Power system state dynamic evaluation method, system and device for abnormal signal diffusion and medium

Abstract

The invention discloses a power system state dynamic evaluation method, system and device for abnormal signal diffusion and a medium, and belongs to the technical field of power system safety and dispatching control, and the method comprises the steps: collecting multiple abnormal signals, and constructing a directed multi-source heterogeneous graph; carrying out system state modeling by utilizing a multi-stage dynamic propagation model based on the graph; recognizing an abnormal propagation path by adopting an optimized graph search algorithm in combination with the power grid topology and the weighted propagation weight; and evaluating the system stability influence according to the path identification result, and generating an intelligent auxiliary decision. According to the method, the abnormal signal propagation path identification and diffusion model is constructed, so that accurate simulation and path identification of concurrent propagation of multiple abnormal signals of the power system are realized; an optimized graph search algorithm and a multi-dimensional evaluation system are combined, so that the efficiency and accuracy of anomaly analysis in a large-scale power grid environment are improved; and finally, through an intelligent decision generation mechanism, a scientific basis for quick response is provided for dispatchers.

Description

Technical Field

[0001] This invention relates to the field of power system safety and dispatch control technology, specifically to a method, system, device, and medium for dynamic assessment of power system status in response to the spread of abnormal signals. Background Technology

[0002] With the expansion of power system scale and the increase in operational complexity, the frequent occurrence of abnormal signals has become a significant factor affecting the safe and stable operation of the power grid. Traditional abnormal signal processing mainly relies on manual experience or static rules, which is difficult to adapt to scenarios with multiple abnormal signals occurring concurrently and their cascading effects.

[0003] In power dispatching systems, dispatchers need to determine the scope of anomalies and take appropriate action within a very short time. However, there is currently a lack of methods and system tools that can systematically assess the propagation paths of multiple anomaly signals and their dynamic impact on the system state, making it difficult to meet the real-time and accuracy requirements of dispatching responses.

[0004] During power system operation, equipment failures, communication anomalies, network attacks, and weather disasters can lead to the simultaneous or continuous occurrence of multiple abnormal signals. These abnormal signals typically exhibit complex spatiotemporal coupling characteristics and may propagate through the power grid via different paths, causing a chain reaction of changes in the system's operating state. Traditional methods relying on human experience and rule bases for judgment prove inadequate when faced with multiple abnormal signals. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention provides a method, system, device and medium for dynamic assessment of power system status in response to the spread of abnormal signals.

[0006] Therefore, the technical problem solved by this invention is: This invention aims to address the system security risks in modern power systems caused by the lack of a dynamic assessment mechanism for the concurrent propagation of multiple abnormal signals. Specifically: First, relying on manual experience and static rules makes it impossible to handle scenarios involving multiple interconnected signals and predict their interactive effects; second, existing assessment methods are limited to equipment-level static models and do not comprehensively consider factors such as signal propagation speed, node response delays, and historical data, resulting in an inability to accurately quantify the spatiotemporal impact range of anomaly propagation; third, the lack of intelligent decision support leads to slow response and low decision-making efficiency for dispatchers when facing complex power grid anomalies.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a method for dynamic assessment of power system status in response to the spread of abnormal signals, comprising: collecting multiple abnormal signals, classifying the types of abnormal signals, extracting feature parameters from the abnormal signals, constructing graph structure nodes for each abnormal signal to form a directed multi-source heterogeneous graph; and modeling the operating status of the power system based on the directed multi-source heterogeneous graph data. Based on the power system topology, an abnormal signal propagation path identification and diffusion model is constructed. A weighted adjacency matrix is ​​formed by calculating the propagation weights of edges, and an optimized graph search algorithm is used for propagation path identification. The abnormal signal propagation path identification and diffusion model is used to assess the impact on power system stability, and combined with the dispatch system database and control strategy, intelligent auxiliary decision-making is generated.

[0008] As a preferred embodiment of the power system state dynamic assessment method for addressing the spread of abnormal signals described in this invention, the steps of collecting multiple abnormal signals, classifying the types of abnormal signals, and extracting feature parameters from the abnormal signals include: Collect multi-source abnormal signals from the power system and classify them according to their fault characteristics.

[0009] Extract multi-dimensional signal parameters that include spatiotemporal features and electrical characteristics.

[0010] Construct time-labeled graph structure nodes based on feature parameters.

[0011] A multi-source heterogeneous graph reflecting the spatiotemporal distribution of abnormal signals is constructed by associating nodes.

[0012] As a preferred embodiment of the power system state dynamic assessment method for addressing the spread of abnormal signals described in this invention, the step of modeling the power system operating state based on directed multi-source heterogeneous graph data includes: Construct a state vector matrix for the graph nodes.

[0013] The state transition matrix of a node is constructed based on a multi-stage dynamic propagation model.

[0014] As a preferred embodiment of the power system state dynamic assessment method for abnormal signal propagation described in this invention, the state transition matrix of the node constructed based on the multi-stage dynamic propagation model includes, By introducing the SEIR model from epidemiology into the power system, the state of power grid nodes is analogized to multiple progressive stages of influence.

[0015] Based on the SEIR model, transition rules and conditions between node states are defined to dynamically describe the propagation process of abnormal signals.

[0016] The complete lifecycle of a node is quantified using a state transition matrix.

[0017] As a preferred embodiment of the dynamic power system state assessment method for abnormal signal propagation described in this invention, the step of constructing an abnormal signal propagation path identification and propagation model based on the power system topology graph, and forming a weighted adjacency matrix by calculating the propagation weights of edges, includes: Construct a power system topology diagram based on the physical connections of the power system.

[0018] By integrating physical distance, response delay, and historical propagation data, dynamic weights of edges are calculated to form a weighted adjacency matrix.

[0019] The beneficial effects of this preferred technical solution are that by constructing a weighted adjacency matrix, the physical topology of the power grid is organically combined with the dynamic characteristics of anomaly propagation. This takes into account both the physical distance factor in the spatial dimension and the response delay characteristics in the time dimension. At the same time, historical propagation data is introduced to reflect the statistical laws of anomaly propagation, so that the constructed propagation model can more accurately reflect the multi-dimensional propagation characteristics of abnormal signals in the actual power grid.

[0020] As a preferred embodiment of the power system state dynamic assessment method for abnormal signal propagation described in this invention, the step of identifying propagation paths using an optimized graph search algorithm includes: A priority queue-based node access optimization mechanism is adopted to optimize the traversal order of large-scale power grid nodes.

[0021] A multi-source node synchronous computing architecture is established to realize parallel propagation path analysis of multi-point abnormal signals.

[0022] Set a propagation time threshold and perform computational pruning on network nodes that exceed the preset range.

[0023] A hierarchical regional search strategy is implemented, first completing the location of cross-regional backbone paths, and then carrying out calculations within the region.

[0024] The beneficial effects of this preferred technical solution are that by introducing a priority queue to optimize the node access order, establishing a multi-source synchronous computing architecture, setting a propagation time threshold, and implementing a hierarchical regional search strategy, an efficient path search mechanism adapted to the characteristics of the power system is constructed. While ensuring the accuracy of the calculation, the search efficiency of abnormal propagation paths in a large-scale power grid environment is improved, and the computational bottleneck problem of traditional algorithms in dealing with multi-source concurrent anomalies and complex power grid topologies is effectively solved.

[0025] As a preferred embodiment of the power system state dynamic assessment method for abnormal signal propagation described in this invention, the step of assessing the system stability impact through abnormal signal propagation path identification and diffusion model includes, Based on the propagation path identification results, a dynamic assessment system for system impact is established.

[0026] By assessing the hazard factor of the critical path, we can identify the propagation path that has the greatest impact on system stability.

[0027] By analyzing the impact of node propagation, we can identify the key nodes that play a central role in signal propagation.

[0028] The number of affected nodes and the frequency offset range are statistically analyzed to quantify the overall impact of the abnormal propagation.

[0029] The beneficial effects of this preferred technical solution are that by constructing a systematic evaluation system that includes critical path identification, core node positioning, and multi-dimensional impact statistics, it achieves accurate quantitative analysis of the impact from propagation path to system stability, and can comprehensively assess the spatiotemporal impact of abnormal signal propagation on the power system.

[0030] This invention provides a dynamic assessment system for the state of a power system in response to the spread of abnormal signals.

[0031] To address the aforementioned technical problems, this invention provides the following technical solution: a dynamic assessment system for power system status targeting the spread of abnormal signals, comprising: a signal acquisition and feature processing module, a graph model construction and status modeling module, a propagation path simulation calculation module, a system impact dynamic assessment module, and an intelligent auxiliary decision generation module.

[0032] The intelligent auxiliary decision generation module collects multiple abnormal signals, classifies the types of abnormal signals, and extracts feature parameters from the abnormal signals.

[0033] The graph model construction and state modeling module constructs graph structure nodes for each abnormal signal, forming a directed multi-source heterogeneous graph.

[0034] Based on directed multi-source heterogeneous graph data, the operating status of the power system is modeled.

[0035] The propagation path simulation calculation module constructs an abnormal signal propagation path identification and diffusion model based on the power system topology diagram. It forms a weighted adjacency matrix by calculating the propagation weights of the edges and uses an improved Dijkstra algorithm for propagation path identification.

[0036] The system impact dynamic assessment module constructs a power system impact dynamic assessment method through abnormal signal propagation path identification and diffusion model.

[0037] The intelligent auxiliary decision generation module combines the scheduling system database and control strategy to generate intelligent auxiliary decisions.

[0038] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the power system state dynamic assessment method for abnormal signal propagation.

[0039] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the power system state dynamic assessment method for abnormal signal propagation.

[0040] The beneficial effects of this invention are as follows: By constructing an abnormal signal propagation path identification and diffusion model, this invention achieves accurate simulation and path identification of the concurrent propagation of multiple abnormal signals in the power system; combined with an optimized graph search algorithm and a multi-dimensional evaluation system, it improves the efficiency and accuracy of anomaly analysis in a large-scale power grid environment; and finally, through an intelligent decision generation mechanism, it provides dispatchers with a scientific basis for rapid response, thereby improving the safety and stability of the power grid in dealing with complex abnormal operating conditions. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 The above is a flowchart of a dynamic power system state assessment method for abnormal signal propagation, provided as an embodiment of the present invention.

[0043] Figure 2 This is a schematic diagram of Dijkstra's algorithm for a dynamic evaluation method of power system state for abnormal signal propagation, provided as an embodiment of the present invention.

[0044] Figure 3 The flowchart of Dijkstra's algorithm for a dynamic evaluation method of power system state for abnormal signal propagation is provided as an embodiment of the present invention.

[0045] Figure 4 This is a schematic diagram of a power system state dynamic assessment system for abnormal signal propagation, provided as an embodiment of the present invention. Detailed Implementation

[0046] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0047] Example 1, referring to Figure 1This is one embodiment of the present invention, which provides a method for dynamic assessment of the state of a power system in response to the spread of abnormal signals, comprising: S1. Collect multiple abnormal signals, classify the types of abnormal signals, extract feature parameters from the abnormal signals, construct graph structure nodes for each abnormal signal, and form a directed multi-source heterogeneous graph.

[0048] S2. Based on directed multi-source heterogeneous graph data, model the operating status of the power system.

[0049] S3. Based on the power system topology, an abnormal signal propagation path identification and diffusion model is constructed. A weighted adjacency matrix is ​​formed by calculating the propagation weight of the edges, and an optimized graph search algorithm is used to identify the propagation path.

[0050] S4. The impact of abnormal signal propagation path identification and diffusion model on power system stability is assessed, and intelligent auxiliary decision-making is generated by combining the dispatch system database and control strategy.

[0051] By constructing a dynamic propagation model that integrates the characteristics of multi-source abnormal signals with the power grid topology, a precise analysis of the entire chain from anomaly identification and state evolution to propagation prediction is achieved. This not only solves the technical problem that traditional methods struggle to quantify the interactive effects and propagation paths when multiple abnormal signals occur concurrently, but also improves the power grid's response speed and accuracy in dealing with complex abnormal conditions through intelligent assessment and decision support.

[0052] Example 2, refer to Figures 2-3 As an embodiment of the present invention, based on the previous embodiment, a method for dynamic assessment of power system state targeting the spread of abnormal signals is provided, comprising: The process of acquiring multiple abnormal signals in S1, classifying the abnormal signal types, and extracting feature parameters from the abnormal signals includes steps A1-A4: A1. Collect multi-source abnormal signals in the power system and classify them according to fault characteristics.

[0053] Abnormal signal types are categorized as follows: fault signals, communication anomalies, attack behavior, and disturbance signals.

[0054] A2. Extract multi-dimensional signal parameters that include spatiotemporal features and electrical characteristics.

[0055] Parameters include timestamp, start position, signal strength, frequency, duration, etc.

[0056] A3. Construct graph structure nodes with time labels based on feature parameters.

[0057] A4. Construct a multi-source heterogeneous graph that reflects the spatiotemporal distribution of abnormal signals by associating nodes.

[0058] Furthermore, the modeling of the power system operating state based on directed multi-source heterogeneous graph data in S2 includes steps B1-B2: B1. Construct the state vector matrix for the graph nodes.

[0059] B2. Construct the state transition matrix of the node based on the multi-stage dynamic propagation model.

[0060] In this embodiment, the multi-stage dynamic propagation model in B2 is the SEIR model, a commonly used infectious disease dynamics model in epidemiology to describe the spread of disease in a population. It divides the population into different groups and uses a set of differential equations to describe the transition relationships between these groups. The core idea of ​​the SEIR model is to predict the spread trend of disease based on changes in the health status of the population. By considering the time-varying transition relationships between node states, the SEIR model's differential equations can accurately describe the propagation dynamics of abnormal signals in the power grid. By solving this set of differential equations, the trend of the number of nodes in each state of the system under specific initial conditions can be predicted, providing a mathematical basis for assessing the spatiotemporal evolution of anomaly impacts.

[0061] In one alternative implementation, the multi-stage dynamic propagation model can be a state transition model based on a hidden Markov model. Specifically, it includes defining the state of a power grid node as a hidden state, using observed anomalous signal features as observed states, describing the dynamic transition relationships between node states through a state transition probability matrix, and using the Viterbi algorithm or a forward-backward algorithm to achieve dynamic prediction and evaluation of the node state.

[0062] In another alternative implementation, the multi-stage dynamic propagation model can also be a gridded propagation model based on cellular automata. Specifically, this involves mapping the power system topology to a cellular grid, where each cell represents a grid node. By defining local propagation rules between neighboring cells (such as state transition functions based on electrical coupling strength), the propagation process of abnormal signals in the spatiotemporal dimensions is simulated, thereby achieving system-level dynamic propagation simulation.

[0063] Furthermore, the construction of the node's state transition matrix based on the multi-stage dynamic propagation model in B2 includes steps B21-B23: B21. Introduce the SEIR model from epidemiology into the power system, and compare the state of power grid nodes to multiple progressive stages of influence.

[0064] B22. Based on the SEIR model, define the transition rules and conditions between node states to dynamically describe the propagation process of abnormal signals.

[0065] In the SEIR model, disease propagation is a process that occurs through the interaction between different individuals. Similarly, in a power grid, abnormal signals propagate through the interaction between different grid components. In the grid topology, the connections between components allow an abnormal signal from one device to affect other devices through the network. Similar to the "contagiousness" in disease propagation, a failure in one device may affect neighboring devices, causing more devices to be exposed to the signal. This signal propagation depends not only on the grid topology but also on factors such as device status, load, and operating conditions. Therefore, the SEIR model is applied to the propagation of abnormal signals in the power grid, and a state transition matrix between nodes is constructed based on this model, as shown in Table 1: Table 1. Definition of Power Grid Anomaly Signal Nodes Based on SEIR Model

[0066] B23. Quantify the complete lifecycle of a node using a state transition matrix.

[0067] In this embodiment of the application, the multiple progressive impact stages in B21, i.e., the abnormal propagation trend chain, are as follows: The SEIR model divides the population into four different states, as shown in Table 2: Table 2 Four different states

[0068] In one alternative implementation, the multiple progressive impact stages can be a five-level state model based on equipment health, including: normal state, warning state, degradation state, fault state, and recovery / isolation state. The model divides the state stages by monitoring the deviation of equipment operating parameters from preset thresholds and establishes corresponding state transition conditions, thereby describing the gradual performance change process of power equipment during anomaly propagation.

[0069] In another alternative implementation, the multiple progressive impact stages can also be based on a three-level state model of functional reliability, including: functional integrity state, functional degradation state, and functional failure state. The model defines the state stages from the perspective of power system functional realization, focusing on changes in the ability of nodes to maintain their design functions during anomaly propagation, and is suitable for assessing the impact of anomalous signals on the overall functional integrity of the system.

[0070] In this embodiment, the optimized graph search algorithm in S3 is the improved Dijkstra's algorithm, specifically including finding the shortest propagation path from the source node to all other nodes using the improved Dijkstra's algorithm. The basic idea of ​​Dijkstra's algorithm is: from the vertex... Starting from this point, search for the shortest path from it to all other vertices. Divide the vertex set V of the directed graph into two subsets. and , The set of vertices for which the shortest path has been found. The set of vertices for which the shortest path has not yet been found; iterate through the set. Each time, find the vertex of the shortest path and add it to the set. In, until the set Empty.

[0071] like Figure 2 As shown, suppose a weighted directed graph... Using a weighted adjacency matrix Representation diagram ; Represents arc The weights on vector D are given by S, which represents the set of shortest paths originating from and ending at . This indicates that the result obtained from the starting point... To each vertex The shortest path length, using Dijkstra's algorithm from source point s to destination point t, is as follows: Figure 3 As shown.

[0072] Figure 3 In this context, S represents the set of nodes for which the shortest path has been determined; D represents the shortest path value; Num represents the number of nodes that have been processed; N represents the total number of nodes; t represents the target node; and Are[j,k] represents the edge weights between nodes.

[0073] Dijkstra's algorithm can calculate the optimal solution for the shortest path between two points and is very robust. However, its computational efficiency decreases as the number of nodes increases. Dijkstra's algorithm uses a weighted adjacency matrix to store data, consuming a large amount of memory; furthermore, it is a blind or undirected search algorithm, and for a graph with n nodes, its time complexity is O(n^2). The computation time and storage overhead are both relatively large.

[0074] The optimization of Dijkstra's algorithm in this invention lies in: Optimization 1: Improving computation speed by using a binary heap. A binary heap is a special type of complete binary tree that can be used to implement a priority queue. There are two main types of binary heaps: min-heaps and max-heaps. This paper uses a min-heap, where the value of each parent node is less than or equal to the value of its child nodes. Binary trees can perform the following operations: insertion, deletion of the minimum value, and update. These operations optimize the selection and update process of the minimum path node.

[0075] Therefore, in the optimized algorithm, the total time complexity of the minimum value extraction operation and the update operation are respectively... Each node and each edge may require one operation. Ultimately, the time complexity of the entire algorithm is O(n log n). Where n is the number of nodes and e is the number of edges.

[0076] Optimization 2: Multi-source optimization: Calculating the shortest path from a single source node to all nodes requires repeated calculations for multiple sources. However, abnormal signals in power grids often have multiple source nodes. Adding multiple signal source nodes to a priority queue at once reduces repeated traversal and avoids multiple executions of Dijkstra's algorithm, making it suitable for multiple-point triggering of abnormal signals. A single calculation yields the shortest propagation path from any source to all nodes.

[0077] Optimization 3: Limit propagation radius: Only calculate propagation time ≤ threshold The path is selected, ignoring nodes exceeding a threshold. For modeling anomalous signal propagation, this is equivalent to simulating only the impact range within a certain time window. This can reduce the computation of irrelevant distant nodes.

[0078] Optimization 4: Power grid topologies are typically divided according to geographical / administrative / electrical regions, with dense connections within each region and few cross-regional connections. Therefore, Dijkstra's algorithm is first run at the region level to find the shortest path across regions, followed by fine-tuning the calculation within each region. This can significantly reduce the number of search nodes and is suitable for large-scale power grid models.

[0079] In one alternative implementation, the optimized graph search algorithm can be an efficient path search method based on the A* algorithm. This implementation introduces a heuristic function to guide the search direction, improving search efficiency while retaining the accuracy of Dijkstra's algorithm. Specifically, the electrical distance or communication delay between nodes is used as the heuristic evaluation function to prioritize exploring paths closer to the target with lower propagation costs, thereby avoiding blind searches in irrelevant areas. This is particularly suitable for rapid path localization in target areas with known anomalous signals.

[0080] In another alternative implementation, the optimized graph search algorithm can also be a parallel path exploration algorithm based on bidirectional search. This algorithm simultaneously initiates the search process from the source of the anomalous signal and the critical nodes of the system, terminating and merging paths when the two search fronts meet. By decomposing the single path search task into two parallel subtasks, the search space is effectively halved, reducing computational complexity, making it particularly suitable for emergency scenarios requiring rapid determination of the propagation path between the source of the anomalous signal and the affected critical equipment.

[0081] Furthermore, in S3, based on the power system topology graph, an abnormal signal propagation path identification and diffusion model is constructed. This involves calculating the propagation weights of edges to form a weighted adjacency matrix, including steps C1-C2: C1. Construct a power system topology diagram based on the physical connection relationships of the power system.

[0082] Constructing a power grid topology diagram , where V represents a node and E represents an edge.

[0083] C2. By integrating physical distance, response delay, and historical propagation data, the dynamic weights of edges are calculated to form a weighted adjacency matrix.

[0084] Assume the abnormal signal originates from the source node. The formula for calculating the propagation weight of the triggering abnormal signal is: In the formula: Represents a node and nodes Physical path length between Indicates response delay. This indicates the average time for historical propagation. This is the weighting factor.

[0085] By assigning propagation weights to each edge and constructing a weighted adjacency matrix, the choice of propagation path can be influenced through real-time calculation. The propagation process of anomaly signals can be represented as a directed weighted graph, which can be used for subsequent path search.

[0086] Furthermore, the propagation path identification using an optimized graph search algorithm in S3 includes steps D1-D4: D1. A priority queue-based node access optimization mechanism is adopted to optimize the traversal order of large-scale power grid nodes.

[0087] D2. Establish a multi-source node synchronous computing architecture to realize parallel propagation path analysis of multi-point abnormal signals.

[0088] D3. Set a propagation time threshold and perform computational pruning on network nodes that exceed the preset range.

[0089] D4. Implement a hierarchical regional search strategy, first complete the cross-regional backbone path location, and then carry out calculations within the region.

[0090] In this embodiment, the power system stability impact assessment in S4 refers to the construction of a dynamic assessment method for power system impact, specifically including: critical path identification: identifying the signal propagation path with the greatest impact on system stability; considering propagation costs or risk levels, accumulating the propagation "hazard coefficient" for each path. The calculation formula is as follows: in, , Represents a node. Indicates the propagation path, This represents the propagation weight of the edge (j,k) formed by nodes j and k.

[0091] Critical node identification: Critical nodes refer to nodes that play a core role in signal propagation and have a significant impact on the stability of system operation.

[0092] Directly affecting the number of nodes: Count the number of nodes whose state is E / I / R during the entire propagation of the abnormal signal. in, Let i represent node i in the power grid, specifically a device in the power system.

[0093] Frequency variation: The range of frequency shift during propagation.

[0094] in, This represents the instantaneous frequency of the circuit system at time t. This represents the normal frequency of the power system, and t represents time.

[0095] In one alternative implementation, the assessment of the impact on power system stability can be based on a comprehensive assessment model that combines node importance and network vulnerability. Specifically, this involves identifying structurally critical nodes by analyzing their centrality indices in the power grid topology (including degree centrality, close centrality, and betweenness centrality), while simultaneously calculating their functional importance based on node state transition probabilities. Ultimately, this constructs a comprehensive assessment system that integrates structural and functional indicators, thereby comprehensively identifying weak links and key impact areas in the power system.

[0096] In another optional implementation, the assessment of power system stability impact can also be based on a dynamic security assessment method using energy level functions and stability domains. Specifically, this includes constructing an energy level function describing the system's dynamic behavior to quantitatively assess the accumulation and dissipation characteristics of system energy during anomaly propagation; and simultaneously calculating the minimum distance (stability domain) between the system's operating point and the stability boundary to dynamically assess the system's ability to maintain stability, thereby achieving real-time assessment and early warning of the system's dynamic security level.

[0097] Furthermore, the system stability impact assessment in S4 through the identification and diffusion model of abnormal signal propagation paths includes steps E1-E4: E1. Based on the propagation path identification results, establish a dynamic assessment system for system impact.

[0098] E2. Identify the propagation paths that have the greatest impact on system stability through critical path hazard factor assessment.

[0099] E3. Through node propagation impact analysis, identify the key nodes that play a core role in signal propagation.

[0100] E4. Statistically determine the number of affected nodes and the frequency offset range to quantify the overall impact of abnormal propagation.

[0101] Example 3, referring to Figure 4 This is one embodiment of the present invention, which provides a dynamic assessment system for power system status in response to the spread of abnormal signals, including: a signal acquisition and feature processing module, a graph model construction and status modeling module, a propagation path simulation calculation module, a system impact dynamic assessment module, and an intelligent auxiliary decision generation module.

[0102] The intelligent auxiliary decision generation module collects multiple abnormal signals, classifies the types of abnormal signals, and extracts feature parameters from the abnormal signals.

[0103] The graph model construction and state modeling module constructs graph structure nodes for each abnormal signal, forming a directed multi-source heterogeneous graph; Based on directed multi-source heterogeneous graph data, the operating status of the power system is modeled.

[0104] The propagation path simulation calculation module, based on the power system topology, constructs an abnormal signal propagation path identification and diffusion model. It forms a weighted adjacency matrix by calculating the propagation weights of the edges and uses an improved Dijkstra algorithm for propagation path identification.

[0105] The system impact dynamic assessment module constructs a method for dynamic assessment of power system impacts through abnormal signal propagation path identification and diffusion models.

[0106] The intelligent auxiliary decision generation module combines the scheduling system database and control strategies to generate intelligent auxiliary decisions.

[0107] This embodiment also provides an electronic device applicable to a dynamic power system state assessment method for addressing the spread of abnormal signals, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the dynamic power system state assessment method for addressing the spread of abnormal signals as proposed in the above embodiment.

[0108] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a dynamic power system state assessment method for abnormal signal propagation as proposed in the above embodiments.

[0109] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for dynamic evaluation of power system status against abnormal signal propagation proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0110] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for dynamic assessment of power system state in response to the spread of abnormal signals, characterized in that: include, Collect multiple anomalous signals, classify the types of anomalous signals, extract feature parameters from the anomalous signals, construct graph structure nodes for each anomalous signal, and form a directed multi-source heterogeneous graph. Modeling of power system operating status based on directed multi-source heterogeneous graph data; Based on the power system topology, an abnormal signal propagation path identification and diffusion model is constructed. A weighted adjacency matrix is ​​formed by calculating the propagation weight of the edges, and an optimized graph search algorithm is used to identify the propagation path. The impact of abnormal signal propagation path identification and diffusion model on power system stability is assessed, and intelligent auxiliary decision-making is generated by combining dispatch system database and control strategy.

2. The method for dynamic assessment of power system state in response to the spread of abnormal signals as described in claim 1, characterized in that: The process of collecting multiple abnormal signals, classifying the types of abnormal signals, and extracting feature parameters from the abnormal signals includes: Collect multi-source abnormal signals in the power system and classify them according to fault characteristics; Extract multi-dimensional signal parameters that include spatiotemporal and electrical characteristics; Construct time-labeled graph structure nodes based on feature parameters; A multi-source heterogeneous graph reflecting the spatiotemporal distribution of abnormal signals is constructed by associating nodes.

3. The method for dynamic assessment of power system state in response to the spread of abnormal signals as described in claim 2, characterized in that: The modeling of the power system operating state based on directed multi-source heterogeneous graph data includes, Construct a state vector matrix for the graph nodes; The state transition matrix of a node is constructed based on a multi-stage dynamic propagation model.

4. The method for dynamic assessment of power system state in response to the spread of abnormal signals as described in claim 3, characterized in that: The state transition matrix of the node constructed based on the multi-stage dynamic propagation model includes, By introducing the SEIR model from epidemiology into the power system, the state of power grid nodes is analogized to multiple progressive stages of influence. Based on the SEIR model, transition rules and conditions between node states are defined to dynamically describe the propagation process of abnormal signals; The complete lifecycle of a node is quantified using a state transition matrix.

5. The method for dynamic assessment of power system state in response to the spread of abnormal signals as described in claim 4, characterized in that: The aforementioned model for identifying and disseminating abnormal signal propagation paths based on the power system topology graph, which forms a weighted adjacency matrix by calculating the propagation weights of edges, includes... Constructing a power system topology diagram based on the physical connections of the power system; By integrating physical distance, response delay, and historical propagation data, dynamic weights of edges are calculated to form a weighted adjacency matrix.

6. The method for dynamic assessment of power system state in response to the spread of abnormal signals as described in claim 4, characterized in that: The method of using an optimized graph search algorithm for propagation path identification includes, A priority queue-based node access optimization mechanism is adopted to optimize the traversal order of large-scale power grid nodes; Establish a multi-source node synchronous computing architecture to realize parallel propagation path analysis of multi-point abnormal signals; Set a propagation time threshold and perform computational pruning on network nodes that exceed the preset range; A hierarchical regional search strategy is implemented, first completing the location of cross-regional backbone paths, and then carrying out calculations within the region.

7. The method for dynamic assessment of power system state in response to the spread of abnormal signals as described in claim 4, characterized in that: The system stability impact assessment using the abnormal signal propagation path identification and diffusion model includes... Based on the results of propagation path identification, a dynamic assessment system for system impact is established; By assessing the hazard factor of critical paths, we can identify the propagation paths that have the greatest impact on system stability. By analyzing the impact of node propagation, the key nodes that play a central role in signal propagation are identified. The number of affected nodes and the frequency offset range are statistically analyzed to quantify the overall impact of the abnormal propagation.

8. A dynamic power system state assessment system for addressing the spread of abnormal signals, employing the dynamic power system state assessment method for addressing the spread of abnormal signals as described in any one of claims 1 to 7, characterized in that, include: The system includes a signal acquisition and feature processing module, a graphical model construction and state modeling module, a propagation path simulation calculation module, a system impact dynamic assessment module, and an intelligent auxiliary decision generation module. The intelligent auxiliary decision generation module collects multiple abnormal signals, classifies the types of abnormal signals, and extracts feature parameters from the abnormal signals. The graph model construction and state modeling module constructs graph structure nodes for each abnormal signal, forming a directed multi-source heterogeneous graph; Modeling of power system operating status based on directed multi-source heterogeneous graph data; The propagation path simulation calculation module, based on the power system topology, constructs an abnormal signal propagation path identification and diffusion model, forms a weighted adjacency matrix by calculating the propagation weights of the edges, and uses an improved Dijkstra algorithm for propagation path identification. The system impact dynamic assessment module constructs a power system impact dynamic assessment method through an abnormal signal propagation path identification and diffusion model. The intelligent auxiliary decision generation module combines the scheduling system database and control strategy to generate intelligent auxiliary decisions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the power system state dynamic assessment method for abnormal signal propagation as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the power system state dynamic assessment method for the propagation of abnormal signals as described in any one of claims 1 to 7.

Images