A three-network coupled typhoon prediction and power grid resilience assessment method

Abstract

The present invention discloses a three-network coupled typhoon prediction and power grid resilience assessment method, which belongs to the field of typhoon prediction and distribution network assessment. The method is divided into a typhoon wind field path prediction method and a distribution network resilience assessment method, wherein the typhoon wind field path prediction method: couples the three networks through the information flow matrix, calculates the impact of the typhoon on the information network and the transportation network, and then calculates the impact of the information network and the transportation network on the distribution network, and on the basis of considering these impacts, uses the naive prediction model and the Batts wind field model to calculate the typhoon prediction result with time-shifting properties; and calculates the distribution network resilience assessment result with time-shifting properties through the three-network coupling relationship. Compared with the existing technology, the resilience assessment system established by this method is more comprehensive, has a wider range of considerations, and is more applicable, which is conducive to the power system to make response decisions faster.

Description

Technical Field

[0001] The present invention relates to the technical field of typhoon prediction and distribution network assessment, and in particular to a three-network coupled typhoon prediction and power grid resilience assessment method. Background Art

[0002] Given the frequent occurrence of natural disasters such as typhoons, it is particularly important to comprehensively consider the coupling of three networks to achieve typhoon forecasting and distribution network resilience assessment. When a typhoon passes through a city, it not only severely damages distribution network facilities, leading to power outages and damage to distribution network equipment, but also impacts the city's transportation and information networks, causing severe traffic congestion and information delays, indirectly exacerbating the impact on the city's distribution network. Therefore, typhoon forecasting and distribution network resilience assessment aim to study typhoon paths and the distribution network's ability to withstand typhoon disasters, so as to propose corresponding improvement recommendations and emergency measures to enhance the distribution network's disaster resilience. Existing typhoon forecasting and resilience assessment content is relatively limited and incomplete. There is no relevant research based on the coupling of three networks, and there is a lack of a comprehensive resilience assessment system that comprehensively considers public impact. Summary of the Invention

[0003] The purpose of the present invention is to overcome one or more deficiencies of the prior art and to provide a three-network coupled typhoon prediction and grid resilience assessment method. The present invention specifically addresses how to decide on the quality and efficiency of grid maintenance and reinforcement.

[0004] The object of the present invention is achieved through the following technical solutions:

[0005] A three-network coupled typhoon prediction and power grid resilience assessment method comprises the following steps:

[0006] Step 1: Establish a three-network model based on the distribution network, transportation network, and information network;

[0007] Step 2: Three-network coupling: The distribution network, transportation network, and information network are connected through a matrix model supported by energy and information flows to establish corresponding relationships between the nodes of the three networks;

[0008] Step 3: Predict the typhoon wind field model using the naive prediction model and the Batts wind field model;

[0009] Step 4: Calculate the impact of the typhoon on the distribution network using the tower fault model associated with the node, and convert the impact of the typhoon on the distribution network into the impact of the typhoon on the information network using the corresponding relationship in step 2;

[0010] Step 5: Using the information network delay model, calculate the delay properties of the information results caused by the damage of the information network, and obtain the typhoon forecast results with delay properties;

[0011] Step 6: Based on the failure probability of distribution network nodes and typhoon forecast results, construct a technical resilience assessment matrix. The technical indicators include:

[0012] System load recovery rate:

[0013] ;

[0014] in, The normal operating load level of the system, is the minimum load level after derating operation, At the initial moment of the disaster, For the moment when the disaster ends;

[0015] System average load loss:

[0016] ;

[0017] in, is the minimum load loss in a single simulation, and N is the total number of simulations;

[0018] Step 7: Perform normalization preprocessing on the resilience assessment matrix;

[0019] Step 8: Calculate the distribution network resilience assessment value based on the weight distribution of technical indicators;

[0020] Step 9: Compare the resilience assessment value with the preset threshold to determine the resilience of the distribution network.

[0021] Furthermore, the three-network model of the distribution network, transportation network and information network in step 1 is defined as:

[0022] Distribution network topology model: Assume that the distribution network has nodes, Edges form a network , the adjacency matrix Represents the reactance value between nodes. When there is no edge, the element is inf.

[0023] Transportation network topology model: Assume that the transportation network has nodes, Edges, adjacency matrix , the elements are weighted values of road length, grade and capacity, and inf is taken when there is no access;

[0024] Information network topology model: Assume that the information network has nodes, Edges, adjacency matrix , the element is the communication distance between nodes, and it is inf when there is no edge.

[0025] Furthermore, the optimal path of the transportation network is calculated using the Floyd algorithm:

[0026] ;

[0027] in, is the direct path length between nodes i and j, is the length of the path through the intermediate node k.

[0028] Furthermore, the three-network coupling in step 2 is achieved through the energy flow and information flow support matrix:

[0029] The distribution network supports the energy flow matrix VPC of the information network: If the distribution network node i supplies power to the information network node j, then Otherwise, 0;

[0030] Information network to distribution network information flow support matrix VCP: If information network node i transmits data to distribution network node j, then Otherwise, 0;

[0031] The definitions of the distribution network's energy flow support matrix VPT for the transportation network and the information network's information flow support matrix VCT for the transportation network are consistent with the above matrix logic.

[0032] Furthermore, the typhoon prediction model in step 3 includes:

[0033] Naive prediction model: Based on linear extrapolation of typhoon historical paths, the formula is:

[0034] ;

[0035] ;

[0036] Among them, Lat1 and Lon1 are the longitude and latitude coordinates of the typhoon eye one hour in the future, Lat0 and Lon0 are the longitude and latitude coordinates of the typhoon eye at the current moment, Lat-1 and Lon-1 are the longitude and latitude coordinates of the typhoon eye in the past hour, and tp is the prediction time step;

[0037] Batts wind field model: Calculates surface wind speed distribution based on typhoon center parameters. The formula is:

[0038] ;

[0039] W ( x ) = { ξ [ 1 − exp ( − ψ x ) ] ω m exp ( − ln β R 7 − R max ( x − R max ) ) 0 0 ≤ x ≤ R max R max ≤ x ≤ R 7 x ＞ R 7 ;

[0040] Among them, Rmax is the maximum wind speed radius of the typhoon, △p is the difference between the central pressure of the typhoon and the standard atmospheric pressure; is the latitude of the typhoon center (unit: degree); is the correction coefficient (value ranges from 0 to 0.4), W(x) is the wind speed at the test site at a distance x from the typhoon center, wm is the measured maximum wind speed 10 meters above the ground, K and β are the typhoon boundary modeling factors, R7 is the radius of the typhoon level 7 wind circle, where .

[0041] Furthermore, the tower failure probability model in step 4 is:

[0042] ;

[0043] in, is the tower failure rate, is the wind speed at the tower location, Design wind speed for tower wind resistance, is the model coefficient.

[0044] Furthermore, the information network delay model in step 5 is:

[0045] ;

[0046] in, is the total transmission delay of information from node i to node i, and They are the service uplink and downlink device transmission delay and the intermediate device transmission delay, is the optical fiber transmission distance, is the refractive index of the optical fiber, is the speed of light in vacuum, N(i, j) is the number of intermediate devices, , ,c= .

[0047] Furthermore, the normalization preprocessing formula of step 7 is:

[0048] ;

[0049] Among them, xnew is the normalized data, xmin and xmax are the minimum and maximum values of the matrix columns respectively.

[0050] Furthermore, the weight distribution of technical indicators in step 8 is achieved through the analytic hierarchy process (AHP), which specifically includes:

[0051] Target layer: the weight of the impact of technical indicators on grid resilience;

[0052] Criteria layer: The weight of load restoration rate (PDS) is 0.6, and the weight of average load loss (ALL) is 0.4.

[0053] Furthermore, the typhoon prediction and resilience assessment calculations in steps 3 and 9 are implemented through the MATLAB platform, and visual results are output.

[0054] Furthermore, the resilience threshold in step 9 is set as the statistical median of historical disaster data.

[0055] A three-network coupled typhoon prediction and power grid resilience assessment system is provided, which is used to assess typhoon prediction and power grid resilience based on three-network coupling (distribution network, transportation network and information network).

[0056] The beneficial effects of the present invention are:

[0057] (1) Through the coupled modeling of the distribution network, transportation network, and information network, the direct damage (such as tower failure) and indirect impact (such as traffic interruption delaying emergency repairs and information delay causing control command lag) of the typhoon on the power system are quantified, achieving a leap from single-grid analysis to multi-grid collaborative assessment, and comprehensively identifying system-level risks;

[0058] (2) Based on the typhoon path prediction model (naive prediction and Batts wind field) and the information network delay dynamic correction mechanism, the disaster evolution process is tracked in real time, the distribution network failure probability and resilience indicators are dynamically updated, and the power system is supported to quickly adjust the response strategy during the disaster process, thereby improving the timeliness of decision-making;

[0059] (3) Through normalization and the analytic hierarchy process (AHP) to objectively assign weights, quantifiable and reproducible resilience assessment results are output to directly guide the priority of grid reinforcement and optimal resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A flowchart of the steps of the invention's method for typhoon prediction and distribution network resilience assessment based on the coupling of three networks: distribution network, transportation network, and information network;

[0061] Figure 2 This is a schematic diagram of three-network coupling according to an embodiment of the present invention;

[0062] Figure 3 This is a topological diagram of a distribution network model according to an embodiment of the present invention;

[0063] Figure 4 This is a topological diagram of a transportation network model according to an embodiment of the present invention;

[0064] Figure 5 This is a topology diagram of the information network model according to an embodiment of the present invention;

[0065] Figure 6 This is a wind speed distribution diagram of towers associated with each node in the distribution network according to an embodiment of the present invention;

[0066] Figure 7This is a probability distribution diagram of tower failures associated with each node in the distribution network according to an embodiment of the present invention;

[0067] Figure 8 This is a model topology diagram when a failure occurs in the information network according to an embodiment of the present invention;

[0068] Figure 9 This is a typhoon path prediction trajectory diagram simulated using MATLAB in an embodiment of the present invention;

[0069] Figure 10 This is a typhoon impact range map simulated using MATLAB in an embodiment of the present invention;

[0070] Figure 11 Schematic diagram of the system toughness trapezoid in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0072] See Figure 1 , provides a three-network coupled typhoon prediction and power grid resilience assessment method, including the following steps:

[0073] Step 1: Establish a three-network model based on the distribution network, transportation network, and information network;

[0074] Step 2: Three-network coupling: The distribution network, transportation network, and information network are connected through a matrix model supported by energy and information flows to establish corresponding relationships between the nodes of the three networks;

[0075] Step 3: Predict the typhoon wind field model using the naive prediction model and the Batts wind field model;

[0076] Step 4: Calculate the impact of the typhoon on the distribution network using the tower fault model associated with the node, and convert the impact of the typhoon on the distribution network into the impact of the typhoon on the information network using the corresponding relationship in step 2;

[0077] Step 5: Using the information network delay model, calculate the delay properties of the information results caused by the damage of the information network, and obtain the typhoon forecast results with delay properties;

[0078] Step 6: Based on the failure probability of distribution network nodes and typhoon forecast results, construct a technical resilience assessment matrix. The technical indicators include:

[0079] System load recovery rate:

[0080] ;

[0081] in, The normal operating load level of the system, is the minimum load level after derating operation, At the initial moment of the disaster, For the moment when the disaster ends;

[0082] System average load loss:

[0083] ;

[0084] in, is the minimum load loss in a single simulation, and N is the total number of simulations;

[0085] Step 7: Perform normalization preprocessing on the resilience assessment matrix;

[0086] Step 8: Calculate the distribution network resilience assessment value based on the weight distribution of technical indicators;

[0087] Step 9: Compare the resilience assessment value with the preset threshold to determine the resilience of the distribution network.

[0088] The three-network model of distribution network, transportation network and information network in step 1 is defined as:

[0089] Distribution network topology model: Assume that the distribution network has nodes, Edges form a network , the adjacency matrix Represents the reactance value between nodes. When there is no edge, the element is inf.

[0090] Transportation network topology model: Assume that the transportation network has nodes, Edges, adjacency matrix , the elements are weighted values of road length, grade and capacity, and inf is taken when there is no access;

[0091] Information network topology model: Assume that the information network has nodes, Edges, adjacency matrix , the element is the communication distance between nodes, and it is inf when there is no edge.

[0092] The optimal path of the transportation network is calculated by the Floyd algorithm:

[0093] ;

[0094] in, is the direct path length between nodes i and j, is the length of the path through the intermediate node k.

[0095] The three-network coupling in step 2 is achieved through the energy flow and information flow support matrix:

[0096] The distribution network supports the energy flow matrix VPC of the information network: If the distribution network node i supplies power to the information network node j, then , otherwise 0;

[0097] Information network to distribution network information flow support matrix VCP: If information network node i transmits data to distribution network node j, then Otherwise, 0;

[0098] The definitions of the distribution network's energy flow support matrix VPT for the transportation network and the information network's information flow support matrix VCT for the transportation network are consistent with the above matrix logic.

[0099] The typhoon prediction model in step 3 includes:

[0100] Naive prediction model: Based on linear extrapolation of typhoon historical paths, the formula is:

[0101] ;

[0102] ;

[0103] Among them, Lat1 and Lon1 are the longitude and latitude coordinates of the typhoon eye one hour in the future, Lat0 and Lon0 are the longitude and latitude coordinates of the typhoon eye at the current moment, and Lat1 and Lon1 are the longitude and latitude coordinates of the typhoon eye at the current moment. -1 , Lon -1 is the latitude and longitude coordinates of the typhoon eye in the past hour, t p is the prediction time step;

[0104] Batts wind field model: Calculates surface wind speed distribution based on typhoon center parameters. The formula is:

[0105] ;

[0106] W ( x ) = { ξ [ 1 − exp ( − ψ x ) ] ω m exp ( − ln β R 7 − R max ( x − R max ) ) 0 0 ≤ x ≤ R max R max ≤ x ≤ R 7 x ＞ R 7 ;

[0107] Among them, Rmax is the maximum wind speed radius of the typhoon, △p is the difference between the central pressure of the typhoon and the standard atmospheric pressure; is the latitude of the typhoon center (unit: degree); ξ is the correction coefficient (range: 0~0.4); W(x) is the wind speed at the test site at a distance x from the typhoon center; wm is the measured maximum wind speed 10 meters above the ground; K and β are the typhoon boundary modeling factors, R7 is the radius of the typhoon level 7 wind circle, .

[0108] The tower failure probability model in step 4 is:

[0109] ;

[0110] in, is the tower failure rate, is the wind speed at the tower location, Design wind speed for tower wind resistance, is the model coefficient.

[0111] The information network delay model in step 5 is:

[0112] ;

[0113] in, is the total transmission delay of information from node i to node i, and They are the service uplink and downlink device transmission delay and the intermediate device transmission delay, is the optical fiber transmission distance, is the refractive index of the optical fiber, is the speed of light in vacuum, N(i, j) is the number of intermediate devices, , ,c= .

[0114] The normalization preprocessing formula of step 7 is:

[0115] ;

[0116] Among them, x new is the normalized data, x min and x max are the minimum and maximum values of the matrix columns, respectively.

[0117] The weight allocation of technical indicators in step 8 is achieved through the analytic hierarchy process (AHP), which specifically includes:

[0118] Target layer: the weight of the impact of technical indicators on grid resilience;

[0119] Criteria layer: The weight of load restoration rate (PDS) is 0.6, and the weight of average load loss (ALL) is 0.4.

[0120] The typhoon prediction and resilience assessment calculations in steps 3 and 9 are implemented through the MATLAB platform, and visual results are output.

[0121] The resilience threshold in step 9 is set as the statistical median of historical disaster data.

[0122] This example uses the coastal city distribution network as the object to simulate the disaster impact before and after the typhoon lands. Figure 2 As shown in the figure, by constructing a three-network coupling model of distribution network, transportation network and information network, combined with typhoon path prediction, tower failure probability calculation, information delay analysis and technical level resilience assessment, the final output is the optimization strategy for grid reinforcement.

[0123] See Figure 3 , build a distribution network model:

[0124] Topology: The improved IEEE33 node system is adopted, with nodes numbered 1-33, where node 1 is the balance node and nodes 2-33 are load nodes.

[0125] Adjacency matrix definition: The adjacency matrix is , the element dᵢⱼ represents the equivalent reactance between nodes (unit: Ω), and is set to inf when there is no edge. For example:

[0126] Reactance between nodes 5 and 6 , current power ;

[0127] There is no direct connection between node 7 and node 20, so .

[0128] Node parameters include:

[0129] Power generation P g : The injection power of the balancing node (node 1) is 5MW.

[0130] Load power P l : The power range of each load node is 0.1~2.5MW. For example, the load of node 6 is 1.8MW.

[0131] Phase angle δ: The initial phase angle is set to 0°, and the tidal current distribution is dynamically calculated using the Newton-Raphson method.

[0132] Connection parameters: line reactance The specific value is set according to the line length and material. For example, the line length of node 5-6 is 2km, and the reactance r = 0.12Ω.

[0133] See Figure 4 , build a transportation network model:

[0134] Topology: Construct a 33-node transportation network, simulating urban main roads, secondary roads and branches. Node 1 is the transportation hub center, and nodes 6-7 are coastal high-risk areas.

[0135] Adjacency matrix definition: Adjacency matrix The weight is calculated by weighting the road length (70%), grade (20%), and traffic capacity (10%). The formula is:

[0136] ;

[0137] in: , : Road grade (1-5, 5 is expressway). For example, the road grade of node 6-7 is 4.

[0138] C ij : Traffic capacity (unit: vehicles / hour), for example, the traffic capacity of node 6-7 vehicles / hour. When there is no access road,

[0139] Path optimization: solve the shortest path based on Floyd algorithm, travel time between nodes, ,in The real-time vehicle speed (set to 20km / h during typhoons).

[0140] See Figure 5 , build an information network model:

[0141] Topology: Design a 21-node communication network, with node 1 as the control center and nodes 6-7 as key coastal monitoring points. Adjacency matrix definition: Adjacency matrix. , the element is the communication distance between nodes (unit: km), and it is inf when there is no edge. For example: the communication distance from node 8 to node 5 ; When nodes 6 and 7 fail, the communication path needs to be detoured to nodes 9-10.

[0142] Communication parameters: Fiber optic transmission speed , fiber refractive index , Equipment transmission delay: service uplink and downlink delay Intermediate device latency .

[0143] Build a naive prediction model:

[0144] Input data: typhoon eye coordinates for the past 6 hours (Table 1), time step =1h;

[0145] Table 1 Typhoon eye coordinate data

[0146]

[0147] Path prediction formula:

[0148] ;

[0149] ;

[0150] For example, the typhoon eye coordinates at the current moment ( , coordinates for the past hour Calculate the path for the next hour:

[0151] ;

[0152] ;

[0153] Wind speed forecast: average wind speed in the past 6 hours Forecast wind speed for the next 6 hours .

[0154] Construct the Batts wind field model prediction results:

[0155] Input parameters: Typhoon center pressure: ; Typhoon center latitude =22.5°; Modeling factor , , correction factor ;

[0156] Calculation of maximum wind speed radius:

[0157] ;

[0158] in, , substituting into:

[0159] ;

[0160] Surface wind speed distribution formula:

[0161] W ( x ) = { ξ [ 1 − exp ( − ψ x ) ] ω m exp ( − ln β R 7 − R max ( x − R max ) ) 0 0 ≤ x ≤ R max R max ≤ x ≤ R 7 x ＞ R 7

[0162] Where: Pick (normalized value);

[0163] ;

[0164] Radius of level 7 wind circle: ;

[0165] MATLAB simulation verification: Generate typhoon tracks, such as Figure 9 As shown, the surface wind speed distribution is as follows Figure 10 As shown, the error compared with the actual meteorological data is <5%.

[0166] See Figure 6-7, calculate the probability of tower failure, fault model parameters: tower wind resistance design threshold ;

[0167] Model coefficients , characterizes the exponential growth rate of wind speed on the failure probability. 4.2 Node failure probability calculation:

[0168] Node 6: Predicting wind speed in Range:

[0169] ;

[0170] Determined to be normal state, failure probability .

[0171] Node 7: Predicting wind speed , then the fault is directly determined at this time: ;

[0172] Visualization results: Figure 7 The failure probability around node 7 is 100%, requiring priority repair; node 6 is marked yellow (low risk).

[0173] Three-network node status synchronization: After the failure of distribution network node 6-7, the transportation network is synchronized through the coupling matrix VPC, VPT, VCP, and VCT;

[0174] The status of the corresponding nodes in the information network: Traffic network node 6-7: The path weight is set to inf, prohibiting passage. Information network node 6-7: The communication distance is set to inf, triggering a detour route.

[0175] Information network delay analysis: see Figure 8 ,Normal state delay: Transmission path: Node 8→5→2→1. Number of intermediate devices: ; Transmission distance: ; Delay calculation:

[0176] ;

[0177] Fault state delay: Transmission path: Node 8→9→10→1 Figure 8 As shown. Number of intermediate devices: ; Transmission distance: ; The delay is calculated as:

[0178] ;

[0179] Among them, the impact of time delay increased by 74%, which may cause control instructions to lag and cause overloaded lines to trip.

[0180] In resilience assessment and decision-making, technical indicators are calculated, load recovery rate (PDS):

[0181] ;

[0182] in, , is the normal operating load level of the system; =45%, which is the minimum load level after derating operation; =2h disaster initial time; = The time when the 24h disaster ends. The average load loss (ALL) is:

[0183] ;

[0184] Z min : Minimum load loss in a single simulation; N=3: Total number of simulations.

[0185] Normalization and weight distribution: Normalization processing:

[0186] ;

[0187] PDS normalized value: 0.25; ALL normalized value: 0.35.

[0188] AHP weight distribution:

[0189] Target layer: the impact weight of technical indicators on grid resilience.

[0190] Criteria layer: load recovery rate weight ; Average load loss weight , the toughness evaluation value is:

[0191] ;

[0192] Threshold comparison and decision output: The preset threshold is based on the median of historical disaster data statistics =0.3; judgment result: S=0.29<0.3, it is judged that the distribution network resilience is insufficient. At this time, the reinforcement strategy is:

[0193] 1. Wind resistance modification of node 7: The tower was replaced, and the designed wind speed resistance was increased to 40m / s, reducing the failure probability to 0;

[0194] 2. Information network routing optimization: The added device uses the path 8→4→1, the number of intermediate devices N(8,1)=2, and the latency is reduced to 0.18s.

[0195] Typhoon path verification: Typhoon tracks generated by MATLAB simulation, such as Figure 9 As shown in the figure, compared with the actual meteorological department data, the maximum error is less than 5%, which verifies the reliability of the naive prediction model.

[0196] Toughness curve analysis: Figure 11 As shown, the system =2h start to run at reduced rating until =Recovery to 90% load level in 24 hours, which is in line with the "resilience trapezoid" theory (pre-disaster prevention → disaster invasion → derating operation → post-disaster recovery).

[0197] Verification of reinforcement effect: Wind resistance modification of node 7: wind speed tolerance increased to 440m / s, failure probability Information network backup path: The delay is optimized to 0.18s, calculated as follows:

[0198] .

[0199] This example demonstrates the complete technical process from typhoon prediction, fault calculation, delay analysis to resilience assessment through a three-network coupling model. The final output strategy can significantly improve the grid's disaster resilience, reducing the probability of high-risk node failure to zero; and reducing information network delay by 43%.

[0200] The toughness rating increased from 0.29 to 0.33 (i.e. This solution has clear engineering application value and can provide technical support for the power system to cope with extreme meteorological disasters.

[0201] The foregoing description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be construed as excluding other embodiments. Rather, the present invention can be used in various other combinations, modifications, and environments and can be modified within the scope of the concept described herein through the above teachings or techniques or knowledge in the relevant field. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be protected by the appended claims.

Claims

1. A three-network coupled typhoon prediction and power grid resilience assessment method, characterized in that: The following steps are involved: Step 1: Establish a three-network model based on the distribution network, transportation network, and information network; Step 2: Three-network coupling: The distribution network, transportation network, and information network are connected through a matrix model supported by energy and information flows to establish corresponding relationships between the nodes of the three networks; Step 3: Predict the typhoon wind field model using the naive prediction model and the Batts wind field model; Step 4: Calculate the impact of the typhoon on the distribution network using the tower fault model associated with the node, and convert the impact of the typhoon on the distribution network into the impact of the typhoon on the information network using the corresponding relationship in step 2; Step 5: Using the information network delay model, calculate the delay properties of the information results caused by the damage of the information network, and obtain the typhoon forecast results with delay properties; Step 6: Based on the failure probability of distribution network nodes and typhoon forecast results, construct a technical resilience assessment matrix. The technical indicators include: System load recovery rate: ； in, The normal operating load level of the system, is the minimum load level after derating operation, At the initial moment of the disaster, For the moment when the disaster ends; System average load loss: ； in, is the minimum load loss in a single simulation, and N is the total number of simulations; Step 7: Perform normalization preprocessing on the resilience assessment matrix; Step 8: Calculate the distribution network resilience assessment value based on the weight distribution of technical indicators; Step 9: Compare the resilience assessment value with the preset threshold to determine the resilience of the distribution network.

2. The three-network coupled typhoon prediction and power grid resilience assessment method according to claim 1, characterized in that: The three-network model of distribution network, transportation network and information network in step 1 is defined as: Distribution network topology model: Assume that the distribution network has nodes, Edges form a network , the adjacency matrix Represents the reactance value between nodes. When there is no edge, the element is inf. Transportation network topology model: Assume that the transportation network has nodes, Edges, adjacency matrix , the elements are weighted values of road length, grade and capacity, and inf is taken when there is no access; Information network topology model: Assume that the information network has nodes, Edges, adjacency matrix , the element is the communication distance between nodes, and it is inf when there is no edge.

3. The three-network coupled typhoon prediction and power grid resilience assessment method according to claim 2, characterized in that: The optimal path of the transportation network is calculated by the Floyd algorithm: ； in, is the direct path length of node i, j, is the length of the path through the intermediate node k.

4. The three-network coupled typhoon prediction and power grid resilience assessment method according to claim 2, characterized in that: The three-network coupling in step 2 is achieved through the energy flow and information flow support matrix: The distribution network supports the energy flow matrix VPC of the information network: If the distribution network node i supplies power to the information network node j, then Otherwise, 0; Information network to distribution network information flow support matrix VCP: If information network node i transmits data to distribution network node j, then Otherwise, 0; The definitions of the distribution network's energy flow support matrix VPT for the transportation network and the information network's information flow support matrix VCT for the transportation network are consistent with the above matrix logic.

5. The three-network coupled typhoon prediction and power grid resilience assessment method according to claim 1, characterized in that: The typhoon prediction model in step 3 includes: Naive prediction model: Based on linear extrapolation of typhoon historical paths, the formula is: ； ； Among them, Lat1 and Lon1 are the longitude and latitude coordinates of the typhoon eye one hour in the future, Lat0 and Lon0 are the longitude and latitude coordinates of the typhoon eye at the current moment, and Lat1 and Lon1 are the longitude and latitude coordinates of the typhoon eye at the current moment. -1 , Lon -1 is the latitude and longitude coordinates of the typhoon eye in the past hour, t p is the prediction time step; Batts wind field model: Calculates surface wind speed distribution based on typhoon center parameters. The formula is: ； ； Among them, Rmax is the maximum wind speed radius of the typhoon, △p is the difference between the central pressure of the typhoon and the standard atmospheric pressure; is the latitude of the typhoon center, is the correction coefficient, W(x) is the wind speed at the test site at a distance x from the typhoon center, wm is the measured maximum wind speed 10 meters above the ground, K and β are the typhoon boundary modeling factors, and R7 is the radius of the typhoon level 7 wind circle.

6. The three-network coupled typhoon prediction and power grid resilience assessment method according to claim 1, characterized in that: The tower failure probability model in step 4 is: ； in, is the tower failure rate, is the wind speed at the tower location, Design wind speed for tower wind resistance, is the model coefficient.

7. The three-network coupled typhoon prediction and power grid resilience assessment method according to claim 1, characterized in that: The information network delay model in step 5 is: ； in, is the total transmission delay of information from node i to node i, and They are the service uplink and downlink device transmission delay and the intermediate device transmission delay, is the optical fiber transmission distance, is the refractive index of the optical fiber, is the speed of light in vacuum, and N(i, j) is the number of intermediate devices.

8. The three-network coupled typhoon prediction and power grid resilience assessment method according to claim 1, characterized in that: The normalization preprocessing formula of step 7 is: ； Among them, x new is the normalized data, x min and x max are the minimum and maximum values of the matrix columns, respectively.

9. The three-network coupled typhoon prediction and power grid resilience assessment method according to claim 1, characterized in that: The weight allocation of technical indicators in step 8 is achieved through the hierarchical analysis method, which specifically includes: Target layer: the weight of the impact of technical indicators on grid resilience; Criteria layer: The weight of load recovery rate is 0.6, and the weight of average load loss is 0.

4.

10. The three-network coupled typhoon prediction and power grid resilience assessment method according to claim 1, characterized in that: The typhoon prediction and resilience assessment calculations in steps 3 and 9 are implemented through the MATLAB platform, and visual results are output.

11. The three-network coupled typhoon prediction and power grid resilience assessment method according to claim 1, characterized in that: The resilience threshold in step 9 is set as the statistical median of historical disaster data.

12. A three-network coupled typhoon prediction and power grid resilience assessment system, characterized in that: The system utilizes a three-network coupled typhoon prediction and power grid resilience assessment method as described in any one of claims 1 to 11, and is used for assessing typhoon prediction and power grid resilience based on three-network coupling.

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