Key power transmission section optimization identification method under extreme event N-K disconnection fault scene

Abstract

The invention relates to a key power transmission section optimization identification method in an extreme event N-K disconnection fault scene, and the method comprises the following steps: S1, converting a power grid into an undirected graph, searching and deleting suspension nodes in the undirected graph, and searching cut points of the simplified undirected graph through a Tarjan algorithm; s2, performing load flow calculation on the large power grid to obtain load flow of each line in the power grid, and establishing a network flow diagram of each partition of the large power grid according to load flow calculation results and maximum transmission power rated values of the lines; s3, sequentially eliminating each edge in the initial cut set, repeatedly carrying out iterative search, 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 obtaining a power transmission section; and S4, obtaining a key power transmission section with an overload tripping risk when N-K disconnection occurs. The key power transmission section of the large power grid under extreme events such as natural disasters can be quickly and effectively identified, and safe and reliable operation of the large power grid is ensured.

Description

Technical Field

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

[0002] The critical section considering the static safety of the power grid refers to a set of lines. When a line in the set fails and trips, it will cause overload of the lines in the set, or even other stability risks. Accurately identifying and timely defending the key transmission sections in the daily operation of the power grid can effectively ensure the safe and reliable operation of the power grid and improve the resilience of the large power grid. The flow sections of traditional power systems are relatively fixed. However, in recent years, with the development of new power systems, a large number of new energy sources such as wind power and photovoltaics have been connected, and various controllable loads such as data centers and air conditioners have continued to appear, increasing the diversity of power system flow distribution, which in turn leads to more frequent changes in transmission sections. Therefore, if the key transmission sections of the new power system can be accurately identified online, it is of great significance for the power grid to prevent and control various extreme natural disasters.

[0003] On the other hand, the classic identification of static safety-critical transmission sections generally only considers the "N-1" and "N-2" overload risks. When "N-1" and "N-2" circuit breaks occur in the power grid, only line faults within the critical section set are more likely to cause overload tripping of the remaining lines in the set, and the probability of causing cascading failures is low. However, extreme natural disasters have occurred frequently in recent years. In extreme events such as floods, typhoons, ice disasters, and earthquakes, a large number of lines will be damaged. Therefore, it is increasingly necessary to perform NK inspections on large power grids and screen critical transmission sections that may have circuit break risks. When an NK circuit break event occurs in the power grid, due to the large number of broken lines and the large amount of power flow transfer, it is not limited to the line tripping within the transmission section set that will trigger a cascading failure of the remaining lines in the set.

[0004] Consider the characteristics of the transmission section in the power grid: 1) It is a cut set of the power grid topology; 2) The active power flow direction of the lines in the section is consistent. Therefore, the screening of transmission sections for static safety can be transformed into the minimum cut screening problem of the graph. Related studies have taken the minimum cut between partitions as the initial section, and then adjusted it according to the actual direction of the flow and the transfer factor. On the basis of this method, there are also studies that consider the identification of key lines that limit the power transmission limit of the transmission section. The above studies all consider the power grid as an unweighted undirected graph. Considering the direction of the actual flow, there are also studies that divide the power grid into partitions, convert several power grid partitions into directed network flow graphs, and assign the capacity of the edges based on the actual flow of the lines. Then, based on the maximum flow-minimum cut theorem, an exhaustive search for the minimum cut is performed on the network flow graph. 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 key sections. In addition, this study only considers performing "N-1" and "N-2" tests on the lines within the transmission section to screen whether other lines within the transmission section have overload risks, but has not yet considered performing NK tests on power grid lines outside the transmission section line set. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for optimizing the identification of key transmission sections in an extreme event NK line break fault scenario, which can quickly and effectively identify the key transmission sections 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 invention to solve the technical problem is: constructing a key transmission section optimization identification method in an extreme event NK line break fault scenario, comprising the following steps:

[0007] 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 the Tarjan algorithm;

[0008] S2. According to the cut points, the undirected graph is divided into large power grids to obtain several undirected subgraphs. The power flow calculation of the large power grid is performed to obtain the power flow P of each line in the power grid. f , establish the network flow diagram of each partition of the large power grid based on the power flow calculation results and the maximum transmission power rating of the line;

[0009] 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 points 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 the search of all minimum cut sets in the network flow graph is completed, and the transmission section is obtained;

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

[0011] According to the above scheme, in step S2, a network flow diagram of a 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, checking whether the minimum cut edge set that has been searched out 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 key transmission sections is to maximize the number of minimum cuts with the risk of disconnection when NK disconnection occurs under extreme events, and its 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 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 this minimum cut;

[0022] The constraints of the optimization model are:

[0023] ∑ l w l ≤K (3)

[0024]

[0025] A bg P g0 -A bd D d =A bf f l (6)

[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 select any K line breaks, and then consider the possibility that the K line breaks cause the flow to transfer, and then cause the flow of the adjacent lines to exceed the limit, and the protection device may trip. In the above constraints, l represents the line index, n represents the node index; Ψ(c) represents the line set in each minimum cut c; Ω represents the line set to be screened formed by the minimum cut; A bg , A bd , A bf are the node-generator association matrix, node load association matrix, and node-line association matrix respectively; w l , v l is a 0-1 variable, which indicates the line disconnection status caused by extreme events in the first stage and whether the line may trip 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. l =1, line l has a 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 key transmission section optimization identification method in the extreme event NK line disconnection fault scenario of the present invention 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 the maximum flow and minimum cut of network flow, 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 the present invention is not limited to considering the "N-1" and "N-2" line breaks within the transmission section, but considers any "NK" line breaks that may occur in the entire power grid under extreme events, thereby screening key transmission sections with overload risks after NK line breaks occur. The obtained key transmission sections are more comprehensive than those in existing research. 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 1It is a key transmission section identification algorithm flow chart of the key transmission section optimization identification method in the extreme event NK line disconnection fault scenario of the present invention;

[0044] Figure 2 is a schematic diagram of a test case of an IEEE 39-node system according to an embodiment of the present invention;

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

[0046] Figure 4 is an initial minimum cut set recognition result diagram with 25 source points in an embodiment of the present invention;

[0047] Figure 5 It is the line tripping probability diagram of the present invention. DETAILED DESCRIPTION

[0048] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0049] like Figure 1-5 As shown, the key transmission section optimization identification method under the extreme event NK line break fault scenario of the present invention comprises 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 the Tarjan algorithm.

[0051] S2. Divide the undirected graph into large power grids according to the cut points to obtain several undirected subgraphs. At the same time, the power flow of the large power grid is calculated to obtain the power flow P of each line in the power grid. f ,The network flow diagram of each partition of the large power grid is established based on the ,flow calculation results and the maximum transmission power rating of the line.

[0052] The grid is partitioned based on cut points rather than geographical features. The network flow graph of each partition has no hanging nodes and only one source and one sink, but different generator nodes or pure source nodes and combinations of pure source nodes can be connected to the source as needed. Similarly, different load nodes or pure sink nodes and combinations of pure sink nodes can be connected to the sink. The capacity of each edge (f, t) in the network flow graph is:

[0053]

[0054] 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). 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, 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 the search of all minimum cut sets in the network flow graph is completed, which is the transmission section obtained by the search.

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

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

[0058] S302: Determine whether the network flow graph is still an unconnected graph after deleting the edge 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 a number of unconnected subgraphs, then add the currently searched minimum cut set to the transmission section set.

[0059] S4. Establish a NK test mixed integer programming model for the large power grid under extreme events, form a set of transmission section lines to be screened based on the transmission sections identified in step 3, input them into the NK test model for calculation, and 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 breaking risk when NK circuit breaking 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 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 for this minimum cut.

[0063] The constraints of the optimization model are:

[0064] ∑ l w l ≤K (3)

[0065]

[0066] A bg P g0 -A bd D d =A bf f l (6)

[0067]

[0068] The model mainly considers two stages. The first stage is to simulate the NK test under extreme events, that is, to select any K lines to be broken, and then consider the possibility that the K lines will cause the flow to transfer, and then the flow of the adjacent lines will exceed the limit, and the protection device will 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 association matrix, node load association matrix, and node-line association matrix respectively; w l , v l are 0-1 variables, indicating the line disconnection status caused by extreme events in the first stage and whether the line may trip 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. l =1, line l has a tripping risk, v l When =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 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;

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

[0073] Constraint (7) represents the DC power flow constraint of the line. When wl 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-node 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 hanging nodes. Remove nodes 16 and 16 from the undirected graph. Figure 3 As shown, the Tarjan algorithm searches for cut points in an 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 calculating the power flow of the IEEE 39-node 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, such as 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 value of the 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 4As 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 in the figure shows that 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 obtained cut set set is shown in Table 1.

[0087] Table 1 Comparison of maximum flow minimum cut algorithm results

[0088]

[0089] like Figure 4 As shown in the figure, when the line {(26,27), (25,2)} is short-circuited, the system is divided into two separate subsystems without disconnecting the three lines (18,3), (11,6), and (11,12). If the three lines {(26,27), (25,2), and (11,6)} are selected as the cut set from the initial cut set, it can be found that (11,6) does not help the system become a disconnected subgraph. In addition, in actual engineering practice, the probability of disconnecting two lines at the same time is much greater than the probability of disconnecting five lines at the same time. It can also be seen from Table 1 that the minimum cut obtained by the maximum flow minimum cut search method is a subset searched in the literature, and redundant cut set combinations and redundant edges are proposed. Therefore, the effective cut set can be accurately searched.

[0090] Each pure source node and pure source node combination is connected to the source point, and all pure sink nodes are connected to the sink point. The method is used to search for the minimum cut, and the corresponding transmission section can be obtained. The results are shown in Appendix 2. According to the power flow calculation results, it can be verified that disconnecting any minimum cut set in Table 2 can cause the power flow of the remaining lines in the transmission section to increase, thereby increasing the risk of triggering cascading failures.

[0091] Table 2 Transmission section search results

[0092]

[0093]

[0094] A cascading failure is a process in which the first-stage line disconnection causes the power flow of the adjacent line to exceed the limit, thereby causing the protection device to operate and cut off the adjacent line. The probability of line tripping increases with the power transmitted by the line in a piecewise linear function relationship, such as Figure 5 As shown. Therefore, 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. This allows different key transmission sections to be screened out.

[0095] The transmission sections obtained by the improved minimum cut search method are taken as the line set Ω to be searched, and 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 section corresponding to the minimum cut is the key transmission section.

[0096] The critical transmission sections when K=1 to 5 are calculated respectively. The power flow calculation results verify that the proposed method can identify the critical transmission sections after the NK circuit breaker event occurs in the power grid under extreme events. When the power grid has an N-1 fault, at most only the minimum cut {(16,21), (23,24)} group may have a circuit breaker risk, causing the system to be disconnected. When the power grid has an N-2 or even N-3 fault, there are still not many critical transmission sections with overload tripping risks, and at most only three groups of minimum cut sets are identified. However, if the N-4 or N-5 circuit breakage may occur under extreme events such as natural disasters, the critical transmission sections with overload risks will increase sharply, as shown in Table 3. Therefore, in the case of frequent extreme natural disasters this year, it is very important to identify the critical sections under the NK circuit breaker condition.

[0097] Table 3 Identification results of key transmission sections

[0098]

[0099] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

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 through the Tarjan algorithm; S2. Divide the undirected graph into large power grids according to the cut points to obtain several undirected subgraphs, perform power flow calculation on the large power grid to obtain the power flow of each line in the power grid, and establish network flow graphs for each partition of the large power grid according to 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 points 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 the search of all minimum cut sets in the network flow graph is completed, and the transmission section is obtained; S4. Establish an NK test mixed integer programming model for the large power grid under extreme events, form a set of transmission section lines to be screened based on the transmission sections identified in step S3, input them into the NK test mixed integer programming model for calculation, and obtain the key transmission sections with the risk of overload tripping when NK line breaks occur.

2. The method for optimizing and identifying key transmission sections in the extreme event NK line break 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 the extreme event NK line break 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 the extreme event NK line disconnection 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 the extreme event NK line break fault scenario according to claim 4 is characterized in that: The method for conducting inspection and rejection includes the following steps: S301, checking whether the minimum cut edge set that has been searched out 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 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.

6. The method for optimizing and identifying key transmission sections in the extreme event NK line break fault scenario according to claim 1 is characterized in that: In step S4, the goal of the mixed integer optimization model for identifying key transmission sections is to maximize the number of minimum cuts with the risk of disconnection when NK disconnection occurs under extreme events, and its objective function is: max∑ c a c (2) Among them, c represents the minimum cut index of the network flow graph, a c 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 this minimum cut; The constraints of the optimization model are: ∑ l In l ≤K (3) A bg P g0 -A bd D d =A bf f l (6) 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 select any K line breaks, and then consider the possibility that the K line breaks cause the flow to transfer, and then cause the flow of the adjacent lines to exceed the limit, and the protection device may trip. In the above constraints, l represents the line index, n represents the node index; Ψ(c) represents the line set in each minimum cut c; Ω represents the line set to be screened formed by the minimum cut; A bg , A bd , A bf are the node-generator association matrix, node load association matrix, and node-line association matrix respectively; w l , v l is a 0-1 variable, which indicates the line disconnection status caused by extreme events in the first stage and whether the line may trip 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. l =1, line l has a 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 the extreme event NK line break fault scenario according to claim 6 is characterized in that: The 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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