Multi-dimensional resilience assessment method for one-two coupled power system under snowstorm and freezing disaster

Abstract

The present application relates to a method for evaluating the multi-dimensional resilience of a primary-secondary coupled power system under snow and ice disaster, comprising: obtaining the meteorological parameters of the power system to be evaluated and the snow and ice disaster; based on the meteorological parameters, constructing a synchronous damage model of primary-secondary equipment under snow and ice disaster to obtain the failure probability of primary-side equipment and the failure probability of secondary-side equipment, thereby determining the initial fault equipment set of the power system to be evaluated; based on the initial fault equipment set, constructing a cascading failure iteration deduction model of primary-secondary coupling, performing iteration deduction until the convergence condition is met; based on the state data of the power system to be evaluated recorded in each round of iteration, calculating the multi-dimensional resilience index according to the resilience evaluation index system covering the three time dimensions of before disaster, during disaster and after disaster, to evaluate the resilience level of the power system to be evaluated under the disaster scenario corresponding to the meteorological parameters. The method can significantly improve the accuracy and practicality of the resilience evaluation of the power system under snow and ice disaster.

Description

Technical Field

[0001] This invention belongs to the field of power system technology, specifically relating to a multidimensional resilience assessment method for secondary coupled power systems under rain, snow, and ice disasters. Background Technology

[0002] With the rapid development of new power systems and the continuous expansion of power grid scale, along with the large-scale integration of new energy sources such as wind power and photovoltaics, the operating characteristics of power systems are becoming increasingly complex. At the same time, the deep integration of power systems and communication systems has resulted in modern power grids exhibiting significant cyber-physical coupling characteristics—the operation of the primary system (power grid) depends on the monitoring and control of the secondary system (communication network), while the operation of the secondary system depends on the energy supply of the primary system.

[0003] Rain, snow, and ice storms are among the major natural disasters threatening the safe operation of power systems. In such weather, excessive icing on transmission lines can lead to conductor breakage and tower collapse. Simultaneously, the fiber-optic composite overhead ground wire (OPGW) attached to transmission lines may also break due to excessive icing. Physical damage to primary equipment directly alters the power grid topology and causes power outages, while physical damage to secondary equipment disrupts communication links, causing the dispatch center to lose "observability" and "controllability" over certain areas. More seriously, faults in the primary and secondary systems can interact and amplify each other: communication interruptions prevent the dispatch center from timely controlling the power grid, potentially triggering new overload trips; and power outages can cause communication equipment attached to substations to lose power, further expanding communication blind spots. This two-way "physical-information" coupled fault propagation mechanism makes the disaster process of the power system under rain, snow, and ice storms extremely complex.

[0004] Currently, methods for assessing the resilience of power systems typically focus on fault analysis of single systems or assess the system's defense and recovery capabilities solely from the perspective of static indicators. However, existing methods struggle to comprehensively reflect the integrated defense and recovery level of power systems under rain, snow, and ice disasters. They lack an assessment indicator system covering the entire process of pre-disaster defense, disaster resistance, and post-disaster recovery, and cannot describe the changes in the system's resilience level over all time periods. More importantly, existing technologies lack a systematic analysis of the evolution process of primary and secondary coupled cascading faults, considering only isolated primary system physical faults or secondary system communication faults. They ignore the interaction between the two during the disaster evolution process and the cascading effect of faults, leading to insufficient accuracy in assessing grid sensing and transmission capabilities, and significant deviations between resilience assessment results and actual conditions.

[0005] In summary, the biggest problem with existing technologies is the lack of systematic modeling of the synchronous damage and bidirectional coupling fault evolution process of secondary systems under rain, snow, and ice disasters. This makes it impossible to accurately characterize the interactive effects and chain-reaction effects between communication failures and physical faults. At the same time, there is also a lack of a multi-dimensional comprehensive evaluation system to systematically characterize the resilience of power systems coupled with secondary systems under rain, snow, and ice disasters. This leads to inaccurate power grid resilience assessment results and makes it difficult to provide reliable decision-making basis for disaster prevention and resilience improvement of the power grid. Summary of the Invention

[0006] To address the aforementioned problems in the existing technology, this invention provides a multidimensional resilience assessment method for secondary coupled power systems under rain, snow, and freezing disasters. The technical problem to be solved by this invention is achieved through the following technical solution: This invention provides a multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and freezing disasters, comprising: S1: Obtain meteorological parameters of the power system to be evaluated, as well as rain, snow and ice disasters. The meteorological parameters include historical observation data, weather forecast data or hypothetical disaster scenario data. S2: Based on the meteorological parameters, construct a synchronous damage model of primary and secondary equipment under rain, snow and ice disasters to obtain the failure probability of primary equipment and the failure probability of secondary equipment, thereby determining the initial set of faulty equipment in the power system to be evaluated; S3: Based on the initial set of faulty devices, construct a cascaded fault iteration model with primary and secondary coupling, and execute the iterative process of "dynamic evolution and observability determination of communication network - scheduling decision and physical response - bidirectional propagation of primary and secondary coupled faults" until the convergence condition is met; S4: Based on the state data of the power system to be evaluated recorded in each round during the iteration process, calculate the multidimensional resilience index according to the resilience assessment index system covering the three time dimensions of pre-disaster, during-disaster and post-disaster, so as to assess the resilience level of the power system to be evaluated under the disaster scenario corresponding to the meteorological parameters.

[0007] Compared with the prior art, the beneficial effects of the present invention are as follows: To address the biggest problem with existing technologies, this invention provides a multi-dimensional resilience assessment method for cyber-physical coupled power systems under rain, snow, and ice disasters. By acquiring multi-source meteorological parameters such as historical observations, weather forecasts, or hypothetical disaster scenarios, a synchronous damage model of secondary and primary equipment under rain, snow, and ice disasters is constructed to accurately recreate the initial impact of the disaster on the cyber-physical coupled system. Then, based on the initial set of faulty equipment, an iterative model of cascading faults in primary and secondary coupling is constructed. Through iterative execution of the process of "dynamic evolution and observability determination of the communication network—scheduling decision and physical response—bidirectional propagation of primary and secondary coupled faults," the dynamic evolution process of mutual induction and cascading amplification of communication interruptions and physical faults is fully simulated. Finally, based on the state data recorded in each round of iteration, multi-dimensional resilience indicators are calculated according to a resilience assessment index system covering three time dimensions: pre-disaster, during-disaster, and post-disaster, achieving a full-cycle quantitative assessment from defense potential and resistance capacity to recovery efficiency. This invention systematically solves the problems of missing coupling modeling, single assessment dimensions, and neglect of dynamic processes in existing technologies, significantly improving the accuracy and practicality of power system resilience assessment under rain, snow, and ice disasters. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating the multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and ice disasters provided in this embodiment of the invention. Figure 2 This is a detailed flowchart of S3 provided in the embodiment of the present invention. Detailed Implementation

[0009] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0010] The following is a detailed description of a multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and ice disasters, proposed in this invention, with reference to the accompanying drawings.

[0011] Figure 1 This is a flowchart illustrating the multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and ice disasters provided in this embodiment of the invention. Figure 1 As shown, the method includes S1-S4; specifically: S1: Obtain meteorological parameters for the power system to be evaluated, as well as for rain, snow and ice disasters. Meteorological parameters include historical observation data, weather forecast data or hypothetical disaster scenario data.

[0012] S2: Based on meteorological parameters, construct a synchronous damage model of primary and secondary equipment under rain, snow and ice disasters to obtain the failure probability of primary equipment and the failure probability of secondary equipment, thereby determining the initial set of faulty equipment in the power system to be evaluated.

[0013] Specifically, S2 includes: S2.1: Construct a primary-side failure probability model to calculate the failure probability of primary-side equipment in the power system to be evaluated, and obtain multiple primary-side equipment failure probabilities; the primary-side equipment is the transmission line in the primary power system topology of the power system to be evaluated, and the transmission line includes at least conductors, towers, and insulators.

[0014] In one possible implementation, the primary-side failure probability model is calculated using the following formula:

[0015] In the formula, Let be the failure probability of the primary side equipment of the transmission line at time t. Let be the probability of conductor breakage in the transmission line at time t. Let be the probability of the tower collapsing under snow cover at time t. Let t be the probability that a transmission line will fail due to wind damage during an ice storm. Let t be the probability of insulator flashover due to icing on the transmission line.

[0016] It should be noted that the probabilities involved on the right side of the equation (such as the probability of fracture, the probability of collapse, etc.) all have mature calculation methods. These methods are based on existing research results in the fields of mechanics, statistics, or electrical engineering, and are common knowledge to those skilled in the art or can be obtained through existing literature.

[0017] S2.2: Construct a secondary side failure probability model to calculate the failure probability of the secondary side equipment of the power system to be evaluated, and obtain the failure probabilities of multiple secondary side equipment; the secondary side equipment includes at least optical cables.

[0018] In one possible implementation, the secondary failure probability model is calculated using the following formula:

[0019] In the formula, It is the failure probability of the secondary side equipment. It refers to the reliability of optical cables under icing conditions. It is a node in the secondary power system communication network. It is the set of all optical links in the system.

[0020] It should be noted that all the values ​​involved on the right side of the equation have mature calculation methods. These methods are based on existing research results in the fields of mechanics, statistics, or electrical engineering, and are common knowledge to those skilled in the art or can be obtained from existing literature.

[0021] S2.3: Based on the failure probabilities of multiple primary-side devices and multiple secondary-side devices, the fault status of each device is determined by a random simulation method, and all devices identified as faulty are combined into an initial set of faulty devices for the power system to be evaluated.

[0022] For example, in S2.1 The value is 0.2, in S2.2 The value is 0.08; generate a random number in the interval [0,1], let's say 0.12; 0.12 is less than 0.2 and greater than 0.08, meaning that if the random number represents a primary-side device, it means that the device is faulty, and if it represents a secondary-side device, it means that the device is functioning normally. Only when the random number is greater than... Or the random number is greater than In this case, the primary or secondary device represented by the random number is normal.

[0023] S3: Based on the initial set of faulty devices, construct a cascaded fault iteration model with primary and secondary coupling. Through the iterative process of “dynamic evolution and observability determination of communication network - scheduling decision and physical response - bidirectional propagation of primary and secondary coupled faults”, until the convergence condition is met.

[0024] Here, after obtaining the initial set of faulty devices, the system iteratively simulates the changes in power system faults under rain, snow, and ice disasters, observing the impact of the "physical-information" bidirectional coupling fault propagation mechanism (e.g., communication interruptions prevent the dispatch center from timely controlling the power grid, potentially triggering new overload trips; while power grid outages can lead to power loss for communication equipment attached to substations, further expanding the communication blind spot), until the power system reaches a new steady state or completely loses its power supply capacity. Because this iterative simulation process involves complex interactions between multiple sub-steps, the following section will combine... Figure 2 Provide a detailed description.

[0025] It should be noted that the power system to be evaluated includes both primary and secondary power system topologies. The primary power system topology includes a set of physical nodes and a set of physical branches, with each physical node corresponding to a substation or power plant. The secondary power system topology includes a set of communication nodes and a set of communication links, with each communication node corresponding to a communication device. Both primary and secondary power system topologies are known topologies pre-generated based on the actual structure of the power grid to be evaluated.

[0026] Figure 2 This is a detailed flowchart of S3 provided in an embodiment of the present invention. For example... Figure 2 As shown, S3 includes S3.1-S3.8; specifically: S3.1: Based on the current communication network topology and the communication link failure information in the set of faulty devices updated in the previous round, a communication network traffic redistribution and overload cascading model based on node degree is adopted to simulate the cascading failure of communication links caused by traffic overload or physical damage, and to calculate the end-to-end communication delay from each node to the scheduling center; wherein, in the first iteration, the set of faulty devices updated in the previous round is the initial set of faulty devices.

[0027] Specifically, S3.1 includes: S3.1.1: Remove the set of faulty devices updated in the previous round from the secondary power system topology to obtain the current communication network topology; wherein, in the first iteration, the set of faulty devices updated in the previous round is the initial set of faulty devices.

[0028] S3.1.2: Based on the degree values ​​of each communication node in the current communication network topology, recalculate the initial information flow of each communication link.

[0029] It should be understood that in complex networks, the higher the degree of a node, the more connections it has with other nodes. In this case, the degree value of the endpoint is used to calculate the information flow of each access layer link. The higher the degree value of the link, the greater the information flow.

[0030] Here, the expression for calculating the initial information flow is:

[0031] In the formula, Let i be the initial information flow of the communication link formed by communication nodes i and j. Let i and j represent the degree values ​​of communication node i and communication node j, respectively. These are parameters for flow rate regulation.

[0032] S3.1.3: Based on the communication link failure information in the set of faulty devices updated in the previous round, identify the communication links that have failed due to physical damage, and redistribute the corresponding information flow to the adjacent normal links according to the preset allocation strategy.

[0033] For example, when communication link ij is disconnected due to a fault, the traffic it carries is redistributed according to the principle of local reallocation. The load of neighboring normal links ia is allocated to node i according to the initial load proportion of neighboring links. The proportion allocated to link ia is:

[0034] In the formula, It is the proportion allocated to link ia. It is on a side road The initial information flow, It is a side road The initial information flow on It is a side road The initial information stream on m It is a branch road J The initial information flow on This represents the set of all adjacent normal links of node i.

[0035] S3.1.4: Based on the preset maximum link capacity and overload coefficient, determine whether each communication link after redistribution is overloaded and fail, and redistribute the information flow of the communication link determined to be overloaded to the adjacent normal link according to the redistribution strategy.

[0036] Specifically, S3.1.4 includes: calculating the link load ratio of each redistributed communication link and comparing it with a preset value; wherein, if the link load ratio of the communication link is less than 1, it is determined that the link is normal; if the link load ratio of the communication link is greater than the overload coefficient, it is determined that it is overloaded and failed; if the link load ratio of the communication link falls within the interval formed by 1 and the overload coefficient, it is determined that it is in a random congestion state; for the communication link determined to be overloaded and failed, its overflow load is calculated, and the overflow load is the difference between the current information flow of the communication link and its maximum capacity; the overflow load is redistributed to the adjacent normal links of the two end nodes according to the proportion of the remaining capacity of each adjacent normal link.

[0037] Here, the maximum capacity that the link can withstand is defined. , This is the overload factor. It calculates the link load ratio after traffic redistribution. If The link is normal; if This indicates that the link is severely overloaded and has failed due to direct fuse failure; if This indicates that the link is in a state of random congestion, with a failure probability of . , It is the weighting coefficient.

[0038] Here, for each link judged as overloaded Calculate its overflow load: In the formula, After redistribution, the branch The current information flow on the screen, It is a side road The maximum physical capacity of the link.

[0039] Here, the redistribution strategy is as follows: ;

[0040] in, , These are nodes Adjacent normal links Maximum capacity and current information flow, , These are nodes Adjacent normal links Maximum capacity and current information flow, , These are nodes Adjacent normal links Maximum capacity and current information flow, The overloaded branch overflows its load to the surrounding normal links. The proportion of traffic during secondary redistribution. Let be the allocation strategy for overloaded branches, let be the set of branches in the normal state among the neighboring nodes of node i, and let be the set of branches in the normal state among the neighboring nodes of node a.

[0041] S3.1.5: Remove communication links that are determined to be overloaded and fail from the current communication network topology, mark the relevant communication nodes as communication failure nodes, and update the current communication network topology using the remaining communication links and remaining communication nodes to obtain the updated communication network topology.

[0042] S3.1.6: Using the shortest path algorithm, calculate the end-to-end communication delay from each communication node to the scheduling center in the updated communication network topology, and obtain multiple communication delays.

[0043] Here, the expression for calculating communication delay is: ; in, It is the end-to-end communication delay from communication node u to scheduling center node v. This refers to the processing delay of the source and destination devices; The processing delay for intermediate forwarding devices is approximately 10μs. This represents the number of intermediate devices along the path. The length of the optical fiber. This is the refractive index of the optical fiber, typically 1.48. It is the speed of light.

[0044] The above is the complete content of S3.1. After obtaining multiple communication delays, continue with subsequent operations.

[0045] S3.2: Based on communication latency and preset latency threshold, determine the observability of nodes and dynamically update the uplink and downlink channel matrices.

[0046] Here, the secondary power system topology is defined. , and These represent the sets of communication nodes and communication links in the communication layer, respectively; define the uplink channel matrix. With downlink channel matrix It is used to describe the transmission status of information between the physical layer and the information layer.

[0047] Among them, the uplink channel matrix The calculation expression is: ; It should be noted that, This means that the node The physical nodes and communication nodes are connected, and the nodes Measurements can be uploaded; This means that the node The communication link between node j and branch is connected. The measurements can be uploaded.

[0048] Downlink Channel Matrix The calculation expression is: ; This means that the node The communication center is connected to its corresponding communication node, that is, the scheduling center communicates with the node. Control commands (such as power adjustment commands) can be sent to the actuators of this node; This means that the node The communication link between node j and the branch is connected, that is, the dispatch center has communication with the branch. Control commands (such as circuit breaker opening and closing commands) can be sent to the actuators at both ends of the branch.

[0049] Here, S3.2 includes: S3.2.1: If the communication delay of each communication node among all communication nodes corresponding to a certain physical node is greater than the preset delay threshold, or there is no connection path between all communication nodes and the scheduling center, the physical node is determined to be an unobservable node.

[0050] It should be noted that the statement "no communication path between all communication nodes and the scheduling center" refers to the situation where the communication links that existed in the previous round disappear due to the removal of some communication nodes in S3.1.

[0051] S3.2.2: For a physical node that is determined to be unobservable, set the diagonal elements in its uplink and downlink channels that represent the connectivity between the physical node and its own communication nodes to zero, and set the off-diagonal elements that represent the transmission status of branch measurements between the physical node and other physical nodes to zero, so as to obtain the updated uplink and downlink channels.

[0052] For example, suppose a power system contains 3 physical nodes (nodes 1, 2, and 3), and its initial uplink channel matrix... In a certain iteration, node 2 is determined to be an unobservable node, and the updated uplink channel matrix is ​​represented as: Downlink channel matrix The update method is exactly the same, and after the update, node 2 will be unable to receive any control commands.

[0053] S3.3: Based on the updated uplink channel matrix, construct a mirror network for the dispatch center. The mirror network is used to represent the actual power grid status perceived by the dispatch center in the current round.

[0054] Specifically, S3.3 includes: determining the physical nodes in the updated uplink channel matrix whose diagonal elements are not zero as observable nodes that can effectively upload measurement data to the dispatch center, and adding them to the observable node set; based on the observable node set, extracting the corresponding physical node and physical branch information from the primary power system topology to construct a mirror network.

[0055] Here, definition 1-th order matrix The power flow information receiving matrix corresponds to the telemetry signal; its calculation expression is: ; In the formula, It is the RTU (Remote Terminal Unit) layout matrix of the primary side equipment. It is a set of nodes in a physical system. It is the power grid matrix of the system.

[0056] definition 1-th order matrix This is the topology information reception matrix, corresponding to the remote signaling signal; its calculation expression is: ; In the formula, It is the topology matrix of the power grid.

[0057] The dispatch center is based solely on the uplink channel matrix. Valid measurement data with state 1 in the middle, combined with and By extracting observable nodes and branches, the current actual perceived state of the system can be reconstructed, which is called a "mirror network".

[0058] It should be noted that the constructed mirror network may lack information on some faulty lines or misjudge the status of normal nodes.

[0059] S3.4: The dispatch center makes optimization decisions based on the mirror network, generates control instructions, and sends them to the physical execution end through the updated downlink channel matrix to update the power network topology and operating status.

[0060] Specifically, S3.4 includes: S3.4.1: Based on the observable node set and physical branch information in the mirror network, with the minimum total load shedding of the system as the objective function, generate the active power adjustment amount of each physical node in the mirror network as the power adjustment command, and generate the circuit breaker status change signal of each physical branch as the circuit breaker opening and closing command.

[0061] Here, the specific formulas for the power adjustment command and the circuit breaker opening and closing command are as follows: ; ; In the formula, It is the active power regulation command transmission matrix for communication substations. It is the circuit breaker opening and closing command transmission matrix for communication substation nodes. It is a matrix showing the monitoring relationship between the scheduling center and each node's injected power. It is a control matrix showing the opening and closing relationships of circuit breakers for all branches from the dispatch center. It is the generalized optimization decision function of the information network control center. It is a power flow information receiving matrix. It is the topology information receiving matrix.

[0062] S3.4.2: Based on the updated downlink channel matrix, determine the controllable physical nodes in the mirror network that can effectively receive instructions.

[0063] S3.4.3: Through the available channels in the downlink channel matrix, power adjustment commands and circuit breaker opening and closing commands are sent to the corresponding controllable physical nodes so that after all controllable physical nodes have executed the received commands, the updated power network topology is obtained.

[0064] For example, a controllable physical node receives a node active power adjustment command. Then, through the downlink communication channel The data is transmitted to the RTU actuator to adjust the injected power of the node. The formula for calculating the injected power is as follows: ; In the formula, It is the node injected power vector A diagonal element matrix, It is the active power vector injected into the node.

[0065] And, upon receiving the circuit breaker opening and closing command Then, it is transmitted to via the downlink communication channel. The RTU actuator changes the system topology, and the updated power network topology is calculated as follows: .

[0066] S3.4.4: Based on the updated power network topology and node injected power, the DC power flow model is used to calculate the power flow distribution of each physical branch in order to update the corresponding operating status; Furthermore, the susceptance matrix is ​​updated using the updated power network topology and node injected power. This yields the updated running status. The specific expression is: ; In the formula, It is the node voltage phase angle vector. It is the physical branch power flow matrix after executing control commands and updating the power grid topology.

[0067] S3.4.5: If the power flow of a physical branch exceeds its limit, the branch will be automatically disconnected.

[0068] S3.4.6: If a power island is formed due to branch disconnection, and the power generation in the island cannot meet the load demand, then all load nodes in the island shall be disconnected. A load node refers to a physical node with a load.

[0069] S3.5: Based on the coupling dependency relationship between primary and secondary systems, simulate the bidirectional propagation of communication faults and physical faults, update the set of fault nodes of the power system to be evaluated, and obtain the updated set of faulty devices in the current round; the set of fault nodes includes physical fault nodes and communication fault nodes.

[0070] Specifically, S3.5 includes: S3.5.1: Obtain the updated communication network topology, the updated power network topology, and the set of faulty nodes for the current round.

[0071] S3.5.2: According to the preset first coupling strength, each physical node that loses communication support is probabilistically determined as a newly added physical fault node; wherein, the physical nodes that lose communication support include the set of unobservable nodes in S3.2, as well as physical nodes that lose connection with the scheduling center due to communication network topology updates.

[0072] For example, define the coupling edge matrix The coupling strength is defined to represent the information dependency of the physical grid on the information network and the energy dependency of the information network on the physical grid. Its basic rules are: (1) If the communication node is connected to the scheduling center and its coupled physical node is working normally, then the communication node is working normally. (2) If the coupled physical node fails, the communication node is in a high-risk state, and the probability is high. A malfunction occurred; (3) If the physical node is in the main network or power island and its coupled communication node is working normally, then the physical node is working normally. (4) If the coupled communication node fails, the physical node is in a high-risk state and is subject to probability. A malfunction has occurred.

[0073] After this round of communication network topology iteration, the updated communication network topology is obtained. And the set of faulty nodes in the current round. After removing communication nodes that cannot connect to the scheduling center, the set of working nodes in the communication layer is denoted as: ;in, This represents the complete set of communication layer nodes in the original system. This is the initial set of faulty nodes. Represents the set of all nodes China The operation of finding the complement, i.e., removing The set of remaining nodes after that.

[0074] Further removing nodes with no connection to the scheduling center yields the effective working subset of the communication layer, denoted as: .

[0075] Based on this, the newly added physical fault nodes in this round can be represented as: ,in, This refers to the set of physical nodes that lost information support due to communication node failure or latency exceeding limits during this iteration. In the entire set, according to the coupling strength The probability of excluding affected physical nodes. Then, the complement of the remaining surviving nodes is obtained through calculation.

[0076] S3.5.3: According to the preset second coupling strength, the communication node to which the out-of-operation physical node is attached is probabilistically determined to be a newly added communication fault node; wherein, the out-of-operation physical node is a physical fault node in the set of fault nodes in the current round.

[0077] For example, based on the same coupling dependency and coupling strength The newly added communication failure nodes in this round can be represented as: ; This is the set of newly added physically faulty nodes that have experienced physical outages or island removals in this iteration. In the set of communication working nodes, according to coupling strength The probability of eliminating communication nodes affected by physical shutdowns. The complement operation is then obtained.

[0078] S3.5.4: Add the newly added physical fault nodes and the newly added communication fault nodes to the fault node set of the current round to obtain the updated fault device set of the current round.

[0079] S3.6: Determine whether the convergence conditions are met. The convergence conditions include no new physical fault nodes or communication fault nodes being generated, or the physical layer having lost its power supply capability.

[0080] S3.7: If not satisfied, return to sub-step 3.1 to continue iteration.

[0081] S3.8: If satisfied, terminate the iteration and proceed to S4.

[0082] Through iterative simulations in steps S3.1 to S3.8, the dynamic evolution of the power system under rain, snow, and ice disasters is fully simulated. In each iteration, the system records multi-dimensional state data, including the communication network status, power network status, set of fault nodes, set of observable nodes, mirror network information, and execution results of control commands. When the convergence condition is met (no new physical or communication fault nodes are generated, or the physical layer has lost its power supply capability), the iteration terminates, and the system enters its final steady state.

[0083] Next, based on the state data of the power system to be evaluated recorded in each round during the iteration process, S4 is executed.

[0084] S4: Based on the state data of the power system to be evaluated recorded in each round during the iteration process, multidimensional resilience indicators are calculated according to the resilience assessment index system covering the three time dimensions of pre-disaster, during-disaster and post-disaster, in order to assess the resilience level of the power system to be evaluated under the disaster scenarios corresponding to the meteorological parameters.

[0085] (1) Pre-disaster stage The pre-disaster assessment metrics focus on the system's defensive capabilities, evaluating the system's topological redundancy and sensing and control potential before the disaster occurs. The assessment formula is as follows:

[0086] In the formula, The total number of nodes. This represents the percentage of physical nodes that can receive at least one control command when there are no disasters or time delays.

[0087] (2) Disaster Phase The assessment metrics during the disaster phase focus on the system's resilience, evaluating its ability to maintain dynamic levels of energy supply and information exchange during a cascading failure evolution. The metrics include: 1) Primary side power supply retention rate

[0088] In the formula, Refers to the first The set of physical worker nodes after rounds of iteration (mainnet + surviving nodes in the power island). Refers to the corresponding set of working branches. It is the first Physical node power retention rate after round iteration It is the first The physical branch connectivity retention rate after round iteration.

[0089] 2) Working node retention rate on the secondary side

[0090] In the formula, It is the first The proportion of communication worker nodes after round iteration Indicates the first After each iteration, the set of communication nodes that remain connected to the scheduling center and have not failed.

[0091]

[0092] In the formula, It is the first The proportion of reachable nodes that meet the latency threshold after round iteration. It is the first wheel node Minimum delay to the dispatch center It is the maximum transmission delay allowed by the service.

[0093]

[0094] In the formula, It is the first In each iteration, the set of physical nodes that can be both observed and controlled by the scheduling center is... It is the first The diagonal elements of the upward channel matrix in each iteration. yes The off-diagonal elements of the uplink channel matrix during round iteration.

[0095] Among them, when When this occurs, it indicates that the physical node and the communication node are still connected, while when... This indicates that there is a connection between node i and control center node j, and measurement information and instructions can be sent.

[0096]

[0097] In the formula, It represents the total number of physical nodes in a power system.

[0098] (3) Post-disaster phase Post-disaster assessment metrics focus on the system's resilience, evaluating the efficiency of restoring the system's remaining structure and functions after fault convergence. The assessment primarily considers the following aspects: 1) Power supply retention rate

[0099] In the formula, For the final load shedding amount, This represents the initial total power supply load.

[0100] 2) Load recovery efficiency

[0101] In the formula, It is the total active power load of the inventory nodes in the system at time t. It is the total active power load of the inventory nodes in the system at the end of the disaster. This is the active load when the system is working normally. The end time of the disaster; This is the start time for recovery; This represents the initial recovery point. A higher R value indicates a stronger recovery capability of the system.

[0102] Through step S4 above, based on the state data of the power system to be evaluated recorded in each round during the iteration process, and combined with a resilience assessment index system covering three time dimensions—pre-disaster defense capability, in-disaster resistance capability, and post-disaster recovery capability—multi-dimensional resilience indices are calculated. These indices quantify the resilience level of the power system to be evaluated under rain, snow, and ice disaster scenarios from different perspectives, including but not limited to the proportion of observable and controllable nodes before the disaster, the power supply retention rate and communication node survival rate during the disaster, and the load recovery efficiency after the disaster.

[0103] Thus, this method completes a comprehensive quantitative assessment of the resilience of secondary coupled power systems during rain, snow, and ice storms. The assessment results can be used for post-disaster analysis to reveal weaknesses in the system's fault evolution process; they can also be used for pre-disaster early warning and planning decisions, providing quantitative basis for differentiated power grid upgrades, optimized allocation of emergency resources, and the formulation of resilience enhancement strategies.

[0104] To address the biggest problem with existing technologies, this invention provides a multidimensional resilience assessment method for secondary coupled power systems under rain, snow, and freezing disasters, achieving beneficial effects through the following technical means: First, this invention establishes a meteorological parameter-driven synchronous damage model for primary and secondary equipment, which uniformly quantifies the physical damage of rain, snow and ice disasters to primary power equipment (such as transmission lines, towers and insulators) and secondary equipment (such as communication optical cables), realistically reproducing the initial impact of disasters on cyber-physical coupled systems, and overcoming the shortcomings of traditional methods that consider single system failures in isolation.

[0105] Secondly, this invention innovatively introduces a dynamic evolution mechanism for communication networks, simulating cascading failures of communication links due to traffic overload or physical damage, and calculating the end-to-end communication delay from nodes to the dispatch center. Based on the delay threshold, it determines the observability and controllability of nodes, constructing a "mirror network" that reflects the actual perception capabilities of the dispatch center. This mechanism accurately characterizes the constraints of information layer performance degradation on power grid regulation capabilities, making the evaluation model closer to actual operating scenarios.

[0106] Based on this, this invention constructs a bidirectional propagation model of primary and secondary coupled faults. By iteratively deduce the mutual induction process of communication faults and physical faults, it fully reproduces the vicious cycle of "communication interruption → scheduling failure → physical fault → communication re-interruption" until the system reaches steady state or collapses. This dynamic evolution process can quantify the cascading amplification effect of faults, providing accurate fault evolution path data for resilience assessment.

[0107] Finally, this invention establishes a multi-dimensional resilience index system covering three time dimensions: pre-disaster defense, disaster resistance, and post-disaster recovery. It quantitatively assesses system resilience from multiple dimensions, including observable and controllable capabilities, power supply retention rate, communication survival rate, and load recovery efficiency. This system not only outputs a final comprehensive resilience score but also reveals the system's performance trajectory throughout the entire disaster cycle, providing a comprehensive and scientific basis for identifying weaknesses, optimizing defense strategies, and improving recovery capabilities.

[0108] In summary, this invention systematically addresses the problems of missing coupling modeling, single evaluation dimension, and neglect of dynamic process in existing technologies by using synchronous damage modeling of primary and secondary equipment, dynamic evolution of communication, mirror network construction, bidirectional coupling iterative deduction, and three-stage multidimensional assessment. It significantly improves the accuracy and practicality of power system resilience assessment under rain, snow, and ice disasters, and provides strong support for disaster prevention and resilience enhancement of power grids.

[0109] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and freezing disasters, characterized in that, include: S1: Obtain meteorological parameters of the power system to be evaluated, as well as rain, snow and ice disasters. The meteorological parameters include historical observation data, weather forecast data or hypothetical disaster scenario data. S2: Based on the meteorological parameters, construct a synchronous damage model of primary and secondary equipment under rain, snow and ice disasters to obtain the failure probability of primary equipment and the failure probability of secondary equipment, thereby determining the initial set of faulty equipment in the power system to be evaluated; S3: Based on the initial set of faulty devices, construct a cascaded fault iteration model with primary and secondary coupling, and execute the iterative process of "dynamic evolution and observability determination of communication network - scheduling decision and physical response - bidirectional propagation of primary and secondary coupled faults" until the convergence condition is met; S4: Based on the state data of the power system to be evaluated recorded in each round during the iteration process, calculate the multidimensional resilience index according to the resilience assessment index system covering the three time dimensions of pre-disaster, during-disaster and post-disaster, so as to assess the resilience level of the power system to be evaluated under the disaster scenario corresponding to the meteorological parameters.

2. The multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and freezing disasters according to claim 1, characterized in that, The power system to be evaluated includes a primary power system topology and a secondary power system topology. The primary power system topology includes a set of physical nodes and a set of physical branches, with each physical node corresponding to a substation or power plant. The secondary power system topology includes a set of communication nodes and a set of communication links, with each communication node corresponding to a communication device. S3 includes: S3.1: Based on the current communication network topology and the communication link failure information in the set of faulty devices updated in the previous round, a node-degree-based communication network traffic redistribution and overload cascading model is adopted to simulate the cascading failure of communication links caused by traffic overload or physical damage, and to calculate the end-to-end communication delay from each node to the scheduling center. S3.2: Based on the communication delay and the preset delay threshold, determine the observability of the node and dynamically update the uplink channel matrix and downlink channel matrix; S3.3: Based on the updated uplink channel matrix, construct a mirror network for the dispatch center. The mirror network is used to characterize the power grid status actually perceived by the dispatch center in the current round. S3.4: The dispatch center makes optimization decisions based on the mirror network, generates control instructions, and sends them to the physical execution end through the updated downlink channel matrix to update the power network topology and operating status; S3.5: Based on the coupling dependency relationship between the primary and secondary systems, simulate the bidirectional propagation of communication faults and physical faults, update the set of fault nodes of the power system to be evaluated, and obtain the updated set of faulty devices for the current round; the set of fault nodes includes physical fault nodes and communication fault nodes; S3.6: Determine whether the convergence conditions are met. The convergence conditions include no new physical fault nodes and communication fault nodes being generated, or the physical layer having lost its power supply capability. S3.7: If not satisfied, return to sub-step 3.1 and continue iterating; S3.8: If satisfied, terminate the iteration and proceed to S4.

3. The multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and freezing disasters according to claim 2, characterized in that, S3.1 includes: The current communication network topology is obtained by removing the set of faulty devices updated in the previous round from the secondary power system topology; wherein, in the first iteration, the set of faulty devices updated in the previous round is the initial set of faulty devices; Based on the degree value of each communication node in the current communication network topology, the initial information flow of each communication link is recalculated; Based on the communication link failure information in the set of faulty devices updated in the previous round, the communication links that have failed due to physical damage are identified, and the corresponding information streams are redistributed to adjacent normal links according to a preset allocation strategy. Based on the preset maximum link capacity and overload coefficient, it is determined whether each communication link after redistribution is overloaded and fails, and the information flow of the communication link determined to be overloaded and failed is redistributed to the adjacent normal link according to the redistribution strategy. The communication links that are determined to be overloaded and failed are removed from the current communication network topology, and the relevant communication nodes are marked as the communication failure nodes. The current communication network topology is updated using the remaining communication links and remaining nodes to obtain the updated communication network topology. Using the shortest path algorithm, the end-to-end communication delay from each communication node to the scheduling center in the updated communication network topology is calculated, resulting in multiple communication delays.

4. The multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and freezing disasters according to claim 3, characterized in that, The process of determining whether each communication link after redistribution is overloaded based on a preset maximum link capacity and overload coefficient, and redistributing the information flow of the communication links determined to be overloaded to the adjacent normal links according to the redistribution strategy, includes: Calculate the link load ratio of each redistributed communication link and compare it with a preset value. If the link load ratio of the communication link is less than 1, the link is considered to be normal. If the link load ratio of the communication link is greater than the overload coefficient, it is considered to be overload failure. If the link load ratio of the communication link falls within the range formed by 1 and the overload coefficient, it is considered to be in a random congestion state. For communication links that are determined to be overloaded and failed, calculate their overflow load, which is the difference between the current information flow and the maximum capacity of the communication link. The overflow load is redistributed to the adjacent normal links of the two endpoints according to the proportion of the remaining capacity of each adjacent normal link.

5. The multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and freezing disasters according to claim 2, characterized in that, S3.2 includes: If the communication delay of each communication node among all communication nodes corresponding to a certain physical node is greater than the preset delay threshold, or if there is no connection path between all communication nodes and the scheduling center, the physical node is determined to be an unobservable node. For a physical node that is determined to be an unobservable node, the diagonal elements representing the connection status between the physical node and its own communication node in its uplink channel matrix and downlink channel matrix are set to zero, and the off-diagonal elements representing the branch measurement upload status between the physical node and other physical nodes are set to zero, thus obtaining the updated uplink channel matrix and the updated downlink channel matrix.

6. The multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and freezing disasters according to claim 5, characterized in that, S3.3 includes: Physical nodes whose diagonal elements are not zero in the updated uplink channel matrix are identified as observable nodes that can effectively upload measurement data to the scheduling center and added to the observable node set. Based on the observable node set, the corresponding physical node and physical branch information is extracted from the primary power system topology to construct the mirror network.

7. The multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and freezing disasters according to claim 6, characterized in that, S3.4 includes: Based on the observable node set and physical branch information in the mirror network, with the minimum total load shedding of the system as the objective function, the active power adjustment of each physical node in the mirror network is generated as a power adjustment command, and the circuit breaker status change signal of each physical branch is generated as a circuit breaker opening and closing command. Based on the updated downlink channel matrix, determine the controllable physical nodes in the mirror network that can effectively receive instructions. Through the available channels in the downlink channel matrix, the power adjustment command and the circuit breaker opening and closing command are sent to the corresponding controllable physical nodes, so that after all controllable physical nodes have executed the received commands, the updated power network topology is obtained. Based on the updated power network topology and node injected power, a DC power flow model is used to calculate the power flow distribution of each physical branch in order to update the corresponding operating status. If the power flow of a physical branch exceeds its limit, the branch will be automatically disconnected. If a power island is formed due to branch disconnection, and the power generation within the island cannot meet the load demand, then all load nodes within the island will be disconnected. The load node refers to a physical node with a load.

8. The multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and freezing disasters according to claim 7, characterized in that, S3.5 includes: Obtain the updated communication network topology, the updated power network topology, and the set of faulty nodes for the current round; According to the preset first coupling strength, each physical node that loses communication support is probabilistically determined as a newly added physical fault node; wherein, the physical nodes that lose communication support include the set of unobservable nodes in S3.2, as well as physical nodes that lose connection with the scheduling center due to communication network topology updates; According to the preset second coupling strength, the communication node to which the out-of-operation physical node is attached is probabilistically determined to be a newly added communication failure node; wherein, the out-of-operation physical node is a physical failure node in the set of failure nodes in the current round. The newly added physical fault nodes and the newly added communication fault nodes are added to the fault node set of the current round to obtain the updated fault device set of the current round.

9. The multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and freezing disasters according to claim 1, characterized in that, S2 includes: A primary-side failure probability model is constructed to calculate the failure probability of primary-side equipment in the power system to be evaluated, thereby obtaining multiple primary-side equipment failure probabilities; the primary-side equipment is the transmission line in the primary power system topology of the power system to be evaluated, and the transmission line includes at least conductors, towers, and insulators; A secondary-side failure probability model is constructed to calculate the failure probability of the secondary-side equipment in the power system to be evaluated, thereby obtaining multiple failure probabilities of the secondary-side equipment; the secondary-side equipment includes at least optical cables. Based on the failure probabilities of the multiple primary-side devices and the multiple secondary-side devices, the fault status of each device is determined by a random simulation method, and all devices determined to be faulty are combined into an initial set of faulty devices for the power system to be evaluated.

10. The multidimensional resilience assessment method for a secondary coupled power system under rain, snow, and freezing disasters according to claim 9, characterized in that, The primary failure probability model is calculated using the following formula: ； In the formula, Let be the failure probability of the primary side equipment of the transmission line at time t. Let be the probability of conductor breakage in the transmission line at time t. Let be the probability of the tower collapsing under snow cover at time t. Let t be the probability that the transmission line will fail due to wind damage during an ice storm. The probability of insulator flashover due to icing on the transmission line at time t; The secondary side failure probability model is calculated using the following formula: ； In the formula, This is the failure probability of the secondary side equipment. This refers to the reliability of the optical cable under icing conditions. It is a node in the secondary power system communication network. It is the set of all fiber optic links in the system.

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