Construction Method for Multi-Level Vulnerability Quantification Index Based on Topological Power Grid Operation

Abstract

The present invention relates to the technical field of power grid operation, and discloses a construction method for multi-level vulnerability quantification indexes based on a topological power grid operation, including: obtaining topological structure data of a power grid dispatching system for hierarchical division, analyzing node data to form a hierarchical matrix, calculating a shortest path length matrix, and analyzing betweenness centrality. By dividing the power grid into a high-voltage level, a medium-voltage level, and a low-voltage level according to the voltage level, the topological structure of each level is independently analyzed. Based on the Louvain algorithm, the modularity increment change of each node moving to an adjacent node is calculated, and the subgroup structure is gradually optimized by maximizing the modularity increment. Combining the modularity of the level with the node vulnerability index of the level forms a more comprehensive vulnerability assessment model. Through the comprehensive analysis of cross-level and intra-level vulnerability indexes, the coupling problems between levels can be more effectively identified and solved during the power grid optimization process.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation, and specifically to a construction method for multi-level vulnerability quantification indexes based on topological power grid operation. Background Art

[0002] In order to ensure the security and stability of the power grid, traditional power grid vulnerability analysis usually focuses on global evaluation, such as the importance of nodes, the connectivity of the network, etc. The above methods can usually accurately reflect the manifestation of different nodes and network connectivity in the power grid vulnerability. However, with the expansion of the power grid scale and the complexity of the operation situation, traditional methods are inconvenient for the complex correlation between different voltage levels and the potential risks within each level;

[0003] Existing vulnerability quantification methods often cannot timely reflect the dynamic changes of multi-level power grid nodes in actual operation, and ignore the impact of the dynamic changes of the power grid in actual operation on the potential risks between different levels, and fail to effectively combine power grid vulnerability indexes for comprehensive evaluation, reducing the efficiency in dealing with power grid disturbances. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that existing vulnerability quantification methods often cannot timely reflect the dynamic changes of multi-level power grid nodes in actual operation, and ignore the impact of the dynamic changes of the power grid in actual operation on the potential risks between different levels, and fail to effectively combine power grid vulnerability indexes for comprehensive evaluation, reducing the efficiency in dealing with power grid disturbances.

[0006] To solve the above technical problem, the present invention provides the following technical solution: a construction method for multi-level vulnerability quantification indexes based on topological power grid operation, including: collecting topological structure data of the power grid, where the topological structure data includes node data and edge data, forming a hierarchical matrix according to the node data, and analyzing the correlation between nodes at each level;

[0007] Preliminarily dividing node subgroups, calculating modularity changes at different levels and across levels, calculating the modularity increment change of node movement based on the Louvain algorithm, and performing repeated iterations to determine the subgroup structure, determining hierarchical vulnerability indexes based on the node data, and calculating multi-level vulnerability quantification indexes of the power grid;

[0008] Drawing a vulnerability index curve, setting an early warning mechanism, analyzing the power grid stability according to the power grid fluctuation amplitude, frequency deviation and power stability, determining a high-stability topological structure by expanding the topological branch in cooperation with the hierarchical vulnerability index, verifying the high-stability topological structure and recording data.

[0009] As a preferred embodiment of the method for constructing a multi - level vulnerability quantification index based on the operation of a topological power grid according to the present invention, the steps of forming a hierarchical matrix based on node data and analyzing the correlation between nodes at each level include pre - processing the topological structure data and constructing an adjacency matrix of the power grid based on whether there is a connection edge between nodes. ;

[0010] Count the number of nodes directly connected to node i as the degree of the node. ;

[0011] Based on the voltage levels of the power grid, perform hierarchical division into three levels: high - voltage level, medium - voltage level, and low - voltage level.

[0012] The high - voltage level includes all substations and transmission lines of 220 kV and above, the medium - voltage level includes all substations and transmission lines between 35 kV and 220 kV, and the low - voltage level includes all distribution substations and distribution lines of 35 kV and below.

[0013] Based on the voltage levels of the nodes in the adjacency matrix, allocate them according to the high - voltage level, medium - voltage level, and low - voltage level to form a hierarchical matrix. , the hierarchical matrix includes a high - voltage level matrix , a medium - voltage level matrix and a low - voltage level matrix ;

[0014] For each hierarchical matrix , use the Floyd - Warshall algorithm to calculate the shortest path length matrix between any two nodes. ;

[0015] Analyze the topological distance matrix of each level and calculate the betweenness centrality value of each node.

[0016] Based on whether two nodes belong to different levels and are directly connected, analyze the correlation between nodes at each level and establish a cross - level connection relationship matrix. .

[0017] As a preferred embodiment of the method for constructing a multi - level vulnerability quantification index based on the operation of a topological power grid according to the present invention, the steps of initially dividing node subgroups, calculating modularity for different levels and cross - levels, calculating the change in modularity increment of node movement based on the Louvain algorithm, and performing repeated iteration to determine the subgroup structure include, based on the hierarchical matrix , if two nodes belong to the same level, they are regarded as a subgroup. For each node , through the calculated shortest path length matrix , calculate the average path length of all nodes as the length threshold, and divide two nodes with the shortest path length less than the length threshold into the same subgroup;

[0018] Based on the adjacency matrix of each layer of the power grid, calculate the modularity within the layer, which is expressed as:

[0019]

[0020] Among them, represents the modularity of the corresponding power grid layer, represents the total number of edges in this layer network, represents node 's degree, is an indicator function, represents node 's degree, and respectively represent the subgroups to which the nodes belong;

[0021] At the same time, calculate the cross-layer modularity, which is expressed as:

[0022]

[0023] Among them, represents the cross-layer modularity;

[0024] Based on the Louvain algorithm, according to the divided subsets to form a network structure, calculate the change in the modularity increment of each node moving to an adjacent node , which is expressed as:

[0025]

[0026]

[0027] Among them, represents the total number of cross-layer edges, represents nodes and 's direct connection, represents the total number of edges, represents the change in the internal connection of the atomic group after node i is removed from the atomic group, represents the connection situation between node i and node , and respectively represent nodes and node 's degrees;

[0028] For each node , determine the largest 's largest adjacent node , and move the node to the same subgroup as , completing the merger and recalculation of the subgroup structure;

[0029] Repeat the iteration for the merger and recalculation of the subgroup structure. Based on the shortest path length matrix values between nodes, calculate the standard deviation and multiply it by the sensitivity constant as the subgroup threshold;

[0030] Monitor the modularity increment in each iteration in real time , and stop the iteration when it is less than the subgroup threshold;

[0031] Recalculate the modularity within each level and the cross-level modularity based on the subgroup structure after the iteration is completed, and perform normalization processing.

[0032] As a preferred solution of the method for constructing a multi-level vulnerability quantification index based on the topological power grid operation described in the present invention, wherein: determining the level vulnerability index based on node data and calculating the multi-level vulnerability quantification index of the power grid includes constructing a node vulnerability index based on nodes, and statistically calculating the betweenness centrality value and the node degree of the nodes;

[0033] Calculate the average distance between a node and other nodes based on the shortest path length matrix, and use the average distance as the node compactness;

[0034] Use the reciprocal of the maximum fluctuation amplitude of the node voltage as the voltage stability index of each node;

[0035] Use the sum of the comprehensive betweenness centrality value, the node degree, the node compactness, and the voltage stability index as the node vulnerability index of the node;

[0036] Divide the modularity of each level by the total number of subgroups in the corresponding level, and consider the node vulnerability index of the level to calculate the vulnerability index of each level, expressed as:

[0037] ;

[0038] Wherein, represents the vulnerability index of each level, represents the modularity of each level, represents the node set of the corresponding level, represents the node vulnerability index of node i, represents the number of nodes in the corresponding level;

[0039] Divide the cross - level modularity by the sum of the number of node connections across different voltage levels to obtain the cross - level vulnerability index. Then, perform a weighted comprehensive calculation on the vulnerability index of each level and the cross - level vulnerability index to form the final multi - level vulnerability quantification index of the power grid.

[0040] As a preferred solution of the method for constructing a multi - level vulnerability quantification index based on the topological power grid operation described in the present invention, wherein: the drawing of the vulnerability index curve and the setting of the early warning mechanism include drawing the vulnerability index curve based on the real - time collected operation data during the power grid operation, and the operation data includes voltage, power, frequency, and load data;

[0041] Pre - process the collected data, and use a median filter to eliminate the noise in the data;

[0042] Calculate the vulnerability index of the node according to the real - time collected operation data and the vulnerability index of the corresponding level , draw a curve graph based on the MATLAB tool for visual display;

[0043] According to the historical data and the operation requirements of the power grid, calculate the sum of the mean value of the historical vulnerability index of the node and twice the standard deviation of the historical vulnerability index as the node vulnerability threshold;

[0044] Calculate the sum of the mean value of the historical vulnerability index of different levels and twice the standard deviation of the historical vulnerability index as the level vulnerability threshold;

[0045] If the vulnerability index of the node is greater than the node vulnerability threshold, trigger the node early warning alarm. If the vulnerability index of different levels is greater than the level vulnerability threshold, trigger the level early warning alarm.

[0046] As a preferred solution of the method for constructing a multi - level vulnerability quantification index based on the topological power grid operation described in the present invention, wherein: the analysis of the power grid stability according to the power grid fluctuation amplitude, frequency deviation, and power stability includes, based on the maximum fluctuation amplitude value of the voltage, calculating the average inverse value of the voltage fluctuation by integrating all nodes as the voltage stability value;

[0047] Calculate the deviation value of the frequency based on the power grid frequency, and perform an average calculation on the absolute value of the frequency deviation to represent the frequency stability, expressed as:

[0048]

[0049] wherein, represents the frequency deviation at time , represents the total number of sampling points, represents the frequency stability;

[0050] Based on the power output of each node and the total power demand, calculate the power stability, expressed as:

[0051]

[0052] where, represents the power stability, represents the total power output of the node, represents the total power demand of the system to which the node belongs, represents the power imbalance;

[0053] Normalize and add the comprehensive voltage stability value, frequency stability value and power stability value as the stability value of the power grid.

[0054] As a preferred solution of the method for constructing a multi-level vulnerability quantification index based on topological power grid operation according to the present invention, wherein: the cooperation level vulnerability index expands the topological branch to determine the high-stability topological structure, including early warning alerts based on the node vulnerability threshold and the level vulnerability threshold, confirming the over-standard nodes and the corresponding subgroups, based on the topological structure data, visual display through the branch and bound algorithm and the MATLAB picture tool, generating a branch topological structure by deleting the connection edges of the high-vulnerability index nodes, and repeating the expansion of the branch;

[0055] Calculate the corresponding level vulnerability index for each new topological structure until the vulnerability index of the level in the new topological structure is lower than the level vulnerability threshold and the power grid stability continues to increase, then continue to expand the branch, and stop expanding the branch when the power grid stability stops increasing;

[0056] Use the boundary evaluation mechanism and cooperate with the power grid simulation tool PSS / E to simulate and verify the scenarios of each generated branch, prune the branches whose adjustment results cannot meet the requirement of reducing the vulnerability index, and select the topological structure with a low vulnerability index and the highest power grid stability as the high-stability structure.

[0057] As a preferred solution of the method for constructing a multi-level vulnerability quantification index based on topological power grid operation according to the present invention, wherein: verifying the high-stability topological structure and recording data includes applying the optimal topological structure to the power grid, starting the power grid operation and collecting key index data in real time, and calculating the adjusted level vulnerability index and the power grid stability value in real time;

[0058] Compare the level vulnerability index and the power grid stability value before and after adjustment. If the change value of the level vulnerability index is less than or equal to 0, and the change value of the power grid stability value is less than or equal to 0, it means that the optimization is incorrect;

[0059] Summarize the changes in vulnerability indicators and stability indicators in all tests, and create a comprehensive report for recording and preservation.

[0060] A computer device, comprising: a memory and a processor; the memory stores a computer program, including: when the processor executes the computer program, the steps of the method described in any one of the present inventions are implemented.

[0061] A computer-readable storage medium, on which a computer program is stored, including: when the computer program is executed by a processor, the steps of the method described in any one of the present inventions are implemented.

[0062] Advantages of the present invention: By dividing the power grid into high-voltage levels, medium-voltage levels, and low-voltage levels according to voltage levels, the topological structure of each level is independently analyzed. Based on the Louvain algorithm, the modularity increment change of each node moving to an adjacent node is calculated, and by maximizing the modularity increment, the subgroup structure is gradually optimized. Combining the modularity of the level with the node vulnerability indicators of that level, a more comprehensive vulnerability assessment model is formed. Through the comprehensive analysis of cross-level and intra-level vulnerability indicators, the coupling problems between levels can be more effectively identified and solved during the power grid optimization process. By using the branch and bound algorithm, the connection edges of nodes with high vulnerability indicators are gradually deleted to generate different branch topologies, optimizing the topological structure, and achieving the balance and improvement of multiple important performance indicators of the power grid. Description of the Drawings

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0064] Figure 1 It is the overall flowchart of the method for constructing multi-level vulnerability quantification indicators based on topological power grid operation provided by an embodiment of the present invention;

[0065] Figure 2 It is the schematic diagram of the vulnerability quantification indicator process of the method for constructing multi-level vulnerability quantification indicators based on topological power grid operation provided by an embodiment of the present invention;

[0066] Figure 3 It is the schematic diagram of the topological optimization process of the method for constructing multi-level vulnerability quantification indicators based on topological power grid operation provided by an embodiment of the present invention. Detailed Embodiments

[0067] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0068] Example 1. Referring to Figures 1 - 3 , which is an embodiment of the present invention, a method for constructing a multi-level vulnerability quantification index based on the operation of a topological power grid is provided, including:

[0069] S1: Obtain the topological structure data of the power grid dispatching system for hierarchical division, analyze the node data to form a hierarchical matrix, calculate the shortest path length matrix and analyze the betweenness centrality, and analyze the correlation between nodes at each level.

[0070] Preferably, obtaining the topological structure data of the power grid dispatching system for hierarchical division, analyzing the node data to form a hierarchical matrix, calculating the shortest path length matrix and analyzing the betweenness centrality, and analyzing the correlation between nodes at each level, includes,

[0071] Based on the power grid dispatching system, collect the topological structure data of the power grid. The data includes information on nodes, such as substations, distribution stations, etc., and edges, such as transmission lines, distribution lines, etc.;

[0072] Preprocess the obtained data, and construct the adjacency matrix A of the power grid based on whether there is an edge between nodes, where indicates that there is an edge between nodes i and j, indicates that there is no edge;

[0073] Count the number of nodes directly connected to node i as the degree of the node ;

[0074] Perform hierarchical division based on the voltage level of the power grid, including high-voltage level, medium-voltage level, and low-voltage level;

[0075] Among them, the high-voltage level includes all substations and transmission lines of 220 kV and above, the medium-voltage level includes all substations and transmission lines between 35 kV and 220 kV, and the low-voltage level includes all distribution stations and distribution lines of 35 kV and below.

[0076] Based on the voltage level of the nodes in the adjacency matrix, allocate them according to the high-voltage level, medium-voltage level, and low-voltage level to form a hierarchical matrix , including High-voltage level matrix, Medium-voltage level matrix and Low-voltage level matrix, , and are respectively expressed as:

[0077]

[0078]

[0079]

[0080] For each hierarchical matrix , use the Floyd - Warshall algorithm to calculate the shortest path length matrix between any two nodes , which is expressed as:

[0081]

[0082] where represents the shortest path length between nodes i and j within this level, and are respectively the path lengths from node i to the intermediate node b, and from the intermediate node b to node j, indicating that the above formula should be applied for all node pairs i and j in the network, and all possible intermediate nodes b.

[0083] Analyze the topological distance matrix for each level , and calculate the betweenness centrality value of each node to evaluate the importance of the node in the hierarchical network, which is expressed as:

[0084]

[0085] where represents the number of shortest paths from node s to node t, represents the number of shortest paths from node s to node t passing through node i, represents the betweenness centrality value of node i, indicates the summation over all different node pairs s and t, where s and t represent any two different nodes, and s is not equal to i, and t is not equal to i.

[0086] Based on whether two nodes belong to different levels and are directly connected, analyze the relevance between nodes at each level, and establish a cross - level connection relationship matrix , which is expressed as:

[0087]

[0088] Through the construction of the adjacency matrix, it provides a basis for subsequent calculations and defines the topological structure of the power grid, providing the necessary input data for calculating the shortest path length between nodes, betweenness centrality, etc. By calculating the shortest path length matrix, which is used to store the shortest path lengths between any two nodes in the network, it directly affects the accuracy of betweenness centrality. Without this matrix, it is impossible to effectively measure the connectivity between nodes, and thus impossible to accurately evaluate the importance of nodes. By calculating the betweenness centrality value, it represents the proportion of the number of times node i acts as a mediator in the shortest paths between all node pairs. By counting the frequency of nodes as mediator nodes, it evaluates their importance in the network, which provides a basis for identifying key nodes in the power grid. Betweenness centrality is a key indicator for vulnerability quantification, capable of identifying nodes with high mediating roles in the power grid. Once these nodes fail, it will have a significant impact on the connectivity of the network. Through the analysis and judgment of the connection relationship matrix, it helps to analyze the overall connectivity of the power grid, especially in the case of cross-level, to identify the dependency relationships between different levels;

[0089] By dividing the power grid into high-voltage level, medium-voltage level, and low-voltage level according to voltage levels and constructing independent level matrices (high-voltage level matrix, medium-voltage level matrix, low-voltage level matrix), the topological structure of each level is independently analyzed. Different from traditional power grid topology analysis methods, this hierarchical modeling method can more meticulously analyze the relationships between levels, especially the cross-level correlations. Among them, the Floyd-Warshall algorithm is usually applied to the global shortest path calculation of graphs, but here it is applied to the analysis of hierarchical matrices, which can refine the network structure of each level and identify potential weaknesses within and across levels.

[0090] S2: Initially divide the node subgroups, calculate the modularity of different levels and cross-levels, calculate the change in modularity increment of node movement based on the Louvain algorithm, and perform repeated iterations to determine the subgroup structure. Based on the node data, determine the level vulnerability indicators and analyze the cross-level vulnerability indicators to calculate the multi-level vulnerability quantification indicators of the power grid.

[0091] Preferably, initially divide the node subgroups, calculate the modularity of different levels and cross-levels, calculate the change in modularity increment of node movement based on the Louvain algorithm, and perform repeated iterations to determine the subgroup structure, including,

[0092] Based on the level matrix , if two nodes belong to the same level, they are regarded as a subgroup. For each node i, through the calculated shortest path length matrix , calculate the average path length of all nodes as the length threshold, and divide two nodes with the shortest path length less than the length threshold into the same subgroup;

[0093] Based on the adjacency matrix of each level (high voltage, medium voltage, low voltage) of the power grid, calculate the modularity within the level, expressed as:

[0094]

[0095]

[0096]

[0097] where, represents the modularity of the corresponding power grid level, represents the total number of edges in the network at this level, represents the degree of node i, is an indicator function. If nodes i and j belong to the same subgroup, it represents 1; otherwise, it represents 0, represents the summation over all node pairs in the level, represents the degree of node j, and respectively represent the subgroups to which the nodes belong.

[0098] At the same time, calculate the cross-level modularity, expressed as:

[0099]

[0100] where, represents the total number of edges across levels ( ), represents the cross-level modularity.

[0101] Based on the Louvain algorithm, construct a network structure according to the divided subsets, and calculate the change in the modularity increment when each node moves to an adjacent node , expressed as:

[0102]

[0103]

[0104] where, represents the total number of edges across levels, represents node and the direct connection between, represents the total number of edges, represents the change in the internal connection of the original subgroup after node i is removed from the original subgroup, represents the connection situation between node i and node , and respectively represent node and node Degree.

[0105] For each node i, determine the maximum adjacent node j, and move node i to the same subgroup as j to complete the merger and recalculation of the subgroup structure;

[0106] Repeat the iteration for the merger and recalculation of the subgroup structure. Based on the shortest path length matrix values between nodes, calculate the standard deviation and multiply it by a sensitivity constant (set to a small value based on historical experience, such as 0.01) as the subgroup threshold.

[0107] Monitor the modularity increment in each iteration in real time , and stop the iteration when it is less than the subgroup threshold;

[0108] Recalculate the modularity within each level and the cross-level modularity based on the subgroup structure after the iteration is completed, and perform normalization processing.

[0109] By calculating the average path length of all nodes as the length threshold, two nodes with a shortest path length less than this threshold are assigned to the same subgroup, innovatively combining global path information and local node connections, avoiding the problem of detail loss that may occur in traditional subgroup division based on direct adjacency relationships. Based on the Louvain algorithm, calculate the change in modularity increment when each node moves to an adjacent node, and gradually optimize the subgroup structure by maximizing the modularity increment. Not only consider the initial subgroup membership of the nodes, but also iteratively optimize the subgroup position of each node to achieve the optimal overall modularity. By introducing the statistical characteristics of the path length distribution, more reasonable stopping conditions for subgroup structure merger are determined, avoiding the risks of over-merger or premature stopping. By calculating the modularity within each level, independent analysis of the cross-level modularity is also carried out, and the results are finally normalized. This method can more comprehensively reflect the multi-level coupling characteristics of the power grid, help identify potential risks and dependencies between different voltage levels, and ensure reasonable structural complexity and level coordination while optimizing the modularity.

[0110] Furthermore, based on the node data, determine the level vulnerability index, analyze the cross-level vulnerability index, and calculate the multi-level vulnerability quantification index of the power grid, including

[0111] Construct a node vulnerability index based on the nodes, and count the betweenness centrality value and the node degree of the nodes;

[0112] Calculate the average distance between a node and other nodes based on the shortest path length matrix as the node compactness;

[0113] The reciprocal of the maximum fluctuation amplitude based on the node voltage is used as the voltage stability index for each node;

[0114] The sum of the betweenness centrality value, node degree, node closeness, and voltage stability index is used as the node vulnerability index for the node.

[0115] Based on the modularity of each level divided by the total number of subgroups in the corresponding level, and considering the node vulnerability index for each level to calculate the vulnerability index for each level, expressed as:

[0116] ;

[0117] where, represents the vulnerability index for each level, represents the modularity for each level, represents the node set for the corresponding level, represents the node vulnerability index of node i, represents the number of nodes for the corresponding level;

[0118] For the cross-level modularity divided by the total number of node connections spanning different voltage levels, the cross-level vulnerability index is obtained. For the vulnerability index of each level and the cross-level vulnerability index, weighted comprehensive calculation is performed to form the final multi-level vulnerability quantification index of the power grid, expressed as:

[0119]

[0120] where, represents the comprehensive vulnerability index, represents the vulnerability index of the high-voltage level, represents the vulnerability index of the medium-voltage level, represents the vulnerability index of the low-voltage level, represents the cross-level vulnerability index.

[0121] By comprehensively considering the betweenness centrality value, node degree, node compactness, and voltage stability index of nodes, a multi-dimensional node vulnerability index is constructed, forming a more comprehensive vulnerability assessment model. By combining the modularity of each level with the node vulnerability index of that level, the vulnerability index of each level is calculated. Through iterative calculation and optimization of subgroups, and the content of calculating modularity is actually applied, which can not only reflect the structural compactness within the level, but also reflect the potential weak points in the structure through the weighting of the node vulnerability index, realizing a more comprehensive assessment of the level vulnerability. By using the cross-level modularity and the total number of node connections spanning different voltage levels, the cross-level vulnerability index is obtained, and it is weighted and integrated with the vulnerability indexes of each level to form the final multi-level vulnerability quantification index of the power grid. Through the calculation of the level vulnerability by combining the multi-dimensional node vulnerability index and modularity, the assessment process is more comprehensive and accurate, which can better identify the weak links and key nodes in the power grid, improve the reliability and stability of the power grid. Through the comprehensive analysis of the cross-level and intra-level vulnerability indexes, the coupling problems between levels can be more effectively identified and solved during the power grid optimization process, ensuring the balance and stability of the power grid between each level, reducing potential risks. Through the formation of the comprehensive vulnerability index, a systematic reference basis is provided for the dispatching and protection measures of the power grid, which can timely adjust the power grid structure during actual operation to prevent the spread of faults and the occurrence of systemic risks.

[0122] S3: Draw the vulnerability index curve based on the power grid operation, set the early warning alarm mechanism, analyze the power grid stability according to the power grid fluctuation amplitude, frequency deviation, and power stability, and cooperate with the level vulnerability index to expand the topological branches to determine the high-stability topological structure, verify the high-stability topological structure and record the data.

[0123] Preferably, draw the vulnerability index curve based on the power grid operation, and set the early warning alarm mechanism, including,

[0124] Draw the vulnerability index curve based on the real-time collected operation data during the power grid operation, and the operation data includes voltage, power, frequency, and load data;

[0125] Preprocess the collected data, and use the median filter to eliminate the noise in the data;

[0126] Calculate the vulnerability index of the node according to the real-time collected operation data and the vulnerability index of the corresponding level , draw a curve graph based on the MATLAB tool for visual display;

[0127] According to the historical data and the operation requirements of the power grid, calculate the sum of the mean value of the historical vulnerability index of the node and twice the standard deviation of the historical vulnerability index as the node vulnerability threshold;

[0128] Calculate the sum of the mean of the historical vulnerability indicators at different levels and twice the standard deviation of the historical vulnerability indicators as the level vulnerability threshold;

[0129] If the vulnerability indicator of a node is greater than the node vulnerability threshold, a node warning alarm is triggered. If the vulnerability indicators at different levels are greater than the level vulnerability threshold, a level warning alarm is triggered.

[0130] By combining the real-time collected data with the historical data, dynamically set the vulnerability thresholds of nodes and levels. In data preprocessing, a median filter is used to eliminate noise, making the subsequent calculation of vulnerability indicators more accurate and reliable. Through MATLAB tools, visualize the vulnerability indicators of nodes and levels, enabling complex power grid vulnerability information to be presented in an intuitive chart form. The visualization display not only facilitates real-time monitoring by power grid management personnel but also provides clear warning information in case of anomalies. The hierarchical warning mechanism can more accurately locate the risk points in the power grid, avoiding misoperations or neglect of problems caused by excessive or insufficient warnings. The hierarchical warning mechanism for nodes and levels can more accurately locate and respond to the risk points in the power grid, preventing local problems from evolving into global failures, and providing more powerful guarantees for the stable operation of the power grid.

[0131] Furthermore, analyze the power grid stability based on the power grid fluctuation amplitude, frequency deviation, and power stability, including,

[0132] Based on the maximum fluctuation amplitude value of the voltage, calculate the average inverse value of the voltage fluctuation by comprehensively considering all nodes as the voltage stability value;

[0133] Based on the power grid frequency, calculate the deviation value of the frequency, and calculate the average of the absolute values of the frequency deviations, representing the frequency stability, expressed as:

[0134]

[0135] Among them, represents the frequency deviation at time , represents the total number of sampling points, represents the frequency stability.

[0136] Based on the collected power output and total power demand of each node, calculate the power stability, expressed as:

[0137]

[0138] Among them, represents the power stability, represents the total power output of the node, Represents the total power demand of the system to which the node belongs. Represents the power imbalance, which is calculated by subtracting the total power demand of the system to which the node belongs from the total power output of the node.

[0139] The comprehensive voltage stability value, frequency stability value, and power stability value are normalized and added together as the stability value of the power grid.

[0140] By comprehensively considering voltage stability, frequency stability, and power stability, a multi-dimensional power grid stability index is formed. Among them, by inversely measuring voltage fluctuations, it can more intuitively reflect the impact of voltage fluctuations on power grid stability. The smaller the fluctuation, the larger the voltage stability value. Frequency stability is measured by calculating the average absolute value of frequency deviation. Power stability of the power grid is measured by calculating the difference between power output and power demand, that is, the power imbalance. Based on the inverse value of the voltage fluctuation amplitude, the average absolute value of the frequency deviation, and the quantification of the power imbalance, the real-time monitoring and dispatching decision-making of the power grid can be based on more sensitive and accurate indicators, improving the operational safety and efficiency of the power grid. Through the normalization processing and comprehensive calculation of various stability indicators, the final power grid stability value formed can be used as the basis for power grid stability early warning, enabling the early warning system to identify potential stability problems earlier and more accurately, and preventing the expansion of faults.

[0141] Among them, for the calculation of voltage stability, the inverse value of voltage fluctuation is used to measure stability. When the voltage fluctuation is large, the stability value will decrease rapidly, and vice versa, the stability value will increase rapidly, which can sensitively reflect the voltage fluctuation situation and can timely warn of the impact of voltage fluctuation on power grid stability. For the calculation of frequency stability, by calculating the average absolute value of frequency deviation, the influence of the deviation sign can be eliminated, ensuring that both positive and negative deviations are taken into account. Different from the maximum deviation value, the average absolute deviation can avoid the excessive influence of extreme values on stability assessment, making the assessment result more robust. Especially when the frequency fluctuation is small, it can more truly reflect the actual operating state of the power grid. For the calculation of power stability, it directly measures the balance between power supply and demand, which can accurately reflect the power stability of the power grid. By adopting an exponentially decaying method for the power imbalance, the influence of the imbalance on power stability can be highlighted, enabling minor imbalances to be identified in a timely manner, while the influence of larger imbalances will be significantly amplified, thus enhancing the early warning effect. By comprehensively considering the stability indicators of voltage, frequency, and power, the key stability factors in power grid operation are comprehensively covered, and through normalization processing, it is ensured that each stability indicator is summed up on the same order of magnitude, enabling stability indicators in different dimensions to fairly occupy corresponding weights in the overall stability, thus avoiding the excessive influence of a certain indicator on the overall result.

[0142] Furthermore, expand the topological branches in coordination with the hierarchical vulnerability index to determine a high-stability topological structure, including

[0143] Based on the warning alerts of the node vulnerability threshold and the hierarchical vulnerability threshold, identify the over-standard nodes and the corresponding subgroups, and based on the initial topological structure data through the branch and bound algorithm, and visually display it through the MATLAB image tool. Generate a branch topological structure by deleting the connection edges of the high-vulnerability index nodes, and repeat the expansion of the branches.

[0144] Calculate the corresponding hierarchical vulnerability index for each new topological structure until the hierarchical vulnerability index in the new topological structure is lower than the hierarchical vulnerability threshold, and if the grid stability continues to increase, continue to expand the branch. If the change in the vulnerability index is significantly reduced, and the grid stability stops increasing or continues to decrease, then stop expanding this branch.

[0145] Use the boundary evaluation mechanism and cooperate with the grid simulation tool PSS / E to simulate and verify the scenarios for each generated branch, prune the branches whose adjustment results cannot meet the requirement of reducing the vulnerability index, and select the topological structure with a low vulnerability index and the highest grid stability as the high-stability structure.

[0146] Through the branch and bound algorithm, gradually delete the connection edges of the high-vulnerability index nodes to generate different branch topological structures, and expand and evaluate these structures to achieve the purpose of reducing the hierarchical vulnerability index. On the basis of the branch and bound algorithm, introduce a boundary evaluation mechanism to evaluate each generated branch, and prune according to the changes in the vulnerability index and the grid stability, effectively avoiding the expansion of invalid branches, improving the efficiency of the algorithm, and ensuring that the finally selected topological structure can significantly reduce the vulnerability index and improve the grid stability. By considering both the reduction of the vulnerability index and the increase of the grid stability, formulate the criteria for branch expansion and pruning, so that the final optimized structure can not only reduce the grid vulnerability, but also improve the overall stability of the grid, achieving the balance and improvement of multiple important performance indicators of the grid, avoiding the negative impact of single optimization, and having important innovation and practical application value. Through the dynamic hierarchical vulnerability assessment method, the expansion and pruning of branches are made more scientific, which can reflect the impact of the change of the grid structure on the vulnerability in real time. By coordinately optimizing the grid stability, it is ensured that the grid not only avoids the increase of vulnerability risk during the optimization process, but also further improves the overall stability, providing a more reliable guarantee for the safe operation of the grid.

[0147] Furthermore, verify the high-stability topological structure and record the data, including

[0148] Apply the optimal topological structure to the power grid, start the operation of the power grid, and collect key index data in real time, and calculate the adjusted hierarchical vulnerability index and the power grid stability value in real time;

[0149] Compare the hierarchical vulnerability index and the power grid stability value before and after adjustment. If the change value of the hierarchical vulnerability index is less than or equal to 0, and the change value of the power grid stability value is less than or equal to 0, it indicates an optimization error;

[0150] Summarize the changes in the vulnerability index and the stability index in all tests, and create a comprehensive report for recording and saving.

[0151] By applying the optimal topological structure to the power grid, collecting and calculating key index data in real time, and immediately comparing the vulnerability index and the power grid stability value before and after adjustment, the optimization effect can be dynamically verified during operation, ensuring that the optimization results can truly improve the stability of the power grid and reduce vulnerability. Through the real-time dynamic feedback mechanism and the ability to immediately identify errors, it is ensured that there are no ineffective or harmful measures during the optimization process, thereby improving the safety and reliability of the power grid operation. Through the systematic recording and saving of the comprehensive report, it provides a scientific basis for power grid optimization decisions, and improves the transparency and traceability of the optimization process, providing data support for future optimization improvements.

[0152] Embodiment 2

[0153] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. And the aforementioned storage medium includes: USB flash drive, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other various media that can store program codes.

[0154] The logic and / or steps represented in the flowchart or otherwise described herein can, for example, be considered as a definitional sequence of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. As used in this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0155] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise appropriate processing as necessary, and then storing it in a computer memory.

[0156] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), and the like.

[0157] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A construction method for multi - level vulnerability quantification indexes based on the operation of a topological power grid, characterized in that, Including: Collecting the topological structure data of the power grid, where the topological structure data includes node data and edge data, forming a hierarchical matrix based on the node data, and analyzing the correlation between nodes at each level; Preliminarily dividing node subgroups, calculating the modularity of different levels and cross-levels, calculating the change in modularity increment of node movement based on the Louvain algorithm, and performing repeated iterations to determine the subgroup structure. Based on the node data, determining the hierarchical vulnerability index and calculating the multi-level vulnerability quantification index of the power grid; Drawing the vulnerability index curve, setting up an early warning mechanism, analyzing the stability of the power grid according to the power grid fluctuation amplitude, frequency deviation, and power stability, and cooperating with the hierarchical vulnerability index to expand the topological branches to determine the high-stability topological structure, verifying the high-stability topological structure and recording the data; The preliminary division of node subgroups, calculating modularity at different levels and cross-levels, calculating the change in modularity increment of node movement based on the Louvain algorithm, and performing repeated iterations to determine the subgroup structure includes, based on the hierarchical matrix A e , if two nodes belong to the same level, they are regarded as subgroups. For each node i, through the calculated shortest path length matrix D e , calculate the average path length of all nodes as the length threshold, and divide two nodes with the shortest path length less than the length threshold into the same subgroup.

2. The method for constructing a multi-level vulnerability quantification index based on the operation of a topological power grid according to claim 1, characterized in that: The forming of the hierarchical matrix based on the node data and analyzing the correlation between nodes at each level includes preprocessing the topological structure data and constructing the adjacency matrix A of the power grid based on whether there is an edge between nodes; The number of nodes directly connected to node i is counted as the degree K of the node i ; Performing hierarchical division based on the voltage level of the power grid, dividing it into three levels: high-voltage level, medium-voltage level, and low-voltage level; The high-voltage level includes all substations and transmission lines of 220 kV and above, the medium-voltage level includes all substations and transmission lines between 35 kV and 220 kV, and the low-voltage level includes all distribution substations and distribution lines of 35 kV and below; The voltage levels of the nodes based on the adjacency matrix are assigned according to the high-voltage level, medium-voltage level, and low-voltage level to form a hierarchical matrix A e , the hierarchical matrix A e includes the high-voltage hierarchical matrix A h , the medium-voltage hierarchical matrix A m and the low-voltage hierarchical matrix A o ; For each hierarchical matrix A e , use the Floyd-Warshall algorithm to calculate the shortest path length matrix D between any two nodes e ; For the topological distance matrix D at each level e perform an analysis and calculate the betweenness centrality value of each node; Analyze the relevance between nodes at each level based on whether two nodes belong to different levels and are directly connected, and establish a cross-level connection relationship matrix C i .

3. The method for constructing a multi-level vulnerability quantification index based on the operation of a topological power grid according to claim 2, wherein: The calculating of the modularity of different levels and cross-levels is based on calculating the modularity within the level for the adjacency matrix of each level of the power grid, expressed as: Among them, Q e represents the modularity corresponding to the power grid level, m e represents the total number of edges in the network at this level, k i represents the degree of node i, δ(c i , c j ) is the indicator function, k j represents the degree of node j, c i and c j respectively represent the subgroups to which the nodes belong; At the same time, calculating the cross-level modularity, expressed as: Among them, Q u represents the cross-level modularity; Based on the Louvain algorithm, according to the divided subsets to form a network structure, calculating the change in modularity increment ΔQ of each node moving to an adjacent node for each node, expressed as: Among them, m c represents the total number of cross-level connected edges, A ij represents the direct connection between nodes i and j, m represents the total number of connected edges, and ΔQ i represents the change in the internal connection of the atomic group after node i is removed from the atomic group, A ik represents the connection situation between node i and node k, k k and k i respectively represent the degrees of nodes k and i; For each node i, determining the adjacent node j with the largest ΔQ, and moving node i to the same subgroup as j to complete the merging and recalculation of the subgroup structure; Repeatedly iterating for the merging and recalculation of the subgroup structure, calculating the standard deviation based on the shortest path length matrix values between nodes and multiplying it by the sensitivity constant as the subgroup threshold; Real-time monitoring the modularity increment ΔQ in each iteration, and stopping the iteration when ΔQ is less than the subgroup threshold; Recalculating the modularity within each level and the cross-level modularity based on the subgroup structure after completing the iteration, and performing normalization processing.

4. The method for constructing a multi - level vulnerability quantification index based on the operation of a topological power grid according to claim 3, wherein: The determining of the hierarchical vulnerability index based on the node data and calculating the multi-level vulnerability quantification index of the power grid includes constructing a node vulnerability index based on the nodes, and statistically calculating the betweenness centrality value and the node degree of the nodes; Calculating the average distance between a node and other nodes based on the shortest path length matrix, and taking the average distance as the node compactness; Taking the reciprocal of the maximum fluctuation amplitude of the node voltage as the voltage stability index of each node; Taking the sum of the comprehensive betweenness centrality value, node degree, node compactness, and voltage stability index as the node vulnerability index of the node; Dividing the modularity of each level by the total number of subgroups in the corresponding level, and considering the node vulnerability index of the level nodes to calculate the vulnerability index of each level, expressed as: Among them, V e represents the vulnerability index of each level, Q e represents the modularity of each level, E represents the set of nodes at the corresponding level, V i represents the node vulnerability index of node i, N e represents the number of nodes at the corresponding level; Divide the cross - level modularity by the sum of the number of node connections across different voltage levels to obtain the cross - level vulnerability index. Weight and comprehensively calculate the vulnerability index of each level and the cross - level vulnerability index to form the final multi - level vulnerability quantification index of the power grid.

5. The method for constructing a multi-level vulnerability quantification index based on the operation of a topological power grid according to claim 4, characterized in that: The drawing of the vulnerability index curve and the setting of the early warning mechanism include drawing the vulnerability index curve based on the real - time operation data collected during the operation of the power grid. The operation data includes voltage, power, frequency, and load data. Pre - process the collected data and use a median filter to eliminate the noise in the data. Calculate the vulnerability index V of the node according to the real-time collected operation data i and the vulnerability index V of the corresponding level e , draw a curve graph based on the MATLAB tool for visual display; According to the historical data and the operation requirements of the power grid, calculate the sum of the mean of the historical vulnerability index of the node and twice the standard deviation of the historical vulnerability index as the node vulnerability threshold. Calculate the sum of the mean of the historical vulnerability index of different levels and twice the standard deviation of the historical vulnerability index as the level vulnerability threshold. If the vulnerability index V of the node i is greater than the node vulnerability threshold, a node warning alarm is triggered. If the vulnerability index V at different levels e is greater than the level vulnerability threshold, a level warning alarm is triggered.

6. The method for constructing a multi-level vulnerability quantification index based on the operation of a topological power grid according to claim 5, characterized in that: The analysis of the power grid stability according to the power grid fluctuation amplitude, frequency deviation, and power stability includes, based on the maximum fluctuation amplitude value of the voltage, comprehensively calculating the average inverse value of the voltage fluctuation of all nodes as the voltage stability value. Calculate the deviation value of the frequency based on the power grid frequency, and calculate the average of the absolute value of the frequency deviation to represent the frequency stability, expressed as: where Δf(t) represents the frequency deviation at time t, T represents the total number of sampling points, and S f represents the frequency stability; Based on the power output and total power demand of each node collected, calculate the power stability, expressed as: Among them, S p represents power stability, P t (t) represents the total power output of the node, P d (t) represents the total power demand of the system to which the node belongs, and ΔP(t) represents the power imbalance; Normalize and add the voltage stability value, frequency stability value, and power stability value as the power grid stability value.

7. The method for constructing a multi-level vulnerability quantification index based on the operation of a topological power grid according to claim 6, wherein: The determination of the high - stability topological structure by cooperating with the level vulnerability index to expand the topological branches includes, based on the early warning alarms of the node vulnerability threshold and the level vulnerability threshold, confirming the over - standard nodes and the corresponding subgroups. Based on the topological structure data, use the branch - and - bound algorithm and MATLAB image tools for visual display. Generate a branch topological structure by deleting the connection edges of the nodes with high vulnerability indexes, and repeat the expansion of branches. Calculate the corresponding level vulnerability index for each new topological structure until the level vulnerability index of the new topological structure is lower than the level vulnerability threshold and the power grid stability continues to increase, then continue to expand the branches. Stop expanding the branches when the power grid stability stops increasing. Use the boundary evaluation mechanism and cooperate with the power grid simulation tool PSS / E to simulate and verify each generated branch. Prune the branches whose adjustment results cannot meet the requirement of reducing the vulnerability index, and select the topological structure with a low vulnerability index and the highest power grid stability as the high - stability structure.

8. The method for constructing a multi-level vulnerability quantification index based on the operation of a topological power grid according to claim 7, characterized in that: The verification of the high - stability topological structure and data recording include applying the optimal topological structure to the power grid, starting the power grid operation and collecting key index data in real - time, and calculating the adjusted level vulnerability index and power grid stability value in real - time. Compare the level vulnerability index and the power grid stability value before and after adjustment. If the change value of the level vulnerability index is less than or equal to 0, and the change value of the power grid stability value is less than or equal to 0, it means an optimization error. Summarize the changes in the vulnerability index and stability index in all tests, and create a comprehensive report for recording and saving.

9. A computer device, comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the method for constructing a multi-level vulnerability quantification index based on the operation of a topological power grid as described in any one of claims 1-8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the method for constructing a multi-level vulnerability quantification index based on the operation of a topological power grid as described in any one of claims 1-8 are implemented.

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