Power grid weather communication multi-system coupling risk analysis method, device and equipment and storage medium

Abstract

The application discloses a power grid weather communication multi-system coupling risk analysis method and device, equipment and storage medium, relates to the technical field of smart grid, and includes: acquiring the physical topology matrix, electrical measurement value, communication link state data and weather disturbance intensity value of each power grid node of the urban agglomeration power grid, and determining the corresponding communication link availability according to the communication link state data; according to the physical topology matrix, the electrical measurement value, the communication link availability and the weather disturbance intensity value of each power grid node, the symmetrization risk scale is calculated; the electrical signal three-dimensional space coupling topology matrix is constructed; the nonlinear coupling calculation of the risk amplification factor is performed to obtain the coupling risk value; and the evolution deduction result is obtained based on the electrical signal three-dimensional space coupling topology matrix and the coupling risk value. Therefore, the comprehensive perception and quantitative characterization of the multi-system influencing factors of the power grid, communication and weather are realized, and the limitation that only single-dimensional factors are considered in traditional risk analysis is overcome.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, and in particular to a method, apparatus, equipment and storage medium for risk analysis of multi-system coupling of power grid meteorological communication. Background Technology

[0002] With the expansion of coastal and near-shore urban clusters, extreme weather events such as typhoons exhibit characteristics such as uncertain paths and significant chain reactions, easily triggering a series of cascading risks, including damage to power transmission and transformation facilities, large-scale power outages in distribution networks, fluctuations in renewable energy output, and damage to critical communications. At the urban cluster scale, there are strong electrical couplings, coordinated operational strategies, and cross-regional risk propagation among multiple regional power grids. Risk analysis and control methods based on a single city or a single voltage level are no longer sufficient to effectively address systemic risks under typhoon scenarios.

[0003] Existing technologies typically focus on power grid security assessments for specific voltage levels or local geographical areas, but they are significantly inadequate when dealing with complex disasters that cross regions and systems. Summary of the Invention

[0004] This application provides a method, device, equipment, and storage medium for risk analysis of multiple coupled power grid, meteorological, and communication systems, which enables comprehensive perception and quantitative characterization of influencing factors of multiple systems such as power grid, communication, and meteorology, overcoming the limitation of traditional risk analysis that only considers single-dimensional factors.

[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, a method for risk analysis of multi-system coupling in power grid meteorological communication is provided, including: The physical topology matrix, electrical measurements, communication link status data, and meteorological disturbance intensity values ​​of each power grid node of the urban agglomeration power grid are obtained, and the availability of the corresponding communication links is determined based on the communication link status data. Based on the physical topology matrix, electrical measurements, communication link availability corresponding to communication link status data, and the intensity of meteorological disturbances experienced by each power grid node, a symmetric risk scale is calculated. A three-dimensional spatial coupling topology matrix for electrical and communications is constructed to characterize the interaction strength among power grid, communications, and meteorology. Based on the availability of communication links and the symmetric risk scale, a nonlinear coupling calculation of the risk amplification factor is performed to obtain the coupling risk value; Based on the three-dimensional spatial coupling topology matrix and coupling risk value of electrical and information systems, the evolution path of the power grid state is dynamically predicted at different spatiotemporal scales, and the evolution inference results are obtained.

[0006] In one implementation, the communication link status data includes the link status between the substation control layer and the bay layer, the link status between the bay layer and the process layer, and the core business link connectivity status between the dispatch master station and the substation. Based on the communication link status data, the availability of the corresponding communication link is determined, including: Based on the communication link status data, determine whether the target communication link is interrupted; If the target communication link is interrupted, the availability of the corresponding communication link is determined to be 0; If the target communication link is connected, the availability of the corresponding communication link is determined to be 1.

[0007] In one implementation, a symmetric risk scale is calculated based on the physical topology matrix, electrical measurements, communication link availability corresponding to communication link status data, and the intensity of meteorological disturbances experienced by each power grid node, including: The physical topology matrix, electrical measurements, meteorological disturbance intensity values, and communication link status data are encapsulated into a multi-dimensional risk feature vector for the communication link availability. The multidimensional risk feature vector is subjected to dimensionless normalization to obtain standardized feature quantities; Calculate the symmetric risk scale based on the standardized characteristic quantities.

[0008] In one implementation, the symmetric risk scale is calculated based on standardized characteristic quantities, including: Based on the pre-defined first model and standardized feature quantities, calculate the symmetric risk scale. The formula for the first model is:

[0009] in, This is a cross-regional scale correction factor. Let k be the k-th standardized feature quantity in the standardized feature quantity. The weight factor is the standardized feature quantity of the k-th dimension. To symmetricize the risk scale.

[0010] In one implementation, a nonlinear coupling calculation of the risk amplification factor is performed based on communication link availability and a symmetric risk scale to obtain a coupling risk value, including: Based on the preset second model, the communication link availability corresponding to the communication link status data, and the symmetric risk scale, a nonlinear coupling calculation of the risk amplification factor is performed to obtain the coupling risk value. The formula for the second model is:

[0011] in, For coupling risk value, To symmetricize the risk scale, For communication link availability, This is the risk sensitivity adjustment coefficient.

[0012] In one implementation, after dynamically predicting the power grid state evolution path at different spatiotemporal scales based on the three-dimensional spatial coupling topology matrix and coupling risk value of electrical and information systems, and obtaining the evolution inference results, the method further includes: Based on the evolutionary simulation results, high-risk nodes in the urban agglomeration power grid were identified. Calculate the risk contribution index of the target high-risk node. The risk contribution index is defined as the sensitivity coefficient of the failure of the target high-risk node to the decrease in the overall connectivity of the system. High-risk nodes whose risk contribution index exceeds a preset threshold are marked as priority defense targets.

[0013] In one implementation, the method further includes: A multi-objective optimization decision model is constructed. The objective function of the multi-objective optimization decision model includes the expected power loss, the expected disaster recovery time, and weight coefficients. Based on the multi-objective optimization decision-making model, a set of coordinated decision-making instructions is generated. The decision-making instructions from the coordination decision-making instruction center are distributed to the dispatch centers at all levels of the urban agglomeration power grid.

[0014] Secondly, a multi-system coupled risk analysis device for power grid meteorological communication is provided, comprising: The acquisition module is used to acquire the physical topology matrix, electrical measurement values, communication link status data, and meteorological disturbance intensity values ​​of each power grid node of the urban agglomeration power grid, and to determine the availability of the corresponding communication link based on the communication link status data. The calculation module is used to calculate the symmetric risk scale based on the physical topology matrix, electrical measurement values, communication link availability corresponding to communication link status data, and meteorological disturbance intensity values ​​experienced by each power grid node. The module is used to construct the three-dimensional spatial coupling topology matrix of electrical and information systems, which is used to characterize the interaction strength among power grid, communication and meteorology. The execution module is used to perform nonlinear coupling calculations of the risk amplification factor based on the availability of communication links and the symmetric risk scale, so as to obtain the coupling risk value; The prediction module is used to dynamically predict the power grid state evolution path at different spatiotemporal scales based on the three-dimensional coupled topology matrix and coupled risk value of electrical and information systems, and obtain the evolution inference results.

[0015] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the power grid meteorological communication multi-system coupling risk analysis method as described in any of the first aspects above.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power grid meteorological communication multi-system coupling risk analysis method as described in any of the first aspects above.

[0017] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to execute the power grid meteorological communication multi-system coupling risk analysis method described in any of the first aspects above.

[0018] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0019] In this embodiment, the physical topology matrix, electrical measurements, communication link status data, and meteorological disturbance intensity values ​​of each grid node of the urban agglomeration power grid are first obtained. Based on the communication link status data, the availability of the corresponding communication links is determined. Then, based on the physical topology matrix, electrical measurements, communication link status data, and meteorological disturbance intensity values ​​of each grid node, a symmetric risk scale is calculated. Next, a three-dimensional coupled topology matrix of electrical and communication systems is constructed to characterize the interaction strength among the power grid, communication, and meteorology. Then, based on the communication link availability and the symmetric risk scale, a nonlinear coupling calculation of the risk amplification factor is performed to obtain the coupled risk value. Finally, based on the three-dimensional coupled topology matrix of electrical and communication systems and the coupled risk value, the evolution path of the power grid state is dynamically predicted at different spatiotemporal scales, yielding the evolution prediction results. Thus, by integrating multi-source information from power grid topology, electrical operation data, communication link status, and meteorological disturbances, and simultaneously quantifying communication link availability and the symmetric risk scale, a comprehensive perception and quantitative characterization of the influencing factors of multiple systems (power grid, communication, and meteorology) is achieved, overcoming the limitation of traditional risk analysis that only considers single-dimensional factors. The coupled risk value is obtained through nonlinear calculation of the risk amplification factor, accurately reflecting the superposition effect of risks from multiple systems, and dynamically extrapolating the evolution of the power grid state at multiple spatiotemporal scales. This scheme achieves accurate analysis and prediction of coupled risks from multiple systems, making up for the shortcomings of traditional methods that are limited in dimension and cannot reflect the interconnected effects between systems. It can effectively support the safe operation of urban power grids and disaster prevention and control.

[0020] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0021] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a flowchart illustrating the multi-system coupling risk analysis method for power grid meteorological communication provided in this application embodiment; Figure 2 This is a flowchart illustrating the hierarchical architecture of the multi-system coupling risk analysis method for power grid meteorological communication in this application embodiment; Figure 3 This is a flowchart illustrating the multi-system coupling risk analysis device for power grid meteorological communication provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0022] The embodiments of the technical solutions of this application will now be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of this application, and are therefore merely examples and should not be used to limit the scope of protection of this application. When the following description relates to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. Various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but can be changed as will become apparent upon understanding this disclosure, except for operations that must be performed in a specific order. Furthermore, for clarity and conciseness, descriptions of features known in the art may be omitted.

[0023] The embodiments described in the following examples of this disclosure are not representative of all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0024] It should be noted that this application primarily addresses the technical problem of the difficulty in accurately predicting and quantifying cross-regional, multi-system cascading risks caused by extreme weather disasters at the urban agglomeration scale using existing technologies. Specifically, this manifests in the following aspects: 1. Asymmetry problem in risk modeling: Since urban agglomeration power grids involve multiple administrative regions, different voltage levels and heterogeneous operating entities, traditional models are difficult to achieve equivalent analysis under a unified framework, resulting in incomparable risk states at different levels.

[0025] 2. Lack of identification of risks associated with multi-system coupling: Existing technologies often overlook the strong coupling relationship between the "power, meteorology, and communication" systems, and cannot characterize the nonlinear amplification mechanism of communication interruptions or meteorological disturbances on power system faults.

[0026] 3. The challenge of dynamic prediction of spatiotemporal evolution characteristics: The randomness of typhoon path and intensity leads to complex evolution of risk in time and space dimensions. Existing static assessment methods are difficult to predict the propagation path of risk from local nodes to the region.

[0027] 4. Risk of failure in cross-regional collaborative decision-making: Without multi-system coupling risk analysis, the defense strategy of a single system is prone to "local optimization, overall failure", which is difficult to support the source-grid-load-storage linkage response at the city cluster level.

[0028] Therefore, this application proposes a method, device, equipment, and storage medium for multi-system coupling risk analysis of power grid meteorological communication, in order to solve the technical defects of the existing technology.

[0029] See Figure 1 This is a flowchart illustrating the multi-system coupling risk analysis method for power grid meteorological communication provided in this application embodiment. The specific implementation methods of this application will be described in detail below with reference to the accompanying drawings.

[0030] like Figure 1 As shown in the figure, the multi-system coupling risk analysis method for power grid meteorological communication provided in this embodiment includes the following steps: Step 101: Obtain the physical topology matrix, electrical measurements, communication link status data, and meteorological disturbance intensity values ​​of each power grid node of the urban agglomeration power grid, and determine the availability of the corresponding communication links based on the communication link status data.

[0031] Among them, the urban agglomeration power grid refers to a regional interconnected power network composed of interconnected power grids of multiple adjacent cities across voltage levels, including the main grid, distribution grid, new energy grid connection nodes and interconnection branches of each city.

[0032] Among them, the physical topology matrix is ​​a structured matrix used to mathematically represent the connection relationship between power grid nodes and branches in urban agglomerations, recording the power grid node number, branch connection relationship, and network connectivity architecture.

[0033] Among them, electrical measurements refer to the steady-state operating parameters of the power grid collected in real time by the power grid dispatching system, including quantifiable operating indicators such as node power flow, bus voltage, and branch current, which are used to characterize the real-time load level and operating margin of the power grid.

[0034] Among them, the meteorological disturbance intensity value is the quantitative value of the external stress generated by the combined effects of typhoon wind speed and rainfall at each power grid node at the corresponding time, which is used to characterize the original damage driving intensity of meteorology on power equipment.

[0035] As one possible approach, the system accesses the physical topology matrix of the power grid at various voltage levels within the urban agglomeration in real time through the northbound interface of the scheduling data network, and fully acquires the connection relationships of power grid nodes and branches, as well as network architecture parameters; at the same time, it synchronously collects real-time electrical measurements of the power grid, including but not limited to steady-state operating parameters such as power flow at each node, bus voltage, and branch current.

[0036] Furthermore, the system can call the meteorological numerical forecasting interface in parallel to obtain the spatial distribution of wind speed field, typhoon path prediction sequence, and regional hourly rainfall gridded data in real time for the entire typhoon process. This application can use inverse distance weighted interpolation algorithm or Kriging interpolation algorithm to accurately map the discrete gridded meteorological parameters to the actual coordinate positions of each power grid equipment node, and quantitatively calculate the initial meteorological disturbance intensity experienced by each power grid node i at time t.

[0037] Among them, the inverse distance weighted interpolation algorithm is a spatial interpolation algorithm that assigns weights inversely proportional to the spatial distance between meteorological grid points and power nodes, thereby mapping discrete grid meteorological data to the coordinates of power equipment nodes with high precision.

[0038] Among them, the Kriging interpolation algorithm is an optimal interpolation algorithm based on spatial statistics, which is suitable for refined meteorological mapping in extreme weather scenarios.

[0039] Among them, grid node i: any power equipment node in the urban agglomeration power grid, including substation nodes, bus nodes, new energy grid connection points and other basic power grid analysis units.

[0040] The meteorological numerical forecasting interface is a data call interface that connects to the meteorological forecasting platform, used to obtain gridded meteorological forecast data such as typhoon track, wind speed spatial distribution, and rainfall distribution in real time. The wind speed field spatial distribution is a gridded dataset of wind speed spatial distribution across the entire typhoon-affected area, representing the magnitude and spatial coverage of typhoon wind speeds at different geographical locations and times. The typhoon track prediction sequence is a time-series prediction data of the typhoon center location dynamically changing over time, output by the meteorological system, used to characterize the typhoon's movement trajectory, speed, and temporal extent of impact. The hourly rainfall gridded data is regional rainfall data with meteorological grids as units and hourly statistical scales.

[0041] Optionally, the initial meteorological disturbance intensity value experienced by each power grid node i at time t can be calculated using the following formula. :

[0042] in, Let j be the spatial weight of the j-th meteorological grid point to the i-th power node. and These represent the normalized wind speed and hourly rainfall at time t, respectively. and These represent the contribution weights of wind speed and rainfall to equipment losses, respectively, and N is the total number of sampling grid points within the affected area. Through this step, the system establishes a spatial mapping benchmark between meteorological disturbances and the physical entities of the power grid.

[0043] Specifically, the system can retrieve the connectivity status of core business links between the substation control layer and the bay layer, the bay layer and the process layer, and the dispatch master station and the substation in real time through the communication management system interface.

[0044] The communication management system interface serves as the data interaction interface for the power-dedicated communication monitoring system, used to retrieve the real-time operating status of core communication links related to power dispatching and substation control. The station control layer and bay layer link is the business communication link between the upper-level monitoring unit and the lower-level measurement and control protection unit within the substation. The bay layer and process layer link is the real-time communication link between the substation's measurement and control protection devices and the intelligent terminals of primary equipment, undertaking real-time control communication functions such as electrical quantity sampling and equipment action signal interaction. The core business link between the dispatch master station and substations is the remote dispatch communication link between the municipal and provincial dispatch master stations and the substations of various city substations, serving as a crucial communication channel for cross-regional power grid dispatching, remote monitoring, and power regulation.

[0045] Among them, communication link status data is real-time operational status data that characterizes the connectivity, interruption, packet loss, and offline status of dedicated power communication links at each level, and is a core information-side parameter for judging the controllability of the power grid.

[0046] Among them, the communication link availability feature is a binary state quantification index used to standardize and characterize the smoothness and accessibility of the power control communication link, taking only two states: 0 or 1.

[0047] Optionally, the communication link status data includes the link status between the substation control layer and the bay layer, the link status between the bay layer and the process layer, and the connectivity status of the core business link between the dispatch master station and the substation. Based on the communication link status data, the availability of the corresponding communication link is determined, including: determining whether the target communication link is interrupted; if the target communication link is interrupted, the availability of the corresponding communication link is determined to be 0; if the target communication link is detected to be connected, the availability of the corresponding communication link is determined to be 1. Here, the target communication link is any one of the communication links.

[0048] It should be noted that, based on the acquired communication link status data, the connectivity of the core communication links between each node is further assessed one-to-one to form a standardized communication link availability feature. The specific assessment rules are as follows: If a target communication link is detected to be interrupted, experiencing excessive packet loss, or going offline in real time, the corresponding control link is deemed to be faulty, and the communication link availability is defined as 0. This indicates that the power grid in that area has entered a control blind zone, where dispatching instructions cannot be effectively issued and field status cannot be uploaded. If the target communication link is detected to be connected in real time and service transmission is normal, the communication link availability is defined as 1, indicating that the power grid communication control loop in that area is normal and controllable.

[0049] For example, we can define a feature of the communication link availability between node i and node j. When a link interruption is detected This indicates that the relevant area has entered a regulatory blind spot. When normal... This variable will serve as a key control factor for subsequent coupling calculations and risk amplification.

[0050] Step 102: Calculate the symmetric risk scale based on the physical topology matrix, electrical measurement values, communication link availability corresponding to communication link status data, and meteorological disturbance intensity values ​​experienced by each power grid node.

[0051] Among them, the symmetric risk scale is a unified quantitative risk indicator used for heterogeneous power grids in multiple regions and at multiple levels in urban agglomerations.

[0052] Optionally, the physical topology matrix, electrical measurements, meteorological disturbance intensity, and communication link availability are encapsulated into a multidimensional risk feature vector. Then, the multidimensional risk feature vector is subjected to dimensionless normalization to obtain standardized feature quantities. Finally, a symmetric risk scale is calculated based on the standardized feature quantities.

[0053] First, a multi-dimensional risk feature vector encapsulation is performed. This involves fusing and encapsulating the physical topology matrix features, electrical measurement operation features, meteorological disturbance intensity features of each power grid node, and the quantified communication link availability features obtained in step 101. This constructs a multi-dimensional risk feature vector that can simultaneously characterize the physical operating status of the power grid, the intensity of external meteorological disturbances, and the information and communication control capabilities. This multi-dimensional risk feature vector comprehensively covers the main grid operation characteristics, distribution network operation characteristics, and the differences in operation between the renewable energy grid-connected side.

[0054] Furthermore, dimensionless normalization processing is performed on the multidimensional risk feature vectors. Because the encapsulated multidimensional risk features suffer from inconsistent dimensions, large differences in numerical scales, and strong heterogeneity at different equipment levels, they cannot be directly used for unified risk modeling calculations. Therefore, it is necessary to normalize all heterogeneous feature data to eliminate dimensional and scale deviations between different equipment types, different power grid levels, and parameters with different physical meanings, thus constructing a globally equivalent data analysis object.

[0055] Optionally, the specific normalization calculation method is as follows: calculate the normalized value of the k-th dimension feature according to the preset Min-Max normalization formula. Here, the original input parameter is the original collected value of the feature in that dimension, and Xmax and Xmin are the maximum and minimum values ​​of the feature quantity in that dimension within the sample space of the city cluster's entire power grid system, respectively. Linear normalization is achieved by traversing the extreme values ​​of the entire sample, uniformly mapping the risk features of all dimensions to the same numerical interval.

[0056] The calculation of the symmetric risk scale based on standardized characteristic quantities includes: Based on the pre-defined first model and standardized feature quantities, calculate the symmetric risk scale. The formula for the first model is:

[0057] in, This is a cross-regional scale correction factor. Let k be the k-th standardized feature quantity in the standardized feature quantity. The weight factor is the standardized feature quantity of the k-th dimension. To achieve a symmetrical risk scaling, this step ensures that the risk status between the main grid and distribution network, and between city A and city B, is statistically comparable.

[0058] It should be noted that by utilizing symmetric modeling theory, the complex characteristics of multiple regions and multiple levels coexisting in urban agglomeration power grids can be addressed by mapping the physical characteristics of the main grid, distribution network and new energy side into a unified risk state representation vector. This enables consistent characterization and equivalent analysis of risk states for different regions, voltage levels and different operating entities, effectively eliminating the risk incomparability problem caused by differences in dimensionality and scale in traditional models, and ensuring accurate mapping of risk states across regional power grids.

[0059] Step 103: Construct the three-dimensional spatial coupling topology matrix of electrical and telecommunications systems. The three-dimensional spatial coupling topology matrix of electrical and telecommunications systems is used to characterize the interaction strength among power grid, communication and meteorology.

[0060] Specifically, the principles of cyber-physical systems can be used to construct an electrical-information three-dimensional spatial coupling topology matrix. This matrix is ​​used to characterize the interaction intensity among power grid, communications, and meteorology.

[0061] Here, matrix elements are defined. The combined coupling weights between nodes i and j: .in, Elements representing the admittance matrix of the physical power grid, This represents the availability of the communication link between node i and node j. and These represent the coupling contribution coefficients on the physical and information sides, respectively. Using this matrix, the system can identify the set of physical devices affected by the cascading disaster when a communication link fails.

[0062] Among them, the electrical and information three-dimensional spatial coupling topology matrix is ​​a custom multi-system coupling quantization matrix in this application embodiment, which integrates three-dimensional elements of power physical topology, communication dynamic status, and meteorological spatial disturbance, and is used to quantify the comprehensive interaction strength and risk correlation of multiple systems between nodes in the whole domain.

[0063] Among them, the comprehensive coupling weight This characterizes the real-time correlation strength between any two power grid nodes under the combined influence of physical electrical constraints and information communication constraints. The admittance matrix elements... This reflects the tightness of the electrical connection between branches of two nodes; a larger value indicates stronger electrical coupling and closer power transfer correlation between nodes. Among these, the physical-side coupling contribution coefficient... This is used to characterize the basic contribution of the power grid's physical network structure in a multi-system coupled system. Among them, the information-side coupling contribution coefficient... It is used to characterize the degree of correction and influence of the communication control link state on the system coupling characteristics, and to reflect the control and constraint role of the information system on the power physical system.

[0064] Step 104: Based on the availability of the communication link and the symmetric risk scale, perform a nonlinear coupling calculation of the risk amplification factor to obtain the coupling risk value.

[0065] Optionally, based on the preset second model, the communication link availability corresponding to the communication link status data, and the symmetric risk scale, a nonlinear coupling calculation of the risk amplification factor can be performed to obtain the coupling risk value. The formula for the second model is:

[0066] in, For coupling risk value, To symmetricize the risk scale, For communication link availability, This represents the risk sensitivity adjustment coefficient. The second model indicates that when communication availability decreases, the loss of controllability will lead to an exponential amplification of the total system risk, revealing the nonlinear disaster chain effect under multi-system coupling.

[0067] Step 105: Based on the three-dimensional spatial coupling topology matrix and coupling risk value of electrical and information systems, dynamically predict the power grid state evolution path at different spatiotemporal scales to obtain the evolution inference results.

[0068] Optionally, a dynamic evolution model of typhoon driving forces can be established based on stochastic differential equations to simulate the intensity of meteorological disturbances. Random fluctuations in space and time.

[0069] The dynamic evolution model can be expressed using the following formula: .

[0070] in, The drift coefficient represents the trend of typhoon evolution. This is the fluctuation intensity coefficient. The increment is a standard Brownian motion value, representing the path randomness. This allows for the provision of external input driving sequences with stochastic characteristics for power grid risk simulation.

[0071] Then, a multi-scale spatiotemporal evolution simulation engine is activated to dynamically predict the power grid state evolution path using Markov state transition matrices. The evolution simulation results can refer to the state transition probabilities defined by combining the electrical-information three-dimensional spatial coupled topology matrix and coupled risk values, which can be expressed using the following formula: .

[0072] in, Let represent the probability distribution of the power grid system being in a specific risk state at time t. Indicating the intensity of meteorological disturbances, For coupling risk value, The electrical and information linkage three-dimensional spatial coupling topology matrix is ​​used. Through iterative calculations at the minute to day scale, the spatiotemporal propagation process of risks along electrical and information links within and between urban clusters is simulated.

[0073] Furthermore, based on the evolutionary simulation results, high-risk nodes in urban agglomeration power grids can be identified, and the risk contribution index of the high-risk nodes can be calculated. The risk contribution index is defined as the sensitivity coefficient of the failure of the high-risk nodes to the overall connectivity reduction of the system. Then, high-risk nodes whose risk contribution index exceeds a preset threshold are marked as priority defense targets.

[0074] Specifically, based on the obtained risk evolution simulation results, that is, the state transition probabilities corresponding to each node, the system automatically screens the entire power grid of the urban agglomeration and identifies the key weak links and target high-risk nodes in the power grid operation.

[0075] Among them, the risk contribution index is used to quantify the degree of system harm of each node. The risk contribution index is the sensitivity coefficient of the decline in the overall connectivity of the urban agglomeration power grid after the failure of the target high-risk node.

[0076] Specifically, substations and key communication hubs with risk contribution indices exceeding a preset threshold can be identified as high-risk weak points and automatically marked as priority defense targets. This enables precise location of hidden dangerous nodes in extreme typhoon scenarios, solving the problem that traditional methods struggle to identify hidden weak points with low initial risk and high-coupling amplified risk.

[0077] Furthermore, a multi-objective optimization decision-making model can be constructed. The objective function of the multi-objective optimization decision-making model includes the expected load loss, the expected disaster recovery time, and weight coefficients. Based on the multi-objective optimization decision-making model, a coordinated decision-making instruction set is generated, and the decision-making instructions in the coordinated decision-making instruction set are sent to the dispatch centers at all levels of the urban agglomeration power grid.

[0078] The objective function of the target optimization decision model is expressed by the following formula: .

[0079] Where F is the total loss cost, and These are the weighting coefficients. For the expected load loss, This is the system's expected disaster recovery time.

[0080] Among them, the multi-objective optimization decision model takes reducing the overall loss cost of the power grid as its core objective. The objective function comprehensively considers the expected power loss of the system and the expected disaster recovery time of the system, and configures corresponding weight coefficients to balance power supply reliability and post-disaster recovery efficiency.

[0081] Then, based on the optimal solution of the multi-objective optimization model, the system generates a coordination decision instruction set. This set includes control methods such as primary-side power flow optimization and adjustment, cross-regional power support, regional reserve resource sharing, and source-grid-load-storage coordinated linkage. It also covers secondary-side communication link redundancy switching and critical communication channel protection strategies. By establishing a multi-dimensional resource linkage mechanism, the system ensures overall stability even when extreme weather causes local control failures, through cross-regional coordination.

[0082] Finally, the system adopts a hierarchical and partitioned execution path, distributing the generated coordination and decision instructions layer by layer to dispatch centers and substation execution units at the city cluster level, city level, and station level.

[0083] After the instructions are implemented, the system continuously monitors the power grid topology, electrical operating status, load level and communication link recovery status in real time. Based on real-time feedback from the field, it dynamically corrects the risk coefficient and subsequent risk evolution path, and updates the new round of risk simulation and decision-making strategies in real time.

[0084] In this embodiment, the physical topology matrix, electrical measurements, communication link status data, and meteorological disturbance intensity values ​​of each grid node of the urban agglomeration power grid are first obtained. Based on the communication link status data, the availability of the corresponding communication links is determined. Then, a symmetric risk scale is calculated based on the physical topology matrix, electrical measurements, communication link status data, and meteorological disturbance intensity values ​​of each grid node. Next, a three-dimensional coupled topology matrix of electrical and communication systems is constructed to characterize the interaction strength among the power grid, communication, and meteorology. Then, based on the communication link availability and the symmetric risk scale, a nonlinear coupling calculation of the risk amplification factor is performed to obtain the coupled risk value. Finally, based on the three-dimensional coupled topology matrix of electrical and communication systems and the coupled risk value, the evolution path of the power grid state is dynamically predicted at different spatiotemporal scales, yielding the evolutionary projection results. This solves the problem of incompatibility in assessment models caused by voltage levels, geographical spans, and subject differences in traditional methods, enabling a consistent and fair comparison of the risk status of power grids in different areas within the urban agglomeration. The application introduces a new modeling step for the risk amplification mechanism of multi-system coupling involving electricity, gas, and telecommunications. This step quantifies and identifies dangerous nodes that, while initially posing low physical risks, are highly susceptible to systemic collapse due to their location in communication-vulnerable or meteorological extreme zones. This significantly improves the comprehensiveness and accuracy of risk identification in extreme scenarios. It overcomes the limitations of existing technologies that only offer "post-event analysis" or "static snapshots," enabling real-time prediction of the dynamic displacement of risks during typhoon movement. This provides dispatchers with ample time for defense and coordinated response of power generation, grid, load, and storage systems. Through the newly added cross-regional collaborative decision-making support logic, this application directly facilitates vertical integration and horizontal mutual support across multiple regions, from "city cluster level" to "city level" to "station level." This effectively prevents the blind and isolated operation of local power grids during typhoons, significantly enhancing the resilience and disaster recovery capabilities of the entire power system.

[0085] Figure 2This is a flowchart illustrating the hierarchical architecture of the multi-system coupling risk analysis method for power grid meteorological communication in this application embodiment. The architecture consists of four layers from top to bottom: a data perception layer, a data processing layer, an inference layer, and a decision layer. The data perception layer is responsible for acquiring power grid physical topology and power flow data, typhoon meteorological field data V and P, and communication network management status η. It performs spatial coordinate mapping between the power grid and meteorological data and identifies service links in the communication network management status. The data processing layer is responsible for constructing a multi-dimensional risk feature vector X, which, after being processed by Min-Max dimensionless transformation, is scaled and symmetrically corrected to obtain a symmetric risk scale R_sym. The inference layer is responsible for constructing the electrical-information CPS coupling matrix M_CPS, combining it with the typhoon intensity SDE evolution model to calculate the risk amplification factor RAF, and generating a multi-scale spatiotemporal state transition matrix P. The decision layer is responsible for identifying weak links and calculating risk contribution, inputting the multi-target defense optimization model to generate a coordinated decision instruction set, and forming a complete risk control process through hierarchical and partitioned execution and closed-loop feedback.

[0086] Corresponding to the power grid meteorological communication multi-system coupling risk analysis method in the above embodiment, Figure 3 This is a structural block diagram of the power grid meteorological communication multi-system coupling risk analysis device provided in the embodiments of this application.

[0087] Reference Figure 3 The power grid meteorological communication multi-system coupled risk analysis device 200 includes: The acquisition module 210 is used to acquire the physical topology matrix, electrical measurement values, communication link status data, and meteorological disturbance intensity values ​​of each power grid node of the urban agglomeration power grid, and to determine the corresponding communication link availability based on the communication link status data. The calculation module 220 is used to calculate the symmetric risk scale based on the physical topology matrix, the electrical measurement values, the communication link availability corresponding to the communication link status data, and the meteorological disturbance intensity values ​​experienced by each power grid node. Module 230 is used to construct a three-dimensional spatial coupling topology matrix of electrical and communication systems, which is used to characterize the interaction strength among power grid, communication and meteorology. The execution module 240 is used to perform a nonlinear coupling calculation of the risk amplification factor based on the availability of the communication link and the symmetric risk scale, so as to obtain the coupling risk value; The prediction module 250 is used to dynamically predict the power grid state evolution path at different spatiotemporal scales based on the electrical and information three-dimensional spatial coupling topology matrix and the coupling risk value, and obtain the evolution inference results.

[0088] Optionally, the communication link status data includes the link status between the substation control layer and the bay layer, the link status between the bay layer and the process layer, and the connection status of the core business link between the dispatch master station and the substation; the acquisition module is specifically used to: determine whether the target communication link is interrupted based on the communication link status data; if the target communication link is determined to be interrupted, the corresponding communication link availability is assigned a value of 0; if the target communication link is determined to be connected, the corresponding communication link availability is assigned a value of 1.

[0089] Optionally, the calculation module includes a feature processing unit and a scaling calculation unit; the feature processing unit is used to encapsulate the physical topology matrix, the electrical measurement value, the meteorological disturbance intensity value, and the communication link availability into a multi-dimensional risk feature vector, and to perform dimensionless normalization processing on the multi-dimensional risk feature vector to obtain a standardized feature quantity; the scaling calculation unit is used to calculate a symmetric risk scale based on the standardized feature quantity.

[0090] Optionally, the execution module is specifically used to: call a preset second model, combine the communication link availability, the symmetric risk scale, and the risk sensitivity adjustment coefficient, perform nonlinear coupling calculation of the risk amplification factor, and obtain the coupling risk value.

[0091] Optionally, the device further includes a node identification module, an index calculation module, and a marking module; the node identification module is used to identify target high-risk nodes in the urban agglomeration power grid based on the evolutionary simulation results; the index calculation module is used to calculate the risk contribution index of the target high-risk node, the risk contribution index being a sensitivity coefficient of the failure of the target high-risk node to the overall system connectivity reduction; the marking module is used to mark target high-risk nodes whose risk contribution index exceeds a preset threshold as priority defense targets.

[0092] Optionally, the device further includes a decision modeling module and an instruction issuance module; the decision modeling module is used to construct a multi-objective optimization decision model, wherein the objective function of the multi-objective optimization decision model includes the expected load loss, the expected disaster recovery time, and weight coefficients; the instruction issuance module is used to generate a coordinated decision instruction set based on the multi-objective optimization decision model, and issue the decision instructions in the coordinated decision instruction set to the dispatch centers at all levels of the urban agglomeration power grid.

[0093] In this embodiment, the physical topology matrix, electrical measurements, communication link status data, and meteorological disturbance intensity values ​​of each grid node of the urban agglomeration power grid are first obtained. Based on the communication link status data, the availability of the corresponding communication links is determined. Then, based on the physical topology matrix, electrical measurements, communication link status data, and meteorological disturbance intensity values ​​of each grid node, a symmetric risk scale is calculated. Next, a three-dimensional coupled topology matrix of electrical and communication systems is constructed to characterize the interaction strength among the power grid, communication, and meteorology. Then, based on the communication link availability and the symmetric risk scale, a nonlinear coupling calculation of the risk amplification factor is performed to obtain the coupled risk value. Finally, based on the three-dimensional coupled topology matrix of electrical and communication systems and the coupled risk value, the evolution path of the power grid state is dynamically predicted at different spatiotemporal scales, yielding the evolutionary projection results. Thus, by integrating multi-source information from power grid topology, electrical operation data, communication link status, and meteorological disturbances, and simultaneously quantifying communication link availability and the symmetric risk scale, a comprehensive perception and quantitative characterization of the influencing factors of multiple systems (power grid, communication, and meteorology) is achieved, overcoming the limitation of traditional risk analysis that only considers single-dimensional factors. The coupled risk value is obtained through nonlinear calculation of the risk amplification factor, accurately reflecting the superposition effect of risks from multiple systems, and dynamically extrapolating the evolution of the power grid state at multiple spatiotemporal scales. This scheme achieves accurate analysis and prediction of coupled risks from multiple systems, making up for the shortcomings of traditional methods that are limited in dimension and cannot reflect the interconnected effects between systems. It can effectively support the safe operation of urban power grids and disaster prevention and control.

[0094] The application areas of this application include power system automation, smart grid resilient operation, and urban cluster emergency management. It can provide risk assessment and auxiliary decision support for power dispatching departments, energy regulatory agencies, and urban lifeline management platforms under extreme weather scenarios.

[0095] In terms of specific applications, this application can be deployed in provincial or cross-regional power grid dispatching systems. By sensing meteorological disturbances, the physical state of the power grid, and the coupling relationship between key communication links within substations and between master and slave stations in real time, it can identify control blind spots caused by communication anomalies and quantify the degree of system risk amplification.

[0096] Meanwhile, this application can be applied to the integrated energy management system of urban agglomerations. By dynamically predicting the cross-regional evolution path of risks throughout the entire typhoon process, it can guide the coordinated linkage and emergency mutual assistance of power generation, grid, load and storage resources, realize the proactive defense, risk mitigation and rapid recovery of the power system under extreme weather scenarios, and significantly improve the coordination decision-making efficiency and operational stability of key infrastructure in urban agglomerations in complex environments.

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0098] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. For example... Figure 4 As shown, the electronic device 5 of this embodiment includes: at least one processor 50 ( Figure 4 (Only one is shown in the diagram) a processor, a memory 51, and a computer program 52 stored in the memory 51 and executable on the at least one processor 50, wherein the processor 50 executes the computer program 52 to implement the steps in any of the above embodiments of the power grid meteorological communication multi-system coupling risk analysis method.

[0099] The electronic device may be a desktop computer, laptop, handheld computer, or cloud server, etc. This electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 5 and does not constitute a limitation on electronic device 5. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0100] The processor 50 may be a central processing unit, or it may be other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0101] In some embodiments, the memory 51 may be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. In other embodiments, the memory 51 may be an external storage device of the electronic device 5, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc., equipped on the electronic device 5. Further, the memory 51 may include both internal storage units and external storage devices of the electronic device 5. The memory 51 is used to store operating systems, applications, boot loaders, data, and other programs, such as the program code of the computer program. The memory 51 can also be used to temporarily store data that has been output or will be output.

[0102] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps in the above-described method embodiments.

[0103] This application provides a computer program product that, when run on an electronic device, enables the electronic device to implement the steps described in the various method embodiments above.

[0104] If the integrated unit is implemented as a software functional unit and used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / electronic device, a recording medium, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0105] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0106] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0107] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0108] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0109] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for risk analysis of multi-system coupling in power grid meteorological communication, characterized in that, include: The physical topology matrix, electrical measurements, communication link status data, and meteorological disturbance intensity values ​​of each power grid node of the urban agglomeration power grid are obtained, and the availability of the corresponding communication links is determined based on the communication link status data. Based on the physical topology matrix, the electrical measurement values, the communication link availability corresponding to the communication link status data, and the meteorological disturbance intensity values ​​experienced by each power grid node, a symmetric risk scale is calculated. A three-dimensional spatial coupling topology matrix for electrical and communications is constructed, which is used to characterize the interaction strength among power grid, communications and meteorology. Based on the communication link availability and the symmetric risk scale, a nonlinear coupling calculation of the risk amplification factor is performed to obtain the coupling risk value; Based on the electrical and information three-dimensional spatial coupling topology matrix and the coupling risk value, the power grid state evolution path is dynamically predicted at different spatiotemporal scales, and the evolution inference results are obtained.

2. The method according to claim 1, characterized in that, The communication link status data includes the link status between the substation control layer and the bay layer, the link status between the bay layer and the process layer, and the core business link connectivity status between the dispatch master station and the substation. The step of determining the corresponding communication link availability based on the communication link status data includes: Based on the communication link status data, determine whether the target communication link is interrupted; If the target communication link is interrupted, the availability of the corresponding communication link is determined to be 0. If the target communication link is found to be connected, the availability of the corresponding communication link is determined to be 1.

3. The method according to claim 1, characterized in that, The step of calculating the symmetric risk scale based on the physical topology matrix, the electrical measurement values, the communication link availability corresponding to the communication link status data, and the meteorological disturbance intensity values ​​experienced by each power grid node includes: The physical topology matrix, the electrical measurement values, the meteorological disturbance intensity values, and the communication link availability corresponding to the communication link status data are encapsulated into a multi-dimensional risk feature vector; The multidimensional risk feature vector is subjected to dimensionless normalization to obtain standardized feature quantities; Calculate the symmetric risk scale based on the standardized feature quantities.

4. The method according to claim 3, characterized in that, The step of calculating the symmetric risk scale based on the standardized feature quantity includes: The symmetric risk scale is calculated based on the preset first model and the standardized feature quantities. The formula for the first model is: in, This is a cross-regional scale correction factor. Let k be the k-th dimension of the standardized feature quantity. The weight factor is the standardized feature quantity of the k-th dimension. To symmetricize the risk scale.

5. The method according to claim 1, characterized in that, The nonlinear coupling calculation of the risk amplification factor based on the communication link availability and the symmetric risk scale to obtain the coupling risk value includes: Based on the preset second model, the communication link availability corresponding to the communication link status data, and the symmetric risk scale, a nonlinear coupling calculation of the risk amplification factor is performed to obtain the coupling risk value, wherein the formula of the second model is: in, For coupling risk value, To symmetricize the risk scale, The availability of the communication link, This is the risk sensitivity adjustment coefficient.

6. The method according to claim 5, characterized in that, After obtaining the evolution inference results by dynamically predicting the power grid state evolution path at different spatiotemporal scales based on the electrical-information three-dimensional spatial coupling topology matrix and the coupling risk value, the method further includes: Based on the evolutionary results, high-risk nodes in the urban agglomeration power grid were identified; Calculate the risk contribution index of the target high-risk node, which is defined as the sensitivity coefficient of the failure of the target high-risk node to the decrease in the overall connectivity of the system. High-risk nodes whose risk contribution index exceeds a preset threshold are marked as priority defense targets.

7. The method according to claim 6, characterized in that, The method further includes: A multi-objective optimization decision model is constructed, wherein the objective function of the multi-objective optimization decision model includes the expected power loss, the expected disaster recovery time, and weight coefficients; Based on the multi-objective optimization decision-making model, a coordinated decision-making instruction set is generated; The decision instructions in the coordination decision instruction set are sent to the dispatch centers at all levels of the urban agglomeration power grid.

8. A multi-system coupled risk analysis device for power grid meteorological communication, characterized in that, include: The acquisition module is used to acquire the physical topology matrix, electrical measurement values, communication link status data, and meteorological disturbance intensity values ​​of each power grid node of the urban agglomeration power grid, and to determine the corresponding communication link availability based on the communication link status data. The calculation module is used to calculate the symmetric risk scale based on the physical topology matrix, the electrical measurement values, the communication link availability corresponding to the communication link status data, and the meteorological disturbance intensity values ​​experienced by each power grid node. The construction module is used to construct a three-dimensional spatial coupling topology matrix of electrical and information systems, which is used to characterize the interaction strength among power grid, communication and meteorology. The execution module is used to perform a nonlinear coupling calculation of the risk amplification factor based on the availability of the communication link and the symmetric risk scale, so as to obtain the coupling risk value; The prediction module is used to dynamically predict the power grid state evolution path at different spatiotemporal scales based on the electrical and information three-dimensional spatial coupling topology matrix and the coupling risk value, and obtain the evolution inference results.

9. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program, which is loaded by a processor to perform the steps of the method according to any one of claims 1 to 7.

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