Power distribution system weak link identification method and device, electronic equipment and program product

Abstract

The invention discloses a weak link identification method and device for a power distribution system, electronic equipment and a program product, and relates to the field of power distribution systems.The method comprises the steps that classified typical disaster scenes and line fault probabilities in the typical disaster scenes are analyzed; evaluating a power supply operation state of the power distribution system after each typical disaster scene occurs by using a maximum power supply capability model, calculating element risk parameters of the power distribution system based on the power supply operation state and the line fault probability, and weighting and calculating a state weakness index by combining the element risk parameters of the power distribution system and scene weight parameters; and calculating a structure vulnerability index based on the centrality index of the node and the scene weight parameter weighting, fusing the state vulnerability index and the structure vulnerability index, and identifying a node weak link in the power distribution system. According to the invention, a technical problem of low accuracy of an evaluation result because only evaluation of a physical structure of the power distribution system is carried out in weak link evaluation of the power distribution system in related technologies is solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution system technology or other related fields. Specifically, it relates to a method and apparatus for identifying weak links in a power distribution system, as well as electronic equipment and software products. Background Technology

[0002] In recent years, global climate change has intensified, and extreme weather events have occurred more frequently, posing unprecedented challenges to the safe and stable operation of power systems, especially distribution networks. Natural disasters such as ice storms, floods, earthquakes, and typhoons not only directly damage distribution infrastructure but can also trigger a series of chain reactions, leading to large-scale power outages and severely impacting socio-economic activities and people's daily lives. Therefore, it is necessary to begin to emphasize vulnerability assessments of distribution systems under extreme disasters and improve the grid's response capabilities and recovery speed.

[0003] In related technologies, research on the vulnerability assessment of power distribution systems mainly focuses on two aspects: first, physical structure vulnerability assessment, which focuses on the disaster resistance capabilities of hardware facilities such as lines, towers, and substations; and second, operational status vulnerability assessment, which analyzes the system's ability to cope with disasters under different operating conditions. However, these technologies have the following obvious limitations in addressing these issues: they emphasize the vulnerability of a single aspect of the power distribution system's structure or operational status, neglecting the interaction and comprehensive impact between the two. For example, they only consider physical damage in specific geographical environments or only assess the performance degradation of the system under specific operating modes, without combining the two, thus lacking a comprehensive vulnerability assessment system. Furthermore, the assessment methods in these technologies are often based on static or typical scenarios, lacking the ability to simulate the dynamic evolution of extreme disasters. They cannot accurately reflect the spatiotemporal characteristics of disasters on the power distribution system, resulting in inaccurate assessment results that are difficult to use for real-time monitoring and emergency decision-making.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, and program product for identifying weak links in a power distribution system, to at least solve the technical problem in related technologies where the assessment of weak links in a power distribution system only assesses the physical structure of the power distribution system, resulting in low accuracy of the assessment results.

[0006] According to one aspect of the present invention, a method for identifying weak links in a power distribution system is provided, comprising: determining a disaster scenario set based on regional historical meteorological data and climate trend analysis results; classifying the disaster scenario set; analyzing each typical disaster scenario after classification and the line fault probability under the typical disaster scenario; evaluating the power supply operation status of the power distribution system after each typical disaster scenario occurs using a maximum power supply capacity model; calculating component risk parameters of the power distribution system based on the power supply operation status and the line fault probability; calculating a state vulnerability index by combining the component risk parameters and scenario weight parameters of the power distribution system; calculating the centrality index of nodes in the network structure of the power distribution system; calculating a structural vulnerability index by combining the centrality index of nodes and scenario weight parameters; fusing the state vulnerability index and the structural vulnerability index to obtain a comprehensive node vulnerability index; and identifying weak links in the power distribution system according to the index ranking results.

[0007] Optionally, the steps of analyzing the typical disaster scenarios after classification and the line fault probability under the typical disaster scenarios include: when the typical disaster scenario is an ice storm scenario, constructing an ice storm scenario mathematical model, wherein the ice storm scenario mathematical model is used to simulate the impact process of ice storms on the power distribution system and calculate the change in the ice thickness of transmission lines; using a transmission line ice thickness growth model, analyzing the ice load per unit length of transmission line generated by the weight of ice in the vertical direction and the wind load per unit length of transmission line in the horizontal direction under the condition of ice accumulation, and calculating the ice wind load borne by the transmission line per unit length by combining the ice load per unit length of transmission line and the wind load per unit length of transmission line; using the ice storm scenario mathematical model to analyze the ice wind load borne by the transmission line per unit length of transmission line and the design load and ultimate load of the transmission line to withstand ice, determining the line failure rate of the line per unit length at a predetermined time; and calculating the line fault probability of the transmission line under the ice storm scenario based on the line failure rate of the line per unit length at the predetermined time.

[0008] Optionally, before using the transmission line icing thickness growth model to analyze the accumulation of icing on the transmission line, the method further includes: obtaining the icing thickness and freezing rain amount on the transmission line at the predetermined time; and inputting the icing thickness, freezing rain amount, ice density, water density, and ambient air moisture content on the transmission line into the transmission line icing thickness growth model.

[0009] Optionally, before classifying the disaster scenario set using a preset clustering strategy, the method further includes: Step 1, for ice storm scenarios, using a Monte Carlo sampling strategy to select transmission line numbers and random numbers; Step 2, using a line fault simulation model to analyze the line breakage rate of the transmission line corresponding to the transmission line number; Step 3, determining whether the random number is greater than or equal to the line breakage rate of the transmission line at a predetermined time; Step 4, if the random number is greater than or equal to the line breakage rate of the transmission line at the predetermined time, determining that the transmission line is in normal operation and proceeding to the next time, determining whether the next time is greater than the end time of the impact of the ice storm scenario; if so, determining that the transmission line has not experienced a fault during the entire ice storm impact process; Step 5, if the random number is less than the line breakage rate of the transmission line at the predetermined time, determining that the transmission line is in a fault state, recording the previous time as the continuous operating time of the transmission line, and obtaining the changes in the operating state of the transmission line; repeating steps 1 to 5 until the selected transmission line number is greater than the total number of lines in the distribution system, ending the Monte Carlo sampling.

[0010] According to another aspect of the present invention, a power distribution system weak link identification device is also provided, comprising: a disaster scenario analysis unit, configured to determine a disaster scenario set based on regional historical meteorological data and climate trend analysis results, classify the disaster scenario set, and analyze each typical disaster scenario after classification and the line fault probability under the typical disaster scenario; a state vulnerability index calculation unit, configured to evaluate the power supply operation status of the power distribution system after each typical disaster scenario occurs using a maximum power supply capacity model, and calculate the component risk parameters of the power distribution system based on the power supply operation status and the line fault probability, and calculate the state vulnerability index by combining the component risk parameters of the power distribution system and the scenario weight parameters; a structural vulnerability index calculation unit, configured to calculate the centrality index of nodes in the network structure of the power distribution system, and calculate the structural vulnerability index by weighting based on the centrality index of the nodes and the scenario weight parameters; and a system node weak link identification unit, configured to fuse the state vulnerability index and the structural vulnerability index to obtain a node comprehensive vulnerability index, and identify the node weak links in the power distribution system according to the index size ranking result.

[0011] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the power distribution system weak link identification method described above.

[0012] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the power distribution system weak link identification method described above.

[0013] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the power distribution system weak link identification method described in any of the above embodiments.

[0014] In this disclosure, a disaster scenario set is determined based on regional historical meteorological data and climate trend analysis results. The disaster scenario set is classified, and the typical disaster scenarios after classification and the line fault probability under the typical disaster scenarios are analyzed. The power supply operation status of the distribution system after the occurrence of each typical disaster scenario is evaluated using the maximum power supply capacity model. Based on the power supply operation status and the line fault probability, the component risk parameters of the distribution system are calculated. The state vulnerability index is calculated by combining the component risk parameters of the distribution system and the scenario weight parameters. The centrality index of the nodes in the network structure of the distribution system is calculated, and the structural vulnerability index is calculated by combining the node centrality index and the scenario weight parameters. The state vulnerability index and the structural vulnerability index are fused to obtain the node comprehensive vulnerability index, and the weak links of the nodes in the distribution system are identified according to the index size ranking results.

[0015] In this disclosure, after classifying all disaster scenarios, the typical disaster scenarios after classification and the line fault probability under the typical disaster scenarios can be analyzed. The state vulnerability index and structural vulnerability index of the power distribution system can be evaluated. By combining structural vulnerability and state vulnerability, the true risk of the power distribution system can be more accurately reflected, and the accuracy of the assessment of the weak links of the power distribution system can be improved. This solves the technical problem in related technologies where the assessment of the weak links of the power distribution system only evaluates the physical structure of the power distribution system, resulting in low accuracy of the assessment results. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 This is a flowchart of an optional method for identifying weak links in a power distribution system according to an embodiment of the present invention;

[0018] Figure 2This is a schematic diagram of another optional method for identifying vulnerable links in a power distribution system based on scene clustering and network centrality according to an embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram illustrating an optional stress analysis of an icing-covered railway line according to an embodiment of the present invention;

[0020] Figure 4 This is a flowchart of an optional Monte Carlo sampling method according to an embodiment of the present invention;

[0021] Figure 5 This is a schematic diagram of an optional power distribution system weak link identification device according to an embodiment of the present invention;

[0022] Figure 6 This is a hardware structure block diagram of an electronic device (or mobile device) for performing a method for identifying weak links in a power distribution system according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] To facilitate understanding of the present invention by those skilled in the art, some terms or nouns involved in the various embodiments of the present invention are explained below:

[0026] Reconfigurable Distributed Generation (RDG) is a rapidly responding distributed generation system that can include solar photovoltaic power, wind turbines, and micro gas turbines. It can provide localized power support during main grid failures. In disaster scenarios, RDG can quickly adapt to changes in network structure, providing necessary power output, reducing load losses, and improving the resilience and flexibility of the distribution system.

[0027] k-means clustering, a clustering algorithm used in data mining and machine learning, aims to divide a dataset into k subsets with similar characteristics. In this invention, the k-means algorithm is used to cluster scenarios of power distribution systems affected by extreme disasters, thereby identifying representative and typical disaster patterns. Furthermore, the probability of occurrence for each pattern is calculated, providing a basis for subsequent vulnerability assessments and emergency response strategies.

[0028] Monte Carlo sampling (MCS) is a numerical computation method based on random sampling used to simulate and predict the probability distribution of random events. This invention utilizes MCS to simulate the failure rate of lines in a power distribution system. By using extensive random sampling, it estimates the average performance of the system under various extreme disaster scenarios, thereby calculating structural vulnerability indicators and providing decision support for system planning and emergency resource deployment.

[0029] The Jones icing model is a mathematical model used to predict the growth of icing on transmission lines, taking into account meteorological factors such as temperature, humidity, and wind speed. In ice storm scenarios, the Jones model can predict the accumulation process of icing on lines based on historical meteorological data and climate trends, and then calculate the line load and failure probability, providing a quantitative analysis tool for system design and risk assessment.

[0030] Maximum power supply capacity model is a model that assesses the maximum power output that a power distribution system can provide under specific conditions (such as after a disaster). It is used to quantify the power supply capacity of a power distribution system after a disaster by simulating changes in network structure and power output to determine which nodes may suffer load losses, thereby achieving an accurate assessment of conditional vulnerability.

[0031] Breadth-First Search (BFS) is a graph traversal algorithm that starts from a given node and explores all possible neighboring nodes in hierarchical order. When evaluating the effectiveness of power distribution system islanding, BFS can quickly identify power islands formed due to line faults, thus determining which loads cannot receive power, providing a rapid analytical tool for emergency resource allocation and network reconfiguration.

[0032] It should be noted that in this disclosure, customer information is collected and analyzed, and users are provided with corresponding operation entry points to choose whether to agree to or reject the automated decision results; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0033] The following embodiments of the present invention can be applied to various systems / applications / equipment for identifying weak points in power distribution systems. The present invention is applicable to scenarios such as smart grid management, emergency management, and disaster prevention. For example, in disaster risk assessment, before extreme disasters such as ice storms, typhoons, and earthquakes, the present invention can identify potential weak points in the power distribution system, predict the system's performance under disaster, provide early warning information to the power sector, and enable early reinforcement measures to be taken.

[0034] This invention can be used to improve the structural layout and equipment selection of power distribution networks to enhance the system's resilience to extreme disasters. It overcomes the limitations of related technical methods that rely on a single assessment dimension, combining structural vulnerability with state vulnerability to form a more comprehensive vulnerability index that more accurately reflects the true risks of the power distribution system. By introducing icing growth models and probabilistic failure models, it can dynamically simulate disaster processes, predict line failure rates, and improve the accuracy and reliability of assessment results.

[0035] This invention can also employ scenario clustering and simplified load loss estimation models, which significantly reduces computational complexity, supports rapid evaluation of large-scale power distribution systems, and improves the timeliness of emergency decision-making.

[0036] The present invention will now be described in detail with reference to various embodiments.

[0037] Example 1

[0038] According to an embodiment of the present invention, an embodiment of a method for identifying weak links in a power distribution system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0039] In this embodiment, by constructing a multi-dimensional and probabilistic set of extreme disaster scenarios, the system failure process under extreme weather conditions such as ice storms can be accurately simulated. Combining the structural characteristics and operating status of the power distribution system, a comprehensive vulnerability index is proposed to achieve a comprehensive assessment of the system's vulnerability. Furthermore, by clustering scenarios and simplifying models, computational efficiency can be improved, supporting rapid assessment and emergency decision-making for large-scale power distribution systems.

[0040] Figure 1 This is a flowchart of an optional method for identifying weak points in a power distribution system according to an embodiment of the present invention, such as... Figure 1As shown, the method includes the following steps:

[0041] Step S101: Based on regional historical meteorological data and climate trend analysis results, determine the disaster scenario set, classify the disaster scenario set, and analyze the typical disaster scenarios after classification and the line failure probability under the typical disaster scenarios.

[0042] In this embodiment, multiple types of disasters are assessed for any region (e.g., a mountainous area, plain, or urban area). Before the assessment begins, historical meteorological data of the region needs to be collected. This mainly includes collecting the occurrence time, duration, damage, and scale of each type of disaster. Historical records of key parameters such as precipitation and wind speed also need to be analyzed. By analyzing the historical meteorological data of the region, the climate trend of the region is analyzed to obtain the climate trend analysis results (a pre-trained climate change prediction model can be used here to generate extreme climate event sequences, each event sequence representing a specific disaster scenario). Multiple types of disasters with a probability greater than a preset probability value are identified (e.g., ice storms, droughts, floods, etc.). Then, the disaster scenario set can be classified to obtain the disaster classification results.

[0043] Furthermore, advanced data mining and clustering algorithms, such as k-means clustering, can be used to classify the generated disaster scenario set. Clustering is based on the similarity of meteorological parameter combinations, disaster intensity, and distribution patterns within the scenarios, aiming to identify representative and typical disaster scenarios. This classification process not only reduces the workload of subsequent analysis but also ensures the refinement and focus of the analysis results. Optionally, the steps for classifying the disaster scenario set include: calculating the total generating capacity parameter and total load demand parameter within the island formed after a transmission line fault in the distribution system under each disaster scenario; confirming load loss within the island when the total generating capacity parameter is less than the total load demand parameter; using a graph traversal algorithm to query all power-depleted islands in the distribution system, summing the loads of all islands to obtain the total load loss value under each disaster scenario; and classifying the disaster scenario set based on the total load loss value.

[0044] In various disaster scenarios, once a transmission line fails, the power distribution system may split into multiple electrically isolated parts, i.e. islands. This embodiment evaluates the self-sufficiency of each island by calculating the total power generation capacity (from distributed power sources or other available power sources) and total load demand in each island after the failure. The total power generation capacity parameter is calculated based on the maximum output power of all available power sources on the island, including but not limited to solar photovoltaic panels, wind turbines, energy storage systems, and possibly connected micro gas turbines.

[0045] The total load demand parameter is calculated based on the electricity demand of all load points on the island, including the needs of various users such as residential buildings, commercial buildings, hospitals, and schools. When the total generating capacity on the island is less than the total load demand, it means that the island cannot meet the electricity needs of all users, resulting in load loss. At this time, emergency measures must be taken, such as starting backup power and limiting non-critical loads, to minimize losses. By quantifying load loss, the impact of disasters on the power distribution system can be assessed more accurately.

[0046] Efficient graph traversal algorithms, such as breadth-first search (BFS) or depth-first search (DFS), are employed to locate all power outage islands in the power distribution system caused by line faults. The algorithm starts from any breakpoint in the network and progressively probes connected isolated sections until all affected islands are identified. By utilizing knowledge of the network topology, it ensures that no potential power outage area is overlooked, thus providing a comprehensive understanding of the disaster's impact on the power distribution system.

[0047] By identifying power outage islands using a graph traversal algorithm, this embodiment sums the load losses of each island to obtain the total load loss value under a specific disaster scenario, reflecting the overall service interruption level of the power distribution system under that disaster. The calculation of the total load loss value provides an intuitive and quantitative indicator for subsequent vulnerability scoring, helping to understand the system's overall resilience. Based on the calculated total load loss value, this embodiment classifies the disaster scenario set. The classification criteria can be pre-defined; for example, scenarios with loss values ​​below a certain threshold are classified as minor disasters, those within a certain range are classified as moderate disasters, and those exceeding another threshold are considered severe disasters.

[0048] Optionally, the steps for analyzing the typical disaster scenarios after classification and the line fault probability under the typical disaster scenarios include: in the case of an ice storm scenario, constructing a mathematical model of the ice storm scenario, wherein the mathematical model of the ice storm scenario is used to simulate the impact process of the ice storm on the power distribution system and calculate the change in the ice thickness of the transmission line; using a transmission line ice thickness growth model, analyzing the ice load per unit length of the transmission line generated by the weight of ice in the vertical direction and the wind load per unit length of the transmission line in the horizontal direction under the condition of ice accumulation, and calculating the ice and wind load borne by the transmission line per unit length by combining the ice load per unit length of the transmission line and the wind load per unit length of the transmission line; using the ice storm scenario mathematical model to analyze the ice and wind load borne by the transmission line per unit length of the transmission line and the design load and ultimate load of the transmission line to withstand ice, and determining the line failure rate per unit length of the line at a predetermined time; and calculating the line fault probability of the transmission line under the ice storm scenario based on the line failure rate per unit length of the line at a predetermined time.

[0049] This embodiment uses an ice storm as an example to illustrate the impact of ice storms on the power transmission system. Based on the spatiotemporal characteristics of the disaster, a mathematical model of the ice storm scenario is constructed to simulate the impact of ice storms on the power transmission system and calculate the changes in the ice thickness of the transmission lines.

[0050] It should be noted that before simulating the impact of ice storms on transmission lines, it is first necessary to obtain the ice thickness on the lines at a specific moment (such as the start time of the simulation or the previous time point). Optionally, before using the transmission line ice thickness growth model to analyze the accumulation of ice on the transmission lines, the following steps are also included: obtaining the ice thickness and freezing rain amount on the transmission lines at a predetermined time; and inputting the ice thickness, freezing rain amount, ice density, water density, and ambient air moisture content on the transmission lines into the transmission line ice thickness growth model.

[0051] Freezing rain is a key meteorological factor contributing to power line icing, and its magnitude directly affects the rate and thickness of icing. Collecting freezing rain data at predetermined times helps the model more accurately simulate the dynamic growth process of icing. Density parameters are an important component in calculating icing loads. Ice is generally denser than water; therefore, icing significantly increases the weight of transmission lines, posing a major challenge to their load-bearing capacity. The moisture content in the air affects the formation of freezing rain and the accumulation of icing. Under high moisture content conditions, the probability of freezing rain formation increases, and the rate of icing also accelerates. These parameters are input into the power line icing thickness growth model, and these parameters work together to simulate the growth process of icing on the transmission line surface. By inputting environmental parameters at a specific time, the thickness of the icing on the line surface at that time is calculated. The icing thickness data reflects the impact of icing on the transmission line load.

[0052] To determine the probability of a line failure at a specific location based on the spatiotemporal characteristics of ice storms, appropriate line design standards are selected during the design and planning of transmission lines, taking into account local climate conditions and historical weather data. Therefore, two thresholds can be derived from the line design standards to calculate the line failure rate, representing the design load and ultimate load of the line's ability to withstand icing. When the ice and wind load on the line is less than its design load, no line breakage will occur. When the ice and wind load exceeds the design load but is less than the ultimate load, the line faces the risk of a line breakage, and the failure rate increases with the increase in ice and wind load. When the ice and wind load exceeds the ultimate load, the accumulated ice on the line exceeds its capacity, resulting in a line breakage, which is the line breakage failure rate.

[0053] In this embodiment, in order to more accurately assess the failure rate of transmission lines caused by ice storms, a series of simulations were conducted using a Monte Carlo sampling strategy. Through random sampling and probability calculation, dynamic simulation of line fault states was achieved. Optionally, before classifying the disaster scenario set using a preset clustering strategy, the method further includes: Step 1, for ice storm scenarios, using a Monte Carlo sampling strategy to select transmission line numbers and random numbers; Step 2, using a line fault simulation model to analyze the line breakage rate of the transmission line corresponding to the transmission line number; Step 3, determining whether the random number is greater than or equal to the line breakage rate of the transmission line at a predetermined time; Step 4, if the random number is greater than or equal to the line breakage rate of the transmission line at the predetermined time, determining that the transmission line is in normal operation and proceeding to the next time step, determining whether the next time step is greater than the end time of the impact of the ice storm scenario; if so, determining that the transmission line has not experienced a fault during the entire impact of the ice storm; Step 5, if the random number is less than the line breakage rate of the transmission line at the predetermined time, determining that the transmission line is in a fault state, recording the previous time step as the continuous operating time of the transmission line, and obtaining the changes in the operating state of the transmission line; repeating steps 1 to 5 until the selected transmission line number is greater than the total number of lines in the distribution system, ending the Monte Carlo sampling.

[0054] Before performing Monte Carlo sampling, the transmission line number is first selected to ensure that each sampling targets a specific line in the distribution system. Simultaneously, a uniformly distributed random number between 0 and 1 is generated. This random number is compared with the line's out-of-line failure rate to determine the line's fault state. Then, a line fault simulation model is used, inputting the environmental conditions and line attributes under an ice storm scenario, to calculate the out-of-line failure rate of a specific transmission line at a predetermined time. The generated random number is then compared with the transmission line out-of-line failure rate at the predetermined time. If the random number is greater than or equal to the failure rate, it means that the transmission line has not experienced a fault in the current sampling and is maintaining normal operation. When the transmission line is in normal operation, this embodiment advances the time to the next moment and executes the Monte Carlo sampling strategy again until the impact of the ice storm scenario ends.

[0055] If the line remains in normal operation throughout the entire process, this embodiment confirms that the transmission line did not experience a fault during the ice storm. If the random number is less than the transmission line disconnection failure rate at a predetermined time, this embodiment marks the line as faulty, indicating that the line was affected by the ice storm and failed in the current sampling simulation. Then, the previous moment is recorded as the line's continuous operating time, the line's operating status is updated to faulty, and sampling continues. This continues until all transmission line numbers are covered; that is, sampling is complete when the selected transmission line numbers are greater than the total number of lines in the distribution system. This ensures that the fault states of all lines are simulated and analyzed, thereby providing comprehensive fault rate data.

[0056] Step S102: Use the maximum power supply capacity model to evaluate the power supply operation status of the power distribution system after each typical disaster scenario occurs, and calculate the component risk parameters of the power distribution system based on the power supply operation status and line fault probability. Combine the component risk parameters of the power distribution system with the scenario weight parameters to calculate the state vulnerability index.

[0057] This embodiment uses a maximum power supply capacity model to conduct an in-depth analysis of the power supply operation status of the power distribution system under various typical disaster scenarios. It not only involves the quantitative assessment of system state changes, but also comprehensively considers the line fault probability to calculate the component risk parameters of the power distribution system. Furthermore, it combines the scenario weights to calculate the weighted state vulnerability index.

[0058] First, a graphical model of the power distribution system is constructed using the network topology, power supply configuration, load demand, and fault probability of each line as inputs. Then, the maximum power output that the system can provide under typical disaster scenarios is calculated using a maximum power supply capacity model, along with the matching between this output and load demand. The maximum power supply capacity model considers the physical limitations of the system, such as line transmission capacity, power supply output capacity, and power demand at load points. It also considers the destructive effects of disasters on the system structure, such as line disconnection and power supply damage, ensuring that the assessment results reflect the actual power supply capacity of the system under extreme conditions. The calculation of component risk parameters is based on the power supply operating status and line fault probability. Specifically, it analyzes how the fault probability of each line affects the system's power supply stability under typical disaster scenarios, and the load loss caused by the fault. For each line, if its fault probability is high in a certain scenario, and the system's power supply capacity decreases significantly after the fault, then the component risk parameter of that line will increase accordingly. The component risk parameters are combined with the weights of typical disaster scenarios to calculate the vulnerability index. The scenario weights reflect the severity and probability of different disaster scenarios affecting the system, and are derived based on historical data and climate trend analysis. A higher vulnerability index means a worse power supply stability of the power distribution system under disaster conditions and a higher risk of load loss.

[0059] Optionally, the construction of the maximum power supply capacity model includes: defining each equipment component in the power distribution system as a node, and the connection relationship between each equipment component as a connection edge; determining the power supply network of the initial maximum power supply capacity model, wherein the failure probability of each transmission line in the power supply network is used as the prior probability of the line; using the minimum load loss cost as the objective function of the maximum power supply capacity model; and setting the constraints of the maximum power supply capacity model, including: power balance constraints; constraints on the difference between load power and its load, load loss, and connected power supply power; constraints on power supply power and distributed power supply and energy storage output; constraints on load reactive power and connected distributed power supply reactive power; upper and lower limit constraints on line transmission power; constraints on load loss and load loss amount; node voltage balance constraints; node voltage upper and lower limit constraints; power balance constraints of energy storage devices at a predetermined time; constraints on the unique charging and discharging state of energy storage devices at a predetermined time; energy capacity constraints of energy storage devices; upper and lower limit constraints on the charging and discharging power of energy storage devices; and upper and lower limit constraints on the power of distributed power sources.

[0060] In this embodiment, when constructing the maximum power supply capacity model, the objective function and various constraints are gradually introduced starting from defining the network topology to ensure that the model can accurately reflect the power supply capacity and vulnerability of the power distribution system in the event of extreme weather events. First, various equipment components in the power distribution system, such as generators, transformers, load points, and energy storage devices, are defined as nodes in the network graph. The electrical connections between these equipment components are represented as connection edges between nodes, including transmission lines and distribution lines. The attributes of these connection edges are determined by the physical characteristics of the lines, such as resistance, reactance, and maximum transmission capacity. In the constructed power supply network, considering the impact of extreme weather events, the failure probability of each transmission line is used as the prior probability of the line. This probability is calculated based on historical meteorological data and line design standards, reflecting the failure risk of the line under specific disaster conditions.

[0061] Furthermore, this embodiment sets minimizing the load loss cost as the objective of the maximum power supply capacity model. This means that in the model calculation, the goal is to reduce load losses caused by line faults while satisfying all constraints, so as to maintain the power supply capacity of the power system to the greatest extent.

[0062] Optionally, the step of calculating the state vulnerability index by combining the component risk parameters and scenario weight parameters of the power distribution system includes: obtaining the probability of line fault occurrence and the load loss caused by the fault for each transmission line in the power distribution system; calculating the scenario over-limit risk index under typical disaster scenarios based on the load loss; obtaining the scenario weight parameters for typical disaster scenarios; and calculating the state vulnerability index based on the component risk parameters, scenario weight parameters, and scenario over-limit risk index of the power distribution system.

[0063] Step S103: Calculate the centrality index of nodes in the network structure of the power distribution system, and calculate the structural vulnerability index based on the centrality index of nodes and the scenario weight parameters.

[0064] Optionally, step S103 includes: obtaining the number of nodes associated with each equipment element node in the network structure of the power distribution system to obtain the node degree centrality index; traversing all node pairs in the network structure of the power distribution system and recording the number of node pairs with the shortest path through the target node to obtain the node betweenness centrality index; and combining the node degree centrality index with the corresponding first centrality index weight, the node betweenness centrality index with the corresponding second centrality index weight, and the scenario weight parameter to calculate the structural vulnerability index.

[0065] In step S103 of this embodiment, a structural vulnerability index can be derived by calculating the degree centrality and betweenness centrality of nodes and combining them with scenario weights. This structural vulnerability index reflects the structural stability of the network and the vulnerability of critical nodes under extreme weather events. Specifically, node degree centrality is an indicator that measures the degree of connectivity of a node in a power distribution system network, reflecting the importance of the node. In this embodiment, the node degree centrality index is calculated by obtaining the number of other nodes associated with each equipment component node. Based on the network structure of the power distribution system, the degree centrality index of each node is equal to the number of nodes directly connected to that node. Nodes with higher degree centrality indices have more connections in the system and are key components of the network; a failure in a node can affect more equipment and loads.

[0066] Node betweenness centrality calculates the frequency with which a node acts as an intermediary node in a network for shortest paths; that is, how many pairs of nodes have a shortest path that passes through that node. In this embodiment, the network structure of the power distribution system is traversed, and for each pair of nodes, the number of shortest paths passing through the target node is recorded to calculate the node betweenness centrality index. Calculating the node betweenness centrality index involves a deep analysis of the entire network graph, using a shortest path algorithm to traverse all node pairs and determine which pairs' shortest paths must pass through the target node. The higher the node betweenness centrality index, the stronger the node's mediating role in the network. If this node or its connected lines fail, it can lead to power loss at numerous load points, increasing the overall vulnerability of the system.

[0067] Furthermore, in this embodiment, the calculation of the structural vulnerability index combines node degree centrality, node betweenness centrality, and scenario weight parameters. The specific calculation method is as follows: First, weights for the first and second centrality indicators are set, corresponding to the weighted averages of node degree centrality and node betweenness centrality, respectively, reflecting the emphasis placed on these two centrality indicators when assessing structural vulnerability. Then, based on the severity and probability of occurrence of typical disaster scenarios, weights are assigned to each scenario, i.e., scenario weight parameters. These weight parameters reflect the importance of each scenario when calculating the structural vulnerability index, ensuring the comprehensiveness and reliability of the assessment results. Subsequently, by integrating node connectivity and network mediation effects, as well as the severity and probability of occurrence of scenarios, the structural vulnerability index of the power distribution system under different disaster scenarios is calculated. This can identify key weak points in the system, guide the pre-deployment of emergency resources and the optimization of network structure, thereby improving the resilience of the power system under extreme weather events.

[0068] Step S104: Combine the state vulnerability index and the structural vulnerability index to obtain the node comprehensive vulnerability index, and identify the weak links in the power distribution system according to the index size ranking results.

[0069] In step S104 of this embodiment, the state vulnerability index and the structural vulnerability index are fused to obtain the comprehensive node vulnerability index, thereby accurately identifying key weak links in the power distribution system and providing a scientific basis for emergency resource deployment, power access point optimization, and islanding strategy formulation. After calculating the comprehensive vulnerability index of all nodes, this embodiment sorts the results from high to low to identify key weak links in the system. Nodes with higher vulnerability indices indicate higher vulnerability at both the state and structural levels. This sorting allows for a clear view of which nodes are most vulnerable to extreme weather events and which node failures or abnormal states may have the greatest impact on the stable operation of the entire system.

[0070] Optionally, the identified vulnerabilities include, but are not limited to: critical load nodes: these nodes are highly vulnerable at the state level due to their high load demand or functional importance; hub nodes in the network: these nodes are highly vulnerable at the structural level, as they connect to a large number of other nodes, and a failure could cause widespread power outages or system disconnection; sensitive line connection nodes: nodes located at both ends of sensitive transmission or distribution lines, which are also relatively vulnerable due to the high failure rate of the lines under specific conditions.

[0071] Through the above steps, a disaster scenario set can be determined based on regional historical meteorological data and climate trend analysis results. The disaster scenario set is then classified, and the typical disaster scenarios after classification and the line fault probability under typical disaster scenarios are analyzed. The power supply operation status of the distribution system after the occurrence of each typical disaster scenario is evaluated using the maximum power supply capacity model. Based on the power supply operation status and line fault probability, the component risk parameters of the distribution system are calculated. The state vulnerability index is calculated by combining the component risk parameters of the distribution system with the scenario weight parameters. The centrality index of nodes in the network structure of the distribution system is calculated, and the structural vulnerability index is calculated by combining the node centrality index with the scenario weight parameters. The state vulnerability index and the structural vulnerability index are fused to obtain the comprehensive node vulnerability index, and the weak links of nodes in the distribution system are identified according to the index ranking results. In this embodiment, after classifying all disaster scenarios, the typical disaster scenarios after classification and the line fault probability under the typical disaster scenarios can be analyzed. The state vulnerability index and structural vulnerability index of the power distribution system can be evaluated. By combining structural vulnerability and state vulnerability, the true risk of the power distribution system can be more accurately reflected, and the accuracy of the assessment of the weak links of the power distribution system can be improved. This solves the technical problem in related technologies where the assessment of the weak links of the power distribution system only evaluates the physical structure of the power distribution system, resulting in low accuracy of the assessment results.

[0072] The following describes in detail another optional implementation method.

[0073] Figure 2 This is a schematic diagram of another optional method for identifying vulnerable points in a power distribution system based on scene clustering and network centrality according to an embodiment of the present invention, as shown below. Figure 2 As shown, it includes:

[0074] S1. Investigate historical meteorological data of the region and extract effective information to construct a mathematical model of ice storm scenarios;

[0075] S2, construct a model of the maximum power supply capacity of the power distribution system, use Monte Carlo sampling to generate a large number of disaster scenarios, and cluster the scenarios according to the loss reduction.

[0076] S3, calculation of state vulnerability index and structural vulnerability index;

[0077] S4 assesses network vulnerability based on vulnerability indicators and provides a basis for resource deployment and islanding.

[0078] Specifically, in the implementation process, step S1 includes: generating typical extreme disaster scenarios based on regional historical meteorological data and climate trend analysis, constructing a set of extreme disaster scenarios, and constructing a mathematical model for ice disaster scenarios by retrieving the required data conditions based on on-site meteorological information.

[0079] The Jones model is used to model the growth of icing thickness on transmission lines.

[0080]

[0081] In the formula, Let t be the thickness of the ice layer on the line at time t; Let be the amount of freezing rain at time t; and The density of ice and the density of water; The moisture content of ambient air.

[0082] Figure 3 This is a schematic diagram illustrating an optional stress analysis of an icing-covered railway line according to an embodiment of the present invention, as shown below. Figure 3 As shown, when ice accumulates on a transmission line, the load on the line is the ice force load generated in the vertical direction due to the weight of the ice. and horizontal wind load Synthesis. The ice load per unit length of the line can be obtained based on the icing thickness at time t. for:

[0083]

[0084] In the formula, d is the diameter of the line.

[0085] The wind load per unit length of the line L can be obtained. W (t) is:

[0086]

[0087] In the formula, Let be the wind speed at the line location at time t, and C be a constant coefficient with a value of . S is the span factor, and the specific calculation formula is as follows:

[0088]

[0089] The ice wind load L per unit length of transmission line can be obtained. IW (t).

[0090]

[0091] To determine the probability of a line failure at a specific location based on the spatiotemporal characteristics of ice storms, appropriate line design standards are selected during the design and planning of transmission lines, taking into account local climate conditions and historical weather data. Therefore, two thresholds can be derived from the line design standards to calculate the line failure rate, representing the design load and ultimate load of the line's ability to withstand icing. When the ice and wind load on the line is less than its design load, no line breakage will occur. When the ice and wind load exceeds the design load but is less than the ultimate load, the line faces the risk of a line breakage, and the failure rate increases with the increase in ice and wind load. When the ice and wind load exceeds the ultimate load, the accumulated ice on the line exceeds its capacity, resulting in a line breakage failure, with a failure rate of 1.

[0092] According to the icing and wind load that the line bears In addition to the design and ultimate load of the line to withstand icing, the line breakage failure rate per unit length at time t can be determined. for

[0093]

[0094] In the formula, and These represent the design load and ultimate load of the line's ice-bearing capacity, respectively.

[0095] The failure rate of a transmission line of length l under ice storm conditions is... for:

[0096] .

[0097] Step S2: Power distribution system modeling. Construct a distributed power source, load, and multi-node power distribution system network topology model. Utilize load loss reduction scenarios and k-means clustering to obtain typical disaster scenarios and their probabilities.

[0098] Construct a model of the maximum power supply capacity of the power distribution system, and build the network after the ice storm as a set. :

[0099] ,in, Represents a set of nodes; Represents a set of directed edges; This represents the prior probability of each line, which is composed of the failure probability of each line.

[0100] The objective function is to minimize the cost of load loss, that is:

[0101]

[0102] in This is the node weight coefficient, determined by the importance of the node's function.

[0103] The constraints to be considered are:

[0104] 1) Power balance constraints.

[0105]

[0106]

[0107] In the formula Indicates the line On the node ; Indicates the line On the node ; Indicates the line At any moment The beneficial current that flowed through; Represents a node At any moment The active power demand; Indicates the line exist The ever-flowing, beneficial current; Indicates the line At any moment The reactive current that flows through; Represents a node At any moment The reactive power demand; Indicates the line exist The reactive current that flows by constantly;

[0108] 2) Constraints on load power, load and load loss, and power supply power.

[0109]

[0110] Represents a node At any moment The active power of the connected load; Represents a node At any moment Lost active power of the load; Represents a node At any moment The connected power source has active power output;

[0111] 3) Constraints on power supply capacity and output of distributed power sources and energy storage.

[0112]

[0113] Indicates the first A distributed power source at time Those who have made meritorious contributions; express Energy release power of energy storage devices on nodes; express Energy storage capacity of energy storage devices on nodes;

[0114] 4) Constraints on the reactive power of the load and the reactive power of the connected distributed power sources.

[0115]

[0116] Indicates the first Taiwan distributed power supply Reactive power output at any given moment; It represents the set of nodes containing distributed power sources in a power distribution system;

[0117] 5) Upper and lower limits of line transmission power constraints.

[0118]

[0119]

[0120] This represents the set of lines connecting all nodes; , Indicates transmission line Upper and lower limits of transmitted active power; , Indicates transmission line Upper and lower limits of transmitted reactive power;

[0121] 6) Load loss amount and load loss constraints.

[0122]

[0123] 7) Node voltage balance constraints.

[0124]

[0125] Represents a node At any moment The square of the voltage; , Indicates transmission line Resistance and reactance;

[0126] 8) Node voltage upper and lower limit constraints.

[0127]

[0128] Represents a node The lower limit of the square of the voltage;

[0129] 9) Energy storage equipment Power balance constraints at time

[0130]

[0131] Represents a node On-site energy storage devices The energy storage capacity status at any given time; Indicates the energy conversion efficiency of energy storage devices;

[0132] 10) Energy storage equipment Unique state constraint for charging and discharging at all times

[0133]

[0134] Represents a node On-site energy storage devices Always in a charged state; Represents a node On-site energy storage devices Constantly in a state of energy release;

[0135] 11) Energy capacity constraints of energy storage devices.

[0136]

[0137] , Indicates the minimum and maximum capacity of the energy storage device;

[0138] 12) Upper and lower limits of charging and discharging power of energy storage devices.

[0139]

[0140]

[0141] , This indicates the minimum active power required for the energy storage device to charge and discharge energy. , This indicates the maximum active power of the energy storage device during charging and discharging.

[0142] 13) No. Power upper and lower limit constraints for distributed power sources.

[0143] ;

[0144] .

[0145] For the first Taiwan distributed power supply Active power output at any given moment; For the first Taiwan distributed power supply Reactive power output at any given moment; , Indicates the upper and lower limits of the active power output of distributed power sources; , This indicates the upper and lower limits of the reactive power output of distributed generation.

[0146] Monte Carlo sampling was used to generate a large number of disaster scenarios, and these scenarios were then reduced and classified based on approximate load loss.

[0147] Figure 4 This is a flowchart of an optional Monte Carlo sampling method according to an embodiment of the present invention, such as... Figure 4 As shown, the Monte Carlo sampling method process is as follows:

[0148] Step 1: Set time and line number ;

[0149] Step 2: Based on the ice storm scenario and line failure rate model, input... Disconnection failure rate of line k at time k ;

[0150] Step 3: Generate Uniformly distributed random numbers ;

[0151] Step 4: Determine the random number Is it greater than or equal to? Timetable Wire breakage failure rate If so, then the line In normal operating condition (denoted as) If not, proceed to step 5; otherwise, the line is in a fault state (denoted as...). ),remember For the line The continuous running time is used to obtain the line The system will then monitor the changes in the operating status and proceed to step 6.

[0152] Step 5: Let ,judge Is it longer than the end time of the ice storm's impact? If so, then the line No malfunctions occurred during the entire ice storm impact period; otherwise, return to step 2.

[0153] Step 6: Let and ,judge Is it greater than the total number of lines in the system? If not, return to step 2; otherwise, it means that all lines have completed the transition from failure rate to operating status change, and the Monte Carlo sampling process ends.

[0154] In step S2, instead of running a full, complex simulation, a highly simplified model is used to quickly estimate load loss. Based on the disaster intensity and component vulnerability curves, the failure state of components is rapidly simulated. The total generating capacity and total load demand within the islands formed after a line fault are calculated. If the generating capacity is less than the demand, the load loss within the island is considered significant. Then, a graph traversal algorithm (such as breadth-first search, BFS) is used to quickly identify all islands in the system that have lost power as a result. The loads within these islands are summed to obtain an approximate value of the total load loss in this scenario. The advantage of this approach is its extremely fast computation speed, the elimination of complex power flow calculations, and the ability to capture the influence of network structure.

[0155] Based on the approximate load loss, the generated scenarios are reduced according to their severity and categorized into minor, moderate, and severe disasters, with each scenario having a probability of P. S1 P S2 P S3 This facilitates improved simulation efficiency.

[0156] Step S3: Calculation of state vulnerability index. Under each typical disaster scenario in S2 cluster, the maximum power supply capacity model is used to reflect the power supply operation status of the power distribution system after the disaster failure. Based on the simulated node failure probability in the typical scenario, the component risk of the power distribution system is determined. Based on the scenario weight, the scenario-weighted expected state vulnerability index is calculated.

[0157] Since the fault probability in the model uses the line fault rate, and the operating state of the distribution network is mainly represented by node state variables, it is necessary to simulate and explain the impact of line faults on the node operating state. The specific method is as follows:

[0158] For each typical disaster scenario s, execute the following loop:

[0159] 1. Traverse all lines: For each line in the system ;

[0160] 2. Simulate faults: Manually set the circuit. The fault is disconnected.

[0161] 3. Run the maximum power supply capacity model: Based on the model, obtain the power supply status of each node and calculate the load loss of each node. This allows us to determine the impact of each line fault.

[0162] It should be noted that the effects of a line fault are not linearly additive, but... Scanning plus linear superposition remains the mainstream method in engineering, and its rationale lies in the great simplification of computational complexity and the low probability of higher-order events.

[0163] In step 3, the extent of functional or operational impairment of the power distribution system after an ice storm is assessed, and condition vulnerability indicators are calculated. These indicators include the probability of line faults and the load loss caused by the faults. According to the formula

[0164]

[0165] The scenario exceedance risk index is calculated, which is the vulnerability of a state in a scenario.

[0166] Similarly, calculate the state vulnerabilities for each scenario using the formula:

[0167] The scenario-weighted expected state vulnerability index was calculated.

[0168] Step S4: Calculate the structural vulnerability index. Under each typical disaster scenario in the S2 cluster, recalculate the centrality index (node ​​degree centrality, node betweenness centrality) in the power distribution system graph structure. Based on this, calculate the weighted structural vulnerability index and the scenario-weighted structural index.

[0169] In step 4, the structural vulnerability of the power distribution system under extreme disasters is assessed by abstracting the system into a complex network and calculating its topological centrality index.

[0170] For a given node, its degree is the number of nodes directly connected to it. In a network, the more connections a node has, the more important it is. The original degree of this node is denoted as _____. According to the formula

[0171]

[0172] Standardize the degree centrality of nodes, where This represents the maximum number of connections a node can have in the network.

[0173] Betweenness centrality measures the degree to which a node plays a mediating role in a network. If a node is located on the shortest path between many other node pairs, then this node is a critical bottleneck, and many optimal power transmission paths must pass through it. Calculating node betweenness centrality involves traversing all node pairs in the network and recording how many node pairs have a shortest path that passes through the target node. According to the formula for calculating betweenness centrality

[0174]

[0175] Where s and t are any two source nodes. For nodes With nodes The total number of shortest paths between them. for and The nodes passed through in all shortest paths between them The number of paths. According to the formula

[0176]

[0177] Standardize the betweenness centrality of nodes.

[0178] Depending on the different emphases placed on degree centrality and betweenness centrality in engineering, a weighted calculation is performed for both:

[0179]

[0180] Obtain the comprehensive centrality index of each node. ,in 、 These are the weighting coefficients of the two centrality indicators.

[0181] For ease of calculation, the line failure rate is mapped to the average failure rate of all connected lines. Specifically, the average value method is used, where the failure rate at a given point is equivalent to the average failure rate of all connected lines. However, the line failure rate changes over time; therefore, this embodiment employs the "barrel theory" to determine the optimal failure rate. The core logic is that the vulnerability of a node is affected by the vulnerability of all its associated paths, as shown in the formula:

[0182]

[0183] in For nodes A set of connected lines.

[0184] The formula for calculating the structural vulnerability index in a given scenario is:

[0185] ;

[0186] Similarly, the structural weaknesses in each scenario are calculated using the formula:

[0187] ;

[0188] The scenario-weighted expected structural weakness index was calculated.

[0189] Based on the engineering requirements, a comprehensive weakness index is obtained by weighting the scenario-weighted expected state weakness index and the scenario-weighted expected structure weakness index.

[0190] Step S5 involves a comprehensive vulnerability score, combining state-based and structural vulnerability indicators to obtain a comprehensive node vulnerability index, ranked from highest to lowest. A higher index indicates a more vulnerable node. The results can be used for emergency resource pre-deployment, power access point optimization, and islanding strategy formulation.

[0191] In this embodiment of the invention, structural vulnerability and state vulnerability can be combined in a weighted manner, breaking the limitations of previous single-dimensional assessments. This allows for a comprehensive consideration of the vulnerability of the system's internal structure and the impact of external environmental changes on its operational state, thereby providing a more comprehensive and realistic vulnerability score.

[0192] By introducing a probabilistic failure model, this invention takes into account the uncertainty of disaster occurrence and the randomness of system response, thus enabling vulnerability assessment to move beyond static conditions and dynamically reflect the impact of different stages of a disaster on the power system. Simultaneously, by utilizing scenario clustering and a simplified load loss model, this invention reduces the computation time and resources required by traditional simulation methods, enabling rapid generation of assessment results when dealing with large-scale networks, accelerating emergency response, and lowering computational costs. This makes it more suitable for applications involving large-scale real-time monitoring and long-term planning.

[0193] The following is a detailed description with reference to another embodiment.

[0194] Example 2

[0195] The power distribution system weak link identification device provided in this embodiment includes multiple implementation units, each of which corresponds to a specific implementation step in Embodiment 1 above.

[0196] Figure 5 This is a schematic diagram of an optional power distribution system weak link identification device according to an embodiment of the present invention, such as... Figure 5As shown, the power distribution system weak link identification device may include: a disaster scenario analysis unit 51, a state weakness index calculation unit 52, a structural vulnerability index calculation unit 53, and a system node weak link identification unit 54.

[0197] Among them, the disaster scenario analysis unit 51 is used to determine the disaster scenario set based on regional historical meteorological data and climate trend analysis results, classify the disaster scenario set, and analyze the typical disaster scenarios after classification and the line failure probability under the typical disaster scenarios.

[0198] The vulnerability index calculation unit 52 is used to evaluate the power supply operation status of the power distribution system after the occurrence of various typical disaster scenarios using the maximum power supply capacity model, and calculate the component risk parameters of the power distribution system based on the power supply operation status and line fault probability, and calculate the vulnerability index by combining the component risk parameters of the power distribution system and scenario weight parameters.

[0199] The structural vulnerability index calculation unit 53 is used to calculate the centrality index of nodes in the network structure of the power distribution system, and to calculate the structural vulnerability index based on the centrality index of nodes and the scenario weight parameters.

[0200] The system node weak link identification unit 54 is used to integrate the state weakness index and the structural vulnerability index to obtain the node comprehensive vulnerability index, and to identify the node weak links in the power distribution system according to the index size ranking result.

[0201] The aforementioned power distribution system weak link identification device can determine a set of disaster scenarios based on regional historical meteorological data and climate trend analysis results through the disaster scenario analysis unit 51, classify the disaster scenario set, and analyze the typical disaster scenarios after classification and the line fault probability under the typical disaster scenarios. The state vulnerability index calculation unit 52 uses the maximum power supply capacity model to evaluate the power supply operation status of the power distribution system after the occurrence of each typical disaster scenario, and calculates the component risk parameters of the power distribution system based on the power supply operation status and the line fault probability. The state vulnerability index is calculated by combining the component risk parameters of the power distribution system and the scenario weight parameters. The structural vulnerability index calculation unit 53 calculates the centrality index of the nodes in the network structure of the power distribution system, and calculates the structural vulnerability index by weighting the node centrality index and the scenario weight parameters. The system node weak link identification unit 54 integrates the state vulnerability index and the structural vulnerability index to obtain the node comprehensive vulnerability index, and identifies the node weak links in the power distribution system according to the index size ranking result. In this embodiment, after classifying all disaster scenarios, the typical disaster scenarios after classification and the line fault probability under the typical disaster scenarios can be analyzed. The state vulnerability index and structural vulnerability index of the power distribution system can be evaluated. By combining structural vulnerability and state vulnerability, the true risk of the power distribution system can be more accurately reflected, and the accuracy of the assessment of the weak links of the power distribution system can be improved. This solves the technical problem in related technologies where the assessment of the weak links of the power distribution system only evaluates the physical structure of the power distribution system, resulting in low accuracy of the assessment results.

[0202] Optionally, the disaster scenario analysis unit includes: an ice disaster scenario model construction module, used to construct a mathematical model of an ice disaster scenario when the typical disaster scenario is an ice disaster scenario. The ice disaster scenario mathematical model is used to simulate the impact of ice disasters on the power distribution system and calculate the changes in the thickness of ice covering the transmission lines; a transmission line load analysis module, used to analyze the ice load per unit length of the transmission line due to the weight of ice covering in the vertical direction and the wind load per unit length of the transmission line in the horizontal direction when ice covering accumulates, using a transmission line ice thickness growth model, and calculating the ice and wind load borne by the transmission line per unit length; a line failure rate determination module, used to analyze the ice and wind load borne by the transmission line per unit length and the design load and ultimate load of the transmission line to ice covering using the ice disaster scenario mathematical model, and determine the line failure rate of the line per unit length at a predetermined time; and a line failure probability calculation module, used to calculate the line failure probability of the transmission line under the ice disaster scenario based on the line failure rate of the line per unit length at a predetermined time.

[0203] Optionally, the power distribution system weak link identification device further includes: a line icing data acquisition module, used to acquire the icing thickness and freezing rain amount on the transmission line at a predetermined time before analyzing the icing accumulation on the transmission line using the transmission line icing thickness growth model; and a model parameter input module, used to input the icing thickness, freezing rain amount, ice density, water density, and ambient air moisture content on the transmission line into the transmission line icing thickness growth model.

[0204] Optionally, the power distribution system weak link identification device further includes: a Monte Carlo sampling module, used to perform the following steps before classifying the disaster scenario set using a preset clustering strategy: Step 1, for ice storm scenarios, use the Monte Carlo sampling strategy to select transmission line numbers and random numbers; Step 2, use a line fault simulation model to analyze the line failure rate of the transmission line corresponding to the transmission line number; Step 3, determine whether the random number is greater than or equal to the line failure rate of the transmission line at a predetermined time; Step 4, if the random number is greater than or equal to the line failure rate of the transmission line at the predetermined time, determine... The transmission line is in normal operation and enters the next moment. It is determined whether the next moment is greater than the end time of the impact of the ice disaster scenario. If so, it is determined that the transmission line has not experienced a fault during the entire impact of the ice disaster. Step 5: If the random number is less than the line failure rate of the transmission line at the predetermined time, it is determined that the transmission line is in a fault state. The previous moment is recorded as the continuous operating time of the transmission line, and the operating status of the transmission line is obtained. Steps 1 to 5 are repeated until the selected transmission line number is greater than the total number of lines in the distribution system, and the Monte Carlo sampling ends.

[0205] Optionally, when classifying the disaster scenario set, the disaster scenario analysis unit includes: a power generation capacity calculation module, used to calculate the total power generation capacity parameters and total load demand parameters of the island formed after the transmission line fault in the distribution system under each disaster scenario; an island load confirmation module, used to confirm the occurrence of load loss in the island when the total power generation capacity parameter in the island is less than the total load demand parameter; an island load accumulation module, used to use a graph traversal algorithm to query all power-outage islands in the distribution system, add up the loads of all islands, and obtain the total load loss value under each disaster scenario; and a disaster classification module, used to classify the disaster scenario set according to the total load loss value.

[0206] Optionally, when constructing the maximum power supply capacity model, the distribution system weak link identification device includes: an initial network definition module, used to define each equipment component in the distribution system as a node, and the connection relationship between each equipment component as a connection edge, to determine the power supply network of the initial maximum power supply capacity model, wherein the failure probability of each transmission line in the power supply network is used as the prior probability of the line; an objective function determination module, used to use the minimum load loss cost as the objective function of the maximum power supply capacity model; and a model constraint setting module, used to set the constraint conditions of the maximum power supply capacity model, including: power balance constraints; constraints on the difference between load power and its load, load loss, and connected power supply power; constraints on power supply power and distributed power supply and energy storage output; constraints on load reactive power and connected distributed power reactive power; upper and lower limit constraints on line transmission power; constraints on load loss and load loss amount; node voltage balance constraints; node voltage upper and lower limit constraints; power balance constraints of energy storage devices at predetermined times; constraints on the unique charging and discharging state of energy storage devices at predetermined times; energy capacity constraints of energy storage devices; upper and lower limit constraints on the charging and discharging power of energy storage devices; and upper and lower limit constraints on the power of distributed power sources.

[0207] Optionally, the condition vulnerability index calculation unit includes: a load loss acquisition module, used to acquire the probability of line fault occurrence and the load loss caused by the fault for each transmission line in the power distribution system; a scenario limit exceedance risk index calculation module, used to calculate the scenario limit exceedance risk index under typical disaster scenarios based on the load loss; and to acquire the scenario weight parameters for typical disaster scenarios; and a condition vulnerability index calculation module, used to calculate the condition vulnerability index based on the component risk parameters, scenario weight parameters, and scenario limit exceedance risk index of the power distribution system.

[0208] Optionally, the structural vulnerability index calculation unit includes: a node degree centrality index acquisition module, used to acquire the number of nodes associated with each equipment component node in the network structure of the power distribution system, and obtain the node degree centrality index; a node betweenness centrality index recording module, used to traverse all node pairs in the network structure of the power distribution system, record the number of node pairs with the shortest path passing through the target node, and obtain the node betweenness centrality index; and a structural vulnerability index calculation module, used to calculate the structural vulnerability index by combining the node degree centrality index and the corresponding first centrality index weight, the node betweenness centrality index and the corresponding second centrality index weight, and the scenario weight parameter.

[0209] The aforementioned power distribution system weak link identification device may also include a processor and a memory. The aforementioned disaster scenario analysis unit 51, state weakness index calculation unit 52, structural vulnerability index calculation unit 53, system node weak link identification unit 54, etc., are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0210] The aforementioned processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and kernel parameters can be adjusted to implement disaster vulnerability analysis of power distribution systems based on scenario clustering and network centrality.

[0211] The aforementioned memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0212] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the power distribution system weak link identification method of any one of the above embodiments.

[0213] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the power distribution system weak link identification method of any one of the above embodiments.

[0214] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the power distribution system weak link identification method described in various embodiments of this application.

[0215] This application also provides a computer program product, including a non-volatile computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the power distribution system weak link identification method described in various embodiments of this application.

[0216] Figure 6 This is a hardware structure block diagram of an electronic device (or mobile device) for implementing a method for identifying weak points in a power distribution system according to an embodiment of the present invention. Figure 6 As shown, an electronic device may include one or more ( Figure 6The processor (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and memory 604 for storing data are also included. In addition, it may include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 6 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device may also include components that are more... Figure 6 The more or fewer components shown, or having the same Figure 6 The different configurations shown.

[0217] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0218] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0219] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0220] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0221] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0222] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0223] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for identifying weak links in a power distribution system, characterized in that, include: Based on regional historical meteorological data and climate trend analysis results, a disaster scenario set is determined, the disaster scenario set is classified, and the typical disaster scenarios after classification and the line failure probability under the typical disaster scenarios are analyzed. The power supply capacity model is used to evaluate the power supply operation status of the distribution system after the occurrence of various typical disaster scenarios. Based on the power supply operation status and the line fault probability, the component risk parameters of the distribution system are calculated. The state vulnerability index is calculated by combining the component risk parameters of the distribution system and the scenario weight parameters. Calculate the centrality index of nodes in the network structure of the power distribution system, and calculate the structural vulnerability index based on the centrality index of the nodes and the scenario weight parameter. By integrating the state vulnerability index and the structural vulnerability index, a comprehensive node vulnerability index is obtained, and the weak links in the power distribution system are identified based on the index ranking results.

2. The method according to claim 1, characterized in that, The steps for analyzing the categorized typical disaster scenarios and the line fault probability under the typical disaster scenarios include: In the case of a typical disaster scenario, an ice storm scenario mathematical model is constructed. The ice storm scenario mathematical model is used to simulate the impact of ice storms on the power distribution system and to calculate the changes in the thickness of ice covering the transmission lines. A transmission line icing thickness growth model is used to analyze the ice load per unit length of the transmission line due to the weight of ice in the vertical direction and the wind load per unit length of the transmission line in the horizontal direction under the condition of ice accumulation. The ice and wind load per unit length of the transmission line is calculated by combining the ice load per unit length and the wind load per unit length of the transmission line. The ice storm scenario mathematical model is used to analyze the ice wind load borne by the unit length of the transmission line, as well as the design load and ultimate load of the transmission line to withstand ice accumulation, and to determine the line breakage failure rate of the unit length of the line at a predetermined time. Based on the line breakage failure rate per unit length at a predetermined time, the line failure probability of the transmission line under the ice disaster scenario is calculated.

3. The method according to claim 2, characterized in that, Before analyzing the accumulation of icing on transmission lines using a transmission line icing thickness growth model, the following steps are also included: Obtain the ice thickness and freezing rain amount on the transmission line at the predetermined time. The ice thickness, freezing rain, ice density, water density, and ambient air moisture content on the transmission line are input into the ice thickness growth model of the transmission line.

4. The method according to claim 1, characterized in that, Before classifying the disaster scenario set using a preset clustering strategy, the following steps are also included: Step 1: For ice storm scenarios, a Monte Carlo sampling strategy is adopted to select transmission line numbers and random numbers; Step 2: Using a line fault simulation model, analyze the line breakage rate of the transmission line corresponding to the transmission line number. Step 3: Determine whether the random number is greater than or equal to the line breakage failure rate of the transmission line at the predetermined time. Step 4: If the random number is greater than or equal to the line failure rate of the transmission line at the predetermined time, determine that the transmission line is in normal operation and proceed to the next time. Determine whether the next time is greater than the end time of the impact of the ice disaster. If so, determine that the transmission line has not experienced any failure during the entire ice disaster impact process. Step 5: If the random number is less than the line failure rate of the transmission line at a predetermined time, determine that the transmission line is in a fault state, record the previous time as the continuous operating time of the transmission line, and obtain the changes in the operating state of the transmission line. Repeat steps one through five until the selected transmission line number is greater than the total number of lines in the distribution system, then end the Monte Carlo sampling.

5. The method according to claim 4, characterized in that, The steps for classifying the disaster scenario set include: Under various disaster scenarios, calculate the total power generation capacity parameters and total load demand parameters within the isolated island formed after a transmission line fault in the power distribution system; If the total power generation capacity parameter within the island is less than the total load demand parameter, load loss within the island is confirmed. The graph traversal algorithm is used to query all power outage islands in the power distribution system, and the loads of all islands are added together to obtain the total load loss value under each disaster scenario. The disaster scenario set is classified according to the total load loss value.

6. The method according to claim 1, characterized in that, When constructing the maximum power supply capacity model, the following are included: Define each device component in the power distribution system as a node, and the connection relationship between each device component as a connection edge, to determine the power supply network of the initial maximum power supply capacity model, wherein the failure probability of each transmission line is used as the prior probability of the line in the power supply network. The objective function of the maximum power supply capacity model is the minimum load loss cost. The constraints of the maximum power supply capacity model are defined, including: power balance constraints; constraints on the difference between load power and its load, load loss, and connected power supply power; constraints on power supply power and distributed power supply and energy storage output; constraints on load reactive power and connected distributed power supply reactive power; upper and lower limit constraints on line transmission power; constraints on load loss and load loss amount; node voltage balance constraints; node voltage upper and lower limit constraints; power balance constraints of energy storage devices at a predetermined time; constraints on the unique charging and discharging state of energy storage devices at a predetermined time; energy capacity constraints of energy storage devices; upper and lower limit constraints on the charging and discharging power of energy storage devices; and upper and lower limit constraints on the power of distributed power supply.

7. The method according to claim 1, characterized in that, The steps for calculating the state vulnerability index by weighting the component risk parameters and scenario weight parameters of the power distribution system include: Obtain the probability of line faults and the load loss caused by each transmission line in the power distribution system; Based on the aforementioned load loss, calculate the scenario over-limit risk index under typical disaster scenarios; Obtain the scene weight parameters for the typical disaster scenarios; The state vulnerability index is calculated based on the component risk parameters of the power distribution system, the scenario weight parameters, and the scenario over-limit risk index.

8. The method according to claim 1, characterized in that, The steps of calculating the centrality index of nodes in the network structure of the power distribution system, and calculating the structural vulnerability index based on the centrality index of the nodes and the scenario weight parameters, include: The number of nodes associated with each equipment element node in the network structure of the power distribution system is obtained to obtain the node degree centrality index. Traverse all node pairs in the network structure of the power distribution system, record the number of node pairs with the shortest path to the target node, and obtain the node betweenness centrality index. The structural vulnerability index is calculated by combining the node degree centrality index and its corresponding first centrality index weight, the node betweenness centrality index and its corresponding second centrality index weight, and the scenario weight parameter.

9. A device for identifying weak links in a power distribution system, characterized in that, include: The disaster scenario analysis unit is used to determine a disaster scenario set based on regional historical meteorological data and climate trend analysis results, classify the disaster scenario set, and analyze each typical disaster scenario after classification and the line failure probability under the typical disaster scenario. The vulnerability index calculation unit is used to evaluate the power supply operation status of the power distribution system after the occurrence of various typical disaster scenarios using the maximum power supply capacity model, and calculate the component risk parameters of the power distribution system based on the power supply operation status and the line fault probability, and calculate the vulnerability index by combining the component risk parameters of the power distribution system and the scenario weight parameters. The structural vulnerability index calculation unit is used to calculate the centrality index of nodes in the network structure of the power distribution system, and to calculate the structural vulnerability index based on the centrality index of the nodes and the scenario weight parameters. The system node vulnerability identification unit is used to fuse the state vulnerability index and the structural vulnerability index to obtain a comprehensive node vulnerability index, and to identify the node vulnerability in the power distribution system according to the index size ranking result.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the power distribution system weak link identification method according to any one of claims 1 to 8.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for identifying weak links in a power distribution system as described in any one of claims 1 to 8.

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