Key transmission section optimization identification method under extreme event N-K broken line fault scenario

Abstract

The application relates to a key power transmission section optimization identification method under an extreme event N-K broken line fault scene, which comprises the following steps: S1, converting a power grid into a non-directed graph, searching and deleting a hanging node in the non-directed graph, and finding a cut point of the simplified non-directed graph through a Tarjan algorithm; S2, performing power flow calculation on a large power grid to obtain power flow of each line in the power grid, and establishing a network flow graph of each partition of the large power grid according to a power flow calculation result and a maximum transmission power rating of the line; S3, iteratively searching by excluding each edge in an initial cut set in turn, identifying and deleting redundant cut edges and invalid cut edges, until the search of all minimum cut sets in the network flow graph is completed, and a power transmission section is obtained; and S4, obtaining a key power transmission section with an overload tripping risk when N-K lines are broken. The application can quickly and effectively identify the key power transmission section of the large power grid under an extreme event such as a natural disaster, and ensures safe and reliable operation of the large power grid.

Description

Technical Field

[0001] The present invention relates to power transmission network fault optimization, and more specifically to a method for optimizing and identifying key power transmission sections in an extreme event NK line break fault scenario. Background Art

[0002] A critical section for the static security of a power grid refers to a collection of lines. When a line within this section trips due to a fault, it can cause overloads and even other stability risks. Accurately identifying and promptly preventing critical transmission sections during daily grid operations can effectively ensure safe and reliable grid operation and enhance the resilience of the larger grid. Traditional power system flow sections are relatively fixed. However, with the recent development of new power systems, the massive integration of renewable energy sources such as wind power and photovoltaics, and the emergence of various controllable loads such as data centers and air conditioning, the power system flow distribution has become more diverse, leading to more frequent changes in transmission sections. Therefore, accurately identifying critical transmission sections in new power systems online is crucial for preventing and controlling various extreme natural disasters.

[0003] On the other hand, classic approaches to identifying static safety-critical transmission sections generally only consider "N-1" and "N-2" overload risks. When an "N-1" or "N-2" outage occurs in a power grid, only line failures within the critical section cluster are more likely to trigger overload tripping of the remaining lines within the cluster, with a lower probability of cascading failures. However, extreme natural disasters have become frequent in recent years, with significant damage to lines in extreme events such as floods, typhoons, ice storms, and earthquakes. Therefore, it is increasingly necessary to perform NK inspections on large power grids and identify critical transmission sections with potential outage risks. When an NK outage occurs in a power grid, due to the large number of outage lines and the significant power flow transfer, it is not necessary for the tripping of lines within the transmission section cluster to trigger cascading failures of the remaining lines within the cluster.

[0004] The characteristics of the power transmission section in the power grid are considered: 1) it is a cut set of the power grid topology; 2) the active power flow directions of the lines in the section are consistent. Therefore, the static security transmission section screening can be converted into the minimum cut screening problem of the graph. Related researches take the minimum cut between the partitions as the initial section, and then adjust according to the actual direction of the flow and the transfer factor. On the basis of this method, there are also researches considering the identification of the key lines that limit the power transmission limit of the transmission section. The above researches all consider the power grid as an unweighted undirected graph. Considering the direction of the actual flow, there are also researches that divide the power grid into several partitions, and then convert several power grid partitions into a directed network flow graph, and assign the capacity of the edge according to the actual flow of the line. Then, based on the maximum flow-minimum cut theorem, the minimum cut of the network flow graph is searched exhaustively. However, the minimum cut set obtained by this method will produce a large number of invalid cuts and redundant cut edges, which will greatly increase the difficulty and cost of monitoring the key section. In addition, this research only considers performing "N-1" and "N-2" checks on the lines in the transmission section, to screen whether the other lines in the transmission section have the risk of overload, but has not considered performing N-K checks on the lines outside the transmission section line set. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a key transmission section optimization identification method under the N-K line fault scenario of extreme events, which can quickly and effectively identify the key transmission section of a large power grid under extreme events such as natural disasters, and ensure the safe and reliable operation of the large power grid.

[0006] The technical solution adopted by the present application to solve the technical problem is to construct a key transmission section optimization identification method under the N-K line fault scenario of extreme events, comprising the following steps:

[0007] S1, converting the power grid into an undirected graph, searching for and deleting the hanging nodes in the undirected graph, and finding the cut points of the simplified undirected graph through the Tarjan algorithm;

[0008] S2, partitioning the undirected graph according to the cut points to obtain several undirected subgraphs, and performing power flow calculation on the large power grid to obtain the power flow P of each line in the power grid f , and establishing a network flow graph for each partition of the large power grid according to the power flow calculation results and the maximum transmission power rating of the line;

[0009] S3, searching for pure sink nodes and pure source nodes in each network flow graph, connecting different pure source nodes and pure sink nodes to the source point and sink point in the network flow graph as needed, searching for the initial minimum cut set in each network flow graph based on the maximum flow-minimum cut theorem in graph theory, iteratively searching each edge in the initial cut set in turn, and identifying and deleting redundant cut edges and invalid cut edges until the search of all minimum cut sets in the network flow graph is completed, to obtain the transmission section;

[0010] S4. Establish an NK test mixed integer programming model for the large power grid under extreme events. Based on the transmission sections identified in step S3, a set of transmission section lines to be screened is formed, and the set is input into the NK test mixed integer programming model for calculation to obtain the key transmission sections with the risk of overload tripping when an NK line break occurs.

[0011] According to the above scheme, in step S2, a network flow diagram of the large power grid partition is established, in which the network flow diagram of each partition has a source point and a sink point, different generator nodes or pure source nodes and pure source node combinations are connected to the source point, and different load nodes or pure sink nodes and pure sink node combinations are connected to the sink point.

[0012] According to the above scheme, the capacity of each edge (f, t) in the network flow graph is:

[0013]

[0014] Among them, p max (f, t) is the maximum transmission power limit of the line between nodes f and t in the power grid, r max (f, t) is the calculated value of the transmission power flow of line (f, t), and Π is the set of multiple lines between nodes f and t.

[0015] According to the above scheme, in step S3, the network flow graph minimum cut set identification algorithm is specifically as follows: each edge in the initial cut set is excluded from the set of edges to be searched in turn, and then the minimum cut of the network flow graph is searched again. If there are redundant edges and invalid edges in the minimum cut searched each time, the redundant edges or invalid edges are checked and eliminated.

[0016] According to the above scheme, the method for inspection and rejection includes the following steps:

[0017] S301, check whether the minimum cut edge set that has been searched is a subset of the currently searched cut set;

[0018] S302. Determine whether the network flow graph is still an unconnected graph after deleting the edges obtained by the current search: If the power set that satisfies the minimum cut set currently searched does not contain any existing cut sets, and after deleting the minimum cut set currently searched, the network flow graph becomes several unconnected subgraphs, then add the minimum cut set currently searched to the transmission section set.

[0019] According to the above scheme, in step S4, the goal of the mixed integer optimization model for identifying critical transmission sections is to maximize the number of minimum cuts with the risk of circuit breaker when an NK circuit breaker occurs under extreme events. The objective function is:

[0020] max∑ c a c(2)

[0021] Among them, c represents the minimum cut index of the network flow graph, a c It is a 0-1 variable, indicating whether the minimum cut of each screened network flow graph has a risk of disconnection. c =1, the minimum cut has the risk of breaking the circuit, which will cause the system to be disconnected. c =0, there is no risk of circuit breaking in the minimum cut;

[0022] The constraints of the optimization model are:

[0023] ∑ l w l ≤K(3)

[0024]

[0025]

[0026]

[0027] The NK test mixed integer programming model considers two stages. The first stage is to simulate the NK test under extreme events, that is, to screen any K line breaks, and then consider the possibility that the K line breaks will cause the flow to transfer, thereby causing the flow of adjacent lines to exceed the limit and the protection device to trip. In the above constraints, l represents the line index and n represents the node index; Ψ(c) represents the set of lines in each minimum cut c; Ω represents the set of lines to be screened formed by the minimum cut; A bg 、A bd 、A bf are the node-generator correlation matrix, the node load correlation matrix, and the node-line correlation matrix respectively; w l , v l is a 0-1 variable, which indicates the line disconnection status caused by the extreme event in the first stage and the possibility of line tripping due to power flow transfer in the second stage. l =1 means line l will be disconnected, w l = 0, the line l is in normal working state, when v l =1, line l has tripping risk, v l =0, there is no tripping risk on line l;

[0028] Constraint (3) is the NK test constraint;

[0029] Constraint (4) is used to indicate that for the line to be screened, if it is disconnected in the first stage, it is impossible for power flow transfer to occur, thus causing line l to be disconnected;

[0030] Constraint (5) indicates that if the line in the minimum cut c does not break and there is no possibility of tripping due to power flow exceeding the limit, then this minimum cut c cannot occur;

[0031] Constraint (6) represents the node power balance equation;

[0032] Constraint (7) represents the DC power flow constraint of the line. When w l =1, line l is disconnected and the line flow is 0;

[0033] Constraint (8) indicates that the line may trip if it exceeds 1.1 times the maximum transmission power limit;

[0034] Constraint (9) represents the upper and lower limits of the node voltage phase angle.

[0035] According to the above scheme, the logical constraint (8) is linearized by the big M method as follows:

[0036]

[0037] Among them, M is a very large constant and E is a very small constant.

[0038] The implementation of the present invention's method for optimizing and identifying key transmission sections in an extreme event NK line breakage fault scenario has the following beneficial effects:

[0039] 1. The present invention improves the network flow maximum flow-minimum cut transmission section search method and the method framework of the mixed integer optimization model for key transmission section screening;

[0040] 2. The present invention improves the search method based on network flow maximum flow minimum cut, which can identify and eliminate invalid edges and redundant edges in the minimum cut set, ensure the effectiveness of screening transmission sections, and reduce the monitoring quantity and defense cost of key transmission sections.

[0041] 3. The mixed integer optimization model proposed in this invention is not limited to considering "N-1" and "N-2" line breaks within the transmission section, but considers any "NK" line break that may occur in the entire power grid under extreme events, thereby screening key transmission sections with overload risks after the occurrence of NK line break. The obtained key transmission sections are more comprehensive than those in existing studies. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0043] Figure 1 This is a flow chart of a key transmission section identification algorithm of a key transmission section optimization identification method under an extreme event NK line disconnection fault scenario of the present invention;

[0044] Figure 2 is an embodiment of the present application IEEE 39 node system test case schematic diagram;

[0045] Figure 3 is an embodiment of the present application IEEE 39 node system corresponding to the undirected graph and partition graph;

[0046] Figure 4 is the initial minimum cut set identification result graph of the source point 25 in the embodiment of the present application;

[0047] Figure 5 is the line trip probability graph of the present application. DETAILED DESCRIPTION

[0048] In order to have a clearer understanding of the technical features, objects and effects of the present application, the specific embodiments of the present application will be described in detail with reference to the drawings.

[0049] As shown in Figure 1-5 , the key power transmission section optimization identification method of the present application under the extreme event N-K line break fault scenario includes the following steps:

[0050] S1, convert the power grid into an undirected graph, search and delete the hanging nodes in the undirected graph, and find the cut points of the simplified undirected graph through Tarjan algorithm.

[0051] S2, divide the undirected graph according to the cut points to obtain several undirected subgraphs. At the same time, the power flow calculation of the large power grid is carried out to obtain the power flow P f of each line in the power grid, and the network flow graph of each partition of the large power grid is established according to the power flow calculation result and the maximum transmission power rating of the line.

[0052] The power grid is divided according to the cut points rather than the geographical characteristics. The network flow graph of each partition has no hanging nodes and only one source point and one sink point, but different generator nodes or pure source nodes and pure source node combinations can be connected to the source point according to the need. Similarly, different load nodes or pure sink nodes and pure sink node combinations can be connected to the sink point. The capacity of each edge (f, t) in the network flow graph is:

[0053]

[0054] Where, p max (f, t) is the maximum transmission power limit value of the line connecting nodes f and t in the power grid, r max (f, t) is the transmission power flow calculation value of the line (f, t). Π is the set of multiple lines between nodes f and t.

[0055] S3. Search for pure sink nodes and pure source nodes in each network flow graph and connect different pure source nodes and pure sink nodes to the source and sink points in the network flow graph as needed. Based on the maximum flow-minimum cut theorem in graph theory, search for the initial minimum cut set in each network flow graph. Repeat the iterative search by excluding each edge in the initial cut set in turn, identifying and removing redundant and invalid cut edges, until all minimum cut sets in the network flow graph are searched. This is the transmission section obtained by the search.

[0056] Each edge in the initial cut set is removed from the set of edges to be searched, and the minimum cut of the network flow graph is searched again. For each minimum cut found, there may be redundant edges and invalid edges, which need to be checked and removed. The method of checking and removing redundant edges and invalid edges includes the following steps:

[0057] S301, check whether the minimum cut edge set that has been searched is a subset of the currently searched cut set;

[0058] S302: Determine whether the network flow graph is still disconnected after deleting the edges currently searched. If the power set that satisfies the currently searched minimum cut set does not contain any existing cut sets, and after deleting the currently searched minimum cut set, the network flow graph becomes several disconnected subgraphs, then add the currently searched minimum cut set to the transmission section set.

[0059] S4. Establish an NK test mixed integer programming model for the large power grid under extreme events. Based on the transmission sections identified in step 3, a set of transmission section lines to be screened is formed, which is input into the NK test model for calculation to obtain the key transmission sections with overload tripping risks when NK line breaks occur.

[0060] The goal of the mixed integer optimization model is to maximize the number of minimum cuts with circuit breaker risks when an NK circuit breaker occurs under extreme events. Its objective function is:

[0061] max∑ c a c (2)

[0062] Among them, c represents the minimum cut index of the network flow graph, a c It is a 0-1 variable, indicating whether the minimum cut of each screened network flow graph has a risk of disconnection. c =1, the minimum cut has the risk of breaking the circuit, which will cause the system to be disconnected. c =0, there is no risk of circuit breaking in the minimum cut.

[0063] The constraints of the optimization model are:

[0064] ∑ l w l ≤K(3)

[0065]

[0066]

[0067]

[0068] The model mainly considers two stages. The first stage is to simulate the NK test under extreme events, that is, to screen any K lines for disconnection, and then consider the possibility that these K lines will cause the flow to shift, and then cause the flow of the adjacent lines to exceed the limit, causing the protection device to trip. In the above constraints, l represents the line index, n represents the node index; Ψ(c) represents the set of lines in each minimum cut c; Ω represents the set of lines to be screened formed by the minimum cut; A bg 、A bd 、A bf are the node-generator correlation matrix, the node load correlation matrix, and the node-line correlation matrix respectively; w l , v l is a 0-1 variable, which indicates the line disconnection status caused by the extreme event in the first stage and the possibility of line tripping due to power flow transfer in the second stage. l =1 means line l will be disconnected, w l = 0, the line l is in normal working state, when v l =1, line l has tripping risk, v l =0, there is no tripping risk on line l.

[0069] Constraint (3) is the NK test constraint;

[0070] Constraint (4) is used to indicate that for the line to be screened, if it is disconnected in the first stage, it is impossible for power flow transfer to occur, thus causing line l to be disconnected;

[0071] Constraint (5) indicates that if there is no disconnection of the line in the minimum cut c and there is no possibility of tripping due to power flow exceeding the limit, then this minimum cut c cannot occur;

[0072] Constraint (6) represents the node power balance equation;

[0073] Constraint (7) represents the DC power flow constraint of the line. When w l When =1, line l is disconnected and the line flow is 0.

[0074] Constraint (8) indicates that the line may trip if it exceeds 1.1 times the maximum transmission power limit.

[0075] Constraint (9) represents the upper and lower limits of the node voltage phase angle.

[0076] Among them, the logical constraint (8) can be linearized by the big M method as follows:

[0077]

[0078] Among them, M is a very large constant and E is a very small constant.

[0079] Example

[0080] like Figure 2 As shown, the IEEE 39-bus system is used as a case to analyze and verify the effectiveness of the method and model of the present invention.

[0081] Convert the IEEE 39-node system into an undirected graph and search to find that nodes 16 and 26 are dangling nodes. Remove nodes 16 and 16 from the undirected graph. Figure 3 As shown in the figure, the Tarjan algorithm searches for cut points in the undirected graph and divides the undirected graph into three independent connected undirected subgraphs based on the cut points.

[0082] Yizi Figure 1 For example, by performing power flow calculation on the IEEE 39-bus system standard example, the line power flow and power flow direction can be obtained, and the undirected subgraph can be converted into a network flow graph based on this, as shown in Figure 3 As shown. The capacity of each edge in the network flow graph is:

[0083]

[0084] Among them, p max (f, t) is the maximum transmission power limit of the line between nodes f and t in the power grid, r max (f, t) is the calculated transmission power flow of line (f, t). Π is the set of multiple lines between nodes f and t.

[0085] The pure source nodes and pure sink nodes of the search network flow graph are {25, 16, 10} and {39, 4, 12, 15, 27} respectively.

[0086] Take node 25 as the source point, connect the sink points with all the pure sink nodes in the system, and search for the transmission section where node 25 transmits power to the entire network flow graph (in Figure 4 As an example, if we first obtain the initial cut set {(26,27),(25,2),(18,3),(11,6),(11,12)} according to the method in the literature, then Figure 4 The dotted line intercepts the image. Then, the capacity of any edge in the initial cut set is set to infinity, and the maximum flow minimum cut search process is repeated. The resulting cut sets are shown in Table 1.

[0087] Table 1 Comparison of Maximum Flow Minimum Cut Algorithm Results

[0088]

[0089] like Figure 4 As shown, when lines {(26,27), (25,2)} are short-circuited, the system is divided into two separate, independent subsystems, without the need to disconnect lines (18,3), (11,6), and (11,12). However, if lines {(26,27), (25,2), and (11,6)} are selected from the initial cut set as the cut set, it can be found that (11,6) does not help the system become a disconnected subgraph. Furthermore, in actual engineering practice, the probability of disconnecting two lines simultaneously is much greater than the probability of disconnecting five lines simultaneously. Table 1 also shows that the minimum cut obtained by the maximum flow minimum cut search method is a subset of the searched-for subset in the literature, and that redundant cut set combinations and redundant edges are proposed. Therefore, a valid cut set can be accurately searched.

[0090] By connecting each pure source node and a combination of pure source nodes to the source point, and all pure sink nodes to the sink point, this method is used to search for the minimum cut, resulting in the corresponding transmission section. The results are shown in Appendix 2. The power flow calculation results verify that disconnecting any of the minimum cut sets in Table 2 will increase the power flow on the remaining lines within the transmission section, thereby increasing the risk of triggering a cascading failure.

[0091] Table 2 Transmission section search results

[0092]

[0093]

[0094] A cascading failure is a process in which a line trip in the first stage causes the power flow in the adjacent lines to exceed the limit, which in turn causes the protection device to operate and cut off the adjacent lines. The probability of line tripping is a piecewise linear function as the power transmitted by the line increases, such as Figure 5 As shown in Figure 2, the constant 1.1 in constraint (8) or constraint (14) in the proposed mixed integer optimization model can be adjusted within the range of 1.1 to 1.7 as needed, thereby screening out different key transmission sections.

[0095] By taking the transmission sections obtained by the improved minimum cut search method as the line set Ω to be searched, the proposed mixed integer optimization model is calculated to solve which transmission sections belonging to the minimum cut have the risk of overload tripping when any NK line break event occurs. The transmission sections corresponding to the minimum cut are the critical transmission sections.

[0096] The critical transmission sections when K=1 to 5 are calculated respectively, and the power flow calculation results can verify that the method can identify the critical transmission sections after the N-K circuit breaking event of the power grid under the extreme event. When the power grid has an N-1 fault, at most only one set of minimum cut {(16, 21), (23, 24)} has a risk of circuit breaking, causing the system to split. When the power grid has an N-2 or even N-3 fault, the critical transmission sections with the risk of overload tripping are still not many, and at most only three sets of minimum cut sets are identified. However, if the N-4 or N-5 circuit breaking under the extreme event such as natural disasters is considered, the critical transmission sections with the risk of overload increase sharply, and the results are shown in Table 3. Therefore, under the condition that extreme natural disasters occur frequently in recent years, it is very important to consider the critical section identification under the N-K circuit breaking condition.

[0097] Table 3 critical transmission section identification results

[0098]

[0099] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the above specific embodiments, and the above specific embodiments are only illustrative but not restrictive. Those skilled in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, and these all belong to the protection of the present application.

Claims

1. A method for optimizing and identifying key transmission sections in an extreme event NK line breakage fault scenario, characterized in that: The following steps are involved: S1. Convert the power grid into an undirected graph, search and delete the hanging nodes in the undirected graph, and find the cut points of the simplified undirected graph using the Tarjan algorithm; S2. Partition the large power grid into several undirected subgraphs based on the cut points. Perform power flow calculation on the large power grid to obtain the power flow of each line in the power grid. Establish network flow graphs for each partition of the large power grid based on the power flow calculation results and the maximum transmission power rating of the line. S3. Search for pure sink nodes and pure source nodes in each network flow graph, connect different pure source nodes and pure sink nodes to the source and sink points in the network flow graph as needed, search for the initial minimum cut set in each network flow graph based on the maximum flow-minimum cut theorem in graph theory, exclude each edge in the initial cut set in turn, repeat the iterative search, identify and delete redundant cut edges and invalid cut edges, until all minimum cut sets in the network flow graph are searched and the transmission section is obtained; S4. Establish an NK test mixed integer programming model for the large power grid under extreme events. Based on the transmission sections identified in step S3, a set of transmission section lines to be screened is formed, and the set is input into the NK test mixed integer programming model for calculation to obtain the key transmission sections with the risk of overload tripping when an NK line break occurs.

2. The method for optimizing and identifying key transmission sections in an extreme event NK line breakage fault scenario according to claim 1 is characterized in that: In step S2, in establishing a network flow diagram for partitioning the large power grid, the network flow diagram for each partition has a source point and a sink point, different generator nodes or pure source nodes and pure source node combinations are connected to the source point, and different load nodes or pure sink nodes and pure sink node combinations are connected to the sink point.

3. The method for optimizing and identifying key transmission sections in an extreme event NK line breakage fault scenario according to claim 2 is characterized in that: The capacity of each edge (f, t) in the network flow graph is: Among them, p max (f, t) is the maximum transmission power limit of the line between nodes f and t in the power grid, r max (f, t) is the calculated value of the transmission power flow of line (f, t), and Π is the set of multiple lines between nodes f and t.

4. The method for optimizing and identifying key transmission sections in an extreme event NK line breakage fault scenario according to claim 1 is characterized in that: In step S3, the network flow graph minimum cut set identification algorithm is specifically as follows: each edge in the initial cut set is excluded from the set of edges to be searched in turn, and then the minimum cut of the network flow graph is searched again. If there are redundant edges and invalid edges in the minimum cut searched each time, the redundant edges or invalid edges are checked and eliminated.

5. The method for optimizing and identifying key transmission sections in an extreme event NK line breakage fault scenario according to claim 4 is characterized in that: The method for conducting inspection and rejection includes the following steps: S301, check whether the minimum cut edge set that has been searched is a subset of the currently searched cut set; S302. Determine whether the network flow graph is still an unconnected graph after deleting the edges obtained by the current search: If the power set that satisfies the minimum cut set obtained by the current search does not contain any existing cut sets, and after deleting the minimum cut set obtained by the current search, the network flow graph becomes several unconnected subgraphs, then add the minimum cut set obtained by the current search to the transmission section set.

6. The method for optimizing and identifying key transmission sections in an extreme event NK line breakage fault scenario according to claim 1 is characterized in that: In step S4, the goal of the mixed integer optimization model for identifying critical transmission sections is to maximize the number of minimum cuts with the risk of a circuit breaker when an NK circuit breaker occurs under extreme events. The objective function is: max∑ c a c (2) Among them, c represents the minimum cut index of the network flow graph, a c It is a 0-1 variable, indicating whether the minimum cut of each screened network flow graph has a risk of disconnection. c =1, the minimum cut has the risk of breaking the circuit, which will cause the system to be disconnected. c =0, there is no risk of circuit breaking in the minimum cut; The constraints of the optimization model are: ∑ l In l ≤K (3) The NK test mixed integer programming model considers two stages. The first stage is to simulate the NK test under extreme events, that is, to screen any K line breaks, and then consider the possibility that the K line breaks will cause the flow to transfer, thereby causing the flow of adjacent lines to exceed the limit and the protection device to trip. In the above constraints, l represents the line index and n represents the node index; Ψ(c) represents the set of lines in each minimum cut c; Ω represents the set of lines to be screened formed by the minimum cut; A bg 、A bd 、A bf are the node-generator correlation matrix, the node load correlation matrix, and the node-line correlation matrix respectively; w l , v l is a 0-1 variable, which indicates the line disconnection status caused by the extreme event in the first stage and the possibility of line tripping due to power flow transfer in the second stage. l =1 means line l will be disconnected, w l = 0, the line l is in normal working state, when v l =1, line l has tripping risk, v l =0, there is no tripping risk on line l; Constraint (3) is the NK test constraint; Constraint (4) is used to indicate that for the line to be screened, if it is disconnected in the first stage, it is impossible for power flow transfer to occur, thus causing line l to be disconnected; Constraint (5) indicates that if the line in the minimum cut c does not break and there is no possibility of tripping due to power flow exceeding the limit, then this minimum cut c cannot occur; Constraint (6) represents the node power balance equation; Constraint (7) represents the DC power flow constraint of the line. When w l =1, line l is disconnected and the line flow is 0; Constraint (8) indicates that the line may trip if it exceeds 1.1 times the maximum transmission power limit; Constraint (9) represents the upper and lower limits of the node voltage phase angle.

7. The method for optimizing and identifying key transmission sections in an extreme event NK line breakage fault scenario according to claim 6 is characterized in that: Logical constraint (8) is linearized by the Big M method as follows: Among them, M is a very large constant and E is a very small constant.

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