Key power transmission section rapid identification method considering geographic partition optimization and graph theory search theory

Abstract

The invention provides a key power transmission section rapid identification method considering geographic partition optimization and a graph theory search theory, and relates to the field of key power transmission section identification of a power system, and the method comprises the steps: firstly, carrying out the optimization of an existing power grid geographic partition according to a geographic partition optimization rule, and carrying out the optimization of a power grid geographic partition according to the optimized power grid geographic partition; and constructing a state diagram of the power grid partitions. And then, based on the obtained power grid partition state diagram, obtaining an adjoint matrix of the state diagram by utilizing a graph theory, obtaining all power transmission sections of the power grid according to the adjoint matrix, calculating improved power flow betweenness index values of all the power transmission sections, and screening out the power transmission section with the maximum improved power flow betweenness index value as the key power transmission section of the power grid. According to the method, the power transmission section of the power grid is rapidly obtained by using the graph theory, the reasonable key power transmission section is screened out according to the improved power flow betweenness index, and a scientific and reliable basis can be provided for rapid identification of the key power transmission section of the power grid.

Description

Technical Field

[0001] The present invention relates to the field of identification of key transmission sections in power systems, and in particular to a method for rapid identification of key transmission sections taking into account geographical partition optimization and graph search theory. Background Art

[0002] Critical transmission sections in a power system are those with low safety margins, where failures can have a significant impact on grid security. These sections are identified from a selection of all transmission sections, representing weak links between regions and prone to failure. In today's power system scheduling and operation, a series of power system operating rules are often required to ensure safe and stable grid operation. These operating rules are typically developed for specific critical sections to achieve "dimensionality reduction" control of complex power systems.

[0003] Currently, most international experts and scholars define a closely connected family of interconnected lines as a transmission section. Section identification and analysis primarily focuses on identifying families of parallel lines with close electrical connections and similar power profiles within the system, and analyzing their transmission capacity and safety. Based on the actual conditions of my country's power grid, domestic researchers have clearly defined the concept of a transmission section. Its original meaning refers to a collection of transmission lines that connect various power supply areas and enable power exchange. The following methods are primarily used to identify critical sections in power systems:

[0004] (1) Transmission section selection method based on natural areas and artificial experience

[0005] Initially, transmission section identification was performed solely by grid operation experts, relying on their extensive dispatching experience and combining it with the electrical distribution characteristics of the administrative area through offline analysis and manual selection. This manual selection method suffers from poor timeliness and flexibility. Furthermore, with the increasing complexity of grid structures and the diversification of power system operation modes, previous manual identification methods are no longer able to meet the actual operational requirements of the grid.

[0006] (2) Critical section identification method based on physical model

[0007] Critical section identification methods based on physical models typically employ time-domain simulation or direct methods to analyze the impact of different transmission sections on transient stability. However, these methods typically involve transient simulation of the entire system, resulting in complex and time-consuming calculations. With the integration of a large number of distributed energy resources into the power grid, the uncertainty of the power system is increasing, and operational states are changing more rapidly, placing higher demands on algorithm speed, making these methods difficult to implement in practice.

[0008] (3) Data-driven critical section identification method

[0009] With the development of artificial intelligence (AI) technology, data-driven methods are increasingly being used in power system analysis and operational optimization, offering significant advantages in computational speed. AI-based critical section screening methods can provide a new solution for identifying critical sections in power systems. However, AI-based critical section identification methods suffer from poor interpretability, making it difficult to construct meaningful identification models. Furthermore, AI-based identification typically requires extensive data training and high data quality, presenting numerous obstacles in practical application.

[0010] (4) Transmission section identification method based on network graph theory

[0011] Graph-theory-based identification methods typically define a transmission section as a set of parallel transmission lines connecting two regional power grids in a power system, analogizing transmission sections in a power grid to the concept of cut sets in graph theory. This method can account for the power flow distribution characteristics and electrical distance constraints in real power grids, and its calculation and identification speed are relatively fast. Therefore, graph-theory-based section search methods have gradually become an important tool for studying transmission sections.

[0012] Based on the above technical background analysis, it can be seen that the theory of graph search is a reasonable method for quickly identifying key transmission sections in power systems. Summary of the Invention

[0013] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for quickly identifying key transmission sections by considering geographical partition optimization and graph search theory.

[0014] The present invention is achieved through the following technical solutions.

[0015] According to one aspect of the present invention, a method for rapidly identifying key transmission sections taking into account geographical partitioning optimization and graph search theory is provided, comprising:

[0016] Optimize the existing power grid geographical partitions based on geographical partition optimization rules and construct the optimized partition status diagram;

[0017] Based on graph theory, the adjoint matrix of the geographical partition state diagram of the power grid is obtained, and all the transmission sections of the power grid are obtained using the adjoint matrix.

[0018] Based on the improved power flow betweenness index, the transmission section with the largest improved power flow betweenness index value is screened out as the key transmission section of the power grid.

[0019] Preferably, the geographical partitioning optimization rule is used to optimize the existing geographical partitioning of the power grid, and an optimized partitioning state diagram is constructed:

[0020] The geographical partition optimization rules are used to optimize the existing power grid geographical partitions and construct their partition state diagrams. The steps of partition optimization and state diagram construction are as follows:

[0021] Step 1: Treat the geographic partition of the existing power grid as an undirected graph containing nodes and branches, and shrink the branch end nodes. Nodes with a node degree of 1 are branch end nodes, and the only node connected to them should be in the same geographic partition. Therefore, all branch end nodes with a node degree of 1 are shrunk to the geographic partition where the node connected to them is located.

[0022] Step 2: Determine whether the flow direction of the tie lines between geographical zones is consistent. If there is a tie line, for example, ij If the tidal current direction is opposite to the overall tidal current direction, check whether the proportion of its tidal current in the overall tidal current of the corresponding tie line is less than a certain value (taken as 10%). If so, the tidal current direction of the tie line is considered consistent. If it is greater than the value, change the geographical partition where the tie line node is located, such as merging node i into the geographical partition where node j is located, or perform the opposite operation, and change the tie line between the two geographical partitions until the tidal currents are consistent. Usually, it is easier to achieve tidal current consistency by changing nodes with smaller node degrees. Generally, a single change of the node is sufficient.

[0023] Step 3: Perform the above optimization operation on all terminal nodes and tie lines of the power grid to obtain the optimized geographical partitions, and construct a state diagram on this partition. That is, each partition is regarded as a new node, and all tie lines between partitions are regarded as new branches between nodes. This branch has directionality, that is, the flow direction of the tie lines between partitions. The resulting state diagram is a directed graph.

[0024] Preferably, the method for rapidly identifying key transmission sections considering geographic partition optimization and graph theory search theory adopts graph theory to obtain the adjoint matrix of the geographic partition state diagram of the power grid, and uses the adjoint matrix to obtain all transmission sections of the power grid, including:

[0025] Step 1: First, obtain the adjacency matrix A of the optimized geographic partition state graph. Assume that the state graph G has n nodes, then its adjacency matrix is ​​recorded as an n-order square matrix (a ij ) n×n The adjacency matrix is ​​obtained by: if there is a branch L between node i and node j from node i to node j ij , then it is recorded as a ij =1, if it does not exist, it is recorded as a ij = 0. The state diagram is represented as follows:

[0026]

[0027] Step 2. Secondly, obtain the path matrix P of the optimized geographic partition state graph. Based on the adjacency matrix A obtained in step 1, the calculation process of the path matrix P is as follows:

[0028] 1) Let P = A, j = 1;

[0029] 2) Let i = 1;

[0030] 3) When the element p in P ij =1, increase k from 1 to n and repeat step 4);

[0031] 4) Let p ik =p ik ||p jk , where || represents the logical OR operation;

[0032] 5) Let i = i + 1. If i ≤ n, return to step 3);

[0033] 6) Let j = j + 1. If j ≤ n, return to step 2. Otherwise, terminate the calculation process.

[0034] Step 3: Next, add an n×n identity matrix E to the path matrix P to obtain the matrix Q.

[0035] Step 4. Finally, the adjoint matrix T of the state diagram is obtained based on the matrix Q and the adjacency matrix A, and the transmission section of the system is obtained based on the adjoint matrix T. Let Q i is the i-th column vector of the matrix Q, then the adjoint matrix is ​​calculated as shown in formula (2), which corresponds to the branch L of the state diagram ij Therefore, the corresponding adjoint matrix can be calculated for all branches of the state diagram. For each adjoint matrix T, all its non-zero elements (for T, its element value is 0 or 1) correspond to a transmission section of the system, and the rows and columns of the non-zero elements correspond to the labels of the branches of the transmission section. For example, if a non-zero element of T is T ij , it means that the transmission section contains the branch L of the state diagram ij , the set of branches corresponding to all nonzero elements of T is the transmission section obtained from the adjoint matrix. By calculating all adjoint matrices of the state diagram, all transmission sections of the system can be obtained. It is worth noting that since the adjoint matrices corresponding to all branches of the state diagram are calculated, the resulting transmission sections may contain duplicates. Secondly, since the branches of the state diagram are composed of interconnecting lines with consistent flow directions between optimized geographical partitions of the power grid, the branches of the state diagram generally include a set of branches from the actual power grid.

[0036]

[0037] Where, Express Q i Invert all elements of ; Represents a logical AND operation on two matrices.

[0038] Preferably, the method for rapidly identifying key transmission sections considering geographical partition optimization and graph search theory adopts an improved power flow betweenness index to screen out the transmission section with the largest improved power flow betweenness index value as the key transmission section of the power grid, including:

[0039] The calculated improved power flow intermediate index values ​​of all transmission sections of the power grid. ij , and the calculation method of the improved power flow betweenness index is as follows:

[0040]

[0041] Where, The improved power flow betweenness index of the tie line; F ij Represents the flow betweenness index of the tie line; M ij Indicates the transmission capacity margin of the tie line.

[0042] For the transmission section x, the improved power flow betweenness index value is calculated as follows:

[0043]

[0044] Where, represents the improved power flow betweenness index of transmission section x; Ω x Represents the set of tie lines for transmission section x.

[0045] For the tie line l ij The transmission capacity margin M ij , which is calculated as follows:

[0046]

[0047] Where, P ij Indicates the tie line l ij The transmission power;

[0048] Calculate the power flow betweenness index F of the tie line ij The calculation of the power flow betweenness index requires solving two key quantities: (1) the transmission power between the node pair (m, n) consisting of the power source node and the load node is allocated on the tie line l ij Share P l (m,n); (2) The active power P(m,n) flowing from the power source node m to the load node n.

[0049] Solving these two key quantities involves determining the power transmission path within the power grid. This is accomplished using a power flow tracing method. By solving the power grid's sequential and reverse-sequence distribution matrices and calculating the downstream and upstream power flows in the interconnection lines, the components of the transmission power within the interconnection lines can be determined.

[0050] Through the contact line ij Power P flowing to load node n l,n for:

[0051]

[0052] Where: P ij For the contact line l ij Active power transmitted in the i is the node flow of node i, where the sum of the flow into or out of node i is taken, that is, for node i, take P i =P i_in or P i =P i_out ;P Ln A is the node load power of load node n; d Assign a matrix to the order of the network; A d The inverse matrix of .

[0053] Matrix element [A d ] ij for:

[0054]

[0055] Where: P j is the node flow of node j, that is, the sum of the flows flowing into or out of node j; is the set of nodes in the power grid that are directly connected to node i through a branch.

[0056] Contact Line ij The power P carried by node m l,m for:

[0057]

[0058] Where: P m_out is the active power flow out of node m, A u Assign the matrix to the network in reverse order; A u The inverse matrix of .

[0059] Matrix element [A u ] ij for:

[0060]

[0061] Where: is the set of nodes in the power grid that are directly connected to node j through a branch.

[0062] By reverse tracking, the actual active power flowing from the power source node m to the load node n can be obtained as:

[0063]

[0064] The transmission power between the node pair (m,n) is shared on the tie line l ij The share on is:

[0065]

[0066] In summary, the contact line l ij The calculation formula of the trend betweenness index is:

[0067]

[0068] Where S m and S n are the injected powers of the power source node m and the load node n respectively; M and D are the sets of power source nodes and load nodes respectively.

[0069] Due to the adoption of the above technical solution, the present invention has at least one of the following beneficial effects:

[0070] The present invention provides a method for rapidly identifying key transmission sections by considering geographical partitioning optimization and graph search theory. The method uses the proposed geographical partitioning optimization rules to rationally optimize the existing geographical partitioning of the power grid and obtains a state diagram of the optimized geographical partitioning.

[0071] The method for obtaining transmission sections provided by the present invention uses graph theory to obtain the adjoint matrix of the obtained power grid partition state diagram, and obtains all transmission sections of the power grid based on the adjoint matrix;

[0072] The key section screening method provided by the invention selects reasonable key transmission sections based on the improved power flow betweenness index, and can provide a scientific and reliable basis for the rapid identification of key transmission sections in the power grid.

[0073] The method for quickly identifying key transmission sections by considering geographical partition optimization and graph search theory provided by the present invention has great engineering practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A flow chart of a method for rapidly identifying key transmission sections in accordance with a preferred embodiment of the present invention, taking into account geographical partitioning optimization and graph search theory; DETAILED DESCRIPTION

[0075] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process. It should be noted that those skilled in the art may make various modifications and improvements without departing from the scope of the present invention, and these modifications and improvements fall within the scope of protection of the present invention.

[0076] This invention provides a method for rapidly identifying critical transmission sections based on geographic partitioning optimization and graph search theory. Based on geographic partitioning optimization rules, the existing geographic partitions of the power grid are optimized and a state diagram of the optimized geographic partitions is obtained. Based on the adjoint matrix of the resulting grid partition state diagram, all transmission sections of the power grid are obtained. Using an improved power flow betweenness metric, reasonable critical transmission sections are screened, representing the critical transmission sections of the power system. This method has practical theoretical significance and potential for widespread application in identifying critical transmission sections of power systems.

[0077] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0078] Please see first Figure 1 , Figure 1 This is a flow chart of a design method for a method for rapidly identifying key transmission sections that considers geographic partitioning optimization and graph theory search theory, provided by an embodiment of the present invention. As shown in the figure, the method for rapidly identifying key transmission sections that considers geographic partitioning optimization and graph theory search theory, provided by an embodiment of the present invention, includes the following steps:

[0079] Step 1): The geographical partition optimization rule optimizes the existing geographical partitions of the power grid and obtains a state diagram of the optimized geographical partitions.

[0080] As a preferred embodiment, the method of step 1) is:

[0081] The steps for partition optimization and state diagram construction are as follows:

[0082] Step 1: Treat the geographic partition of the existing power grid as an undirected graph containing nodes and branches, and shrink the branch end nodes. Nodes with a node degree of 1 are branch end nodes, and the only node connected to them should be in the same geographic partition. Therefore, all branch end nodes with a node degree of 1 are shrunk to the geographic partition where the node connected to them is located.

[0083] Step 2: Determine whether the flow direction of the tie lines between geographical zones is consistent. If there is a tie line, for example, ijIf the tidal current direction is opposite to the overall tidal current direction, check whether the proportion of its tidal current in the overall tidal current of the corresponding tie line is less than a certain value (taken as 10%). If so, the tidal current direction of the tie line is considered consistent. If it is greater than the value, change the geographical partition where the tie line node is located, such as merging node i into the geographical partition where node j is located, or perform the opposite operation, and change the tie line between the two geographical partitions until the tidal currents are consistent. Usually, it is easier to achieve tidal current consistency by changing nodes with smaller node degrees. Generally, a single change of the node is sufficient.

[0084] Step 3: Perform the above optimization operation on all terminal nodes and tie lines of the power grid to obtain the optimized geographical partitions, and construct a state diagram on this partition. That is, each partition is regarded as a new node, and all tie lines between partitions are regarded as new branches between nodes. This branch has directionality, that is, the flow direction of the tie lines between partitions. The resulting state diagram is a directed graph.

[0085] Step 2) Based on the adjoint matrix of the obtained power grid partition state diagram, all transmission sections of the power grid are obtained.

[0086] As a preferred embodiment, the method of step 2) is:

[0087] Graph theory is used to obtain the adjoint matrix of the power grid geographical partition state diagram, and all transmission sections of the power grid are obtained using the adjoint matrix. The steps are as follows:

[0088] Step 1: First, obtain the adjacency matrix A of the optimized geographic partition state graph. Assume that the state graph G has n nodes, then its adjacency matrix is ​​recorded as an n-order square matrix (a ij ) n×n The adjacency matrix is ​​obtained by: if there is a branch L between node i and node j from node i to node j ij , then it is recorded as a ij =1, if it does not exist, it is recorded as a ij = 0. The state diagram is represented as follows:

[0089]

[0090] Step 2. Secondly, obtain the path matrix P of the optimized geographic partition state graph. Based on the adjacency matrix A obtained in step 1, the calculation process of the path matrix P is as follows:

[0091] 1) Let P = A, j = 1;

[0092] 2) Let i = 1;

[0093] 3) When the element p in P ij =1, increase k from 1 to n and repeat step 4);

[0094] 4) Let p ik =pik ||p jk , where || represents the logical OR operation;

[0095] 5) Let i = i + 1. If i ≤ n, return to step 3);

[0096] 6) Let j = j + 1. If j ≤ n, return to step 2. Otherwise, terminate the calculation process.

[0097] Step 3: Next, add an n×n identity matrix E to the path matrix P to obtain the matrix Q.

[0098] Step 4. Finally, the adjoint matrix T of the state diagram is obtained based on the matrix Q and the adjacency matrix A, and the transmission section of the system is obtained based on the adjoint matrix T. Let Q i is the i-th column vector of the matrix Q, then the adjoint matrix is ​​calculated as shown in formula (2), which corresponds to the branch L of the state diagram ij Therefore, the corresponding adjoint matrix can be calculated for all branches of the state diagram. For each adjoint matrix T, all its non-zero elements (for T, its element value is 0 or 1) correspond to a transmission section of the system, and the rows and columns of the non-zero elements correspond to the labels of the branches of the transmission section. For example, if a non-zero element of T is T ij , it means that the transmission section contains the branch L of the state diagram ij , the set of branches corresponding to all nonzero elements of T is the transmission section obtained from the adjoint matrix. By calculating all adjoint matrices of the state diagram, all transmission sections of the system can be obtained. It is worth noting that since the adjoint matrices corresponding to all branches of the state diagram are calculated, the resulting transmission sections may contain duplicates. Secondly, since the branches of the state diagram are composed of interconnecting lines with consistent flow directions between optimized geographical partitions of the power grid, the branches of the state diagram generally include a set of branches from the actual power grid.

[0099]

[0100] Where, Express Q i Invert all elements of ; Represents a logical AND operation on two matrices.

[0101] Step 3) Based on the improved power flow betweenness index, reasonable key transmission sections are screened out, namely the key transmission sections of the power system.

[0102] As a preferred embodiment, the method of step 3) is:

[0103] Using the improved power flow betweenness index, the transmission section with the largest improved power flow betweenness index value is selected as the key transmission section of the power grid. The steps are as follows:

[0104] The calculated improved power flow intermediate index values ​​of all transmission sections of the power grid. ij , and the calculation method of the improved power flow betweenness index is as follows:

[0105]

[0106] Where, The improved power flow betweenness index of the tie line; F ij Represents the flow betweenness index of the tie line; M ij Indicates the transmission capacity margin of the tie line.

[0107] For the transmission section x, the improved power flow betweenness index value is calculated as follows:

[0108]

[0109] Where, represents the improved power flow betweenness index of transmission section x; Ω x Represents the set of tie lines for transmission section x.

[0110] For the tie line l ij The transmission capacity margin M ij , which is calculated as follows:

[0111]

[0112] Where, P ij Indicates the tie line l ij The transmission power; Indicates the tie line l ij The maximum operating transmission power.

[0113] Calculate the power flow betweenness index F of the tie line ij The calculation of the power flow betweenness index requires solving two key quantities: (1) the transmission power between the node pair (m, n) consisting of the power source node and the load node is allocated on the tie line l ij Share P l (m,n); (2) The active power P(m,n) flowing from the power source node m to the load node n.

[0114] Solving these two key quantities involves determining the power transmission path within the power grid. This is accomplished using a power flow tracing method. By solving the power grid's sequential and reverse-sequence distribution matrices and calculating the downstream and upstream power flows in the interconnection lines, the components of the transmission power within the interconnection lines can be determined.

[0115] Through the contact line ij Power P flowing to load node n l,n for:

[0116]

[0117] Where: P ij For the contact line l ij Active power transmitted in the i is the node flow of node i, where the sum of the flow into or out of node i is taken, that is, for node i, take P i =P i_in or P i =P i_out ;P Ln A is the node load power of load node n; d Assign a matrix to the order of the network; A d The inverse matrix of .

[0118] Matrix element [A d ] ij for:

[0119]

[0120] Where: P j is the node flow of node j, that is, the sum of the flows flowing into or out of node j; is the set of nodes in the power grid that are directly connected to node i through a branch.

[0121] Contact Line ij The power P carried by node m l,m for:

[0122]

[0123] Where: P m_out is the active power flow out of node m, A u Assign the matrix to the network in reverse order; A u The inverse matrix of .

[0124] Matrix element [A u ] ij for:

[0125]

[0126] Where: is the set of nodes in the power grid that are directly connected to node j through a branch.

[0127] By reverse tracking, the actual active power flowing from the power source node m to the load node n can be obtained as:

[0128]

[0129] The transmission power between the node pair (m,n) is shared on the tie line l ij The share on is:

[0130]

[0131] In summary, the contact line l ij The calculation formula of the trend betweenness index is:

[0132]

[0133] Where S m and S n are the injected powers of the power source node m and the load node n respectively; M and D are the sets of power source nodes and load nodes respectively.

[0134] The present invention provides a method for rapidly identifying critical transmission sections that considers geographic zoning optimization and graph search theory. Based on geographic zoning optimization rules, the existing geographic zoning of the power grid is optimized and a state diagram of the optimized geographic zoning is obtained. Based on the adjoint matrix of the resulting grid zoning state diagram, all transmission sections of the power grid are obtained. Based on an improved power flow betweenness index, reasonable key transmission sections are screened, namely the power system's critical transmission sections. Compared with traditional power system critical transmission section screening methods, this invention fully considers the characteristics of the power grid's geographic zoning and proposes an optimization method for it, making the power grid's geographic zoning more reasonable and suitable for critical section identification. Furthermore, a graph search method is proposed to search all transmission sections of the system, and an improved power flow betweenness index is provided for critical transmission section screening, effectively improving the efficiency of critical section identification.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for rapid identification of key transmission sections considering geographical partition optimization and graph search theory, characterized in that: include: (1) Optimize the existing power grid geographical partitions based on geographical partition optimization rules and construct the optimized partition state diagram; (2) Based on graph theory, the adjoint matrix of the power grid geographical partition state diagram is obtained, and all transmission sections of the power grid are obtained using the adjoint matrix; (3) Based on the improved power flow betweenness index, the transmission section with the largest improved power flow betweenness index value is selected as the key transmission section of the power grid.

2. The method for rapidly identifying key transmission sections considering geographical partition optimization and graph search theory according to claim 1 is characterized in that: The step (1) is specifically as follows: Step 1.1: Consider the geographical partition of the existing power grid as an undirected graph containing nodes and branches. Contract the end nodes of the branches. Nodes with a node degree of 1 are considered branch end nodes. Contract all branch end nodes with a node degree of 1 to the geographical partition where the nodes connected to them are located. Step 1.2: Determine whether the tidal current direction of the tie lines between geographical zones is consistent. If there is a tie line with a tidal current direction opposite to the overall tidal current direction, check whether the proportion of its tidal current in the overall tidal current of the corresponding tie line is less than a certain value. If so, the tidal current direction of the tie line is considered consistent. If it is greater than a certain value, change the geographical zone where the tie line node is located. Step 1.3: Perform the above optimization operation on all terminal nodes and tie lines of the power grid to obtain the optimized geographical partitions, and construct a state diagram on these partitions. That is, each partition is regarded as a new node, and all tie lines between partitions are regarded as new branches between nodes. The new branches have directionality, that is, the flow direction of the tie lines between partitions. The resulting state diagram is a directed graph.

3. The method for rapidly identifying key transmission sections considering geographical partition optimization and graph search theory according to claim 1 is characterized in that: The step (2) is specifically as follows: Step 2.1, obtain the adjacency matrix A of the optimized geographic partition state graph. Assume that the state graph G has n nodes, then its adjacency matrix is ​​recorded as an n-order square matrix (a ij ) n×n , the adjacency matrix A is obtained as follows: if there is a branch L from node i to node j between node i and node j ij , then it is recorded as a ij =1, if it does not exist, it is recorded as a ij =0, the state diagram is represented as follows: Step 2.2: Obtain the path matrix P of the optimized geographic partition state graph. Based on the adjacency matrix A obtained in step 2.1, the calculation process of the path matrix P is as follows: 1) Let P = A, j = 1; 2) Let i = 1; 3) When the element p in P ij =1, increase k from 1 to n and repeat step 4); 4) Let p ik =p ik ||p jk , where || represents the logical OR operation; 5) Let i = i + 1, and when i ≤ n, return to step 3); 6) Let j = j + 1. When j ≤ n, return to step 2). Otherwise, end the calculation process. Step 2.

3. Add an n×n identity matrix E to the path matrix P to obtain the matrix Q. Step 2.4, obtain the adjoint matrix T of the state diagram according to the matrix Q and the adjacency matrix A, and obtain the transmission section of the system according to the adjoint matrix T. Let Q i is the i-th column vector of the matrix Q, then the adjoint matrix is ​​calculated as shown in formula (2), and the adjoint matrix corresponds to the branch L of the state diagram ij , the corresponding adjoint matrix can be calculated for all branches of the state diagram. For each adjoint matrix T, all its non-zero elements correspond to the transmission section of a system, and the rows and columns of the non-zero elements correspond to the labels of the branches of the transmission section. For example, if a non-zero element of T is T ij , it means that the transmission section contains the branch L of the state diagram ij , the branch set corresponding to all non-zero elements of T is the transmission section obtained by the adjoint matrix. By calculating all adjoint matrices of the state diagram, all transmission sections of the system can be obtained. Where, Express Q i Invert all elements of ; Represents a logical AND operation on two matrices.

4. The method for rapidly identifying key transmission sections considering geographical partition optimization and graph search theory according to claim 1 is characterized in that: The step (3) is specifically as follows: The calculated improved power flow betweenness index values ​​of all transmission sections of the power grid are as follows: ij , and the calculation method of the improved power flow betweenness index is as follows: Where, The improved power flow betweenness index of the tie line; F ij Represents the flow betweenness index of the tie line; M ij Indicates the transmission capacity margin of the tie line; For the transmission section x, the improved power flow betweenness index value is calculated as follows: Where, represents the improved power flow betweenness index of transmission section x; Ω x represents the set of tie lines of transmission section x; For the tie line l ij The transmission capacity margin M ij , which is calculated as follows: Where, P ij Indicates the tie line l ij The transmission power; Indicates the tie line l ij Maximum operating transmission power; Calculate the power flow betweenness index F of the tie line ij : Through the contact line ij Power P flowing to load node n l,n for: Where: P ij For the contact line l ij Active power transmitted in the i is the node flow of node i, where the sum of the flow into or out of node i is taken, that is, for node i, take P i =P i_in or P i =P i_out ;P Ln A is the node load power of load node n; d Assign a matrix to the order of the network; A d The inverse matrix of Matrix element [A d ] ij for: Where: P j is the node flow of node j, that is, the sum of the flows flowing into or out of node j; is the set of nodes in the power grid that are directly connected to node i by a branch; Contact Line ij The power P carried by node m l,m for: Where: P m_out is the active power flow out of node m, A u Assign the matrix to the network in reverse order; A u The inverse matrix of Matrix element [A u ] ij for: Where: is the set of nodes in the power grid that are directly connected to node j by a branch; The actual active power flowing from the power source node m to the load node n is obtained by reverse tracking: The transmission power between the node pair (m,n) is shared on the tie line l ij The share on is: Contact Line ij The calculation formula of the trend betweenness index is: Where S m and S n are the injected powers of the power source node m and the load node n respectively; M and D are the sets of power source nodes and load nodes respectively.

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