Power distribution network equipment vulnerability evaluation method in extreme scene and related equipment

Abstract

The invention discloses a power distribution network equipment vulnerability assessment method in an extreme scene and related equipment, and belongs to the technical field of power equipment disaster influence assessment, and the method comprises the steps: constructing a grid water accumulation model, and calculating the water accumulation height of each grid based on the grid water accumulation model and a rainfall center position; calculating the fault probability of the distribution transformer and the line based on the ponding height and the environmental factor; carrying out fault sampling based on the fault probability of the distribution transformer and the line, and determining which components may have faults; and outputting the fault component list and the fault probability. Through ponding dynamic simulation and equipment failure probability calculation, the accuracy of vulnerability evaluation of the power system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of disaster impact assessment of power equipment, and specifically relates to a method for assessing the vulnerability of distribution network equipment in extreme scenarios and related equipment. Background Art

[0002] With global climate change and the frequent occurrence of extreme weather events, the challenges faced by distribution network equipment are becoming increasingly severe. Extreme scenarios, such as strong storms, high temperatures, ice and snow disasters, etc., reduce the power supply reliability of the distribution network. These extreme conditions not only increase the maintenance cost of power facilities, but also directly affect the continuity and reliability of power supply, and have a potential huge impact on social and economic activities. Therefore, establishing a model that can accurately assess the vulnerability of distribution network equipment under extreme scenarios is crucial to preventing and mitigating the impact of natural disasters on the power system.

[0003] Existing distribution network equipment vulnerability assessment methods are usually based on historical disaster data and basic properties of equipment, such as materials, structures, and geographical locations, to estimate the probability of equipment being damaged under specific disaster conditions. However, these traditional methods have the following main shortcomings and deficiencies: Traditional models mostly rely on statistical analysis and lack a dynamic response mechanism to extreme weather events. This means that they may not be able to capture the suddenness and nonlinear characteristics of extreme events, resulting in limited accuracy of prediction results. Existing technologies often ignore the interaction of multiple factors in extreme scenarios. For example, strong winds may be accompanied by heavy rains, while ice and snow cover may aggravate the impact of low temperatures on equipment. The consideration of a single factor is not enough to reflect the complexity of the real world, which limits the model's predictive ability and scope of application. Summary of the invention

[0004] The purpose of the present invention is to provide a method for assessing the vulnerability of distribution network equipment under extreme scenarios and related equipment, aiming to improve the accuracy of the vulnerability assessment of the power system through dynamic simulation of water accumulation and calculation of equipment failure probability.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for assessing the vulnerability of distribution network equipment in extreme scenarios, comprising: Construct a gridded waterlogging model, and calculate the waterlogging height of each grid based on the gridded waterlogging model and the rainfall center position; Calculate the failure probability of distribution transformers and lines based on water accumulation height and environmental factors; Fault sampling based on the failure probability of distribution transformers and lines to determine which components are likely to fail; Output a list of failed components and their failure probabilities.

[0006] Furthermore, a gridded waterlogging model is constructed. Based on the gridded waterlogging model and the rainfall center position, the waterlogging height of each grid is calculated, including: Establish a gridded waterlogging model, create a grid coordinate system, and define the longitude and latitude range; Grid the power supply area of ​​the distribution network and set the water accumulation height in the same grid to be the same; Dynamically update the water accumulation height of each grid based on rainfall, drainage capacity and terrain.

[0007] Furthermore, the iterative calculation formula for the water accumulation height used when dynamically updating the water accumulation height of each grid is:

[0008]

[0009] in: Indicates along Direction of water flow, >0 indicates water inflow, otherwise it indicates water outflow; for Rainfall intensity at each moment; is the time variation, is the time-dependent depth of ponding; is the water depth of grid z at time t; for The water depth of grid z at time instant; is the grid width; For along Direction of water flow velocity, symbol Refers to the grid z surrounding , , , Direction, grid z and diagonal grid , , , There is no flow relationship between them; q z (t )for Drainage flow in grid z at time; is the duration of the simulation segment; , , , are the grids in the east, west, north and south directions of grid z respectively. , , , They are the grids in the northeast, northwest, southeast and southwest directions of grid z respectively.

[0010] Furthermore, based on the degree of water accumulation and environmental factors, the failure probability of distribution transformers and lines is calculated including: Calculate the probability of distribution transformer failure based on the water accumulation height of the grid where the distribution transformer is located; Calculate wind load, rain load and ice load of overhead lines; Based on the overhead line wind load, overhead line rain load and overhead line ice load, the failure probability and fault probability of the line are obtained through the joint probability distribution function; Based on the distribution transformer failure probability and line failure probability, the overall distribution network component failure probability is calculated.

[0011] Furthermore, the failure rate of the distribution transformer is calculated by the following formula:

[0012] Where: for Time equipment The water level of the grid; for Time equipment The degree of water accumulation in the grid; Design flood-proof height for distribution station (room) or box-type substation; To mark the height of the cable joints of the high-voltage switch cabinet in the station; is the attenuation coefficient; is the damping coefficient.

[0013] Furthermore, the wind load of the overhead line is calculated by the following formula:

[0014] In the formula, is the wind load on overhead lines, N; Wind pressure unevenness coefficient is related to wind speed; Wind speed at a base height of 10m, ; The coefficient of wind pressure height variation is related to the surface roughness and the altitude of the line; The shape factor of the conductor; Wind vibration coefficient; Conductor outer diameter or calculated outer diameter when covered with ice, mm; Horizontal span of tower, m; Wind load increase factor when covered with ice; Angle between wind direction and conductor; The rain load on overhead lines is calculated by the following formula:

[0015] In the formula, is the overhead line rain load, N; The diameter of the water drop, mm; n is the number of water droplets per unit volume, pieces / ; b The area of ​​the rain-facing surface of the component, ; The final velocity of the raindrop, m / s; Ice load on overhead lines is calculated by:

[0016] In the formula, is the ice load on the overhead line, N; b is the ice thickness, mm; d is the outer diameter of the overhead line, mm; A is the cross-sectional area of ​​the overhead line, ; g acceleration due to gravity, .

[0017] Furthermore, based on the overhead line wind load, the overhead line rain load and the overhead line ice load, the failure probability and fault probability of the line are obtained through the joint probability distribution function, including: Calculate the combined line load W and derive the mean of the overhead line load probability density function based on the normal distribution of W and the mean of the probability density function of overhead line strength ; The line combined load W is determined by the following formula:

[0018] Where G is the weight of the conductor; The mean of the joint distribution function Z(t) is calculated by the following formula With variance :

[0019] Where: The overhead line strength and load both obey normal distribution, is the mean value of the probability density function of overhead line strength; is the standard deviation of the probability density function of overhead line strength; The joint probability density function is transformed into a standard normal distribution form, and the failure probability of the line is:

[0020] Where: is the failure probability of the line at time t, is the standard normal distribution function, is the mean of the joint distribution function at time t, is the variance of the joint distribution function at time t; Calculate line failure rate based on line failure probability : .

[0021] In a second aspect, the present invention provides a distribution network equipment vulnerability assessment system under extreme scenarios, comprising: A gridded waterlogging model building module is used to build a gridded waterlogging model and calculate the waterlogging height of each grid based on the gridded waterlogging model and the rainfall center position; Failure probability calculation module, used to calculate the failure probability of distribution transformers and lines based on water accumulation height and environmental factors; A fault sampling module is used to perform fault sampling based on the fault probability of distribution transformers and lines to determine which components may fail; The output module is used to output the list of faulty components and the probability of failure.

[0022] The present invention is further improved in that: In a third aspect, the present invention provides an electronic device, comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for assessing the vulnerability of distribution network equipment in extreme scenarios as described in any one of the first aspects of the present invention.

[0023] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for assessing the vulnerability of distribution network equipment in an extreme scenario as described in any one of the first aspects of the present invention.

[0024] Compared with the prior art, the present invention has at least the following beneficial technical effects: (1) The present invention provides a method for assessing the vulnerability of distribution network equipment in extreme scenarios that takes into account environmental factors and dynamic changes in water accumulation. It uses advanced water accumulation dynamic simulation technology and equipment failure probability calculation models to solve the shortcomings of traditional methods in predicting the failure of distribution network equipment under rainstorm disasters. By gridding the distribution network area and considering the influence of multiple factors such as rainfall, topography, drainage system, and urban drainage capacity, this method can more accurately simulate the water accumulation process and predict the failure probability of equipment. This not only improves the accuracy of the prediction, but also helps the power sector take measures in advance to reduce the impact of natural disasters such as rainstorms on the power system and improve the safety and reliability of the power system.

[0025] (2) The exponential function fitting method adopted by the present invention calculates the failure rate of power distribution equipment according to the dynamic change of water accumulation height. Compared with the traditional static threshold judgment, this method can more accurately reflect the actual risk status of equipment under extreme weather conditions. This dynamic accumulation model is similar to the icing disaster model, which takes into account the characteristics that the failure rate of equipment changes with meteorological changes, thereby improving the accuracy and reliability of the prediction.

[0026] (3) The present invention comprehensively considers various loads on overhead lines under extreme weather conditions, including ice load, wind load, and rain load. By accurately calculating the impact of these loads on the lines, the safety status of the lines can be more comprehensively evaluated. This comprehensive load risk analysis helps the power sector identify potential line failure points and take reinforcement or maintenance measures in a timely manner to ensure the stability of power transmission.

[0027] (4) The present invention integrates geographic information system (GIS) data to achieve an organic combination of geographic spatial information and equipment failure prediction, so that the prediction results can be intuitively displayed on the map, which is convenient for the power department to make spatial decisions and resource scheduling. This visual analysis tool improves the practical value of the prediction results and helps to quickly respond and deal with emergencies. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flow chart of the method for assessing the vulnerability of distribution network equipment in extreme scenarios provided by the present invention; Figure 2 A structural block diagram of a distribution network equipment vulnerability assessment system under extreme scenarios provided by an embodiment of the present invention; Figure 3 A block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] The present invention is further described in detail below in conjunction with the accompanying drawings: Example 1 Reference Figure 1 This embodiment provides a method for evaluating the vulnerability of distribution network equipment in extreme scenarios, including the following steps: Step 1: Data preprocessing and environmental parameter collection: Convert the distribution network data, historical data and environmental parameters required for distribution network component failure prediction into a format suitable for analysis (e.g., a neatly classified table that can be called by the program); In some embodiments, converting the historical data and environmental parameters required for distribution network component failure prediction into a format suitable for analysis and setting initial parameters includes the following steps: Step 1.1: Read the technical parameters of the distribution network, including the technical parameters of the distribution transformer and the line, including the relevant geographic information system (GIS) data, the design flood prevention height, the elevation of the cable joint, the horizontal span of the tower, the line direction, the conductor mass (kg), the relevant geographic information system (GIS) data including the equipment location, the line location, etc., read the distribution network data from the Excel file and clean up the missing values.

[0032] Collect environmental parameters, including rainfall level, rainfall center coordinates, extreme weather types, wind speed, and ice thickness in the area where the distribution network is located; The historical data includes historical failure records of equipment, and the historical failure records are used to determine the coefficients in the calculation formula of the present invention; Step 1.2: Geographic coordinate conversion: convert the geographic coordinates of distribution transformers and lines into a grid coordinate system that is convenient for calculation.

[0033] Step 2: Waterlogging simulation: Establish a gridded waterlogging model and calculate the waterlogging degree of each grid based on the grid and rainfall center position; In some embodiments, based on the grid and the rainfall center position, calculating the water accumulation degree of each grid comprises the following steps: Step 2.1: Establish a gridded waterlogging model, initialize the gridded waterlogging model parameters, create a gridded coordinate system, and define the latitude and longitude range. Use the Digital Elevation Model (DEM) data to grid the distribution network power supply area, and set the same waterlogging height in the same grid.

[0034] The parameters of the gridded waterlogging model include grid width, simulation period length, rainfall level, rainfall center coordinates, extreme weather type, wind speed, ice thickness, etc. Among them, rainfall level, rainfall center coordinates, extreme weather type, wind speed, and ice thickness are the rainfall level, rainfall center coordinates, extreme weather type, wind speed, and ice thickness of the distribution network area collected; Step 2.2: Dynamic calculation of waterlogging, dynamically update the waterlogging height of each grid according to factors such as rainfall, drainage capacity, terrain, drainage system function, and urban drainage capacity. The iterative calculation formula for waterlogging height is:

[0035]

[0036] Where: Indicates along Direction of water flow, >0 indicates water inflow, otherwise it indicates water outflow; for Rainfall intensity at each moment; is the time variation, is the time-dependent depth of ponding; is the water depth of grid z at time t; for The water depth of grid z at time instant; is the grid width; For along Direction of water flow velocity, symbol Refers to the grid z surrounding , , , Direction, grid z and diagonal grid , , , There is no flow relationship between them; q z (t )for Drainage flow in grid c at time; The duration of the simulation segment. , , , are the grids in the east, west, north and south directions of grid z respectively. , , , They are the grids in the northeast, northwest, southeast and southwest directions of grid z respectively.

[0037] Step 2.3: Output the water accumulation height and record the grid water accumulation situation at each time point.

[0038] Step 3: Calculation of component failure probability: Calculate the failure probability of distribution transformers and lines based on water accumulation height and other environmental factors; In some embodiments, based on the degree of water accumulation and other environmental factors, calculating the failure probability of distribution transformers and lines includes the following steps: Step 3.1: Calculation of distribution transformer failure probability. For each distribution transformer, the failure probability is calculated based on the water accumulation level of the grid where it is located. The relationship between the failure rate of the distribution transformer and the water accumulation height is as follows:

[0039] Where: for Time distribution transformer The degree of water accumulation in the grid; for Time distribution transformer The degree of water accumulation in the grid; Design flood-proof height for distribution station (room) or box-type substation; To mark the height of the cable joints of the high-voltage switch cabinet in the station; is the attenuation coefficient; is the damping coefficient.

[0040] Step 3.2: Calculate wind load, rain load and ice load. The calculation formula for overhead line wind load is:

[0041] In the formula, is the wind load on overhead lines, N; Wind pressure unevenness coefficient is related to wind speed; Wind speed at a base height of 10m, ; The coefficient of wind pressure height variation is related to the surface roughness and the altitude of the line; The shape factor of the conductor; Wind vibration coefficient; Conductor outer diameter or calculated outer diameter when covered with ice, mm; Horizontal span of tower, m; Wind load increase factor when covered with ice; The angle between wind direction and conductor is obtained from wind direction and line direction.

[0042] Calculation formula for rain load on overhead lines

[0043] In the formula, is the overhead line rain load, N; The diameter of the water drop, mm; n is the number of water droplets per unit volume, pieces / ; b The area of ​​the rain-facing surface of the component, ; Terminal velocity of raindrop, m / s.

[0044] Calculation formula for ice load on overhead lines

[0045] In the formula, is the ice load on the overhead line, N; b is the ice thickness, mm; d is the outer diameter of the overhead line, mm; A is the cross-sectional area of ​​the overhead line, ; g acceleration due to gravity, .

[0046] Step 3.3: Obtain the failure probability and fault probability of the line through the joint probability distribution function Calculate the combined line load W and derive the mean of the overhead line load probability density function based on the normal distribution of W and the mean of the probability density function of overhead line strength ; The line combined load W is determined by the following formula:

[0047] Among them, G is the weight of the wire, which is the product of the mass of the wire and the acceleration due to gravity.

[0048] The mean of the joint distribution function Z(t) With variance The relationship is as follows:

[0049] Where: The overhead line strength and load both obey normal distribution, is the mean value of the probability density function of overhead line strength; is the standard deviation of the probability density function of overhead line strength; is the mean value of the probability density function of overhead line load; is the mean of the probability density function of overhead line strength.

[0050] By the mean With variance The joint probability density function is calculated using the standard normal distribution formula. The joint probability density function is converted into a standard normal distribution form, and the failure probability of the line is:

[0051] Where: is the failure probability of the line at time t, is the standard normal distribution function, is the mean of the joint distribution function at time t, is the variance of the joint distribution function at time t.

[0052] Line failure rate The relationship with failure probability is:

[0053] Step 3.4: Summarize the failure probabilities to obtain the overall distribution network component failure probability.

[0054] Step 4: Fault sampling: Fault sampling is performed based on the failure probability of distribution transformers and lines to determine which components may fail. Fault sampling is randomly obtained based on the failure probability, or quantitatively selected based on the failure probability ranking. Step 5: Output the results, including a list of faulty components and a fault probability analysis report. The analysis report includes the calculated fault probability and an analysis of whether the faulty components are fragile or not based on the frequency.

[0055] The following is a system embodiment of the present invention, which can be used to implement the method embodiment of the present invention. For details not disclosed in the device embodiment, please refer to the method embodiment of the present invention.

[0056] Example 2 See also Figure 2 In this embodiment, a distribution network equipment vulnerability assessment system under extreme scenarios is provided, including: A gridded waterlogging model building module is used to build a gridded waterlogging model and calculate the waterlogging height of each grid based on the gridded waterlogging model and the rainfall center position; Failure probability calculation module, used to calculate the failure probability of distribution transformers and lines based on water accumulation height and environmental factors; A fault sampling module is used to perform fault sampling based on the fault probability of distribution transformers and lines to determine which components may fail; The output module is used to output the list of faulty components and the probability of failure.

[0057] All relevant contents of each step involved in the embodiment of the aforementioned distribution network equipment vulnerability assessment method under an extreme scenario can be referred to the functional description of the functional modules corresponding to the distribution network equipment vulnerability assessment system under an extreme scenario in an embodiment of the present invention, and will not be repeated here.

[0058] Example 3 Reference Figure 3 , this embodiment provides an electronic device, which includes a processor and a memory, and the processor and the memory are connected through a bus; the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a method for vulnerability assessment of distribution network equipment in an extreme scenario. The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The fact that only one line is used in the diagram does not mean that there is only one bus or only one type of bus.

[0059] Example 4 This embodiment provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the above-mentioned embodiment of the method for assessing the vulnerability of distribution network equipment in an extreme scenario.

[0060] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0061] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0064] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the vulnerability of distribution network equipment in extreme scenarios, characterized in that: include: Construct a gridded waterlogging model, and calculate the waterlogging height of each grid based on the gridded waterlogging model and the rainfall center position; Calculate the failure probability of distribution transformers and lines based on water accumulation height and environmental factors; Fault sampling based on the failure probability of distribution transformers and lines to determine which components are likely to fail; Output a list of failed components and their failure probabilities.

2. According to a method for assessing the vulnerability of distribution network equipment in extreme scenarios in claim 1, it is characterized in that: The step of constructing a gridded waterlogging model and calculating the waterlogging height of each grid based on the gridded waterlogging model and the rainfall center position includes: Establish a gridded waterlogging model, create a grid coordinate system, and define the longitude and latitude range; Grid the power supply area of ​​the distribution network and set the water accumulation height in the same grid to be the same; Dynamically update the water accumulation height of each grid based on rainfall, drainage capacity and terrain.

3. The method for assessing the vulnerability of distribution network equipment in extreme scenarios according to claim 2 is characterized in that: The iterative calculation formula for the water accumulation height used when dynamically updating the water accumulation height of each grid is: in: Indicates along Direction of water flow, >0 indicates water inflow, otherwise it indicates water outflow; for Rainfall intensity at each moment; is the time variation, is the depth of ponding related to time; is the water depth of grid z at time t; for The water depth of grid z at time instant; is the grid width; For along Direction of water flow velocity, symbol Refers to the grid z surrounding , , , Direction, grid z and diagonal grid , , , There is no flow relationship between them; q z (t )for Drainage flow in grid z at time; is the duration of the simulation segment; , , , are the grids in the east, west, north and south directions of grid z respectively. , , , They are the grids in the northeast, northwest, southeast and southwest directions of grid z respectively.

4. The method for assessing the vulnerability of distribution network equipment in extreme scenarios according to claim 1 is characterized in that: The calculation of the failure probability of distribution transformers and lines based on the degree of water accumulation and environmental factors includes: Calculate the probability of distribution transformer failure based on the water accumulation height of the grid where the distribution transformer is located; Calculate wind load, rain load and ice load of overhead lines; Based on the overhead line wind load, overhead line rain load and overhead line ice load, the failure probability and fault probability of the line are obtained through the joint probability distribution function; Based on the distribution transformer failure probability and line failure probability, the overall distribution network component failure probability is calculated.

5. The method for assessing the vulnerability of distribution network equipment in extreme scenarios according to claim 4 is characterized in that: The failure rate of the distribution transformer is calculated by the following formula: Where: for Time equipment The water level of the grid; for Time equipment The degree of water accumulation in the grid; Design flood-proof height for distribution station (room) or box-type substation; To mark the height of the cable joints of the high-voltage switch cabinet in the station; is the attenuation coefficient; is the damping coefficient.

6. The method for assessing the vulnerability of distribution network equipment in extreme scenarios according to claim 4 is characterized in that: The wind load on overhead lines is calculated by the following formula: In the formula, is the wind load on overhead lines, N; Wind pressure unevenness coefficient is related to wind speed; Wind speed at a base height of 10m, ; The wind pressure height variation coefficient is related to the surface roughness and the altitude of the line; The shape factor of the conductor; Wind vibration coefficient; Conductor outer diameter or calculated outer diameter when covered with ice, mm; Horizontal span of tower, m; Wind load increase factor when covered with ice; Angle between wind direction and conductor; The rain load on overhead lines is calculated by the following formula: In the formula, is the overhead line rain load, N; The diameter of the water drop, mm; n is the number of water droplets per unit volume, pieces / ; b The area of ​​the rain-facing surface of the component, ; The final velocity of the raindrop, m / s; Ice load on overhead lines is calculated by: In the formula, is the ice load on the overhead line, N; b is the ice thickness, mm; d is the outer diameter of the overhead line, mm; A is the cross-sectional area of ​​the overhead line, ; g acceleration due to gravity, .

7. The method for assessing the vulnerability of distribution network equipment in extreme scenarios according to claim 4 is characterized in that: The method of obtaining the failure probability and fault probability of the line based on the wind load of the overhead line, the rain load of the overhead line and the ice load of the overhead line by combining the probability distribution function includes: Calculate the combined line load W and derive the mean of the overhead line load probability density function based on the normal distribution of W and the mean value of the probability density function of the overhead line strength ; The line combined load W is determined by the following formula: Where G is the weight of the conductor; The mean of the joint distribution function Z(t) is calculated by the following formula With variance : Where: The overhead line strength and load both obey normal distribution, is the mean value of the probability density function of overhead line strength; is the standard deviation of the probability density function of overhead line strength; The joint probability density function is transformed into a standard normal distribution form, and the failure probability of the line is: Where: is the failure probability of the line at time t, is the standard normal distribution function, is the mean of the joint distribution function at time t, is the variance of the joint distribution function at time t; Calculate line failure rate based on line failure probability : .

8. A distribution network equipment vulnerability assessment system under extreme scenarios, characterized in that: include: A gridded waterlogging model building module is used to build a gridded waterlogging model and calculate the waterlogging height of each grid based on the gridded waterlogging model and the rainfall center position; Failure probability calculation module, used to calculate the failure probability of distribution transformers and lines based on water accumulation height and environmental factors; A fault sampling module is used to perform fault sampling based on the fault probability of distribution transformers and lines to determine which components may fail; The output module is used to output the list of faulty components and the probability of failure.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for assessing the vulnerability of distribution network equipment in an extreme scenario as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a method for assessing the vulnerability of distribution network equipment in extreme scenarios according to any one of claims 1 to 7 is implemented.

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