Power grid line identification method, device and equipment for natural disaster scenarios

Abstract

This application relates to the field of power system control technology, and in particular to a method, apparatus, and equipment for power grid line identification in natural disaster scenarios. The method includes: acquiring original power grid topology data, line vulnerability parameters, and natural disaster data; generating multiple fault scenarios under natural disaster conditions based on the power grid topology data, line vulnerability parameters, and natural disaster data; evaluating the comprehensive performance gain of each power grid line under each fault scenario based on the original power grid topology data; generating a line importance ranking sequence for each fault scenario based on the comprehensive performance gain of each power grid line under each fault scenario; and identifying the target power grid line based on the line importance ranking sequence for each fault scenario. This solves the problems of fragmented analysis processes, lack of unified coordination mechanisms, insufficient consideration of extreme natural disaster risk characteristics, lack of consistent correlation between results from multiple scenarios, and insufficient global ranking and fusion capabilities in related technologies.

Description

Technical Field

[0001] This application relates to the field of power system control technology, and in particular to a method, apparatus and equipment for identifying power grid lines in natural disaster scenarios. Background Technology

[0002] Power transmission networks are a vital infrastructure for modern urban operations. Natural disasters can easily cause multiple lines to fail simultaneously, triggering cascading failures and large-scale power outages. Therefore, research on the identification of critical power lines is of great significance.

[0003] The relevant technologies employ complex network theory, seepage theory, and Monte Carlo simulation methods to analyze disaster scenarios, and combine topological analysis and statistical evaluation methods to assess the importance of key system elements. However, the overall analysis process is relatively fragmented, and there is a lack of a unified coordination mechanism between the various stages. The relevant technologies are based on average operating conditions or normal operating conditions for evaluation, and fail to fully consider the complex risk characteristics under extreme natural disaster conditions. In addition, in the process of multi-scenario analysis, the comparative relationships between different scenarios are relatively incomplete, making it difficult to achieve consistent ranking and effective integration of the overall results. Summary of the Invention

[0004] This application provides a method, device, and equipment for identifying power grid lines in natural disaster scenarios, in order to solve problems such as the fragmented technical analysis process, lack of unified coordination mechanism, insufficient consideration of extreme natural disaster risk characteristics, lack of consistency between results in multiple scenarios, and insufficient global ranking and fusion capabilities.

[0005] The first aspect of this application provides a method for identifying power grid lines in natural disaster scenarios, comprising the following steps: acquiring original power grid topology data, line vulnerability parameters, and natural disaster data; generating multiple fault scenarios under the natural disaster scenario based on the power grid topology data, line vulnerability parameters, and natural disaster data; evaluating the comprehensive performance gain of each power grid line under each fault scenario based on the original power grid topology data; generating a line importance ranking sequence under each fault scenario based on the comprehensive performance gain of each power grid line under each fault scenario; and identifying the target power grid line based on the line importance ranking sequence under each fault scenario.

[0006] Optionally, in one embodiment of this application, multiple fault scenarios under a natural disaster scenario are generated based on power grid topology data, line vulnerability parameters, and natural disaster data, including: using power grid topology data, line vulnerability parameters, and natural disaster data as input, and employing the Monte Carlo simulation method to generate multiple fault scenarios under a natural disaster scenario.

[0007] Optionally, in one embodiment of this application, evaluating the comprehensive performance gain of each power grid line under each fault scenario based on the original power grid topology data includes: extracting the initial set of failed lines in each fault scenario; obtaining reference power grid topology data after the failed lines in the initial set of failed lines have been restored; calculating the topology connectivity gain and power supply capacity gain based on the original power grid topology data and the reference power grid topology data; and calculating the comprehensive performance gain of the corresponding power grid line based on the topology connectivity gain and power supply capacity gain.

[0008] Optionally, in one embodiment of this application, calculating the topology connectivity gain and power supply capacity gain based on the original power grid topology data and the reference power grid topology data includes: calculating the relative size of the first maximum connected component of the original power grid topology based on the original power grid topology data; calculating the relative size of the second maximum connected component of the reference power grid topology based on the reference power grid topology data; calculating the topology connectivity gain based on the relative size of the first maximum connected component and the relative size of the second maximum connected component; calculating the first power supply load ratio based on the original power grid state corresponding to the original power grid topology data; calculating the second power supply load ratio based on the current power grid state corresponding to the reference power grid topology data; and calculating the power supply capacity gain based on the first power supply load ratio and the second power supply load ratio.

[0009] Optionally, in one embodiment of this application, the formula for calculating the overall performance gain is:

[0010] in, For overall performance gain; The line is faulty; For topological connectivity gain; To balance the weights; To balance the weights; This is the power supply capacity gain.

[0011] Optionally, in one embodiment of this application, generating a line importance ranking sequence for each fault scenario based on the comprehensive performance gain of each power grid line under each fault scenario includes: obtaining the current set of failed lines and the current power grid state; selecting at least one failed line for recovery based on the comprehensive performance gain of each power grid line under each fault scenario, and updating the current set of failed lines and the current power grid state; re-evaluating the comprehensive performance gain of each power grid line based on the updated current set of failed lines until a stopping condition is met, so as to obtain the line importance ranking sequence for each fault scenario.

[0012] Optionally, in one embodiment of this application, identifying target power grid lines based on the line importance ranking sequence under each fault scenario includes: determining the ranking position of power grid lines based on the line importance ranking sequence under each fault scenario; converting the ranking position of power grid lines into standardized values ​​based on a pre-set target conversion format; extracting the initial system performance loss degree under each fault scenario, and calculating the weight of each power grid line based on the initial system performance loss degree and the standardized values; constructing the preference value of each power grid line based on the relative preference relationship between power grid lines, calculating the weighted average preference value based on the preference value and weight of each power grid line, calculating the global dominance of each power grid line based on the weighted average preference value; and determining the target power grid line from each power grid line based on the global dominance.

[0013] A second aspect of this application provides a power grid line identification device for natural disaster scenarios, comprising: an acquisition module for acquiring original power grid topology data, line vulnerability parameters, and natural disaster data, and generating multiple fault scenarios under the natural disaster scenario based on the power grid topology data, line vulnerability parameters, and natural disaster data; a generation module for evaluating the comprehensive performance gain of each power grid line under each fault scenario based on the original power grid topology data, and generating a line importance ranking sequence under each fault scenario based on the comprehensive performance gain of each power grid line under each fault scenario; and an identification module for identifying target power grid lines based on the line importance ranking sequence under each fault scenario.

[0014] Optionally, in one embodiment of this application, the acquisition module is further configured to use power grid topology data, line vulnerability parameters, and natural disaster data as inputs to generate multiple fault scenarios under natural disaster scenarios using the Monte Carlo simulation method.

[0015] Optionally, in one embodiment of this application, the generation module is further configured to extract the initial set of failed lines in each fault scenario; obtain reference grid topology data after the failed lines in the initial set of failed lines have been restored; calculate the topology connectivity gain and power supply capacity gain based on the original grid topology data and the reference grid topology data; and calculate the comprehensive performance gain of the corresponding grid lines based on the topology connectivity gain and power supply capacity gain.

[0016] Optionally, in one embodiment of this application, the generation module is further configured to calculate the first maximum connectivity component relative size of the original power grid topology based on the original power grid topology data, calculate the second maximum connectivity component relative size of the reference power grid topology based on the reference power grid topology data, calculate the topology connectivity gain based on the first maximum connectivity component relative size and the second maximum connectivity component relative size; calculate the first power supply load ratio based on the original power grid state corresponding to the original power grid topology data, calculate the second power supply load ratio based on the current power grid state corresponding to the reference power grid topology data, and calculate the power supply capacity gain based on the first power supply load ratio and the second power supply load ratio.

[0017] Optionally, in one embodiment of this application, the formula for calculating the overall performance gain is:

[0018] in, For overall performance gain; The line is faulty; For topological connectivity gain; To balance the weights; To balance the weights; This is the power supply capacity gain.

[0019] Optionally, in one embodiment of this application, the generation module is further configured to obtain the current set of failed lines and the current power grid state; select at least one failed line for recovery based on the comprehensive performance gain of each power grid line under each fault scenario, and update the current set of failed lines and the current power grid state; re-evaluate the comprehensive performance gain of each power grid line based on the updated current set of failed lines until the stopping condition is met, so as to obtain the line importance ranking sequence under each fault scenario.

[0020] Optionally, in one embodiment of this application, the identification module is further configured to determine the ranking position of the power grid lines according to the line importance ranking sequence under each fault scenario; convert the ranking position of the power grid lines into standardized values ​​based on a pre-set target conversion format; extract the initial system performance loss degree under each fault scenario; calculate the weight of each power grid line based on the initial system performance loss degree and the standardized values; construct the preference value of each power grid line according to the relative preference relationship between power grid lines; calculate the weighted average preference value based on the preference value and weight of each power grid line; calculate the global dominance of each power grid line based on the weighted average preference value; and determine the target power grid line from each power grid line based on the global dominance.

[0021] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the power grid line identification method for natural disaster scenarios as described in the above embodiments.

[0022] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the power grid line identification method for natural disaster scenarios as described in the above embodiments.

[0023] Therefore, this application has the following beneficial effects: First, raw power grid topology data, line vulnerability parameters, and natural disaster data are acquired. Multiple fault scenarios under natural disaster conditions are generated based on this data, enabling unified modeling of power grid structural characteristics, line failure probabilities, and external disaster disturbances. This constructs a basic analysis scenario covering various uncertainties, improving the scenario completeness of subsequent risk assessments. Second, the comprehensive performance gain of each power grid line under each fault scenario is evaluated based on the raw power grid topology data. A line importance ranking sequence is generated for each fault scenario based on this comprehensive performance gain, transforming the line's impact in the fault scenarios into a unified quantitative indicator and forming comparable ranking results. Then, target power grid lines are identified based on the line importance ranking sequence for each fault scenario. By comprehensively filtering the ranking results across multiple fault scenarios, consistent identification of high-impact lines across scenarios is achieved. This addresses issues such as fragmented technical analysis processes, lack of a unified collaborative mechanism, insufficient consideration of extreme natural disaster risk characteristics, lack of consistent correlation between results from multiple scenarios, and insufficient global ranking and fusion capabilities.

[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0025] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a power grid line identification method for natural disaster scenarios according to an embodiment of this application; Figure 2 This is a flowchart illustrating single-scene recognition according to an embodiment of this application; Figure 3 This is a flowchart illustrating multi-scenario fusion according to an embodiment of this application; Figure 4 This is an example diagram illustrating the technical framework of a power grid line identification method for natural disaster scenarios according to embodiments of this application; Figure 5 This is an example diagram of a power grid line identification device for natural disaster scenarios according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0026] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0027] The following description, with reference to the accompanying drawings, describes a method, apparatus, and device for identifying power grid lines in natural disaster scenarios according to embodiments of this application. Addressing the problems mentioned in the background section, this application provides a method for identifying power grid lines in natural disaster scenarios. In this method, original power grid topology data, line vulnerability parameters, and natural disaster data are acquired. Multiple fault scenarios under a natural disaster scenario are generated based on the power grid topology data, line vulnerability parameters, and natural disaster data. The comprehensive performance gain of each power grid line under each fault scenario is evaluated based on the original power grid topology data. A line importance ranking sequence is generated for each fault scenario based on the comprehensive performance gain of each power grid line under each fault scenario. The target power grid line is identified based on the line importance ranking sequence for each fault scenario. This solves the problems of fragmented analysis processes, lack of a unified collaborative mechanism, insufficient consideration of extreme natural disaster risk characteristics, lack of consistent correlation between results from multiple scenarios, and insufficient global ranking and fusion capabilities in related technologies.

[0028] Specifically, Figure 1 This is a flowchart illustrating a power grid line identification method for natural disaster scenarios, provided as an embodiment of this application.

[0029] like Figure 1 As shown, this power grid line identification method for natural disaster scenarios includes the following steps: In step S101, the original power grid topology data, line vulnerability parameters and natural disaster data are obtained, and multiple fault scenarios under the natural disaster scenario are generated based on the power grid topology data, line vulnerability parameters and natural disaster data.

[0030] Among them, the power grid topology data is the basic network data used to characterize the connection relationship and structural attributes of power grid nodes and lines. In this application, it represents the physical connection relationship and topological structure information between nodes in the power system. The line vulnerability parameters are a set of parameters used to characterize the probability of failure or damage of power grid lines under the action of natural disasters. In this application, they represent the failure sensitivity index of each transmission line under external disturbance conditions such as typhoons, ice storms or earthquakes. The natural disaster data is disaster information used to describe the intensity, path and impact range of natural disasters in the target area within a preset time window. In this application, it represents the external input variables of disasters used to drive the generation of fault scenarios and to characterize the spatiotemporal distribution characteristics of disasters.

[0031] Understandably, by jointly modeling power grid topology data, line vulnerability parameters, and natural disaster data and generating multiple fault scenarios, a unified representation of the uncertainty of natural disasters and multi-source disturbance factors can be achieved, thereby avoiding the assessment bias caused by the analysis of a single deterministic scenario.

[0032] In one embodiment of this application, multiple fault scenarios under a natural disaster scenario are generated based on power grid topology data, line vulnerability parameters, and natural disaster data. This includes generating multiple fault scenarios under a natural disaster scenario using a Monte Carlo simulation method with power grid topology data, line vulnerability parameters, and natural disaster data as input.

[0033] Among them, the fault scenario is the combination of possible failure states of the power grid under the action of a specific natural disaster.

[0034] Understandably, generating multiple fault scenarios through Monte Carlo simulation allows for the systematic expression of possible line failure combinations under different random disturbance conditions, thereby providing diverse and comparable scenario inputs for line importance assessment.

[0035] Before initiating a risk assessment for disasters, this application first generates multiple failure scenarios. These scenarios are inputted using power grid topology data, line vulnerability parameters, and typhoon path forecast data. The line vulnerability parameters include a median disaster intensity parameter and a logarithmic standard deviation parameter. The median disaster intensity parameter represents the disaster intensity parameter corresponding to a probability of 0.5 for the line to reach a failure state; the logarithmic standard deviation parameter represents the degree of uncertainty and dispersion in the line failure probability as a function of disaster intensity.

[0036] Furthermore, this application uses the Monte Carlo simulation method to generate N evaluation scenarios (e.g., N=10000), wherein each scenario includes: (1) scenario identifier. (2) Initial set of failed lines (3) The degree of initial system performance loss, expressed in terms of initial power supply capacity; (4) Optionally, for each failed line failure probability .

[0037] Specifically, in this embodiment, the disaster intensity at each transmission line or tower location is first determined based on disaster data. Then, the corresponding disaster intensity is converted into the failure probability of each line based on the line vulnerability parameters. Subsequently, for each Monte Carlo sampling, a random number following a uniform distribution is generated for each line, and this random number is compared with the failure probability of that line. When the random number is less than or equal to the failure probability of that line, it is determined that the line has experienced initial failure in this sampling; otherwise, it is determined that the line remains in normal operation in this sampling. The above sampling process is repeated N times to obtain N fault scenarios.

[0038] All scenario data is output in MATLAB data file format. The output format of scenario data is standardized, and all subsequent units read data through a unified data interface to ensure that the entire framework uses the same set of scenario foundations.

[0039] In step S102, the comprehensive performance gain of each power grid line under each fault scenario is evaluated based on the original power grid topology data, and a line importance ranking sequence under each fault scenario is generated based on the comprehensive performance gain of each power grid line under each fault scenario.

[0040] Among them, the comprehensive performance gain is a comprehensive evaluation index used to characterize the degree of improvement of the overall performance of the power grid after the restoration of a single power grid line under a specific fault scenario; the line importance ranking sequence is an ordered arrangement result used to characterize the relative importance of each power grid line under the same fault scenario, and in this application, it represents the line priority sequence obtained by sorting each line from largest to smallest based on the comprehensive performance gain of each line.

[0041] Understandably, mapping the impact of line restoration on the power grid topology and power supply capacity under various fault scenarios into a unified comprehensive performance gain index transforms the degree of impact of different lines, which was originally difficult to compare directly, into quantifiable results under the same evaluation scale. Based on this, a sequence of line importance ranking under each fault scenario is formed, thereby realizing a structured expression from changes in power grid state to the ranking of the relative importance of lines.

[0042] In one embodiment of this application, the comprehensive performance gain of each power grid line under each fault scenario is evaluated based on the original power grid topology data, including: extracting the initial set of failed lines in each fault scenario; obtaining reference power grid topology data after the failed lines in the initial set of failed lines have been restored; calculating the topology connectivity gain and power supply capacity gain based on the original power grid topology data and the reference power grid topology data; and calculating the comprehensive performance gain of the corresponding power grid line based on the topology connectivity gain and power supply capacity gain.

[0043] The initial set of failed lines refers to the set of power grid lines that initially fail due to natural disasters under a specific fault scenario. In this application, it represents the combination of lines that first go out of service or are damaged in a single fault scenario generated by Monte Carlo simulation. The reference power grid topology data refers to the power grid topology data obtained after restoring the failed lines in the initial set of failed lines. In this application, it represents the topology reconstruction result used to characterize the changes in the power grid structure and connection relationships after line restoration. The topology connectivity gain is an evaluation index used to characterize the change in the degree of connectivity of the power grid topology. In this application, it represents the quantitative result of the change in the relative size of the maximum connected component before and after the restoration of the failed lines. The power supply capacity gain is an evaluation index used to characterize the degree of power supply load restoration of the power grid. In this application, it represents the quantitative result of the change in the proportion of power supply load before and after the restoration of the failed lines.

[0044] Understandably, by comparing and analyzing the power grid state before and after line restoration, changes in power grid topology connectivity and power supply capacity can be uniformly quantified as a comprehensive performance gain, thereby enabling a comparable marginal assessment of the system impact of restoring a single line.

[0045] After the new fault scenario data is output, the critical path identification unit is activated. This unit reads: all assessment scenario data; and the original power grid topology data.

[0046] For each fault scenario First execute as follows Figure 2 The single-scenario evaluation process is shown below: (1) Initialization. Let the current initial set of failed lines be... The current power grid status is ; (2) Marginal contribution calculation. Based on the original power grid topology data and the reference power grid topology data, the changes in topology connectivity and power supply capacity are compared and calculated to obtain the topology connectivity gain. and power supply capacity gain Furthermore, the comprehensive performance gain of the corresponding power grid line is calculated based on the weighted relationship. The formula for calculating the comprehensive performance gain is as follows:

[0047] in, For overall performance gain; The line is faulty; For topological connectivity gain; To balance the weights; This is the power supply capacity gain.

[0048] In one embodiment of this application, calculating topology connectivity gain and power supply capacity gain based on original power grid topology data and reference power grid topology data includes: calculating a first maximum connectivity component relative size of the original power grid topology based on the original power grid topology data; calculating a second maximum connectivity component relative size of the reference power grid topology based on the reference power grid topology data; calculating topology connectivity gain based on the first maximum connectivity component relative size and the second maximum connectivity component relative size; calculating a first power supply load ratio based on the original power grid state corresponding to the original power grid topology data; calculating a second power supply load ratio based on the current power grid state corresponding to the reference power grid topology data; and calculating power supply capacity gain based on the first power supply load ratio and the second power supply load ratio.

[0049] Wherein, the first maximum connectivity component relative size is the ratio between the number of nodes contained in the maximum connectivity component in the original power grid topology and the total number of nodes in the original power grid topology, used to characterize the overall connectivity retention degree of the original power grid topology after a fault; the second maximum connectivity component relative size is the ratio between the number of nodes contained in the maximum connectivity component in the reference power grid topology and the total number of nodes in the reference power grid topology, used to characterize the overall connectivity retention degree of the reference power grid topology after a fault; the first power supply load ratio is the load power supply ratio that the power grid can meet under the original power grid state, and in this application represents a benchmark index of the system power supply capacity based on the original topology; the second power supply load ratio is the load power supply ratio that the power grid can meet under the current power grid state, and in this application represents an evaluation index of the system power supply capacity based on the reference topology.

[0050] It is understandable that by calculating the relative scale of the maximum connectivity components and the power supply load ratio under the original grid state and the reference grid state respectively, the changes in grid topology connectivity and power supply capacity can be decomposed into two independently quantifiable indicators, thereby providing a clear component basis for the calculation of comprehensive performance gains.

[0051] For example, under a certain fault scenario, the power grid is modeled as a graph structure, where nodes represent substation nodes and edges represent transmission lines; generator output and load demand are configured as electrical attributes of the corresponding nodes for power flow calculation and power supply capacity assessment, and are not considered as independent nodes in topology connectivity calculation. The relative size of the largest connected component calculated from the original power grid topology data is the first relative size of the largest connected component. The relative size of the second largest connected component was calculated from the reference power grid topology data. Then through and The difference quantifies the connectivity changes of the topology under the change of the line state; at the same time, the proportion of powerable loads is obtained based on power flow calculation under the original grid state. After reallocating the power flow under the reference grid conditions, the power supply load ratio is obtained as follows: Then through and The difference characterizes the impact of line state changes on the system's power supply capacity, thereby obtaining the topology connectivity gain and power supply capacity gain respectively.

[0052] In one embodiment of this application, the formula for calculating the overall performance gain is as follows:

[0053] in, For overall performance gain; The line is faulty; For topological connectivity gain; To balance the weights; This is the power supply capacity gain.

[0054] It is understandable that by introducing a weighted combination method to map the topological connectivity gain and power supply capacity gain into a single evaluation index, the power grid impact quantities of different dimensions can be comprehensively characterized under the same quantitative framework, thereby achieving a unified measurement of the contribution to line recovery.

[0055] In one embodiment of this application, a line importance ranking sequence for each fault scenario is generated based on the comprehensive performance gain of each power grid line under each fault scenario. This includes: obtaining the current set of failed lines and the current power grid state; selecting at least one failed line for recovery based on the comprehensive performance gain of each power grid line under each fault scenario, and updating the current set of failed lines and the current power grid state; re-evaluating the comprehensive performance gain of each power grid line based on the updated set of failed lines until a stopping condition is met, so as to obtain the line importance ranking sequence for each fault scenario.

[0056] The current set of failed lines refers to the set of power grid lines that are in a failed state due to natural disasters under a specific fault scenario. In this application, it represents the set of lines to be repaired that dynamically changes with the recovery process. The current power grid state is the system state used to characterize the power grid topology and operating capacity under the current recovery step. In this application, it represents the combination state of the power grid structure and power supply capacity that is continuously updated with the line recovery operation. The stopping condition is the judgment rule used to determine whether the iterative recovery process has ended. In this application, it represents the constraint condition that triggers termination when the set of failed lines is empty or the system performance no longer changes.

[0057] It is understandable that this application introduces an iterative evaluation and update mechanism based on gradual recovery, so that the power grid state can dynamically evolve with the line recovery process. This enables the marginal contribution of each line to the system performance recovery to be gradually identified in the continuous state changes, and finally forms a line importance ranking sequence consistent with the recovery process.

[0058] Specifically, the process by which this application generates a sequence of line importance rankings for each fault scenario based on the comprehensive performance gain of each power grid line under each fault scenario is as follows: (1) Marginal contribution calculation. For each initially failed line... The evaluation process for improving system performance after restoration is as follows: Calculate topological connectivity gain : Methods including but not limited to changes in the relative size of the largest connected component can be used to calculate it; Calculate power supply capacity gain Methods including but not limited to changes in the power supply load ratio can be used to calculate this. This leads to the overall performance gain. ,in To balance the weights; (2) Recovery Selection. Select the line with the lowest overall performance gain. To restore it, remove it from the initial set of failed lines. Remove from the list and update the power grid status. ; (3) Status Update. After recovery, the system status is reassessed. If the recovery causes changes in the status of other lines (such as cascading failures), the initial set of failed lines is updated accordingly. ; (4) Iterate until termination. Repeat steps (1)-(4) above until the initial set of failed lines is reached. Record the recovery sequence; (5) Generate a fault scenario ranking. Reverse the recovery order to obtain a line importance ranking sequence under the fault scenario; The order of line importance for each fault scenario is in a standardized format and includes: scenario identifier, a list of line identifiers and their order position in that scenario.

[0059] In step S103, the target power grid line is identified according to the line importance ranking sequence under each fault scenario.

[0060] The target power grid lines are a set of critical lines that have a significant impact on the risk of power grid operation, selected according to the line importance ranking sequence under each fault scenario. In this application, they represent transmission lines that have been identified as priority lines for attention or priority reinforcement after cross-scenario assessment.

[0061] It is understandable that the ranking of line importance under a single fault scenario is transformed into a target line screening result for all scenarios, thereby achieving unified identification of lines that consistently exhibit high impact under different disaster scenarios, avoiding local biases caused by relying solely on a single scenario, and improving the stability and representativeness of critical line identification results under multiple disaster conditions.

[0062] In one embodiment of this application, identifying target power grid lines based on the line importance ranking sequence under each fault scenario includes: determining the ranking position of power grid lines based on the line importance ranking sequence under each fault scenario; converting the ranking position of power grid lines into standardized values ​​based on a pre-set target conversion format; extracting the initial system performance loss degree under each fault scenario, and calculating the weight of each power grid line based on the initial system performance loss degree and the standardized values; constructing a preference value for each power grid line based on the relative preference relationship between power grid lines, calculating a weighted average preference value based on the preference value and weight of each power grid line, calculating the global dominance of each power grid line based on the weighted average preference value; and determining the target power grid line from each power grid line based on the global dominance.

[0063] In this application, the grid line ranking position refers to the relative position of each grid line in the sequence determined according to its importance under a single fault scenario, representing the importance ranking level of the line under that fault scenario. The target transformation format is a preset data transformation rule used to map the line ranking position to a unified numerical expression form, representing a mapping system that transforms discrete ranking positions into a computable standardized score. The standardized value is a unified quantitative index obtained by processing the line ranking position using the target transformation format, representing a normalized ranking value used for cross-scenario comparability analysis. The initial system performance loss degree is the degree of decline in the overall power supply capacity of the grid due to line failure under a specific fault scenario. The preference relationship describes the comparative relationship between the relative importance of two grid lines under the same or different fault scenarios. In this application, the term represents the set of comparative relationships between lines based on the ranking results; the preference value is a numerical value calculated based on the preference relationship to quantify the relative priority of lines, and in this application, it represents a quantitative indicator to express the relative strength of the advantages between lines; the weighted average preference value is a comprehensive evaluation result obtained by weighting and summing the preference values ​​in combination with the weights of each fault scenario, and in this application, it represents the comprehensive preference degree of lines after considering the importance of scenarios; the global dominance is a unified evaluation index used to characterize the comprehensive importance of power grid lines under all fault scenarios, and in this application, it represents the basis for global ranking of lines obtained through multi-scenario preference fusion calculation; the target power grid line is a set of key lines that have a significant impact on the risk of power grid operation, selected based on the global dominance, and in this application, it represents the set of key transmission lines determined after cross-scenario comprehensive evaluation.

[0064] Understandably, by uniformly converting the ranking of line importance under various failure scenarios into standardized values, the ranking results that are not directly comparable due to differences in failure combinations under different scenarios are transformed into comparable evaluation data under the same scale. Furthermore, by combining the degree of initial system performance loss of each failure scenario with differentiated weights, the disaster scenarios with higher severity have a greater influence in the global evaluation, thereby avoiding the information dilution problem caused by simple averaging. At the same time, by constructing relative preference relationships between lines, the original discrete ranking information is transformed into a preference expression form that can be used for cross-scenario fusion, so that the problem of missing lines or incomplete ranking in different scenarios can be effectively included in a unified calculation framework. On this basis, the weighted average preference value and global dominance are calculated.

[0065] In this embodiment of the application, after completing the calculation of the importance ranking sequence of all lines, cross-scenario fusion is performed, and the fusion process is executed. The specific process is as follows: Figure 3 As shown: (1) Standardization Conversion. After reading the importance ranking sequence of all power grid lines under all fault scenarios, the ranking position of each power grid line in each fault scenario is converted into a standardized value. The standardized value is used to characterize the relative importance of the power grid line in the corresponding fault scenario. The larger the value, the more important the line is in the fault scenario. (2) Weight allocation. The weight of each failure scenario is calculated based on the initial system performance loss of the failure scenario. The weight is used to characterize the influence of different failure scenarios in the global evaluation, and the sum of the weights is 1. The greater the initial system performance loss, the higher the corresponding weight. (3) Preference Modeling. For any two power grid lines, a set of fault scenarios that simultaneously include both power grid lines is collected, and preference values ​​are constructed based on the fault scenario set. The preference values ​​are used to characterize the relative importance between the two power grid lines; where preference represents the relative importance relationship between the two power grid lines under the same fault scenario. For any two power grid lines, if they are both included in the line importance ranking sequence corresponding to the same fault scenario, the corresponding preference values ​​are determined according to their relative ranking positions in the ranking sequence. Wherein, the relative ranking position is the standardized ranking result obtained by the standardization transformation in step (1), and the preference value is used to reflect the relative importance between the two power grid lines under the current fault scenario, which can be calculated in the following way:

[0066] in Indicates the line The relative sorting position after standardization transformation. Indicates the line The relative ranking positions after standardization transformation. For each fault scenario, the preference values ​​corresponding to the two power grid lines are calculated to form a set of preference values ​​for the two power grid lines under each fault scenario; subsequently, the preference value sets are weighted and aggregated according to the weights corresponding to each fault scenario to obtain the comprehensive preference relationship between the two power grid lines.

[0067] (4) Weighted aggregation. For fault scenario pairs with preference information, the preference values ​​are weighted and averaged to obtain a weighted preference value. For fault scenario pairs lacking preference information, they are set to a no-preference state. (5) Dominance calculation. The global dominance of each power grid line is calculated based on the weighted preference value, and the power grid lines are sorted from largest to smallest according to the global dominance to obtain the global line importance sequence.

[0068] The global path importance sequence output by the critical path identification unit is used as the input to the risk assessment unit as a global risk map. The risk assessment unit then performs the following steps: (6) Critical Path Identification. Based on preset risk thresholds or sorting truncation rules, critical and vulnerable paths are identified from the global path importance sequence, and a critical path list is generated; (7) Strategy generation guidance. Output the critical route list to the visualization module to generate optimization strategies for route reinforcement priority ranking and inspection plan formulation.

[0069] In summary, the technical framework of the power grid line identification method for natural disaster scenarios in this application is as follows: Figure 4 As shown, it mainly consists of three functional units: (1) Fault scenario set construction unit: responsible for generating and continuously updating multiple fault scenarios that reflect the uncertainty of typhoon disasters; (2) Critical route identification unit: responsible for independently performing route importance assessment in each scenario, generating a ranking sequence, and merging the ranking sequences of all scenarios into a global route importance ranking sequence; (3) Risk assessment unit: responsible for identifying critical vulnerable lines based on the importance ranking sequence of the lines, and outputting a list of critical lines to guide the formation of optimization strategies.

[0070] These three units have clear data dependencies and form an organic whole triggered by an inherent time sequence.

[0071] The power grid line identification method for natural disaster scenarios proposed in this application first acquires original power grid topology data, line vulnerability parameters, and natural disaster data. Based on this data, multiple fault scenarios under natural disaster conditions are generated, achieving unified modeling of power grid structural characteristics, line failure probabilities, and external disaster disturbance factors. This constructs a basic analysis scenario covering multiple uncertainties, improving the scenario completeness of subsequent risk assessments. Second, the comprehensive performance gain of each power grid line under each fault scenario is evaluated based on the original power grid topology data. A line importance ranking sequence is generated based on the comprehensive performance gain, transforming the influence of lines in fault scenarios into a unified quantitative indicator and forming comparable ranking results. Then, the target power grid line is identified based on the line importance ranking sequence under each fault scenario. By comprehensively filtering the ranking results across multiple fault scenarios, consistent identification of high-impact lines across scenarios is achieved. This solves problems such as fragmented technical analysis processes, lack of a unified collaborative mechanism, insufficient consideration of extreme natural disaster risk characteristics, lack of consistent correlation between results from multiple scenarios, and insufficient global ranking and fusion capabilities.

[0072] Next, referring to the accompanying drawings, a power grid line identification device for natural disaster scenarios is described according to an embodiment of this application.

[0073] Figure 5 This is a block diagram of a power grid line identification device for natural disaster scenarios according to an embodiment of this application.

[0074] like Figure 5 As shown, the power grid line identification device 10 for this natural disaster scenario includes: an acquisition module 100, a generation module 200, and an identification module 300.

[0075] The module 100 is used to acquire original power grid topology data, line vulnerability parameters, and natural disaster data, and generate multiple fault scenarios under natural disaster scenarios based on the power grid topology data, line vulnerability parameters, and natural disaster data; the generation module 200 is used to evaluate the comprehensive performance gain of each power grid line under each fault scenario based on the original power grid topology data, and generate a line importance ranking sequence under each fault scenario based on the comprehensive performance gain of each power grid line under each fault scenario; the identification module 300 is used to identify the target power grid line based on the line importance ranking sequence under each fault scenario.

[0076] In one embodiment of this application, the acquisition module 100 is further configured to generate multiple fault scenarios under natural disaster scenarios using the Monte Carlo simulation method, with power grid topology data, line vulnerability parameters and natural disaster data as input.

[0077] In one embodiment of this application, the generation module 200 is further configured to extract the initial set of failed lines in each fault scenario; obtain reference grid topology data after the failed lines in the initial set of failed lines have been restored; calculate the topology connectivity gain and power supply capacity gain based on the original grid topology data and the reference grid topology data; and calculate the comprehensive performance gain of the corresponding grid line based on the topology connectivity gain and power supply capacity gain.

[0078] In one embodiment of this application, the generation module 200 is further configured to calculate the first maximum connectivity component relative size of the original power grid topology based on the original power grid topology data, calculate the second maximum connectivity component relative size of the reference power grid topology based on the reference power grid topology data, calculate the topology connectivity gain based on the first maximum connectivity component relative size and the second maximum connectivity component relative size; calculate the first power supply load ratio based on the original power grid state corresponding to the original power grid topology data, calculate the second power supply load ratio based on the current power grid state corresponding to the reference power grid topology data, and calculate the power supply capacity gain based on the first power supply load ratio and the second power supply load ratio.

[0079] In one embodiment of this application, the formula for calculating the overall performance gain is as follows:

[0080] in, For overall performance gain; The line is faulty; For topological connectivity gain; To balance the weights; To balance the weights; This is the power supply capacity gain.

[0081] In one embodiment of this application, the generation module 200 is further configured to obtain the current set of failed lines and the current power grid state; select at least one failed line for recovery based on the comprehensive performance gain of each power grid line under each fault scenario, and update the current set of failed lines and the current power grid state; re-evaluate the comprehensive performance gain of each power grid line based on the updated current set of failed lines until the stopping condition is met, so as to obtain the line importance ranking sequence under each fault scenario.

[0082] In one embodiment of this application, the identification module 300 is further configured to determine the ranking position of the power grid lines according to the line importance ranking sequence under each fault scenario; convert the ranking position of the power grid lines into standardized values ​​based on a pre-set target conversion format; extract the initial system performance loss degree under each fault scenario; calculate the weight of each power grid line based on the initial system performance loss degree and the standardized values; construct the preference value of each power grid line according to the relative preference relationship between power grid lines; calculate the weighted average preference value based on the preference value and weight of each power grid line; calculate the global dominance of each power grid line based on the weighted average preference value; and determine the target power grid line from each power grid line based on the global dominance.

[0083] It should be noted that the foregoing explanation of the power grid line identification method embodiment for natural disaster scenarios also applies to the power grid line identification device for natural disaster scenarios in this embodiment, and will not be repeated here.

[0084] According to the power grid line identification device for natural disaster scenarios proposed in this application, firstly, it acquires original power grid topology data, line vulnerability parameters, and natural disaster data, and generates multiple fault scenarios under natural disaster scenarios based on the data. This enables unified modeling of power grid structural characteristics, line failure probabilities, and external disaster disturbance factors, thereby constructing a basic analysis scenario covering multiple uncertain conditions and improving the scenario completeness of subsequent risk assessment. Secondly, it evaluates the comprehensive performance gain of each power grid line under each fault scenario based on the original power grid topology data, and generates a line importance ranking sequence under each fault scenario based on the comprehensive performance gain. This transforms the influence of lines in fault scenarios into a unified quantitative index and forms a comparable ranking result. Then, it identifies target power grid lines based on the line importance ranking sequence under each fault scenario. By comprehensively filtering the ranking results under multiple fault scenarios, it achieves the identification of consistently high-impact lines across scenarios. This solves the problems of fragmented related technical analysis processes, lack of a unified collaborative mechanism, insufficient consideration of extreme natural disaster risk characteristics, lack of consistent correlation between results from multiple scenarios, and insufficient global ranking and fusion capabilities.

[0085] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0086] When the processor 602 executes the program, it implements the power grid line identification method for natural disaster scenarios provided in the above embodiments.

[0087] Furthermore, electronic devices also include: Communication interface 603 is used for communication between memory 601 and processor 602.

[0088] The memory 601 is used to store computer programs that can run on the processor 602.

[0089] The memory 601 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0090] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0091] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0092] The processor 602 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0093] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described power grid line identification method for natural disaster scenarios.

[0094] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0095] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0096] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0097] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0098] Those skilled in the art will understand that all or part of the steps of the methods implementing the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0099] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for identifying power grid lines in natural disaster scenarios, characterized in that, Includes the following steps: Obtain raw power grid topology data, line vulnerability parameters, and natural disaster data; and generate multiple fault scenarios under natural disaster scenarios based on the power grid topology data, the line vulnerability parameters, and the natural disaster data. The overall performance gain of each power grid line under each fault scenario is evaluated based on the original power grid topology data, and the line importance ranking sequence under each fault scenario is generated based on the overall performance gain of each power grid line under each fault scenario. The target power grid line is identified based on the line importance ranking sequence under each fault scenario.

2. The power grid line identification method for natural disaster scenarios according to claim 1, characterized in that, The generation of multiple fault scenarios under natural disaster conditions based on the power grid topology data, the line vulnerability parameters, and the natural disaster data includes: Using the power grid topology data, the line vulnerability parameters, and the natural disaster data as inputs, the Monte Carlo simulation method is used to generate multiple fault scenarios under natural disaster conditions.

3. The power grid line identification method for natural disaster scenarios according to claim 1, characterized in that, The evaluation of the comprehensive performance gain of each power grid line under each fault scenario based on the original power grid topology data includes: Extract the initial set of failed lines in each fault scenario; Obtain reference power grid topology data after the recovery of failed lines in the initial set of failed lines; Calculate the topology connectivity gain and power supply capacity gain based on the original power grid topology data and the reference power grid topology data, and calculate the comprehensive performance gain of the corresponding power grid line based on the topology connectivity gain and the power supply capacity gain.

4. The power grid line identification method for natural disaster scenarios according to claim 3, characterized in that, The calculation of topology connectivity gain and power supply capacity gain based on the original power grid topology data and the reference power grid topology data includes: The first maximum connectivity component relative size of the original power grid topology is calculated based on the original power grid topology data; the second maximum connectivity component relative size of the reference power grid topology is calculated based on the reference power grid topology data; and the topology connectivity gain is calculated based on the first maximum connectivity component relative size and the second maximum connectivity component relative size. The first power supply load ratio is calculated based on the original power grid state corresponding to the original power grid topology data. The second power supply load ratio is calculated based on the current power grid state corresponding to the reference power grid topology data. The power supply capacity gain is calculated based on the first power supply load ratio and the second power supply load ratio.

5. The power grid line identification method for natural disaster scenarios according to claim 3, characterized in that, The formula for calculating the overall performance gain is as follows: in, For overall performance gain; The line is faulty; For topological connectivity gain; To balance the weights; To balance the weights; This is the power supply capacity gain.

6. The power grid line identification method for natural disaster scenarios according to claim 1, characterized in that, The step of generating a line importance ranking sequence for each fault scenario based on the comprehensive performance gain of each power grid line under each fault scenario includes: Get the set of currently failed lines and the current power grid status; Based on the comprehensive performance gain of each power grid line under each fault scenario, at least one failed line is selected for recovery, and the current set of failed lines and the current power grid state are updated. The overall performance gain of each power grid line is reassessed based on the updated set of currently failed lines until a stopping condition is met, in order to obtain a line importance ranking sequence for each fault scenario.

7. The power grid line identification method for natural disaster scenarios according to claim 1, characterized in that, The step of identifying the target power grid line based on the line importance ranking sequence under each fault scenario includes: The ranking position of the power grid lines is determined based on the ranking sequence of line importance under each fault scenario. The power grid line sorting positions are converted into standardized values ​​based on a pre-set target conversion format; Extract the initial system performance loss level under each fault scenario, and calculate the weight of each power grid line based on the initial system performance loss level and the standardized value; A preference value for each power grid line is constructed based on the relative preference relationship between power grid lines. A weighted average preference value is calculated based on the preference value and weight of each power grid line. The global dominance of each power grid line is calculated based on the weighted average preference value. The target power grid line is determined from each power grid line based on the global dominance.

8. A power grid line identification device for natural disaster scenarios, characterized in that, include: The acquisition module is used to acquire raw power grid topology data, line vulnerability parameters, and natural disaster data, and generate multiple fault scenarios under natural disaster scenarios based on the power grid topology data, the line vulnerability parameters, and the natural disaster data; The generation module is used to evaluate the comprehensive performance gain of each power grid line under each fault scenario based on the original power grid topology data, and generate the line importance ranking sequence under each fault scenario based on the comprehensive performance gain of each power grid line under each fault scenario. The identification module is used to identify the target power grid line according to the line importance ranking sequence under each fault scenario.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the power grid line identification method for natural disaster scenarios as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they implement the power grid line identification method for natural disaster scenarios as described in any one of claims 1-7.

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