Power grid communication simulation method and system based on area interconnection

Abstract

The present invention relates to the field of network communication technology, specifically to a power grid communication simulation method and system based on the interconnection of substations, comprising the following steps: capturing the interconnection data between substations and the initial node information of the power grid communication simulation, setting a hypergraph according to the physical connection, storing the node data by classification according to the connection type and frequency, entering the hypergraph model, and obtaining the initial hypergraph structure data. In the present invention, by capturing the initial node information of the power grid communication simulation and setting the hypergraph, the fine management of the power grid data flow is realized, the node data is stored by classification according to the connection type and frequency, the accuracy and speed of data management are improved, the network performance is optimized, and the data flow direction is automatically optimized by analyzing the data flow and communication frequency, and weighting high-flow nodes, reducing communication delays, marking key nodes and recalculating communication paths, reducing delays and blockages, improving the network response speed and processing capacity, and enhancing the stability and efficiency of the network under high load and emergencies.

Description

Technical Field

[0001] The present invention relates to the field of network communication technology, and in particular to a power grid communication simulation method and system based on interconnection of substations. Background Art

[0002] The field of network communication technology mainly involves the transmission and exchange methods of various data, voice, video and other information. The field uses a variety of protocols and technologies to ensure that information is transmitted efficiently and securely in the global network. Network communication technology includes wired and wireless communication technology, including local area network (LAN), wide area network (WAN), mobile communication network and Internet technology. Network communication also emphasizes the application of real-time data processing and big data technology to support complex data analysis and decision support systems. With the development of the Internet of Things (IoT) and cloud computing, network communication technology is also evolving to adapt to a wider range of application scenarios and improve the reliability and scalability of network communication technology.

[0003] Among them, the grid communication simulation method is a technology that simulates the information communication flow in the power system, which is used to optimize the operation efficiency and reliability of the power grid. This method assists engineers and researchers to analyze and predict the performance of the power grid in normal operation and emergency situations by simulating the generation, transmission and reception of various information in the power network. The main uses include testing the reaction time, load management capabilities, fault detection and response mechanisms of the power grid. Through grid communication simulation, the effects of new communication technologies, protocols and network architectures, as well as their impact on the stability and efficiency of the power grid can be evaluated without affecting the actual operation of the power grid.

[0004] Although existing technologies cover a wide range of protocols and methods to ensure efficient and secure information transmission worldwide, they are deficient in handling real-time big data flows and peak load management. In the field of power grid communications, existing technologies fail to respond to changes in network loads in real time, resulting in data congestion and communication delays at critical moments in power system operation. In terms of fault detection and response mechanisms, traditional network communication technologies can handle routine situations, but in emergencies or extreme load conditions, the technology fails to effectively predict and process complex data flow changes, increasing the risk of instability in the power grid. For example, when the power grid suffers from sudden batch power consumption or equipment failure, the delayed response of existing technologies leads to damage to key equipment or widespread system shutdown, causing serious economic and safety consequences. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a power grid communication simulation method and system based on area interconnection.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, which is a power grid communication simulation method based on the interconnection of substations, comprising the following steps:

[0007] S1: Capture the interconnection data between stations and the initial node information of the power grid communication simulation, set up a hypergraph according to the physical connection, classify and store the node data according to the connection type and frequency, enter the hypergraph model, and obtain the initial hypergraph structure data;

[0008] S2: According to the initial hypergraph structure data, identify data traffic peaks and communication frequencies, assign weights to key nodes, adjust the weights through a criticality scoring mechanism, and obtain reconfiguration weight data;

[0009] S3: By reconfiguring weight data, detecting node connectivity, marking key nodes and connections, analyzing data flow and communication error rate, and generating a list of key nodes and connections;

[0010] S4: based on the key nodes and the connection list, recalculate the path of the power grid communication, calculate the optimal data transmission path between the nodes, adjust the path, and generate optimized communication path data;

[0011] S5: using the optimized communication path data, dynamically adjusting the hypergraph structure of the power grid communication simulation to match the real-time demand of the power grid operation, and obtaining the dynamically adjusted network structure data;

[0012] S6: Perform simulation test on the dynamically adjusted network structure data to verify the stability of data transmission and communication efficiency, simulate the network performance under the interconnection load and emergency conditions, and generate network simulation test results.

[0013] As a further solution of the present invention, the initial hypergraph structure data includes node type, connection frequency, and storage structure; the reconfiguration weight data includes node weight value, adjusted weight distribution, and optimized data flow direction; the key node and connection list includes key node identification, key connection type, and communication error points; the optimized communication path data includes path optimization scheme, response speed improvement point, and processing capability enhancement area; the dynamically adjusted network structure data includes network node update, connection adjustment result, and real-time demand response; the network simulation test results include stability verification data, efficiency evaluation results, and load response strategy.

[0014] As a further solution of the present invention, the steps of capturing the interconnection data between substations and the initial node information of the power grid communication simulation, setting up a hypergraph according to the physical connection, classifying and storing the node data according to the connection type and frequency, and entering the hypergraph model to obtain the initial hypergraph structure data are specifically as follows:

[0015] S101: Capture the interconnection data between stations and the initial node information of the power grid communication simulation, use physical and communication parameters to classify and store the data, and establish a node data classification and storage framework by dividing the node connection type and transmission frequency parameters;

[0016] S102: using the classification information in the node data classification storage framework, performing preliminary construction of a hypergraph structure, organizing supernodes and defining edge attributes through node connection types and data transmission frequency parameters, performing preliminary mapping of physical and logical relationships between nodes in the hypergraph, and generating a hypergraph structure sketch;

[0017] S103: Based on the hypergraph structure sketch, refine the connection mode and communication protocol details between the hypergraph nodes, adjust the attribute definitions of the nodes and edges, optimize the data storage strategy, and obtain initial hypergraph structure data.

[0018] As a further solution of the present invention, according to the initial hypergraph structure data, the data traffic peak and the communication frequency are identified, weights are assigned to key nodes, and the weights are adjusted through a criticality scoring mechanism. The steps of obtaining the reconfiguration weight data are specifically as follows:

[0019] S201: extracting the data flow and communication frequency of each node from the initial hypergraph structure data, counting the flow peaks and communication frequency averages of multiple nodes in a target time period, performing time series analysis, identifying periodic changes in flow and communication, and generating a flow frequency summary table;

[0020] S202: using the traffic frequency summary table, performing a data sorting operation, arranging the traffic and communication frequency of each node, selecting key nodes from the sorted data, performing feature analysis on the nodes, identifying common features of traffic and frequency nodes, marking the nodes as priority key nodes, and generating a key node list;

[0021] S203: According to the key node list, the weights of the nodes in the list are adjusted to optimize the data flow of the power grid communication network, and the weight allocation and network structure are reorganized to obtain reconfigured weight data.

[0022] As a further solution of the present invention, the steps of reconfiguring weight data, detecting node connectivity, marking key nodes and connections, analyzing data flow and communication error rate, and generating a list of key nodes and connections are specifically as follows:

[0023] S301: According to the reconfigured weight data, a connectivity test is performed by real-time monitoring of the connection status of the node and the weight value of the data packet, and the communication frequency and data packet size of each node are analyzed to determine the active and inactive connection paths in the network and generate a connectivity test result;

[0024] S302: Based on the connectivity detection result, determine the nodes and connections of key roles in the power grid by evaluating the data transmission records of each node and the network position of the node, including recording and analyzing the data transmission frequency of the node and the stability of the connection, marking the key transmission nodes and key connections of the power grid, and generating a key node and connection map;

[0025] S303: Using the key node and connection map, count the data flow direction of the key nodes and the communication error rate of the connection, including the data transmission efficiency of the nodes and connections, error records and communication frequency records, identify potential problem areas, and generate a list of key nodes and connections.

[0026] As a further solution of the present invention, based on the key nodes and the connection list, the path of the power grid communication is recalculated, and the optimal data transmission path between nodes is calculated, the path is adjusted, and the steps of generating optimized communication path data are specifically as follows:

[0027] S401: Based on the key nodes and connection list, identify potential communication paths between nodes, use a network topology view to display the connection status between nodes, evaluate the delay and blocking data of the communication paths of the nodes, calculate the response time path between multiple nodes, and generate a preliminary optimization path table;

[0028] S402: using the data in the preliminary optimized path table, monitoring the real-time traffic conditions of multiple paths in the simulated network, marking the mined abnormal traffic and blocked paths and analyzing the causes, adjusting the routing settings of the associated paths, and generating an adjusted path demand table;

[0029] S403: Based on the adjusted path demand table, the communication weights of the affected nodes are adjusted, including the adjustment of the target node weights, the power grid communication network structure is optimized, and optimized communication path data is obtained.

[0030] As a further solution of the present invention, the optimized communication path data is used to dynamically adjust the hypergraph structure of the power grid communication simulation to match the real-time needs of the power grid operation, and the steps of obtaining the dynamically adjusted network structure data are specifically as follows:

[0031] S501: using the optimized communication path data, applying a decision tree algorithm, collecting dynamic information of real-time transmission efficiency and network load, identifying performance standards of multiple nodes and connections, analyzing the situation of nodes being overloaded and having low transmission efficiency, verifying adjustment targets through real-time comparison of data frequency and load, and generating a list of node and connection adjustment requirements;

[0032] S502: Based on the node and connection adjustment requirement list, dynamically adjust the connection relationship between nodes in the network and update the connection weight, configure the connection lines between nodes, adjust the weight values ​​of multiple connection points and match the differentiated transmission requirements, reallocate network resources according to the real-time network load situation, and generate an optimized hypergraph structure sketch;

[0033] S503: Use the optimized hypergraph structure sketch to perform adjustment verification, refine the parameter settings of nodes and connections, lock the network structure, and obtain dynamically adjusted network structure data.

[0034] As a further solution of the present invention, the formula of the decision tree algorithm is as follows:

[0035] ,

[0036] Among them, T represents the average transmission efficiency of the node, L represents the network load, and C represents the number of connections in the current network. is the weight coefficient of transmission efficiency, is the weight coefficient of the network load, is the adjustment coefficient of the load growth rate, V represents the average load growth rate of nodes in the network, and S represents the score of network performance.

[0037] As a further solution of the present invention, a simulation test is performed on the dynamically adjusted network structure data to verify the stability and communication efficiency of data transmission, simulate the network performance under the interconnection load and emergency conditions of the substation area, and generate the network simulation test results in the following steps:

[0038] S601: Based on the dynamically adjusted network structure data, configure the parameter settings of the simulation environment, the node configuration of the simulation network and the initialization of the communication protocol, including the setting of the number of nodes, connection type and transmission rate, and generate the simulation environment configuration result;

[0039] S602: Using the simulation environment configuration result, perform key network load simulation, including injecting traffic data into the network model and simulating sudden disconnection events, monitoring the response and recovery capabilities of the network, recording key performance parameters, including response time and network recovery rate, and obtaining batch load response data;

[0040] S603: Based on the batch load response data, analyze network performance, evaluate the network's ability to match high loads and emergencies, analyze latency, packet loss rate, and recovery time, determine the degree of matching between network design and operational requirements, and generate network simulation test results.

[0041] A power grid communication simulation system based on area interconnection, which is used to execute the above-mentioned power grid communication simulation method based on area interconnection, comprises:

[0042] The hypergraph initialization module captures the interconnection data between stations and the initial node information of the power grid communication simulation, determines the connection between nodes through physical connections, integrates the data to set up the hypergraph, and stores the data nodes according to the connection type and frequency to obtain the initial hypergraph structure set;

[0043] The data flow analysis module uses the initial hypergraph structure set to count the data transmission frequency and flow between multiple nodes, assigns weights to nodes with frequent transmission, optimizes the data flow line through a dynamic weight adjustment strategy, and obtains a reconfiguration weight set;

[0044] The key node identification module uses the reconfiguration weight set to evaluate and measure the connectivity of multiple nodes, identify and record key nodes and connections for data flow and communication efficiency, and establish a list of key nodes and connections;

[0045] The path optimization module recalculates the data transmission path according to the key nodes and connection list, adjusts the path and matches the real-time network traffic changes, reduces delays, optimizes response speed and processing capacity, and obtains optimized communication path data;

[0046] The dynamic adjustment and testing module uses the optimized communication path data to adjust the hypergraph structure of the power grid communication simulation and match the real-time network load, performs simulation tests to evaluate communication stability and efficiency, and constructs network simulation test results.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by capturing the initial node information of the power grid communication simulation and constructing the initial hypergraph structure based on the physical connection, the fine management of the power grid data flow is realized. By classifying and storing the node data according to the connection type and frequency, the accuracy and access speed of data management are effectively improved, thereby optimizing the overall performance of the power grid communication network. The data flow peak and communication frequency between stations are analyzed, and weights are given to high flow and frequency nodes, so that the network can automatically optimize the data flow during the peak flow period and reduce the communication delay caused by traffic congestion. By marking key nodes and connections and recalculating the power grid communication path, the delay and blockage in data transmission are further reduced, and the response speed and processing capacity of the network are significantly improved. This strategy of dynamically adjusting the network structure not only matches the real-time needs of power grid operation, but also enhances the stability and communication efficiency of the network under high load and emergency conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0058] In the description of the present invention, it is necessary to interpret that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be interpreted as a limitation on the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a power grid communication simulation method based on area interconnection, comprising the following steps:

[0060] S1: Capture the interconnection data between substations and the initial node information of the power grid communication simulation, build the initial hypergraph structure according to the physical connection, classify and store the node data according to the connection type and frequency, enter the hypergraph model, and obtain the initial hypergraph structure data;

[0061] S2: Based on the initial hypergraph structure data, analyze the data flow peak and communication frequency between stations, assign weights to nodes with large flow and high frequency, adjust the weights of multiple nodes through the node criticality scoring mechanism, optimize the data flow lines of the power grid communication network, and obtain reconfiguration weight data;

[0062] S3: By reconfiguring weight data, detecting the connectivity between multiple nodes, identifying and marking the key nodes and connections of the interconnected areas in the power grid, analyzing the data flow and communication error rate of key elements, and generating a list of key nodes and connections;

[0063] S4: Based on the key nodes and connection lists, the path of power grid communication is recalculated, and the optimal data transmission path between nodes is calculated. The path is adjusted in real time according to the network traffic simulated by power grid communication, the delay and blockage in data transmission are reduced, the response speed and processing capacity of the network are optimized, and the optimized communication path data is generated;

[0064] S5: Using the optimized communication path data, dynamically adjust the hypergraph structure of the power grid communication simulation, adjust the nodes and connections according to the real-time data and network load conditions, match the real-time needs of the power grid operation, and obtain the dynamically adjusted network structure data;

[0065] S6: Conduct simulation tests on the dynamically adjusted network structure data to verify the stability of data transmission and communication efficiency, simulate the network performance under the interconnection load and emergency conditions, verify the matching and effectiveness of the network design, and generate network simulation test results.

[0066] The initial hypergraph structure data includes node type, connection frequency, and storage structure. The reconfiguration weight data includes node weight value, adjusted weight distribution, and optimized data flow direction. The key node and connection list includes key node identification, key connection type, and communication error points. The optimized communication path data includes path optimization solutions, response speed improvement points, and processing capacity enhancement areas. The dynamically adjusted network structure data includes network node updates, connection adjustment results, and real-time demand response. The network simulation test results include stability verification data, efficiency evaluation results, and load response strategies.

[0067] Specifically, if Figure 2 As shown in the figure, the interconnection data between substations and the initial node information of the power grid communication simulation are captured, a hypergraph is set according to the physical connection, the node data is classified and stored according to the connection type and frequency, and the hypergraph model is entered to obtain the initial hypergraph structure data. Specifically, the steps are as follows:

[0068] S101: Capture the interconnection data between stations and the initial node information of the power grid communication simulation, use physical and communication parameters to classify and store data, and divide by node connection type and transmission frequency parameters to establish the execution process of the node data classification storage framework as follows;

[0069] In capturing the interconnection data between substations and the initial node information of the power grid communication simulation, the interconnection data between substations include physical parameters such as voltage, current, power and frequency. The initial node information of the power grid communication simulation involves the type of node, connection status and communication protocol. By collecting data, the status of the power grid can be monitored in real time. The data is preliminarily classified and stored to facilitate subsequent data call and analysis. The classification is mainly based on the node connection type and transmission frequency parameters. Such classification helps to optimize data retrieval efficiency and reduce the complexity of data processing. For the establishment of the data storage framework, a database management system is adopted. By setting reasonable database indexes, the query and storage speeds are improved. The establishment of the node data classification storage framework provides data support for the stable operation of the power grid.

[0070] S102: Use the classification information in the node data classification storage framework to perform preliminary construction of the hypergraph structure, organize the supernodes and define the edge attributes through the node connection type and data transmission frequency parameters, perform preliminary mapping of the physical and logical relationships of the nodes in the hypergraph, and generate the hypergraph structure sketch. The execution process is as follows;

[0071] Use node data classification to store classification information in the framework, according to the formula:

[0072] ,

[0073] Calculate the preliminary mapping of the physical and logical relationships of nodes in the hypergraph, where E represents the energy of the hypergraph nodes, m represents the connection density of the nodes, and v represents the data transmission frequency parameter;

[0074] Referring to a node in a power grid, its connection density m is 5 (indicates that one node is connected to five nodes), and the data transmission frequency v is 10Hz, substitute the value into the energy formula In the calculation Unit energy;

[0075] This result shows that in a hypergraph structure, when the connection density and transmission frequency of a node are high, the energy of the node is also relatively high, which affects the importance of the node in the hypergraph and the priority of data processing.

[0076] S103: Based on the hypergraph structure sketch, refine the connection mode and communication protocol details between hypergraph nodes, adjust the attribute definition of nodes and edges, optimize the data storage strategy, and obtain the initial hypergraph structure data. The execution process is as follows;

[0077] In refining the connection mode and communication protocol details between hypergraph nodes based on the hypergraph structure sketch, the refinement includes determining whether the nodes are directly or indirectly connected, and selecting appropriate communication protocols to ensure the security and efficiency of data transmission. For example, for nodes that need to exchange data frequently, you can choose efficient communication protocols such as MQTT or CoAP, optimize the attribute definitions of nodes and edges, including the role and function of the node, and the weight and direction of the edge. The adjustment of attributes is to better reflect the actual role of the node in the power grid and the flow of data. Optimizing data storage strategies involves selecting appropriate data compression methods and data backup strategies to reduce storage space and increase the speed of data recovery. In the process of obtaining initial hypergraph structure data, it is necessary to ensure the integrity and consistency of the data. The refined operations provide a guarantee for the effective operation of the hypergraph structure.

[0078] Specifically, if Figure 3 As shown, according to the initial hypergraph structure data, the data traffic peak and communication frequency are identified, weights are assigned to key nodes, and the weights are adjusted through the criticality scoring mechanism. The specific steps for obtaining the reconfiguration weight data are as follows:

[0079] S201: extracting the data flow and communication frequency of each node from the initial hypergraph structure data, counting the peak flow and average communication frequency of multiple nodes in the target time period, and performing time series analysis to identify the periodic changes in flow and communication, and generating a flow frequency summary table. The execution process is as follows;

[0080] In the process of extracting the data flow and communication frequency of each node from the initial hypergraph structure data, the flow data of each node is monitored, including the total input and output. The data reflects the working intensity of the node during the target time period. Combined with the communication frequency data of the node, time series analysis can be performed. The analysis involves statistical methods and mathematical models, such as the autoregressive moving average model, to identify the periodic changes in flow and communication. By identifying periodic changes, future data flow peaks can be predicted more accurately, which is crucial for optimizing network resource allocation and traffic management. The final generated flow frequency summary table includes the flow and frequency data of all nodes during the monitoring period, providing basic data for the next step of data sorting operations.

[0081] S202: Using the traffic frequency summary table, perform a data sorting operation, arrange the traffic and communication frequency of each node, select key nodes from the sorted data, perform feature analysis on the nodes, identify common features of traffic and frequency nodes, mark the nodes as priority key nodes, and generate the key node list. The execution process is as follows;

[0082] Using the flow frequency summary table, according to the formula:

[0083] ,

[0084] Calculate the average communication frequency of each node, where R represents the average communication frequency, represents the communication frequency of the ith node, represents the traffic of the ith node, and n represents the total number of nodes;

[0085] Assume that there are 5 nodes in a network configuration, and the communication frequencies of multiple nodes are 10Hz, 20Hz, 15Hz, 10Hz and 5Hz respectively. The corresponding traffic is 100MB, 150MB, 120MB, 80MB and 60MB respectively. Substitute the values ​​into the formula Calculated in ;

[0086] This result shows the average communication frequency of nodes in the network configuration, which has important guiding significance for network optimization and resource allocation.

[0087] S203: According to the key node list, the weights of the nodes in the list are adjusted to optimize the data flow of the power grid communication network, and the weight distribution and network structure are reorganized. The execution process of obtaining the reconfigured weight data is as follows;

[0088] In the process of adjusting the weights of the nodes in the list according to the key node list, the data transmission rate, node computing power and connection reliability of each node are analyzed. These factors directly determine the role and importance of the node in the power grid communication network. Weight allocation involves the reorganization of the network structure and how to reasonably allocate resources to improve overall efficiency. For example, increase the weight of high-efficiency nodes and reduce the weight of inefficient nodes. Such adjustments help optimize data flow, make data transmission more efficient, reduce latency, and improve response speed. In the process of obtaining reconfiguration weight data, it is necessary to refer to the real-time and reliability of the network to ensure that all modifications will not affect the stability of the power grid communication network. This is crucial to maintaining the normal operation of the power grid.

[0089] Specifically, if Figure 4 As shown in the figure, by reconfiguring weight data, detecting node connectivity, marking key nodes and connections, analyzing data flow and communication error rate, the steps of generating a list of key nodes and connections are as follows:

[0090] S301: According to the reconfigured weight data, the connectivity test is performed by real-time monitoring of the connection status of the node and the weight value of the data packet, and the communication frequency and data packet size of each node are analyzed to determine the active and inactive connection paths in the network. The execution process of generating the connectivity test result is as follows;

[0091] According to the need to reconfigure weight data, connectivity tests are performed by real-time monitoring of the connection status of each node and the weight value of the data packet, and analyzing the communication frequency and data packet size of each node. This analysis helps determine which connection paths in the network are active and which are inactive, which is crucial for network management. This method can effectively monitor and adjust network configuration to ensure efficient operation of the network, generate connectivity test results, and reflect the real-time status of each connection path in the network.

[0092] S302: Based on the connectivity detection result, by evaluating the data transmission record of each node and the network position of the node, the nodes and connections of key roles in the power grid are determined, including recording and analyzing the data transmission frequency of the nodes and the stability of the connection, marking the key transmission nodes and key connections of the power grid, and generating the key node and connection map. The execution process is as follows;

[0093] Based on the connectivity test results, the system evaluates the data transmission record and network position of each node according to the formula:

[0094] ,

[0095] Calculate the importance of key nodes in the power grid, where represents the importance score of node i, represents the frequency of data transmission from node i to node j, Represents the stability of the connection;

[0096] Consider a specific network with three nodes. The data transmission frequency from node 1 to node 2 is , the stability of the connection , the data transmission frequency from node 1 to node 3 is , the stability of the connection , the importance score of node 1 Calculated as ;

[0097] This calculation process illustrates how to evaluate the criticality of a node by combining its communication frequency and connection stability.

[0098] S303: Using the key node and connection map, the data flow direction of the key nodes and the communication error rate of the connection are counted, including the data transmission efficiency of the nodes and connections, error records and communication frequency records, and potential problem areas are identified. The execution process of generating a list of key nodes and connections is as follows;

[0099] Using key node and connection maps, the data flow of key nodes and the communication error rate of connections are counted. Data analysis includes the data transmission efficiency of nodes and connections, error records and communication frequency records. Data helps identify potential problem areas in the network. Through this comprehensive analysis, network performance can be effectively monitored and optimized, the stability and efficiency of key communication paths can be ensured, and a list of key nodes and connections can be generated, providing important decision-making support information for network management.

[0100] Specifically, if Figure 5 As shown, based on the key nodes and connection lists, the path of power grid communication is recalculated, and the optimal data transmission path between nodes is calculated, the path is adjusted, and the steps of generating optimized communication path data are as follows:

[0101] S401: Based on the key node and connection list, identify the potential communication paths between nodes, use the network topology view to display the connection status between nodes, evaluate the delay and blocking data of the communication paths of the nodes, calculate the response time path between multiple nodes, and generate the preliminary optimization path table. The execution process is as follows;

[0102] The communication path of the node is evaluated for delay and blocking data according to the formula:

[0103] ,

[0104] Calculate the response time path between multiple nodes, where O represents the total response time path, represents the delay time of the i-th node, represents the blocking time of the i-th node;

[0105] Referring to a simple network model, including three nodes, the delay and blocking data of each node are:

[0106] = 2 ms, = 1 ms;

[0107] = 3 ms, = 1.5 ms;

[0108] = 2.5 ms, = 0.5 ms;

[0109] Applying the above formula, the total response time path is calculated as: millisecond;

[0110] The result shows that the average response time of the entire network is 3.5 milliseconds, which is an important indicator for evaluating network communication efficiency.

[0111] S402: using the data in the preliminary optimized path table, monitoring the real-time traffic conditions of multiple paths in the simulated network, marking the mined abnormal traffic and blocked paths and analyzing the causes, adjusting the routing settings of the associated paths, and generating the adjusted path demand table, the execution process is as follows;

[0112] Based on the data in the preliminary optimized path table, the real-time traffic conditions of multiple paths in the simulated network are monitored, the mined abnormal traffic and blocked paths are marked and the causes are analyzed, and the routing settings of the associated paths are adjusted. The process involves real-time monitoring and analysis of network traffic data, which requires specific algorithms or software tools to identify traffic patterns and anomalies, collect traffic data in real time, and use statistical analysis methods to determine the standard deviation and average of traffic. Statistical indicators assist in identifying traffic fluctuations that deviate from the normal state. Once abnormal traffic or high congestion is detected, it is immediately marked and analyzed for causes involving network configuration errors, hardware failures, or external attacks. According to the analysis results, the network routing settings are adjusted to optimize the paths, ensure data transmission efficiency and stable network operation, and generate an adjusted path demand table.

[0113] S403: Based on the adjusted path demand table, the communication weights of the affected nodes are adjusted, including the adjustment of the target node weights, the power grid communication network structure is optimized, and the execution process of obtaining the optimized communication path data is as follows;

[0114] Based on the adjusted path demand table, the communication weights of the affected nodes are adjusted. The process involves dynamic adjustment of the weight parameters of the communication nodes in the network management system. The purpose is to optimize the entire power grid communication network structure and achieve path optimization. During the operation, it is necessary to judge the communication load of each node based on the real-time performance data and historical performance indicators of the network, and adjust the weight of each node according to the communication volume, delay time and historical blocking records, in order to achieve the optimization of the entire network communication. The adjustment of parameters depends on complex algorithm models, including the application of network algorithms and dynamic analysis of network topology. After optimization, the optimized communication path data is obtained.

[0115] Specifically, if Figure 6 As shown, the hypergraph structure of the power grid communication simulation is dynamically adjusted by optimizing the communication path data to match the real-time needs of the power grid operation. The specific steps for obtaining the dynamically adjusted network structure data are as follows:

[0116] S501: Utilizing the optimized communication path data, applying the decision tree algorithm, collecting dynamic information of real-time transmission efficiency and network load, identifying the performance standards of multiple nodes and connections, analyzing the situation of nodes being overloaded and having low transmission efficiency, verifying the adjustment target through real-time comparison of data frequency and load volume, and generating the node and connection adjustment requirement list, the execution process is as follows;

[0117] The formula for the decision tree algorithm is as follows:

[0118] ,

[0119] Among them, T represents the average transmission efficiency of the node, L represents the network load, and C represents the number of connections in the current network. is the weight coefficient of transmission efficiency, is the weight coefficient of the network load, is the adjustment coefficient of the load growth rate, V represents the average load growth rate of nodes in the network, and S represents the score of network performance.

[0120] Detailed explanation of the formula and the process of formula calculation and derivation:

[0121] T (average node transmission efficiency) is obtained from the network monitoring configuration, recording the transmission data of each node over a period of time, and setting the average transmission efficiency obtained within one hour to 5Mbps;

[0122] L (network load) is also obtained from the network monitoring configuration and represents the total load. The total load at the same time point is set to 2000MB.

[0123] C (number of network connections) is obtained from the network management tool. This value indicates the current number of active connections. Let the current number of connections be 50.

[0124] and is the weight coefficient, which is determined based on historical data analysis. The weight is set based on the network's priority service principle and business needs. =0.3 and =0.7, reflecting that in this case, the network load has a greater impact on the overall performance than the transmission efficiency;

[0125] (Load growth rate adjustment factor) is derived from long-term data analysis and is used to adjust the real-time impact of load growth on performance. It is set to 0.05, indicating the impact of each 1% increase in load on performance;

[0126] V (average load growth rate) is obtained by analyzing the load data within a certain time window in the past and is set to 2%, which represents the average growth rate of the load of all nodes;

[0127] Calculate the weighted sum: Weighted sum = ;

[0128] Divide the weighted sum by the absolute number of connections and take the square root: Square root term = ;

[0129] Calculate the effect of load growth rate in the denominator: Load Growth Adjustment = ;

[0130] Calculate the final score S: ;

[0131] The result shows that under the current network load and transmission efficiency conditions, the adjusted network performance score is 4.82. This value can be used to evaluate the current performance of the network and compare it with the predetermined performance standards to determine whether further adjustments to the network configuration or resource allocation are needed to ensure network performance optimization.

[0132] S502: Based on the node and connection adjustment requirement list, by dynamically adjusting the connection relationship between nodes in the network and updating the connection weights, configuring the connection lines between nodes, adjusting the weight values ​​of multiple connection points and matching differentiated transmission requirements, and reallocating network resources according to the real-time network load, the execution process of generating the optimized hypergraph structure sketch is as follows;

[0133] Based on the node and connection adjustment requirements list, we first collect data on existing nodes and connections in the network, including the transmission capacity of each node, the bandwidth utilization rate and latency of the current connection. According to the performance index requirements proposed by the data center and the user end, we make preliminary weight settings. The weight of each node depends on its transmission capacity and criticality, and the weight of each connection is adjusted based on its bandwidth utilization rate and historical stability. We use a dynamic weight adjustment strategy to optimize the connection relationship between nodes in the network, including the weight value between multiple connection points. By calculating the contribution of each connection to the overall network performance, we dynamically adjust the weight to improve the overall response speed and data processing capability of the network. For differentiated transmission requirements, such as different treatment of data-intensive and delay-sensitive applications, we reallocate network resources according to the real-time network load and optimize the connection lines, thereby reducing congestion and latency and improving service quality. Based on the adjustment, we generate a sketch of the optimized hypergraph structure, which shows the adjusted network structure, including the new connection relationship of the nodes and the updated weight information, for further analysis and verification.

[0134] S503: Use the optimized hypergraph structure sketch to perform adjustment verification, refine the parameter settings of nodes and connections, lock the network structure, and obtain the dynamically adjusted network structure data. The execution process is as follows;

[0135] Using the optimized hypergraph structure sketch, first set the performance parameters of each node and connection in detail according to the node and connection data in the sketch, including determining the maximum load capacity of each node, the maximum bandwidth of the connection, and setting the priority level for specific applications. Use simulation tools to simulate network operation, monitor the performance of the network under high and low load conditions, and ensure that all settings meet design expectations. On this basis, lock the network structure. In the process, it is necessary to further adjust the parameters of the nodes or connections according to the simulation results. Through the dynamic adjustment verification process, accurately capture the impact of each adjustment on network performance, and obtain the dynamically adjusted network structure data. The data not only reflects the final configuration of the network, but also provides an important basis for future network upgrades and maintenance.

[0136] Specifically, if Figure 7 As shown, the dynamically adjusted network structure data is simulated and tested to verify the stability and communication efficiency of data transmission, simulate the network performance under the interconnection load and emergency conditions of the substation area, and generate the network simulation test results in the following steps:

[0137] S601: Based on the dynamically adjusted network structure data, configure the parameter settings of the simulation environment, the node configuration of the simulation network and the initialization of the communication protocol, including the setting of the number of nodes, connection type and transmission rate, and generate the simulation environment configuration result. The execution process is as follows;

[0138] Based on the dynamically adjusted network structure data, the parameter settings of the simulation environment are configured. The process includes the node configuration of the simulated network and the initialization of the communication protocol. It is necessary to refer to the settings of the number of nodes, connection type and transmission rate. Each node in the simulation environment should have parameters that can represent its behavior in the actual network, such as transmission rate and connection type. The setting of parameters needs to be based on the requirements of network design and the expected network load. By setting parameters, the consistency of the simulation environment and the actual environment can be ensured, thereby effectively evaluating the performance and reliability of the network. This not only assists network designers in understanding the performance of the network under differentiated configurations, but also enables them to foresee potential problems and challenges before the network is implemented, and generate simulation environment configuration results.

[0139] S602: Using the simulation environment configuration results, perform key network load simulation, including injecting traffic data into the network model and simulating sudden disconnection events, monitoring the response and recovery capabilities of the network, and recording key performance parameters, including response time and network recovery rate. The execution process of obtaining batch load response data is as follows;

[0140] Perform critical network load simulation, according to the formula:

[0141] ,

[0142] Calculate the network recovery rate, where A represents the network recovery rate, represents the recovery time of the xth simulation, and N represents the number of simulations;

[0143] Five critical network load simulations were performed, and the network recovery time for each simulation was as follows:

[0144] = 120 seconds, = 150 seconds, = 130 seconds, = 140 seconds, = 125 seconds,

[0145] Apply the above formula to calculate the network recovery rate: Second;

[0146] The results show that the average network recovery rate is 133 seconds, which is a key performance indicator for evaluating the network's response to emergencies and helps analyze the network's stability and reliability in practical applications.

[0147] S603: Based on the batch load response data, the network performance is analyzed, the network's matching capability for high load and emergencies is evaluated, the delay, data packet loss rate and recovery time are analyzed, the matching degree between the network design and the operation requirements is determined, and the execution process of generating the network simulation test results is as follows;

[0148] Based on batch load response data, the network performance is analyzed, including the evaluation of the network's ability to match high loads and emergencies. During the execution process, the delay, data packet loss rate and recovery time need to be analyzed. The process uses statistical and data analysis methods to evaluate the performance indicators of the network, such as the average value of the delay time, the standard deviation and the distribution of the recovery time. Through the data, the matching degree of the network design and operational requirements can be determined. For example, if the delay time is generally long or the data packet loss rate is high, it indicates that the network capacity needs to be further enhanced or its routing strategy needs to be optimized. This analysis assists network engineers in optimizing the network structure, improving the overall performance and reliability of the network, and generating network simulation test results.

[0149] like Figure 8 As shown, the power grid communication simulation system based on the interconnection of substations includes:

[0150] The hypergraph initialization module captures the interconnection data between stations and the initial node information of the power grid communication simulation, determines the connection between nodes through physical connections, integrates the data to set up the hypergraph, and stores the data nodes according to the connection type and frequency to obtain the initial hypergraph structure set;

[0151] The data flow analysis module uses the initial hypergraph structure set to count the data transmission frequency and flow between multiple nodes, assigns weights to nodes with frequent transmission, optimizes the data flow line through a dynamic weight adjustment strategy, and obtains a reconfigured weight set;

[0152] The key node identification module uses a reconfiguration weight set to evaluate and measure the connectivity of multiple nodes, identify and record the key nodes and connections for data flow and communication efficiency, and establish a list of key nodes and connections;

[0153] The path optimization module recalculates the data transmission path based on the key nodes and connection lists, adjusts the path and matches the real-time network traffic changes, reduces delays, optimizes response speed and processing capabilities, and obtains optimized communication path data;

[0154] The dynamic adjustment and testing module uses the optimized communication path data to adjust the hypergraph structure of the power grid communication simulation and match the real-time network load, performs simulation tests to evaluate communication stability and efficiency, and constructs network simulation test results.

[0155] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A power grid communication simulation method based on area interconnection, characterized in that: The following steps are involved: Capture the interconnection data between stations and the initial node information of the power grid communication simulation, set up a hypergraph according to the physical connection, classify and store the node data according to the connection type and frequency, enter it into the hypergraph model, and obtain the initial hypergraph structure data; According to the initial hypergraph structure data, identifying data traffic peaks and communication frequencies, assigning weights to key nodes, adjusting the weights through a criticality scoring mechanism, and obtaining reconfiguration weight data; By reconfiguring the weight data, detecting node connectivity, marking key nodes and connections, analyzing data flow and communication error rate, and generating a list of key nodes and connections; The steps of key nodes and connection lists are specifically as follows: According to the reconfigured weight data, a connectivity test is performed by real-time monitoring of the connection status of the nodes and the weight value of the data packet, and the communication frequency and data packet size of each node are analyzed to determine the active and inactive connection paths in the network and generate a connectivity test result; Based on the connectivity detection results, determine the nodes and connections of key roles in the power grid by evaluating the data transmission records of each node and the network location of the node, including recording and analyzing the data transmission frequency of the node and the stability of the connection, marking the key transmission nodes and key connections of the power grid, and generating a key node and connection map; Using the key node and connection map, counting the data flow direction of the key nodes and the communication error rate of the connection, including the data transmission efficiency of the nodes and connections, error records and communication frequency records, identifying potential problem areas, and generating a list of key nodes and connections; Based on the key nodes and connection list, recalculate the path of power grid communication, calculate the optimal data transmission path between nodes, adjust the path, and generate optimized communication path data; The steps of optimizing the communication path data are specifically as follows: Based on the key nodes and connection list, potential communication paths between nodes are identified, the connection status between nodes is displayed using a network topology view, delay and blocking data of the communication paths of the nodes are evaluated, response time paths between multiple nodes are calculated, and a preliminary optimized path table is generated; Using the data in the preliminary optimized path table, monitoring the real-time traffic conditions of multiple paths in the simulated network, marking the mined abnormal traffic and blocked paths and analyzing the causes, adjusting the routing settings of the associated paths, and generating an adjusted path demand table; Based on the adjusted path demand table, adjusting the communication weights of the affected nodes, including adjusting the weights of the target nodes, optimizing the power grid communication network structure, and obtaining optimized communication path data; Using the optimized communication path data, dynamically adjusting the hypergraph structure of the power grid communication simulation to match the real-time needs of the power grid operation, and obtaining dynamically adjusted network structure data; The dynamically adjusted network structure data is simulated and tested to verify the stability of data transmission and communication efficiency, simulate the network performance under the interconnection load and emergency conditions, and generate network simulation test results.

2. The power grid communication simulation method based on area interconnection according to claim 1 is characterized in that: The initial hypergraph structure data includes node type, connection frequency, and storage structure; the reconfiguration weight data includes node weight value, adjusted weight distribution, and optimized data flow direction; the key node and connection list includes key node identification, key connection type, and communication error points; the optimized communication path data includes path optimization solutions, response speed improvement points, and processing capability enhancement areas; the dynamically adjusted network structure data includes network node updates, connection adjustment results, and real-time demand response; the network simulation test results include stability verification data, efficiency evaluation results, and load response strategies.

3. The power grid communication simulation method based on area interconnection according to claim 1 is characterized in that: The steps of capturing the interconnection data between substations and the initial node information of the power grid communication simulation, setting up a hypergraph according to the physical connection, storing the node data by connection type and frequency, and entering the hypergraph model to obtain the initial hypergraph structure data are as follows: Capture the interconnection data between stations and the initial node information of the power grid communication simulation, use physical and communication parameters to classify and store data, and establish a node data classification and storage framework through node connection type and transmission frequency parameter division; Using the classification information in the node data classification storage framework, a preliminary construction of a hypergraph structure is performed, supernodes are organized and edge attributes are defined through node connection types and data transmission frequency parameters, a preliminary mapping of physical and logical relationships between nodes in the hypergraph is performed, and a hypergraph structure sketch is generated; Based on the hypergraph structure sketch, the connection mode and communication protocol details between the hypergraph nodes are refined, the attribute definitions of the nodes and edges are adjusted, the data storage strategy is optimized, and the initial hypergraph structure data is obtained.

4. The power grid communication simulation method based on area interconnection according to claim 1 is characterized in that: According to the initial hypergraph structure data, the data traffic peak and communication frequency are identified, weights are assigned to key nodes, and the weights are adjusted through a criticality scoring mechanism. The steps of obtaining reconfiguration weight data are specifically as follows: Extracting the data flow and communication frequency of each node from the initial hypergraph structure data, counting the flow peaks and communication frequency averages of multiple nodes in a target time period, performing time series analysis, identifying periodic changes in flow and communication, and generating a flow frequency summary table; Using the traffic frequency summary table, perform data sorting operations, arrange the traffic and communication frequency of each node, select key nodes from the sorted data, perform feature analysis on the nodes, identify common features of traffic and frequency nodes, mark the nodes as key nodes with priorities, and generate a key node list; According to the key node list, the weights of the nodes in the list are adjusted to optimize the data flow of the power grid communication network, and the weight distribution and network structure reorganization are performed to obtain reconfigured weight data.

5. The power grid communication simulation method based on area interconnection according to claim 1 is characterized in that: The steps of dynamically adjusting the hypergraph structure of the power grid communication simulation by using the optimized communication path data to match the real-time demand of the power grid operation and obtaining the dynamically adjusted network structure data are as follows: Utilizing the optimized communication path data, applying a decision tree algorithm, collecting dynamic information on real-time transmission efficiency and network load, identifying performance standards for multiple nodes and connections, analyzing situations where nodes are overloaded and transmission efficiency is low, verifying adjustment targets through real-time comparison of data frequency and load volume, and generating a list of node and connection adjustment requirements; Based on the node and connection adjustment requirement list, by dynamically adjusting the connection relationship between nodes in the network and updating the connection weights, configuring the connection lines between nodes, adjusting the weight values ​​of multiple connection points and matching differentiated transmission requirements, and reallocating network resources according to the real-time network load conditions, an optimized hypergraph structure sketch is generated; The optimized hypergraph structure sketch is used to perform adjustment verification, refine the parameter settings of nodes and connections, lock the network structure, and obtain dynamically adjusted network structure data.

6. The power grid communication simulation method based on area interconnection according to claim 5 is characterized in that: The formula of the decision tree algorithm is as follows: ， Among them, T represents the average transmission efficiency of the node, L represents the network load, and C represents the number of connections in the current network. is the weight coefficient of transmission efficiency, is the weight coefficient of the network load, is the adjustment coefficient of the load growth rate, V represents the average load growth rate of nodes in the network, and S represents the score of network performance.

7. The power grid communication simulation method based on area interconnection according to claim 1 is characterized in that: The steps of performing simulation test on the dynamically adjusted network structure data to verify the stability and communication efficiency of data transmission, simulating the network performance under the interconnection load and emergency conditions of the substation area, and generating the network simulation test results are as follows: Based on the dynamically adjusted network structure data, configure the parameter settings of the simulation environment, the node configuration of the simulation network and the initialization of the communication protocol, including the setting of the number of nodes, the connection type and the transmission rate, and generate the simulation environment configuration result; Using the simulation environment configuration results, perform critical network load simulation, including injecting traffic data into the network model and simulating sudden disconnection events, monitoring the response and recovery capabilities of the network, recording key performance parameters, including response time and network recovery rate, and obtaining batch load response data; Based on the batch load response data, the network performance is analyzed, the network's ability to match high loads and emergencies is evaluated, the delay, data packet loss rate and recovery time are analyzed, the matching degree between the network design and operational requirements is determined, and the network simulation test results are generated.

8. A power grid communication simulation system based on area interconnection, characterized in that: According to the power grid communication simulation method based on substation interconnection according to any one of claims 1 to 7, the system comprises: The hypergraph initialization module captures the interconnection data between stations and the initial node information of the power grid communication simulation, determines the connection between nodes through physical connections, integrates the data to set up the hypergraph, and stores the data nodes according to the connection type and frequency to obtain the initial hypergraph structure set; The data flow analysis module uses the initial hypergraph structure set to count the data transmission frequency and flow between multiple nodes, assigns weights to nodes with frequent transmission, optimizes the data flow line through a dynamic weight adjustment strategy, and obtains a reconfiguration weight set; The key node identification module uses the reconfiguration weight set to evaluate and measure the connectivity of multiple nodes, identify and record key nodes and connections for data flow and communication efficiency, and establish a list of key nodes and connections; The path optimization module recalculates the data transmission path according to the key nodes and connection list, adjusts the path and matches the real-time network traffic changes, reduces delays, optimizes response speed and processing capacity, and obtains optimized communication path data; The dynamic adjustment and testing module uses the optimized communication path data to adjust the hypergraph structure of the power grid communication simulation and match the real-time network load, performs simulation tests to evaluate communication stability and efficiency, and constructs network simulation test results.

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