System and method for improving toughness of power distribution network under typhoon disaster based on digital twinborn

Abstract

The invention discloses a digital twinning-based power distribution network toughness improvement system under typhoon disasters, and the system comprises a physical model layer which is used for obtaining power distribution network geographic information, power distribution network equipment ledger data, a power distribution network topological structure, user load information, and a historical typhoon scene library; the data interaction layer is used for accessing the data acquired by the physical model layer; the simulation model layer is used for simulating a typhoon wind field, calculating an equipment failure rate, simulating a typhoon scene, calculating load loss, calculating equipment toughness and sequencing equipment vulnerability; and the application service layer provides a reinforcement strategy for the power distribution network to cope with typhoon disasters. The invention further discloses a method for improving the toughness of the power distribution network under the typhoon disaster based on digital twinning. Real-time synchronization of a physical entity and a digital model is achieved, multi-source heterogeneous data are deeply fused, and the reinforcing requirements of power distribution network equipment in two different reinforcing modes facing typhoon disasters are met by embedding two different toughness improving models.

Description

Technical Field

[0001] This invention relates to a system and method for improving the resilience of power distribution networks under typhoon disasters based on digital twins. Background Technology

[0002] As the "last mile" of the power system, the power distribution network directly serves a vast number of users, and its safe and stable operation is crucial to social production and residents' lives. However, most power distribution network equipment (such as poles, conductors, and transformers) is installed outdoors, making it highly vulnerable to extreme natural disasters such as typhoons, torrential rains, and snowstorms. Due to its wide distribution, complex topology, and flexible operation, damage to power distribution network equipment during extreme weather can not only cause localized power outages but also trigger chain reactions, leading to regional power supply paralysis. Typhoons, with their combined destructive effects of strong winds, torrential rains, and storm surges, pose one of the most serious threats to power distribution networks in coastal areas. Typhoons often cause large-scale failures such as toppled poles, broken conductors, fallen trees damaging power lines, and water ingress into equipment, resulting in widespread power outages and significant socio-economic losses. Therefore, effectively improving the resilience of the power distribution network under typhoon disasters—that is, the system's ability to withstand shocks, maintain critical functions, and recover quickly from failures—has become a major challenge for the power industry.

[0003] Currently, strategies to enhance the resilience of power distribution networks against typhoons mainly revolve around preventative reinforcement before disasters and emergency recovery after disasters. Pre-disaster equipment reinforcement is considered the most cost-effective and proactive defense method, and also a direct means of improving the physical strength of the power distribution network. It is divided into two categories: permanent reinforcement and temporary reinforcement. 1. Permanent reinforcement: This includes raising design standards, replacing materials with high-strength ones, and modifying weak structures. Examples include using poles with higher wind resistance ratings, increasing foundation size, and undergrounding power lines. These measures involve large investments, long cycles, and are difficult to adjust once implemented. 2. Temporary reinforcement: This is implemented in the short period before the typhoon arrives, such as adding temporary guy wires, support poles, and sandbag protection. These measures are lower in cost and more flexible in implementation.

[0004] Defects and shortcomings of existing technology:

[0005] 1. The existing power distribution network resilience enhancement model does not distinguish between the two different reinforcement methods, nor does it consider the significant differences in the timing and objectives of the two reinforcement methods (permanent reinforcement: during routine planning, construction, and maintenance, fundamentally improving power grid resilience; temporary reinforcement: a few hours to a few days before a typhoon arrives, to respond to a single typhoon threat).

[0006] 2. Existing technologies lack high-fidelity digital mirroring of distribution networks, and lack real-time perception of physical conditions and synchronous updates of digital models.

[0007] Therefore, a system and method for improving the resilience of power distribution networks under typhoon disasters based on digital twins are provided. Summary of the Invention

[0008] To address the aforementioned problems in existing technologies, this invention provides a digital twin-based system and method for enhancing the resilience of power distribution networks under typhoon disasters. This system achieves real-time synchronization between physical entities and digital models, deeply integrates multi-source heterogeneous data, and by embedding two different resilience enhancement models, meets the reinforcement needs of power distribution network equipment for two different reinforcement methods in the face of typhoon disasters.

[0009] The technical solution to achieve the above objectives is:

[0010] One of the present inventions is a power distribution network resilience enhancement system based on digital twins under typhoon disasters, comprising:

[0011] The physical model layer is used to acquire geographical information of the power distribution network, equipment ledger data of the power distribution network, topology of the power distribution network, user load information, and historical typhoon scenario database.

[0012] The data interaction layer is used to access data obtained from the physical model layer, as well as real-time forecast data from local weather stations, to obtain the typhoon's predicted path, intensity, central pressure, and landfall time.

[0013] The simulation model layer is used to simulate typhoon wind fields, calculate equipment failure rates, simulate typhoon scenarios, calculate load losses, calculate equipment resilience, and rank equipment vulnerability.

[0014] The application service layer is used to call the calculation results of the simulation model layer and the data provided by the data interaction layer. Through two embedded resilience enhancement decision models, it provides reinforcement strategies for the power distribution network to cope with typhoon disasters.

[0015] Preferably, the physical model layer includes:

[0016] The first data acquisition module is used to build a geographic information system for the power distribution network based on GIS, and to assign three-dimensional coordinates to the power distribution network equipment, as well as the surrounding terrain features of the power distribution network equipment.

[0017] The second data acquisition module is used to collect and record equipment ledger data;

[0018] The structure building module is used to associate the equipment ledger with spatial objects in GIS one by one through the equipment ID, analyze the power distribution network topology to build a node-branch model, and describe the current flow and network connectivity.

[0019] The user electricity consumption analysis module is used to determine the electricity load level of users at each node based on the user load data of each node, deploy smart meters, and analyze the 24-hour electricity consumption patterns of users on typical weekdays, weekends, holidays, and different seasons.

[0020] The Typhoon Scene Database module is used to record local historical typhoon data, including parameters such as path, intensity, central pressure, and landfall time.

[0021] Preferably, the data interaction layer includes:

[0022] The user load analysis module is used to access real-time forecast data from local weather stations to obtain typhoon forecast paths, intensity, central pressure, and landfall time. It also uses the power grid monitoring system to obtain real-time user electricity consumption data and analyze user load.

[0023] Preferably, the simulation model layer includes:

[0024] The Typhoon Wind Field Simulation Module is used to simulate the changing process of typhoons using the Batts Typhoon Wind Field Model.

[0025] The equipment failure rate calculation module is used to establish equipment failure rate models for conductors and towers under different wind speeds and calculate the equivalent failure probability of power distribution lines.

[0026] The typhoon scenario simulation module is used to perform Monte Carlo simulation for each typhoon scenario. 5000 fault scenarios are generated under one typhoon scenario, and a total of 5000×n fault scenarios are generated for n typhoon scenarios.

[0027] The load loss calculation module is used to combine network topology and user load level data, calculate the load loss under each fault scenario through a power flow calculation program, and associate the load loss under the fault scenario with the damaged line number to calculate the expected load loss after each line fault occurs.

[0028] The equipment resilience and vulnerability ranking module is used to calculate the resilience index of each line through the equipment resilience assessment model, calculate the vulnerability of each line through the equipment vulnerability model, and rank each line according to vulnerability from largest to smallest, generating a priority list for the reinforcement of distribution network equipment.

[0029] Preferably, the application service layer includes:

[0030] The permanent reinforcement scheme module is used to manually input a series of pending reinforcement schemes for the top-ranked lines in the priority list. For each reinforcement scheme, it is virtually implemented in a digital twin model, and a cost database is established for each reinforcement scheme.

[0031] The temporary reinforcement optimization scheme module is used to drive the typhoon wind field model and equipment failure rate model through accurate typhoon path prediction, and build an emergency temporary reinforcement optimization model to obtain the most economically optimal temporary reinforcement scheme.

[0032] The output decision recommendation module is used to generate a comprehensive decision report containing information on the reinforcement target, recommended reinforcement scheme, estimated investment, expected resilience improvement benefits, and cost-benefit ratio, providing data-driven decision support for the power grid company's annual investment and renovation plans.

[0033] Preferably, in the first acquisition module, the power distribution network equipment includes, but is not limited to, conductors, towers, and substations; the three-dimensional coordinates include longitude, latitude, and altitude; and the surrounding terrain features of the power distribution network equipment include, but are not limited to, buildings and vegetation cover.

[0034] In the second data acquisition module, the equipment ledger data includes identity information, lifecycle information, physical material information, and design parameters. Among them, the identity information includes equipment ID, type, model, and manufacturer; the lifecycle information includes commissioning date, maintenance date, and defect record; the physical material information includes the materials of the tower and conductor; and the design parameters include design wind resistance level, conductor design breaking strength, and tower design bending moment.

[0035] Preferably, in the typhoon wind field simulation module, the Batts typhoon wind field model simulates the typhoon's changing process, as follows:

[0036] Calculate the central pressure difference Δp(t):

[0037] Δp(t)=p a +p ty (t);

[0038] In the formula, Δp(t) is the central air pressure difference, p a Environmental pressure is a constant parameter, p ty (t) represents the pressure at the typhoon center;

[0039] The wind speed at the component location is obtained based on the central air pressure difference Δp(t):

[0040]

[0041] In the formula, Let be the gradient wind speed of the typhoon at time t. Let v be the maximum wind speed at time t. com R(t) represents the wind speed at the typhoon center at time t, d(t) represents the distance between the typhoon center and the component location at time t, and R max (t) represents the radius of the typhoon's maximum wind speed at time t;

[0042] The distance d(t) between the typhoon center and the component position at time t is calculated using the following formula:

[0043] d(t)=((x com -x(t))+(y com -y(t)))0.5 ;

[0044] In the formula, (x com ,y com (x(t), y(t)) represents the geographical coordinates of the power distribution network components, and (x(t), y(t)) represents the geographical coordinates of the typhoon center at time t.

[0045] Preferably, in the equipment failure rate calculation module, the cumulative failure probability of each tower within a duration T is calculated. Represented as:

[0046]

[0047] In the formula, (m,k) represents the k-th tower of the m-th distribution line. Let be the failure rate at time t. Let γ be the wind speed at the component's position at time t, and γ be the shape factor. The design wind speed for the tower is given, N is the total number of typhoon impact intervals, and Δt is the time interval, which is taken as 1 hour.

[0048] Cumulative failure probability of each conductor over duration T It can be represented as:

[0049]

[0050] In the formula, (m,l) represents the l-th segment of the m-th distribution line. Let be the failure rate at time t. Both ρ and ρ are coefficient parameters. Let be the wind speed at the center of the component at time t. Design wind speed for the conductor;

[0051] Since a long power distribution line is considered as a combination of conductors and towers, the equivalent fault probability of this power distribution line is expressed as:

[0052]

[0053] In the formula, K and L are the total number of distribution towers and conductors of the m-th distribution line, respectively.

[0054] Preferably, in the equipment resilience and vulnerability ranking module, the calculation formula for the equipment resilience assessment model is as follows:

[0055]

[0056] In the formula, T i λ is a resilience index. n Let P be the probability of scenario n occurring. nLet N be the load retention rate for scenario n, N be the number of fault scenarios, T0 be the disaster duration of the distribution network (i.e., the time from the occurrence of the fault to the restoration of the original normal power supply state), and F(t) be the load level of the distribution network under extreme weather disasters. T (t) represents the load level of the distribution network during normal operation, W RES n This refers to the load loss caused by a fault in the distribution network.

[0057] The formula for calculating the equipment vulnerability model is as follows:

[0058] CVI(m)=E(P m )×E(W RES m );

[0059] In the formula, CVI(m) is the vulnerability index of line m, and E(P) is the vulnerability index of line m. m Let E(W) represent the expected failure probability of line m under all typhoon scenarios. RES m ) represents the expected load loss caused by a fault in line m under all scenarios.

[0060] Preferably, in the permanent reinforcement scheme module, for each reinforcement scheme, it is virtually implemented in the digital twin model, specifically including:

[0061] Modify the design wind resistance rating parameters of the corresponding equipment in the physical model layer for this line;

[0062] By accessing historical typhoon data from a historical typhoon scenario database or using user-input typhoon data, a typhoon scenario database is constructed, containing multiple typical historical typhoon paths, intensities, central pressures, and landfall times. Simulations are then performed using this database to obtain new vulnerability indices for the power line after implementing the reinforcement scheme. The improvement in resilience resulting from the reinforcement is represented by the reduction in the vulnerability index.

[0063] ΔCVI=CVI-CVI′;

[0064] In the formula, ΔCVI is the reduction value of the vulnerability index, CVI is the vulnerability index without reinforcement, and CVI′ is the vulnerability index after reinforcement.

[0065] Establish a cost database for each reinforcement scheme, including material costs, labor costs, construction period, etc., and calculate the cost-benefit ratio of each reinforcement scheme:

[0066] CBR = Cost / ΔCVI;

[0067] In the formula, CBR is the cost-benefit ratio, and Cost is the value of the reinforcement of the line. The smaller the CBR value, the greater the resilience improvement benefit that can be obtained per unit investment.

[0068] Preferably, in the temporary reinforcement optimization scheme module, an emergency temporary reinforcement optimization model is constructed to obtain the economically optimal temporary reinforcement scheme, including:

[0069] Using accurate typhoon path prediction as input to the typhoon wind field model, the model is driven to predict in real time the precise wind speed at the coordinates of each power distribution network device along the typhoon path for the next few hours to days.

[0070] The predicted wind speed at the equipment location is input into the equipment failure rate model to calculate the cumulative failure probability of each tower and line segment under the influence of this specific typhoon, and then the cumulative failure probability of each line is calculated.

[0071] The objective function for constructing an emergency temporary reinforcement optimization model is:

[0072] Minimum load loss and reinforcement cost:

[0073]

[0074] In the formula, Y1 is the comprehensive power loss cost, Y2 is the reinforcement cost of power grid components, and ω i For load level weighting, α i The load reduction ratio for node i. Let t be the active load at node i. i Let be the power outage duration of node i, be 'a' be the average electricity price, and be 'x' be the average electricity price. ij This is a binary variable representing whether line ij is reinforced, c ij Let be the reinforcement cost of line ij, n be the total number of nodes, and m be the total number of branches.

[0075] The second invention provides a method for improving the resilience of a power distribution network under typhoon disasters based on digital twins, comprising: permanent reinforcement decision-making and temporary reinforcement decision-making;

[0076] Permanent reinforcement decisions include:

[0077] Step S1: The user issues a decision command to carry out permanent reinforcement, retrieves all historical typhoon path and intensity data that have affected the area in the past period, or manually inputs typhoon path and intensity data to form a database containing dozens of typical typhoon disaster scenarios.

[0078] Step S2: Take each scenario in the historical typhoon database as input to drive the typhoon wind field model and equipment failure rate model, calculate the failure probability of each line in the power distribution network under that scenario, and calculate the expected failure probability of each line under all typhoon scenarios.

[0079] Step S3: Perform Monte Carlo random sampling for each typhoon scenario. For each sampling, a specific power grid fault state will be generated based on the fault probability of each device.

[0080] Step S4: Combining network topology and user load level data, calculate the load loss under each fault scenario through power flow, and correspond the load loss under the fault scenario with the damaged line number to calculate the expected load loss after each line fault occurs.

[0081] Step S5: By statistically averaging the calculation results, calculate the expected failure probability and expected load loss of each line under all historical typhoon scenarios, and calculate the vulnerability index of each line to generate a vulnerability ranking of the entire network lines.

[0082] Step S6: The planning engineer proposes a variety of feasible reinforcement solutions for the vulnerable lines in the upper row, and permanently reinforces the lines.

[0083] Step S7: For the modified digital twin power grid, the system re-executes steps S1-S5 to obtain the new vulnerability index after reinforcement. Based on the preset material and labor cost database, the system estimates the total investment of various reinforcement schemes and calculates the cost-benefit ratio.

[0084] In step S8, the system automatically generates a comprehensive decision support report on permanent reinforcement, complete with illustrations and text.

[0085] Temporary reinforcement decisions include:

[0086] Step S9: After the typhoon warning is issued, obtain the latest typhoon location and intensity forecast data from the meteorological department at a certain frequency, and call up equipment ledger data, power grid topology data, user load data, and geographic information data;

[0087] Step S10: Using the latest forecast, drive the typhoon wind field model and equipment failure rate model to predict the detailed wind speed process that each tower and conductor on the typhoon path will experience in the next few hours to days, and calculate their respective cumulative failure probability.

[0088] Step S11: Perform Monte Carlo random sampling for each typhoon scenario to generate thousands of specific power grid fault states. Call the temporary reinforcement optimization model and input the fault states into the temporary reinforcement optimization model for solution.

[0089] Step S12: After the optimization model is solved, the list of lines that need to be reinforced is displayed, including the device ID, geographical information, and type of the equipment that needs to be reinforced.

[0090] Step S13: The maintenance personnel carry out reinforcement work based on the results. After a group of personnel completes a reinforcement task, they return the reinforcement status information to the system. Upon receiving the information, the system immediately updates the status of the digital twin of the tower.

[0091] In step S14, the system automatically generates a temporary reinforcement decision support report with illustrations and text.

[0092] Preferably, in step S4, the load loss W due to the distribution network fault RES n The calculation formula is as follows:

[0093]

[0094] In the formula, T i λ is a resilience index. n Let P be the probability of scenario n occurring. n Let N be the load retention rate for scenario n, N be the number of fault scenarios, T0 be the disaster duration of the distribution network (i.e., the time from the occurrence of the fault to the restoration of the original normal power supply state), and F(t) be the load level of the distribution network under extreme weather disasters. T (t) represents the load level of the distribution network during normal operation, W RES n This refers to the load loss caused by a fault in the distribution network.

[0095] In step S5, the vulnerability index CVI(m) is calculated using the following formula:

[0096] CVI(m)=E(P m )×E(W RES m );

[0097] In the formula, CVI(m) is the vulnerability index of line m, and E(P) is the vulnerability index of line m. m Let E(W) represent the expected failure probability of line m under all typhoon scenarios. RES m ) represents the expected load loss caused by a fault in line m under all scenarios.

[0098] Preferably, in step S7, the typhoon scenario library is used again for simulation to obtain the new vulnerability index of the line after implementing the reinforcement scheme. The improvement in toughness brought about by the reinforcement is the reduction in the vulnerability index.

[0099] ΔCVI=CVI-CVI′;

[0100] In the formula, ΔCVI is the reduction value of the vulnerability index, CVI is the vulnerability index without reinforcement, and CVI′ is the vulnerability index after reinforcement.

[0101] Establish a cost database for each reinforcement scheme, including material costs, labor costs, construction period, etc., and calculate the cost-benefit ratio of each reinforcement scheme:

[0102] CBR = Cost / ΔCVI;

[0103] In the formula, CBR is the cost-benefit ratio, and Cost is the value of the reinforcement of the line. The smaller the CBR value, the greater the resilience improvement benefit that can be obtained per unit investment.

[0104] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention is geared towards long-term planning. Through statistical analysis and cost-benefit assessment of historical typhoon big data, it identifies inherent and long-standing weaknesses in the power grid. Its decision-making results aim to fundamentally and systematically improve the physical strength and resilience of the power grid, making investment decisions more forward-looking and scientific. This invention constructs a high-fidelity digital twin system, significantly improving the accuracy of disaster simulation and prediction. Furthermore, the typhoon wind field model and equipment failure rate model of this invention can calculate the degree of impact and failure probability of each tower and each line segment at a specific time and spatial location. Its prediction accuracy is higher than that of traditional macro-assessment methods based on regional averages, providing a solid data foundation for subsequent accurate decision-making. Attached Figure Description

[0105] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0106] Figure 1 This is a block diagram of a power distribution network resilience enhancement system based on digital twins under typhoon disasters according to the present invention.

[0107] Figure 2 This is a flowchart of the permanent reinforcement decision-making process in a method for improving the resilience of a power distribution network under typhoon disasters based on digital twins, as described in this invention.

[0108] Figure 3 This is a flowchart of a temporary reinforcement decision in a method for improving the resilience of a power distribution network under typhoon disasters based on digital twins, as described in this invention. Detailed Implementation

[0109] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0110] like Figure 1 As shown, a power distribution network resilience enhancement system based on digital twins under typhoon disasters includes:

[0111] The physical model layer is used to acquire geographical information of the power distribution network, equipment ledger data of the power distribution network, topology of the power distribution network, user load information, and historical typhoon scenario database.

[0112] In this embodiment, the physical model layer includes: a first acquisition module 1, a second acquisition module 2, a structure construction module 3, a user power consumption analysis module 4, and a typhoon scenario library module 5.

[0113] The first data acquisition module 1 is used to build a geographic information system for the power distribution network based on GIS, and to assign three-dimensional coordinates to the power distribution network equipment and the surrounding terrain features of the power distribution network equipment.

[0114] In this embodiment, the power distribution network equipment includes, but is not limited to, conductors, towers, and substations; the three-dimensional coordinates include longitude, latitude, and altitude; and the surrounding terrain features of the power distribution network equipment include, but are not limited to, buildings and vegetation cover.

[0115] The second acquisition module 2 is used to collect and record equipment ledger data.

[0116] In this embodiment, the equipment ledger data includes identity information, lifecycle information, physical material information, and design parameters; wherein, the identity information includes equipment ID, type, model, and manufacturer; the lifecycle information includes commissioning date, maintenance date, and defect record; the physical material information includes the materials of the tower and conductor; and the design parameters include design wind resistance level, conductor design breaking strength, and tower design bending moment.

[0117] The structure construction module 3 is used to associate the equipment ledger with spatial objects in the GIS one by one through the equipment ID, analyze the power distribution network topology to construct a node-branch model (nodes represent electrical connection points of the power distribution network, such as substation busbars, load access points, etc., parameters; branches represent electrical paths connecting two nodes, such as overhead lines, parameters include resistance, reactance and operating status), and describe the current flow and network connectivity.

[0118] User electricity consumption analysis module 4 is used to determine the electricity load level of each node user based on the user load data of each node (Level 1 load: absolutely no power outage allowed, even momentary interruption is not allowed, such as hospitals, financial data centers, water supply plants, etc.; Level 2 load: short-term power outages are allowed, but restoration needs to be done as soon as possible, such as general production lines of large manufacturing industries, office buildings of medium and large enterprises, etc.; Level 3 load: power outages are allowed, and the losses caused by power outages are relatively small. In the event of a power grid emergency, these are the objects that should be prioritized for reduction or interruption, such as residential electricity consumption, agricultural production electricity, etc.), deploy smart meters, and analyze the 24-hour electricity consumption patterns of users on typical weekdays, weekends, holidays, and different seasons.

[0119] Typhoon Scene Library Module 5 is used to record local historical typhoon data, including parameters such as path, intensity, central pressure, and landfall time.

[0120] The data interaction layer is used to access data obtained from the physical model layer, as well as real-time forecast data from local weather stations, to obtain the typhoon's predicted path, intensity, central pressure, and landfall time.

[0121] In this embodiment, the data interaction layer includes: user load analysis module 6.

[0122] User load analysis module 6 is used to access real-time forecast data from local meteorological stations, obtain typhoon forecast paths, intensity, central pressure, and landfall time, and acquire user electricity consumption data in real time through the power grid monitoring system to analyze user load.

[0123] The simulation model layer is used to simulate typhoon wind fields, calculate equipment failure rates, simulate typhoon scenarios, calculate load losses, calculate equipment resilience, and rank equipment vulnerabilities.

[0124] In this embodiment, the simulation model layer includes: a typhoon wind field simulation module 7, an equipment failure rate calculation module 8, a typhoon scenario simulation module 9, a load loss calculation module 10, and an equipment resilience and vulnerability ranking module 11.

[0125] Typhoon wind field simulation module 7 is used to simulate the changing process of typhoons using the Batts typhoon wind field model.

[0126] In this embodiment, the Batts typhoon wind field model simulates the typhoon's change process, as follows:

[0127] Calculate the central pressure difference Δp(t):

[0128] Δp(t)=p a +p ty (t);

[0129] In the formula, Δp(t) is the central air pressure difference, p a Environmental pressure is a constant parameter, p ty (t) represents the pressure at the typhoon center;

[0130] further:

[0131]

[0132] In the formula, t land α represents the time of typhoon landfall, β represents the typhoon attenuation rate, β represents the basic attenuation coefficient, χ represents the environmental adjustment factor, and ξ and η are both empirical parameters of the model. ξ controls the baseline scale and reflects the influence of the typhoon's initial state or climate background, while η adjusts the effect of pressure difference on R.max The nonlinear effect of (t), v H (t land R represents the maximum wind speed of the typhoon at landfall. max (t land () represents the radius of the typhoon's maximum wind speed at landfall;

[0133] The wind speed at the component location is obtained based on the central air pressure difference Δp(t):

[0134]

[0135] In the formula, Let be the gradient wind speed of the typhoon at time t. Let v be the maximum wind speed at time t. com R(t) represents the wind speed at the typhoon center at time t, d(t) represents the distance between the typhoon center and the component location at time t, and R max (t) represents the radius of the typhoon's maximum wind speed at time t;

[0136] in,

[0137]

[0138]

[0139] In the formula, k, δ, and ε are all parameter coefficients, and v H (t) represents the ambient wind speed of the typhoon at time t;

[0140] The distance d(t) between the typhoon center and the component position at time t is calculated using the following formula:

[0141] d(t)=((x com -x(t))+(y com -y(t))) 0.5 ;

[0142] In the formula, (x com ,y com (x(t), y(t)) represents the geographical coordinates of the power distribution network components, and (x(t), y(t)) represents the geographical coordinates of the typhoon center at time t.

[0143] in,

[0144]

[0145] In the formula, (x land ,y land ( ) represents the geographical coordinates of the coastline where the typhoon made landfall. The angles of the typhoon's path and the coastline.

[0146] The equipment failure rate calculation module 8 is used to establish equipment failure rate models for conductors and towers under different wind speeds and calculate the equivalent failure probability of power distribution lines.

[0147] In the embodiment, the cumulative failure probability of each tower over a duration T Represented as:

[0148]

[0149] In the formula, (m,k) represents the k-th tower of the m-th distribution line. Let be the failure rate at time t. Let γ be the wind speed at the component's position at time t, and γ be the shape factor. The design wind speed for the tower is given, N is the total number of typhoon impact intervals, and Δt is the time interval, which is taken as 1 hour.

[0150] Cumulative failure probability of each conductor over duration T It can be represented as:

[0151]

[0152] In the formula, (m,l) represents the l-th segment of the m-th distribution line. Let be the failure rate at time t. Both ρ and ρ are coefficient parameters. Let be the wind speed at the center of the component at time t. Design wind speed for the conductor;

[0153] Since a long power distribution line is considered as a combination of conductors and towers, the equivalent fault probability of this power distribution line is expressed as:

[0154]

[0155] In the formula, K and L are the total number of distribution towers and conductors of the m-th distribution line, respectively.

[0156] Typhoon scenario simulation module 9 is used to perform Monte Carlo simulation for each typhoon scenario. 5000 fault scenarios are generated under one typhoon scenario, and a total of 5000×n fault scenarios are generated for n typhoon scenarios.

[0157] The load loss calculation module 10 is used to combine network topology and user load level data, and through a power flow calculation program, calculate the load loss under each fault scenario, and associate the load loss under the fault scenario with the damaged line number to calculate the expected load loss after each line fault occurs.

[0158] The equipment resilience and vulnerability ranking module 11 is used to calculate the resilience index of each line through the equipment resilience assessment model, calculate the vulnerability of each line through the equipment vulnerability model, and rank each line according to vulnerability from largest to smallest to generate a priority list for the reinforcement of distribution network equipment.

[0159] In this embodiment, the calculation formula for the equipment resilience assessment model is as follows:

[0160]

[0161] In the formula, T i λ is a resilience index. n Let P be the probability of scenario n occurring. n Let N be the load retention rate for scenario n, N be the number of fault scenarios, T0 be the disaster duration of the distribution network (i.e., the time from the occurrence of the fault to the restoration of the original normal power supply state), and F(t) be the load level of the distribution network under extreme weather disasters. T (t) represents the load level of the distribution network during normal operation, W RES n This refers to the load loss caused by a fault in the distribution network.

[0162] The formula for calculating the equipment vulnerability model is as follows:

[0163] CVI(m)=E(P m )×E(W RES m );

[0164] In the formula, CVI(m) is the vulnerability index of line m, and E(P) is the vulnerability index of line m. m Let E(W) represent the expected failure probability of line m under all typhoon scenarios. RES m ) represents the expected load loss caused by a fault in line m under all scenarios.

[0165] The application service layer is used to call the calculation results of the simulation model layer and the data provided by the data interaction layer. Through two embedded resilience enhancement decision models, it provides reinforcement strategies for the power distribution network to cope with typhoon disasters.

[0166] In this embodiment, the application service layer includes: a permanent reinforcement scheme module 12, a temporary reinforcement optimization scheme module 13, and an output decision suggestion module 14.

[0167] The Permanent Reinforcement Scheme Module 12 is used to manually input a series of pending reinforcement schemes for lines ranked high on the priority list (e.g., Scheme A: Replace existing standard poles with concrete poles or iron towers with higher wind resistance; Scheme B: Reinforce the foundations of critical towers; Scheme C: Convert the overhead line into an underground cable). For each reinforcement scheme, it is virtually implemented in a digital twin model, and a cost database is established for each reinforcement scheme.

[0168] In this embodiment, for each reinforcement scheme, it is virtually implemented in a digital twin model, specifically including:

[0169] Modify the design wind resistance rating parameters of the corresponding equipment in the physical model layer for this line;

[0170] By accessing historical typhoon data from a historical typhoon scenario database or using user-input typhoon data, a typhoon scenario database is constructed, containing multiple typical historical typhoon paths, intensities, central pressures, and landfall times. Simulations are then performed using this database to obtain new vulnerability indices for the power line after implementing the reinforcement scheme. The improvement in resilience resulting from the reinforcement is represented by the reduction in the vulnerability index.

[0171] ΔCVI=CVI-CVI′;

[0172] In the formula, ΔCVI is the reduction value of the vulnerability index, CVI is the vulnerability index without reinforcement, and CVI′ is the vulnerability index after reinforcement.

[0173] Establish a cost database for each reinforcement scheme (such as replacing utility poles or burying power lines), including material costs, labor costs, and construction time, and calculate the cost-benefit ratio of each reinforcement scheme:

[0174] CBR = Cost / ΔCVI;

[0175] In the formula, CBR is the cost-benefit ratio, and Cost is the value of the reinforcement of the line. The smaller the CBR value, the greater the resilience improvement benefit that can be obtained per unit investment.

[0176] The temporary reinforcement optimization scheme module 13 is used to drive the typhoon wind field model and equipment failure rate model through accurate typhoon path prediction, and construct an emergency temporary reinforcement optimization model to obtain the economically optimal temporary reinforcement scheme.

[0177] In this embodiment, an emergency temporary reinforcement optimization model is constructed to obtain the economically optimal temporary reinforcement scheme, including:

[0178] Based on accurate typhoon path prediction, which involves accessing the latest typhoon forecast information (center location, movement speed, central pressure, etc.) released by authoritative meteorological departments, the model is used as input to drive the typhoon wind field model and predict in real time the precise wind speed at the coordinates of each power distribution network device (tower, conductor) along the typhoon path for the next few hours to days.

[0179] The predicted wind speed at the equipment location is input into the equipment failure rate model to calculate the cumulative failure probability of each tower and line segment under the influence of this specific typhoon, and then the cumulative failure probability of each line is calculated.

[0180] The objective function for constructing an emergency temporary reinforcement optimization model is:

[0181] Minimum load loss and reinforcement cost:

[0182]

[0183] In the formula, Y1 is the comprehensive power loss cost, Y2 is the reinforcement cost of power grid components, and ω i For load level weighting, α i The load reduction ratio for node i. Let t be the active load at node i. i Let be the power outage duration of node i, be 'a' be the average electricity price, and be 'x' be the average electricity price. ij This is a binary variable representing whether line ij is reinforced. If x ij A value of 0 indicates no reinforcement; if x ij A value of 1 represents reinforcement, and c ij Let be the reinforcement cost of line ij, n be the total number of nodes, and m be the total number of branches, where ;

[0184] The constraints include topological constraints for the radial operation of the distribution network, voltage constraints, generator output constraints, power balance constraints, line power flow constraints, second-order cone constraints, branch current constraints, and Ohm's law constraints.

[0185] in,

[0186] Distribution network radial operation topology constraints:

[0187] g k ∈G k ;

[0188] In the formula, g k For the distribution network topology, G k Configure a set of topologies for all radiation states;

[0189] Voltage constraint:

[0190]

[0191] In the formula, U is the lower limit of the voltage amplitude at node i. i Let be the square of the voltage amplitude at node i. This represents the upper limit of the voltage amplitude at node i.

[0192] Unit output constraints:

[0193]

[0194]

[0195] In the formula, and Let i represent the active and reactive power outputs of the power source at node i. and To represent the upper and lower limits of the active and reactive power output of the power source at node i;

[0196] Power balance constraints:

[0197]

[0198] In the formula, P ij and Q ij For the active and reactive power flow of line ij, P ji and Q ji For the active and reactive power flow of the line, and Let represent the active and reactive power of the power source at node i. and Let Ω represent the active and reactive loads at node i. B For distribution network nodes;

[0199] Power flow constraints:

[0200] -(1-u ij +u ij x ij )P ij,max ≤P ij ≤(1-u ij +u ij x ij )P ij,max ;

[0201] -(1-u ij +u ij x ij )Q ij,max ≤Q ij ≤(1-u ij +u ij x ij )Q ij,max ;

[0202] In the formula, P ij,max and P ij,max For the upper limit of active and reactive power flow of the line, u ij This represents the connectivity status of the line between node i and node j under the influence of the typhoon.

[0203] Second-order cone constraint:

[0204]

[0205] In the formula, L ij Let Ω be the square of the current magnitude flowing through branch ij. L A collection of distribution network lines;

[0206] Branch current constraints:

[0207]

[0208] In the formula, I ij,max Let be the maximum current amplitude allowed to pass through branch ij;

[0209] Ohm's Law constraint:

[0210]

[0211] In the formula, M is a sufficiently large positive number, and R ij and X ij These represent the line resistance and reactance.

[0212] The output decision recommendation module 14 is used to output a comprehensive decision report containing information on the reinforcement object, recommended reinforcement scheme, estimated investment, expected resilience improvement benefits, and cost-benefit ratio, providing data-driven decision support for the power grid company's annual investment and renovation plans.

[0213] like Figure 2 As shown in Figure 3, a method for improving the resilience of power distribution networks under typhoon disasters based on digital twins includes: permanent reinforcement decision-making and temporary reinforcement decision-making;

[0214] Permanent reinforcement decisions include:

[0215] Step S1: The user issues a decision command to carry out permanent reinforcement, retrieves all historical typhoon path and intensity data that have affected the area in the past period, or manually inputs typhoon path and intensity data to form a database containing dozens of typical typhoon disaster scenarios.

[0216] Step S2: Take each scenario in the historical typhoon database as input to drive the typhoon wind field model and equipment failure rate model, calculate the failure probability of each line in the power distribution network under that scenario, and calculate the expected failure probability of each line under all typhoon scenarios.

[0217] Step S3: Perform Monte Carlo random sampling for each typhoon scenario. For each sampling, a specific power grid fault state will be generated based on the fault probability of each device.

[0218] Step S4: Combining network topology and user load level data, calculate the load loss under each fault scenario through power flow, and correlate the load loss under the fault scenario with the damaged line number to calculate the expected load loss after each line fault occurs.

[0219] In the embodiment, the load loss W due to a distribution network fault RES n The calculation formula is as follows:

[0220]

[0221] In the formula, T i λ is a resilience index. n Let P be the probability of scenario n occurring. n Let N be the load retention rate for scenario n, N be the number of fault scenarios, T0 be the disaster duration of the distribution network (i.e., the time from the occurrence of the fault to the restoration of the original normal power supply state), and F(t) be the load level of the distribution network under extreme weather disasters. T (t) represents the load level of the distribution network during normal operation, W RES n This refers to the load loss caused by a fault in the distribution network.

[0222] Step S5: By statistically averaging the calculation results, calculate the expected failure probability and expected load loss of each line under all historical typhoon scenarios, and calculate the vulnerability index of each line to generate a vulnerability ranking of the entire network of lines.

[0223] In this embodiment, the vulnerability index CVI(m) is calculated using the following formula:

[0224] CVI(m)=E(P m )×E(W RES m );

[0225] In the formula, CVI(m) is the vulnerability index of line m, and E(P) is the vulnerability index of line m. m Let E(W) represent the expected failure probability of line m under all typhoon scenarios. RES m ) represents the expected load loss caused by a fault in line m under all scenarios.

[0226] In step S6, the planning engineer proposes a variety of feasible reinforcement solutions for the vulnerable lines in the upper row, and permanently reinforces the lines.

[0227] In step S7, for the modified digital twin power grid, the system re-executes steps S1-S5 to obtain the new vulnerability index after reinforcement. Based on the preset material and labor cost database, the system estimates the total investment of various reinforcement schemes and calculates the cost-benefit ratio.

[0228] In this embodiment, a typhoon scenario library was used again for simulation to obtain a new vulnerability index for the line after implementing the reinforcement scheme. The improvement in toughness brought about by the reinforcement is the reduction in the vulnerability index.

[0229] ΔCVI=CVI-CVI′;

[0230] In the formula, ΔCVI is the reduction value of the vulnerability index, CVI is the vulnerability index without reinforcement, and CVI′ is the vulnerability index after reinforcement.

[0231] Establish a cost database for each reinforcement scheme, including material costs, labor costs, construction period, etc., and calculate the cost-benefit ratio of each reinforcement scheme:

[0232] CBR = Cost / ΔCVI;

[0233] In the formula, CBR is the cost-benefit ratio, and Cost is the value of the reinforcement of the line. The smaller the CBR value, the greater the resilience improvement benefit that can be obtained per unit investment.

[0234] In step S8, the system automatically generates a comprehensive decision support report on permanent reinforcement, complete with illustrations and text.

[0235] Temporary reinforcement decisions include:

[0236] Step S9: After the typhoon warning is issued, obtain the latest typhoon location and intensity forecast data from the meteorological department at a certain frequency, and call up equipment ledger data, power grid topology data, user load data, and geographic information data.

[0237] Step S10: Using the latest forecast, drive the typhoon wind field model and equipment failure rate model to predict the detailed wind speed process that each tower and conductor on the typhoon path will experience in the next few hours to days, and calculate their respective cumulative failure probabilities.

[0238] Step S11: Perform Monte Carlo random sampling for each typhoon scenario to generate thousands of specific power grid fault states. Call the temporary reinforcement optimization model and input the fault states into the temporary reinforcement optimization model for solution.

[0239] Step S12: After the optimization model is solved, the list of lines that need to be reinforced is displayed, including the device ID, geographical information, and type.

[0240] Step S13: The maintenance personnel carry out reinforcement work based on the results. After a group of personnel completes a reinforcement task, they return the reinforcement status information to the system. Upon receiving the information, the system immediately updates the status of the digital twin of the tower.

[0241] In step S14, the system automatically generates a temporary reinforcement decision support report with illustrations and text.

[0242] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A typhoon disaster-based power distribution network resilience improvement system based on digital twinning, characterized in that, include: The physical model layer is used to acquire geographical information of the power distribution network, equipment ledger data of the power distribution network, topology of the power distribution network, user load information, and historical typhoon scenario database. The data interaction layer is used to access data obtained from the physical model layer, as well as real-time forecast data from local weather stations, to obtain the typhoon's predicted path, intensity, central pressure, and landfall time. The simulation model layer is used to simulate typhoon wind fields, calculate equipment failure rates, simulate typhoon scenarios, calculate load losses, calculate equipment resilience, and rank equipment vulnerability. The application service layer is used to call the calculation results of the simulation model layer and the data provided by the data interaction layer. Through two embedded resilience enhancement decision models, it provides reinforcement strategies for the power distribution network to cope with typhoon disasters.

2. The typhoon disaster-based power distribution network resilience improvement system based on digital twinning of claim 1, wherein, The physical model layer includes: The first data acquisition module is used to build a geographic information system for the power distribution network based on GIS, and to assign three-dimensional coordinates to the power distribution network equipment, as well as the surrounding terrain features of the power distribution network equipment. The second data acquisition module is used to collect and record equipment ledger data; The structure building module is used to associate the equipment ledger with spatial objects in GIS one by one through the equipment ID, analyze the power distribution network topology to build a node-branch model, and describe the current flow and network connectivity. The user electricity consumption analysis module is used to determine the electricity load level of users at each node based on the user load data of each node, deploy smart meters, and analyze the 24-hour electricity consumption patterns of users on typical weekdays, weekends, holidays, and different seasons. The Typhoon Scene Database module is used to record local historical typhoon data, including parameters such as path, intensity, central pressure, and landfall time.

3. The typhoon disaster-based power distribution network resilience improvement system based on digital twinning of claim 2, wherein, The data interaction layer includes: The user load analysis module is used to access real-time forecast data from local weather stations to obtain typhoon forecast paths, intensity, central pressure, and landfall time. It also uses the power grid monitoring system to obtain real-time user electricity consumption data and analyze user load.

4. The typhoon disaster-based power distribution network resilience improvement system based on digital twinning of claim 3, wherein, The simulation model layer includes: The Typhoon Wind Field Simulation Module is used to simulate the changing process of typhoons using the Batts Typhoon Wind Field Model. The equipment failure rate calculation module is used to establish equipment failure rate models for conductors and towers under different wind speeds and calculate the equivalent failure probability of power distribution lines. The typhoon scenario simulation module is used to perform Monte Carlo simulation for each typhoon scenario. 5000 fault scenarios are generated under one typhoon scenario, and a total of 5000×n fault scenarios are generated for n typhoon scenarios. The load loss calculation module is used to combine network topology and user load level data, and calculate the load loss under each fault scenario through a power flow calculation program. It also associates the load loss under each fault scenario with the damaged line number and calculates the expected load loss after each line fault occurs. The equipment resilience and vulnerability ranking module is used to calculate the resilience index of each line through the equipment resilience assessment model, calculate the vulnerability of each line through the equipment vulnerability model, and rank each line according to vulnerability from largest to smallest, generating a priority list for the reinforcement of distribution network equipment.

5. The typhoon disaster-based power distribution network resilience improvement system based on digital twinning of claim 4, wherein, The application service layer includes: The permanent reinforcement scheme module is used to manually input a series of pending reinforcement schemes for the top-ranked lines in the priority list. For each reinforcement scheme, it is virtually implemented in a digital twin model, and a cost database is established for each reinforcement scheme. The temporary reinforcement optimization scheme module is used to drive the typhoon wind field model and equipment failure rate model through accurate typhoon path prediction, and build an emergency temporary reinforcement optimization model to obtain the most economically optimal temporary reinforcement scheme. The output decision recommendation module is used to generate a comprehensive decision report containing information on the reinforcement target, recommended reinforcement scheme, estimated investment, expected resilience improvement benefits, and cost-benefit ratio, providing data-driven decision support for the power grid company's annual investment and renovation plans.

6. The typhoon disaster-based power distribution network resilience improvement system based on digital twinning of claim 2, wherein, In the first data acquisition module, the power distribution network equipment includes, but is not limited to, conductors, towers, and substations; the three-dimensional coordinates include longitude, latitude, and altitude; and the surrounding terrain features of the power distribution network equipment include, but are not limited to, buildings and vegetation cover. In the second data acquisition module, the equipment ledger data includes identity information, lifecycle information, physical material information, and design parameters. Among them, the identity information includes equipment ID, type, model, and manufacturer; the lifecycle information includes commissioning date, maintenance date, and defect record; the physical material information includes the materials of the tower and conductor; and the design parameters include design wind resistance level, conductor design breaking strength, and tower design bending moment.

7. The typhoon disaster-based power distribution network resilience improvement system based on digital twinning of claim 4, wherein, In the typhoon wind field simulation module, the Batts typhoon wind field model simulates the changing process of a typhoon, as follows: Calculate the central pressure difference Δp(t): Δp(t) = p a + p ty (t); where Δp(t) is the central pressure difference, p a is the ambient pressure, which is a constant parameter, p ty (t) is the typhoon central pressure; The wind speed at the component location is obtained based on the central air pressure difference Δp(t): wherein is the gradient wind speed of the typhoon at time t, is the maximum wind speed at time t, v com (t) is the wind speed at the typhoon center at time t, d(t) is the distance between the typhoon center and the element position at time t, R max (t) is the maximum wind speed radius of the typhoon at time t; The distance d(t) between the typhoon center and the component position at time t is calculated using the following formula: d(t) = ((x com - x(t)) + (y com - y(t))) 0.5 ; In the formula, (x com ,y com ) are geographical coordinates of the power distribution network element, and (x(t), y(t)) are geographical coordinates of the typhoon center at time t.

8. The typhoon disaster-based power distribution network resilience enhancement system based on digital twins according to claim 4, characterized in that, The cumulative failure probability of each tower in the duration T in the device failure rate calculation module is represented as: In the formula, (m, k) is the kth tower of the mth distribution line, is the failure rate at time t, is the element position wind speed at time t, and γ is a shape coefficient, is the tower design wind speed, N is the total number of typhoon influence intervals, and Δt is the time interval, which is taken as 1 hour; Cumulative failure probability of each wire over a time duration T Can be expressed as: In the formula, (m, l) is the mth power distribution line and the lth line segment, is the failure rate at time t, and ρ are coefficient parameters, is the element center position wind speed at time t, is the conductor design wind speed; Since a long power distribution line is considered as a combination of conductors and towers, the equivalent fault probability of this power distribution line is expressed as: In the formula, K and L are the total number of distribution towers and conductors of the m-th distribution line, respectively.

9. The typhoon disaster-based power distribution network resilience improvement system based on digital twinning of claim 4, wherein, In the equipment resilience and vulnerability ranking module, the calculation formula for the equipment resilience assessment model is as follows: where T i is the toughness index, λ n is the probability of occurrence of scenario n, P n is the load retention rate of scenario n, N is the number of failure scenarios, T0 is the disaster time of the distribution network, i.e., the time from failure occurrence to recovery to the original normal power supply state after failure end, F(t) is the load level of the distribution network under extreme weather disasters, F T (t) is the load level of the distribution network in normal operation, W RES n is the load loss amount of the distribution network failure; The formula for calculating the equipment vulnerability model is as follows: CVI(m) = E(P m ) x E(W RES m ); where CVI(m) is the vulnerability index of line m, E(P m ) is the expected failure probability of line m under all typhoon scenarios, and E(W RES m ) is the expected load loss amount caused by line m failure under all scenarios.

10. The typhoon disaster-based power distribution network resilience improvement system based on digital twinning of claim 5, wherein, In the permanent reinforcement scheme module, each reinforcement scheme is virtually implemented in a digital twin model, specifically including: Modify the design wind resistance rating parameters of the corresponding equipment in the physical model layer for this line; By accessing historical typhoon data from a historical typhoon scenario database or using user-input typhoon data, a typhoon scenario database is constructed, containing multiple typical historical typhoon paths, intensities, central pressures, and landfall times. Simulations are then performed using this database to obtain new vulnerability indices for the power line after implementing the reinforcement scheme. The improvement in resilience resulting from the reinforcement is represented by the reduction in the vulnerability index. ΔCVI=CVI-CVI′; In the formula, ΔCVI is the reduction value of the vulnerability index, CVI is the vulnerability index without reinforcement, and CVI′ is the vulnerability index after reinforcement. Establish a cost database for each reinforcement scheme, including material costs, labor costs, construction period, etc., and calculate the cost-benefit ratio of each reinforcement scheme: CBR = Cost / ΔCVI; In the formula, CBR is the cost-benefit ratio, and Cost is the value of the reinforcement of the line. The smaller the CBR value, the greater the resilience improvement benefit that can be obtained per unit investment.

11. The typhoon disaster-based power distribution network resilience improvement system based on digital twinning of claim 5, wherein, In the temporary reinforcement optimization scheme module, an emergency temporary reinforcement optimization model is constructed to obtain the economically optimal temporary reinforcement scheme, including: Using accurate typhoon path prediction as input to the typhoon wind field model, the model is driven to predict in real time the precise wind speed at the coordinates of each power distribution network device along the typhoon path for the next few hours to days. The predicted wind speed at the equipment location is input into the equipment failure rate model to calculate the cumulative failure probability of each tower and line segment under the influence of this specific typhoon, and then the cumulative failure probability of each line is calculated. The objective function for constructing an emergency temporary reinforcement optimization model is: Minimum load loss and reinforcement cost: where Y1 is the comprehensive electricity loss cost value, Y2 is the reinforcement cost of the grid element, ω i is the load level weight, α i is the load reduction ratio of node i, is the active load at node i, t i is the outage duration of node i, a is the average electricity price, x ij is a binary variable, indicating whether the line ij is reinforced or not, c ij is the reinforcement cost of the line ij, n is the total number of nodes, and m is the total number of branches.

12. A typhoon disaster distribution network resilience improvement method based on the digital twin-based typhoon disaster distribution network resilience improvement system of claim 1, characterized in that, include: Permanent reinforcement decisions and temporary reinforcement decisions; Permanent reinforcement decisions include: Step S1: The user issues a decision command to carry out permanent reinforcement, retrieves all historical typhoon path and intensity data that have affected the area in the past period, or manually inputs typhoon path and intensity data to form a database containing dozens of typical typhoon disaster scenarios. Step S2: Take each scenario in the historical typhoon database as input to drive the typhoon wind field model and equipment failure rate model, calculate the failure probability of each line in the power distribution network under that scenario, and calculate the expected failure probability of each line under all typhoon scenarios. Step S3: Perform Monte Carlo random sampling for each typhoon scenario. For each sampling, a specific power grid fault state will be generated based on the fault probability of each device. Step S4: Combining network topology and user load level data, calculate the load loss under each fault scenario through power flow, and correspond the load loss under the fault scenario with the damaged line number to calculate the expected load loss after each line fault occurs. Step S5: By statistically averaging the calculation results, calculate the expected failure probability and expected load loss of each line under all historical typhoon scenarios, and calculate the vulnerability index of each line to generate a vulnerability ranking of the entire network lines. Step S6: The planning engineer proposes a variety of feasible reinforcement solutions for the vulnerable lines in the upper row, and permanently reinforces the lines. Step S7: For the modified digital twin power grid, the system re-executes steps S1-S5 to obtain the new vulnerability index after reinforcement. Based on the preset material and labor cost database, the system estimates the total investment of various reinforcement schemes and calculates the cost-benefit ratio. In step S8, the system automatically generates a comprehensive decision support report on permanent reinforcement, complete with illustrations and text. Temporary reinforcement decisions include: Step S9: After the typhoon warning is issued, obtain the latest typhoon location and intensity forecast data from the meteorological department at a certain frequency, and call up equipment ledger data, power grid topology data, user load data, and geographic information data; Step S10: Using the latest forecast, drive the typhoon wind field model and equipment failure rate model to predict the detailed wind speed process that each tower and conductor on the typhoon path will experience in the next few hours to days, and calculate their respective cumulative failure probability. Step S11: Perform Monte Carlo random sampling for each typhoon scenario to generate thousands of specific power grid fault states. Call the temporary reinforcement optimization model and input the fault states into the temporary reinforcement optimization model for solution. Step S12: After the optimization model is solved, the list of lines that need to be reinforced is displayed, including the device ID, geographical information, and type of the equipment that needs to be reinforced. Step S13: The maintenance personnel carry out reinforcement work based on the results. After a group of personnel completes a reinforcement task, they return the reinforcement status information to the system. Upon receiving the information, the system immediately updates the status of the digital twin of the tower. In step S14, the system automatically generates a temporary reinforcement decision support report with illustrations and text.

13. The method of claim 12, wherein, In step S4, the load loss amount W of the power distribution network at the time of the fault RES n The calculation formula is as follows: In the formula, T i is a toughness index, λ n is a scene n occurrence probability, P n is a scene n load retention rate, N is a number of fault scenes, T0 is a distribution network disaster time, i.e. a time from fault occurrence to fault end recovery to an original normal power supply state, F(t) is a load level of the distribution network under an extreme weather disaster, F T (t) is a load level of the distribution network in normal operation, W RES n is a load loss amount of the distribution network fault; In step S5, the vulnerability index CVI(m) is calculated using the following formula: CVI(m) = E(P m ) x E(W RES m ); where CVI(m) is the vulnerability index of line m, E(P m ) is the expected probability of failure of line m under all typhoon scenarios, and E(W RES m ) is the expected amount of load loss caused by line m under all scenarios after it fails.

14. The method of claim 12, wherein, In step S7, the typhoon scenario library is used again for simulation to obtain the new vulnerability index of the line after implementing the reinforcement scheme. The improvement in toughness brought about by the reinforcement is the reduction in the vulnerability index. ΔCVI=CVI-CVI′; In the formula, ΔCVI is the reduction value of the vulnerability index, CVI is the vulnerability index without reinforcement, and CVI′ is the vulnerability index after reinforcement. Establish a cost database for each reinforcement scheme, including material costs, labor costs, construction period, etc., and calculate the cost-benefit ratio of each reinforcement scheme: CBR = Cost / ΔCVI; In the formula, CBR is the cost-benefit ratio, and Cost is the value of the reinforcement of the line. The smaller the CBR value, the greater the resilience improvement benefit that can be obtained per unit investment.

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