Random fault scene set-based power distribution network vulnerability assessment method and system

Abstract

The invention discloses a power distribution network vulnerability assessment method and system based on a random fault scene set, and belongs to the technical field of power equipment disaster influence assessment. According to the method, the fault probability of an upstream distribution transformer and the fault probability of a line which are possibly caused in a single random extreme condition scene are considered; obtaining a downstream distribution transformer which may have a fault based on the line which may have the fault; according to the method, the land where all the distribution transformers which may fail is located is judged, third-level indexes can be obtained by combining the importance of the land where the distribution transformers are located and the probability of occurrence of the distribution transformers, second-level indexes and first-level indexes are further obtained, and after all the first-level indexes are combined, the vulnerability of the whole power distribution network in a random fault scene can be judged. According to the method, through in-depth analysis of specific extreme events, areas and facilities which are affected most seriously can be rapidly identified, the exported indexes can guide short-term emergency resource layout, and a scientific basis is provided for emergency rescue and resource allocation.

Description

Technical Field

[0001] The present invention belongs to the technical field of power equipment disaster impact assessment, and particularly relates to a method and system for assessing the vulnerability of a distribution network based on a set of random fault scenarios. Background Art

[0002] With the increase of global environmental changes and extreme climate events, the vulnerability of infrastructure such as transportation, communication, and energy systems has gradually emerged. In particular, extreme weather scenarios, such as abnormal high temperatures, severe floods, strong earthquakes, etc., have posed unprecedented challenges to the robustness and recovery ability of these systems. Such extreme situations not only cause physical damage to urban distribution facilities, but also indirectly affect the normal operation of social and economic activities, and even threaten public safety. Therefore, constructing an index system that can effectively evaluate and quantify the vulnerability of infrastructure under extreme conditions is of great significance for improving the ability of the distribution network to cope with disasters.

[0003] Existing vulnerability assessment methods mostly focus on the analysis of conventional risks, and use historical data and static attributes (such as the design standards and material properties of buildings, etc.) to predict the possible loss degree of facilities under normal circumstances. However, when facing extreme conditions, these methods show obvious limitations and deficiencies. First of all, traditional assessment means often adopt frequency-based probability models, and the effectiveness of such models in dealing with low-probability high-impact events is questioned because they are difficult to fully consider the uniqueness and uncertainty of extreme events. Secondly, existing technologies have shortcomings in dealing with the interactions between complex systems, and fail to comprehensively consider the superimposed effects between different types of disasters, as well as the interactive effects of natural and human factors, and these chain reactions are difficult to accurately predict by existing assessment models. In addition, for rapidly changing extreme weather, such as sudden short-term heavy rainfall, existing methods lack the ability of real-time monitoring and immediate response, which further weakens their effectiveness in practical applications. Summary of the Invention

[0004] The purpose of the present invention is to overcome the above-mentioned shortcomings of the existing technologies, and provide a method and system for assessing the vulnerability of a distribution network based on a set of random fault scenarios, so as to solve the problem that the existing assessment models in the existing technologies lack the ability of real-time monitoring and immediate response.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: A method for assessing the vulnerability of a distribution network based on a set of random fault scenarios, comprising the following steps: Based on extreme scenario conditions, calculate the fault probability of upstream distribution transformers and the fault probability of lines; Based on the fault probability, perform sampling to obtain the upstream distribution transformers and lines that may fail under a single random extreme condition scenario; According to the line where a fault may occur, obtain the downstream distribution transformers where a fault may occur, add tags indicating whether there is a fault to the upstream and downstream distribution transformers, and obtain a list of faulty distribution transformers; Determine the land ownership of all distribution transformers in the list of faulty distribution transformers, obtain the third-level indicators based on the distribution transformers and their land ownership, obtain the second-level indicators based on the third-level indicators, and obtain the first-level indicators based on the second-level indicators; Combine all the first-level indicators to judge the vulnerability of the distribution network under a single randomly generated extreme condition scenario.

[0006] A further improvement of the present invention lies in: Preferably, the extreme scenario conditions include water accumulation at the location of the distribution transformer, wind load on the overhead line, rain load on the overhead line, and ice load on the overhead line.

[0007] Preferably, calculate the failure probability of the distribution transformer based on the water accumulation depth at the location of the distribution transformer.

[0008] Preferably, based on the wind load on the overhead line, rain load on the overhead line, and ice load on the overhead line, obtain the failure probability and fault probability of the line through the joint probability distribution function.

[0009] Preferably, the sampling method is Monte Carlo sampling or fixed-output number sampling.

[0010] Preferably, the third-level indicators are the number of power outage users and the number of water outage users affected by the upstream and downstream distribution transformers on the land.

[0011] Preferably, the second-level indicators include the equivalent number of power outage users under the jurisdiction, the equivalent number of power outage users of high-end users, the number of distribution transformers with a failure probability higher than a specific value, the number of distribution transformers directly causing faults, the number of distribution transformers caused by line faults, the number of water outage users associated with distribution transformers under the jurisdiction, and the number of water outage users associated with high-end distribution transformers.

[0012] Preferably, the first-level indicators include the equivalent number of power outage users, the number of lost distribution transformers, and the number of water outage users.

[0013] Preferably, the calculation formula for the equivalent number of power outage users is:

[0014] Wherein, EF ( s ) is the set of out-of-service distribution transformers in the s th scenario; is the number of industrial power outage users, is the number of power outage users of government and public service facilities, is the number of power outage users of commerce and service industries, is the number of users with power outages in public facilities, is the number of users with power outages in transportation facilities, is the number of users with power outages in residential areas; The calculation formula for the number of lost distribution transformers is:

[0015] Among them, is the number of lost distribution transformers, ∆NT Hprob is the number of distribution transformers with a failure probability higher than a specific value, ∆NT Direct is the number of distribution transformers directly failed due to extreme events, ∆NT Line is the number of distribution transformers failed due to line failures; , , are the weights corresponding to the secondary indicators respectively; The calculation formula for the number of users with water outages is:

[0016] Among them, WF ( s ) is the set of water outage blocks in the s th scenario, is the number of industrial users with water outages, is the number of government and public service facility users with water outages, is the number of commercial / service industry users with water outages, is the number of users with power outages in public facilities, is the number of users with power outages in transportation facilities, is the number of users with power outages in residential areas, is the weight value corresponding to the secondary indicator, adjusted according to the real - world scenario data.

[0017] A distribution network vulnerability assessment system based on a set of random failure scenarios includes: A failure probability module for calculating the failure probability of upstream distribution transformers and the failure probability of lines based on extreme scenario conditions; A direct failure module for sampling based on the failure probability to obtain the distribution network components that may fail and generating a single - time random extreme condition scenario; A failure list module for obtaining the downstream distribution transformers that may fail according to the lines that may fail, adding tags indicating whether the upstream and downstream distribution transformers are faulty, and obtaining a list of faulty distribution transformers; An index calculation module for determining the land use of all distribution transformers in the list of faulty distribution transformers, obtaining tertiary indicators based on the distribution transformers and their land use, obtaining secondary indicators based on the tertiary indicators, and obtaining primary indicators based on the secondary indicators; An evaluation module for judging the vulnerability of a distribution network under random extreme conditions by combining all first-level indicators.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a method for evaluating the vulnerability of a distribution network based on a set of random fault scenarios. This method takes into account the fault probability of the distribution network under extreme scenario conditions, analyzes the fault probabilities of upstream distribution transformers and lines that may be caused under a single random extreme condition scenario; based on the lines where faults may occur, downstream distribution transformers where faults may occur are obtained; the land use to which all possible faulty transformers belong is judged, and by combining the importance of the land use and the probability of occurrence of the distribution transformers, third-level indicators can be obtained, and further second-level and first-level indicators can be obtained. After combining all the first-level indicators, the vulnerability of the entire distribution network under random fault scenarios can be judged. Through in-depth analysis of specific extreme events, this method can quickly identify the areas and facilities most severely affected. The derived indicators can guide the layout of short-term emergency resources, provide a scientific basis for emergency rescue and resource allocation, and help relevant departments efficiently allocate rescue forces in the initial stage of a disaster, minimizing casualties and property losses. The indicators derived from comprehensive analysis of multiple random extreme condition scenarios of this method can guide the layout of long-term emergency resources. Through long-term data accumulation and multi-scenario comparative analysis, the long-term impact laws of different types of extreme events on infrastructure can be revealed, providing support for formulating more reasonable and effective long-term emergency management and resource planning strategies. This big data-based analysis method can not only enhance the disaster resistance ability of infrastructure, but also promote the continuous improvement and development of emergency plans, and improve the disaster response level of the whole society.

[0019] Furthermore, the multi-dimensional hierarchical index system constructed in the present invention has the following core advantages: First, the hierarchical design of the three-level indicators achieves a balance between the systematicness and fineness of the evaluation - the first-level indicators (equivalent number of power outage users / number of lost distribution transformers / number of water supply cut-off users) construct the basic evaluation framework, the second-level indicators refine the scenario classification, and the third-level indicators establish a differential evaluation model for specific user types. This tree-like hierarchical structure not only ensures the integrity of the evaluation system but also accurately depicts the vulnerability differences among different industries and facilities. Second, the calculation method of dynamically weighted coefficients and multi-source data fusion significantly improves the scientificity of the evaluation when calculating the first-level indicators: for industrial users, the profit / GDP ratio is used to reflect economic sensitivity, for government agencies, a product model of the population in the jurisdiction and the importance coefficient is innovatively introduced, and for transportation facilities, the real-time passenger flow is used to capture spatio-temporal dynamic characteristics. This calculation system combining quantitative and qualitative methods effectively balances multiple dimensions such as economic value, social impact, and people's livelihood guarantee. In particular, through the differential weighting mechanism (such as setting a high weight in the lost distribution transformer module to highlight the cascading risk of line failures), the vulnerability of critical infrastructure is presented explicitly. In addition, the modular design of the third-level indicators and weight parameters not only maintains the unity of the evaluation criteria but also allows for dynamic parameter adjustment according to regional characteristics, providing an expandable standardized tool for vulnerability comparison across regions and scenarios. This hierarchical and dynamically weighted evaluation framework can not only support the accurate positioning of short-term emergencies (such as quickly identifying key rescue nodes through the 1.2.2 government facility indicators) but also provide multi-dimensional decision-making basis for long-term resilience construction (such as optimizing the collaborative protection of infrastructure through the correlation analysis of the number of water supply cut-off users in 3.x), ultimately realizing an evaluation closed-loop from single-point vulnerability detection to system resilience improvement.

[0020] Furthermore, the vulnerability assessment method for distribution network equipment in extreme scenarios considering environmental factors and dynamic changes in water accumulation provided by the present invention uses advanced dynamic water accumulation simulation technology and equipment failure probability calculation models to solve the deficiencies of traditional methods in predicting the failure of distribution network equipment under rainstorm disasters. By gridifying the distribution network area and considering the influence of multiple factors such as rainfall, terrain, drainage system, and urban drainage capacity, this method can more accurately simulate the water accumulation process and then predict the equipment failure probability. This not only improves the accuracy of the prediction but also helps the power department take measures in advance to reduce the impact of rainstorm disasters on the power system.

[0021] Furthermore, the exponential function fitting method adopted in the present invention calculates the failure rate of distribution equipment according to the dynamic change of water accumulation depth. Compared with the traditional static threshold judgment, this method can more precisely reflect the actual risk state of equipment under extreme weather conditions. This dynamic cumulative model is similar to the icing disaster model, considering the characteristic that the equipment failure rate changes with meteorological conditions, thereby improving the accuracy and reliability of the prediction.

[0022] Furthermore, the present invention comprehensively considers various loads of overhead lines in extreme weather, including ice load, wind load and rain load, and can more comprehensively evaluate the safety status of the lines by accurately calculating the impact of these loads on the lines. This comprehensive load risk analysis helps the power sector identify potential line fault points and take reinforcement or maintenance measures in a timely manner to ensure the stability of power transmission.

[0023] Furthermore, the present invention realizes the organic combination of geographic spatial information and equipment failure prediction by integrating with GIS data, 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

[0024] Figure 1 It is a flow chart of the method for assessing the vulnerability of a distribution network of the present invention; Figure 2 is a flow chart for obtaining a downstream distribution transformer of the present invention; Figure 3 This is a diagram of the distribution network vulnerability assessment system of the present invention. DETAILED DESCRIPTION

[0025] In the following, the terms "first", "second", "third", and "fourth" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first", "second", "third", and "fourth" may explicitly or implicitly include one or more of the features.

[0026] The co-shooting method provided in the embodiment of the present application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), etc. The embodiment of the present application does not impose any restrictions on the specific type of the terminal device.

[0027] It should be noted that the terms "first", "second", etc. in the specification and drawings of the present invention are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0028] The object of the present invention is to provide a method for evaluating the vulnerability of distribution network equipment under extreme scenarios, aiming to identify the vulnerable nodes of the urban distribution network by the possible damaged equipment in the distribution network and reduce the impact of natural disasters on power supply. The method of the present invention includes the following steps: S1, based on extreme scenario conditions, calculate the failure probability of upstream distribution transformers and the failure probability of lines; S2, based on the failure probability, conduct sampling to obtain the upstream distribution transformers and lines that may fail under a single random extreme condition scenario; S3, according to the lines that may fail, obtain the downstream distribution transformers that may fail, add tags indicating whether they are faulty to the upstream and downstream distribution transformers, and obtain a list of faulty distribution transformers; S4, determine the land use to which all distribution transformers in the list of faulty distribution transformers belong, obtain the third-level indicators based on the distribution transformers and their land use, obtain the second-level indicators based on the third-level indicators, and obtain the first-level indicators based on the second-level indicators; S5, combine all the first-level indicators to judge the vulnerability of the distribution network under random extreme conditions.

[0029] In some embodiments of the present invention, in S1, the extreme scenarios include water accumulation in the grid where the distribution transformer is located, wind load on overhead lines, rain load on overhead lines, and ice load on overhead lines. Calculate the failure probability of the distribution transformer under the condition of water accumulation in the grid where the distribution transformer is located, and the failure probability of the line under the conditions of wind load on overhead lines, rain load on overhead lines, and ice load on overhead lines; further, based on the above-mentioned failure probability of the distribution transformer and the failure probability of the line, calculate the overall failure probability of the distribution network components.

[0030] S11, construct a grid water accumulation model, based on the grid water accumulation model and the position of the rainfall center, calculate the water accumulation depth of each grid; based on the water accumulation depth of the grid where the distribution transformer is located, calculate the failure probability of the distribution transformer. It includes the following steps: S111. Read the distribution network data, including distribution transformer switch and line data, read the relevant data from the Excel file and clean the missing values.

[0031] S112. Geographic coordinate conversion, convert the geographic coordinates of the distribution transformer switch and line to a grid coordinate system that is convenient for calculation.

[0032] S113. Set the environmental parameters, define the grid width, simulation period length, rainfall level, extreme weather type, wind speed, ice coating thickness, etc.

[0033] S114. Collect the historical failure records of distribution network equipment and related Geographic Information System (GIS) data, including parameters such as Digital Elevation Model (DEM), equipment location, designed flood prevention height, cable joint elevation relative to the ground, etc.; S115. Convert the collected data into an original sample set suitable for the grid waterlogging model; S116. Establish a grid waterlogging model, create a grid coordinate system, and define the longitude and latitude range; S117. Perform grid processing on the power supply area of the distribution network, assuming that the waterlogging depth is the same within the same grid; S118. Dynamically update the waterlogging depth of each grid according to rainfall, drainage capacity, and terrain; The iterative calculation formula for waterlogging depth is:

[0034]

[0035] Among them: Represents the Water flow rate in the * direction, >0 indicates waterlogging inflow, otherwise it indicates outflow; Is Rainfall intensity at time Is the time variation, Is the waterlogging depth related to time; Is the waterlogging depth of grid z at time t; Is Waterlogging depth of grid z at time; Is the grid width; Is the waterlogging flow velocity in the * direction, and the symbol * refers to around grid z , , , Direction, there is no flow relationship between grid z and the diagonal grid , , , ; Is Drainage flow within the time grid z Is the simulated segmented duration 、 、 、 Are the grids in the due east, due west, due north, and due south directions of grid z respectively 、 、 、 Are the grids in the northeast, northwest, southeast, and southwest directions of grid z respectively

[0036] S119. Calculate the failure probability of the distribution transformer based on the waterlogging depth in the grid:

[0037] In the formula: Is The waterlogging depth of the grid where the device is located at time ; Is The waterlogging degree of the grid where the device is located at time ; Is the designed flood prevention height of the substation (room) or box-type substation Is the ground elevation of the cable joint of the high-voltage switchgear in the station Is the attenuation coefficient Is the damping coefficient

[0038] The process of S12 calculating the impact of the load of the overhead line on the distribution network fault is as follows: S121. Calculate the wind load, rain load, and ice coating load of the overhead line Among them, the wind load of the overhead line:

[0039] In the formula, Is the wind load of the overhead line, N The wind pressure uneven coefficient, related to the wind speed The wind speed at a reference height of 10 m ; The wind pressure height change coefficient, related to the surface roughness and the elevation of the line The shape coefficient of the conductor The wind vibration coefficient The outer diameter of the conductor or the calculated outer diameter during ice coating, mm The horizontal span of the pole tower, m The wind load increase coefficient during ice coating The included angle between the wind direction and the conductor Among them, the rain load of the overhead line:

[0040] In the formula, is the rain load on the overhead line, N; is the diameter of the water droplet, mm; n is the number of water droplets per unit volume, pieces / ; b is the area of the component's rain-facing surface, ; is the terminal velocity of the raindrop, m / s; Among them, the ice load on the overhead line:

[0041] 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 is the acceleration due to gravity, .

[0042] S122. Based on the wind load, rain load and ice load of the overhead line, the failure probability and fault probability of the line are obtained through the joint probability distribution function, including: Calculate the combined load W of the line, and obtain the mean value of the probability density function of the overhead line load according to the normal distribution of W and the mean value of the probability density function of the overhead line strength ; The combined load W of the line is determined by the following formula:

[0043] Among them, G is the self-weight of the conductor; Calculate the mean value and variance of the joint distribution function Z(t) through the following formula:

[0044] In the formula: Both the overhead line strength and load follow a normal distribution, is the mean value of the probability density function of the overhead line strength; is the standard deviation of the probability density function of the overhead line strength; Convert the joint probability density function into the standard normal distribution form, then the failure probability of the line is:

[0045] In the formula: is t the failure probability of the line at time is the standard normal distribution function, is t the mean value of the joint distribution function at time ist Variance of the moment joint distribution function; Calculating the line failure rate based on the line failure probability :

[0046] S13. Calculate the overall failure probability of the distribution network components based on the above-mentioned failure probabilities of the distribution transformers and lines.

[0047] The specific steps of S2 are as follows: Based on the calculated failure probabilities of the distribution transformers and lines, sample to obtain a single random extreme condition scenario. The sampling method can be selected by the user to use Monte Carlo sampling or fixed output number sampling; Monte Carlo sampling: Generate a random number set with the same size as the failure probability set. The value range of the random numbers is between 0 and 1. Compare the failure probability set with the random number set, and the components with random numbers less than the failure probability are considered to have failed; Fixed output number sampling: Sort the failure probability set from largest to smallest, and select the failed components in the set order of the set number to obtain a single random extreme condition scenario.

[0048] The specific steps of S3 are as follows: Refer to Figure 2 It can be seen that in the entire distribution network structure, the line is connected to the corresponding distribution transformer. Therefore, if the line fails, it will inevitably cause the failure of the downstream distribution transformer connected to it; in this step, through the line that may fail, obtain the downstream distribution transformers that may fail affected by this line. During the process of confirming the downstream distribution transformers, refer to Figure 2 , and proceed according to the following steps to ensure that all downstream transformers can be considered: S3.1. According to the network topology relationship, determine whether the downstream distribution transformer is the line-end transformer. If it is, execute S3.3; otherwise, execute S3.2; S3.2. According to the network topology relationship, return to this line, continue to search for the downstream distribution transformer, and then execute S3.1; S3.3. List the obtained downstream distribution transformers in the list of failed distribution transformers.

[0049] Based on the topology structure of the distribution network, obtain the list of upstream distribution transformers that may fail and downstream distribution transformers affected by the line. After adding failure labels to them respectively, add them to the list of failed distribution transformers and output; the failure list contains the failure probability of the distribution transformer and the number of users affected by this failed transformer.

[0050] S41. The six types of land use for the third-level indicators come from the data annotation of the Urban Planning and Design Institute. Combining with the urban land classification in GB50137-2011 "Standard for Urban Land Classification and Planning Construction Land" (as shown in Table 1 below), by introducing this table, the land use type of the area where the faulty distribution transformer is located can be divided, and then the degree of influence on this land use type can be judged.

[0051] Table 1 Urban Land Classification

[0052] The values of twelve third-level indicators are obtained by classification, screening and statistics from the result table according to various index conditions. The values of seven second-level indicators are extracted according to whether it is bureau-owned, whether the fault is caused by the line, and the probability label of the distribution transformer fault, including: the number of equivalent bureau-owned power outage users , the number of equivalent high-class users with power outage , the number of distribution transformers with a fault probability higher than a specific value , the number of distribution transformers directly causing faults , the number of distribution transformers with faults caused by line faults , the number of water supply cut-off users associated with bureau-owned distribution transformers and the number of water supply cut-off users associated with high-class distribution transformers . Further, the first-level indicators are obtained based on the second-level indicators. The first-level indicators include: the number of equivalent power outage users , the number of lost distribution transformers ( ) and the number of water supply cut-off users ( ), as shown in Table 2 below: Table 2 List of Vulnerability Indicators Based on Importance

[0053] Among them, the calculation formulas corresponding to the first-level indicators are as follows: The number of equivalent power outage users

[0054]

[0055] Among them, EF ( s ) represents the set of out-of-service distribution transformers in the s th scenario; EF ( s ) is calculated through the power grid connection relationship; is the weight value corresponding to the second-level indicator, adjusted according to the real-scenario data.

[0056] The number of lost distribution transformers

[0057]

[0058] Among them, is the loss allocation variable, ∆NT Hprob is the number of distribution variables with a distribution change failure probability higher than a specific value, ∆NT Direct is the number of distribution variables directly caused by extreme events to fail, ∆NT Line is the number of distribution variables caused by line failures; , , are the weights corresponding to the secondary indicators respectively.

[0059] The number of users with water cut

[0060]

[0061] Among them, WF ( s ) represents the set of water cut blocks in the s th scenario; WF ( s ) is calculated through the electricity-water failure conduction model.

[0062] In the above table, the fourth column is the calculation method of the weight coefficient. For example, the corresponding weight coefficient The calculation formula is the total profit of users / per capita GDP of the target jurisdiction. the corresponding weight coefficient According to the differences between government agencies and public services, there are different calculation methods, etc. The following are all the calculation methods of the corresponding weight coefficients when calculating the tertiary or secondary indicators.

[0063] The second aspect of the present invention discloses a distribution network vulnerability assessment system based on a set of random fault scenarios, including: A fault probability module for calculating the fault probability of upstream distribution transformers and the fault probability of lines based on extreme scenario conditions; A direct fault module for sampling based on the fault probability to obtain the distribution network accessories that may fail and generating a single random extreme condition scenario; A fault list module for obtaining the downstream distribution transformers that may fail according to the lines that may fail, adding labels indicating whether the upstream and downstream distribution transformers are faulty, and obtaining a list of faulty distribution transformers; An index calculation module for determining the land ownership of all distribution transformers in the list of faulty distribution transformers, obtaining tertiary indicators based on the distribution transformers and their land ownership, obtaining secondary indicators based on the tertiary indicators, and obtaining primary indicators based on the secondary indicators; An evaluation module for judging the vulnerability of the distribution network under random extreme conditions by combining all the primary indicators.

[0064] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A vulnerability assessment method for distribution networks based on a set of random fault scenarios, characterized in that, It includes the following steps: Based on extreme scenario conditions, calculate the failure probability of upstream distribution transformers and the failure probability of lines; Based on the failure probability, conduct sampling to obtain the upstream distribution transformers and lines that may fail under a single random extreme condition scenario; According to the lines that may fail, obtain the downstream distribution transformers that may fail, add tags indicating whether they are faulty to the upstream and downstream distribution transformers, and obtain a list of faulty distribution transformers; Determine the land ownership of all distribution transformers in the list of faulty distribution transformers, obtain the third-level indicators based on the distribution transformers and their land ownership, obtain the second-level indicators based on the third-level indicators, and obtain the first-level indicators based on the second-level indicators; Combine all the first-level indicators to judge the vulnerability of the distribution network under a single random extreme condition scenario.

2. The vulnerability assessment method of a distribution network based on a set of random fault scenarios according to claim 1, wherein The extreme scenario conditions include waterlogging at the location of the distribution transformer, wind load on overhead lines, rain load on overhead lines, and ice coating load on overhead lines.

3. The vulnerability assessment method of a distribution network based on a set of random fault scenarios according to claim 2, wherein Calculate the failure probability of the distribution transformer based on the waterlogging depth at the location of the distribution transformer.

4. A vulnerability assessment method for a distribution network based on a set of random fault scenarios according to claim 2, characterized in that Based on the wind load, rain load, and ice coating load on the overhead lines, obtain the failure probability and fault probability of the lines through the joint probability distribution function.

5. A vulnerability assessment method for a distribution network based on a set of random fault scenarios according to claim 1, characterized in that, The sampling method is Monte Carlo sampling or fixed-output number sampling.

6. A vulnerability assessment method for a distribution network based on a set of random fault scenarios according to claim 1, characterized in that The third-level indicators are the number of power outage users and the number of water supply interruption users affected by the upstream and downstream distribution transformers on the land.

7. A vulnerability assessment method for a distribution network based on a set of random fault scenarios according to claim 1, characterized in that The second-level indicators include the equivalent number of power outage users under the jurisdiction, the equivalent number of power outage users for high-end users, the number of distribution transformers with a failure probability higher than a specific value, the number of directly faulty distribution transformers, the number of distribution transformers faulty due to line failures, the number of water supply interruption users associated with distribution transformers under the jurisdiction, and the number of water supply interruption users associated with distribution transformers for high-end users.

8. A vulnerability assessment method for a distribution network based on a set of random fault scenarios according to claim 1, characterized in that The first-level indicators include the equivalent number of power outage users, the number of faulty distribution transformers, and the number of water supply interruption users.

9. A vulnerability assessment method for a distribution network based on a set of random fault scenarios according to claim 8, characterized in that The calculation formula for the equivalent number of power outage users is: Among them, EF ( s ) is the set of de-energized distribution transformers in the s th scenario; is the number of industrial power outage users, is the number of government and public service facility power outage users, is the number of commercial and service industry power outage users, is the number of public utility power outage users, is the number of transportation facility power outage users, is the number of residential power outage users; The calculation formula for the number of faulty distribution transformers is: Among them, is the loss allocation variable, ∆NT Hprob is the number of distribution variables where the distribution change failure probability is higher than a specific value, ∆NT Direct is the number of distribution variables directly caused by extreme events to fail, ∆NT Line is the number of distribution variables caused by line failures; , , are the weights corresponding to the secondary indicators respectively; The calculation formula for the number of water supply interruption users is: Among them, WF ( s ) is the set of water cut-off areas in the s th scenario, is the number of industrial users with water cut-off, is the number of government and public service facility users with water cut-off, is the number of commercial / service industry users with water cut-off, is the number of utility users with power cut-off, is the number of transportation facility users with power cut-off, is the number of residential users with power cut-off, is the weight value corresponding to the secondary index, which is adjusted according to the real scenario data.

10. A vulnerability assessment system for a distribution network based on a set of random fault scenarios, characterized in that, It includes: A failure probability module, which is used to calculate the failure probability of upstream distribution transformers and the failure probability of lines based on extreme scenario conditions; A direct fault module, which is used to conduct sampling based on the failure probability to obtain the distribution network components that may fail and generate a single random extreme condition scenario; A fault list module, which is used to obtain the downstream distribution transformers that may fail according to the lines that may fail, add tags indicating whether they are faulty to the upstream and downstream distribution transformers, and obtain a list of faulty distribution transformers; An indicator calculation module, which is used to determine the land ownership of all distribution transformers in the list of faulty distribution transformers, obtain the third-level indicators based on the distribution transformers and their land ownership, obtain the second-level indicators based on the third-level indicators, and obtain the first-level indicators based on the second-level indicators; An evaluation module, which is used to judge the vulnerability of the distribution network under random extreme conditions by combining all the first-level indicators.

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