply of the transmission line , is the importance of load supply of the transmission line The operating state of the power transmission line When the operating state of the power transmission line is normal (i.e., operating normally) The power transmission line When the operating state of the power transmission line is damaged (i.e., the line fails) , For the operating state of the power transmission line The power transmission line When the operating state of the power transmission line is normal (i.e., the line is operating normally) The power transmission line When the operating state of the power transmission line is damaged (i.e., the line fails) .

[0133] is the set of power transmission lines in the power network, is the node The active power consumed is the node The active power demanded

[0134] For example, the load supply importance is shown in Table 7

[0135] Table 7

[0136]

[0137] Based on Table 7, the restoration plan for the power network under the damage evolution scenario can be obtained as follows: Repair the power transmission lines in the order {1, 2, 3, 4, 5, 6, 22, 25, 7, 26, 23, 27, 28, 8, 29, 9, 10, 11, 12, 24, 30, 13, 18, 14, 19, 31, 15, 20, 16, 17, 21, 32}

[0138] 106. Based on the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario, conduct a dynamic resilience assessment of the power network

[0139] The implementation process of step 106 is as follows

[0140] 106-1. Establish an optimal power flow model

[0141] Specifically, when implementing, based on the power network line damage evolution scenario, with the goal of maximizing the load supply rate and using the damage scenario as the model input, establish an optimal power flow model

[0142] This optimal power flow model can calculate the evolution of the load supply rate of the power network under the disaster evolution scenario

[0143] For example, the optimal power flow model is .

[0144] s.t.

[0145] , .

[0146] , .

[0147] , .

[0148] , .

[0149] , .

[0150] , .

[0151] , .

[0152] , .

[0153] , .

[0154] , .

[0155] , .

[0156] , .

[0157] Among them, is the set of all substation nodes and user load nodes in the power network, , and are node identifiers, is the transmission line composed of nodes and , is the transmission line composed of nodes and .

[0158] is the set of transmission lines in the power network.

[0159] is the active power transmitted on the transmission line , is the transmission line The active power transmitted upward is the transmission line The reactive power transmitted is the transmission line The reactive power transmitted is the transmission line The maximum active power transmission is the transmission line The maximum reactive power transmission is the active power consumed by node is the active power consumed by node is the active power consumed by node is the active power consumed by node is the reactive power consumed by node is the reactive power consumed by node is the reactive power consumed by node is the reactive power consumed by node is the active power demanded by node is the active power demanded by node is the reactive power demanded by node is the reactive power demanded by node is the maximum active power provided by node is the maximum active power provided by node is the maximum reactive power provided by node is the maximum reactive power provided by node is the active power provided by node is the active power provided by node is the active power provided by node is the active power provided by node is the reactive power provided by node is the reactive power provided by node is the reactive power provided by node is the reactive power provided by node

[0160] is the lower voltage limit of node is the lower voltage limit of node is the voltage of node is the voltage of node is the upper voltage limit of node is the upper voltage limit of node is the voltage of node is the voltage of node is the operating status of transmission line is the operating status of transmission line is the power factor of node is the power factor of node is the capacitance of transmission line is the capacitance of transmission line is the reactance of transmission line is the reactance of transmission line is any large number

[0161] The parameter table of the optimal power flow model is shown in Table 8

[0162] Table 8

[0163]

[0164] The objective function of the optimal power flow model is as follows: , which can maximize the total load supply rate of all nodes.

[0165] The constraints of the optimal power flow model are as follows:

[0166] 1. Power transmission balance constraint

[0167] , .

[0168] , .

[0169] 2. Output power and load power upper limit constraint

[0170] , .

[0171] , .

[0172] , .

[0173] , .

[0174] The output power and load power upper limit constraint can ensure that the output power and consumption power of the node do not exceed their upper limit values.

[0175] 3. Node voltage range constraint

[0176] , .

[0177] The node voltage range constraint can ensure that the node voltage meets the normal operation requirements.

[0178] 4. Line transmission power constraint

[0179] , .

[0180] , .

[0181] The line transmission power constraint can ensure that the actual transmission power of the line does not exceed its upper limit value. When the operating state of the transmission line is damaged (i.e., the line fails), When it is, the upper limit value of the line transmission power is 0.

[0182] 5. Power factor constraint of line transmission power

[0183] , .

[0184] The power factor constraint of line transmission power can ensure that the actual transmission power meets the power factor requirements.

[0185] 6. Linear power flow constraint

[0186] , .

[0187] , .

[0188] When the operating state of the transmission line is damaged (i.e., the line fails), that is when it is, this constraint does not work.

[0189] 106 - 2. Input the power network line damage evolution scenario and the recovery plan under the power network damage evolution scenario into the optimal power flow model to obtain the power network load supply rate evolution scenario.

[0190] 106 - 3. Conduct power network dynamic resilience assessment based on the power network load supply rate evolution scenario.

[0191] The specific implementation process of step 106 - 3 is as follows: Based on the power network load supply rate evolution scenario, determine the load supply rate of the power network at each moment. Evaluate the power network dynamic resilience based on the load supply rate .

[0192] Among them, is the power network dynamic resilience value, is the time identifier, is the power network at the th moment of the load supply rate, is the load supply rate of the power network at the 0th moment, that is, the initial performance of 100%.

[0193] When evaluating the power network dynamic resilience, the load supply rate can be used as a power network performance index to establish a power network dynamic resilience assessment model under rainstorm - flood - waterlogging disasters, such as Figure 19 shown.

[0194] See Figure 19, under the multiple impacts of the "rainstorm - flood - waterlogging" disaster, the power grid first enters the resistance process. As the disaster gradually evolves and impacts, the performance of the power grid gradually decreases; after the disaster evolution ends, the power grid enters the recovery process and its performance gradually increases. Therefore, .

[0195] For example, the evolution of the load supply rate under a set of scenarios is as Figure 20 shown. The resilience index under this scenario is 47.25%. Based on the dynamic resilience assessment model of the power grid under the rainstorm - flood - waterlogging disaster, calculate the resilience index of the power grid under each scenario, and the expected value can be taken to determine the resilience index of the power grid, which is 45.75%.

[0196] The dynamic resilience assessment method of the power grid provided in this embodiment is a dynamic resilience assessment method that characterizes the resistance and recovery capabilities of the power grid under the "rainstorm - flood - waterlogging" chain disaster by refining the mapping model between disaster characteristics and power grid performance.

[0197] The beneficial effects of the dynamic resilience assessment method of the power grid provided in this embodiment of the present invention are as follows: The method of the present invention proposes a dynamic resilience assessment method for the power grid, which can achieve the spatio - temporal evolution analysis of the "rainstorm - flood - waterlogging" disaster chain for the rainstorm disaster scenario, use the Monte Carlo sampling method to obtain the damage evolution scenarios of the power grid, use the line importance method based on load supply to determine the recovery plan, and based on the optimal power flow model, obtain the dynamic evolution scenarios of the power grid load supply rate, realizing the dynamic resilience assessment of the power grid and fully reflecting the response ability of the power grid to the "rainstorm - flood - waterlogging" disaster.

[0198] This embodiment provides a dynamic resilience assessment method for the power grid, which splices the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters to obtain a spatio - temporal evolution Bayesian network model of the rainstorm - flood - waterlogging disaster; according to the spatio - temporal evolution Bayesian network model of the rainstorm - flood - waterlogging disaster and the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and waterlogging disasters respectively, obtain the line damage evolution scenarios of the power grid and the recovery plan under the power grid damage evolution scenarios; based on the line damage evolution scenarios of the power grid and the recovery plan under the power grid damage evolution scenarios, conduct the dynamic resilience assessment of the power grid, providing a theoretical support for improving the resilience of the power grid and reducing the impact of disasters.

[0199] Based on the same inventive concept of the dynamic resilience assessment method of the power grid, this embodiment provides a dynamic resilience assessment device for the power grid. See Figure 21 , and this device includes:

[0200] The acquisition module 2101 is used to acquire the topological structure of the power network and the geographical distribution of the transmission lines.

[0201] The division module 2102 is used to divide the power network into regions based on the topological structure of the power network and the geographical distribution of the transmission lines acquired by the acquisition module 2101.

[0202] The first construction module 2103 is used to splice the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters based on the spatial factors between adjacent regions and the temporal factors within the regions after division by the division module 2102, to obtain a spatio-temporal evolution Bayesian network model of rainstorm-flood-waterlogging disasters. Among them, the Bayesian network model of rainstorm disasters takes the characteristic index of rainstorm disasters as the output node, and the characteristic index of rainstorm disasters is the 24-hour precipitation. The Bayesian network model of flood disasters takes the disaster-forming environmental factors of flood disasters as the input nodes and the characteristic index of flood disasters as the output node. The disaster-forming environmental factors of flood disasters are the 24-hour precipitation, normalized difference vegetation index, proportion of basin area, land use type, and proportion of impervious surface. The characteristic index of flood disasters is the peak flow and flood volume. The Bayesian network model of waterlogging disasters takes the disaster-forming environmental factors of waterlogging disasters as the input nodes and the characteristic index of waterlogging disasters as the output node. The disaster-forming environmental factors of waterlogging disasters are the 24-hour precipitation, flood volume, land use type, terrain wetness index, normalized difference vegetation index, and proportion of impervious surface. The characteristic index of waterlogging disasters is the waterlogging depth.

[0203] The first evaluation module 2104 is used to evaluate the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and waterlogging disasters respectively according to the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters.

[0204] The second construction module 2105 is used to obtain the damage evolution scenarios of the power network lines and the recovery plans under the power network damage evolution scenarios according to the spatio-temporal evolution Bayesian network model of rainstorm-flood-waterlogging disasters constructed by the first construction module 2103 and the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and waterlogging disasters evaluated by the first evaluation module 2104. Among them, the recovery plan under the power network damage evolution scenario is determined according to the importance of load supply of the transmission lines.

[0205] The second evaluation module 2106 is used to conduct dynamic resilience assessment of the power network based on the damage evolution scenarios of the power network lines constructed by the second construction module 2105 and the recovery plans under the power network damage evolution scenarios.

[0206] Among them, the 24-hour precipitation node is a discrete node, and its values include: below 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, above 250.0 mm.

[0207] The normalized difference vegetation index node is a discrete node, and its values include: [-1, -0.35), [-0.35, 0.35), [0.35, 1].

[0208] The basin area proportion node is a discrete node, and its values include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%.

[0209] The land use type node is a discrete node, and its values include: forest, shrub, construction land, grassland, permanent ice and snow, water body, farmland, and wasteland.

[0210] The impervious surface proportion node is a discrete node, and its values include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%.

[0211] The flood volume node is a discrete node, and its values include: below 10 million cubic meters, 10 - 30 million cubic meters, 30 - 50 million cubic meters, above 50 million cubic meters.

[0212] The terrain wetness index node is a discrete node, and its values include: 0 - 10, 10 - 20, 20 - 30.

[0213] Among them, the first evaluation module 2104 is used to evaluate the probability of damage to power facilities under rainstorm disasters . Among them, is the characteristic index value of the rainstorm disaster obtained from the rainstorm disaster Bayesian network model, is the maximum value of the characteristic index of the rainstorm disaster that the power facilities can withstand.

[0214] Evaluate the probability of damage to power facilities under flood disasters . Among them, is the probability of damage to power facilities corresponding to the peak flood flow under flood disasters, is the probability of damage to power facilities corresponding to the flood volume under flood disasters, is the weight coefficient of the peak flood flow under flood disasters, , is the peak flood flow index value under flood disasters obtained from the flood disaster Bayesian network model, is the maximum value of the peak flood flow index that the power facilities can withstand under flood disasters, is the index value of flood volume under flood disasters obtained from the Bayesian network model of flood disasters. is the maximum value of the flood volume index that the power facilities can withstand under flood disasters.

[0215] Evaluate the probability of damage to power facilities under waterlogging disasters Among them, is the characteristic index value of waterlogging disasters obtained from the Bayesian network model of waterlogging disasters. is the maximum value of the characteristic index of waterlogging disasters that the power facilities can withstand.

[0216] Among them, the load supply importance of any transmission line , .

[0217] Among them, is the set of all power generation nodes, substation nodes, transmission nodes, distribution nodes and user nodes in the power network. , , and are node identifiers. is the transmission line composed of nodes and . is the transmission line composed of nodes and . is the load supply importance of the transmission line . is the operating state of the transmission line . When the operating state of the transmission line is normal , when the operating state of the transmission line is damaged . is the operating state of the transmission line . When the operating state of the transmission line is normal , when the operating state of the transmission line is damaged is the set of transmission lines in the power network. is the active power consumed by the node . is the active power demanded by the node .

[0218] Among them, the second evaluation module 2106 is used to establish an optimal power flow model. The power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario are input into the optimal power flow model to obtain the power network load supply rate evolution scenario. The dynamic resilience of the power network is evaluated based on the power network load supply rate evolution scenario.

[0219] Among them, the optimal power flow model is: 。

[0220] s.t.

[0221] , 。

[0222] , 。

[0223] , 。

[0224] , 。

[0225] , 。

[0226] , 。

[0227] , 。

[0228] , 。

[0229] , 。

[0230] , 。

[0231] , 。

[0232] , 。

[0233] Among them, is the set of all substation nodes and user load nodes in the power network, 、 and are node identifiers, is from node and The transmission line formed is the transmission line formed by nodes and is the set of transmission lines in the power network is the active power transmitted on the transmission line is the active power transmitted on the transmission line is the reactive power transmitted on the transmission line is the reactive power transmitted on the transmission line is the maximum active power transmission capacity of the transmission line is the maximum reactive power transmission capacity of the transmission line is the active power consumed by node is the active power consumed by node is the reactive power consumed by node is the reactive power consumed by node is the active power demanded by node is the reactive power demanded by node is the maximum active power provided by node is the maximum reactive power provided by node is the active power provided by node is the active power provided by node is the reactive power provided by node is the reactive power provided by node is the lower voltage limit of node is the voltage of node is the upper voltage limit of node is the voltage of node is the operating state of the transmission line is the power factor of node ​​​​​​​​​​​​​​​​​​​​​​​​​For the transmission line capacitance, For the transmission line reactance, is any large number.

[0234] Among them, the dynamic resilience assessment of the power network based on the evolution scenario of the power network load supply rate includes:

[0235] Based on the evolution scenario of the power network load supply rate, determine the load supply rate of the power network at each moment.

[0236] Evaluate the dynamic resilience of the power network .

[0237] Among them, is the dynamic resilience value of the power network, is the time identifier, is the load supply rate of the power network at the th moment, is the load supply rate of the power network at the 0th moment.

[0238] The device provided in this embodiment obtains the evolution scenario of the power network line damage and the recovery plan under the power network damage evolution scenario according to the spatio-temporal evolution Bayesian network model of rainstorm-flood-internal waterlogging disasters and the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and internal waterlogging disasters; based on the evolution scenario of the power network line damage and the recovery plan under the power network damage evolution scenario, conduct dynamic resilience assessment of the power network, providing theoretical support for improving the resilience of the power network and reducing the impact of disasters.

[0239] Based on the same inventive concept of the power network dynamic resilience assessment method, this embodiment provides an electronic device, as shown in Figure 22 , including: a memory 2201, a processor 2202, and a computer program.

[0240] Among them, the computer program is stored in the memory 2201 and is configured to be executed by the processor 2202 to implement the above-mentioned power network dynamic resilience assessment method.

[0241] Specifically,

[0242] Collect the topological structure of the power network and the geographical distribution of the transmission lines.

[0243] Based on the topological structure of the power network and the geographical distribution of the transmission lines, divide the power network into regions.

[0244] Based on the spatial factors between adjacent regions after division and the temporal factors within the regions, splice the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters to obtain the spatio-temporal evolution Bayesian network model of rainstorm-flood-waterlogging disasters. Among them, the Bayesian network model of rainstorm disasters takes the characteristic indicators of rainstorm disasters as output nodes, and the characteristic indicator of rainstorm disasters is the 24-hour precipitation. The Bayesian network model of flood disasters takes the disaster-forming environmental factors of flood disasters as input nodes and the characteristic indicators of flood disasters as output nodes. The disaster-forming environmental factors of flood disasters are the 24-hour precipitation, normalized difference vegetation index, proportion of basin area, land use type, and proportion of impervious surface. The characteristic indicators of flood disasters are peak flow and flood volume. The Bayesian network model of waterlogging disasters takes the disaster-forming environmental factors of waterlogging disasters as input nodes and the characteristic indicators of waterlogging disasters as output nodes. The disaster-forming environmental factors of waterlogging disasters are the 24-hour precipitation, flood volume, land use type, terrain wetness index, normalized difference vegetation index, and proportion of impervious surface. The characteristic indicator of waterlogging disasters is the waterlogging depth.

[0245] According to the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters, evaluate the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and waterlogging disasters respectively.

[0246] According to the spatio-temporal evolution Bayesian network model of rainstorm-flood-waterlogging disasters and the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and waterlogging disasters respectively, obtain the damage evolution scenarios of power network lines and the recovery plans under the power network damage evolution scenarios. Among them, the recovery plans under the power network damage evolution scenarios are determined according to the importance of load supply of transmission lines.

[0247] Based on the damage evolution scenarios of power network lines and the recovery plans under the power network damage evolution scenarios, conduct the dynamic resilience assessment of the power network.

[0248] Optionally, the 24-hour precipitation node is a discrete node, and the values include: below 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, above 250.0 mm.

[0249] The normalized difference vegetation index node is a discrete node, and the values include: [-1, -0.35), [-0.35, 0.35), [0.35, 1].

[0250] The proportion of basin area node is a discrete node, and the values include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%.

[0251] The land use type node is a discrete node, and its values include: forest, shrub, construction land, grassland, permanent ice and snow, water body, farmland, and wasteland.

[0252] The impervious surface ratio node is a discrete node, and its values include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%.

[0253] The flood volume node is a discrete node, and its values include: less than 10 million cubic meters, 10 - 30 million cubic meters, 30 - 50 million cubic meters, more than 50 million cubic meters.

[0254] The terrain wetness index node is a discrete node, and its values include: 0 - 10, 10 - 20, 20 - 30.

[0255] Optionally, according to the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters, evaluate the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and waterlogging disasters respectively, including:

[0256] Evaluate the probability of power facilities being damaged under rainstorm disasters . Among them, is the characteristic index value of rainstorm disasters obtained according to the Bayesian network model of rainstorm disasters, is the maximum value of the characteristic index of rainstorm disasters that power facilities can withstand.

[0257] Evaluate the probability of power facilities being damaged under flood disasters . Among them, is the probability of power facilities being damaged corresponding to the peak flood flow under flood disasters, is the probability of power facilities being damaged corresponding to the flood volume under flood disasters, is the weight coefficient of the peak flood flow under flood disasters, , is the peak flood flow index value under flood disasters obtained according to the Bayesian network model of flood disasters, is the maximum value of the peak flood flow index that power facilities can withstand under flood disasters, is the flood volume index value under flood disasters obtained according to the Bayesian network model of flood disasters, is the maximum value of the flood volume index that power facilities can withstand under flood disasters.

[0258] Evaluate the probability of power facilities being damaged under waterlogging disasters . Among them, is the characteristic index value of waterlogging disasters obtained according to the Bayesian network model of waterlogging disasters, The maximum value of the characteristic index of the waterlogging disaster that the power facilities can withstand.

[0259] Optionally, the load supply importance of any transmission line , .

[0260] Among them, is the set of all power generation nodes, substation nodes, transmission nodes, distribution nodes and user nodes in the power network, , , and are node identifiers, is the transmission line composed of nodes and , is the transmission line composed of nodes and . is the load supply importance of the transmission line , is the operating state of the transmission line . When the operating state of the transmission line is normal , when the operating state of the transmission line is damaged , is the operating state of the transmission line . When the operating state of the transmission line is normal , when the operating state of the transmission line is damaged is the set of transmission lines in the power network, is the active power consumed by the node , is the active power demanded by the node .

[0261] Optionally, based on the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario, perform power network dynamic resilience assessment, including:

[0262] Establish an optimal power flow model.

[0263] Input the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario into the optimal power flow model to obtain the power network load supply rate evolution scenario.

[0264] Perform power network dynamic resilience assessment based on the power network load supply rate evolution scenario.

[0265] Optionally, the optimal power flow model is: 。

[0266] s.t.

[0267] , 。

[0268] , 。

[0269] , 。

[0270] , 。

[0271] , 。

[0272] , 。

[0273] , 。

[0274] , 。

[0275] , 。

[0276] , 。

[0277] , 。

[0278] , 。

[0279] Among them, is the set of all substation nodes and user load nodes in the power network, 、 and are node identifiers, is the transmission line composed of nodes and , is the transmission line composed of nodes and 。 is the set of transmission lines in the power network. is the active power transmitted on the transmission line , is the transmission line The active power transmitted above is the transmission line The reactive power transmitted above is the transmission line The reactive power transmitted above is the transmission line The maximum active power transmission is the transmission line The maximum reactive power transmission is the active power consumed by node is the active power consumed by node is the reactive power consumed by node is the reactive power consumed by node is the active power demanded by node is the reactive power demanded by node is the maximum active power provided by node is the maximum reactive power provided by node is the active power provided by node is the active power provided by node is the reactive power provided by node is the reactive power provided by node is the lower voltage limit of node is the voltage of node is the upper voltage limit of node is the voltage of node is the operating state of transmission line is the power factor of node is the capacitance of transmission line is the reactance of transmission line is any large number

[0280] Optionally, a dynamic resilience assessment of the power network is performed based on the evolution scenario of the power network load supply rate, including:

[0281] ​​​​​​​​​​​​​​​​​​​​Determine the load supply rate of the power grid at each moment based on the evolution scenario of the load supply rate of the power grid.

[0282] Evaluate the dynamic resilience of the power grid 。

[0283] Among them, is the dynamic resilience value of the power grid, is the time identifier, is the load supply rate of the power grid at the th moment, is the load supply rate of the power grid at the 0th moment.

[0284] The electronic device provided in this embodiment, when the computer program thereon is executed by a processor, obtains the evolution scenario of the power grid line damage and the recovery plan under the evolution scenario of the power grid damage according to the spatio-temporal evolution Bayesian network model of rainstorm-flood-internal waterlogging disasters and the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and internal waterlogging disasters; based on the evolution scenario of the power grid line damage and the recovery plan under the evolution scenario of the power grid damage, conducts the dynamic resilience assessment of the power grid, providing a theoretical support for improving the resilience of the power grid and reducing the impact of disasters.

[0285] Based on the same inventive concept of the power grid dynamic resilience assessment method, this embodiment provides a computer-readable storage medium, and a computer program is stored thereon. The computer program is executed by a processor to implement the above-mentioned power grid dynamic resilience assessment method.

[0286] Specifically,

[0287] Collect the topological structure of the power grid and the geographical distribution of the transmission lines.

[0288] Based on the topological structure of the power grid and the geographical distribution of the transmission lines, divide the power grid into regions.

[0289] Based on the spatial factors between adjacent regions after division and the time factors within the regions, splice the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters to obtain the spatio-temporal evolution Bayesian network model of rainstorm-flood-waterlogging disasters. Among them, the Bayesian network model of rainstorm disasters takes the characteristic indicators of rainstorm disasters as output nodes, and the characteristic indicator of rainstorm disasters is the 24-hour precipitation. The Bayesian network model of flood disasters takes the disaster-forming environmental factors of flood disasters as input nodes and the characteristic indicators of flood disasters as output nodes. The disaster-forming environmental factors of flood disasters are 24-hour precipitation, normalized difference vegetation index, proportion of basin area, land use type, and proportion of impervious surface. The characteristic indicators of flood disasters are peak flood discharge and flood volume. The Bayesian network model of waterlogging disasters takes the disaster-forming environmental factors of waterlogging disasters as input nodes and the characteristic indicators of waterlogging disasters as output nodes. The disaster-forming environmental factors of waterlogging disasters are 24-hour precipitation, flood volume, land use type, terrain wetness index, normalized difference vegetation index, and proportion of impervious surface. The characteristic indicator of waterlogging disasters is waterlogging depth.

[0290] According to the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters, evaluate the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and waterlogging disasters respectively.

[0291] According to the spatio-temporal evolution Bayesian network model of rainstorm-flood-waterlogging disasters and the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and waterlogging disasters respectively, obtain the damage evolution scenarios of power network lines and the restoration plans under the power network damage evolution scenarios. Among them, the restoration plan under the power network damage evolution scenario is determined according to the importance of load supply of transmission lines.

[0292] Based on the damage evolution scenarios of power network lines and the restoration plans under the power network damage evolution scenarios, conduct a dynamic resilience assessment of the power network.

[0293] Optionally, the 24-hour precipitation node is a discrete node, and the values include: below 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, above 250.0 mm.

[0294] The normalized difference vegetation index node is a discrete node, and the values include: [-1, -0.35), [-0.35, 0.35), [0.35, 1].

[0295] The proportion of basin area node is a discrete node, and the values include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%.

[0296] The land use type node is a discrete node, and its values include: forest, shrub, construction land, grassland, permanent ice and snow, water body, farmland, and wasteland.

[0297] The impervious surface ratio node is a discrete node, and its values include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%.

[0298] The flood volume node is a discrete node, and its values include: less than 10 million cubic meters, 10 - 30 million cubic meters, 30 - 50 million cubic meters, more than 50 million cubic meters.

[0299] The terrain humidity index node is a discrete node, and its values include: 0 - 10, 10 - 20, 20 - 30.

[0300] Optionally, according to the Bayesian network models of rainstorm disasters, flood disasters, and waterlogging disasters, evaluate the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and waterlogging disasters, including:

[0301] Evaluate the probability of power facilities being damaged under rainstorm disasters . Among them, is the characteristic index value of rainstorm disasters obtained according to the Bayesian network model of rainstorm disasters, is the maximum value of the characteristic index of rainstorm disasters that power facilities can withstand.

[0302] Evaluate the probability of power facilities being damaged under flood disasters . Among them, is the probability of power facilities being damaged corresponding to the peak flood flow under flood disasters, is the probability of power facilities being damaged corresponding to the flood volume under flood disasters, is the weight coefficient of the peak flood flow under flood disasters, , is the peak flood flow index value under flood disasters obtained according to the Bayesian network model of flood disasters, is the maximum value of the peak flood flow index that power facilities can withstand under flood disasters, is the flood volume index value under flood disasters obtained according to the Bayesian network model of flood disasters, is the maximum value of the flood volume index that power facilities can withstand under flood disasters.

[0303] Evaluate the probability of power facilities being damaged under waterlogging disasters . Among them, is the characteristic index value of waterlogging disasters obtained according to the Bayesian network model of waterlogging disasters, The maximum value of the characteristic index of the waterlogging disaster that the power facilities can withstand.

[0304] Optionally, the load supply importance of any transmission line , .

[0305] Among them, is the set of all power generation nodes, substation nodes, transmission nodes, distribution nodes and user nodes in the power network, , , and are node identifiers, is the transmission line composed of nodes and , is the transmission line composed of nodes and . is the load supply importance of the transmission line , is the operating state of the transmission line . When the operating state of the transmission line is normal , when the operating state of the transmission line is damaged , is the operating state of the transmission line . When the operating state of the transmission line is normal , when the operating state of the transmission line is damaged is the set of transmission lines in the power network, is the active power consumed by the node , is the active power demanded by the node .

[0306] Optionally, based on the power network line damage evolution scenario and the recovery plan under the power network damage evolution scenario, perform power network dynamic resilience assessment, including:

[0307] Establish an optimal power flow model.

[0308] Input the power network line damage evolution scenario and the recovery plan under the power network damage evolution scenario into the optimal power flow model to obtain the power network load supply rate evolution scenario.

[0309] Perform power network dynamic resilience assessment based on the power network load supply rate evolution scenario.

[0310] Optionally, the optimal power flow model is: ​。

[0311] such that

[0312] , 。

[0313] , 。

[0314] , 。

[0315] , 。

[0316] , 。

[0317] , 。

[0318] , 。

[0319] , 。

[0320] , 。

[0321] , 。

[0322] , 。

[0323] , 。

[0324] Among them, is the set of all substation nodes and user load nodes in the power network, 、 and are node identifiers, is the transmission line composed of nodes and , is the transmission line composed of nodes and 。 is the set of transmission lines in the power network. is the active power transmitted on the transmission line , is the transmission line The active power transmitted above is the transmission line The reactive power transmitted above is the transmission line The reactive power transmitted above is the transmission line The maximum active power transmission is the transmission line The maximum reactive power transmission is the active power consumed by node is the active power consumed by node is the reactive power consumed by node is the reactive power consumed by node is the active power demanded by node is the reactive power demanded by node is the maximum active power provided by node is the maximum reactive power provided by node is the active power provided by node is the active power provided by node is the reactive power provided by node is the reactive power provided by node is the lower voltage limit of node is the voltage of node is the upper voltage limit of node is the voltage of node is the operating state of transmission line is the power factor of node is the capacitance of transmission line is the reactance of transmission line is any large number

[0325] Optionally, perform a dynamic resilience assessment of the power network based on the evolution scenario of the power network load supply rate, including:

[0326] ​​​​​​​​​​​​​​​​​​​​Determine the load supply rate of the power grid at each moment based on the evolution scenario of the load supply rate of the power grid.

[0327] Evaluate the dynamic resilience of the power grid 。

[0328] Wherein, is the dynamic resilience value of the power grid, is the time identifier, is the load supply rate of the power grid at the th moment, is the load supply rate of the power grid at the 0th moment.

[0329] The computer-readable storage medium provided in this embodiment, on which the computer program is executed by a processor to obtain the evolution scenario of the power grid line damage and the recovery plan under the evolution scenario of the power grid damage according to the spatio-temporal evolution Bayesian network model of rainstorm-flood-internal waterlogging disasters and the probabilities of power facilities being damaged under rainstorm disasters, flood disasters, and internal waterlogging disasters; based on the evolution scenario of the power grid line damage and the recovery plan under the evolution scenario of the power grid damage, conduct the dynamic resilience assessment of the power grid, providing a theoretical support for improving the resilience of the power grid and reducing the impact of disasters.

[0330] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0331] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0332] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the function specified in one or more processes and / or blocks Figure 1 in the flowchart Figure 1 represented by one or more blocks or blocks

[0333] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing steps for implementing the function specified in one or more processes and / or blocks Figure 1 in the flowchart Figure 1 represented by one or more blocks or blocks

[0334] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application

[0335] It is obvious that those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations

Claims

1. A method for evaluating dynamic resilience of a power network, characterized in that: The method comprises: Collect the topology of the power network and the geographical distribution of transmission lines; Based on the topological structure of the power network and the geographical distribution of the transmission lines, the power network is divided into regions; Based on the spatial factors between the divided adjacent regions and the temporal factors within the regions, the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster are spliced ​​to obtain the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters; among them, the Bayesian network model of rainstorm disaster takes the characteristic index of rainstorm disaster as the output node, and the characteristic index of rainstorm disaster is the 24-hour precipitation; the Bayesian network model of flood disaster takes the disaster-prone environmental factors of flood disaster as the input node and the characteristic index of flood disaster as the output node. The disaster-prone environmental factors of flood disasters are 24-hour precipitation, normalized difference vegetation index, proportion of watershed area, land use type, and proportion of impervious surface; the characteristic indicators of flood disasters are peak flow and flood volume; the Bayesian network model of waterlogging disasters takes the disaster-prone environmental factors of waterlogging disasters as input nodes and the characteristic indicators of waterlogging disasters as output nodes, and the disaster-prone environmental factors of waterlogging disasters are 24-hour precipitation, flood volume, land use type, terrain humidity index, normalized difference vegetation index, and proportion of impervious surface; the characteristic indicator of waterlogging disasters is water accumulation depth; According to the Bayesian network model of rainstorm disaster, flood disaster and waterlogging disaster, the probability of damage to power facilities in rainstorm disaster, flood disaster and waterlogging disaster were evaluated respectively; According to the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters and the probability of damage to power facilities under rainstorm disasters, flood disasters, and waterlogging disasters, the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario are obtained; wherein the restoration plan under the power network damage evolution scenario is determined according to the load supply importance of the transmission line; Based on the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario, the dynamic resilience assessment of the power network is carried out; The Bayesian network model for rainstorm disaster, the Bayesian network model for flood disaster and the Bayesian network model for waterlogging disaster are used to evaluate the probability of damage to power facilities under rainstorm disaster, flood disaster and waterlogging disaster, respectively, including: Assess the probability of damage to power facilities during heavy rain disasters ;in, is the characteristic index value of rainstorm disaster obtained according to the Bayesian network model of rainstorm disaster, It is the maximum value of the characteristic index of rainstorm disaster that the power facilities can withstand; Assess the probability of damage to power facilities in flood disasters ;in, is the probability of damage to power facilities corresponding to the peak flow under flood disasters, is the probability of damage to power facilities corresponding to the flood volume under flood disaster, is the weight coefficient of peak flow under flood disaster, ; , is the flood peak flow index value under flood disasters obtained according to the Bayesian network model of flood disasters, It is the maximum value of the peak flow index under flood disasters that the power facilities can withstand. is the flood index value under flood disaster obtained according to the Bayesian network model of flood disaster, It is the maximum value of the flood index under flood disaster that the power facilities can withstand; Assess the probability of damage to power facilities due to waterlogging disasters ;in, is the characteristic index value of waterlogging disaster obtained according to the Bayesian network model of waterlogging disaster, It is the maximum value of the characteristic index of urban flood disaster that the power facilities can withstand.

2. The method according to claim 1, characterized in that: The 24-hour precipitation node is a discrete node, and the values ​​include: below 49.9 mm, 50.0-99.9 mm, 100.0-249.9 mm, and above 250.0 mm; The NDVI node is a discrete node, and its values ​​include: [-1, -0.35), [-0.35, 0.35), [0.35, 1]; The basin area ratio node is a discrete node, and its values ​​include: 0~20%, 20%~40%, 40%~60%, 60%~80%, 80%~100%; The land use type node is a discrete node, and its values ​​include: forest, shrub, construction land, grassland, permanent ice and snow, water body, farmland and wasteland; The impervious surface ratio node is a discrete node, and its values ​​include: 0~20%, 20%~40%, 40%~60%, 60%~80%, 80%~100%; The flood node is a discrete node, and the values ​​include: less than 10 million cubic meters, 10-30 million cubic meters, 30-50 million cubic meters, and more than 50 million cubic meters; The terrain humidity index node is a discrete node with values ​​of 0~10, 10~20, and 20~30.

3. The method according to claim 1, characterized in that: The importance of load supply of any transmission line , ; in, It is the collection of all power generation nodes, substation nodes, transmission nodes, distribution nodes and user nodes in the power network. , , and is the node identifier, For the node and The transmission lines formed, For the node and The transmission lines formed; For transmission lines The importance of load supply, For transmission lines The operating status of the transmission line When the operating status is normal , transmission lines When the operating status is damaged , For transmission lines The operating status of the transmission line When the operating status is normal , transmission lines When the operating status is damaged ; is the set of transmission lines in the power network, For Node Active power consumed, For Node The active power required.

4. The method according to claim 1, characterized in that The power network dynamic resilience assessment based on the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario includes: Establish optimal power flow model; Inputting the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario into the optimal power flow model to obtain the power network load supply rate evolution scenario; The dynamic resilience of the power network is evaluated based on the power network load supply rate evolution scenario.

5. The method according to claim 4, characterized in that The optimal power flow model is: ; st ， ； ， ； ， ； ， ； ， ； ， ； ， ； ， ； ， ； ， ； ， ； ， ； in, is the collection of all substation nodes and user load nodes in the power network, , and is the node identifier, For the node and The transmission lines formed, For the node and The transmission lines formed; It is a collection of transmission lines in the power network; For transmission lines is the active power transmitted, For transmission lines The active power transmitted on For transmission lines The reactive power transmitted on For transmission lines The reactive power transmitted on For transmission lines The maximum active transmission power, For transmission lines Maximum reactive transmission power; For Node Active power consumed, For Node Active power consumed, For Node Reactive power consumed, For Node Reactive power consumed; For Node The active power required, For Node The reactive power required, For Node The maximum active power provided, For Node Maximum reactive power provided; For Node The active power provided, For Node The active power provided, For Node The reactive power provided, For Node Reactive power provided; For Node The voltage lower limit, For Node The voltage, For Node The upper voltage limit, For Node Voltage; For transmission lines The operating status of For Node The power factor, For transmission lines The capacitance, For transmission lines The reactance, Any large number.

6. The method according to claim 4, characterized in that The performing of the power network dynamic resilience assessment based on the power network load supply rate evolution scenario includes: Determining the load supply rate of the power network at each moment based on the load supply rate evolution scenario of the power network; Assessing the dynamic resilience of power networks ; in, is the dynamic resilience value of the power network, To mark the moment, For the power network The load supply rate at the time, is the load supply rate of the power network at time 0.

7. An electronic device, characterized in that: include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and is configured to be executed by the processor to implement the method according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon; the computer program is executed by a processor to implement the method according to any one of claims 1 to 6.

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