Power distribution network resilience evaluation method and device for rainstorm waterlogging disaster

Abstract

The application provides a power distribution network elasticity evaluation method and device for rainstorm waterlogging disasters, the method comprising: acquiring weather data and geographical data in a preset time period; determining a position coefficient and a scale coefficient according to a plurality of rainfall amounts; determining a sample mean and a sample standard deviation according to a plurality of rainfall durations; constructing a first distribution function and a second distribution function; determining a reference rainfall amount range and a reference rainfall duration range, and randomly generating target weather data in the reference rainfall amount range and the reference rainfall duration range; generating A random waterlogging disaster events according to the target weather data and the geographical data, performing reduction processing on the A random waterlogging disaster events, and obtaining B reference waterlogging disaster events; acquiring a target waterlogging disaster event; acquiring a plurality of elasticity evaluation indexes and constructing a multi-index elasticity evaluation model; and evaluating the target waterlogging disaster event according to the multi-index elasticity evaluation model to obtain a target elasticity evaluation value.

Description

Technical Field

[0001] This application relates to the field of distribution network resilience assessment, and in particular to a method and apparatus for assessing the resilience of distribution networks in the event of rainstorm flooding. Background Technology

[0002] In recent years, the frequency of extreme weather disasters has been on the rise. Among them, rainstorm flooding is one of the most frequent natural disasters. It usually overloads urban drainage systems, causes severe water accumulation in streets, residential areas and infrastructure, results in large-scale power outages, and seriously affects people's daily lives and socio-economic activities.

[0003] Against this backdrop, assessing the resilience of distribution networks, especially under the threat of torrential rain and urban flooding, has become crucial for improving urban emergency management capabilities and reducing disaster losses. However, current research largely focuses on assessing specific disaster scenarios, neglecting the diversity and uncertainty of disasters. Furthermore, most studies propose or employ single resilience assessment indicators to evaluate the resilience of the power grid in the face of natural disasters, which has limitations. A single indicator cannot capture these complex interdependencies and their impact on the overall system resilience, thus failing to comprehensively reflect the distribution network's integrated response capabilities in the face of natural disasters.

[0004] How to conduct a more comprehensive and accurate resilience assessment of the distribution network is an urgent issue that needs to be addressed. Summary of the Invention

[0005] This application provides a method and apparatus for assessing the resilience of a power distribution network in the event of rainstorm flooding. By analyzing multiple resilience assessment indicators, the resilience level of the power distribution network under rainstorm flooding disasters can be assessed more comprehensively and accurately.

[0006] In a first aspect, embodiments of this application provide a method for assessing the resilience of a power distribution network in response to rainstorm-induced urban flooding disasters, the method comprising:

[0007] Acquire weather and geographic data of the power distribution network within a preset time period under rainstorm and urban flooding disaster. The weather data includes multiple rainfall amounts and multiple rainfall durations, and the geographic data includes multiple ground saturation levels and multiple drainage system efficiencies.

[0008] The location coefficient and scale coefficient are determined based on the multiple rainfall amounts;

[0009] The sample mean and sample standard deviation are determined based on the multiple rainfall durations.

[0010] A first distribution function and a second distribution function are constructed based on the location coefficient, the scale coefficient, the sample mean, and the sample standard deviation, respectively. The horizontal axis of the first distribution function is the reference rainfall, and the vertical axis is the first probability. The horizontal axis of the second distribution function is the reference rainfall duration, and the vertical axis is the second probability.

[0011] Determine the reference rainfall range and reference rainfall duration range corresponding to the first distribution function and the second distribution function being greater than the preset first probability threshold and the second probability threshold, respectively, and randomly generate target weather data within the reference rainfall range and the reference rainfall duration range;

[0012] Based on the target weather data and the geographic data, A random urban flooding disaster events are generated. The A random urban flooding disaster events are then reduced to obtain B reference urban flooding disaster events, where A and B are both integers greater than 1, and A is greater than B.

[0013] Acquire a target urban flooding disaster event, wherein the target urban flooding disaster event is any one or more of the B reference urban flooding disaster events;

[0014] Multiple resilience assessment metrics are obtained, including load failure rate, weighted load recovery rate, islanded power supply coverage rate, and reserve capacity utilization rate.

[0015] A multi-index elasticity assessment model is constructed based on the aforementioned multiple elasticity assessment indicators;

[0016] The target urban flooding disaster event is evaluated based on the multi-index resilience assessment model to obtain the target resilience assessment value.

[0017] Secondly, embodiments of this application provide a distribution network resilience assessment device for rainstorm and urban flooding disasters. The device includes a first acquisition module, a determination module, a first construction module, a generation module, a processing module, a second acquisition module, a third acquisition module, a second construction module, and an assessment module, wherein:

[0018] The first acquisition module is used to acquire weather data and geographical data of the power distribution network within a preset time period under rainstorm and waterlogging disaster. The weather data includes rainfall and rainfall duration, and the geographical data includes ground saturation and drainage system efficiency.

[0019] The determination module is used to determine the location coefficient and scale coefficient based on the multiple rainfall amounts; and to determine the sample mean and sample standard deviation based on the multiple rainfall durations.

[0020] The first construction module is used to construct a first distribution function and a second distribution function based on the location coefficient, the scale coefficient, the sample mean, and the sample standard deviation, respectively. The horizontal axis of the first distribution function is the reference rainfall amount, and the vertical axis is the first probability. The horizontal axis of the second distribution function is the reference rainfall duration, and the vertical axis is the second probability.

[0021] The generation module is used to determine the reference rainfall range and reference rainfall duration range corresponding to the first distribution function and the second distribution function being greater than the preset first probability threshold and the second probability threshold, respectively, and to randomly generate target weather data within the reference rainfall range and the reference rainfall duration range;

[0022] The processing module is used to generate A random urban flooding disaster events based on the target weather data and the geographic data, and to reduce the A random urban flooding disaster events to obtain B reference urban flooding disaster events, wherein A and B are both integers greater than 1, and A is greater than B;

[0023] The second acquisition module is used to acquire a target urban flooding disaster event, wherein the target urban flooding disaster event is any one or more of the B reference urban flooding disaster events;

[0024] The third acquisition module is used to acquire multiple resilience assessment indicators, including load failure rate, weighted load recovery rate, islanded power supply coverage rate, and reserve capacity utilization rate.

[0025] The second construction module is used to construct a multi-index elasticity assessment model based on the multiple elasticity assessment indicators.

[0026] The assessment module is used to assess the target urban flooding disaster event based on the multi-index resilience assessment model to obtain the target resilience assessment value.

[0027] Thirdly, embodiments of this application provide an electronic device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing steps in any method of the first aspect of this application.

[0028] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in any method of the first aspect of this application.

[0029] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program being operable to cause a computer to perform some or all of the steps described in any method of the first aspect of this application. The computer program product may be a software installation package.

[0030] By implementing the embodiments of this application and analyzing multiple resilience assessment indicators of the distribution network, the resilience level of the distribution network under rainstorm and flood disasters can be assessed more comprehensively and accurately. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a block diagram of the module composition of a resilience assessment system provided in an embodiment of this application;

[0033] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0034] Figure 3 This is a flowchart illustrating a method for assessing the resilience of a power distribution network in response to rainstorm-induced urban flooding, as provided in an embodiment of this application.

[0035] Figure 4 This is a schematic diagram illustrating a risk level classification provided in an embodiment of this application;

[0036] Figure 5 This is a block diagram of the functional modules of a power distribution network resilience assessment device for rainstorm and urban flooding disasters provided in this application embodiment. Detailed Implementation

[0037] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0038] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0039] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document indicates that the preceding and following related objects are in an "or" relationship. In the embodiments of this application, "multiple" refers to two or more.

[0040] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.

[0041] In this application, the term "connection" refers to various connection methods, such as direct connection or indirect connection, to achieve communication between devices. This application does not impose any limitations on this.

[0042] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0043] The following is an explanation of the relevant terms used in this application:

[0044] Vulnerability: The sensitivity and vulnerability of load nodes to disasters such as rainstorms and flooding.

[0045] Importance: The impact of load nodes on the overall stable operation of the distribution network.

[0046] Offload ratio: The degree to which a distribution network cannot meet load demand due to insufficient power supply capacity when encountering a disaster. The lower the offload ratio, the stronger the resilience of the distribution network and the better it can meet power demand; the higher the offload ratio, the weaker the resilience of the distribution network when subjected to shocks.

[0047] Weighted load recovery rate: The load recovery rate after assigning weights to loads according to their load levels reflects the degree of recovery of important loads. The more loads with higher load levels are restored, the higher the weighted load recovery rate will be.

[0048] Islanded power supply coverage: During severe rainstorms and flooding, the distribution network can maintain power supply to critical areas by forming islands. The higher the islanded power supply coverage, the stronger the distribution network's ability to provide power support to critical loads during severe rainstorms and flooding.

[0049] Reserve capacity utilization rate: The extent to which a distribution network can utilize its reserve capacity to maintain operation or quickly restore service in the event of a disaster. A higher reserve capacity utilization rate indicates that the distribution network faces greater pressure during a disaster, while a lower reserve capacity utilization rate indicates that the distribution network has sufficient capacity to cope with emergencies.

[0050] In recent years, the frequency of extreme weather disasters has been on the rise. Among them, rainstorm flooding is one of the most frequent natural disasters, often leading to overload of urban drainage systems, severe flooding in streets, residential areas, and infrastructure, causing widespread power outages, and seriously affecting people's daily lives and socio-economic activities. Against this backdrop, assessing the resilience of power distribution networks, especially under rainstorm flooding, has become crucial for improving urban emergency management capabilities and reducing disaster losses. However, current research mostly focuses on assessing specific disaster scenarios, neglecting the diversity and uncertainty of disasters. Furthermore, most studies propose or use single resilience assessment indicators to evaluate the resilience of the power grid in the face of natural disasters, which has certain limitations. A single indicator cannot capture these complex interdependencies and their impact on the overall system resilience, resulting in an inability to comprehensively reflect the comprehensive response capacity of the power distribution network in the face of natural disasters. How to conduct a more comprehensive and accurate resilience assessment of the power distribution network is an urgent problem to be solved.

[0051] To address the aforementioned problems, this application provides a method and apparatus for assessing the resilience of a power distribution network in response to rainstorm-induced urban flooding. The method involves acquiring weather and geographical data of the power distribution network over a preset time period during a rainstorm-induced urban flooding disaster. The weather data includes multiple rainfall amounts and multiple rainfall durations, while the geographical data includes multiple ground saturation levels and multiple drainage system efficiencies. A location coefficient and a scale coefficient are determined based on the multiple rainfall amounts. A sample mean and a sample standard deviation are determined based on the multiple rainfall durations. A first distribution function and a second distribution function are constructed based on the location coefficient, the scale coefficient, the sample mean, and the sample standard deviation, respectively. The first distribution function has a reference rainfall amount on the horizontal axis and a first probability on the vertical axis, while the second distribution function has a reference rainfall duration on the horizontal axis and a second probability on the vertical axis. The method determines that the first distribution function and the second distribution function are respectively greater than a preset first probability threshold and a preset second probability threshold. The second probability threshold corresponds to a reference rainfall range and a reference rainfall duration range. Target weather data is randomly generated within these ranges. Based on the target weather data and the geographical data, A random urban flooding disaster events are generated. These A random urban flooding disaster events are then reduced to B reference urban flooding disaster events, where A and B are both integers greater than 1, and A is greater than B. Target urban flooding disaster events are obtained, which are any one or more of the B reference urban flooding disaster events. Multiple resilience assessment indicators are obtained, including load shedding rate, weighted load recovery rate, islanded power supply coverage rate, and reserve capacity utilization rate. A multi-indicator resilience assessment model is constructed based on these indicators. The target urban flooding disaster events are then assessed using the multi-indicator resilience assessment model to obtain the target resilience assessment value. Therefore, by analyzing multiple resilience assessment indicators, the resilience level of the distribution network under rainstorm and urban flooding disasters can be assessed more comprehensively and accurately.

[0052] The following is combined Figure 1 The system architecture of a power distribution network resilience assessment method for rainstorm-induced urban flooding disasters, as described in the embodiments of this application, is explained. Figure 1 This is a block diagram of the module composition of a resilience assessment system provided in this application embodiment. The resilience assessment system 110 includes four main modules: a data collection module 111, a preprocessing module 112, a resilience assessment module 113, and a display module 114.

[0053] Among them, the data collection module 111 is used to collect weather data and geographical data of the power distribution network under rainstorm and waterlogging disasters.

[0054] In one possible embodiment, historical statistics on local heavy rainfall are obtained from the meteorological system, including but not limited to rainfall amount and duration, and a sample dataset is constructed based on these statistics.

[0055] The preprocessing module 112 is used to preprocess the collected data.

[0056] In one possible embodiment, to address issues such as missing data and substandard data quality in the sample dataset, data filling and data replacement are used to preprocess the sample dataset, which can be done using interpolation methods.

[0057] In one possible implementation, for the sample dataset, the Pearson coefficient is used to quantitatively calculate the correlation between different data points, analyze the correlation between different data points, and use the validated effective coefficients to measure the correlation, screening out strongly correlated factors for rainstorm scene generation. The Pearson coefficient can effectively measure the linear correlation between two variables. For multivariate problems, it can be regarded as several bivariate problems, and its Pearson coefficient can be calculated for each, and the strength of their correlation can be analyzed.

[0058] For the variable X = [x1, x2, ..., x...] n ] T Y = [y1, y2, ..., y n ] T The formula for calculating its Pearson correlation coefficient is:

[0059]

[0060] Here, x0 and y0 are the average values ​​of n sample data corresponding to X and Y, respectively. The correlation coefficient r ranges from [-1, 1], and is defined as follows: the closer the correlation coefficient r is to 1, the higher the correlation between x and y; conversely, the closer the correlation coefficient r is to 0, the lower the correlation between x and y. When creating complex rainstorm scenario simulation models, more realistic parameters can be set based on the correlation between different rainstorm data, such as the correlation between rainfall amount and rainfall duration, so that the generated scenario more accurately reflects the actual situation.

[0061] In one possible implementation, to eliminate the influence of different dimensions between variables and to make the data more standardized for subsequent analysis, data normalization can be performed. This requires obtaining the minimum and maximum values ​​of parameters such as rainfall and rainfall duration. The normalization formula is as follows:

[0062]

[0063] Where y is the result of data normalization; x is the original data value; x min It is the minimum value in the sample dataset; x max It is the maximum value in the sample dataset.

[0064] Among them, the elasticity assessment module 113 is used to construct a multi-index elasticity assessment model and to perform elasticity assessment on the input data.

[0065] In one possible embodiment, a multi-index resilience assessment model is constructed based on the load loss rate, weighted load recovery rate, islanded power supply coverage rate, and reserve capacity utilization rate. The preprocessed data is input into the established multi-index resilience assessment model for model calculation to obtain a comprehensive assessment value of the flood resilience level of the distribution network.

[0066] The display module 114 is used to analyze and evaluate the results and present them to the user in a visual form.

[0067] In one possible embodiment, assuming the resilience assessment indicators are drainage system condition, rainfall, topography, and urban construction density, the scores of these indicators are expressed on a percentage basis, resulting in an evaluation result: drainage system condition corresponds to 80 points; rainfall corresponds to 75 points; topography corresponds to 90 points; and urban construction density corresponds to 70 points. If a radar chart is used for display, each vertex represents an assessment indicator, and the length of the side represents the score for that indicator. This clearly shows the scores of each indicator and their relative importance. If a bar chart is used, each bar represents an assessment indicator, and the height of the bar represents the score for that indicator. This allows for a direct comparison and ranking of the scores of each indicator. If a map is used for display, different colors or markers can be used to represent the assessment scores of different areas in the distribution network, visually demonstrating the flood resilience of each area.

[0068] As can be seen, the above system architecture can improve data accuracy and reliability by preprocessing the data; improve the accuracy of resilience assessment results by analyzing and evaluating the data through a multi-indicator evaluation model; and facilitate intuitive understanding of the assessment results by displaying the results in a visual manner, thereby improving the efficiency of information transmission and communication.

[0069] The following is combined Figure 2 The electronic devices in the embodiments of this application will be described. Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 2 As shown, the electronic device 20 includes one or more processors 220, a memory 230, a communication interface 240, and one or more programs 231. The processor 220 is communicatively connected to the memory 230 and the communication interface 240 via an internal communication bus.

[0070] The processor 220 is mainly used for:

[0071] Acquire weather and geographic data of the power distribution network within a preset time period under rainstorm and urban flooding disaster. The weather data includes multiple rainfall amounts and multiple rainfall durations, and the geographic data includes multiple ground saturation levels and multiple drainage system efficiencies.

[0072] Location and scale factors are determined based on multiple rainfall amounts;

[0073] The sample mean and sample standard deviation were determined based on multiple rainfall durations.

[0074] The first distribution function and the second distribution function are constructed based on the location coefficient, the scale coefficient, the sample mean, and the sample standard deviation, respectively. The horizontal axis of the first distribution function is the reference rainfall, and the vertical axis is the first probability. The horizontal axis of the second distribution function is the reference rainfall duration, and the vertical axis is the second probability.

[0075] Determine the reference rainfall range and reference rainfall duration range corresponding to the first distribution function and the second distribution function being greater than the preset first probability threshold and the second probability threshold, respectively, and randomly generate target weather data within the reference rainfall range and the reference rainfall duration range;

[0076] A random urban flooding disaster events are generated based on target weather data and geographic data. The A random urban flooding disaster events are then reduced to obtain B reference urban flooding disaster events, where A and B are both integers greater than 1, and A is greater than B.

[0077] Obtain the target urban flooding disaster event, which is any one or more of the B reference urban flooding disaster events;

[0078] Multiple resilience assessment metrics are obtained, including load shedding rate, weighted load recovery rate, islanded power supply coverage, and reserve capacity utilization.

[0079] A multi-index elasticity assessment model is constructed based on multiple elasticity assessment indicators;

[0080] The target urban flooding disaster event is assessed based on a multi-index resilience assessment model to obtain the target resilience assessment value.

[0081] The one or more programs 231 are stored in the memory 230 and configured to be executed by the processor 220. The one or more programs 231 include instructions for performing any step in the above method embodiments.

[0082] The processor 220 may be, for example, a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, cells, and circuits described in conjunction with the disclosure of this application. The processor 220 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication unit may be a communication interface 240, a transceiver, transceiver circuitry, etc., and the storage unit may be a memory 230.

[0083] The memory 230 can be volatile memory or non-volatile memory, or may include both. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0084] It is understood that the electronic device 20 may include more or fewer structural elements than those shown in the above structural block diagram, such as a power module, physical buttons, a Wi-Fi module, a speaker, a Bluetooth module, sensors, a display module, etc., without limitation. It is understood that the electronic device 20 may be equipped with... Figure 1 The aforementioned module composition structure.

[0085] After understanding the software and hardware architecture of this application, the following will be combined with... Figure 3 This application describes a method for assessing the resilience of a power distribution network in response to rainstorm-induced urban flooding. Figure 3 A flowchart illustrating a method for assessing the resilience of a power distribution network in response to rainstorm-induced urban flooding, as provided in this application embodiment, specifically includes the following steps:

[0086] Step S301: Obtain weather and geographic data of the power distribution network within a preset time period under rainstorm and waterlogging disaster.

[0087] The weather data includes multiple rainfall amounts and multiple rainfall durations, and the geographic data includes multiple ground saturation levels and multiple drainage system efficiencies.

[0088] Specifically, weather and geographic data of the power distribution network to be assessed under rainstorm and urban flooding disasters are obtained, selecting data within a preset time period, which includes but is not limited to one day, one week, or one month. Ground saturation represents the degree of water saturation in the soil, i.e., the proportion of soil pores filled with water. Ground saturation is usually expressed as a percentage and reflects the soil's moisture level. Drainage system efficiency refers to the operational efficiency of the urban drainage system, i.e., the system's ability to collect, transport, and treat rainwater and sewage. An efficient drainage system can effectively and promptly remove rainwater, reducing the risk of urban flooding and protecting urban facilities and residents' property. An inefficient drainage system may lead to rainwater accumulation, increasing the risk of urban flooding and impacting facilities within the power distribution network.

[0089] Step S302: Determine the location coefficient and scale coefficient based on the multiple rainfall amounts.

[0090] Among them, the location coefficient considers the impact of geographical location on rainfall. Different geographical locations have different climate conditions, topographic features, etc., which leads to uneven spatial distribution of rainfall. The scale coefficient considers the impact of the research scale on rainfall. Different research scales may lead to different manifestations of rainfall.

[0091] Specifically, statistical analysis was conducted on rainfall data from different regions across multiple rainfall datasets, revealing significant differences in rainfall across mountainous areas, city centers, and suburbs within the power distribution network's coverage area. For example, mountainous areas may experience higher rainfall, while city centers may be affected by the artificial heat island effect, resulting in relatively lower rainfall. Based on the analysis results, location coefficients for different geographical locations were determined. Furthermore, the variation in rainfall at different scales was considered. For instance, rainfall at a local scale may be significantly influenced by topography, while at a regional scale, it may be affected by factors such as atmospheric circulation. Scale coefficients at different scales were determined through statistical analysis of multiple rainfall datasets.

[0092] Step S303: Determine the sample mean and sample standard deviation based on the multiple rainfall durations.

[0093] Specifically, multiple rainfall durations are statistically analyzed. The sample mean is the sum of all sample data divided by the number of samples. That is, the sum of multiple rainfall durations is calculated and then divided by the number of multiple rainfall durations to obtain the sample mean. The sample standard deviation measures the degree of deviation of each data point in the dataset from the mean. That is, the difference between each rainfall duration and the sample mean is calculated, resulting in multiple differences. Then, each of the multiple differences is squared to obtain multiple squared differences. Finally, the sum of the multiple squared differences is divided by the number of multiple rainfall durations and the square root is taken to obtain the sample standard deviation.

[0094] Step S304: Construct a first distribution function and a second distribution function based on the location coefficient, the scale coefficient, the sample mean, and the sample standard deviation, respectively.

[0095] Wherein, the horizontal axis of the first distribution function is the reference rainfall amount, and the vertical axis is the first probability; the horizontal axis of the second distribution function is the reference rainfall duration, and the vertical axis is the second probability.

[0096] The first distribution function is constructed based on the location coefficient α and the scale coefficient β, where x represents the reference rainfall and f1 represents the first probability. The first distribution function is as follows:

[0097]

[0098] The second distribution function is constructed based on the sample mean μ and sample standard deviation σ, where y represents the reference rainfall duration and f2 represents the second probability. The second distribution function is then expressed as follows:

[0099]

[0100] Step S305: Determine the reference rainfall range and reference rainfall duration range corresponding to the first distribution function and the second distribution function being greater than the preset first probability threshold and the second probability threshold, respectively, and randomly generate target weather data within the reference rainfall range and the reference rainfall duration range.

[0101] Specifically, based on a preset first probability threshold, the rainfall data corresponding to the first probability greater than the first probability threshold in the first distribution function is determined, thus obtaining the corresponding reference rainfall range. Then, based on a preset second probability threshold, the rainfall duration data corresponding to the second probability greater than the second probability threshold in the second distribution function is determined, thus obtaining the corresponding reference rainfall duration range. Finally, rainfall data is randomly generated within the reference rainfall range, and rainfall duration data is randomly generated within the reference rainfall duration range, thus obtaining the target weather data.

[0102] Step S306: Generate A random urban flooding disaster events based on the target weather data and the geographic data, and reduce the A random urban flooding disaster events to obtain B reference urban flooding disaster events.

[0103] Where A and B are both integers greater than 1, and A is greater than B. The target weather data includes randomly generated rainfall data and rainfall duration data.

[0104] Specifically, the target weather data and the geographic data are randomly combined to obtain A random urban flooding disaster events; H random urban flooding disaster events are randomly selected from the A random urban flooding disaster events, where H is a positive integer and H is less than or equal to B; the event characteristics corresponding to the H random urban flooding disaster events are determined according to the K-means clustering method, and the event characteristics include weather characteristics and geographic characteristics; the A random urban flooding disaster events are reduced according to the event characteristics to obtain B reference urban flooding disaster events.

[0105] Specifically, from the target weather data, any rainfall amount and any rainfall duration are randomly selected. Then, from the geographical data, any ground saturation and any drainage system efficiency are randomly selected. These are combined to obtain a random urban flooding disaster event. This process is repeated to obtain A random urban flooding disaster events. From these A random urban flooding disaster events, H random urban flooding disaster events are randomly selected, and the event characteristics corresponding to these H random urban flooding disaster events are determined, namely weather characteristics and geographical characteristics, such as the rainfall amount, rainfall duration, ground saturation, and drainage system efficiency corresponding to the random urban flooding disaster events. Based on these event characteristics, and using the K-means clustering method, the A random urban flooding disaster events are divided into H clusters with similar characteristics. Then, one or more random urban flooding disaster events most similar to each of the H clusters are selected, thus reducing the A random urban flooding disaster events to B reference urban flooding disaster events.

[0106] In one possible embodiment, different historical flooding disaster events are randomly selected from weather and geographic data over a preset time period to obtain multiple reference flooding disaster events. This method is relatively simple and does not require detailed analysis of specific event characteristics; simulations can be performed directly based on historical weather and geographic data.

[0107] Step S307: Obtain the target urban flooding disaster event.

[0108] The target urban flooding disaster event is any one or more of the B reference urban flooding disaster events.

[0109] Step S308: Obtain multiple resilience assessment metrics.

[0110] The aforementioned resilience assessment indicators include load shedding rate, weighted load recovery rate, islanded power supply coverage, and reserve capacity utilization rate.

[0111] The formula for obtaining the load failure rate is as follows:

[0112]

[0113] Among them, R L P is the load loss rate. L,i P represents the load power of load node i under normal operating conditions. loss,i Let be the unload power of load node i; N be the total number of load nodes in the distribution network; wherein, the lower the unload rate, the stronger the resilience of the distribution network and the better it can meet the power demand; the higher the unload rate, the weaker the reliability of the distribution network when it is subjected to shocks, that is, the weaker its resilience.

[0114] The formula for obtaining the weighted load recovery rate is as follows:

[0115]

[0116] Among them, R WLR w is the weighted load recovery rate. i U represents the load weight of load node i; i The state value of load node i is defined as follows: when load node i is restored to power, the state value is 1; when load node i is not restored to power, the state value is 0. The weighted load recovery rate reflects the degree of recovery of important loads. The more loads with higher load levels are restored, the higher the weighted load recovery rate will be.

[0117] The formula for obtaining the island power supply coverage rate is as follows:

[0118]

[0119] During periods of severe rainstorms and flooding, the power distribution network can maintain power supply capacity in critical areas by forming isolated islands (i.e., partially independent power grid areas). Among these, R... land The power supply coverage rate of the isolated islands is given by M, where M is the total number of isolated islands formed under the rainstorm and flood disaster, and w is the power supply coverage rate of the isolated islands. land,j P represents the load weight of island j; landj Let j be the load power of the isolated island. The higher the power supply coverage of the isolated island, the stronger the ability of the distribution network to provide power support for critical loads under rainstorm and flood disasters.

[0120] The formula for obtaining the reserve capacity utilization rate is as follows:

[0121]

[0122] Among them, R S P represents the reserve capacity utilization rate. u P represents the actual reserve margin used by the power distribution network during rainstorm and urban flooding disasters. rated This represents the total reserve margin of the distribution network. A high reserve capacity utilization rate indicates that the distribution network faces significant pressure during disasters, while a low reserve capacity utilization rate indicates that the distribution network has sufficient capacity to cope with emergencies.

[0123] The process involves: acquiring basic information about each load node in the distribution network, including load type and load location; determining evaluation indicators for each load node based on the basic information, including vulnerability value and importance; constructing an evaluation indicator system based on the vulnerability value and importance; determining the load level of load node i corresponding to the target urban flooding disaster event based on the evaluation indicator system; and determining the load weight w corresponding to the load level of load node i based on a preset mapping relationship between load level and load weight. i .

[0124] Specifically, the basic information of each load node in the distribution network is obtained, namely, the load type and load location of each load node. Load types include, but are not limited to, residential electricity load, commercial electricity load, industrial electricity load, agricultural electricity load, transportation electricity load, government electricity load, and other electricity loads. Load locations include, but are not limited to, urban loads, suburban loads, rural loads, industrial area loads, commercial area loads, and loads in other locations. Evaluation indicators for load nodes are determined based on different load types and load locations, namely, vulnerability value and importance. Vulnerability value refers to the sensitivity and vulnerability of the load node to the impact of rainstorms and flooding disasters, while importance refers to the influence of the load node on the overall stable operation of the distribution network. Both vulnerability value and importance range from 0 to 1. For example, power facilities in low-lying rural areas, which are considered rural loads, are more susceptible to damage due to water accumulation, with a vulnerability value of 0.8. In contrast, high-lying communication base stations, which are considered urban loads, are relatively safe, with a vulnerability value of 0.4. Hospitals and major transportation hubs are considered more important, with an importance value of 0.8, while scenic spots and park lighting are considered less important, with an importance value of 0.5. Then, an evaluation index system is constructed based on the vulnerability value and importance of each load node. This system is used to determine the load level of load node i corresponding to the target flooding disaster event. For example, in the evaluation index system, the preset vulnerability value threshold and importance threshold are both 0.5. If the threshold is exceeded, it is considered high; otherwise, it is considered low. Assuming that a type A load has a vulnerability value of 0.7 and an importance value of 0.8, then the load level corresponding to type A load is high vulnerability and high importance. Similarly, a type B load has a vulnerability value of 0.5 and an importance value of 0.4, then the load level corresponding to type A load is low vulnerability and low importance. Finally, based on the preset mapping relationship between load level and load weight, the load weight w corresponding to the load level of load node i is determined. i .

[0125] The evaluation index system is constructed based on vulnerability value and importance, specifically including: determining the weight of the evaluation index for each load node according to the preset evaluation index priority; determining the weight vector according to the weight of the evaluation index for each load node; constructing a fuzzy evaluation matrix according to the evaluation index for each load node; performing fuzzy operation on the weight vector and the fuzzy evaluation matrix to obtain the comprehensive membership degree of each load node in the evaluation level; and performing a comprehensive evaluation based on the comprehensive membership degree to obtain the evaluation index system.

[0126] Specifically, the priority of evaluation indicators for load nodes varies depending on the scenario. For example, in a relatively flat area, all load nodes are unlikely to fail, so the vulnerability value of load nodes in this scenario has a lower priority, while the importance value has a higher priority. Weights are determined based on the priority of vulnerability value and importance, with higher priority resulting in a larger weight, to obtain the weight of each load node's evaluation indicator. A weight vector is then determined based on the weight of each load node's evaluation indicator. A fuzzy evaluation matrix is ​​constructed based on the evaluation indicators of each load node, mapping the specific data of each load node to the fuzzy evaluation matrix. The element values ​​in the fuzzy evaluation matrix represent the membership degree of the corresponding indicator at the evaluation level, expressing the degree to which the indicator meets the evaluation level for fuzzy calculation. Finally, fuzzy operations are performed on the weight vector and the fuzzy evaluation matrix to obtain the comprehensive membership degree of each load node at the evaluation level. This comprehensive membership degree is then used for comprehensive evaluation, determining the comprehensive score of each load node on the evaluation indicator, thus obtaining the evaluation indicator system.

[0127] Step S309: Construct a multi-index elasticity assessment model based on the multiple elasticity assessment indices.

[0128] The multi-index elasticity assessment model is as follows:

[0129]

[0130] Among them, R BN N is the target elasticity assessment value. BN w represents the total number of the target urban flooding disaster events. BN,k R is the disaster severity factor of the target urban flooding disaster event k. L,k R is the load loss rate of the target urban flooding disaster event k. WLR,k R is the weighted load recovery rate for the target urban flooding disaster event k. land,k R represents the island power supply coverage rate for the target flooding disaster event k. S,k R is the reserve capacity utilization rate for the target flooding disaster event k, where R BN The higher the value, the stronger the resilience and the better the recovery capability of the power distribution network, enabling it to more effectively cope with the impact of rainstorms and urban flooding.

[0131] Wherein, w1 is the preset weighting factor for the load failure rate in the resilience assessment, w2 is the preset weighting factor for the weighted load recovery rate in the resilience assessment, w3 is the preset weighting factor for the islanded power supply coverage rate in the resilience assessment, and w4 is the preset weighting factor for the reserve capacity utilization rate in the resilience assessment.

[0132] Among them, an urban flooding intensity model is established based on the weather data and the geographical data;

[0133] The waterlogging intensity model is as follows:

[0134] H W =αS W β T R γ exp(uB R -vD P )

[0135] Among them, H W For the intensity of urban flooding, B R For rainfall, T R S represents the duration of rainfall. W D represents the surface water content before rainfall. P The drainage system efficiency is used to represent the capacity of a regional drainage system to handle accumulated water, where α, β, γ, u, and v are preset intensity coefficients.

[0136] The risk level of the target urban flooding disaster event k is determined based on the aforementioned urban flooding intensity model.

[0137] Based on the preset mapping relationship between risk level and disaster level factor, the disaster level factor w corresponding to the risk level of the target urban flooding disaster event k is determined. BN,k .

[0138] In one possible embodiment, the risk level of rainstorm-induced urban flooding disaster events is classified according to an urban flooding intensity model. For ease of understanding, see [link to relevant documentation]. Figure 4 , Figure 4 This is a schematic diagram illustrating the risk level classification of urban flooding disaster events provided in this application embodiment. The risk level is divided into three levels: low risk, medium risk, and high risk, based on the flooding intensity and rainfall duration corresponding to the urban flooding disaster event. Clearly defined risk levels help identify which urban flooding disaster events are more harmful, allowing for more targeted selection of representative events during sampling. For example, urban flooding disaster event A corresponds to a flooding intensity of 20cm and a rainfall duration of 25min, thus its risk level is low risk; urban flooding disaster event B corresponds to a flooding intensity of 60cm and a rainfall duration of 50min, thus its risk level is high risk. If extreme cases of urban flooding disasters need to be analyzed, urban flooding disaster event B is selected for resilience assessment.

[0139] Step S310: Evaluate the target urban flooding disaster event according to the multi-index resilience assessment model to obtain the target resilience assessment value.

[0140] Specifically, the data corresponding to the target urban flooding disaster event is input into the multi-index resilience assessment model for calculation, resulting in a target resilience assessment value. Based on this value, the resilience of the power distribution network in the face of rainstorm-induced urban flooding disasters is determined, and key factors affecting the resilience level are identified. Improvement suggestions can also be generated based on the target resilience assessment value, including but not limited to strengthening drainage system construction, improving urban flooding emergency plans, and optimizing urban planning.

[0141] It is evident that by using this multi-index resilience assessment model, the ability of the power distribution network to cope with and adapt to rainstorm and urban flooding disasters can be more comprehensively evaluated, providing a scientific basis for urban management and disaster response.

[0142] The above primarily describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the electronic device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0143] This application embodiment can divide the electronic device into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0144] When dividing each function into modules according to its corresponding function. Figure 5 This application provides a functional module block diagram of a distribution network resilience assessment device for rainstorm-induced urban flooding disasters. The device 500 includes a first acquisition module 510, a determination module 520, a first construction module 530, a generation module 540, a processing module 550, a second acquisition module 560, a third acquisition module 570, a second construction module 580, and an assessment module 590.

[0145] The first acquisition module 510 is used to acquire weather data and geographical data of the power distribution network within a preset time period under rainstorm and waterlogging disaster. The weather data includes rainfall and rainfall duration, and the geographical data includes ground saturation and drainage system efficiency.

[0146] The determination module 520 is used to determine the location coefficient and scale coefficient based on the multiple rainfall amounts; and to determine the sample mean and sample standard deviation based on the multiple rainfall durations.

[0147] The first construction module 530 is used to construct a first distribution function and a second distribution function based on the location coefficient, the scale coefficient, the sample mean, and the sample standard deviation, respectively. The horizontal axis of the first distribution function is the reference rainfall amount, and the vertical axis is the first probability. The horizontal axis of the second distribution function is the reference rainfall duration, and the vertical axis is the second probability.

[0148] The generation module 540 is used to determine the reference rainfall range and reference rainfall duration range corresponding to the first distribution function and the second distribution function being greater than the preset first probability threshold and the second probability threshold, respectively, and to randomly generate target weather data within the reference rainfall range and the reference rainfall duration range;

[0149] The processing module 550 is used to generate A random urban flooding disaster events based on the target weather data and the geographic data, and to reduce the A random urban flooding disaster events to obtain B reference urban flooding disaster events, wherein A and B are both integers greater than 1, and A is greater than B;

[0150] The second acquisition module 560 is used to acquire a target urban flooding disaster event, wherein the target urban flooding disaster event is any one or more of the B reference urban flooding disaster events;

[0151] The third acquisition module 570 is used to acquire multiple resilience assessment indicators, including load failure rate, weighted load recovery rate, islanded power supply coverage rate, and reserve capacity utilization rate.

[0152] The second construction module 580 is used to construct a multi-index elasticity assessment model based on the multiple elasticity assessment indices.

[0153] The assessment module 590 is used to assess the target urban flooding disaster event according to the multi-index resilience assessment model and obtain the target resilience assessment value.

[0154] It is evident that by analyzing multiple resilience assessment indicators, the resilience level of the power distribution network under rainstorm and urban flooding disasters can be assessed more comprehensively and accurately.

[0155] It should be noted that the specific implementation of each operation can be described in the corresponding description of the method embodiments shown above. The distribution network resilience assessment device 500 for rainstorm and urban flood disasters can be used to execute the above method embodiments of this application, and will not be described again here.

[0156] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.

[0157] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0158] It should be noted that, for the sake of simplicity, the above embodiments are all described as a series of actions. Those skilled in the art should understand that this application is not limited to the described order of actions, as some steps in the embodiments of this application can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions, steps, modules, or units involved are not necessarily essential to the embodiments of this application.

[0159] In the above embodiments, the descriptions of each embodiment in this application have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0160] In summary, by implementing the embodiments of this application, combining risk level, load level classification and other relevant indicators, and by comprehensively considering different factors, the response capability and system resilience of the distribution network under rainstorm and urban flooding disasters can be more accurately assessed, providing theoretical guidance for the prevention, response and recovery capabilities of the distribution network in the face of rainstorm and urban flooding disasters.

[0161] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0162] The steps of the methods or algorithms described in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, portable hard disk, read-only optical disk (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Furthermore, the ASIC can reside in a terminal device or management device. Alternatively, the processor and storage medium can exist as discrete components in the terminal device or management device.

[0163] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in the embodiments of this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0164] The modules / units included in the various devices and products described in the above embodiments can be software modules / units, hardware modules / units, or a combination of both. For example, for devices and products applied to or integrated into a chip, all modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits. For devices and products applied to or integrated into a chip module, all modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The implementation is achieved through a software program that runs on a processor integrated within the chip module. The remaining modules / units (if any) can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into terminal equipment, each of their modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components within the terminal equipment. Alternatively, at least some modules / units can be implemented using a software program that runs on a processor integrated within the terminal equipment, while the remaining modules / units (if any) can be implemented using hardware methods such as circuits.

[0165] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this application. It should be understood that the above descriptions are merely specific embodiments of the embodiments of this application and are not intended to limit the protection scope of the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solutions of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. A method for assessing the resilience of power distribution networks in response to rainstorm-induced urban flooding disasters, characterized in that, include: Acquire weather and geographic data of the power distribution network within a preset time period under rainstorm and urban flooding disaster. The weather data includes multiple rainfall amounts and multiple rainfall durations, and the geographic data includes multiple ground saturation values ​​and multiple drainage system efficiencies. The ground saturation value is used to represent the amount of surface water before rainfall. The location coefficient and scale coefficient are determined based on the multiple rainfall amounts; The sample mean and sample standard deviation are determined based on the multiple rainfall durations. A first distribution function and a second distribution function are constructed based on the location coefficient, the scale coefficient, the sample mean, and the sample standard deviation, respectively. The horizontal axis of the first distribution function is the reference rainfall, and the vertical axis is the first probability. The horizontal axis of the second distribution function is the reference rainfall duration, and the vertical axis is the second probability. Determine the reference rainfall range and reference rainfall duration range corresponding to the first distribution function and the second distribution function being greater than the preset first probability threshold and the second probability threshold, respectively, and randomly generate target weather data within the reference rainfall range and the reference rainfall duration range; Based on the target weather data and the geographic data, A random urban flooding disaster events are generated. The A random urban flooding disaster events are then reduced to obtain B reference urban flooding disaster events, where A and B are both integers greater than 1, and A is greater than B. Acquire a target urban flooding disaster event, wherein the target urban flooding disaster event is any one or more of the B reference urban flooding disaster events; Multiple resilience assessment metrics are obtained, including load failure rate, weighted load recovery rate, islanded power supply coverage rate, and reserve capacity utilization rate. A multi-index elasticity assessment model is constructed based on the aforementioned multiple elasticity assessment indicators; The target urban flooding disaster event is evaluated based on the multi-index resilience assessment model to obtain the target resilience assessment value; The step of constructing a multi-index elasticity assessment model based on the multiple elasticity assessment indicators includes: The multi-index elasticity assessment model is as follows: Among them, R BN N is the target elasticity assessment value. BN w represents the total number of the target urban flooding disaster events. BN,k R is the disaster severity factor of the target urban flooding disaster event k. L，k R is the load loss rate of the target urban flooding disaster event k. WLR,k R is the weighted load recovery rate for the target urban flooding disaster event k. land,k R represents the island power supply coverage rate for the target flooding disaster event k. S,k The reserve capacity utilization rate for the target urban flooding disaster event k; Wherein, w1 is the preset weighting factor for the load failure rate in the resilience assessment, w2 is the preset weighting factor for the weighted load recovery rate in the resilience assessment, w3 is the preset weighting factor for the islanded power supply coverage rate in the resilience assessment, and w4 is the preset weighting factor for the reserve capacity utilization rate in the resilience assessment.

2. The method as described in claim 1, characterized in that, The process involves generating A random urban flooding disaster events based on the target weather data and the geographic data, and then reducing these A random urban flooding disaster events to obtain B reference urban flooding disaster events, including: The target weather data and the geographic data are randomly combined to obtain the A random urban flooding disaster events; H random waterlogging disaster events are randomly selected from the A random waterlogging disaster events, where H is a positive integer and H is less than or equal to B; The event characteristics corresponding to the H random urban flooding disaster events are determined using the K-means clustering method, and the event characteristics include weather characteristics and geographical characteristics. Based on the event characteristics, the A random urban flooding disaster events are reduced to obtain B reference urban flooding disaster events.

3. The method as described in claim 1, characterized in that, The acquisition of multiple resilience assessment metrics includes: The formula for obtaining the load failure rate is as follows: Among them, R L P is the load failure rate. L,i P represents the load power of load node i under normal operating conditions. loss,i Let N be the unload power of load node i; N is the total number of load nodes in the distribution network. The formula for obtaining the weighted load recovery rate is as follows: Among them, R WLR w is the weighted load recovery rate. i U represents the load weight of load node i; i The state value of load node i is 1 when load node i resumes power supply and 0 when load node i fails to resume power supply. The formula for obtaining the island power supply coverage rate is as follows: Among them, R land The power supply coverage rate of the isolated islands is given by M, where M is the total number of isolated islands formed under the rainstorm and flood disaster, and w is the power supply coverage rate of the isolated islands. land,j P represents the load weight of island j; landj The load power of the isolated island j; The formula for obtaining the reserve capacity utilization rate is as follows: Among them, R S P represents the reserve capacity utilization rate. u P represents the actual reserve margin used by the power distribution network during rainstorm and urban flooding disasters. rated This represents the total reserve margin of the power distribution network.

4. The method as described in claim 3, characterized in that, The method further includes: Obtain basic information for each load node in the power distribution network, including load type and load location; The evaluation index for each load node is determined based on the aforementioned basic information. The evaluation index includes vulnerability value and importance. An evaluation index system is constructed based on the aforementioned vulnerability value and importance. The load level of load node i corresponding to the target urban flooding disaster event is determined according to the evaluation index system. Based on the preset mapping relationship between load level and load weight, the load weight w corresponding to the load level of load node i is determined. i .

5. The method as described in claim 4, characterized in that, The construction of the evaluation index system based on the vulnerability value and the importance includes: The weight of the evaluation index for each load node is determined according to the preset evaluation index priority. The weight vector is determined based on the weight of the evaluation index for each load node. A fuzzy evaluation matrix is ​​constructed based on the evaluation indicators of each load node; The weight vector and the fuzzy evaluation matrix are subjected to fuzzy operation to obtain the comprehensive membership degree of each load node in the evaluation level. The evaluation index system is obtained by conducting a comprehensive evaluation based on the comprehensive membership degree.

6. The method as described in claim 1, characterized in that, The method further includes: An urban flooding intensity model is established based on the aforementioned weather data and geographical data; The waterlogging intensity model is as follows: H W =αS W β T R γ exp(uB R -vD P ) Among them, H W For the intensity of urban flooding, B R For rainfall, T R S represents the duration of rainfall. W D represents ground saturation. P The drainage system efficiency is used to represent the capacity of a regional drainage system to handle accumulated water, where α, β, γ, u, and v are preset intensity coefficients. The risk level of the target urban flooding disaster event k is determined based on the aforementioned urban flooding intensity model. Based on the preset mapping relationship between risk level and disaster level factor, the disaster level factor w of the target urban flooding disaster event k corresponding to the risk level of the target urban flooding disaster event k is determined. BN,k .

7. A power distribution network resilience assessment device for rainstorm-induced urban flooding disasters, used to perform the method as described in any one of claims 1-6, characterized in that, It includes a first acquisition module, a determination module, a first construction module, a generation module, a processing module, a second acquisition module, a third acquisition module, a second construction module, and an evaluation module, wherein: The first acquisition module is used to acquire weather data and geographical data of the power distribution network within a preset time period under rainstorm and waterlogging disaster. The weather data includes multiple rainfall amounts and multiple rainfall durations, and the geographical data includes multiple ground saturation values ​​and multiple drainage system efficiencies. The ground saturation value is used to represent the amount of surface water before rainfall. The determining module is used to determine the location coefficient and scale coefficient based on the multiple rainfall amounts; and to determine the sample mean and sample standard deviation based on the multiple rainfall durations. The first construction module is used to construct a first distribution function and a second distribution function based on the location coefficient, the scale coefficient, the sample mean, and the sample standard deviation, respectively. The horizontal axis of the first distribution function is the reference rainfall amount, and the vertical axis is the first probability. The horizontal axis of the second distribution function is the reference rainfall duration, and the vertical axis is the second probability. The generation module is used to determine the reference rainfall range and reference rainfall duration range corresponding to the first distribution function and the second distribution function being greater than the preset first probability threshold and the second probability threshold, respectively, and to randomly generate target weather data within the reference rainfall range and the reference rainfall duration range. The processing module is used to generate A random urban flooding disaster events based on the target weather data and the geographic data, and to reduce the A random urban flooding disaster events to obtain B reference urban flooding disaster events, wherein A and B are both integers greater than 1, and A is greater than B; The second acquisition module is used to acquire a target urban flooding disaster event, wherein the target urban flooding disaster event is any one or more of the B reference urban flooding disaster events; The third acquisition module is used to acquire multiple resilience assessment indicators, including load failure rate, weighted load recovery rate, islanded power supply coverage rate, and reserve capacity utilization rate. The second construction module is used to construct a multi-index elasticity assessment model based on the multiple elasticity assessment indicators; The assessment module is used to assess the target urban flooding disaster event based on the multi-index resilience assessment model to obtain the target resilience assessment value.

8. An electronic device, characterized in that, include: A processor, a memory, and one or more programs; said one or more programs are stored in said memory and configured to be executed by said processor, said programs including instructions for performing the steps of the method as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The computer storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-6.

Images