Power distribution network node vulnerability and criticality comprehensive evaluation method and system based on multi-dimensional index system

Abstract

The invention provides a power distribution network node vulnerability and criticality comprehensive evaluation method and system based on a multi-dimensional index system, and relates to the field of power grid reliability evaluation and scene generation. Obtaining accumulated water depth time sequence data of node positions by combining a Chicago rain pattern model and a hydrological model; combining the accumulated water depth time sequence data with a direct failure probability model to obtain a direct failure probability; utilizing the indirect failure probability model to obtain an indirect failure probability; fusing the direct failure probability and the indirect failure probability to obtain a node comprehensive failure probability; constructing a key evaluation index and a vulnerability evaluation index; performing dimensionless and weighted aggregation calculation on each sub-index to obtain a key score and a vulnerability score of the node; and constructing a two-dimensional risk quadrant graph, and generating a differentiated operation and maintenance strategy. According to the method, the vulnerability and key indexes of the power distribution network nodes are separated and quantified, and identification, grading and operation and maintenance strategy configuration of node risks can be realized.

Description

Technical Field

[0001] This invention relates to the field of power grid reliability assessment and scenario generation, and more specifically, to a comprehensive assessment method and system for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system. Background Technology

[0002] As climate change intensifies, extreme weather events (especially extreme rainfall and floods) pose a serious threat to the safe operation of urban power distribution networks. Traditional risk assessment methods for power distribution networks often have limitations when facing such complex natural disasters.

[0003] On the one hand, they often conflate the possibility of a node failing due to external shocks (vulnerability) with the severity of the consequences of a node failure on the system operation (criticality), making it difficult to accurately characterize risks and optimize resource allocation.

[0004] On the other hand, existing methods for quantifying the impact of extreme rainfall often remain at the level of single environmental parameters or static topological analysis, failing to systematically integrate meteorological and hydrological physical processes (such as water depth and condensation formation) with the time-varying cumulative damage mechanism of equipment, resulting in a discrepancy between the risk assessment results and the actual composite failure mechanism of equipment.

[0005] Especially when multiple factors such as high humidity and water immersion are involved, traditional assessments are unable to accurately distinguish between direct and indirect failures, which is detrimental to the risk identification, resilience enhancement, and precise and proactive operation and maintenance planning of the power grid.

[0006] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0007] In view of this, the present invention provides a comprehensive assessment method and system for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system, in order to solve the aforementioned problems.

[0008] To solve the above problems, the specific technical solution adopted by the present invention is as follows:

[0009] According to a first aspect of the present invention, a comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system is provided, the method comprising the following steps:

[0010] S1. Geographically register the distribution network topology with the digital elevation model to construct spatial features; based on the spatial features, and combined with the Chicago rainfall pattern model and hydrological model, obtain the time series data of water depth at node locations;

[0011] S2. Using the direct failure probability model, the time series data of water accumulation depth at the node location is calculated to obtain the direct failure probability; using the indirect failure probability model, the condensation risk is quantified to obtain the indirect failure probability; by combining the direct failure probability and the indirect failure probability, the overall failure probability of the node is calculated.

[0012] S3. Based on the functional attributes, topological position, and external environmental risk of nodes, construct key assessment indicators; based on the comprehensive failure probability, and combined with the structural characteristics, operating status, and disaster response capability of the system, construct vulnerability assessment indicators.

[0013] S4. Perform dimensionless and weighted aggregation calculations on each sub-indicator in the critical assessment indicators and vulnerability assessment indicators to obtain the criticality score and vulnerability score of the node; construct a two-dimensional risk quadrant diagram based on the criticality score and vulnerability score of the node, and generate the corresponding differentiated operation and maintenance strategy for the distribution network node.

[0014] Preferably, the distribution network topology is georeferenced with the digital elevation model to construct spatial features; based on these spatial features, and combined with the Chicago rainfall pattern model and hydrological model, the dynamic water depth time series data at node locations is obtained, including the following steps:

[0015] S11. Based on the IEEE-33 node distribution network, construct the distribution network topology; perform geographic registration between the distribution network topology and the digital elevation model to obtain spatial features;

[0016] S12. Based on spatial characteristics, the Chicago rainfall pattern model is used to calculate the rainfall intensity before and after the rain peak of the extreme rainstorm process, respectively, and obtain the time series data of rainfall intensity.

[0017] S13. Based on the time series data of rainfall intensity and combined with the differential equation of water accumulation dynamics in the hydrological model, time integration calculation is performed to obtain the water accumulation depth of each distribution network node at different times, and the time series data of water accumulation depth at the node location is integrated.

[0018] Preferably, the direct failure probability model calculates the direct failure probability using time-series data of water depth at the node location; the indirect failure probability model quantifies the condensation risk to obtain the indirect failure probability; and the comprehensive node failure probability is calculated by fusing the direct and indirect failure probabilities, including the following steps:

[0019] S21. Based on the functional relationship between damage accumulation rate and water depth, a direct failure probability model is constructed, and the direct failure probability model is used to calculate the time series data of water depth at the node location to obtain the direct failure probability of the node.

[0020] S22. Condensation early warning based on dew point temperature; quantify condensation risk using a proportional risk model to obtain the indirect failure probability of nodes.

[0021] S23. The combined failure probability of a node is obtained by comprehensively calculating the direct failure probability and the indirect failure probability.

[0022] Preferably, based on the functional relationship between damage accumulation rate and water depth, a direct failure probability model is constructed, and the direct failure probability model is used to calculate the direct failure probability of the node using dynamic water depth time-series data at the node location. This includes the following steps:

[0023] S211. Based on the physical damage increment of the equipment per unit time, construct a piecewise function for the damage accumulation rate;

[0024] S212. Based on the dynamic water depth time series data and the piecewise function of damage accumulation rate at the node location, the total cumulative damage is calculated by time series integration.

[0025] S213. Construct a cumulative damage-failure probability conversion curve based on the Weibull distribution, and use the cumulative damage-failure probability conversion curve to convert the total cumulative damage amount to obtain the direct failure probability.

[0026] Preferably, condensation early warning is based on dew point temperature; the quantification of condensation risk using a proportional hazards model to obtain the indirect failure probability of nodes includes the following steps:

[0027] S221. Based on the obtained air temperature, relative humidity and saturated water vapor pressure inside and outside the equipment cabinet, calculate the dew point temperature; calculate the temperature difference between the dew point temperature and the obtained equipment surface temperature, classify the condensation risk level of the equipment according to the temperature difference, and trigger the corresponding warning signal; when the warning signal indicates that there is a condensation risk, start the proportional risk model calculation for the equipment.

[0028] S222. For the equipment that triggers the early warning, a proportional risk model is constructed based on the Weibull distribution baseline failure rate and condensation-related covariates. The proportional risk model is used to quantify the condensation risk of the equipment into the cumulative failure probability of the equipment, and the cumulative failure probabilities of all equipment in the node are integrated to obtain the indirect failure probability.

[0029] Preferred key evaluation indicators include sub-indicators of node load capacity, intermediary centrality, degree centrality, power generation node attributes, and composite environmental risk.

[0030] Among them, the node load capacity sub-index is used to quantify the direct impact of node failure on power users;

[0031] The intermediary center property index is used to identify nodes in the network that undertake the task of power flow transmission;

[0032] Degree centrality is a sub-index used to measure the local connectivity and influence of a node.

[0033] The generation node attribute sub-indicators are used to assess the node's ability to provide support and resilience to the power grid;

[0034] The composite environmental risk sub-indicator is used to assess the disaster resistance capacity of the node's geographical location under extreme rainfall.

[0035] Preferably, the vulnerability assessment indicators include the grid topology and Laplace spectrum radius node degree sub-indices, the node transmission betweenness sub-indices based on power flow, the voltage limit exceeding sub-indices, the node vulnerability under extreme rainfall sub-indices, and the comprehensive failure probability sub-indices.

[0036] Among them, the node degree sub-index of power grid topology and Laplace spectrum radius is used to reflect the proportion of the local connectivity strength of a node in the whole network structure relative to the overall structural complexity of the network.

[0037] A power flow-based node transmission betweenness sub-index used to measure the mediating role of a node in the actual power path.

[0038] The voltage exceedance sub-index is used to indicate the degree of voltage deviation under extreme rainfall conditions;

[0039] The vulnerability index of nodes under extreme rainfall is used to comprehensively reflect the vulnerability of nodes under extreme rainfall.

[0040] The comprehensive failure probability sub-index is used to obtain the total failure risk of a node under extreme rainfall based on the comprehensive failure probability.

[0041] Preferably, the criticality score and vulnerability score of a node are obtained by performing dimensionless and weighted aggregation calculations on each sub-indicator in the criticality assessment index and vulnerability assessment index, respectively, including the following steps:

[0042] Using the max-min normalization algorithm, the sub-indicators in the criticality assessment index and the vulnerability assessment index are normalized to obtain the standardized values ​​of each sub-indicator.

[0043] Based on the analytic hierarchy process, a judgment matrix is ​​constructed by combining expert scores, and the weight coefficients of each sub-indicator in the critical assessment indicators and vulnerability assessment indicators are determined by consistency test.

[0044] Based on the weighting coefficients, the standardized values ​​of each sub-indicator are linearly weighted and aggregated to calculate the criticality score and vulnerability score for each node.

[0045] Preferably, based on the criticality and vulnerability scores of nodes, a two-dimensional risk quadrant diagram is constructed, and corresponding differentiated operation and maintenance strategies for distribution network nodes are generated, including:

[0046] Using the criticality scores of each section as the first dimension and the vulnerability scores of each node as the second dimension, a two-dimensional risk quadrant diagram is constructed, which includes the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant.

[0047] Specifically, for nodes falling into the first quadrant, reinforcement, condition-based maintenance, or emergency plans should be immediately formulated and implemented; for nodes falling into the second quadrant, online monitoring and regular inspections should be strengthened; for nodes falling into the third quadrant, preventative maintenance or upgrades should be arranged within the equipment replacement cycle, depending on the operation and maintenance resources and budget; and for nodes falling into the fourth quadrant, the regular inspection cycle and standards should be maintained.

[0048] According to a second aspect of the present invention, a comprehensive assessment system for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system is provided, the system comprising:

[0049] The water depth time series data calculation module is used to georegister the distribution network topology with the digital elevation model to construct spatial features; based on the spatial features, and combined with the Chicago rainfall pattern model and hydrological model, the water depth time series data of the node location is obtained.

[0050] The comprehensive failure probability calculation module is used to calculate the direct failure probability by using the direct failure probability model to calculate the water depth time series data at the node location; to quantify the condensation risk by using the indirect failure probability model to obtain the indirect failure probability; and to calculate the comprehensive failure probability of the node by combining the direct failure probability and the indirect failure probability.

[0051] The vulnerability and criticality indicator construction module is used to construct criticality assessment indicators based on the functional attributes, topological position, and multi-dimensional sub-indicators of external environmental risks of nodes; and to construct vulnerability assessment indicators based on the comprehensive failure probability, combined with the structural characteristics, operating status, and disaster response capability of the system.

[0052] The differentiated operation and maintenance strategy generation module is used to perform dimensionless and weighted aggregation calculations on each sub-indicator in the critical assessment indicators and vulnerability assessment indicators to obtain the criticality score and vulnerability score of the node; based on the criticality score and vulnerability score of the node, a two-dimensional risk quadrant diagram is constructed, and the corresponding differentiated operation and maintenance strategy for the distribution network node is generated.

[0053] The beneficial effects of this invention are as follows:

[0054] 1. This invention proposes a comprehensive assessment method and system for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system. Addressing the difficulty in accurately characterizing the complex failure mechanisms of equipment under extreme rainfall threats, this method constructs a direct failure probability model based on time-varying cumulative damage and an indirect failure probability model based on the proportional hazards model (PHM), integrating flood immersion and high-humidity condensation mechanisms to calculate the comprehensive failure probability of nodes. Recognizing that traditional distribution network risk assessment methods conflate node vulnerability and criticality, this invention independently assesses node criticality by constructing and integrating multi-dimensional indicators such as load importance and network status, and independently assesses node vulnerability by constructing and integrating multi-dimensional indicators such as structure, function, operation, and environment. Finally, by constructing a two-dimensional risk quadrant diagram, the criticality and vulnerability scores are intuitively graded, thereby providing differentiated operation and maintenance strategies.

[0055] 2. This invention systematically separates and quantifies the two core risk dimensions of distribution network nodes: vulnerability and criticality, overcoming the shortcomings of traditional methods that confuse the two. Through meteorological and hydrological models, a direct failure probability model based on cumulative damage (considering time-history effects), and a high-humidity condensation indirect failure model based on proportional risk models, this invention achieves deep integration and precise quantification of the complex failure mechanism of equipment under extreme rainfall. Furthermore, by constructing a multi-dimensional indicator system (structure, function, operation, environment, etc.) and a two-dimensional risk quadrant diagram for result presentation, this invention can intuitively and effectively identify and classify node risks, thus providing a strong scientific basis for formulating differentiated operation and maintenance strategies and resource optimization allocation for distribution networks, such as high-risk (priority handling) and high-value (key monitoring). Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0057] Figure 1 This is a flowchart of a comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system, according to an embodiment of the present invention.

[0058] Figure 2 This is a principle block diagram of a comprehensive assessment system for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system, according to an embodiment of the present invention.

[0059] Figure 3 This is a topology diagram of a 33-node distribution network system in a comprehensive evaluation method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system according to an embodiment of the present invention.

[0060] Figure 4 This is a Chicago rain pattern diagram under different conditions in a comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system according to an embodiment of the present invention;

[0061] Figure 5 This is a graph showing the variation of water accumulation height at 33 nodes in a comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system, according to an embodiment of the present invention.

[0062] Figure 6 This is a contour map of condensation risk in high-voltage switchgear in a comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system according to an embodiment of the present invention.

[0063] Figure 7 This is a graph showing the change in failure rate of 33 nodes in a comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system according to an embodiment of the present invention.

[0064] Figure 8 This is a ranking chart of the criticality scores of 33 nodes in a comprehensive evaluation method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system according to an embodiment of the present invention.

[0065] Figure 9 This is a 33-node vulnerability scoring diagram in a comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system according to an embodiment of the present invention.

[0066] Figure 10 This is a quadrant distribution diagram of the criticality-vulnerability of 33 nodes in a comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system according to an embodiment of the present invention.

[0067] In the picture:

[0068] 1. Water depth time series data calculation module; 2. Comprehensive failure probability calculation module; 3. Vulnerability index and key index construction module; 4. Differentiated operation and maintenance strategy generation module. Detailed Implementation

[0069] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0070] According to an embodiment of the present invention, a method and system for comprehensive assessment of the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system is provided.

[0071] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown in the first embodiment of the present invention, a comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system is provided. This method includes the following steps:

[0072] S1. Geographically register the distribution network topology with the digital elevation model to construct spatial features; based on the spatial features, and combined with the Chicago rainfall pattern model and hydrological model, obtain the time series data of water depth at node locations.

[0073] As a preferred implementation, the distribution network topology is georeferenced with the digital elevation model to construct spatial features; based on these spatial features, and combined with the Chicago rainfall pattern model and hydrological model, the dynamic water depth time series data at node locations is obtained, including the following steps:

[0074] S11. Based on the IEEE-33 node distribution network, construct the distribution network topology; perform geographic registration between the distribution network topology and the digital elevation model to obtain spatial features;

[0075] S12. Based on spatial characteristics, the Chicago rainfall pattern model is used to calculate the rainfall intensity before and after the rain peak of the extreme rainstorm process, respectively, and obtain the time series data of rainfall intensity.

[0076] S13. Based on the time series data of rainfall intensity and combined with the differential equation of water accumulation dynamics in the hydrological model, time integration calculation is performed to obtain the water accumulation depth of each distribution network node at different times, and the time series data of water accumulation depth at the node location is integrated.

[0077] It should be explained that the method of coupling the Chicago rainfall pattern with the hydrological model to simulate the physical processes of the disaster in order to obtain dynamic water depth time series data at the nodal locations is as follows:

[0078] A1: Distribution network system and geographic information modeling.

[0079] First, such as Figure 3 As shown, the IEEE-33 node distribution network system is modeled as an undirected graph G=(V,E), where the node set V represents the 33 buses in the system, and the edge set E represents the distribution lines connecting the buses. Simultaneously, DEM (Digital Elevation Model) data of the area where the distribution network is located is acquired and gridded. The topological geographic information of the distribution network is registered with the DEM data, thereby assigning a terrain elevation attribute to each node i∈V. .

[0080] A2: In order to simulate short-duration, high-intensity extreme rainstorm processes, such as Figure 4 As shown, the Chicago rainfall pattern model is used, which can well reflect the characteristics of early peak time and high peak intensity of urban rainstorms. The calculation formula for instantaneous rainfall intensity is divided into two stages: before the rain peak and after the rain peak.

[0081] Peak phase:

[0082] ;

[0083] Later part of the peak:

[0084] ;

[0085] In the formula, t represents the duration of rainfall; i(t) represents the instantaneous rainfall intensity at time t; r represents the peak rainfall coefficient; and a, b, and c represent empirical parameters obtained through statistical analysis based on local long-term historical rainfall data. These parameters are usually related to the return period of rainfall.

[0086] A3: Calculation of water accumulation depth at nodes.

[0087] like Figure 5 As shown, the water depth at each node is determined by both rainfall and the capacity of the drainage system. Its core differential equation describes the change in water depth over time. Considering that extreme rainfall may lead to performance degradation of the drainage system, drainage efficiency is modeled as a function that deteriorates more rapidly with increasing system overload. By integrating the rainfall intensity over time and subtracting the time-varying drainage volume, the time-series data of the water depth for each grid can be calculated.

[0088] ;

[0089] In the formula, V i (t) represents the volume of water stored in unit i (m³); d represents the differential or small change (such as dV). i (t) represents a small change in water volume), while Represents the derivative or rate of change with respect to time t; Ai represents the effective surface area (m²) of cell i; h i (t) represents the water depth (m) at unit i; Q R , i (t) represents the net rainfall input rate (after deducting losses such as surface interception) (m³ / s); Q D , i (t) represents the outflow rate (m³ / s) caused by the drainage system (controlled by the accelerated decay model); Q NetFlow , i(t) represents the net surface water exchange rate (m³ / s) between adjacent units, which represents the diffusion of water on urban surfaces (e.g., streets); Q I , i (t) represents the seepage rate.

[0090] S2. Using the direct failure probability model, the time series data of water accumulation depth at the node location are calculated to obtain the direct failure probability; using the indirect failure probability model, the condensation risk is quantified to obtain the indirect failure probability; by combining the direct failure probability and the indirect failure probability, the overall failure probability of the node is calculated.

[0091] It should be explained that the method of calculating the comprehensive failure probability of nodes by integrating the mechanisms of direct failure (flood immersion) and indirect failure (high humidity condensation) is used to quantify the combined threat of extreme rainfall to equipment. Specifically:

[0092] B1: Constructing a direct failure probability model. An innovative direct failure probability model based on time-varying cumulative damage is constructed. This model treats the flood soaking process as a process of physical damage accumulating over time. By establishing a functional relationship between the damage accumulation rate and the water depth, and by integrating the damage rate over time to obtain the total cumulative damage, the Weibull distribution is finally used to map this cumulative damage to the direct failure probability of the nodes.

[0093] B2: Construct an indirect failure probability model. Introduce a condensation risk assessment based on physical mechanisms and a risk quantification method based on the proportional risk model (PHM); calculate the dew point temperature to provide early warning of condensation risk, and use the proportional risk model to quantify the dynamic impact of environmental covariates such as high humidity and condensation on equipment failure rate, thereby calculating the indirect failure probability of the node.

[0094] B3: Calculate the overall failure probability. The direct failure probability and indirect failure probability are combined to obtain the overall failure probability of the node under the combined threat of flooding and high humidity condensation.

[0095] As a preferred implementation, the direct failure probability is calculated using the time-series data of water depth at the node location using a direct failure probability model; the indirect failure probability is obtained by quantifying the condensation risk using an indirect failure probability model; and the overall node failure probability is calculated by fusing the direct and indirect failure probabilities, including the following steps:

[0096] S21. Based on the functional relationship between damage accumulation rate and water depth, a direct failure probability model is constructed, and the direct failure probability model is used to calculate the time series data of water depth at the node location to obtain the direct failure probability of the node.

[0097] S22. Condensation early warning based on dew point temperature; quantify condensation risk using a proportional risk model to obtain the indirect failure probability of nodes.

[0098] S23. The combined failure probability of a node is obtained by comprehensively calculating the direct failure probability and the indirect failure probability.

[0099] It should be explained that, considering that direct failure (water immersion) and indirect failure (condensation) are two relatively independent events, the combined failure probability of node i at time t can be calculated using the following formula:

[0100] ;

[0101] In the formula, P direct,i (t) represents the direct failure probability; P indirect,i (t) represents the indirect failure probability; P comp,i (t) represents the overall failure probability. This overall probability more comprehensively reflects the combined threat posed by extreme rainfall to the node.

[0102] As a preferred embodiment, a direct failure probability model is constructed based on the functional relationship between damage accumulation rate and water depth. Using this model, the direct failure probability of the node is calculated from the dynamic water depth time-series data at the node location, including the following steps:

[0103] S211. Based on the physical damage increment of the equipment per unit time, construct a piecewise function for the damage accumulation rate;

[0104] S212. Based on the dynamic water depth time series data and the piecewise function of damage accumulation rate at the node location, the total cumulative damage is calculated by time series integration.

[0105] S213. Construct a cumulative damage-failure probability conversion curve based on the Weibull distribution, and use the cumulative damage-failure probability conversion curve to convert the total cumulative damage amount to obtain the direct failure probability.

[0106] It should be explained that the direct failure probability model assumes that the physical damage to the equipment is a cumulative process over time, and its failure probability depends on the total cumulative damage, rather than the instantaneous environmental state. This model includes two core parts: damage accumulation rate calculation and damage-based failure probability assessment, as detailed below:

[0107] C1: Damage accumulation rate function.

[0108] First, define a damage accumulation rate function. This function quantifies the increment of physical damage suffered by the equipment per unit time at a specific water depth h(t). The function is modeled as a piecewise function to reflect the differences in damage mechanisms at different stages of flooding:

[0109] ;

[0110] The first stage involves the water accumulation height h(t) being lower than the equipment's safe height H. safe At the first stage, the damage accumulation rate is 0; the second stage: when the water height exceeds H... safe However, the height H of critical components (such as cable connectors) was not reached. critical In the first stage, the damage rate increases linearly with the increase of flooding depth, and the coefficient α reflects the impermeability of the equipment shell and foundation seal; in the third stage, when the water level submerges the critical components, the intrusion of water leads to accelerated degradation of insulation performance and a sharp increase in the risk of internal short circuits. The damage rate increases rapidly in the form of a power function, and the intensity of its growth is controlled by the parameters β and γ.

[0111] C2: Calculation of cumulative damage.

[0112] For node i, the total cumulative damage D during the extreme rainfall event i (t) is the integral of the damage accumulation rate over time; in the simulation with a discrete time step Δt, t k The total cumulative damage at each time point is calculated as follows:

[0113] ;

[0114] In the formula, D i (t k () indicates time The integral of the rate of damage accumulation at any given time; t represents the amount of damage caused within the current time period; k This represents the k-th discrete time point (the moment when the k-th time step ends).

[0115] C3: Direct failure probability based on cumulative damage.

[0116] The final failure probability of equipment is not a simple linear relationship with the cumulative damage, but rather exhibits a probabilistic nature. This invention uses the Weibull distribution to construct a vulnerability curve based on cumulative damage, thereby calculating the direct failure probability P. direct,i (t):

[0117] ;

[0118] In the formula, D i (t) represents the total cumulative damage calculated at time t; The scale parameter represents the characteristic damage lifespan that the equipment can withstand; The shape parameter describes the dispersion of failure events around the characteristic damage lifetime; these two parameters are obtained through aging tests of the equipment, material mechanics analysis, or statistical fitting of historical failure data.

[0119] The water depth sequence h(t) obtained in S1 is transformed into a cumulative damage sequence D using a direct failure probability model. i (t), and finally calculate the direct failure probability P of each node i over time. direct,i (t). This method reveals more profoundly the impact of the time-history effect of flood disasters on equipment reliability.

[0120] As a preferred implementation, condensation early warning is based on dew point temperature; the condensation risk is quantified using a proportional risk model to obtain the indirect failure probability of the node, including the following steps:

[0121] S221. Based on the obtained air temperature, relative humidity and saturated water vapor pressure inside and outside the equipment cabinet, the dew point temperature is calculated; the temperature difference between the dew point temperature and the obtained equipment surface temperature is calculated, the condensation risk level of the equipment is classified according to the temperature difference, and the corresponding early warning signal is triggered; when the early warning signal indicates that there is a condensation risk (i.e. ΔT > 0), the proportional risk model calculation for the equipment is started.

[0122] S222. For the equipment that triggers the early warning, a proportional risk model is constructed based on the Weibull distribution baseline failure rate and condensation-related covariates. The proportional risk model is used to quantify the condensation risk of the equipment into the cumulative failure probability of the equipment, and the cumulative failure probabilities of all equipment in the node are integrated to obtain the indirect failure probability.

[0123] It should be explained that extreme rainfall is usually accompanied by near-saturated, high-humidity air. When the surface temperature of equipment is lower than the dew point temperature of the surrounding air, condensation will form on the surface of the insulation components, significantly reducing their insulation performance and thus causing equipment failure. This invention introduces a condensation early warning system based on physical mechanisms and risk quantification based on a proportional hazards model (PHM), as detailed below:

[0124] D1: Condensation risk assessment.

[0125] By real-time monitoring or simulation of the air temperature T and relative humidity RH inside and outside the equipment cabinet, as well as the surface temperature T of key equipment. surf The dew point temperature T can be calculated using the following formula. d .

[0126] Calculate the saturated water vapor pressure:

[0127] ;

[0128] In the formula, Es represents the saturated water vapor pressure (in hPa); T represents the air temperature; E0 represents the saturated water vapor pressure (in hPa) at an air temperature of 0℃; a and b are coefficients. When T>0℃, the water surface values ​​are a=7.5 and b=237.3; when T≤0℃, the ice surface values ​​are a=9.5 and b=265.5.

[0129] Then, calculate the water vapor pressure (in %RH) of air at T℃ with a known relative humidity F, i.e., the saturated water vapor pressure E (in hPa):

[0130] ;

[0131] Calculate the dew point temperature:

[0132] ;

[0133] In the formula, T d The dew point temperature is represented by ; a and b are correction factors; E0 represents the saturated water vapor pressure (in hPa) at 0°C, with a value of 6.11 hPa; Es represents the saturated water vapor pressure (in hPa); and E represents the saturated water vapor pressure of air.

[0134] It can be seen that there are three main factors affecting the occurrence of condensation: temperature T (which will affect the values ​​of a and b), relative humidity F, and saturated water vapor pressure Es.

[0135] like Figure 6 As shown, the final temperature difference is calculated to determine the risk of condensation.

[0136] Calculate the surface temperature T of key equipment surf With the calculated dew point temperature T d The difference between them:

[0137] ;

[0138] When ΔT>0 (i.e., T) d >T surf ): This indicates that the surface temperature of the equipment is lower than the dew point temperature, and water vapor will condense on the surface, resulting in condensation. A high-risk warning should be issued at this time; when the warning signal indicates that there is a risk of condensation (i.e., ΔT > 0), the proportional risk model calculation for the equipment should be initiated.

[0139] When ΔT≤0 (i.e., T) d ≤T surf ): This indicates that the surface temperature of the equipment is higher than or equal to the dew point temperature, water vapor will not condense, and condensation has not occurred. This is a "no condensation risk" or "low risk" state.

[0140] The system will continuously monitor the surface temperature T of critical equipment. surf The system calculates the temperature T, relative humidity F, and saturated water vapor pressure Es, and then calculates ΔT. As long as ΔT remains less than or equal to 0, the system maintains a "no condensation risk" or "low risk" state, without triggering high-level alarms or initiating complex proportional risk models to save computational resources.

[0141] D2: Quantification of Proportional Risk Model (PHM).

[0142] For devices that trigger early warnings, PHM decomposes the device's failure rate function into the product of a baseline failure rate and a covariate function. For power distribution equipment, the baseline failure rate is typically described using a Weibull distribution, reflecting the equipment's inherent lifespan characteristics. The covariate is used to quantify the impact of external factors. This invention constructs an improved PHM instantaneous failure rate:

[0143] ;

[0144] In the formula, β w η w The Weibull distribution shape and scale parameter of device w are represented; t represents time; K t Indicates the conversion factor for time units; X H X T X I These represent covariates such as humidity inside the equipment, temperature of key components, and insulation defects in the equipment cabinet; α H α T α I These represent the corresponding covariate coefficients, reflecting the degree of influence of these factors on the failure rate. These parameters can be determined by fitting and analyzing the correlation between historical failures and environmental data.

[0145] Calculate the cumulative failure probability of a single device over a period of time:

[0146] ;

[0147] like Figure 7 As shown, calculate the total indirect failure probability of all devices in the entire node:

[0148] ;

[0149] In the formula, N i represents the (total) number of devices in node i; w represents the device number in node i.

[0150] S3. Based on the functional attributes, topological position, and external environmental risk of nodes, construct key assessment indicators; based on the comprehensive failure probability, and combined with the structural characteristics, operating status, and disaster response capability of the system, construct vulnerability assessment indicators.

[0151] As a preferred implementation method, the key evaluation indicators include node load capacity sub-indicators, intermediary centrality sub-indicators, degree centrality sub-indicators, power generation node attribute sub-indicators, and composite environmental risk sub-indicators.

[0152] It should be explained that the criticality of nodes is assessed by constructing and integrating multi-dimensional indicators such as load importance and network status. From the perspective of system impact, this invention evaluates the severity of the consequences of node failure. Therefore, this invention constructs a criticality assessment system comprising five categories of indicators. The final results are as follows: Figure 8 As shown.

[0153] Among them, the node load capacity sub-index is used to quantify the direct impact of node failure on power users;

[0154] Specifically, the node load capacity sub-index quantifies the direct impact of node failure on power users, measured by the size of the active power load it bears:

[0155] ;

[0156] In the formula, I c1 (i) represents the normalized load capacity index of node i; P d,i L represents the active power load (i.e., power demand) at node i; L represents the set of all load nodes in the system. It represents the sum of the active power of all load nodes in the system, and represents the total load of the system.

[0157] The intermediary center property index is used to identify nodes in the network that undertake the task of power flow transmission;

[0158] Specifically, the intermediate center property index identifies nodes that act as bridges for power flow transmission in the system and is measured using the electrical betweenness factor, which takes into account the physical characteristics of the power grid.

[0159] ;

[0160] In the formula, I c2 (n) represents the normalized electrical betweenness index of node n; B e (n) represents the electrical betweenness of node n, measuring the magnitude of the power flow through that node; G represents the set of all generator nodes i in the system; L represents the set of all load nodes j in the system; W i W represents the rated active power (generating capacity) of generator node i; j This represents the active power demand of load node j; This represents the sum of weights for all generator-load pairs used for normalization.

[0161] Degree centrality is a sub-index used to measure the local connectivity and influence of a node.

[0162] Specifically, the degree centrality sub-index measures a node's local connectivity and influence, i.e., the number of nodes directly connected to it:

[0163] ;

[0164] In the formula, I c3 (i) represents the normalized centrality index of node i; C D (i) represents the degree of node i, i.e. the number of edges directly connected to the node; N represents the total number of nodes in the network; N-1 represents the maximum degree that a node can have in a simple graph with N nodes, used for normalization.

[0165] The generation node attribute sub-indicators are used to assess the node's ability to provide support and resilience to the power grid;

[0166] Specifically, the generation node attribute sub-indicators assess the node's ability to provide support and resilience to the power grid, measured by its local generation capacity.

[0167] The composite environmental risk sub-indicator is used to assess the disaster resistance capacity of the node's geographical location under extreme rainfall.

[0168] Specifically, the composite environmental risk index comprehensively assesses the disaster resilience of a node's geographical location under extreme rainfall and is a multi-factor weighted model. The composite environmental risk index is calculated using a linear weighted summation model:

[0169] ;

[0170] In the formula, M represents the total number of risk indicators involved in the assessment; I i,k w represents the k-th normalized environmental risk index value of node i; k This represents the weight of the k-th indicator.

[0171] The key environmental risk indicators selected in this invention include the following categories to comprehensively quantify the risks under extreme rainfall: Rainfall intensity indicator: assesses the probability and intensity of extreme rainfall events within the region. Topography indicator: assesses the flood susceptibility of the node's location; the lower the terrain and the gentler the slope, the higher the risk. Waterlogging indicator: assesses the severity of historical waterlogging and flooding at the node. Surface cover indicator: assesses the permeability of the surface, such as vegetation cover rate and the proportion of impermeable area.

[0172] As a preferred implementation, the vulnerability assessment indicators include the grid topology and Laplace spectrum radius node degree sub-indices, the node transmission betweenness sub-indices based on power flow, the voltage limit exceeding sub-indices, the node vulnerability under extreme rainfall sub-indices, and the comprehensive failure probability sub-indices.

[0173] It needs to be explained that, in quantifying the inherent tendency of distribution network nodes to fail due to a combination of factors such as water immersion and high humidity under extreme rainfall, this invention clarifies that vulnerability is the failure tendency of the equipment itself under extreme rainfall. Therefore, this invention constructs a vulnerability assessment system comprising five categories of indicators, aiming to accurately identify the weakest links most likely to fail and have the greatest impact on the system under extreme rainfall. For example... Figure 9 As shown, this is the score for the weakness of 33 nodes.

[0174] Among them, the node degree sub-index of power grid topology and Laplace spectrum radius is used to reflect the proportion of the local connectivity strength of a node in the whole network structure relative to the overall structural complexity of the network.

[0175] Specifically, the power grid topology can be modeled as an undirected graph G=(V,E), where the node set V represents the bus and the edge set E represents the line; the Laplace matrix of the graph is defined as: L=DA.

[0176] In the formula, Degree matrix, The rest are 0; Represents the adjacency matrix. Node i and node j are connected.

[0177] Calculate its largest eigenvalue (spectral radius): ;

[0178] Then, the spectral radius index of node i is defined: ;

[0179] In the formula, The largest eigenvalue of the Laplace matrix L corresponding to the power grid topology diagram, also known as the spectral radius, represents the complexity of the entire network structure; d i The degree of node i represents the number of lines directly connected to that node, and it comes from the degree matrix D.

[0180] The node degree sub-index of the power grid topology and Laplace spectrum radius reflects the proportion of the local connectivity strength of a node in the overall network structure relative to the overall structural complexity of the network.

[0181] A power flow-based node transmission betweenness sub-index used to measure the mediating role of a node in the actual power path.

[0182] Specifically, unlike pure topological centrality, the node transmission betweenness index based on power flow takes into account the directionality and path importance of electrical power flow.

[0183] Define P st Represents the total active power flow from node s to t; define P st (i) This represents the power flow through node i in the path; then the power transfer betweenness index of node i is:

[0184] ;

[0185] ;

[0186] In the formula, The power transfer betweenness is represented by k; the rainfall sensitivity coefficient is represented by k. R represents the power transfer betweenness under extreme rainfall conditions; i This represents the rainfall sensitivity coefficient.

[0187] The node transmission betweenness index based on power flow measures the mediating role of a node in the actual power path of the system, and is a kind of traffic hub in the power transmission network.

[0188] The voltage exceedance sub-index is used to indicate the degree of voltage deviation under extreme rainfall conditions;

[0189] Specifically, the allowable range of the given voltage is: Define the voltage over-limit index for node i:

[0190] ;

[0191] ;

[0192] In the formula, This indicates the voltage limit exceedance index for node i. A value greater than 0 indicates a voltage exceeding the limit, and a larger value indicates a more severe deviation from the normal range. V i V represents the actual voltage value at node i; min V max These represent the preset minimum and maximum allowable operating ranges for node voltages, respectively; R i V represents the rainfall sensitivity coefficient; dev This indicates the voltage exceeding the limit under extreme rainfall conditions.

[0193] The vulnerability index of nodes under extreme rainfall is used to comprehensively reflect the vulnerability of nodes under extreme rainfall.

[0194] Specifically, environmental factors, such as rainfall sensitivity coefficients, are introduced to comprehensively reflect the vulnerability of nodes under extreme rainfall:

[0195] ;

[0196] In the formula, R i Elevation represents the rainfall sensitivity coefficient. i Indicates the elevation of the node; Flood Risk i Indicates the degree of flood susceptibility; Rain Fault History i Indicates the frequency of historical rainfall-related faults; ε, φ, and γ all represent weighting coefficients, satisfying... .

[0197] The comprehensive failure probability sub-index is used to obtain the total failure risk of a node under extreme rainfall based on the comprehensive failure probability.

[0198] Specifically, by integrating structural, state, and environmental factors, the total failure risk of the output node under extreme rainfall is calculated. The specific calculation method for the comprehensive failure probability is shown in S2.

[0199] S4. Perform dimensionless and weighted aggregation calculations on each sub-indicator in the critical assessment indicators and vulnerability assessment indicators to obtain the criticality score and vulnerability score of the node; construct a two-dimensional risk quadrant diagram based on the criticality score and vulnerability score of the node, and generate the corresponding differentiated operation and maintenance strategy for the distribution network node.

[0200] It should be explained that the vulnerability of nodes is assessed by constructing and integrating multi-dimensional indicators such as structure, function, operation and environment. This is used to quantify the inherent tendency of nodes to fail under disasters. The vulnerability score is then combined to identify and classify risks in a comprehensive manner, so as to provide decision support for the differentiated operation and maintenance strategy of the power grid.

[0201] As a preferred implementation, the criticality score and vulnerability score of a node are obtained by performing dimensionless and weighted aggregation calculations on each sub-indicator in the criticality assessment index and vulnerability assessment index, respectively, including the following steps:

[0202] Using the max-min normalization algorithm, the sub-indicators in the criticality assessment index and the vulnerability assessment index are normalized to obtain the standardized values ​​of each sub-indicator.

[0203] Based on the analytic hierarchy process, a judgment matrix is ​​constructed by combining expert scores, and the weight coefficients of each sub-indicator in the critical assessment indicators and vulnerability assessment indicators are determined by consistency test.

[0204] Based on the weighting coefficients, the standardized values ​​of each sub-indicator are linearly weighted and aggregated to calculate the criticality score and vulnerability score for each node.

[0205] It should be explained that the specific calculation methods for the criticality score and vulnerability score of each node are as follows:

[0206] G1: Dimensionless processing of indicators.

[0207] The max-min normalization algorithm is used to process the raw data of all indicators in the evaluation system and map them to intervals to eliminate differences in units and scales between different indicators.

[0208] ;

[0209] In the formula, X norm X represents the value obtained after the indicator has been normalized; X represents the original measurement value of a specific indicator at a certain node; X min This represents the minimum value of this indicator across all evaluated nodes; X max This indicates the maximum value of the indicator across all evaluated nodes.

[0210] G2: Determining the weight of indicators.

[0211] By employing the Analytic Hierarchy Process (AHP) combined with expert scoring, a judgment matrix is ​​constructed and a consistency test is performed to determine the weight coefficients of each indicator in the corresponding evaluation system.

[0212] Calculate the geometric mean M of each row of the judgment matrix. i :

[0213] ;

[0214] In the formula, a i,j This represents an element in a matrix.

[0215] The geometric mean vector is normalized to obtain the final weight vector w. i :

[0216] ;

[0217] G3: Overall score calculation.

[0218] By weighted summing of the dimensionless values ​​of each indicator, the final criticality score and vulnerability score for each node are calculated:

[0219] ;

[0220] In the formula, Si represents the final comprehensive score of node i; n represents the total number of indicators in the corresponding evaluation system (for example, there are 5 indicators in the criticality evaluation system); Wj represents the weight of the j-th indicator, which is determined by the AHP method. This represents the value of the j-th index of node i after min-max normalization.

[0221] As a preferred implementation method, a two-dimensional risk quadrant diagram is constructed based on the criticality score and vulnerability score of the nodes, and corresponding differentiated operation and maintenance strategies for distribution network nodes are generated, including:

[0222] Using the criticality scores of each section as the first dimension and the vulnerability scores of each node as the second dimension, a two-dimensional risk quadrant diagram is constructed, which includes a first quadrant, a second quadrant, a third quadrant, and a fourth quadrant.

[0223] Specifically, for nodes falling into the first quadrant, reinforcement, condition-based maintenance, or emergency plans should be immediately formulated and implemented; for nodes falling into the second quadrant, online monitoring and regular inspections should be strengthened; for nodes falling into the third quadrant, preventative maintenance or upgrades should be arranged within the equipment replacement cycle based on maintenance resources and budget; and for nodes falling into the fourth quadrant, the regular inspection cycle and standards should be maintained.

[0224] It needs to be explained that, for example Figure 10 As shown, the risk comprehensive identification and classification method is as follows: the criticality score of each node obtained from step S3 is used as the first dimension, and the vulnerability score of each node obtained from step S4 is used as the second dimension to construct a two-dimensional risk quadrant map. By mapping each node to different quadrants of the map, the risk of the node can be intuitively identified and classified, thereby providing decision support for differentiated operation and maintenance strategies.

[0225] The risk classification and corresponding differentiated operation and maintenance strategies in the two-dimensional risk quadrant diagram include:

[0226] H1: First Quadrant (High Criticality - High Vulnerability); defined as "High Risk - Priority Response Zone". For nodes falling into this zone, reinforcement, condition-based maintenance, or emergency response plans should be developed and implemented immediately as the highest priority for resource allocation.

[0227] H2: Second Quadrant (High Criticality - Low Vulnerability); defined as "High Value - Key Monitoring Area". Nodes falling into this area should be treated as key monitoring targets, and online monitoring and regular inspections should be strengthened to ensure that they remain in a low vulnerability state.

[0228] H3: Third Quadrant (Low Criticality - High Vulnerability); defined as "Potential Risk - Opportunistic Improvement Zone". For nodes falling into this zone, preventive maintenance or upgrades can be arranged within the equipment replacement cycle, depending on the operation and maintenance resources and budget, to reduce their failure probability.

[0229] H4: Fourth Quadrant (Low Criticality - Low Vulnerability); defined as "Low Risk - Routine Operation and Maintenance Zone". For nodes falling into this zone, it is sufficient to maintain the routine inspection cycle and standards.

[0230] like Figure 2 As shown in the second embodiment of the present invention, a comprehensive assessment system for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system is provided. The system includes:

[0231] The water depth time series data calculation module 1 is used to perform geographic registration between the power distribution network topology and the digital elevation model to construct spatial features; based on the spatial features, and combined with the Chicago rainfall pattern model and hydrological model, the water depth time series data of the node location is obtained;

[0232] The comprehensive failure probability calculation module 2 is used to calculate the direct failure probability by using the direct failure probability model to calculate the water depth time series data at the node location; to quantify the condensation risk by using the indirect failure probability model to obtain the indirect failure probability; and to calculate the comprehensive failure probability of the node by combining the direct failure probability and the indirect failure probability.

[0233] Vulnerability and criticality indicator construction module 3 is used to construct criticality assessment indicators based on the functional attributes, topological position, and multi-dimensional sub-indicators of external environmental risks of nodes; and to construct vulnerability assessment indicators based on comprehensive failure probability, combined with multi-dimensional sub-indicators of system structural characteristics, operating status, and disaster response capabilities.

[0234] The differentiated operation and maintenance strategy generation module 4 is used to perform dimensionless and weighted aggregation calculations on each sub-indicator in the critical assessment indicators and vulnerability assessment indicators to obtain the criticality score and vulnerability score of the node; based on the criticality score and vulnerability score of the node, a two-dimensional risk quadrant diagram is constructed, and the corresponding differentiated operation and maintenance strategy for the distribution network node is generated.

[0235] This invention is applicable to risk assessment, resilience enhancement, and proactive operation and maintenance planning of distribution networks under extreme weather events (especially extreme rainfall). The core of this method lies in systematically differentiating and quantifying two core risk dimensions of distribution network nodes: the probability of a node failing under external disturbances (vulnerability), and the severity of the consequences of a node failure on the entire system (criticality). First, a meteorological and hydrological model is used to simulate the node water accumulation process under extreme rainfall. Second, a composite failure probability model is constructed, integrating the direct impact of flooding and the indirect impact of high humidity condensation. Then, independent vulnerability and criticality evaluation index systems are built from multiple dimensions, including structure, function, operation, and environment, to calculate the corresponding scores for each node. Finally, the evaluation results are plotted on a two-dimensional risk quadrant diagram, enabling intuitive identification and classification of node risks. This invention overcomes the shortcomings of traditional methods that confuse vulnerability and criticality. Through the deep integration of multi-domain physical processes, it achieves a precise characterization of distribution network risks. Its evaluation results are intuitive and highly operable, providing scientific decision support for the optimization of power grid resource allocation and the formulation of resilience enhancement strategies.

[0236] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0237] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0238] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system, characterized in that, The method includes the following steps: S1. Geographically register the distribution network topology with the digital elevation model to construct spatial features; based on the spatial features, and combined with the Chicago rainfall pattern model and hydrological model, obtain the time series data of water depth at node locations; S2. Using the direct failure probability model, the time series data of water accumulation depth at the node location is calculated to obtain the direct failure probability; using the indirect failure probability model, the condensation risk is quantified to obtain the indirect failure probability; by combining the direct failure probability and the indirect failure probability, the overall failure probability of the node is calculated. S3. Based on the functional attributes, topological position, and external environmental risk of nodes, construct key assessment indicators; based on the comprehensive failure probability, and combined with the structural characteristics, operating status, and disaster response capability of the system, construct vulnerability assessment indicators. S4. Perform dimensionless and weighted aggregation calculations on each sub-indicator in the critical assessment indicators and vulnerability assessment indicators to obtain the criticality score and vulnerability score of the node; construct a two-dimensional risk quadrant diagram based on the criticality score and vulnerability score of the node, and generate the corresponding differentiated operation and maintenance strategy for the distribution network node.

2. The comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system as described in claim 1, characterized in that, The process involves geographic registration of the power distribution network topology with the digital elevation model to construct spatial features. Based on spatial characteristics and combined with the Chicago rainfall pattern model and hydrological model, the dynamic water depth time series data at the node locations are obtained through the following steps: S11. Based on the IEEE-33 node distribution network, construct the distribution network topology; perform geographic registration between the distribution network topology and the digital elevation model to obtain spatial features; S12. Based on spatial characteristics, the Chicago rainfall pattern model is used to calculate the rainfall intensity before and after the rain peak of the extreme rainstorm process, respectively, and obtain the time series data of rainfall intensity. S13. Based on the time series data of rainfall intensity and combined with the differential equation of water accumulation dynamics in the hydrological model, time integration calculation is performed to obtain the water accumulation depth of each distribution network node at different times, and the time series data of water accumulation depth at the node location is integrated.

3. The method for comprehensive assessment of the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system according to claim 1, characterized in that, The direct failure probability is calculated by using the direct failure probability model to calculate the time series data of water depth at the node location; The indirect failure probability model is used to quantify the condensation risk and obtain the indirect failure probability. The calculation of the overall node failure probability by combining the direct failure probability and the indirect failure probability includes the following steps: S21. Based on the functional relationship between damage accumulation rate and water depth, a direct failure probability model is constructed, and the direct failure probability model is used to calculate the time series data of water depth at the node location to obtain the direct failure probability of the node. S22. Condensation warning based on dew point temperature; The proportional risk model is used to quantify the condensation risk and obtain the indirect failure probability of the node. S23. The combined failure probability of a node is obtained by comprehensively calculating the direct failure probability and the indirect failure probability.

4. The method for comprehensive assessment of the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system according to claim 3, characterized in that, The process of constructing a direct failure probability model based on the functional relationship between damage accumulation rate and water depth, and then using this model to calculate the direct failure probability of nodes based on the dynamic water depth time-series data at node locations, includes the following steps: S211. Based on the physical damage increment of the equipment per unit time, construct a piecewise function for the damage accumulation rate; S212. Based on the dynamic water depth time series data and the piecewise function of damage accumulation rate at the node location, the total cumulative damage is calculated by time series integration. S213. Construct a cumulative damage-failure probability conversion curve based on the Weibull distribution, and use the cumulative damage-failure probability conversion curve to convert the total cumulative damage amount to obtain the direct failure probability.

5. The method for comprehensive assessment of the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system according to claim 3, characterized in that, The dew point temperature-based condensation early warning system; The proportional hazards model is used to quantify condensation risk, and the indirect failure probability of nodes is obtained by following these steps: S221. Based on the obtained air temperature, relative humidity and saturated water vapor pressure inside and outside the equipment cabinet, calculate the dew point temperature; calculate the temperature difference between the dew point temperature and the obtained equipment surface temperature, classify the condensation risk level of the equipment according to the temperature difference, and trigger the corresponding warning signal; when the warning signal indicates that there is a condensation risk, start the proportional risk model calculation for the equipment. S222. For equipment that triggers an early warning, a proportional risk model is constructed based on the Weibull distribution baseline failure rate and condensation-related covariates. The condensation risk of equipment is quantified into the cumulative failure probability of the equipment using the proportional risk model, and the indirect failure probability is obtained by integrating the cumulative failure probabilities of all equipment in the node.

6. The comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system according to claim 1, characterized in that, The key evaluation indicators include sub-indicators of node load capacity, intermediate centrality, degree centrality, power generation node attributes, and complex environmental risk. The node load capacity sub-index is used to quantify the direct impact of node failure on power users. The intermediary center property index is used to identify nodes in the network that undertake the task of power flow transmission; The degree centrality sub-index is used to measure the local connectivity and influence of a node; The sub-indicators of the power generation node attributes are used to evaluate the node's ability to provide support and resilience to the power grid; The composite environmental risk sub-indicators are used to assess the disaster resistance of the node's geographical location under extreme rainfall.

7. The comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system according to claim 6, characterized in that, The vulnerability assessment metrics include the grid topology and Laplace spectrum radius node degree sub-metric, the node transmission betweenness sub-metric based on power flow, the voltage limit violation sub-metric, the node vulnerability under extreme rainfall sub-metric, and the comprehensive failure probability sub-metric. The power grid topology and Laplace spectral radius node degree sub-index is used to reflect the proportion of the local connectivity strength of a node in the whole network structure relative to the overall structural complexity of the network. The power flow-based node transmission betweenness sub-index is used to measure the mediating role of a node in the actual power path. The voltage over-limit sub-index is used to indicate the degree of voltage deviation under extreme rainfall. The vulnerability sub-indicator of the node under extreme rainfall is used to comprehensively reflect the vulnerability of the node under extreme rainfall. The comprehensive failure probability sub-index is used to obtain the total failure risk of a node under extreme rainfall based on the comprehensive failure probability.

8. The comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system according to claim 1, characterized in that, The process of performing dimensionless and weighted aggregation calculations on each sub-indicator in the criticality assessment indicators and vulnerability assessment indicators to obtain the criticality score and vulnerability score of the node includes the following steps: Using the max-min normalization algorithm, the sub-indicators in the criticality assessment index and the vulnerability assessment index are normalized to obtain the standardized values ​​of each sub-indicator. Based on the analytic hierarchy process, a judgment matrix is ​​constructed by combining expert scores, and the weight coefficients of each sub-indicator in the critical assessment indicators and vulnerability assessment indicators are determined by consistency test. Based on the weighting coefficients, the standardized values ​​of each sub-indicator are linearly weighted and aggregated to calculate the criticality score and vulnerability score for each node.

9. A comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system, as described in claim 8, is characterized in that... The process of constructing a two-dimensional risk quadrant diagram based on the criticality and vulnerability scores of nodes, and generating corresponding differentiated operation and maintenance strategies for distribution network nodes, includes: Using the criticality scores of each section as the first dimension and the vulnerability scores of each node as the second dimension, a two-dimensional risk quadrant diagram is constructed, which includes a first quadrant, a second quadrant, a third quadrant, and a fourth quadrant. Specifically, for nodes falling into the first quadrant, reinforcement, condition-based maintenance, or emergency plans should be immediately formulated and implemented; for nodes falling into the second quadrant, online monitoring and regular inspections should be strengthened; for nodes falling into the third quadrant, preventative maintenance or upgrades should be arranged within the equipment replacement cycle based on maintenance resources and budget; and for nodes falling into the fourth quadrant, the regular inspection cycle and standards should be maintained.

10. A comprehensive assessment system for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system, used to implement the comprehensive assessment method for the vulnerability and criticality of distribution network nodes based on a multi-dimensional index system as described in any one of claims 1-9, characterized in that, The system includes: The water depth time series data calculation module is used to georegister the distribution network topology with the digital elevation model to construct spatial features; based on the spatial features, and combined with the Chicago rainfall pattern model and hydrological model, the water depth time series data of the node location is obtained. The comprehensive failure probability calculation module is used to calculate the direct failure probability by using the direct failure probability model to calculate the water depth time series data at the node location; to quantify the condensation risk by using the indirect failure probability model to obtain the indirect failure probability; and to calculate the comprehensive failure probability of the node by combining the direct failure probability and the indirect failure probability. The vulnerability and criticality indicator construction module is used to construct criticality assessment indicators based on the functional attributes, topological position, and multi-dimensional sub-indicators of external environmental risks of nodes; and to construct vulnerability assessment indicators based on the comprehensive failure probability, combined with the structural characteristics, operating status, and disaster response capability of the system. The differentiated operation and maintenance strategy generation module is used to perform dimensionless and weighted aggregation calculations on each sub-indicator in the critical assessment indicators and vulnerability assessment indicators to obtain the criticality score and vulnerability score of the node; based on the criticality score and vulnerability score of the node, a two-dimensional risk quadrant diagram is constructed, and the corresponding differentiated operation and maintenance strategy for the distribution network node is generated.

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