Urban power distribution network anti-seismic toughness evaluation and dynamic risk method considering seismic oscillation intensity space variability

Abstract

The invention discloses an urban power distribution network anti-seismic toughness evaluation and dynamic risk method considering seismic oscillation intensity spatial variability, and relates to the field of civil engineering disaster prevention and reduction, and the method comprises the steps: determining a seismic oscillation field with the intensity having spatial variability according to historical seismic data and a seismic oscillation attenuation relation; the method comprises the following steps: simplifying a transformer substation into nodes, simplifying a distribution line into edges, determining a topological structure according to network information, constructing a network model based on an improved Space-L method, and reading peak accelerations of each node and a line midpoint of a power network; selecting transformer substation and distribution line vulnerability models, and generating damage samples based on Monte Carlo simulation; according to the method, the system function change condition of the power grid after the earthquake is obtained in combination with the repair model, toughness and risk assessment is carried out, the spatial variability of the seismic field intensity is effectively considered, partition classification dynamic assessment of the power grid earthquake risk is realized, and the system toughness assessment result can provide visual and effective guidance for command decision making.

Description

Technical Field

[0001] This invention relates to the field of disaster prevention and mitigation technology in civil engineering, and in particular to a method for assessing the seismic resilience and dynamic risk of urban power distribution networks that takes into account the spatial variability of seismic intensity. Background Technology

[0002] As a crucial component of urban lifeline engineering, the power distribution network provides the power source for the normal operation of various facilities and equipment. Research on its seismic resilience evaluation has become a focal point in disaster prevention and mitigation. Currently, resilience evaluation methods for power distribution networks can be categorized into three types: first, comprehensive evaluation methods based on indicator systems, which construct quantitative indicators and obtain resilience evaluation values ​​through comprehensive calculations; second, dynamic evaluation methods based on simulation, which quantify the system performance trajectory by simulating the occurrence, failure, and recovery processes of extreme events; and third, goal-oriented evaluation methods based on optimization models, which obtain evaluation results by solving mathematical models with optimal resilience as the objective. However, these methods are designed for non-seismic disaster scenarios and cannot reflect the resilience evaluation and risk analysis results of systems under seismic scenarios.

[0003] For earthquake scenarios, researchers have developed a seismic resilience evaluation method for power distribution networks based on a three-stage resilience curve framework encompassing pre-disaster, during-disaster, and post-disaster stages. This method comprehensively assesses the resilience of the system by quantifying dynamic changes in system performance. However, existing seismic resilience assessment methods still have significant shortcomings: First, they generally assume the seismic field to be spatially uniform, ignoring the spatial variability of actual seismic intensity, thus failing to accurately capture the differences in seismic excitation between geographically located nodes and lines. Second, existing analyses often focus on static or deterministic assessments, neglecting the influence of complex factors such as the uncertainty of the post-disaster system state and the dynamic nature of the recovery process. Finally, the system resilience assessment structure struggles to provide intuitive and effective guidance for command and decision-making, lacking deep integration with engineering practice and emergency command and decision-making needs. It fails to transform system-level performance losses into controllable, spatialized disaster information, cannot examine the dynamic changes in system risk, and struggles to form objective and intuitive decision-making basis. Summary of the Invention

[0004] To address the aforementioned challenges, this invention provides a method for assessing the seismic resilience and dynamic risk of urban power distribution networks that considers the spatial variability of seismic intensity. This method can accurately assess the seismic resilience of urban power distribution networks and achieve dynamic assessment of seismic risk in power grids by region and category, providing intuitive and effective guidance for post-earthquake command and decision-making.

[0005] To achieve the above objectives, this invention provides a method for assessing the seismic resilience and dynamic risk of urban power distribution networks that considers the spatial variability of seismic intensity, comprising the following steps: S1: Based on the historical ground motion observation data of the site and combined with the ground motion attenuation relationship, the peak ground acceleration of the site is determined by the probabilistic seismic hazard analysis method, and a ground motion field with spatial variability is constructed. S2: Simplify substations into nodes and power distribution lines into edges. Determine the topology based on network information, construct a network model based on the improved Space-L method, and read the peak acceleration of each node and the midpoint of the power network. S3: Based on the vulnerability model of substations and distribution lines, the conditional failure probability under a given peak acceleration is obtained, and different scenario samples of post-earthquake physical damage to the power grid are generated using the quasi-Monte Carlo simulation method. S4: Based on the different scenario samples, perform connectivity analysis on the power grid, select functional indicators to describe the system functions, combine the time-varying repair model to generate system function change curves for the damaged samples, and conduct power grid resilience and risk assessment.

[0006] Preferably, S1 specifically includes: S101: Select the seismic source according to the specifications to obtain the site range; S102: Review and collect historical earthquake data to obtain earthquake data within a certain time range, and calculate the intensity value of each unit when each historical earthquake occurred based on the ground motion attenuation relationship formula. S103: Based on the Gutenberg-Richard magnitude-frequency relationship, magnitude is replaced by intensity to calculate the earthquake intensity-frequency relationship for each unit: ; in, f Intensity greater than or equal to I The annual incidence rate, a , b For parameters; S104: Assuming that the random event of an earthquake follows a Poisson process, what is the exceedance probability of an earthquake of a certain magnitude? p : ; in, Let be the average recurrence interval for earthquakes of magnitude M or greater, and e be the base of the natural logarithm. t The duration of existence of a given building or facility; S105: Based on the quantitative relationship between intensity and peak ground acceleration, the peak ground acceleration with a 10% exceedance probability over 50 years is obtained for the study area, i.e., the fortification intensity of each unit. The specific calculation formula is as follows: ; in, I For earthquake intensity, PGA Peak ground acceleration,c , d is the regression coefficient.

[0007] Preferably, the network model constructed in S2 based on the improved Space-L method includes the following steps: S201: Collect relevant information about the power grid, including the location of substations and distribution lines, the voltage level, power, transformer anchoring method, and site type of each distribution station, and the level, structure type, and site type of each distribution line. Simplify substations as nodes and distribution lines as edges. S202: Determine the power grid topology based on relevant information, and establish a network model based on the improved Space-L method. The basic principle of the improved Space-L method is as follows: On the same branch, substations of the same level are modeled in parallel, that is, substations of the same level do not affect each other. This effect is achieved by setting virtual nodes, where the edges between virtual nodes and real nodes will not be destroyed.

[0008] Preferably, S3 specifically includes: S301: Select the vulnerability model of the substation and distribution line, and calculate the failure probability of each component based on the peak acceleration at each node and the midpoint of the edge and the selected vulnerability curve. S302: Generate a set of pseudo-random numbers based on the quasi-Monte Carlo simulation method, and associate each random number with the failure probability of the corresponding device. p Comparison: When the random number is between 0 and... p When the random number is p~1, the device status is marked as 1, indicating normal operation. When the random number is p~1, the device status is marked as 0, indicating that it has stopped working. This is used as a destructive sample.

[0009] Preferably, S4 specifically includes: S401: Modify the adjacency matrix according to the state of nodes and edges in the generated damaged samples. If a node is damaged, delete all edges connected to that node. If an edge is damaged, delete that edge. S402: Use a depth-first search algorithm or a breadth-first search algorithm to perform source node connectivity analysis and statistically analyze the connectivity of each node in the sample. S403: Select the functional indicators of the power grid, combine them with the repair model for analysis, and generate the system function change curve of the damaged sample; S404: Select system resilience index and calculate the resilience value of different samples; S405: Introducing the concept of dynamic risk, and establishing a dynamic analysis method for power distribution networks during earthquake post-disaster recovery based on the risk definition, to conduct risk assessment.

[0010] Preferably, the functional indicator of the power grid in S403 is the normal load factor of the power grid, expressed as: ; in, For the power grid t Functional indicators at any given time Represents a node i exist t The load of time, For the normal operation of the power grid i The load of each node.

[0011] Preferably, the system resilience indicators selected in S404 include system function loss and the average value of system function; The loss of system function is expressed as: ; in, It is the power grid t Functional indicators at any given time t 0 represents the start time when the system is affected by the disaster. t 4 is the moment when the system function returns to normal. In the indicators, the larger the value, the lower the resilience; the smaller the value, the higher the resilience. The average system function is expressed as: ; in, The higher the value, the higher the toughness. The smaller the value, the lower the toughness.

[0012] Preferably, the risk definition in S405 is expressed as follows: ; in, R For risk, P For the probability of disaster, L This is a loss.

[0013] Preferably, in S405, the risk assessment specifies the probability of an event occurring as the probability of post-earthquake connectivity failure at each node, and the degree of impact after the event occurs is measured by the node load impact coefficient.

[0014] Preferably, the node load impact factor is expressed as: ; in, q i For the normal operation of the power grid i The load of each node; For nodes i The maximum load that the power grid can increase after the repair.

[0015] Therefore, the present invention employs the above-mentioned method for assessing the seismic resilience and dynamic risk of urban power distribution networks that considers the spatial variability of seismic intensity, and compared with the prior art, it has the following beneficial effects: (1) This invention considers the spatial variability of ground motion intensity, which is beneficial to capturing the differences in seismic excitation of nodes and lines in different geographical locations, making the seismic resilience results of the power distribution network and the post-earthquake system behavior more accurate; (2) This invention uses the quasi-Monte Carlo simulation method to generate damage samples. By probabilistically calculating random variables, it realizes the stochastic simulation of the seismic damage process of the power grid, thereby systematically grasping the uncertainty factors and improving the scientific nature of the assessment results. (3) This invention establishes a dynamic risk analysis method for power distribution networks after earthquakes, realizes the zoning and classification assessment of power distribution network risks under earthquake disasters, and can intuitively display the dynamic evolution of risks in each service area, providing a scientific basis for precise prevention and control of power grid risks.

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the seismic resilience assessment and dynamic risk method for urban power distribution networks that considers the spatial variability of seismic intensity, as presented in this invention. Figure 2 This is a schematic diagram of the improved Space-L modeling method in an embodiment of the present invention; Figure 3 This is a schematic diagram of the power grid service area in an embodiment of the present invention; Figure 4 This is a schematic diagram showing the distribution of PGA in each unit within the study area under the fortification intensity in an embodiment of the present invention; Figure 5 This is a diagram showing the post-earthquake functional changes of the power grid under the preferred repair strategy in an embodiment of the present invention; Figure 6 This is a diagram showing the post-earthquake functional changes of the power grid under a random repair strategy in an embodiment of the present invention. Figure 7 The following are risk maps of the power grid service areas at different times after the earthquake in the embodiments of the present invention; wherein, (a) is the risk map of the power grid service areas 0 days after the earthquake, (b) is the risk map of the power grid service areas 30 days after the earthquake, (c) is the risk map of the power grid service areas 150 days after the earthquake, and (d) is the risk map of the power grid service areas 300 days after the earthquake. Detailed Implementation

[0018] The following detailed description of embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0019] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0020] The terms "comprising" or "including" as used in this invention mean that the element preceding the term encompasses the element listed after the term, and do not exclude the possibility of encompassing other elements. Terms such as "inner," "outer," "upper," and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. In this invention, unless otherwise explicitly specified and limited, the term "attached" and similar terms should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can refer to a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication of two elements or the interaction relationship between two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0021] Example A method for assessing the seismic resilience and dynamic risk of urban power distribution networks that considers the spatial variability of seismic intensity, such as... Figure 1 As shown, it includes the following steps: S1: Based on the historical ground motion observation data of the site and combined with the ground motion attenuation relationship, the peak ground acceleration of the site is determined by the probabilistic seismic hazard analysis (PSHA) method, and a ground motion field with spatial variability is constructed. S1 specifically includes: S101: Select the seismic source according to the specifications to obtain the site range; S102: Review and collect historical earthquake data to obtain earthquake data within a certain time range, and calculate the intensity value of each unit when each historical earthquake occurred based on the ground motion attenuation relationship formula. S103: Based on the Gutenberg-Richard magnitude-frequency relationship, magnitude is replaced by intensity to calculate the earthquake intensity-frequency relationship for each unit: ; in,f Intensity greater than or equal to I The annual incidence rate, a , b The parameters can be obtained by least squares fitting. S104: Assuming that the random event of an earthquake follows a Poisson process, what is the exceedance probability of an earthquake of a certain magnitude? p : ; in, The mean recurrence interval for earthquakes of magnitude M or greater. e Let be the base of the natural logarithm. t Given the duration of existence of a building or facility; this formula can also be used to estimate the occurrence of events exceeding a specific intensity at a given location. I The probability of an earthquake; S105: Based on the quantitative relationship between intensity and peak ground acceleration, the peak ground acceleration with a 10% exceedance probability over 50 years is obtained for the study area, i.e., the fortification intensity of each unit. The specific calculation formula is as follows: ; in, I For earthquake intensity, PGA Peak ground acceleration, c , d The regression coefficient can be obtained by fitting historical ground motion data and intensity assessment data of the statistical study area.

[0022] S2: Substations are simplified to nodes and distribution lines to edges. The topology is determined based on network information. A network model is constructed based on the improved Space-L method, and the peak acceleration of each node and the midpoint of the line in the power network is read. PGA ); including the following steps: S201: Collect relevant information about the power grid, including the location of substations and distribution lines, the voltage level, power, transformer anchoring method, and site type of each distribution station, and the level, structure type, and site type of each distribution line. Simplify substations as nodes and distribution lines as edges. S202: Determine the power grid topology based on relevant information, and establish a network model based on the improved Space-L method. The basic principle of the improved Space-L method is as follows: On the same branch, substations of the same level are modeled in parallel, meaning that substations of the same level do not affect each other. Specifically, virtual nodes S1, S2, and S3 are set up, and the edges between virtual nodes and real nodes are not disrupted. Figure 2 This is a schematic diagram of the modeling method.

[0023] Subsequently, ArcGIS software was used to connect the power network with the study area. PGA Overlay analysis is performed to read the data from each node of the power network and the midpoint of the line. PGA .

[0024] S3: Based on the vulnerability model of substations and distribution lines, the conditional failure probability under a given peak acceleration is obtained. A quasi-Monte Carlo simulation method is used to generate different scenarios of post-earthquake physical damage to the power grid; specifically including: S301: Select the vulnerability model of the substation and distribution line, and calculate the failure probability of each component based on the peak acceleration at each node and midpoint of the edge and the selected vulnerability curve. p ; S302: Select the interruption criteria, using 0 and 1 to represent the interrupted and running states of nodes and edges, respectively. Generate a set of pseudo-random numbers in the range of 0 to 1 based on the quasi-Monte Carlo simulation method, and associate each random number with the failure probability of the corresponding device. p Comparison: When the random number is between 0 and... p When the random number is in the specified state, the device status is marked as 1, indicating normal operation. p When the value is ~1, the device status is marked as 0, indicating that it has stopped working, and this is used as a sample for destruction.

[0025] S4: Based on the different scenario samples, perform connectivity analysis on the power grid, select functional indicators to describe system functions, generate system function change curves for damaged samples using a time-varying repair model, and conduct power grid resilience and risk assessment. Specifically, this includes: S401: Based on the generated destruction samples, update the state of nodes and edges, and dynamically reconstruct the adjacency matrix. If a node is destroyed, all edges connected to that node are deleted; if an edge is destroyed, that edge is deleted. S402: Use a depth-first search algorithm or a breadth-first search algorithm to perform source node connectivity analysis and statistically analyze the connectivity of each node in the sample. S403: Select the functional indicators of the power grid, combine them with the repair model for analysis, and generate the system function change curve of the damaged sample; Specifically, select indicators that can describe the function of the power grid system, combine them with the repair model, calculate the functional changes during the sample repair process, and repeat the above steps. N Next, statistical analysis N The results of this simulation.

[0026] In this embodiment, the normal load factor of the power grid is selected as the functional indicator of the power grid, and is expressed as: ; in, For the power grid t Functional indicators at any given time Represents a node i exist t The load of time, For the normal operation of the power grid i The load of each node.

[0027] S404: Select system resilience index and calculate the resilience value of different samples; Specifically, the system resilience indicators are selected as follows: Based on the definition of the system resilience index, the resilience values ​​of different samples are calculated. To avoid the limitations of a single index, this embodiment selects the average value of system function loss and system function to define the resilience index and conduct resilience assessment.

[0028] The loss of system function is expressed as: ; in, It is the power grid t Functional indicators at any given time t 0 represents the start time when the system is affected by the disaster. t 4 is the moment when the system function returns to normal. In the indicators, the larger the value, the lower the resilience; the smaller the value, the higher the resilience. The average system function is expressed as: ; in, The higher the value, the higher the toughness. The smaller the value, the lower the toughness.

[0029] S405: Introducing the concept of dynamic risk, and establishing a dynamic analysis method for power distribution networks during earthquake post-disaster recovery based on the risk definition, to conduct risk assessment.

[0030] Specifically, a system risk analysis is conducted, calculating the changes in connectivity of each node at different times after the earthquake. Based on the general definition of risk, the risk of each service area of ​​the power grid at different times after the earthquake is calculated. The general definition of risk is as follows: ; in, R For risk, P For the probability of disaster, L This refers to the loss. Referring to QRE, the probability of an event occurring is specifically defined as the probability of post-earthquake connectivity failure at each node. The degree of impact after the event occurs is measured by the node load impact coefficient, which is expressed as: ; in, q i For the normal operation of the power gridi The load of each node; For nodes i The maximum load that the power grid can increase after the repair.

[0031] Example 1 This embodiment uses, as follows Figure 3 Taking the power grid service area shown as an example, it includes one 220kV substation as the power source for the distribution network; 17 10kV substations to simulate the load demand of the power grid, with each substation serving one area; three 110kV substations; and several lines of different levels for transmitting electrical energy, further illustrating the effectiveness of the method provided in this application.

[0032] First, according to the standard "Earthquake Safety Evaluation of Engineering Sites" (GB17741-2005), the study area was extended by 150 km when selecting seismic sources, resulting in a site latitude and longitude range of 114.5°E~118.2°E, 38.6°N~41.4°N. Considering the regional scope, geological conditions, and other factors, the study area was divided into 544 units of 0.01° × 0.01°.

[0033] After reviewing and collecting relevant data, data on 66 earthquakes of magnitude 4.0 or higher that have occurred since 294 AD were obtained. The intensity values ​​at each unit during each earthquake were calculated using the seismic attenuation formula for bedrock sites. ; ; in, I Intensity; M Magnitude; R The epicentral distance is (km). σ The standard deviation is 0.377. After calculating the intensity value, by analogy with the Gutenberg-Richard magnitude-frequency relationship, the magnitude is replaced by intensity to calculate the seismic intensity-frequency relationship at each unit: ; in, f Intensity greater than or equal to I The annual incidence rate, a , b The parameters can be obtained by least squares fitting; based on the intensity in each unit in this embodiment I The number of data points ≥4.0 did not change significantly, and the fitted values ​​of a and b did not change significantly either, so no adjustment was needed.

[0034] Subsequently, assuming that earthquakes follow a Poisson process, the exceedance probability for an earthquake of a certain magnitude can be calculated. p : ; in, The mean recurrence interval for earthquakes of magnitude M or greater. t The duration of existence of a given building or facility.

[0035] According to intensity I and PGA The quantitative relationship can yield the 10% exceedance probability of the bedrock site in the study area over 50 years. PGA : ; The 50-year exceedance probability of 10% was obtained for the bedrock site in the study area. PGA Afterwards, due to the site amplification effect, it is necessary to... PGA With appropriate adjustments, and by combining the method proposed in the fifth-generation Chinese seismic ground motion parameter zoning map, the site conditions of the study area were obtained. PGA ,like Figure 4 As shown.

[0036] The physical state of each component is then simulated. In this embodiment, the vulnerability curves of substations and distribution lines in earthquake disasters given in the HAZUS manual are used for calculation. This model assumes that the structural vulnerability curves conform to the cumulative log-normal distribution function. ; in, P f Indicates in the given PGA The probability of the substructure experiencing a failure level exceeding a specified damage level; d s Indicates that in a given PGA Seismic response of substructure; d si Indicates the specified damage level i Critical value of seismic response of substructure; S For the seismic intensity index, this invention selects PGA as the seismic intensity index; Φ(·) represents the cumulative probability function of the standard normal distribution; λ i and β i These represent the damage states respectively. i The median and standard deviation of the corresponding fragility curve.

[0037] For substation nodes, this embodiment selects "moderate damage" to the substation with anchoring measures in the HAZUS manual vulnerability model as the basis for unit operation interruption. However, when a node suffers minor damage, it is assumed that its function is reduced to 0.3 of the original value. For distribution lines, "moderate damage" to the line is selected as the basis for unit operation interruption. That is, when a node suffers minor damage, if the node is still connected, it is considered that the node power is reduced to 30% of the normal value. When a moderate or higher degree of damage occurs, the node power is reduced to 0.

[0038] Based on the positions of each node and the midpoint of the edge PGA Calculate its failure probability using the selected fragility curve. p Use MATLAB software to generate a set of pseudo-random numbers, and compare this set of numbers with the probability of damage to each device. When the number is in the range of 0~ p When the device status is set to 1, it is in normal operation; when the number is in a certain state... p When the value is ~1, the device status is 0, which means it has stopped working. This is used as a sample for destruction. Subsequently, the adjacency matrix is ​​modified based on the state of nodes and edges. If a node is damaged, all edges connected to that node are deleted; if an edge is damaged, that edge is deleted. A breadth-first search (BFS) algorithm is applied to analyze the connectivity of the source node. The connectivity of each node in this sample is then statistically analyzed.

[0039] After the above analysis is completed, the nodal impact coefficient is calculated to determine the repair sequence of the repair strategy and subsequent risk analysis. It is defined as the maximum load ratio that the power grid may restore after the nodal repair is completed. ; in, w i Represents a node i Influence coefficient, q i Indicates the first time when the power grid is working normally i The load of each node; Represents a node i The maximum load that the power grid can increase after the repair.

[0040] The repair work requires calculations based on the repair function. In this embodiment, a discrete function model is selected for description. The repair models for substations and distribution lines are shown in Table 1.

[0041] Table 1 Repair functions for each unit

[0042] The post-earthquake power grid function change curve is calculated based on the repair model. This embodiment only considers the case where only one engineering team is carrying out repairs, and considers two strategies: preferred repair and random repair. The preferred repair strategy determines the repair order based on the node influence coefficient, while the random repair strategy does not consider any factors and randomly determines the repair order. Figure 5 and Figure 6 The figures shown are the changes in power grid function after the earthquake under the preferred repair strategy and the random repair strategy, respectively. The sample curves are curves drawn based on the initial sample data of 1000 quasi-Monte Carlo simulations. The sample mean is the curve formed by connecting the mean values ​​of power grid function at each time point after the earthquake. The sample median is the median value of the function at each time point after the earthquake for 1000 samples. The median sample refers to the damage sample corresponding to the initial function after the earthquake being at the median.

[0043] The resilience index of each simulated sample was calculated, and the calculation results of all random samples were obtained by statistical analysis of different samples, as shown in Table 2.

[0044] Table 2. Power grid resilience indices under two repair strategies

[0045] The initial risk level of the distribution network after the earthquake, calculated according to the risk definition, is shown in Table 3. Furthermore, as the repair work progresses, the risk at each node will change at different times. Figure 7 Risks in different service areas of the power grid at different times after the earthquake.

[0046] Table 3 Initial Post-Earthquake Risk Levels of Various Nodes in the Power Grid

[0047] Therefore, the present invention adopts the above-mentioned method for assessing the seismic resilience and dynamic risk of urban power distribution networks that considers the spatial variability of ground motion intensity. This method can accurately assess the seismic resilience of urban power distribution networks and realize the dynamic assessment of seismic risk of power grids by region and category, providing intuitive and effective guidance for post-earthquake command and decision-making.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for assessing the seismic resilience and dynamic risk of urban power distribution networks considering the spatial variability of seismic intensity, characterized in that, Includes the following steps: S1: Based on the historical ground motion observation data of the site and combined with the ground motion attenuation relationship, the peak ground acceleration of the site is determined by the probabilistic seismic hazard analysis method, and a ground motion field with spatial variability is constructed. S2: Simplify substations into nodes and power distribution lines into edges. Determine the topology based on network information, construct a network model based on the improved Space-L method, and read the peak acceleration of each node and the midpoint of the power network. S3: Based on the vulnerability model of substations and distribution lines, the conditional failure probability under a given peak acceleration is obtained, and different scenario samples of post-earthquake physical damage to the power grid are generated using the quasi-Monte Carlo simulation method. S4: Based on the different scenario samples, perform connectivity analysis on the power grid, select functional indicators to describe the system functions, combine the time-varying repair model to generate system function change curves for the damaged samples, and conduct power grid resilience and risk assessment.

2. The method for assessing the seismic resilience and dynamic risk of urban power distribution networks considering the spatial variability of seismic intensity as described in claim 1, characterized in that, S1 specifically includes: S101: Select the seismic source according to the specifications to obtain the site range; S102: Review and collect historical earthquake data to obtain earthquake data within a certain time range, and calculate the intensity value of each unit when each historical earthquake occurred based on the earthquake motion attenuation relationship formula. S103: Based on the Gutenberg-Richard magnitude-frequency relationship, magnitude is replaced by intensity to calculate the earthquake intensity-frequency relationship for each unit: ； in, f Intensity greater than or equal to I The annual incidence rate, I For earthquake intensity, a , b For parameters; S104: Assuming that the random event of an earthquake follows a Poisson process, what is the exceedance probability of an earthquake of a certain magnitude? p : ； in, The mean recurrence interval for earthquakes of magnitude M or greater. e Let be the base of the natural logarithm. t The duration of existence of a given building or facility; S105: Based on the quantitative relationship between intensity and peak ground acceleration, the peak ground acceleration with a 10% exceedance probability over 50 years is obtained for the study area, i.e., the fortification intensity of each unit. The specific calculation formula is as follows: ； in, PGA Peak ground acceleration, c , d is the regression coefficient.

3. The method for assessing the seismic resilience and dynamic risk of urban power distribution networks considering the spatial variability of seismic intensity as described in claim 2, characterized in that... The network model in S2 is constructed based on the improved Space-L method, including the following steps: S201: Collect relevant information about the power grid, including the location of substations and distribution lines, the voltage level, power, transformer anchoring method, and site type of each distribution station, and the level, structure type, and site type of each distribution line. Simplify substations as nodes and distribution lines as edges. S202: Determine the power grid topology based on relevant power grid information, and establish a network model based on the improved Space-L method. The basic principle of the improved Space-L method is as follows: On the same branch, substations of the same level are modeled in parallel, that is, substations of the same level do not affect each other. This effect is achieved by setting virtual nodes, where the edges between virtual nodes and real nodes will not be destroyed.

4. The method for assessing the seismic resilience and dynamic risk of urban power distribution networks considering the spatial variability of seismic intensity as described in claim 3, characterized in that... S3 specifically includes: S301: Select the vulnerability model of the substation and distribution line, and calculate the failure probability of each component based on the peak acceleration at each node and the midpoint of the edge and the selected vulnerability curve. S302: Generate a set of pseudo-random numbers based on the quasi-Monte Carlo simulation method, and associate each random number with the failure probability of the corresponding device. p Comparison: When the random number is between 0 and... p When the random number is in the specified state, the device status is marked as 1, indicating normal operation. p When the value is ~1, the device status is marked as 0, indicating that it has stopped working, and this is used as a sample for destruction.

5. The method for assessing the seismic resilience and dynamic risk of urban power distribution networks considering the spatial variability of seismic intensity as described in claim 4, characterized in that, S4 specifically includes: S401: Modify the adjacency matrix according to the state of nodes and edges in the generated damaged samples. If a node is damaged, delete all edges connected to that node. If an edge is damaged, delete that edge. S402: Use a depth-first search algorithm or a breadth-first search algorithm to perform source node connectivity analysis and statistically analyze the connectivity of each node in the sample. S403: Select the functional indicators of the power grid, combine them with the repair model for analysis, and generate the system function change curve of the damaged sample; S404: Select system resilience index and calculate the resilience value of different samples; S405: Introducing the concept of dynamic risk, and establishing a dynamic analysis method for power distribution networks during earthquake post-disaster recovery based on the risk definition, to conduct risk assessment.

6. The method for assessing the seismic resilience and dynamic risk of urban power distribution networks considering the spatial variability of seismic intensity as described in claim 5, characterized in that, In S403, the functional indicator of the power grid is the normal load rate, expressed as: ； in, For the power grid t Functional indicators at any given time Represents a node i exist t The load of time, For the normal operation of the power grid i The load of each node.

7. The method for assessing the seismic resilience and dynamic risk of urban power distribution networks considering the spatial variability of seismic intensity as described in claim 6, characterized in that, The system resilience indicators selected in S404 include system function loss and the average value of system function; The loss of system function is expressed as: ； in, For the power grid t Functional indicators at any given time t 0 represents the start time when the system is affected by the disaster. t 4 is the moment when the system function returns to normal. In the indicators, the larger the value, the lower the resilience; the smaller the value, the higher the resilience. The average system function is expressed as: ； in, The higher the value, the higher the toughness. The smaller the value, the lower the toughness.

8. The method for assessing the seismic resilience and dynamic risk of urban power distribution networks considering the spatial variability of seismic intensity as described in claim 7, characterized in that, The risk definition in S405 is as follows: ； in, R For risk, P For the probability of disaster, L This is a loss.

9. The method for assessing the seismic resilience and dynamic risk of urban power distribution networks considering the spatial variability of seismic intensity as described in claim 8, characterized in that, In S405, risk assessment specifies the probability of an event occurring as the probability of post-earthquake connectivity failure at each node, and the degree of impact after the event occurs is measured by the node load impact coefficient.

10. A method for assessing the seismic resilience and dynamic risk of urban power distribution networks considering the spatial variability of seismic intensity as described in claim 9, characterized in that, The nodal load impact factor is expressed as: ； in, q i For the normal operation of the power grid i The load of each node; For nodes i The maximum load increased by the power grid after the repair.

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