Three-network coupled typhoon prediction and power grid toughness evaluation method

Abstract

The invention discloses a three-network coupled typhoon prediction and power grid toughness evaluation method, and belongs to the field of typhoon prediction and power distribution network evaluation. The method comprises a typhoon wind field path prediction method and a power distribution network toughness evaluation method, and the typhoon wind field path prediction method comprises the following steps: coupling three networks through an information flow matrix, calculating the influence of typhoon on an information network and a traffic network, and further calculating the influence of the information network and the traffic network on a power distribution network; on the basis of considering the influences, a naive prediction model and a Batts wind field model are used for calculation to obtain a typhoon prediction result with a time shifting property; and calculating through a three-network coupling relationship to obtain a power distribution network toughness evaluation result with a time shifting property. Compared with the prior art, the method has the advantages that a toughness evaluation system established by the method is more comprehensive, wider in consideration range and higher in applicability, and a power system can make a response decision more quickly.

Description

Technical Field

[0001] The present invention relates to the technical fields of typhoon prediction and distribution network assessment, and particularly relates to a typhoon prediction and power grid resilience assessment method with triple-network coupling. Background Art

[0002] In the context of frequent natural disasters such as typhoons, it is particularly important to comprehensively consider triple-network coupling to achieve typhoon prediction and distribution network resilience assessment. After a typhoon passes through a city, it will not only cause serious damage to distribution network facilities, resulting in power outages and equipment damage to the distribution network, but also have an impact on the urban transportation network and information network, leading to serious traffic jams and information delays, which indirectly exacerbates the impact on the urban distribution network. Therefore, typhoon prediction and distribution network resilience assessment aim to study the typhoon path and the risk resistance ability of the distribution network in typhoon disasters, so as to put forward corresponding improvement suggestions and emergency measures, thereby enhancing the disaster resistance ability of the distribution network. The existing content on typhoon prediction and resilience assessment is relatively single and not comprehensive enough. There is no relevant research based on triple-network coupling, and there is a lack of a comprehensive resilience assessment system that considers public influence relatively comprehensively. Summary of the Invention

[0003] The purpose of the present invention is to overcome one or more deficiencies of the prior art and provide a typhoon prediction and power grid resilience assessment method with triple-network coupling. Specifically, the present invention solves how to decide the quality and efficiency of power grid maintenance and reinforcement.

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

[0005] A typhoon prediction and power grid resilience assessment method with triple-network coupling includes the following steps:

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

[0007] Step 2: Triple-network coupling: Establish a corresponding relationship between the nodes of the three networks through a matrix model supported by energy flow and information flow for the distribution network, transportation network, and information network;

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

[0009] Step 4: Calculate the impact of the typhoon on the distribution network through a tower pole failure model associated with the nodes, and convert the impact of the typhoon on the distribution network into the impact of the typhoon on the information network through the corresponding relationship in Step 2;

[0010] Step 5: Calculate the delay property of the information result caused by the damage of the information network through an information network delay model to obtain a typhoon prediction result with delay property;

[0011] Step 6: Based on the fault probability of the distribution network nodes and the typhoon prediction results, construct a resilience evaluation matrix at the technical level. The technical level indicators include:

[0012] System load recovery rate:

[0013] ;

[0014] where is the normal operating load level of the system, is the minimum load level after derated operation, is the initial moment of the disaster, is the end moment of the disaster;

[0015] System average load loss:

[0016] ;

[0017] where is the minimum load shedding amount of a single simulation, and N is the total number of simulations;

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

[0019] Step 8: Calculate the distribution network resilience evaluation value based on the technical index weight assignment;

[0020] Step 9: Compare the resilience evaluation value with the preset threshold to judge the strength of the distribution network resilience.

[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 and connected edges, forming a network . The adjacency matrix represents the reactance value between nodes, and the element is inf when there is no connected edge;

[0023] Transportation network topology model: Assume that the transportation network has nodes and connected edges, and the adjacency matrix , and the element is the weighted value of the road length, grade, and traffic capacity, and takes inf when there is no passage;

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

[0025] Further, the optimal path of the transportation network is calculated by the Floyd algorithm:

[0026] ;

[0027] Among them, is the direct path length between nodes i and j, is the path length via the intermediate node k.

[0028] Further, the three-network coupling in step 2 is realized through the energy flow and information flow support matrices:

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

[0030] The information flow support matrix VCP of the information network for the distribution network: If the information network node i transmits data to the distribution network node j, then Otherwise, it is 0;

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

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

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

[0034] ;

[0035] ;

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

[0037] Batts wind field model: Calculate the surface wind speed distribution according to the 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, and △p is the difference between the typhoon center pressure and the standard atmospheric pressure; is the typhoon center latitude value (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 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 equipment transmission delay and the intermediate equipment 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 normalized 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 allocation of technical indicators in step 8 is implemented by 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] Further, the typhoon prediction and resilience assessment calculations in steps 3 and 9 are implemented through the MATLAB platform and the visualization results are output.

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

[0055] A typhoon prediction and power grid resilience assessment system with triple-network coupling is provided, and the system is used to evaluate typhoon prediction and power grid resilience based on triple-network coupling (distribution network, transportation network, and information network).

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

[0057] (1) Through the triple-network coupling modeling of the distribution network, transportation network, and information network, the direct damage (such as pole failures) and indirect impacts of typhoons on the power system (such as traffic interruptions delaying repair work and information delays resulting in lagging control instructions) are quantified, achieving a leap from single-power-grid analysis to multi-network 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, and the distribution network fault probability and resilience indicators are dynamically updated, supporting the power system to quickly adjust response strategies during the disaster occurrence process and improving the timeliness of decision-making;

[0059] (3) By objectively assigning weights through normalization and the analytic hierarchy process (AHP), a quantifiable and reproducible resilience assessment result is output, directly guiding the power grid reinforcement priority and resource optimization allocation. Description of the Drawings

[0060] Figure 1 It is a flowchart of the steps of the typhoon prediction and distribution network resilience assessment method with triple-network coupling of the distribution network, transportation network, and information network provided by the invention;

[0061] Figure 2 It is a schematic diagram of triple-network coupling of the embodiments of the present invention;

[0062] Figure 3 It is a topological diagram of the distribution network model of the embodiments of the present invention;

[0063] Figure 4 It is a topological diagram of the transportation network model of the embodiments of the present invention;

[0064] Figure 5 It is a topological diagram of the information network model of the embodiments of the present invention;

[0065] Figure 6 It is a wind speed distribution diagram of the associated poles of each node of the distribution network of the embodiments of the present invention;

[0066] Figure 7Distribution map of the failure probability of the poles associated with each node of the distribution network in the embodiment of the present invention;

[0067] Figure 8 Topological model diagram when a failure occurs in the information network in the embodiment of the present invention;

[0068] Figure 9 Typhoon path prediction trajectory diagram simulated by MATLAB in the embodiment of the present invention;

[0069] Figure 10 Typhoon influence range diagram simulated by MATLAB in the embodiment of the present invention;

[0070] Figure 11 Schematic diagram of the system resilience trapezoid in the embodiment of the present invention. Detailed implementation manners

[0071] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0072] Refer to Figure 1 , and a typhoon prediction and power grid resilience evaluation method for triple-network coupling is provided, including the following steps:

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

[0074] Step 2: Triple-network coupling: Establish a corresponding relationship between the nodes of the triple network through a matrix model supported by energy flow and information flow for the distribution network, transportation network, and information network;

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

[0076] Step 4: Calculate the impact of the typhoon on the distribution network through the pole failure 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 through the corresponding relationship in Step 2;

[0077] Step 5: Calculate the delay nature of the information result caused by the damage of the information network through the information network delay model to obtain a typhoon prediction result with delay nature;

[0078] Step 6: Based on the failure probability of the distribution network nodes and the typhoon prediction result, construct a resilience evaluation matrix at the technical level, and the technical level indicators include:

[0079] System load recovery rate:

[0080] ;

[0081] Among them, is the normal operating load level of the system, is the lowest load level after derating operation, is the initial moment of the disaster, is the end moment of the disaster;

[0082] System average load loss:

[0083] ;

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

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

[0086] Step 8: Calculate the resilience evaluation value of the distribution network based on the technical index weight allocation;

[0087] Step 9: Compare the resilience evaluation value with the preset threshold to judge the strength of the distribution network resilience.

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

[0089] Distribution network topology model: Assume that the distribution network has a total of nodes, connected edges, forming a network , and the adjacency matrix represents the reactance value between nodes. When there is no connected edge, the element is inf;

[0090] Transportation network topology model: Assume that the transportation network has a total of nodes, connected edges, and the adjacency matrix , and the element is the weighted value of the road length, grade, and traffic capacity. When there is no passage, take inf;

[0091] Information network topology model: Assume that the information network has a total of nodes, connected edges, and the adjacency matrix , and the element is the communication distance between nodes. When there is no connected edge, take inf.

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

[0093] ;

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

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

[0096] The distribution network supports the information network energy flow matrix VPC: 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 the typhoon's historical path, the formula is:

[0101] ;

[0102] ;

[0103] Among them, Lat1, Lon1 are the longitude and latitude coordinates of the typhoon eye one hour in the future, Lat0, Lon0 are the longitude and latitude coordinates of the typhoon eye at the current moment, and Lat1, 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: Calculate the surface wind speed distribution based on the 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 (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, respectively; R7 is the radius of the typhoon level 7 wind circle; .

[0108] The pole failure probability model in step 4 is as follows:

[0109] ;

[0110] where is the pole failure rate, is the wind speed at the location of the pole, is the wind design speed of the pole, is the model coefficient.

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

[0112] ;

[0113] where is the total transmission delay of information from node i to i, and are the transmission delays of the uplink and downlink devices and the intermediate device respectively, 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 in step 7 is:

[0115] ;

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

[0117] The weight assignment of the technical indicators in step 8 is achieved through the Analytic Hierarchy Process (AHP), specifically including:

[0118] Goal layer: The influence weight of technical indicators on the resilience of the power grid;

[0119] Criterion layer: The weight of the load recovery rate (PDS) is 0.6, and the weight of the 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 the visualization results are output.

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

[0122] This embodiment takes the coastal city distribution network as the object to simulate the disaster impacts before and after the typhoon landing. As Figure 2 shown, by constructing a three-network coupling model of the distribution network, transportation network, and information network, combining typhoon path prediction, pole and tower failure probability calculation, information delay analysis, and resilience assessment at the technical level, an optimized strategy for power grid reinforcement is finally output.

[0123] Refer to Figure 3 to construct a distribution network model:

[0124] Topological structure: The improved IEEE 33-node system is adopted, with node numbers 1 - 33, where node 1 is the slack node and nodes 2 - 33 are load nodes.

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

[0126] The reactance between node 5 and node 6 , and the power flow ;

[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 slack node (node 1) is 5 MW.

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

[0131] Phase angle δ: The initial phase angles are all set to 0°, and the power flow distribution is dynamically calculated by the Newton - Raphson method.

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

[0133] Refer to Figure 4 to construct a transportation network model:

[0134] Topological structure: A 33-node transportation network is constructed to simulate the main roads, secondary roads, and branch roads in the city. Node 1 is the transportation hub center, and nodes 6 - 7 are high-risk coastal areas.

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

[0136] ;

[0137] Among them: , : Road grade (levels 1 - 5, level 5 is the highway), for example, the road grade between nodes 6 - 7 is level 4.

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

[0139] Path optimization: Solve the shortest path based on the Floyd algorithm, and the travel time between nodes, , where is the real-time vehicle speed (set to 20 km / h during typhoon).

[0140] Refer to Figure 5 , and construct an information network model:

[0141] Topology: Design a 21-node communication network, with node 1 as the control center and nodes 6 - 7 as the coastal key monitoring points; Definition of the adjacency matrix: The adjacency matrix , and the elements are the communication distances between nodes (unit: km), taking inf when there is no connection edge. For example: The communication distance from node 8 to node 5 ; When nodes 6 and 7 fail, the communication path needs to detour to nodes 9 - 10.

[0142] Communication parameters: Optical fiber transmission speed , optical fiber refractive index , equipment transmission delay: Service uplink and downlink delay Intermediate equipment delay .

[0143] Construct a naive prediction model:

[0144] Input data: The eye coordinates of the typhoon in 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 ( , the coordinates in the past 1 hour Calculate the path in the next 1 hour:

[0151] ;

[0152] ;

[0153] Wind speed prediction: The average wind speed in the past 6 hours The predicted wind speed in the next 6 hours .

[0154] Build the prediction results of the Batts wind field model:

[0155] Input parameters: Central pressure of the typhoon: ; Central latitude of the typhoon = 22.5°; Modeling factor , , correction coefficient ;

[0156] Calculation of the maximum wind speed radius:

[0157] ;

[0158] Among them, , substituting it in, we get:

[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] Among them: ξ Take (normalized value);

[0163] ;

[0164] Radius of the 7th-level wind circle: ;

[0165] MATLAB simulation verification: Generate the typhoon track, as Figure 9 shown, and the surface wind speed distribution is as Figure 10 shown. The comparison error with the actual meteorological data is <5%.

[0166] Refer to Figures 6 - 7, calculate the failure probability of the pole tower, and the failure model parameters: the wind resistance design threshold of the pole tower ;

[0167] Model coefficient , representing the exponential growth rate of wind speed on the failure probability. 4.2 Calculation of node failure probability:

[0168] Node 6: Predicted wind speed is in interval:

[0169] ;

[0170] It is determined to be in the normal state, and the failure probability .

[0171] Node 7: Predicted wind speed , then directly determine the failure at this time: ;

[0172] Visualization result: Figure 7 The failure probability around Node 7 is shown as 100%, and it needs to be repaired preferentially; Node 6 is marked yellow (low risk).

[0173] Synchronization of the status of the three-network nodes: After the failure of nodes 6-7 in the distribution network, synchronize the traffic network through the coupling matrices VPC, VPT, VCP, and VCT;

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

[0175] Analysis of information network delay: Refer to Figure 8 , normal state delay: Transmission path: Node 8 → 5 → 2 → 1. Number of intermediate devices: ; Transmission distance: ; Delay calculation:

[0176] ;

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

[0178] ;

[0179] Among them, the delay impact increases by 74%, which may cause the control instruction to lag and trigger the tripping of the overloaded line.

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

[0181] ;

[0182] where , is the normal operating load level of the system; = 45%, is the lowest load level after derated operation; = 2 h, is the initial moment of the disaster; = 24 h, is the end moment of the disaster. The average load loss (ALL) is:

[0183] ;

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

[0185] Normalization and weight assignment: Normalization processing:

[0186] ;

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

[0188] AHP weight assignment:

[0189] Goal layer: The influence weight of technical indicators on the power grid resilience.

[0190] Criterion layer: The weight of the load recovery rate ; The weight of the average load loss , and the resilience 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 resilience of the distribution network is insufficient. At this time, the reinforcement strategy is:

[0193] 1. Wind resistance transformation of node 7: Replace the pole tower, design the wind resistance speed to be increased to 40 m / s, and the failure probability is reduced to 0;

[0194] 2. Information network routing optimization: Add a standby path 8 → 4 → 1, the number of intermediate devices N(8, 1) = 2, and the time delay is reduced to 0.18 s.

[0195] Typhoon path verification: The typhoon trajectory generated by MATLAB simulation, as Figure 9 shown; Compared with the actual meteorological department data, the maximum error < 5%, verifying the reliability of the naive prediction model.

[0196] Analysis of the resilience curve: As Figure 11 shown, it shows that the system starts to derate from = 2 h and recovers to 90% load level at = 24 h, which conforms to the "resilience trapezoid" theory (pre-disaster prevention → disaster invasion → derating operation → post-disaster recovery).

[0197] Verification of reinforcement effect: Wind resistance transformation of node 7: Wind speed tolerance is increased to 440 m / s, and the failure probability ; Spare path of the information network: Delay is optimized to 0.18 s, and the calculation formula is:

[0198] .

[0199] In this embodiment, through the three-network coupling model, the technical process from typhoon prediction, fault calculation, delay analysis to resilience assessment is fully demonstrated. The final output strategy can significantly improve the disaster resistance ability of the power grid, reduce the failure probability of high-risk nodes to 0; reduce the delay of the information network by 43%;

[0200] The resilience assessment value is 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 above is only the preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be changed within the scope of the concept described herein through the above teachings or the technology or knowledge in related fields. And the changes and modifications made by those skilled in the art without departing from the spirit and scope of the present invention should all be within the protection scope of the appended claims of the present invention.

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 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 flow and information flow to establish the corresponding relationship 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 through 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 through the corresponding relationship in step 2; Step 5: Calculate the time delay property of the information results caused by the damage of the information network through the information network delay model, and obtain the typhoon prediction results with time delay properties; Step 6: Based on the failure probability of distribution network nodes and typhoon forecast results, a technical resilience assessment matrix is ​​constructed. 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 technical indicator weight allocation; 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 the distribution network, transportation network and information network in step 1 is defined as: Distribution network topology model: Assume that the distribution network has nodes, The 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 takes 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 between nodes i and j, is the path length 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 realized by the energy flow and information flow support matrix: The distribution network supports the information network energy flow matrix VPC: 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 the typhoon's historical path, the formula is: ； ； Among them, Lat1, Lon1 are the longitude and latitude coordinates of the typhoon eye one hour in the future, Lat0, Lon0 are the longitude and latitude coordinates of the typhoon eye at the current moment, and Lat1, 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: Calculate the surface wind speed distribution based on the 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 typhoon center latitude, 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 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 equipment transmission delay and the intermediate equipment 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 step 3 and step 9 are implemented through the MATLAB platform, and the 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 uses a three-network coupled typhoon prediction and power grid resilience assessment method as described in claims 1 to 11, which is used to assess typhoon prediction and power grid resilience based on three-network coupling.

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