Power transmission section identification method and system based on dynamic matching of fragile lines in power transmission channel

Abstract

The invention relates to the technical field of power system dispatching operation control, and discloses a power transmission section identification method and system based on dynamic matching of fragile lines in a power transmission channel. Constructing an improved PageRank algorithm to calculate a key degree index of the node; calculating a transaction volume sequence between a generator and a load node by using the PTDF, screening out a key generator-load node pair, extracting a key line set in a corresponding source-load channel from the updated topological graph by means of network conversion and shortest path search, and giving a vulnerability index of the line in combination with the LODF and the real-time power flow of the line; and matching the highly fragile line identified on line with the minimum cut candidate section obtained by the offline search, and dynamically screening to obtain the optimal power transmission section in the current operation state. The method can adapt to the dynamic change of the operation state of the power grid, quantifies the key of the nodes in real time, has high accuracy and high calculation efficiency, and provides powerful support for safe and stable operation of the power grid.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching and operation control technology, and in particular to a method and system for identifying transmission sections based on dynamic matching of vulnerable lines within transmission channels. Background Technology

[0002] Transmission sections of power grids are a crucial foundation for power system safety and stability analysis, power flow control, and operational monitoring. Existing methods for identifying critical transmission sections based on human experience are limited by the experience level and subjective judgment of operators, resulting in a rather crude approach to operational status analysis and identification rule formulation. In large-scale power grids, such methods typically require traversing a large number of possible section combinations, leading to massive computational loads and low efficiency. They also ignore the power flow distribution under the current operating conditions and the power flow transfer patterns after line failures, causing the identification results to be out of sync with actual operational risks, resulting in omissions or misjudgments in section identification. With the large-scale integration of new energy sources and frequent changes in power flow distribution, traditional methods struggle to reflect the dynamic characteristics of sections in a timely manner, easily leading to omissions or misjudgments of critical sections, and failing to meet the dual requirements of speed and accuracy for modern power systems.

[0003] Especially under complex and ever-changing operating conditions, critical transmission sections are often concentrated within a few transmission corridors. If we can first identify the transmission corridors closely related to the current operating mode, and then conduct focused analysis on line vulnerability within those corridors, the search range for sections will be significantly reduced and the identification efficiency improved. However, existing methods generally lack the ability to specifically identify transmission corridors, and also lack a mechanism for quantifying the importance of line vulnerability by combining power flow transfer characteristics.

[0004] Therefore, it is necessary to propose a transmission section identification method that can quickly locate key transmission channels in their current operating state and uses the vulnerability of the lines within the channel as the core indicator. Summary of the Invention

[0005] Based on this, it is necessary to address the above-mentioned technical problems. This invention provides a method and system for identifying transmission sections based on dynamic matching of vulnerable lines within a transmission channel. By introducing dynamic evaluation methods such as the power flow transfer coefficient after line failure, the safety vulnerability of the line within the channel is quantified, thereby efficiently and accurately identifying key transmission sections under multiple operating modes, balancing computational efficiency and identification accuracy, and meeting the dual requirements of power systems for real-time performance and reliability.

[0006] A method for identifying transmission sections based on dynamic matching of vulnerable lines within a transmission corridor includes the following steps:

[0007] Step 1: Based on the real-time operation data of the power grid, the criticality index of each node in the power grid is calculated using the improved PageRank algorithm. The improved PageRank algorithm uses the line power flow magnitude as an element in the Google matrix to reflect the actual correlation strength between nodes. The restart vector e is improved by combining the adjacency relationship and impedance characteristics of the nodes and weighting them with the total impedance of each node. Virtual nodes and node impedance matrices are introduced to connect the energy exchange relationship between generator nodes and load nodes.

[0008] Step 2: Perform network transformation on the original power grid, converting nodes in the power system into edges and edges into nodes, and using the reciprocal of the power system criticality index calculated in Step 1 as the weight of the edges to form a new topology model.

[0009] Step 3: Based on the power flow transfer distribution factor, sort the energy transaction size between generators and load nodes in the new topology model, search for the shortest path between generator nodes and load nodes, and extract the set of lines in the important transmission channels under the current operating state.

[0010] Step 4: Calculate the vulnerability index of the transmission line by combining the branch disconnection distribution factor. Based on the set of lines in the important transmission channels obtained in Step 3, conduct a quantitative assessment of the vulnerability of the lines in the transmission channels to obtain the corresponding vulnerability index.

[0011] Step 5: Generate a set of candidate transmission sections offline using the minimum cut set search method;

[0012] Step 6: Based on the vulnerability index values ​​in Step 4, sort the lines in the line set within the important transmission channel and match them one by one with the transmission sections in the candidate transmission section set. From the matching results, select the candidate section with the highest sum of vulnerability indices and the fewest number of lines as the final key transmission section.

[0013] A transmission section identification system based on dynamic matching of vulnerable lines within a transmission corridor includes:

[0014] Criticality Index Calculation Unit: Based on real-time power grid operation data, the criticality index of each node in the power grid is calculated using an improved PageRank algorithm. The improved PageRank algorithm uses the line power flow magnitude as an element in the Google matrix to reflect the actual correlation strength between nodes. The restart vector e is improved by combining the adjacency relationship and impedance characteristics of nodes and weighting it with the total impedance of each node. Virtual nodes and node impedance matrices are introduced to connect the energy exchange relationship between generator nodes and load nodes.

[0015] Topology model construction unit: The original power grid is transformed into a network, nodes in the power system are converted into edges, and edges are converted into nodes. The reciprocal of the calculated power system criticality index is used as the weight of the edge to form a new topology model.

[0016] Line set extraction unit in important transmission channels: Based on the power flow transfer distribution factor, the energy transaction size between generator and load nodes in the new topology model is sorted, and the shortest path between generator nodes and load nodes is searched to extract the line set in the important transmission channels under the current operating state;

[0017] Vulnerability index calculation unit: Combines branch disconnection distribution factor to calculate the vulnerability index of transmission lines. Based on the line set in important transmission channels, the vulnerability of the lines in the transmission channels is quantitatively assessed to obtain the corresponding vulnerability index.

[0018] Candidate transmission section set generation unit: The candidate transmission section set is generated offline using the minimum cut set search method;

[0019] Key transmission section generation unit: Based on the magnitude of vulnerability index, the lines in the line set within the important transmission channel are sorted and matched one by one with the transmission sections in the candidate transmission section set. From the matching results, the candidate section with the highest sum of vulnerability index and the fewest number of lines is selected as the final key transmission section.

[0020] The invention has the following advantages and beneficial effects:

[0021] (1) The real-time important transmission channel extraction method based on the improved PageRank and Power Transmission Distribution Factor (PTDF) proposed in this invention can effectively identify channels undertaking critical transmission tasks by combining node importance and power flow distribution information under the real-time operation of the power grid. This method introduces network transformation and source-load node transaction volume ranking methods when searching for the shortest path, which significantly reduces the search space and computational load. Compared with the traditional method that relies on full network traversal, it can more accurately reflect the risk distribution under the current operating state and has higher computational efficiency.

[0022] (2) This invention combines the vulnerability assessment method of Line Outage Distribution Factor (LODF) with a minimum cut set search strategy to dynamically identify key transmission sections based on important transmission channels. By prioritizing candidate sections through vulnerability indicators, it can quantitatively reflect the power flow transfer risk caused by line failures, thereby revealing potential weak links in the power grid more comprehensively. This method supports offline generation of candidate sections and rapid matching in real time, which not only ensures the accuracy of section identification but also significantly improves computational efficiency, making it suitable for power grid security analysis and scheduling decisions under multiple operating scenarios. Attached Figure Description

[0023] Figure 1 This is a diagram illustrating the overall framework of the present invention for rapid identification of power grid transmission sections based on dynamic matching of vulnerable lines within transmission channels.

[0024] Figure 2 This is a schematic diagram of the power grid network conversion method of the present invention.

[0025] Figure 3 This is a flowchart of the solution process for this invention.

[0026] Figure 4 The diagram shows the topology of the IEEE 39-node test system used in the implementation of this invention.

[0027] Figure 5 This is a diagram showing the criticality index of nodes in an embodiment of the present invention.

[0028] Figure 6 This is a schematic diagram of the identification results of transmission section 1 in an embodiment of the present invention.

[0029] Figure 7 This is a schematic diagram of the identification results of transmission section 2 in an embodiment of the present invention.

[0030] Figure 8 This is a schematic diagram of the identification results of transmission section 3 in an embodiment of the present invention.

[0031] Figure 9 This is a schematic diagram of the identification results of transmission section 4 in an embodiment of the present invention. Detailed Implementation

[0032] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0033] To quickly and accurately locate critical transmission sections threatening system safety during actual operation and improve the timeliness of online safety verification and emergency control, this invention designs a reasonable framework for the rapid identification of power grid transmission sections with dynamic matching of vulnerable lines within the transmission channel. The overall framework diagram is shown below. Figure 1As shown, the traditional PageRank algorithm is first improved by modifying the Google matrix and restart vector to adapt to the differences in physical characteristics between the power grid and the Internet, and an improved PageRank algorithm is constructed to calculate the criticality index of nodes. Then, the power flow transfer distribution factor is used to calculate the transaction volume between generator and load nodes and sort them to screen out key generator-load node pairs. Guided by this, network transformation and shortest path search are used to extract the set of critical lines in the corresponding source-load channels in the updated topology graph, and the vulnerability index of the line is given by combining the branch disconnection distribution factor and the real-time power flow of the line. Finally, the highly vulnerable lines identified online are matched with the minimum cut candidate sections obtained by offline search to dynamically select the optimal transmission section in the current operating state. In the IEEE-39 node power system simulation, using the candidate transmission sections obtained by offline search as input, the complete process of the method proposed in this invention can complete the identification of transmission sections in only 5.345 seconds, and the source-load partitioning results are reasonable. After N-1 fault verification, the power flow transfer relationship within the identified transmission sections is significant.

[0034] This invention relates to a method for rapid identification of power grid transmission sections based on dynamic matching of vulnerable lines within transmission channels, as follows: Figure 3 The solution process is shown below. Unlike traditional techniques that rely solely on power grid topology or historical power flow data for transmission section identification, this invention combines an improved PageRank algorithm with minimum cut set search. Based on the structural characteristics of transmission channels and line vulnerability analysis under real-time power grid operation, it achieves rapid and accurate identification of key transmission sections.

[0035] This invention proposes a fast extraction method for important transmission channels based on improved PageRank and PTDF. Specifically, it includes the following steps:

[0036] Step 1: Based on the real-time operation data of the power grid, including power flow distribution, topology and other operating status information, calculate the criticality index of each node using the improved PageRank algorithm. The improved PageRank algorithm introduces virtual nodes and node impedance matrices to reflect the physical characteristics of the power grid that are different from the Internet, thereby improving the accuracy of critical node identification.

[0037] First, the Google matrix is ​​improved by replacing its elements with line power flow magnitude to reflect the actual correlation strength between nodes. The restart vector e is also improved by combining the adjacency relationship and impedance characteristics of nodes and weighting the total impedance of each node to replace the uniform jump probability matrix in the traditional PageRank algorithm, thus reflecting the electrical distance relationship between nodes. Virtual nodes are introduced to connect the energy exchange relationship between generator nodes and load nodes, thereby increasing the importance of generator nodes and load nodes in the criticality calculation.

[0038] The PageRank algorithm is essentially based on a random walk model on a directed graph, which eventually converges to a stationary distribution. The initial PR values ​​are set to a uniform distribution, and the iterative formula for calculating PR values ​​is: (1); Where n is the total number of web pages on the network, and the damping coefficient is... Typically, 0.85 is chosen to balance the effects of the link structure and random jumps. G is the state transition probability matrix, and e is the restart vector.

[0039] A state transition probability matrix (Google matrix) is constructed based on the link relationships within web pages, representing the probability that a web browser will jump from one web page to another. (Google matrix) and its elements as follows: (2); (3); in, This represents the number of web pages linked from web page j.

[0040] The restart vector e is: (4);

[0041] The final value condition for convergence is: (5); in, To ensure the precision of the PR value, it is set to four decimal places.

[0042] The improved Google matrix is ​​as follows: (6);

[0043] The elements in the improved Google matrix are: (7); Among them, P ij It represents the active power flow on the transmission line between nodes i and j; for the connection between a virtual node and a generator node or load node, P ij Take the generator output power or the active load of the load node respectively; Let J be the net outflow power of node j.

[0044] This improvement addresses the problem that the original PageRank algorithm assumes that the PR value of each node in the network is evenly distributed among the other nodes that the node points to. This eliminates the PR value bias caused by the significant differences in the correlation strength between power grid nodes and overcomes the defect that generator and load nodes are often assigned lower PR values ​​because they are often located at the edge of the network, which is inconsistent with their key role in the power system.

[0045] Improved restart vector for: (8); Among them, X ij It is the line impedance between node i and node j. This setting makes the power flow more inclined to flow to the low-impedance node, thus making the restart vector more concentrated on the "easily reachable" node, solving the problem that the uniform distribution of restart vectors in the original PageRank algorithm does not conform to the physical characteristics of power flow being topologically constrained.

[0046] Step 2: Through network transformation, nodes in the power system are converted into edges, and edges are converted into nodes. The reciprocal of the calculated power system criticality index is used as the weight of the edge to form a new topology model for efficient subsequent retrieval.

[0047] To improve the retrieval efficiency of critical transmission channels, this invention performs network transformation on the original power grid to generate an equivalent topology. Network transformation involves converting nodes into edges and edges into nodes, forming a new topology. The reciprocal of the node criticality index obtained in step 1, 1 / PR, is used as the weight of the lines in the new topology. A schematic diagram of the network transformation is shown below. Figure 2 As shown.

[0048] Step 3: Based on the power flow distribution factor (PTDF), sort the energy transaction size between generators and load nodes, with the sorting principle mainly based on the size of active power transaction; and search for the shortest path between generator nodes and load nodes in the new topology model in Step 2 to extract important transmission channels under the current operating state.

[0049] The power flow distribution factor (PTDF) is used to calculate the transaction volume between source and load nodes. The generator and load nodes are sorted according to the size of the active power transaction volume. The shortest path search is performed on the sorted generator-load node pairs in the equivalent topology from high to low. For each shortest path obtained, its equivalent node set is recorded and restored to the corresponding transmission line set. This transmission line set is determined as the important transmission channel for the active power transaction of the source-load node pair.

[0050] The transaction power of generator and load node pairs based on PTDF is calculated as follows: (9); (10); (11); (12); in, This represents the transaction volume between generator node g and load node d. Let g be the magnitude of the active power output of generator g; Let d be the magnitude of the active load. P represents the magnitude of the active power transaction between generator node g and load node d on transmission line l. l Let be the magnitude of the active power flow on transmission line l; Node i and Node j are the nodes at both ends of transmission line l. Let be the element in the i-th row and g-th column of the impedance matrix. Let be the element in the j-th row and g-th column of the impedance matrix; Let be the element in the i-th row and d-th column of the impedance matrix; x is the element in the j-th row and d-th column of the impedance matrix; ij Let L be the impedance of the transmission line l; D, G, and L are the sets of load nodes, generator nodes, and lines, respectively.

[0051] The impedance matrix is ​​an n*n square matrix (n represents the number of nodes in the power system), where the element in the i-th row and j-th column represents the complex impedance from the i-th node to the j-th node. The node impedance matrix is ​​a symmetric matrix because the impedance from node i to node j is equal to the impedance from node j to node i.

[0052] Combining formulas (9)-(10), the magnitude of the active power transaction between the corresponding generator and load nodes can be obtained, and then sorted in descending order. Subsequently, the shortest path search is performed on the sorted generator-load pairs in the equivalent topology; for each shortest path obtained, the corresponding equivalent node set is restored to the actual transmission line set, forming an important transmission channel under the current operating state, that is, an important transmission channel for the active power transaction of the source-load node pair. The effectiveness of the model is verified by using the improved IEEE 39-node system for example analysis.

[0053] Step 4: For the set of lines within important transmission channels, based on the branch disconnection distribution factor (LODF), use the vulnerability assessment method to quantitatively assess the vulnerability of each line, and obtain the set of important vulnerable lines; the vulnerability index reflects the degree of impact on the power flow transfer of other lines in the channel when the line fails.

[0054] A vulnerability index based on LODF is proposed, and its calculation is as follows:

[0055] LODF is defined as: (13); in, Let be the branch disconnection distribution factor of transmission line l with respect to transmission line k. Its physical meaning is the proportion of active power transferred from transmission line l to transmission line k after the branch disconnection. The reactance of transmission line l; Z is the reactance of transmission line k; ll Z represents the self-impedance of transmission line l in the line impedance matrix; kl This represents the mutual impedance between transmission line k and transmission line l in the line impedance matrix.

[0056] The line mutual impedance matrix is ​​obtained by the following formula: (14); in, Let be the node-branch association vector of transmission line l. The starting node of transmission line l is +1, the ending node is -1, and the rest are 0. For M k The transpose of ; Z is the inverse of the susceptance matrix B.

[0057] The susceptance matrix B is calculated as follows: (15);

[0058] Therefore, it is assumed that the candidate transmission channels obtained through shortest path search contain a total of M lines. Using LODF and combining it with the actual power flow on each line, the impact of the failure of any one line on the remaining lines in the channel can be assessed. Taking line l as an example, its vulnerability index is defined as: (16); in, P is the initial vulnerability index for transmission line l. l This represents the active power flow of line l.

[0059] After normalization, we get: (17);

[0060] For each line in the initial set of lines, its LODF (Location of Flow) is calculated, and the absolute values ​​of the branch disconnection distribution factors of the remaining lines in the set are summed. This summation is then multiplied by the absolute power flow value before the line's failure to obtain the line's vulnerability index. A higher vulnerability index indicates that when the line fails, more power flow is transferred to the remaining initial lines, making it more susceptible to large-scale power flow redistribution and cascading failures.

[0061] Step 5: Generate a set of candidate transmission sections using the minimum cut set search method (which can be implemented through offline recursive enumeration). This step is completed offline, effectively reducing the real-time computational burden.

[0062] Specifically, the depth-first search (DFS) algorithm is used to recursively split all possible cut sets in the network. In each search, the edge set that satisfies the "minimum cut" condition is selected by judging the connectivity of the edge set, and all the identified minimum cut sets are stored as candidate section sets.

[0063] Step 6: Based on the vulnerability index in Step 4, sort the lines in the initial line set according to the vulnerability index, and match them one by one with the candidate sections to screen out the candidate transmission section set containing highly vulnerable lines, so as to ensure that highly vulnerable lines are analyzed first.

[0064] The matching process involves summing the vulnerability indices of vulnerable lines in each candidate section to obtain the sum of the vulnerability indices for that section, and recording the number of lines it contains. Finally, the section with the largest sum of vulnerability indices is selected from all candidate sections; if there are ties, the section with the fewest lines is selected and identified as the final critical transmission section, which is then used for power grid security analysis and dispatch decisions.

[0065] A transmission section is defined as a cut set of transmission lines that transmit power from a power source area to a load area under a certain operating condition. According to the definition of a cut set, this set of transmission lines divides the power grid into two distinct regions. If any line is missing from the cut set, the transmission section cannot divide the power grid into two regions. In this invention, based on the vulnerability index of the initial line set, candidate transmission sections obtained through offline recursive enumeration search are matched sequentially, with priority given to sections containing highly vulnerable lines. Finally, candidate sections containing both the most vulnerable lines and the largest number of lines are selected from the matching results as key transmission sections.

[0066] Another embodiment of the present invention:

[0067] A transmission section identification system based on dynamic matching of vulnerable lines within a transmission corridor includes:

[0068] Criticality Index Calculation Unit: Based on real-time power grid operation data, the criticality index of each node in the power grid is calculated using an improved PageRank algorithm. The improved PageRank algorithm uses the line power flow magnitude as an element in the Google matrix to reflect the actual correlation strength between nodes. The restart vector e is improved by combining the adjacency relationship and impedance characteristics of nodes and weighting it with the total impedance of each node. Virtual nodes and node impedance matrices are introduced to connect the energy exchange relationship between generator nodes and load nodes.

[0069] Topology model construction unit: The original power grid is transformed into a network, nodes in the power system are converted into edges, and edges are converted into nodes. The reciprocal of the calculated power system criticality index is used as the weight of the edge to form a new topology model.

[0070] Line set extraction unit in important transmission channels: Based on the power flow transfer distribution factor, the energy transaction size between generator and load nodes in the new topology model is sorted, and the shortest path between generator nodes and load nodes is searched to extract the line set in the important transmission channels under the current operating state;

[0071] Vulnerability index calculation unit: Combines branch disconnection distribution factor to calculate the vulnerability index of transmission lines. Based on the line set in important transmission channels, the vulnerability of the lines in the transmission channels is quantitatively assessed to obtain the corresponding vulnerability index.

[0072] Candidate transmission section set generation unit: The candidate transmission section set is generated offline using the minimum cut set search method;

[0073] Key transmission section generation unit: Based on the magnitude of vulnerability index, the lines in the line set within the important transmission channel are sorted and matched one by one with the transmission sections in the candidate transmission section set. From the matching results, the candidate section with the highest sum of vulnerability index and the fewest number of lines is selected as the final key transmission section.

[0074] To verify the effectiveness and adaptability of the proposed critical transmission section identification framework, the IEEE 39-bus system with renewable energy integration was selected as a test case to evaluate the overall performance of the proposed critical section identification method. See the IEEE 39-bus system topology diagram. Figure 4 .

[0075] Under this operating condition, the generator output and node load of the system are shown in Table 1.

[0076] Table 1. Operational Status of IEEE 39 Nodes .

[0077] Based on the operational status of the IEEE 39-node system, the criticality indicators of each node are calculated as follows: Figure 5 As shown.

[0078] Depend on Figure 5As shown, nodes 6, 16, 29, and 38 have high PR values ​​under this operating condition, indicating that they are critical nodes. Combining generator output data, power flow distribution, and network topology, it can be found that these nodes are located at network hubs or important positions in transmission channels. For example, node 6 is connected to four transmission lines with large power flows of 514MW, 448MW, 338MW, and 625MW respectively. Node 16 is connected to five transmission lines, four of which carry power flows exceeding 200MW, while the loads on node 16 and its adjacent node 15 are 320MW and 329MW respectively. Node 29 is connected to three transmission lines, one of which is the output channel for the largest generator (generator 9). Node 38 is the largest generator node, supplying power to many important nodes. If any of these important nodes fails and goes offline, the system's transmission channels will be disrupted under the current operating condition, the power flow distribution will change drastically, and the power flow may even fail to converge. Therefore, the transmission lines interconnected by these nodes are more important and have a higher probability of forming the transmission sections of the system. By combining the node's own attributes with its network topology, this invention verifies that the improved PageRank algorithm can effectively identify key nodes in the system, providing a data foundation for subsequent identification of vulnerable lines.

[0079] Calculate the transaction volume between generators and load nodes, select the top ten generator-load node pairs in terms of transaction volume, and filter the search range of the transmission channel. The specific transaction volume ranking is shown in Table 2.

[0080] Table 2 Ranking of Active Power Transactions between Generator and Load Nodes in the IEEE-39 System .

[0081] Based on the top 10 generator-load node pairs (GD) in terms of transaction volume, shortest path search is performed on the sorted generator-load node pairs in the equivalent topology in descending order of transaction volume on the new topology after network transformation. For each shortest path obtained, its equivalent node set is recorded and restored to the corresponding transmission line set. The vulnerability index of the line is calculated. For details, please refer to Tables 3 and 4.

[0082] Table 3. Vulnerability Indicators of Lines in the Top 5 Transmission Channels by GD Trading Volume in the IEEE-39 Node System .

[0083] Table 4. Vulnerability Indicators of Transmission Lines in the 6th-10th Nodes of the IEEE-39 Node System by GD Trading Volume .

[0084] Depend on Figure 4 As shown in Tables 1, 2, 3, and 4, the identified critical transmission line sets all correspond to the critical transmission channels between the respective generator-load node pairs. For each generator-load node pair, the matched line set almost covers its most important transmission paths; once these lines are disconnected, the load's power supply capacity will be significantly limited. In particular, lines carrying large power flows not only determine the power transmission within their respective sets but also significantly affect the power flow distribution of other lines in the entire network. For example, after lines 16-19 are disconnected, the system's power flow will no longer converge.

[0085] Taking line 16-17 as an example: This line is located at the hub of the system, handling the power exchange between nodes 37 and 38, as well as between these nodes and external loads. Therefore, it appears in the critical line set of multiple generator-load node pairs. Its vulnerability indices are 0.1574 (unit 37-load 20), 0.2859 (unit 38-load 16), 0.2732 (unit 38-load 15), and 0.1918 (unit 38-load 23), all at relatively high levels. It is evident that if line 16-17 is disconnected, it will simultaneously weaken multiple energy transmission paths, posing a serious threat to the system's power supply reliability.

[0086] The pre-searched minimum cut sets showed 33 combinations of 2 lines, 90 combinations of 3 lines, 197 combinations of 4 lines, and 300 combinations of 5 lines. When matching candidate transmission sections, the sections with the highest sum of vulnerability indices and the fewest candidate paths were prioritized, sorted by the transaction volume of generator and load nodes from largest to smallest. Among the 10 identified critical transmission lines, 8 corresponding cut sets were matched. The transmission sections matched by generator and load node pairs ranked 5th (Unit 31 - Load 24) and 6th (Unit 35 - Load 8) in terms of node transaction volume overlapped with those matched by generator and load node pairs ranked 4th (Unit 31 - Load 20) and 3rd (Unit 33 - Load 3), respectively, and were therefore omitted. Specific results are shown in Table 5.

[0087] Table 5. Transmission section identification results of the IEEE 39-bus system .

[0088] like Figure 6 and Figure 7As shown, transmission sections 1 and 2 are important transmission channels for generator nodes 37 and 38, respectively. Transmission section 1 consists of lines 2-25 and 26-27, dividing the entire network into two regions. Line 26-27 in this section has the highest vulnerability index. If it breaks due to a fault, the power flow on transmission line 2-25 will increase from 261MW to 518MW, an increase of 98.5%, exceeding the thermal stability limit of 500MW for transmission line 2-25. Transmission section 2 consists of lines 25-26 and 26-27. If line 26-27, which has the highest vulnerability index in this section, also breaks due to a fault, the power flow on line 25-26 will reverse, increasing from 54MW to 202MW, significantly exacerbating the regional power exchange imbalance. Therefore, it is necessary to focus on monitoring the operational margin of the relevant transmission lines and, if necessary, take measures such as power diversion in advance to improve their overload capacity and ensure the safe transmission of generator power.

[0089] like Figure 8 and Figure 9 As shown, transmission sections 3 and 4 are important transmission channels supplying load nodes 3 and 20, respectively. Transmission section 3 consists of lines 2-3, 3-4, 16-17, and 17-27, dividing load node 3 and its corresponding generator node 33 into two regions. Disconnecting line 16-17, which has the highest vulnerability index within this section, significantly changes the active power flow of the other three lines: line 2-3 increases from 333MW to 351MW, an increase of 5.4%; line 3-4 surges from 54MW to 294MW, an increase of 444%; and the power flow on line 17-27 increases from 25MW to 87MW, an increase of 248%. Transmission section 4 consists of lines 14-15 and 16-17, separating load node 20 from its corresponding generator node 31. Due to a fault, line 14-15 is disconnected, and its 36MW power flow will be entirely transferred to line 16-17, changing the power flow on line 16-17 from 226MW to 190MW. In addition to the candidate transmission section set obtained through pre-offline search, the entire process from calculating the criticality of nodes to identifying the final transmission section took 5.345 seconds.

[0090] In summary, the method proposed in this invention can quickly and accurately identify the criticality indicators of nodes in the system under its current operating state, and can identify vulnerable lines on critical transmission channels by the magnitude of transaction volume between nodes. After N-1 verification, the finally identified transmission sections can be accurately partitioned, and the power flow transfer relationships within the sections are significant. The following conclusions can be drawn:

[0091] 1) This invention proposes a fast identification method for key nodes in a power system by improving the PageRank algorithm. This method can accurately reflect the spatial distribution of power grid energy exchange under the current operating state. The comparison results with the power flow of the grid show that the algorithm can sensitively capture the impact of the operating state on key links, laying the foundation for subsequent section identification.

[0092] 2) This invention proposes a method for identifying important transmission channels based on generator-load transaction volume ranking, combining a network conversion-shortest path search algorithm. This method can form a set of lines to be evaluated based on important transmission channels, thereby accurately identifying the most vulnerable lines in the transmission channels that are most likely to trigger power flow shifts, effectively improving the pertinence and timeliness of vulnerability assessment.

[0093] 3) To avoid missing critical channels, this invention proposes a transmission section identification method based on vulnerable line matching, which combines minimum cut set search to traverse all potential transmission sections between source and load nodes. The numerical examples show that removing vulnerable lines from the section results in a significant power flow shift, verifying that the proposed method can quickly and accurately identify transmission sections, effectively revealing potential risks and supporting proactive prevention of cascading failures by the dispatching side.

Claims

1. A method for identifying transmission sections based on dynamic matching of vulnerable lines within a transmission channel, characterized in that, Includes the following steps: Step 1: Based on the real-time operation data of the power grid, the criticality index of each node in the power grid is calculated using the improved PageRank algorithm. The improved PageRank algorithm uses the line power flow size as an element in the Google matrix to reflect the actual correlation strength between nodes. The restart vector e is improved by combining the adjacency relationship and impedance characteristics of the nodes and weighting them by the total impedance of each node; virtual nodes and node impedance matrices are introduced to connect the energy exchange relationship between generator nodes and load nodes. Step 2: Perform network transformation on the original power grid, converting nodes in the power system into edges and edges into nodes, and using the reciprocal of the power system criticality index calculated in Step 1 as the weight of the edges to form a new topology model. Step 3: Based on the power flow transfer distribution factor, sort the energy transaction size between generators and load nodes in the new topology model, search for the shortest path between generator nodes and load nodes, and extract the set of lines in the important transmission channels under the current operating state. Step 4: Calculate the vulnerability index of the transmission line by combining the branch disconnection distribution factor. Based on the set of lines in the important transmission channels obtained in Step 3, conduct a quantitative assessment of the vulnerability of the lines in the transmission channels to obtain the corresponding vulnerability index. Step 5: Generate a set of candidate transmission sections offline using the minimum cut set search method; Step 6: Based on the vulnerability index values ​​in Step 4, sort the lines in the line set within the important transmission channel and match them one by one with the transmission sections in the candidate transmission section set. From the matching results, select the candidate section with the highest sum of vulnerability indices and the fewest number of lines as the final key transmission section.

2. The method for identifying transmission sections based on dynamic matching of vulnerable lines within a transmission channel according to claim 1, characterized in that, The PageRank algorithm is as follows: The initial PR value is set to a uniform distribution, and the iterative calculation formula for the PR value is: （1）； Where n is the total number of web pages on the network. Here, G is the damping coefficient, G is the state transition probability matrix, and e is the restart vector; A state transition probability matrix, or Google matrix, is constructed based on the link relationships within web pages. This matrix represents the probability that a web browser will jump from one web page to another. and its elements as follows: （2）； （3）； in, This refers to the number of web pages linked from web page j; The restart vector e is: （4）； The final value condition for convergence is: （5）； in, For the precision of the PR value; and The PR values ​​are obtained at the k-th and k+1-th iterations, respectively.

3. The method for identifying transmission sections based on dynamic matching of vulnerable lines within a transmission channel according to claim 2, characterized in that, In step 1, when calculating the criticality index of each node in the power grid using the improved PageRank algorithm, the PageRank value is used as the criticality index of each node. The iterative calculation formula for the PageRank value is as follows: （6）； Wherein, the total number of web pages n in the network corresponds to the total number of nodes in the power grid. For the improved Google matrix, This is the improved restart vector.

4. The method for identifying transmission sections based on dynamic matching of vulnerable lines within a transmission channel according to claim 3, characterized in that, The improved Google matrix for: （7）； Improved Google matrix elements in for: （8）； Among them, P ij It represents the active power flow on the transmission line between nodes i and j; for the connection between a virtual node and a generator node or load node, P ij Take the generator output power or the active load of the load node respectively; Let J be the net outflow power of node j.

5. The method for identifying transmission sections based on dynamic matching of vulnerable lines within a transmission channel according to claim 4, characterized in that, The improved restart vector for: （9）； Among them, X ij It is the line impedance between node i and node j; Let j be the set of adjacent nodes of node j.

6. The method for identifying transmission sections based on dynamic matching of vulnerable lines within a transmission channel according to claim 1, characterized in that, Step 3 specifically involves: The transaction volume between generator nodes and load nodes is calculated based on the power flow transfer distribution factor. The generator nodes and load nodes are sorted according to the size of the active power transaction volume. The shortest path search is performed on the sorted generator nodes and load nodes in the equivalent topology from high to low. For each shortest path obtained, its equivalent node set is recorded and restored to the corresponding transmission line set. The transmission line set is determined as the important transmission channel for the active power transaction between the generator nodes and load nodes. The transaction power of generator and load node pairs based on the power flow transfer distribution factor is calculated as follows: （10）； （11）； （12）； （13）； in, This represents the transaction volume between generator node g and load node d. Let g be the magnitude of the active power output of generator g; Let d be the magnitude of the active load. P represents the magnitude of the active power transaction between generator node g and load node d on transmission line l. l Let be the magnitude of the active power flow on transmission line l; Node i and Node j are the nodes at both ends of transmission line l. Let be the element in the i-th row and g-th column of the impedance matrix. Let be the element in the j-th row and g-th column of the impedance matrix; Let be the element in the i-th row and d-th column of the impedance matrix; x is the element in the j-th row and d-th column of the impedance matrix; ij Let L be the impedance of the transmission line l; D, G, and L are the sets of load nodes, generator nodes, and lines, respectively.

7. The method for identifying transmission sections based on dynamic matching of vulnerable lines within a transmission channel according to claim 3, characterized in that, Step 4 is specifically as follows: The branch disconnection distribution factor is defined as: （14）； in, Let be the branch disconnection distribution factor of transmission line l with respect to transmission line k. Its physical meaning is the proportion of active power transferred from transmission line l to transmission line k after the branch disconnection. The reactance of transmission line l; Z is the reactance of transmission line k; ll Z represents the self-impedance of transmission line l in the line impedance matrix; kl This represents the mutual impedance between transmission line k and transmission line l in the line impedance matrix; The line mutual impedance matrix is ​​obtained by the following formula: （15）； in, Let be the node-branch association vector of transmission line l. The starting node of transmission line l is +1, the ending node is -1, and the rest are 0. For M k The transpose of ; Z is the inverse of the susceptance matrix B; The susceptance matrix B is calculated as follows: （16）； in, The susceptance relationship between node i and node j; Assuming the transmission line set within the transmission corridor obtained through shortest path search contains M lines, the impact of the failure of any one line on the remaining lines in the corridor is evaluated using the branch disconnection distribution factor and the actual power flow on each line. For transmission line l, its vulnerability index is defined as: （17）； in, This is the initial vulnerability index for transmission line l; After normalization, we get: （18）； in, This is the vulnerability index of transmission line l after normalization. This is the initial vulnerability index for transmission line k.

8. The method for identifying transmission sections based on dynamic matching of vulnerable lines within a transmission channel according to claim 1, characterized in that, Step 5 specifically involves recursively splitting all possible cut sets in the network using a depth-first search algorithm. In each search, the edge sets that satisfy the minimum cut condition are selected by judging the connectivity of the edge sets, and all identified minimum cut sets are stored as candidate section sets.

9. The method for identifying transmission sections based on dynamic matching of vulnerable lines within a transmission channel according to claim 1, characterized in that, In step 6, the matching process involves accumulating the vulnerability indices of vulnerable lines in each candidate section to obtain the sum of the vulnerability indices for that section, and recording the number of lines it contains. Finally, the section with the largest sum of vulnerability indices is selected from all candidate sections. If there are ties, the section with the fewest lines is selected and identified as the final critical transmission section, which is then provided for power grid security analysis and dispatch decision-making.

10. The method for identifying transmission sections based on dynamic matching of vulnerable lines within a transmission channel according to claim 9, characterized in that, A transmission section is defined as a cut set of transmission lines that transmit power from a power source area to a load area under a certain operating condition. This set of transmission lines divides the power grid into two different regions. If any line is missing from the cut set, the transmission section cannot divide the power grid into two regions. Based on the vulnerability index of the initial line set, it is matched sequentially with candidate transmission sections obtained by offline recursive enumeration search, and sections containing highly vulnerable lines are selected first. Finally, the candidate sections that contain both the most vulnerable lines and the largest number of lines are selected from the matching results as key transmission sections.

11. A transmission section identification system based on dynamic matching of vulnerable lines within a transmission channel, characterized in that, include: Criticality Index Calculation Unit: Based on the real-time operation data of the power grid, the criticality index of each node in the power grid is calculated using an improved PageRank algorithm. The improved PageRank algorithm uses the line power flow magnitude as an element in the Google matrix to reflect the actual correlation strength between nodes. The restart vector e is improved by combining the adjacency relationship and impedance characteristics of the nodes and weighting them by the total impedance of each node; virtual nodes and node impedance matrices are introduced to connect the energy exchange relationship between generator nodes and load nodes. Topology model construction unit: The original power grid is transformed into a network, nodes in the power system are converted into edges, and edges are converted into nodes. The reciprocal of the calculated power system criticality index is used as the weight of the edge to form a new topology model. Line set extraction unit in important transmission channels: Based on the power flow transfer distribution factor, the energy transaction size between generator and load nodes in the new topology model is sorted, and the shortest path between generator nodes and load nodes is searched to extract the line set in the important transmission channels under the current operating state; Vulnerability index calculation unit: Combines branch disconnection distribution factor to calculate the vulnerability index of transmission lines. Based on the line set in important transmission channels, the vulnerability of the lines in the transmission channels is quantitatively assessed to obtain the corresponding vulnerability index. Candidate transmission section set generation unit: The candidate transmission section set is generated offline using the minimum cut set search method; Key transmission section generation unit: Based on the magnitude of vulnerability index, the lines in the line set within the important transmission channel are sorted and matched one by one with the transmission sections in the candidate transmission section set. From the matching results, the candidate section with the highest sum of vulnerability index and the fewest number of lines is selected as the final key transmission section.

Images