Method, device, equipment, and medium for evaluating the resilience of a power system under extreme disasters

Abstract

This application relates to the technical field of power systems, and specifically provides a method, device, equipment, and medium for evaluating the resilience of a power system under extreme disasters. The method includes: collecting the topological structure of the power system; determining the scenario state of the power system during the evolution of extreme disasters based on the topological structure; establishing a network public opinion set according to the corresponding levels of network public opinion; evaluating the power supply performance of the power system at each moment after the occurrence of extreme disasters based on the scenario state; evaluating the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters based on the network public opinion set; and evaluating the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after the occurrence of extreme disasters and the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters, which can improve the stability of the power system and reduce the impact of disasters.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and particularly to a method, device, equipment, and medium for evaluating the resilience of a power system under extreme disasters. Background Art

[0002] Extreme natural disasters, such as typhoons, floods, earthquakes, etc., have a devastating impact on power systems. The occurrence of disasters not only directly damages the physical infrastructure of the power grid, such as transmission lines, substations, etc., resulting in power supply interruptions, but also its impact will quickly spread to the entire power system, triggering a chain reaction. This kind of impact is not only reflected in the reduction of power supply, but also involves significant changes in the operating state of the power grid, such as a decrease in power supply capacity, failures of communication and control systems, etc., posing a serious threat to the stability and reliability of the power grid. As extreme disasters evolve, their impact on the power system will be further exacerbated. Disasters may trigger secondary disasters, such as landslides, debris flows, etc., which will further damage the power grid facilities, making the power restoration work more difficult. At the same time, the complexity and interconnectivity of the power grid enable faults to spread quickly, exacerbating the vulnerability of the power grid. In addition, disasters may also cause the power grid to face risks such as overload and overvoltage, further increasing the possibility of damage and failure of power grid equipment.

[0003] As an indicator characterizing the degree of damage to the power system function after extreme events, the resilience of the power system has become a hot research direction. However, in actual operation scenarios, the evolution scenarios of extreme natural disasters are complex, and it is difficult to evaluate the damage of the power system, making it difficult for the power grid to effectively respond. Existing resilience assessment methods often only consider single-impact scenarios and are difficult to accurately characterize the damage and performance recovery stages of the power system during the evolution of extreme natural disasters. There is an urgent need for a power system resilience assessment framework that can comprehensively consider disaster evolution, emergency response, and external environmental factors, identify weak links in the power system, and characterize the performance level of the power grid under extreme natural disasters. Summary of the Invention

[0004] To solve one of the above technical defects, the present application provides a method, device, equipment, and medium for evaluating the resilience of a power system under extreme disasters.

[0005] In the first aspect of the present application, a method for evaluating the resilience of a power system under extreme disasters is provided. The method includes:

[0006] Collect the topological structure of the power system;

[0007] Based on the topological structure, determine the scenario state of the power system during the evolution of extreme disasters;

[0008] According to the level corresponding to the online public opinion, establish an online public opinion set;

[0009] Evaluate the power supply performance of the power system at each moment after an extreme disaster based on the scenario state;

[0010] Evaluate the intensity of online public opinion of the power system at each moment after an extreme disaster based on the collection of online public opinion;

[0011] Evaluate the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after an extreme disaster and the intensity of online public opinion of the power system at each moment after an extreme disaster.

[0012] In the second aspect of this application, a device for evaluating the resilience of a power system under extreme disasters is provided. The device includes:

[0013] A collection module for collecting the topological structure of the power system;

[0014] A determination module for determining the scenario state of the power system during the evolution of extreme disasters based on the topological structure collected by the collection module;

[0015] An establishment module for establishing a collection of online public opinions according to the corresponding levels of online public opinions;

[0016] A first evaluation module for evaluating the power supply performance of the power system at each moment after an extreme disaster based on the scenario state determined by the determination module;

[0017] A second evaluation module for evaluating the intensity of online public opinion of the power system at each moment after an extreme disaster based on the collection of online public opinions established by the establishment module;

[0018] A third evaluation module for evaluating the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after an extreme disaster evaluated by the first evaluation module and the intensity of online public opinion of the power system at each moment after an extreme disaster evaluated by the second evaluation module.

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

[0020] A memory;

[0021] A processor; and a computer program;

[0022] 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.

[0023] In the fourth aspect of this 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.

[0024] The present application provides a method, apparatus, device, and medium for evaluating the resilience of a power system under extreme disasters. The method includes: collecting the topological structure of the power system; determining the scenario state of the power system during the evolution of extreme disasters based on the topological structure; establishing a network public opinion set according to the corresponding levels of network public opinion; evaluating the power supply performance of the power system at each moment after the occurrence of extreme disasters based on the scenario state; evaluating the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters based on the network public opinion set; and evaluating the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after the occurrence of extreme disasters and the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters, which can improve the stability of the power system and reduce the impact of disasters. Description of the Drawings

[0025] The drawings described herein are used to provide a further understanding of the present application and form 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:

[0026] Figure 1 It is a schematic flowchart of a method for evaluating the resilience of a power system under extreme disasters provided by an embodiment of the present application;

[0027] Figure 2 It is a schematic diagram of a power system provided by an embodiment of the present application;

[0028] Figure 3 It is a schematic diagram of an evolution curve of the comprehensive performance of a power system at each moment after the occurrence of extreme disasters provided by an embodiment of the present application;

[0029] Figure 4 It is a schematic diagram of establishing a dynamic Bayesian network model for emergency disposal of a power system provided by an embodiment of the present application;

[0030] Figure 5 It is a schematic diagram of establishing a dynamic Bayesian network model for emergency disposal of a power system within 10 days provided by an embodiment of the present application;

[0031] Figure 6 It is a schematic diagram of another evolution curve of the comprehensive performance of a power system at each moment after the occurrence of extreme disasters provided by an embodiment of the present application;

[0032] Figure 7 It is a schematic structural diagram of an apparatus for evaluating the resilience of a power system under extreme disasters provided by an embodiment of the present application;

[0033] Figure 8 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0034] In order to make the technical solutions and advantages in the embodiments of the present application clearer and more understandable, the exemplary embodiments of the present application will be further described in detail below 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.

[0035] In the process of implementing the present application, the inventors found that power system resilience, as an indicator characterizing the degree of damage to the power system function after extreme events, has become a hot research direction. However, in actual operation scenarios, the evolution scenarios of extreme natural disasters are complex, and it is difficult to evaluate the damage of the power system, making it difficult for the power grid to effectively respond. Existing resilience assessment methods often only consider single-impact scenarios and are difficult to accurately characterize the damage and performance recovery stages of the power system during the evolution of extreme natural disasters. There is an urgent need for a power system resilience assessment framework that can comprehensively consider disaster evolution, emergency response, and external environmental factors to identify weak links in the power system and characterize the performance level of the power grid under extreme natural disasters.

[0036] In view of the above problems, the embodiments of the present application provide a method, device, equipment, and medium for assessing the resilience of a power system under extreme disasters. The method includes: collecting the topological structure of the power system; determining the scenario state of the power system during the evolution of extreme disasters based on the topological structure; establishing a network public opinion set according to the corresponding levels of network public opinion; evaluating the power supply performance of the power system at each moment after the occurrence of extreme disasters based on the scenario state; evaluating the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters based on the network public opinion set; and evaluating the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after the occurrence of extreme disasters and the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters. The method provided in this embodiment evaluates the power supply performance of the power system at each moment after the occurrence of extreme disasters based on the scenario state, evaluates the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters based on the network public opinion set, and evaluates the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after the occurrence of extreme disasters and the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters, which can improve the stability of the power system and reduce the impact of disasters.

[0037] See Figure 1 , this embodiment provides a method for assessing the resilience of a power system under extreme disasters. The implementation process of this method is as follows:

[0038] 101, collect the topological structure of the power system.

[0039] When performing step 101, in addition to collecting the topological structure of the power system, the geographical location of the power system can also be collected, the connection methods of the power generation nodes, transformation nodes, transmission nodes, distribution nodes, and user nodes are determined, and the environmental information of each node and the location of the transmission line are determined.

[0040] For example, taking the IEEE 39-node standard system as an example of the power system, as Figure 2 shown, it contains a total of 10 power generation nodes, 39 load nodes, and 46 transmission lines.

[0041] 102. Based on the topological structure, determine the scenario state of the power system during the extreme disaster evolution process.

[0042] The scenario state is used to describe the performance level of the power system after suffering from extreme natural disasters, mainly including three aspects: power generation performance, transmission performance, and load demand.

[0043] Therefore, in step 102, the possible performance states of the disaster-bearing bodies (including power nodes, transmission lines, and user loads in the power system) during the emergency process of the power system can be determined, and their combinations are formed into the scenario state.

[0044] Among them, the scenario state includes: the performance state of the power generation node, the performance state of the transmission node, the performance state of the transmission line, and the performance state of the load node.

[0045] The implementation process of step 102 is as follows: The power system is divided into four categories: power generation nodes, transmission nodes, transmission lines, and user loads. The performance levels of each of them during the extreme natural disaster evolution process are classified, and a scenario state set is established. It is specifically implemented through steps 102-1 and 102-2.

[0046] 102-1. Based on the topological structure, determine the power generation nodes, transmission nodes, transmission lines, and load nodes in the power system.

[0047] 1. Power generation nodes

[0048] Power generation nodes include thermal power generation, hydropower generation, wind power generation, photovoltaic power generation, and nuclear power generation.

[0049] 2. Transmission nodes

[0050] Transmission nodes include converter stations, substations, and switching substations.

[0051] 3. Load nodes

[0052] Load nodes include households, commercial facilities, and industrial facilities.

[0053] 102-2, determine the performance status of power generation nodes, transmission nodes, transmission lines, and load nodes.

[0054] 1. Performance status of power generation nodes

[0055] Power generation nodes mainly include thermal power generation, hydropower generation, wind power generation, photovoltaic power generation, and nuclear power generation, etc. The method provided in this embodiment uniformly measures their performance using the power generation amount as an indicator, takes the installed design capacity as a reference value, and divides the performance level of power generation nodes according to the percentage of their actual power supply capacity meeting the installed design capacity, specifically divided into five states: "0~20%", "20%~40%", "40%~60%", "60%~80%", and "80%~100%".

[0056] Therefore, the performance status of power generation nodes includes: 0~20%, 20%~40%, 40%~60%, 60%~80%, 80%~100%.

[0057] The performance status of any power generation node is determined according to the percentage of its actual power supply capacity meeting the installed design capacity.

[0058] 2. Performance status of transmission nodes

[0059] Transmission nodes mainly include converter stations, substations, connection substations, etc., which are responsible for the collection, distribution, conversion, and transmission of electric energy. The method provided in this embodiment uniformly measures the performance of such nodes using the upper limit of transmission power as an indicator, takes the rated power as a reference value, and divides the performance level of transmission nodes according to the percentage of its actual upper limit of transmission power meeting the rated power, specifically divided into five states: "0~20%", "20%~40%", "40%~60%", "60%~80%", and "80%~100%".

[0060] Therefore, the performance status of transmission nodes includes: 0~20%, 20%~40%, 40%~60%, 60%~80%, 80%~100%.

[0061] The performance status of any transmission node is determined according to the percentage of its actual upper limit of transmission power meeting the rated power.

[0062] 3. Performance status of transmission lines

[0063] The method provided in this embodiment divides the performance level of transmission lines using the percentage of damaged lines, specifically divided into five states: "0~20%", "20%~40%", "40%~60%", "60%~80%", and "80%~100%".

[0064] Therefore, the performance states of the power transmission lines include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%.

[0065] The performance state of any power transmission line is determined according to the percentage of damaged lines.

[0066] 4. Performance states of load nodes

[0067] Load nodes mainly include households, commercial facilities, industrial facilities, etc. After a disaster, due to factors such as increased emergency demand, increased basic living needs of residents, and electricity demand for reconstruction and recovery work, the post - disaster load demand may be several times that of the normal scenario demand. The method provided in this embodiment measures the performance of load nodes using the load demand as an indicator. Taking the average load demand in the normal scenario as a reference value, the performance level of load nodes is divided according to the ratio of the actual load demand to the average load demand in the normal scenario, specifically divided into five states: "0 - 33%", "33% - 66%", "66% - 100%", "100% - 150%", and "150% - 200%".

[0068] Among them, the performance states of load nodes include: 0 - 33%, 33% - 66%, 66% - 100%, 100% - 150%, 150% - 200. The performance state of any load node is determined according to the ratio of its actual load demand to the average load demand in the normal scenario.

[0069] In addition, a scenario state set can be formed. For example, a vector containing the performance states of four types of main bodies, namely the performance states of power generation nodes, power transmission nodes, power transmission lines, and load nodes, is used to obtain the scenario state set.

[0070] In addition, when implementing the method provided in this embodiment itself, the disposal measures, disaster characteristics of extreme natural disasters, and environmental factors during the emergency process of the power system will also be determined.

[0071] 1. Disposal measures

[0072] Among them, the disposal measures are determined by the emergency response levels of key departments during the emergency process of the power system. For example, by determining the functional division method of key departments during the emergency process of the power system, the disposal measures are obtained.

[0073] Key departments such as: power grid dispatching group, production recovery group, power consumption marketing group, security and protection group, information release group, and material support group.

[0074] The emergency response level of any key department is one of the following: Level I, Level II, Level III, and Level IV.

[0075] In addition, during specific implementation, a set of disposal measures can also be constructed based on the disposal measures. For example, according to the composition of the emergency organization structure and management system of the power system, the departments participating in the emergency activities are divided into the power grid dispatching group, the production recovery group, the power consumption and marketing group, the security and protection group, the information release group, and the material support group. The emergency response levels of each group all include four levels: Level I (extraordinarily serious), Level II (major), Level III (relatively large), and Level IV (general). The emergency response levels of the five groups constitute the set of disposal measures, that is, a five-dimensional vector containing the response levels of different departments.

[0076] 2. Disaster Characteristics of Extreme Natural Disasters

[0077] When the extreme natural disaster is an earthquake, the disaster characteristic is the intensity, and its grading is: 0 (not occurring), Degrees I - IV, Degrees V - VI, Degrees VII - VIII, Degrees IX - X, Degrees XI - XII.

[0078] When the extreme natural disaster is a landslide, the disaster characteristic is the landslide volume, and its grading is: 0 (not occurring), less than 100,000 cubic meters (small landslide), 100,000 - 1,000,000 cubic meters (medium landslide), 1,000,000 - 10,000,000 cubic meters (large landslide), more than 10,000,000 cubic meters (giant landslide).

[0079] When the extreme natural disaster is a rainstorm, the disaster characteristic is the precipitation, and its grading is: less than 49.9 mm (non-rainstorm), 50.0 - 99.9 mm (rainstorm), 100.0 - 249.9 mm (heavy rainstorm), more than 250.0 mm (extra heavy rainstorm).

[0080] When the extreme natural disaster is a flood, the disaster characteristic is the peak flow rate, and its grading is: 0 (not occurring), less than 500 cubic meters per second, 500 - 1000 cubic meters per second, 1000 - 1500 cubic meters per second, more than 1500 cubic meters per second.

[0081] When the extreme natural disaster is an urban waterlogging, the disaster characteristic is the waterlogging depth, and its grading is: less than 15 cm, 15 - 27 cm, 27 - 40 cm, 40 - 60 cm, more than 60 cm.

[0082] For example, according to the disaster type, determine the disaster characteristic elements and the corresponding grading methods to obtain the disaster characteristics shown in Table 1. For disaster types not included in the table, corresponding characteristic indicators can be considered. When the disaster types included in the model are , the natural disaster set is a -dimensional vector containing the levels of various disaster characteristic elements.

[0083] Table 1

[0084]

[0085] 3. Environmental Factors

[0086] Environmental factors, including: altitude, geological structure, 24-hour precipitation, air humidity (relative humidity), air temperature (weighted average air temperature).

[0087] Among them, the altitude is classified as: below 500 meters, 500 - 1000 meters, 1000 - 1500 meters, 1500 - 2000 meters, above 2000 meters.

[0088] The geological structure is classified as: monoclinic structure, fold structure, fault structure, block structure.

[0089] The 24-hour precipitation is classified as: below 0.1 mm (trace rainfall), 0.1 - 9.9 mm (light rain), 10.0 - 24.9 mm (moderate rain), 25.0 - 49.9 mm (heavy rain), 50.0 - 99.9 mm (rainstorm), 100.0 - 249.9 mm (heavy rainstorm), above 250.0 mm (extra heavy rainstorm).

[0090] The air humidity is classified as: 0 - 40%, 40% - 60%, 60% - 80%, above 80%.

[0091] The air temperature is classified as: below 25 °C, 25 - 30 °C, 30 - 35 °C, 35 - 40 °C, above 40 °C.

[0092] For example, according to the type of disaster, determine the environmental factors related to the gestation and development of the disaster, and obtain the environmental factors shown in Table 2. For the environmental factors not included in the table, corresponding classification criteria can be considered. When the types of environmental factors included in the model are , the natural disaster set is the dimensional vector containing the levels of various environmental factors.

[0093] Table 2

[0094]

[0095] In addition, in specific implementation, the natural disaster set and the external environment set can also be constructed based on the disaster characteristics of extreme natural disasters and environmental factors.

[0096] 103. Establish a network public opinion set according to the level corresponding to the network public opinion.

[0097] The network public opinion set is a one-dimensional vector corresponding to the level of the actual network public opinion.

[0098] The network public opinion set includes: extremely major Internet public opinion events (Level IV), major Internet public opinion events (Level III), relatively large Internet public opinion events (Level II), general Internet public opinion events (Level I), no negative public opinion.

[0099] For example, the online public opinion is divided into four levels according to the propagation speed and influence range, and the specific meanings are shown in Table 3.

[0100] Table 3

[0101]

[0102] 104. Based on the scenario state, evaluate the power supply performance of the power system at each moment after the occurrence of an extreme disaster.

[0103] The power supply performance of the power system at each moment after the occurrence of an extreme disaster is determined according to the value of the scenario state set at that moment, that is, determined according to the actual states of the power generation nodes, transmission nodes, transmission lines, and load nodes.

[0104] Since the scenario state includes: the performance state of the power generation nodes, the performance state of the transmission nodes, the performance state of the transmission lines, and the performance state of the load nodes. Therefore, step 104 will evaluate the power supply performance of the power system at any moment after the occurrence of an extreme disaster through the following formula:

[0105] .

[0106] Among them, is the time identifier, is the power supply performance of the power system at time after the occurrence of an extreme disaster, and the value range is [0, 1].

[0107] is the supply-demand ratio, that is, the ratio of the upper limit of the power system load supply to the load demand, , is the performance state of the power generation nodes of the power system at time after the occurrence of an extreme disaster, is the performance state of the transmission nodes of the power system at time after the occurrence of an extreme disaster, is the performance state of the transmission lines of the transmission nodes of the power system at time after the occurrence of an extreme disaster, is the performance state of the load nodes of the power system at time after the occurrence of an extreme disaster.

[0108] is the supply-demand factor, generally not less than 1. , is the upper limit of the load supply when the power system operates normally, is the average load demand when the power system operates normally.

[0109] 105. Based on the collection of online public opinions, evaluate the intensity of online public opinions at each moment after an extreme disaster occurs in the power system.

[0110] The intensity of online public opinions is obtained from the collection of online public opinions. Among them, no negative public opinion, level I, level II, level III, and level IV correspond to 1, 0.75, 0.5, 0.25, and 0 respectively, which are used to characterize the impact of online public opinions in the actual scenario, that is, the smaller the public opinion, the higher the network performance.

[0111] 106. Based on the power supply performance at each moment after an extreme disaster occurs in the power system and the intensity of online public opinions at each moment after an extreme disaster occurs in the power system, evaluate the resilience of the power system under extreme disasters.

[0112] When specifically implemented, the resilience of the power system under extreme disasters can be evaluated through the following formula:

[0113] .

[0114] Among them, is the resilience of the power system under extreme disasters, is the occurrence moment of the extreme disaster, is the total duration of the power system resilience evaluation, is the initial comprehensive performance of the power system, which is 1.

[0115] is the comprehensive performance of the power system at the moment after the extreme disaster occurs, which is jointly characterized by the power supply performance and the intensity of online public opinions at the moment after the extreme disaster occurs, and the value range is [0, 1]. , is the power supply performance of the power system at the moment after the extreme disaster occurs, is the intensity of online public opinions of the power system at the moment after the extreme disaster occurs, is the weight of the power supply performance, is the weight of the intensity of online public opinions, .

[0116] For example, Figure 3 shows the evolution curve of the comprehensive performance of the power system at each moment after the extreme disaster occurs, where the area of the shaded part corresponds to the resilience of the power system under the extreme disaster.

[0117] If the disposal measures in the emergency process of the power system, the disaster characteristics of extreme natural disasters, and environmental factors are also determined during specific implementation, then step 106 can also evaluate the resilience of the power system under extreme disasters based on the disposal measures in the emergency process of the power system, the disaster characteristics of extreme natural disasters, environmental factors, the power supply performance of the power system at each moment after the occurrence of extreme disasters, and the intensity of online public opinion of the power system at each moment after the occurrence of extreme disasters.

[0118] When evaluating the resilience of the power system under extreme disasters based on the disposal measures in the emergency process of the power system, the disaster characteristics of extreme natural disasters, environmental factors, the power supply performance of the power system at each moment after the occurrence of extreme disasters, and the intensity of online public opinion of the power system at each moment after the occurrence of extreme disasters, it can be achieved through the following steps:

[0119] 1. Couple the scenario state, disposal measures, natural disasters, the disaster characteristics of extreme natural disasters, and environmental factors to establish a dynamic Bayesian network model for power system emergency disposal.

[0120] For example, couple the scenario state, disposal measures, natural disasters, external environment, and online public opinion set. In the actual scenario, the environmental factors affect natural disasters at the current moment, the disaster characteristics affect online public opinion and the power grid scenario state, and the emergency management department formulates emergency plans for each department according to the scenario state and online public opinion. After the implementation of the emergency plan, the scenario state and online public opinion at the next moment are updated according to the current scenario state and online public opinion state. At the same time, the environmental factors and natural disasters will be updated over time, thus establishing a dynamic Bayesian network model for power system emergency disposal, as Figure 4 shown.

[0121] For example, taking 24 hours as a unit, establish a dynamic Bayesian network model for power system emergency disposal within 10 days, as Figure 5 shown.

[0122] 2. Evaluate the resilience of the power system under extreme disasters based on the dynamic Bayesian network model for power system emergency disposal, the power supply performance of the power system at each moment after the occurrence of extreme disasters, and the intensity of online public opinion of the power system at each moment after the occurrence of extreme disasters.

[0123] The specific implementation process here is as follows:

[0124] (1) Determine the initial states of the power system, natural disasters, external environment, and online public opinion.

[0125] For example, when determining the initial state of the power system, the power generation nodes, load nodes, and transmission lines are all in normal operating states.

[0126] (2)Based on the dynamic Bayesian network model for power system emergency disposal, the power supply performance of the power system at each moment after an extreme disaster occurs, the intensity of online public opinion of the power system at each moment after an extreme disaster occurs, the initial states of the power system, natural disasters, external environment, and online public opinion, an inference algorithm is used to obtain the evolution of the power system and online public opinion, and the resilience index is calculated.

[0127] For example, based on the power supply performance of the power system at each moment after an extreme disaster occurs, the intensity of online public opinion of the power system at each moment after an extreme disaster occurs, the power system, natural disasters, external environment, and the initial state of online public opinion, the data shown in Table 4 are obtained. Among them, the values "1 - 7" in "Precipitation" respectively represent the corresponding state intervals in the Bayesian network corresponding to the actual data, corresponding to below 0.1 mm, 0.1 - 9.9 mm, 10.0 - 24.9 mm, 25.0 - 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, and above 250.0 mm. The numerical sequence represents the evolution of the precipitation in this area within the next 10 days. Other environmental factors such as the normalized difference vegetation index and basin area are fixed values and do not change with time.

[0128] Table 4

[0129]

[0130] The above data shown in Table 4 are input into the dynamic Bayesian network model for power system emergency disposal, and the junction tree inference algorithm is used to obtain the scenario state and the evolution of online public opinion, and draw Figure 6 the evolution curve of the comprehensive performance of the power system shown, and the resilience index is calculated to be 89.6% by the formula

[0131] The method for evaluating the resilience of the power system under extreme disasters provided in this embodiment is a resilience evaluation method that can characterize the evolution mode of extreme natural disasters, the implementation of emergency disposal plans, the evolution scenarios of damage and power supply levels, thereby characterizing the power supply performance of the power system throughout the disaster cycle. It can help operators carry out the work of evaluating the resilience of the power system under the evolution of extreme natural disasters, and provides theoretical support for improving the stability of the power system and reducing the impact of disasters.

[0132] This embodiment provides a method for evaluating the resilience of a power system under extreme disasters. The method includes: collecting the topological structure of the power system; determining the scenario state of the power system during the evolution of extreme disasters based on the topological structure; establishing a network public opinion set according to the corresponding levels of network public opinion; evaluating the power supply performance of the power system at each moment after the occurrence of extreme disasters based on the scenario state; evaluating the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters based on the network public opinion set; and evaluating the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after the occurrence of extreme disasters and the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters. The method provided in this embodiment evaluates the power supply performance of the power system at each moment after the occurrence of extreme disasters based on the scenario state, evaluates the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters based on the network public opinion set, and evaluates the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after the occurrence of extreme disasters and the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters, which can improve the stability of the power system and reduce the impact of disasters.

[0133] Based on the same inventive concept of the method for evaluating the resilience of a power system under extreme disasters, this embodiment provides an electronic device. Refer to Figure 7 , the device includes:

[0134] A collection module 701, configured to collect the topological structure of the power system.

[0135] A determination module 702, configured to determine the scenario state of the power system during the evolution of extreme disasters based on the topological structure collected by the collection module 701.

[0136] An establishment module 703, configured to establish a network public opinion set according to the corresponding levels of network public opinion.

[0137] A first evaluation module 704, configured to evaluate the power supply performance of the power system at each moment after the occurrence of extreme disasters based on the scenario state determined by the determination module 702.

[0138] A second evaluation module 705, configured to evaluate the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters based on the network public opinion set established by the establishment module 703.

[0139] A third evaluation module 706, configured to evaluate the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after the occurrence of extreme disasters evaluated by the first evaluation module 704 and the intensity of network public opinion of the power system at each moment after the occurrence of extreme disasters evaluated by the second evaluation module 705.

[0140] Wherein, the scenario state includes: the performance state of the power generation node, the performance state of the power transmission node, the performance state of the power transmission line, and the performance state of the load node.

[0141] Based on the topological structure, determine the scenario states of the power system during the evolution process of extreme disasters, including:

[0142] Determine the power generation nodes, transmission nodes, transmission lines, and load nodes in the power system based on the topological structure. Among them, the power generation nodes include thermal power generation, hydropower generation, wind power generation, photovoltaic power generation, and nuclear power generation. The transmission nodes include converter stations, substations, and connection substations. The load nodes include households, commercial facilities, and industrial facilities.

[0143] Determine the performance states of the power generation nodes, transmission nodes, transmission lines, and load nodes. Among them, the performance states of the power generation nodes include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%. The performance state of any power generation node is determined according to the percentage of its actual power supply capacity meeting the installed capacity design. The performance states of the transmission nodes include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%. The performance state of any transmission node is determined according to the percentage of its actual transmission power upper limit meeting the rated power. The performance states of the transmission lines include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%. The performance state of any transmission line is determined according to the damaged line percentage. The performance states of the load nodes include: 0 - 33%, 33% - 66%, 66% - 100%, 100% - 150%, 150% - 200. The performance state of any load node is determined according to the ratio of its actual load demand to the average load demand under normal scenarios.

[0144] Among them, the scenario states include: the performance states of the power generation nodes, transmission nodes, transmission lines, and load nodes.

[0145] Based on the scenario states, evaluate the power supply performance of the power system at each moment after the occurrence of extreme disasters, including:

[0146] Evaluate the power supply performance of the power system at any moment after the occurrence of extreme disasters through the following formula:

[0147] .

[0148] Among them, is the time identifier, is the power supply performance of the power system at moment after the occurrence of extreme disasters, is the supply - demand ratio. , is the power supply performance of the power system at The performance state of the power generation node at a certain moment is the performance state of the transmission node of the power system after an extreme disaster at a certain moment is the performance state of the transmission line of the transmission node of the power system after an extreme disaster at a certain moment is the performance state of the load node of the power system after an extreme disaster at a certain moment is the supply-demand factor , is the upper limit of load supply when the power system is operating normally is the average load demand when the power system is operating normally

[0149] Among them, the third evaluation module 706 is used to evaluate the resilience of the power system under extreme disasters through the following formula

[0150] .

[0151] Among them is the resilience of the power system under extreme disasters is the occurrence time of the extreme disaster is the total duration of the power system resilience evaluation is the initial comprehensive performance of the power system is the comprehensive performance of the power system after the extreme disaster occurs at a certain moment , is the power supply performance of the power system after the extreme disaster occurs at a certain moment is the intensity of online public opinion of the power system after the extreme disaster occurs at a certain moment is the weight of the power supply performance is the weight of the intensity of online public opinion .

[0152] Among them, the online public opinion set includes: extremely major Internet public opinion events, major Internet public opinion events, relatively large Internet public opinion events, general Internet public opinion events, and no negative public opinion

[0153] Among them, the device further includes: a processing module, which is used to determine the disposal measures, disaster characteristics of extreme natural disasters, and environmental factors during the emergency process of the power system. Among them, the disposal measures are determined by the emergency response degree of key departments during the emergency process of the power system

[0154] Among them, the third evaluation module 706 is used to evaluate the resilience of the power system under extreme disasters based on the disposal measures during the emergency process of the power system, the disaster characteristics and environmental factors of extreme natural disasters, the power supply performance of the power system at each moment after the occurrence of extreme disasters, and the intensity of online public opinion of the power system at each moment after the occurrence of extreme disasters.

[0155] Among them, the key departments include: the power grid dispatching group, the production recovery group, the power consumption marketing group, the security and protection group, the information release group, and the material support group. The emergency response level of any key department is one of the following: Level I, Level II, Level III, and Level IV.

[0156] When the extreme natural disaster is an earthquake, the disaster characteristic is the intensity, and its classification is: 0, degrees I - IV, degrees V - VI, degrees VII - VIII, degrees IX - X, degrees XI - XII.

[0157] When the extreme natural disaster is a landslide, the disaster characteristic is the landslide volume, and its classification is: 0, less than 100,000 cubic meters, 100,000 - 1,000,000 cubic meters, 1,000,000 - 10,000,000 cubic meters, more than 10,000,000 cubic meters.

[0158] When the extreme natural disaster is heavy rain, the disaster characteristic is the precipitation, and its classification is: less than 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, more than 250.0 mm.

[0159] When the extreme natural disaster is a flood, the disaster characteristic is the peak flood discharge, and its classification is: 0, less than 500 cubic meters per second, 500 - 1,000 cubic meters per second, 1,000 - 1,500 cubic meters per second, more than 1,500 cubic meters per second.

[0160] When the extreme natural disaster is waterlogging, the disaster characteristic is the waterlogging depth, and its classification is: less than 15 cm, 15 - 27 cm, 27 - 40 cm, 40 - 60 cm, more than 60 cm.

[0161] The environmental factors include: altitude, geological structure, 24 - hour precipitation, air humidity, and temperature.

[0162] Among them, the altitude classification is: less than 500 m, 500 - 1,000 m, 1,000 - 1,500 m, 1,500 - 2,000 m, more than 2,000 m.

[0163] The geological structure classification is: monoclinic structure, fold structure, fault structure, block structure.

[0164] The 24 - hour precipitation classification is: less than 0.1 mm, 0.1 - 9.9 mm, 10.0 - 24.9 mm, 25.0 - 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, more than 250.0 mm.

[0165] The air humidity is classified as: 0 - 40%, 40% - 60%, 60% - 80%, above 80%.

[0166] The air temperature is classified as: below 25 degrees Celsius, 25 - 30 degrees Celsius, 30 - 35 degrees Celsius, 35 - 40 degrees Celsius, above 40 degrees Celsius.

[0167] The device provided in this embodiment evaluates the power supply performance of the power system at each moment after an extreme disaster based on the scenario state; evaluates the intensity of online public opinion of the power system at each moment after an extreme disaster based on the online public opinion set; and evaluates the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after an extreme disaster and the intensity of online public opinion of the power system at each moment after an extreme disaster, which can improve the stability of the power system and reduce the impact of disasters.

[0168] Based on the same inventive concept of the method for evaluating the resilience of the power system under extreme disasters, this embodiment provides an electronic device, which is as shown in Figure 8 and includes: a memory 801, a processor 802, and a computer program.

[0169] Among them, the computer program is stored in the memory 801 and is configured to be executed by the processor 802 to implement the above - mentioned method for evaluating the resilience of the power system under extreme disasters.

[0170] Specifically,

[0171] Collect the topological structure of the power system.

[0172] Based on the topological structure, determine the scenario state of the power system during the evolution of extreme disasters.

[0173] Establish an online public opinion set according to the corresponding levels of online public opinion.

[0174] Based on the scenario state, evaluate the power supply performance of the power system at each moment after an extreme disaster.

[0175] Based on the online public opinion set, evaluate the intensity of online public opinion of the power system at each moment after an extreme disaster.

[0176] Based on the power supply performance of the power system at each moment after an extreme disaster and the intensity of online public opinion of the power system at each moment after an extreme disaster, evaluate the resilience of the power system under extreme disasters.

[0177] Optionally, the scenario state includes: the performance state of power generation nodes, the performance state of transmission nodes, the performance state of transmission lines, and the performance state of load nodes.

[0178] Based on the topological structure, determine the scenario states of the power system during the evolution process of extreme disasters, including:

[0179] Based on the topological structure, determine the power generation nodes, transmission nodes, transmission lines, and load nodes in the power system. Among them, the power generation nodes include thermal power generation, hydropower generation, wind power generation, photovoltaic power generation, and nuclear power generation. The transmission nodes include converter stations, substations, and switching substations. The load nodes include households, commercial facilities, and industrial facilities.

[0180] Determine the performance states of the power generation nodes, transmission nodes, transmission lines, and load nodes. Among them, the performance states of the power generation nodes include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%. The performance state of any power generation node is determined according to the percentage of its actual power supply capacity meeting the installed capacity design. The performance states of the transmission nodes include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%. The performance state of any transmission node is determined according to the percentage of its actual transmission power upper limit meeting the rated power. The performance states of the transmission lines include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%. The performance state of any transmission line is determined according to the damaged line percentage. The performance states of the load nodes include: 0 - 33%, 33% - 66%, 66% - 100%, 100% - 150%, 150% - 200. The performance state of any load node is determined according to the ratio of its actual load demand to the average load demand under normal scenarios.

[0181] Optionally, the scenario states include: the performance states of the power generation nodes, transmission nodes, transmission lines, and load nodes.

[0182] Based on the scenario states, evaluate the power supply performance of the power system at each moment after the occurrence of extreme disasters, including:

[0183] Evaluate the power supply performance of the power system at any moment after the occurrence of extreme disasters through the following formula:

[0184] .

[0185] Among them, is the time identifier, is the power supply performance of the power system at moment after the occurrence of extreme disasters, is the supply - demand ratio. , is the performance state of the power generation nodes at moment after the occurrence of extreme disasters, For the performance status of transmission nodes at the moment after an extreme disaster occurs in the power system For the performance status of transmission lines of transmission nodes at the moment after an extreme disaster occurs in the power system For the performance status of load nodes at the moment after an extreme disaster occurs in the power system For the performance status of transmission lines of transmission nodes at the moment after an extreme disaster occurs in the power system For the performance status of load nodes at the moment after an extreme disaster occurs in the power system For the performance status of load nodes at the moment after an extreme disaster occurs in the power system Is the supply - demand factor. , Is the upper limit of load supply when the power system is operating normally Is the average load demand when the power system is operating normally

[0186] Optionally, based on the power supply performance of the power system at each moment after an extreme disaster occurs and the intensity of online public opinion of the power system at each moment after an extreme disaster occurs, evaluate the resilience of the power system under extreme disasters, including:

[0187] Evaluate the resilience of the power system under extreme disasters through the following formula:

[0188] .

[0189] Among them, Is the resilience of the power system under extreme disasters Is the occurrence time of the extreme disaster Is the total duration of power system resilience evaluation Is the initial comprehensive performance of the power system For the power system after an extreme disaster occurs At the moment of the comprehensive performance , For the power system after an extreme disaster occurs At the moment of the power supply performance For the power system after an extreme disaster occurs At the moment of the intensity of online public opinion Is the power supply performance weight Is the intensity weight of online public opinion .

[0190] Optionally, the set of online public opinions includes: extremely major Internet public opinion events, major Internet public opinion events, relatively large Internet public opinion events, general Internet public opinion events, and no negative public opinions.

[0191] Optionally, the method further includes:

[0192] Determine the disposal measures, disaster characteristics of extreme natural disasters, and environmental factors during the emergency process of the power system. Among them, the disposal measures are determined by the emergency response degree of key departments during the emergency process of the power system.

[0193] Based on the power supply performance of the power system at each moment after an extreme disaster occurs and the intensity of online public opinion of the power system at each moment after an extreme disaster occurs, evaluate the resilience of the power system under extreme disasters, including:

[0194] Based on the disposal measures in the emergency process of the power system, the disaster characteristics and environmental factors of extreme natural disasters, the power supply performance of the power system at each moment after an extreme disaster occurs, and the intensity of online public opinion of the power system at each moment after an extreme disaster occurs, evaluate the resilience of the power system under extreme disasters.

[0195] Optionally, the key departments include: the power grid dispatching group, the production restoration group, the power consumption marketing group, the security and protection group, the information release group, and the material support group. The emergency response level of any key department is one of the following: Level I, Level II, Level III, and Level IV.

[0196] When the extreme natural disaster is an earthquake, the disaster characteristic is the intensity, and its classification is: 0, Degree I - IV, Degree V - VI, Degree VII - VIII, Degree IX - X, Degree XI - XII.

[0197] When the extreme natural disaster is a landslide, the disaster characteristic is the landslide volume, and its classification is: 0, less than 100,000 cubic meters, 100,000 - 1,000,000 cubic meters, 1,000,000 - 10,000,000 cubic meters, more than 10,000,000 cubic meters.

[0198] When the extreme natural disaster is a heavy rain, the disaster characteristic is the precipitation, and its classification is: less than 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, more than 250.0 mm.

[0199] When the extreme natural disaster is a flood, the disaster characteristic is the peak flow rate, and its classification is: 0, less than 500 cubic meters per second, 500 - 1,000 cubic meters per second, 1,000 - 1,500 cubic meters per second, more than 1,500 cubic meters per second.

[0200] When the extreme natural disaster is an urban waterlogging, the disaster characteristic is the waterlogging depth, and its classification is: less than 15 cm, 15 - 27 cm, 27 - 40 cm, 40 - 60 cm, more than 60 cm.

[0201] The environmental factors include: altitude, geological structure, 24-hour precipitation, air humidity, and temperature.

[0202] Among them, the altitude classification is: below 500 meters, 500 - 1,000 meters, 1,000 - 1,500 meters, 1,500 - 2,000 meters, above 2,000 meters.

[0203] The geological structure classification is: monoclinic structure, fold structure, fault structure, block structure.

[0204] The 24-hour precipitation levels are classified as: below 0.1 mm, 0.1 - 9.9 mm, 10.0 - 24.9 mm, 25.0 - 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, and above 250.0 mm.

[0205] The air humidity levels are classified as: 0 - 40%, 40% - 60%, 60% - 80%, and above 80%.

[0206] The temperature levels are classified as: below 25 °C, 25 - 30 °C, 30 - 35 °C, 35 - 40 °C, and above 40 °C.

[0207] The electronic device provided in this embodiment, when the computer program thereon is executed by a processor, is configured to evaluate the power supply performance of the power system at each moment after an extreme disaster based on the scenario state; evaluate the intensity of online public opinion of the power system at each moment after an extreme disaster based on the online public opinion set; and evaluate the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after an extreme disaster and the intensity of online public opinion of the power system at each moment after an extreme disaster, which can improve the stability of the power system and reduce the impact of disasters.

[0208] Based on the same inventive concept of the method for evaluating the resilience of a power system under extreme disasters, this embodiment provides a computer-readable storage medium, on which a computer program is stored. The computer program is executed by a processor to implement the above-mentioned method for evaluating the resilience of a power system under extreme disasters.

[0209] Specifically,

[0210] Collect the topological structure of the power system.

[0211] Based on the topological structure, determine the scenario state of the power system during the evolution of extreme disasters.

[0212] According to the levels corresponding to the online public opinion, establish an online public opinion set.

[0213] Based on the scenario state, evaluate the power supply performance of the power system at each moment after an extreme disaster.

[0214] Based on the online public opinion set, evaluate the intensity of online public opinion of the power system at each moment after an extreme disaster.

[0215] Based on the power supply performance of the power system at each moment after an extreme disaster and the intensity of online public opinion of the power system at each moment after an extreme disaster, evaluate the resilience of the power system under extreme disasters.

[0216] Optionally, the scenario state includes: the performance state of power generation nodes, the performance state of power transmission nodes, the performance state of power transmission lines, and the performance state of load nodes.

[0217] Based on the topological structure, determine the scenario states of the power system during the evolution process of extreme disasters, including:

[0218] Based on the topological structure, determine the power generation nodes, transmission nodes, transmission lines, and load nodes in the power system. Among them, the power generation nodes include thermal power generation, hydropower generation, wind power generation, photovoltaic power generation, and nuclear power generation. The transmission nodes include converter stations, substations, and switching substations. The load nodes include households, commercial facilities, and industrial facilities.

[0219] Determine the performance states of the power generation nodes, transmission nodes, transmission lines, and load nodes. Among them, the performance states of the power generation nodes include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%. The performance state of any power generation node is determined according to the percentage of its actual power supply capacity meeting the installed capacity design. The performance states of the transmission nodes include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%. The performance state of any transmission node is determined according to the percentage of its actual transmission power upper limit meeting the rated power. The performance states of the transmission lines include: 0 - 20%, 20% - 40%, 40% - 60%, 60% - 80%, 80% - 100%. The performance state of any transmission line is determined according to the damaged line percentage. The performance states of the load nodes include: 0 - 33%, 33% - 66%, 66% - 100%, 100% - 150%, 150% - 200%. The performance state of any load node is determined according to the ratio of its actual load demand to the average load demand in the normal scenario.

[0220] Optionally, the scenario states include: the performance states of the power generation nodes, transmission nodes, transmission lines, and load nodes.

[0221] Based on the scenario states, evaluate the power supply performance of the power system at each moment after the occurrence of extreme disasters, including:

[0222] Evaluate the power supply performance of the power system at any moment after the occurrence of extreme disasters through the following formula:

[0223] .

[0224] Among them, is the time identifier, is the power supply performance of the power system at moment after the occurrence of extreme disasters, is the supply - demand ratio. , is the performance state of the power generation nodes at moment after the occurrence of extreme disasters, For the performance state of the transmission nodes at the moment after an extreme disaster occurs in the power system For the performance state of the transmission lines of the transmission nodes at the moment after an extreme disaster occurs in the power system For the performance state of the load nodes at the moment after an extreme disaster occurs in the power system is the supply-demand factor. , is the upper limit of load supply when the power system operates normally, is the average load demand when the power system operates normally.

[0225] Optionally, based on the power supply performance of the power system at each moment after an extreme disaster occurs and the intensity of online public opinion of the power system at each moment after an extreme disaster occurs, evaluate the resilience of the power system under extreme disasters, including:

[0226] Evaluate the resilience of the power system under extreme disasters through the following formula:

[0227] .

[0228] Among them, is the resilience of the power system under extreme disasters, is the occurrence time of the extreme disaster, is the total duration of the power system resilience evaluation, is the initial comprehensive performance of the power system, is the power system at the comprehensive performance at the moment after an extreme disaster occurs. , is the power supply performance of the power system at the moment after an extreme disaster occurs, is the intensity of online public opinion of the power system at the moment after an extreme disaster occurs, is the power supply performance weight, is the intensity weight of online public opinion, .

[0229] Optionally, the online public opinion set includes: extremely major Internet public opinion events, major Internet public opinion events, relatively large Internet public opinion events, general Internet public opinion events, and no negative public opinion.

[0230] Optionally, the method further includes:

[0231] Determine the disposal measures, disaster characteristics of extreme natural disasters, and environmental factors during the emergency process of the power system. Among them, the disposal measures are determined by the emergency response degree of key departments during the emergency process of the power system.​​​

[0232] Based on the power supply performance of the power system at each moment after an extreme disaster and the intensity of online public opinion of the power system at each moment after an extreme disaster, evaluate the resilience of the power system under extreme disasters, including:

[0233] Based on the disposal measures in the emergency process of the power system, the disaster characteristics of extreme natural disasters and environmental factors, the power supply performance of the power system at each moment after an extreme disaster, and the intensity of online public opinion of the power system at each moment after an extreme disaster, evaluate the resilience of the power system under extreme disasters.

[0234] Optionally, the key departments include: the power grid dispatching group, the production recovery group, the power consumption marketing group, the security and protection group, the information release group, and the material support group. The emergency response level of any key department is one of the following: Level I, Level II, Level III, and Level IV.

[0235] When the extreme natural disaster is an earthquake, the disaster characteristic is the intensity, and its classification is: 0, Degree I - IV, Degree V - VI, Degree VII - VIII, Degree IX - X, Degree XI - XII.

[0236] When the extreme natural disaster is a landslide, the disaster characteristic is the landslide volume, and its classification is: 0, less than 100,000 cubic meters, 100,000 - 1,000,000 cubic meters, 1,000,000 - 10,000,000 cubic meters, more than 10,000,000 cubic meters.

[0237] When the extreme natural disaster is heavy rain, the disaster characteristic is the precipitation, and its classification is: less than 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, more than 250.0 mm.

[0238] When the extreme natural disaster is a flood, the disaster characteristic is the peak flow rate, and its classification is: 0, less than 500 cubic meters per second, 500 - 1,000 cubic meters per second, 1,000 - 1,500 cubic meters per second, more than 1,500 cubic meters per second.

[0239] When the extreme natural disaster is waterlogging, the disaster characteristic is the waterlogging depth, and its classification is: less than 15 cm, 15 - 27 cm, 27 - 40 cm, 40 - 60 cm, more than 60 cm.

[0240] Environmental factors include: altitude, geological structure, 24-hour precipitation, air humidity, temperature.

[0241] Among them, the altitude classification is: below 500 m, 500 - 1,000 m, 1,000 - 1,500 m, 1,500 - 2,000 m, above 2,000 m.

[0242] The geological structure classification is: monoclinic structure, fold structure, fault structure, block structure.

[0243] The 24-hour precipitation levels are classified as: below 0.1 mm, 0.1 - 9.9 mm, 10.0 - 24.9 mm, 25.0 - 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, and above 250.0 mm.

[0244] The air humidity levels are classified as: 0 - 40%, 40% - 60%, 60% - 80%, and above 80%.

[0245] The temperature levels are classified as: below 25 °C, 25 - 30 °C, 30 - 35 °C, 35 - 40 °C, and above 40 °C.

[0246] The computer-readable storage medium provided in this embodiment, when the computer program thereon is executed by a processor, is used to evaluate the power supply performance of the power system at each moment after an extreme disaster based on the scenario state; evaluate the intensity of online public opinion of the power system at each moment after an extreme disaster based on the collection of online public opinion; and evaluate the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after an extreme disaster and the intensity of online public opinion of the power system at each moment after an extreme disaster, which can improve the stability of the power system and reduce the impact of disasters.

[0247] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take 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.

[0248] 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 flow and / or block in the flowchart and / or block diagram, and the combination of flows 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, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0249] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in one or more of the flows Figure 1 and / or boxes Figure 1 specified in one or more of the boxes.

[0250] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in one or more of the flows Figure 1 and / or boxes Figure 1 specified in one or more of the boxes.

[0251] 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 cover the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0252] Obviously, 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 the resilience of a power system under extreme disasters, characterized in that: The method comprises: Collect the topology of the power system; Based on the topological structure, determining the scenario state of the power system during the evolution of extreme disasters; Establish a collection of online public opinions according to the corresponding levels of online public opinions; Based on the scenario status, evaluate the power supply performance of the power system at each moment after the extreme disaster occurs; Based on the network public opinion set, evaluate the network public opinion intensity of the power system at each moment after the extreme disaster occurs; Based on the power supply performance of the power system at each moment after the extreme disaster occurs and the intensity of online public opinion of the power system at each moment after the extreme disaster occurs, the resilience of the power system under extreme disasters is evaluated; include: The resilience of the power system under extreme disasters is evaluated by the following formula: ； in, To improve the resilience of power systems under extreme disasters, When extreme disasters occur, is the total duration of the power system resilience assessment, is the initial comprehensive performance of the power system, To strengthen the power system after extreme disasters Comprehensive performance at all times; , To strengthen the power system after extreme disasters Power supply performance at all times, To strengthen the power system after extreme disasters The intensity of online public opinion at all times, is the power supply performance weight, is the network public opinion intensity weight, .

2. The method for evaluating the resilience of a power system under extreme disasters according to claim 1 is characterized in that: The scenario status includes: the performance status of the power generation node, the performance status of the power transmission node, the performance status of the power transmission line, and the performance status of the load node; Determining the scenario state of the power system during the evolution of extreme disasters based on the topological structure includes: Based on the topological structure, the power generation nodes, transmission nodes, transmission lines, and load nodes in the power system are determined; wherein the power generation nodes include thermal power generation, hydropower generation, wind power generation, photovoltaic power generation, and nuclear power generation; the transmission nodes include converter stations, substations, and wiring substations; and the load nodes include households, commercial facilities, and industrial facilities; Determine the performance status of power generation nodes, transmission nodes, transmission lines, and load nodes; the performance status of power generation nodes includes: 0~20%, 20%~40%, 40%~60%, 60%~80%, 80%~100%, and the performance status of any power generation node is determined according to the percentage of its actual power supply capacity meeting the installed design capacity; the performance status of transmission nodes includes: 0~20%, 20%~40%, 40%~60%, 60%~80%, 80%~100%, and the performance status of any transmission node is determined according to the percentage of its actual power supply capacity meeting the installed design capacity. The performance state is determined according to the percentage of the actual transmission power upper limit meeting the rated power; the performance states of the transmission line include: 0~20%, 20%~40%, 40%~60%, 60%~80%, 80%~100%, and the performance state of any transmission line is determined according to the percentage of damaged lines; the performance states of the load nodes include: 0~33%, 33%~66%, 66%~100%, 100%~150%, 150%~200%, and the performance state of any load node is determined according to the ratio of its actual load demand to the average load demand in normal scenarios.

3. The method for evaluating the resilience of a power system under extreme disasters according to claim 1 is characterized in that: The scenario status includes: the performance status of the power generation node, the performance status of the power transmission node, the performance status of the power transmission line, and the performance status of the load node; The evaluating the power supply performance of the power system at each moment after the extreme disaster occurs based on the scenario state includes: The power supply performance of the power system at any time after an extreme disaster occurs is evaluated by the following formula: ； in, To mark the moment, To protect the power system after extreme disasters Power supply performance at all times, is the supply-demand ratio; , To protect the power system after extreme disasters The performance status of the power generation node at the moment, To protect the power system after extreme disasters The performance status of the transmission node at the time, To protect the power system after extreme disasters The performance status of the transmission line at the transmission node at the time, To protect the power system after extreme disasters The performance status of the load node at the time, is the supply and demand factor; , It is the upper limit of load supply during normal operation of the power system. It is the average load demand during normal operation of the power system.

4. The method for evaluating the resilience of a power system under extreme disasters according to claim 1, characterized in that: The collection of online public opinion includes: particularly serious Internet public opinion events, serious Internet public opinion events, relatively serious Internet public opinion events, general Internet public opinion events, and no negative public opinion.

5. The method for evaluating the resilience of a power system under extreme disasters according to claim 1, characterized in that: The method further comprises: Determine the disposal measures of the power system during the emergency process, the disaster characteristics of extreme natural disasters and environmental factors; wherein the disposal measures are determined by the emergency response level of key departments of the power system during the emergency process; The assessment of the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after the extreme disaster occurs and the intensity of network public opinion of the power system at each moment after the extreme disaster occurs includes: The resilience of the power system under extreme disasters is assessed based on the power system's emergency response measures, the disaster characteristics and environmental factors of extreme natural disasters, the power supply performance of the power system at each moment after the extreme disaster occurs, and the intensity of online public opinion of the power system at each moment after the extreme disaster occurs.

6. The method for evaluating the resilience of a power system under extreme disasters according to claim 5 is characterized in that: The key departments include: power grid dispatching group, production recovery group, power marketing group, security group, information release group and material support group; the emergency response level of any key department is one of the following: level I, level II, level III and level IV; When the extreme natural disaster is an earthquake, the disaster characteristic is the intensity, which is graded as follows: 0, I~IV, V~VI, VII~VIII, IX~X, XI~XII; When the extreme natural disaster is a landslide, the disaster characteristic is the volume of the landslide, which is classified as follows: 0, less than 100,000 cubic meters, 100,000 to 1 million cubic meters, 1 million to 10 million cubic meters, and more than 10 million cubic meters; When the extreme natural disaster is a rainstorm, the disaster characteristic is the precipitation, which is classified as follows: less than 49.9 mm, 50.0-99.9 mm, 100.0-249.9 mm, and more than 250.0 mm; When the extreme natural disaster is a flood, the disaster characteristic is the peak flow, which is classified as follows: 0, less than 500 cubic meters per second, 500-1000 cubic meters per second, 1000-1500 cubic meters per second, and more than 1500 cubic meters per second; When the extreme natural disaster is waterlogging, the disaster characteristic is the depth of water accumulation, which is classified as follows: less than 15 cm, 15-27 cm, 27-40 cm, 40-60 cm, and more than 60 cm; The environmental factors include: altitude, geological structure, 24-hour precipitation, air humidity, and temperature; The altitude classification is as follows: below 500 meters, 500-1000 meters, 1000-1500 meters, 1500-2000 meters, and above 2000 meters; The geological structures are classified into: monocline structure, fold structure, fault structure, and block structure; The 24-hour precipitation classification is: less than 0.1 mm, 0.1-9.9 mm, 10.0-24.9 mm, 25.0-49.9 mm, 50.0-99.9 mm, 100.0-249.9 mm, and more than 250.0 mm; Air humidity is graded as follows: 0~40%, 40%~60%, 60%~80%, and above 80%; The temperature is classified into: below 25 degrees Celsius, 25~30 degrees Celsius, 30~35 degrees Celsius, 35~40 degrees Celsius, and above 40 degrees Celsius.

7. A device for evaluating the resilience of a power system under extreme disasters, characterized in that: The device comprises: A collection module, used to collect the topological structure of the power system; A determination module, used to determine the scenario state of the power system during the evolution of extreme disasters based on the topological structure collected by the collection module; Establish a module for establishing a network public opinion collection according to the corresponding levels of network public opinion; A first evaluation module, used to evaluate the power supply performance of the power system at each moment after the occurrence of an extreme disaster based on the scenario state determined by the determination module; A second evaluation module is used to evaluate the intensity of network public opinion of the power system at each moment after the occurrence of an extreme disaster based on the network public opinion set established by the establishment module; The third evaluation module is used to evaluate the resilience of the power system under extreme disasters based on the power supply performance of the power system at each moment after the extreme disaster occurs evaluated by the first evaluation module and the network public opinion intensity of the power system at each moment after the extreme disaster occurs evaluated by the second evaluation module; the resilience of the power system under extreme disasters is evaluated by the following formula: ； in, To improve the resilience of power systems under extreme disasters, When extreme disasters occur, is the total duration of the power system resilience assessment, is the initial comprehensive performance of the power system, To protect the power system after extreme disasters Comprehensive performance at all times; , To protect the power system after extreme disasters Power supply performance at all times, To protect the power system after extreme disasters The intensity of online public opinion at all times, is the power supply performance weight, is the network public opinion intensity weight, .

8. An electronic device, characterized in that: include: Memory; processor; and computer programs; Wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the method for assessing the resilience of a power system under extreme disasters as described in any one of claims 1-6.

9. 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 for assessing the resilience of a power system under extreme disasters as described in any one of claims 1-6.

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