Electric power system key power transmission section identification method considering static safety and stability constraints

Abstract

The invention discloses a power system key transmission section identification method considering static safety and stability constraints, which comprises the following steps of: firstly, putting forward a line importance evaluation index by considering factors such as topological structure characteristics, node electrical coupling relationship, power flow consistency and the like, and constructing an initial transmission section identification model in combination with a Girvan-Newman division method; secondly, an initial power transmission section weak'bayonet 'identification rule is formulated by considering a power flow operation mode and a line current-carrying capacity; then, from the perspective of static safety and stability, establishing a data guidance-mechanism driven static stable key section identification method based on a mass data generation result and a transmission section weak ''bayonet'' heavy load evaluation index system; and finally, based on a static stable key section identification result, generating a corresponding key dominant label, realizing quantitative and classified analysis of the key degree of the initial power transmission section in a multi-power-flow mode, and further realizing key power transmission section identification.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching operation control, and in particular to a method for identifying key transmission sections of a power system taking into account static safety and stability constraints. Background Art

[0002] Formulating reasonable safety control rules for different operation modes of the power system and deducing the stable operation boundary can effectively ensure the safe and stable operation of the power grid. At present, the key section power is often used as the core feature to divide the safe operation area, and then formulate stable control rules for the actual power system. As a key link in the interconnection of power grids, the transmission section is the focus of research and regulation in the actual operation of the power system. Research on the identification of key transmission sections is the basis for section analysis.

[0003] In the past, during the operation and dispatch of power grids, staff usually identified key sections based on their work experience and the principle of administrative work area division. With the access of a high proportion of renewable energy systems, the resulting diversified power flows have made it increasingly difficult to meet the actual production and operation needs based solely on expert experience and traditional offline analysis methods. It is also becoming increasingly difficult to accurately and efficiently identify key transmission sections under different operation modes.

[0004] In general, although the current method has achieved the key section analysis considering the change of operation mode to a certain extent, there are still some problems that need to be solved urgently:

[0005] 1) In order to ensure the completeness of the identification results, the initial sections are often screened by exhaustive or traversal methods, which results in a large number of them and a cumbersome and inefficient process for determining the criticality of the sections;

[0006] 2) Traditional critical section identification methods that take into account static safety and stability constraints are mostly verified using time domain simulation methods, which are not adaptable enough to variable power flow modes and are usually only used to characterize critical sections under relatively severe operating modes. Summary of the invention

[0007] Based on the problems raised by the above background technology, the purpose of the present invention is to provide a method for identifying key transmission sections of an electric power system taking into account static safety and stability constraints. It approaches from both qualitative and quantitative perspectives, considers static safety and stability constraints, and designs a reasonable framework for identifying key transmission sections of an electric power system, so as to improve the understanding of the operating status of the power grid and enhance the flexibility, openness, interactivity, economy and sharing of the power grid.

[0008] The present invention is achieved through the following technical solutions:

[0009] The present invention provides a method for identifying key transmission sections of a power system taking into account static safety and stability constraints, comprising the following steps:

[0010] A topology importance evaluation model based on network constraint coefficients is constructed, and the topology importance evaluation model is used to analyze the importance of the topology structure of the power system line; an electrical coupling importance evaluation model considering the clustering of spatial electrical characteristics is constructed, and the electrical coupling importance evaluation model is used to analyze the importance of electrical coupling of the power system line;

[0011] Determine the importance of the power grid line by combining the shortest path of the topology importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model;

[0012] Taking the importance of the power grid line as the partition principle indicator, the initial transmission section set of the power system is constructed based on the Girvan-Newman splitting algorithm;

[0013] Identify weak points of transmission sections from the initial transmission section set of the power system according to the score coefficients of the power system lines and the long-term allowable flow;

[0014] Establishing a data-guided and mechanism-driven static stability critical transmission section identification model, inputting the weak points of the transmission section into the static stability critical transmission section identification model to identify the static stability critical section;

[0015] Through the static stability key section, the key sections corresponding to different power flow modes are deduced to complete the identification of key transmission sections. In the above technical scheme, firstly, the line importance evaluation index is proposed considering the topological structure characteristics, node electrical coupling relationship, power flow consistency and other factors, and the initial transmission section identification model is constructed in combination with the Girvan-Newman partitioning method; secondly, the initial transmission section weak "checkpoint" identification rules are formulated considering the power flow operation mode and line current carrying capacity; then, from the perspective of static safety and stability, the results and the transmission section weak "checkpoint" overload evaluation index system are generated based on massive data, which can ensure that the transmission section is not missed or wrongly selected, while minimizing the number of initial section identifications and reducing the cumbersomeness of the judgment process, and can be used for power system transmission section identification in different scenarios.

[0016] Considering the static safety and stability constraints, a data-guided and mechanism-driven static stability critical section identification method is proposed. Based on the static stability critical section identification results, the corresponding critical dominant labels are generated to achieve quantitative and qualitative analysis of the criticality of the initial transmission section under multiple power flow modes, thereby realizing the identification of critical transmission sections. It has strong applicability under different power flow modes and greatly reduces the workload of traditional "N-1" and "N-2" safety and stability verification. In addition, compared with the existing technology, this method has better solved the shortcomings of traditional methods that are limited by expert experience and historical laws, and further enhances the application value of static stability critical section identification in different scenarios in theoretical fields and actual engineering cases.

[0017] In an optional embodiment, constructing a topology structure importance evaluation model based on network constraint coefficients includes:

[0018] Calculate the network constraint coefficients of the nodes at both ends of the power grid line ij, and determine the index C reflecting the topological importance of the node i based on the network constraint coefficients L (i) through the indicator C L (i) Construct the topological importance index T of line ij i-j ; The calculation process is as follows:

[0019]

[0020] C L (i) = -log2 C(i)

[0021] T i-j =C L (i)×C L (j)

[0022] Among them, C(i) is the network constraint coefficient of node i, node q is the adjacent node shared by nodes i and j, and p ij represents the proportion of line ij to all lines connected to node j; Γ i Represents the set of adjacent nodes of node i.

[0023] In an optional embodiment, constructing an electrical coupling importance evaluation model considering spatial electrical characteristic clustering includes:

[0024] The line transmission distance and line impedance are taken as input features, the input features are normalized, and the normalized input features are calculated using the density peak clustering algorithm to obtain the line clustering label:

[0025] I i-j =DPC(D ij ,r ij ,x ij )

[0026] The electrical coupling importance is calculated based on the line cluster labels:

[0027]

[0028] Among them, D ij represents the transmission distance of line ij, r ij and x ij represent line resistance and reactance respectively, DPC(·) represents density peak clustering algorithm; N DPC Indicates the number of clusters.

[0029] In an optional embodiment, determining the importance of a power grid line by integrating the shortest path of the topology importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model includes:

[0030]

[0031] in, is the edge betweenness of line ij, represents the frequency of the shortest path between nodes i and j passing through line ij, σ ij Represents the number of shortest paths between nodes i and j.

[0032] In an optional embodiment, taking the importance of the power grid line as the partition principle indicator, constructing the initial power transmission section set of the power system based on the Girvan-Newman splitting algorithm includes:

[0033] Establish a directed graph, perform Girvan-Newman partitioning on the directed graph, and set the initial number of Girvan-Newman partitions k=2;

[0034] The edge betweenness in the directed graph is calculated using the Girvan-Newman splitting algorithm to obtain a partition connection line set Lk;

[0035] Determine the power flow consistency of each line in the partition tie line set Lk, and store the lines with power flow consistency determined in the k-th transmission section set Ck;

[0036] Let k=k+1, and repeatedly perform the calculation of the edge betweenness in the directed graph using the Girvan-Newman splitting algorithm until the edge betweenness of the remaining lines is 0 or the number of Girvan-Newman partitions k reaches an iteration threshold;

[0037] The initial transmission section set C = {C1, C2, C3, ... Ck-1} is constructed based on the iteration results, where the order in the initial transmission section set represents the importance of the section.

[0038] In an optional embodiment, a directed graph is established, and performing Girvan-Newman partitioning on the directed graph includes:

[0039] The transformers, generators and load nodes in the power grid are defined as vertices of a directed graph, and the transmission lines between the transformers, generators and load nodes are defined as directed edges, where the direction indicates the flow direction of electricity;

[0040] Among them, the transmission line does not include the connecting lines within the substation; when there are multiple identical transmission lines at the same time, the identical transmission lines are merged into one edge.

[0041] In an optional embodiment, identifying the weak points of the power transmission sections from the initial power transmission section set of the power system according to the score coefficients and the long-term allowable flow of the power system lines includes:

[0042] Calculate the branching coefficient K of line ij under full wiring operation mode i-j and long-term allowable current carrying capacity L i-j ;

[0043] Calculating the ratio of the line branching factor to the long-term allowable current carrying capacity, and constructing a determination condition based on the ratio;

[0044] The sections in the initial transmission section set of the power system that meet the determination condition are defined as weak transmission section points.

[0045] In an optional embodiment, the branching coefficient K of line ij in the full wiring operation mode is calculated i-j and long-term allowable current carrying capacity L i-j The calculation process is as follows:

[0046]

[0047] |P i-j |≤L i-j

[0048]

[0049] |K i-j / L i-j |≤1 / |P g,sum |

[0050] Among them, K i-j It represents the branch coefficient of line ij in the transmission section to which it belongs, P i-j represents the active power of line ij, represents the active power of transmission section g, which belongs to the initial section set C, U N Indicates rated voltage; I max Indicates the current limit of the line; T c represents the temperature correction factor; Indicates the power factor.

[0051] In an optional embodiment, a data-guided-mechanism-driven static stability key transmission section identification model is established according to active power and long-term allowable current carrying capacity, and the transmission section weak point is input into the static stability key transmission section identification model to identify the static stability key section, including:

[0052] Obtain the active power P of the line ij on the transmission section g to which the weak gate of the transmission section belongs i-jand long-term allowable current carrying capacity L i-j ;

[0053] Using active power P i-j The sum of P g,sum and long-term allowable current carrying capacity L i-j Establish the initial section static stability safety constraint:

[0054] P g,sum >δ·L i-j

[0055] Among them, δ is the overload rate threshold;

[0056] Using active power P i-j The sum of P g,sum and long-term allowable current carrying capacity L i-j The sum of L g,ekl Set the section overload rate η:

[0057] η=P g,sum / L g,ekl

[0058] Among them, L g,ekl It represents the sum of the long-term allowable current carrying capacity of the remaining lines in the transmission section g except the "checkpoint";

[0059] Based on the N-1 principle and the N-2 principle, the transmission section g whose section overload rate η exceeds the overload rate threshold is verified, and the transmission section g that passes the verification is defined as a static stability critical section; wherein, the verification process is as follows:

[0060] P g,check >λ·L g,ekl

[0061] α=P g,check / L g,ekl

[0062] Among them, λ represents the safety margin, P g,check It represents the active power of transmission section g under the “N-1” or “N-2” planned maintenance mode, α represents the calculation margin, and the value range of α is [0,+∞].

[0063] In an optional embodiment, when the value of the calculation margin is in [0, 1), the calculation margin is corrected, and the correction process is as follows:

[0064]

[0065] Among them, α' represents the modified calculation margin, and λ represents the safety margin.

[0066] A second aspect of the present invention provides a key transmission section identification system for a power system taking into account static safety and stability constraints, comprising:

[0067] An evaluation model construction module is used to construct a topology structure importance evaluation model based on network constraint coefficients, and the topology structure importance evaluation model is used to analyze the importance of the topology structure of the power system line; an electrical coupling importance evaluation model is constructed considering the clustering of spatial electrical characteristics, and the electrical coupling importance evaluation model is used to analyze the importance of electrical coupling of the power system line;

[0068] An importance calculation module, used to determine the importance of the power grid line by comprehensively considering the shortest path of the topological structure importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model;

[0069] An initial transmission section module, used to construct an initial transmission section set of the power system based on the Girvan-Newman splitting algorithm, taking the importance of the power grid line as a partition principle indicator;

[0070] A weak checkpoint identification module is used to identify the weak checkpoints of the power transmission section from the initial power transmission section set of the power system according to the score coefficient of the power system line and the long-term allowable flow;

[0071] A static stable critical section identification module is used to establish a data-guided and mechanism-driven static stable critical transmission section identification model, and input the weak points of the transmission section into the static stable critical transmission section identification model to identify the static stable critical section;

[0072] The section identification module is used to deduce the key sections corresponding to different power flow modes through the static stability key sections to complete the identification of key transmission sections.

[0073] A third aspect of the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a method for identifying key transmission sections of an electric power system taking into account static safety and stability constraints is implemented.

[0074] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for identifying key transmission sections of an electric power system taking into account static safety and stability constraints.

[0075] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0076] (1) The line importance evaluation system constructed by considering the topological structure characteristics, node electrical coupling relationship and power flow consistency described in the present invention is combined with the initial transmission section identification model constructed by the Girvan-Newman partitioning algorithm. Compared with the prior art, this scheme can minimize the number of initial section identifications and reduce the cumbersomeness of the determination process while ensuring that no transmission sections are missed or wrongly selected, and can be used for power system transmission section identification in different scenarios;

[0077] (2) The present invention considers the static safety and stability constraints and proposes a data-guided and mechanism-driven critical section identification strategy, which has strong applicability under different tidal modes and greatly reduces the workload of traditional "N-1" and "N-2" safety and stability verification. In addition, compared with the existing technology, this scheme better solves the shortcomings of traditional methods that are limited by expert experience and historical laws, and further enhances the application value of static stability critical section identification in different scenarios in theoretical fields and actual engineering cases. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0079] Figure 1 This is an overall framework diagram of the method for identifying key transmission sections of a power system provided in Example 1 of the present invention.

[0080] Figure 2 This is a flowchart of the steps of the method for identifying key transmission sections of a power system provided in Example 1 of the present invention.

[0081] Figure 3 Schematic diagram of the impact of the transmission section "checkpoint" on the system provided in Example 1 of the present invention.

[0082] Figure 4 This is an overall structural diagram of the data-guided and mechanism-driven static stability discrimination model provided in Example 1 of the present invention.

[0083] Figure 5 This is an IEEE 39-node test system for new energy access provided in Example 2 of the present invention.

[0084] Figure 6 This is a schematic diagram of the initial section overload rate of the new energy access IEEE 39-node system provided in Example 2 of the present invention.

[0085] Figure 7This is the calculation result of the static safety stability margin of the initial section of the new energy access IEEE 39 system provided in Example 2 of the present invention.

[0086] Figure 8 The specific location of the static stability critical section of the new energy access IEEE 39 system provided in Example 2 of the present invention.

[0087] FIG9( a ) is a schematic diagram of the initial section overload rate of the G power grid in Example 3 of the present invention.

[0088] FIG9( b ) is a schematic diagram of the initial section overload rate of the L power grid in Example 3 of the present invention.

[0089] FIG10( a ) is a schematic diagram of the static stability safety margin of the G power grid transmission section in Embodiment 3 of the present invention.

[0090] FIG10( b ) is a schematic diagram of the static stability safety margin of the L power grid transmission section in Embodiment 3 of the present invention.

[0091] FIG. 11( a ) is a schematic diagram of the identification results of the key transmission sections of the G power grid in Example 3 of the present invention.

[0092] FIG11( b ) is a schematic diagram of the identification results of the key transmission sections of the L power grid in Example 3 of the present invention. DETAILED DESCRIPTION

[0093] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0094] Example 1

[0095] like Figure 1 As shown, the present invention includes two aspects: first, an initial transmission section identification method based on line importance and Girvan-Newman partitioning; second, identification of key transmission sections taking into account static safety and stability constraints.

[0096] Specifically, Figure 2 As shown in FIG, the method for identifying key transmission sections of a power system taking into account static safety and stability constraints includes the following steps:

[0097] A topology importance evaluation model based on network constraint coefficients is constructed, and the topology importance evaluation model is used to analyze the importance of the topology structure of the power system line; an electrical coupling importance evaluation model considering the clustering of spatial electrical characteristics is constructed, and the electrical coupling importance evaluation model is used to analyze the importance of electrical coupling of the power system line;

[0098] Determine the importance of the power grid line by combining the shortest path of the topology importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model;

[0099] Taking the importance of the power grid line as the partition principle indicator, the initial transmission section set of the power system is constructed based on the Girvan-Newman splitting algorithm;

[0100] Identify weak points of transmission sections from the initial transmission section set of the power system according to the score coefficients of the power system lines and the long-term allowable flow;

[0101] Establishing a data-guided and mechanism-driven static stability critical transmission section identification model, inputting the weak points of the transmission section into the static stability critical transmission section identification model to identify the static stability critical section;

[0102] The key sections corresponding to different power flow modes are deduced through the static stability key sections to complete the identification of key transmission sections.

[0103] The embodiment of the present invention firstly considers factors such as topological structure characteristics, node electrical coupling relationship, and power flow consistency to propose line importance evaluation indicators, and constructs an initial transmission section identification model in combination with the Girvan-Newman partitioning method; secondly, the initial transmission section weak "checkpoint" identification rules are formulated considering the power flow operation mode and line current carrying capacity; then, from the perspective of static safety and stability, the results and the transmission section weak "checkpoint" overload evaluation indicator system are generated based on massive data, which can minimize the number of initial section identifications and reduce the cumbersomeness of the judgment process while ensuring that no transmission sections are missed or misselected, and can be used for power system transmission section identification in different scenarios.

[0104] Considering the static safety and stability constraints, a data-guided and mechanism-driven static stability critical section identification method is proposed. Based on the static stability critical section identification results, the corresponding critical dominant labels are generated to achieve quantitative and qualitative analysis of the criticality of the initial transmission section under multiple power flow modes, thereby realizing the identification of critical transmission sections. It has strong applicability under different power flow modes and greatly reduces the workload of traditional "N-1" and "N-2" safety and stability verification. In addition, compared with the existing technology, this method has better solved the shortcomings of traditional methods that are limited by expert experience and historical laws, and further enhances the application value of static stability critical section identification in different scenarios in theoretical fields and actual engineering cases.

[0105] In an optional embodiment, constructing a topology structure importance evaluation model based on network constraint coefficients includes:

[0106] Calculate the network constraint coefficients of the nodes at both ends of the power grid line ij, and determine the index C reflecting the topological importance of the node i based on the network constraint coefficients L (i) through the indicator C L (i) Construct the topological importance index T of line ij i-j ; The calculation process is as follows:

[0107]

[0108] C L (i) = -log2 C(i) (2)

[0109] T i-j =C L (i)×C L (j) (3)

[0110] Among them, C(i) is the network constraint coefficient of node i, node q is the adjacent node shared by nodes i and j, and p ij represents the proportion of line ij to all lines connected to node j; Γ i Represents the set of adjacent nodes of node i.

[0111] It should be noted that p iq 、p qj With p ij Similarly, they all represent the proportion of the corresponding line to all lines connected to the node. Based on the network constraint coefficient of the structural hole theory, the topological structure importance T that reflects the importance of the line is proposed from the perspective of the topological characteristics of the power grid. i-j . T i-j It can characterize the connection tightness between line ij and other lines. The larger the value is, the more important line ij is in the system, the stronger the power transmission role it undertakes, and the greater the possibility of causing safety and stability problems.

[0112] In addition, the value of the network constraint coefficient C is between 0 and 1. The smaller the value, the more important the target node is in the topology. In order to better characterize the influence of C, the negative logarithm of C with base 2 is taken as the node importance index C L From formula (2), we know that when C(i) = 0.5, C L (i) = 1, that is, C(i) is bounded by 0.5, and if it is lower than 0.5, then T i-j A positive effect is produced, otherwise a negative effect is produced. C(i) is 1, which means that the node is a hanging node.

[0113] In an optional embodiment, constructing an electrical coupling importance evaluation model considering spatial electrical characteristic clustering includes:

[0114] The line transmission distance and line impedance are taken as input features, the input features are normalized, and the normalized input features are calculated using the density peak clustering algorithm to obtain the line clustering label:

[0115] I i-j =DPC(D ij , r ij , x ij ) (7)

[0116] The electrical coupling importance is calculated based on the line cluster labels:

[0117]

[0118] Among them, D ij represents the transmission distance of line ij, r ij and x ij represent line resistance and reactance respectively, DPC(·) represents density peak clustering algorithm; N DPC Indicates the number of clusters.

[0119] It should be noted that the embodiment of the present invention adopts the Density Peaks Clustering (DPC) algorithm to classify and evaluate the degree of line electrical coupling, where DPC considers both the local density and relative distance of nodes to describe the clustering results. Line impedance is used to characterize the electrical distance between nodes, and the transmission line distance is introduced in combination with line impedance to jointly characterize the degree of line electrical coupling, and different levels of electrical coupling importance E are formulated. i-j The scoring principle.

[0120] Furthermore, in the density peak clustering algorithm, considering the amount of data, the Gaussian kernel is used to calculate the local density ρ(i) of node i:

[0121]

[0122] Among them, d ij represents the Euclidean distance between nodes i and j; d c Represents the neighborhood cutoff distance, which can be set manually according to specific needs.

[0123] The relative distance δ(i) represents the minimum distance between node i and other nodes with higher density.

[0124]

[0125] The relative distance calculation methods of the node with the highest density and other nodes are described respectively. The ρ(i) and δ(i) of the cluster center are relatively high, and the results can be visualized through the decision diagram.

[0126] In addition, the decision value γ can be defined i Select cluster centers:

[0127] γ(i)=ρ i ×δ i (6)

[0128] The purpose of taking the line transmission distance and the line impedance as input features and normalizing the input features is to avoid the magnitude difference of the input parameters interfering with the clustering results. Furthermore, in this embodiment, the upper and lower limits are set to 0.95 and 0.05 respectively.

[0129] In an optional embodiment, determining the importance of a power grid line by integrating the shortest path of the topology importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model includes:

[0130]

[0131]

[0132] in, is the edge betweenness of line ij, represents the frequency of the shortest path between nodes i and j passing through line ij, σ ij Represents the number of shortest paths between nodes i and j.

[0133] It should be noted that step 3 combines the topological structure importance proposed in step 1, the electrical coupling importance proposed in step 2, and the edge betweenness index, and comprehensively considers the importance of the two types of lines and the edge betweenness to jointly constitute an importance evaluation index for the power grid line, and the importance of the power grid line is determined by the importance evaluation index.

[0134] In an optional embodiment, taking the importance of the power grid line as the partition principle indicator, constructing the initial power transmission section set of the power system based on the Girvan-Newman splitting algorithm includes:

[0135] Establish a directed graph, perform Girvan-Newman partitioning on the directed graph, and set the initial number of Girvan-Newman partitions k=2;

[0136] The edge betweenness in the directed graph is calculated using the Girvan-Newman splitting algorithm to obtain a partition connection line set Lk;

[0137] Determine the power flow consistency of each line in the partition tie line set Lk, and store the lines with power flow consistency determined in the k-th transmission section set Ck;

[0138] Let k=k+1, and repeatedly perform the calculation of the edge betweenness in the directed graph using the Girvan-Newman splitting algorithm until the edge betweenness of the remaining lines is 0 or the number of Girvan-Newman partitions k reaches an iteration threshold;

[0139] The initial transmission section set C = {C1, C2, C3, ... Ck-1} is constructed based on the iteration results, where the order in the initial transmission section set represents the importance of the section.

[0140] It should be noted that the embodiment of the present invention uses the importance of the power line obtained in step 3 as the partition principle indicator, and uses the calculation of the line edge betweenness as the basis to establish a directed graph. The importance of the power line is partitioned and calculated using the Girvan-Newman iterative partitioning strategy that considers the power flow consistency constraint. In the Girvan-Newman partition iteration, the removed edges are recorded. These edges define the connection between different communities. Finally, the set of these edges L k It represents the communication lines between communities under the division of k communities, thus obtaining the partition communication line set L k .

[0141] Based on the operating status of the power system, the flow direction of each line can be obtained through real-time monitoring data. k The flow direction of each line is determined. If L k The power flow directions of all lines in the grid are the same, that is, they are all transmitting power to the same area of ​​the grid, then the power flows of these lines can be considered to be consistent. If there are lines with opposite power flow directions, that is, some lines transmit power in one direction, while other lines transmit power in the opposite direction, then the power flows are inconsistent. The lines with consistent power flows are stored in the transmission section set C obtained for the kth time. k If they are inconsistent, the result will be ignored.

[0142] In an optional embodiment, a directed graph is established, and performing Girvan-Newman partitioning on the directed graph includes:

[0143] The transformers, generators and load nodes in the power grid are defined as vertices of a directed graph, and the transmission lines between the transformers, generators and load nodes are defined as directed edges, where the direction indicates the flow direction of electricity;

[0144] Among them, the transmission line does not include the connecting lines within the substation; when there are multiple identical transmission lines at the same time, the identical transmission lines are merged into one edge.

[0145] In an optional embodiment, identifying the weak points of the power transmission sections from the initial power transmission section set of the power system according to the score coefficients and the long-term allowable flow of the power system lines includes:

[0146] Calculate the branching coefficient K of line ij under full wiring operation mode i-j and long-term allowable current carrying capacity L i-j ;

[0147] Calculating the ratio of the line branching factor to the long-term allowable current carrying capacity, and constructing a determination condition based on the ratio;

[0148] The sections in the initial transmission section set of the power system that meet the determination condition are defined as weak transmission section points.

[0149] It should be noted that step 5 is based on the initial transmission section set, and the weak “checkpoint” identification rules of the initial transmission section are designed considering the power flow operation mode and line current carrying capacity. i-j With L i-j The line with the largest ratio is not overloaded, and the other lines in this section meet the static safety constraints, so the line that meets the condition The line is defined as the weak "badge" of the transmission section. The schematic diagram of the impact of the "badge" of the transmission section on the system is as follows Figure 3 As shown in the figure, the transmission section consists of lines L1 and L4. The impact of disconnecting the "battery" line L1 in the transmission section on the entire system is much greater than disconnecting the non-"battery" line L4 in the section. When L1 is disconnected, the impact on the system includes but is not limited to overload of lines L2 and L4, reverse flow of line L5, etc. On the contrary, when L4 is disconnected, the operation of the system does not receive a significant impact. Therefore, more attention should be paid to the operating status of the "battery" in the transmission section.

[0150] In an optional embodiment, the branching coefficient K of line ij in the full wiring operation mode is calculated i-j and long-term allowable current carrying capacity L i-j The calculation process is as follows:

[0151]

[0152] |P i-j |≤L i-j (12)

[0153]

[0154] |K i-j / L i-j |≤1 / |P g,sum | (14)

[0155] Among them, K i-j It represents the branch coefficient of line ij in the transmission section to which it belongs, P i-j represents the active power of line ij, represents the active power of transmission section g, which belongs to the initial section set C, U N Indicates rated voltage; I max Indicates the current limit of the line; T c represents the temperature correction factor; Indicates the power factor.

[0156] It should be noted that the power system transmission section distributes the active power of each line according to the impedance of each line, and uses the branching coefficient to define the ratio of the active power of any line in the transmission section to the section power. Therefore, the line branching coefficient can be approximated as a constant under the full connection operation mode.

[0157] In this embodiment, the ambient temperature is 25°C, the conductor temperature is 80°C, and the temperature correction coefficient T is affected by the ambient temperature and the conductor working temperature. c In this case, the power factor is 1. In this embodiment, the power factor is 0.9 or 0.95.

[0158] In an optional embodiment, a data-guided-mechanism-driven static stability key transmission section identification model is established according to active power and long-term allowable current carrying capacity, and the transmission section weak point is input into the static stability key transmission section identification model to identify the static stability key section, including:

[0159] Obtain the active power P of the line ij on the transmission section g to which the weak gate of the transmission section belongs i-j and long-term allowable current carrying capacity L i-j ;

[0160] Using active power P i-j The sum of P g,sum and long-term allowable current carrying capacity L i-j Establish the initial section static stability safety constraint:

[0161] P g,sum >δ·L i-j (15)

[0162] Among them, δ is the overload rate threshold;

[0163] Using active power P i-j The sum of P g,sum and long-term allowable current carrying capacity L i-j The sum of L g,ekl Set the section overload rate η:

[0164] η=P g,sum / L g,ekl (16)

[0165] Among them, L g,ekl It represents the sum of the long-term allowable current carrying capacity of the remaining lines in the transmission section g except the "checkpoint";

[0166] Based on the N-1 principle and the N-2 principle, the transmission section g whose section overload rate η exceeds the overload rate threshold is verified, and the transmission section g that passes the verification is defined as a static stability critical section; wherein, the verification process is as follows:

[0167] P g,check >λ·L g,ekl (17)

[0168] α=P g,check / L g,ekl (18)

[0169] Among them, λ represents the safety margin, P g,check It represents the active power of transmission section g under the “N-1” or “N-2” planned maintenance mode, α represents the calculation margin, and the value range of α is [0,+∞].

[0170] The calculation margin refers to the calculation margin of the transmission section under the planned maintenance mode.

[0171] It should be noted that step 6: Taking the initial transmission section weak "badge" as input, taking into account the static safety and stability constraints of the online safe and stable operation of the power system, a data-guided-mechanism-driven static stability key transmission section identification model is established. Based on the massive data generation results and the section weak "badge" overload evaluation index system, a static stability key section identification model is established. The overall structure of the static stability discrimination model based on data guidance-mechanism driven is as follows Figure 4 shown.

[0172] Based on the weak points of the transmission section obtained in step 5, the overloaded section is identified. Guided by historical data, the transmission section whose initial section η exceeds the set threshold is set as an overloaded section and included in the consideration of key transmission sections. Based on the "N-1" and "N-2" stability verification principles, the overloaded section is finely identified as a key section.

[0173] Among them, the "N-1" principle means ensuring that the power grid can still maintain stable operation when any single component (such as a line or transformer) fails; the "N-2" principle means further ensuring that the power grid can remain stable when any two components fail at the same time.

[0174] In an optional embodiment, when the value of the calculation margin is in [0, 1), the calculation margin is corrected, and the correction process is as follows:

[0175]

[0176] Among them, α' represents the modified calculation margin, and λ represents the safety margin.

[0177] It should be noted that, usually α≥1 means that the section is out of limit. In order to improve the visualization effect of α in [0,1), it needs to be corrected and verified using the corrected calculation margin. α'≥0 means that the transmission section is overloaded and fails the static safety and stability verification. Otherwise, it is judged to have passed the verification. The transmission sections that fail the "N-1" or "N-2" verification are recorded as static stability key sections.

[0178] Furthermore, by deducing the key sections corresponding to different power flow modes through the static stability key sections, a data-guided-mechanism-driven static stability key transmission section identification model is established based on the active power and the long-term allowable current carrying capacity. The weak points of the transmission section are input into the static stability key transmission section identification model to identify the static stability key sections. The static stability key transmission section identification model finds the transmission section that is greatly affected by static stability, and generates corresponding critical dominant labels to complete the identification of the key transmission section.

[0179] The key section identified by taking into account the static stability safety constraints is the key transmission section from the static safety perspective, which can then be deduced to the key sections corresponding to different power flow modes.

[0180] After the power system is disturbed, if its power flow distribution changes and presents different operating modes, it is necessary to further determine the critical sections corresponding to the target system under different power flow modes, and then analyze the operating characteristics of the key transmission sections under the static stability safety constraints based on the section criticality identification method from the static safety perspective. Based on the static stability safety theme, the critical dominant labels of different key transmission sections are formulated, that is, to determine whether the critical section is susceptible to static stability under a specific operating mode.

[0181] Example 2

[0182] In order to verify the effectiveness and adaptability of the critical transmission section identification framework proposed in Example 1 of the present invention, Example 2 of the present invention selects the IEEE 39-node system with new energy access as a test case. First, based on the IEEE 39-node system, the traditional units connected to nodes 35 and 37 are replaced with wind turbines (W) and photovoltaic units (P) respectively, so as to test the proposed method and verify the comprehensive performance of the proposed critical section identification method. For a diagram of the IEEE 39-node system considering new energy access, see Figure 5 .

[0183] Result analysis:

[0184] The present invention modifies the output of traditional units according to the ±15% fluctuation of standard working conditions, and the uncertain wind and solar stations apply ±30% disturbance to randomly generate scenarios according to the wind and solar characteristics, and a total of 5,000 sets of tidal samples are obtained. The tidal samples of the scenarios where the tidal directions of each line are completely consistent are eliminated, and 349 sets of typical scenarios are obtained. The changes in the tidal flow direction of each line and the probability of occurrence are recorded, as shown in Tables 1 and 2.

[0185] Table 1 Importance ranking of IEEE 39-bus system lines for renewable energy access

[0186]

[0187] Table 2 Flow directions of fixed lines in different scenarios of IEEE 39-bus system with renewable energy access

[0188]

[0189]

[0190] Considering the importance of the line and the edge betweenness, the importance of the power lines is quantitatively ranked, as shown in Table 3. Considering the impact of the unit output volatility on the line flow direction, the initial transmission section is traversed based on the improved Girvan-Newman partitioning algorithm proposed in the present invention. In addition, the flow consistency of the lines in each cut set under different operating modes is analyzed for different scenarios, and the probability of flow consistency is calculated. The cut set with a flow consistency rate exceeding 70% is regarded as the initial transmission section, so the lines with a probability of flow direction change exceeding 30% are preferentially eliminated. Therefore, the sections containing the four lines 3-4, 8-9, 9-39, and 17-27 are eliminated in the verification stage.

[0191] Table 3 Changes and probability of occurrence of non-fixed lines of power flow in different scenarios of new energy access to IEEE 39-node system

[0192]

[0193] Taking the transmission section characteristics as the basic condition, the Girvan-Newman partitioning algorithm is used to identify all the initial transmission section sets for the access of renewable energy to the IEEE 39-bus system, considering the line importance and power flow consistency rate. The initial section results composed of different numbers of lines are shown in Table 4.

[0194] Table 4 Initial section identification results of IEEE 39-bus system with renewable energy access

[0195]

[0196] Based on the initial transmission sections found, the key transmission sections susceptible to static safety and stability are identified by taking into account the static safety and stability constraints. Considering that the lines in the IEEE 39-bus system are all single-circuit lines, the initial sections containing no more than 2 lines will be operated in sections when performing "N-1" or "N-2" stability checks. Therefore, all static stability key sections containing no more than 2 lines are shown in Table 5.

[0197] Table 5 Identification results of key sections of the IEEE 39-bus system with no more than 2 lines for renewable energy access

[0198]

[0199]

[0200] As shown in Table 5, the critical section identification method constructed by the present invention considering the uncertainty of unit output and the power flow consistency constraint found a total of 1 "N-1" critical section and 11 "N-2" critical sections. At the same time, taking into account the influence of the excessive frequency of power flow direction changes or the limit of the line with the opposite power flow direction, in order to avoid the section not meeting the power flow consistency constraint, the present invention eliminates the critical sections that do not meet the power flow consistency constraint. Next, based on formula (14), the "checkpoints" of each initial section containing more than 2 lines are calculated, and based on formula (16), each section η is calculated to identify the sections with static stability risks. The initial sections containing more than 2 lines and the corresponding "checkpoints" can be seen in Table 6.

[0201] Table 6 Initial section and bayonet identification results of improved IEEE 39-node system

[0202]

[0203] The evaluation index η of the initial section overload rate of the IEEE 39 system with new energy access is as follows: Figure 6 shown.

[0204] Considering the section overload rate evaluation index η, the initial section (marked in orange) with η not less than the threshold of 0.7 is subjected to "N-1" or "N-2" static stability refinement verification. The static stability safety margin of the initial over-limit section is calculated based on equations (18) and (19), and the safety margin of the non-over-limit section is calculated as a control group to compare the reliability and applicability of the proposed static stability judgment criterion. According to expert experience, the safety margin λ in formula (17) is set to 0.8. The calculation results of the static safety stability margin of the initial section of the IEEE 39 system with new energy access are as follows: Figure 7 shown.

[0205] like Figure 7As shown, the four initial over-limit sections identified by the IEEE 39-node system for new energy access all exceed the static safety and stability margins; therefore, these four initial over-limit sections can be summarized as static stability critical sections. In contrast, the static safety margins of the remaining 24 initial non-over-limit sections were all within the limit. As mentioned above, the static stability judgment criterion proposed in the present invention has strong applicability, can be used as an important step in the identification of static stability critical sections, and greatly reduces the workload of traditional "N-1" and "N-2" stability verification. The positions of each static safety and stability section in the improved IEEE 39 system are as follows: Figure 8 .

[0206] Example 3

[0207] In order to verify the effectiveness and adaptability of the method proposed in Example 1 of the present invention in actual power grid applications, Example 3 of the present invention selects the power grids of G City and L City in a certain place in the southwest (hereinafter referred to as G power grid and L power grid) as test targets. Among them, the G power grid consists of 30 nodes and 37 transmission lines above 220kV, with a network load of 3286MW, including 1 thermal power unit, 5 hydropower units, 4 wind power units, and a total installed power capacity of 5502MW, with a new energy installed capacity accounting for 18.2%. The L power grid consists of 51 nodes and 60 transmission lines above 220kV, with a network load of 5835MW, a total of 11 hydropower units, and a total installed power capacity of 3550MW.

[0208] Result analysis:

[0209] Grid dispatching and operation personnel usually choose the Xiada mode, which is a relatively severe scenario among typical operating modes, for safety and stability verification. Therefore, this paper first identifies the key transmission sections for the Xiada operating mode of the G grid and the L grid.

[0210] First, obtain the initial transmission section set under the Xiada operation mode of the G power grid and the L power grid, and obtain the initial section "checkpoint", as shown in Table 7 and Table 8 for details.

[0211] Table 7 Initial section and bayonet identification results of the Xiada mode of the G power grid

[0212]

[0213] Table 8 Initial section and bayonet identification results of L power grid Xiada mode

[0214]

[0215] As shown in Tables 7 and 8, considering the importance of the lines and the community division strategy, the G grid and L grid obtained 10 and 15 initial transmission sections respectively, which reduced the workload of identifying the criticality of the sections to a certain extent.

[0216] The visualization results of the initial section overload rate η of the two power grids can be seen in Figure 9(a) and Figure 9(b). Considering the section overload rate η, the initial sections (marked in orange) with η not less than the threshold of 0.7 are subjected to "N-1" or "N-2" static stability refinement verification.

[0217] The "N-1" or "N-2" stability check is performed on each initial section "checkpoint", and the static stability safety margin of the initial over-limit section is calculated. The safety margin of the section that does not exceed the limit is calculated as a control group to compare the reliability and applicability of the proposed static stability judgment criteria. Among them, the safety margin of the L power grid transmission section 4 is as high as 1.72, that is, no matter how the safety margin λ is set, the section will exceed the limit. In addition, according to expert experience, λ is set to 0.8, and the calculation results of the static safety stability margin of other sections of the two power grids are shown in Figure 10(a) and Figure 10(b).

[0218] It is easy to see from the figure that, except for the calculation margin of G power grid transmission section 5 which is lower than the preset λ, the other 9 initial over-limit sections of the two power grids have not passed the static safety and stability verification; that is, except for G power grid transmission section 5, the other 9 initial over-limit sections are classified as static stability critical sections. As a comparison, the 14 initial non-over-limit sections all passed the static safety verification. Therefore, the static stability judgment criterion proposed in this paper has strong applicability in actual power grids and can be used as a priori step for "N-1" and "N-2" stability verification to reduce the workload of static stability critical section identification.

[0219] Combined with the static stability judgment results, the identification results of the key transmission sections of the two power grids are shown in Figure 11(a) and Figure 11(b), and compared with the key transmission sections listed for key monitoring by calculation experts in the actual operation of the two power grids. For details, please refer to Table 9 and Table 10.

[0220] Table 9 Results of identification of key sections of static stability of G power grid in summer mode

[0221]

[0222] As shown in Table 9, there are two key transmission sections in the actual operation of the G power grid. Based on the key sections identified by the proposed method in this paper, which completely correspond to the above two key sections (17-24+28-19, 8-26), two additional static stability key sections (11-20+11-25+11-6, 20-15+25-15) are found. While avoiding omissions, sections with potential risks can also be summarized to obtain better monitoring effects.

[0223] Table 10 Results of identification of key sections of static stability in summer-large mode of L power grid

[0224]

[0225] As shown in Table 10, there are 5 key transmission sections in the actual operation of the L power grid. The key sections identified based on the proposed method can completely correspond to 3 of them (28-34+10-42, 10-42, 50-22+51-22+50-1+51-1), and 2 new static stability key sections (10-36+15-36, 42-32+42-23) are found. While avoiding missing selection, the sections with potential risks can also be summarized to obtain better monitoring effects.

[0226] The present invention proposes a model framework for the identification of critical sections of static stability of power systems. Under this framework, firstly, the line importance evaluation index is proposed considering factors such as topological structure characteristics, node electrical coupling relationship, and power flow consistency, and the initial transmission section identification model is constructed in combination with the Girvan-Newman partitioning method; secondly, the initial transmission section weak "badge" identification rules are formulated considering the power flow operation mode and line current carrying capacity; thirdly, from the perspective of static safety and stability, based on the massive data generation results and the transmission section weak "badge" overload evaluation index system, a data-guided-mechanism-driven static stability critical section identification method is established; then, based on the static stability critical section identification results, the corresponding critical dominant labels are generated to achieve quantitative and qualitative analysis of the criticality of the initial transmission section under multiple power flow modes, and then the critical transmission section identification is realized; finally, the IEEE 39-node system with new energy access and two prefecture-level power grids in the southwest are taken as examples to analyze the identification effect of the static stability critical section of the power system. The results verify the effectiveness of the proposed framework and method. The proposed method helps researchers avoid missing key transmission sections while summarizing key sections with potential risks to achieve better monitoring results.

[0227] (1) The line importance evaluation system constructed by considering the topological structure characteristics, node electrical coupling relationship and power flow consistency can ensure that no transmission section is missed or wrongly selected, while minimizing the number of initial section identifications and reducing the cumbersomeness of the determination process, thus providing a guarantee for identifying transmission sections in more complex and changeable power system operation modes.

[0228] (2) Considering the static safety and stability constraints, a data-guided and mechanism-driven critical section identification strategy is proposed. It has strong applicability under different tidal current modes and greatly reduces the workload of traditional "N-1" and "N-2" safety and stability verification. This model can be used as an important step in the identification of static stability critical sections in different scenarios in the engineering and theoretical fields and provide certain reference significance.

[0229] Example 4

[0230] Embodiment 4 of the present invention provides a key transmission section identification system for a power system taking into account static safety and stability constraints, including:

[0231] An evaluation model construction module is used to construct a topology structure importance evaluation model based on network constraint coefficients, and the topology structure importance evaluation model is used to analyze the importance of the topology structure of the power system line; an electrical coupling importance evaluation model is constructed considering the clustering of spatial electrical characteristics, and the electrical coupling importance evaluation model is used to analyze the importance of electrical coupling of the power system line;

[0232] An importance calculation module, used to determine the importance of the power grid line by comprehensively considering the shortest path of the topological structure importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model;

[0233] An initial transmission section module, used to construct an initial transmission section set of the power system based on the Girvan-Newman splitting algorithm, taking the importance of the power grid line as a partition principle indicator;

[0234] A weak checkpoint identification module is used to identify the weak checkpoints of the power transmission section from the initial power transmission section set of the power system according to the score coefficient of the power system line and the long-term allowable flow;

[0235] A static stable critical section identification module is used to establish a data-guided and mechanism-driven static stable critical transmission section identification model, and input the weak points of the transmission section into the static stable critical transmission section identification model to identify the static stable critical section;

[0236] The section identification module is used to deduce the key sections corresponding to different power flow modes through the static stability key sections to complete the identification of key transmission sections.

[0237] Example 5

[0238] Embodiment 5 of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for identifying key transmission sections of an electric power system taking into account static safety and stability constraints as described in Embodiment 1 is implemented.

[0239] Example 6

[0240] Embodiment 6 of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a method for identifying key transmission sections of a power system taking into account static safety and stability constraints is implemented.

[0241] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying key transmission sections of a power system taking into account static safety and stability constraints, characterized in that: The steps include: A topology importance evaluation model based on network constraint coefficients is constructed, and the topology importance evaluation model is used to analyze the importance of the topology structure of the power system line; an electrical coupling importance evaluation model considering the clustering of spatial electrical characteristics is constructed, and the electrical coupling importance evaluation model is used to analyze the importance of electrical coupling of the power system line; Determine the importance of the power grid line by combining the shortest path of the topology importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model; Taking the importance of the power grid line as the partition principle indicator, the initial transmission section set of the power system is constructed based on the Girvan-Newman splitting algorithm; Identify weak points of transmission sections from the initial transmission section set of the power system according to the score coefficients of the power system lines and the long-term allowable flow; A data-guided and mechanism-driven static stability critical transmission section identification model is established according to active power and long-term allowable current carrying capacity, and the weak points of the transmission section are input into the static stability critical transmission section identification model to identify the static stability critical section; The key sections corresponding to different power flow modes are deduced through the static stability key sections to complete the identification of key transmission sections.

2. The method for identifying key transmission sections of a power system taking into account static safety and stability constraints according to claim 1 is characterized in that: The important evaluation models of topological structures based on network constraint coefficients include: Calculate the network constraint coefficients of the nodes at both ends of the power grid line ij, and determine the index C reflecting the topological importance of the node i based on the network constraint coefficients L (i) through the indicator C L (i) Construct the topological importance index T of line ij i-j ; The calculation process is as follows: C L (i)=-log2 C(i) T i-j =C L (i)×C L (j) Among them, C(i) is the network constraint coefficient of node i, node q is the adjacent node shared by nodes i and j, and p ij represents the proportion of line ij to all lines connected to node j; Γ i Represents the set of adjacent nodes of node i.

3. The method for identifying key transmission sections of a power system taking into account static safety and stability constraints according to claim 1, characterized in that: The construction of an electrical coupling importance evaluation model considering spatial electrical characteristic clustering includes: The line transmission distance and line impedance are taken as input features, the input features are normalized, and the normalized input features are calculated using the density peak clustering algorithm to obtain the line clustering label: I i-j =DPC(D ij ,r ij ,x ij ) The electrical coupling importance is calculated based on the line cluster labels: Among them, D ij represents the transmission distance of line ij, r ij and x ij represent line resistance and reactance respectively, DPC(·) represents density peak clustering algorithm; N DPC Indicates the number of clusters.

4. The method for identifying key transmission sections of a power system taking into account static safety and stability constraints according to claim 1, characterized in that: Determining the importance of the power grid line by combining the shortest path of the topology importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model includes: in, is the edge betweenness of line ij, represents the frequency of the shortest path between nodes i and j passing through line ij, σ ij Represents the number of shortest paths between nodes i and j.

5. The method for identifying key transmission sections of a power system taking into account static safety and stability constraints according to claim 1, characterized in that: Taking the importance of the power grid line as the partition principle indicator, the initial transmission section set of the power system is constructed based on the Girvan-Newman splitting algorithm, including: Establish a directed graph, perform Girvan-Newman partitioning on the directed graph, and set the initial number of Girvan-Newman partitions k=2; The Girvan-Newman splitting algorithm is used to calculate the edge betweenness in the directed graph to obtain the partition connection line set L k ; For the partition tie line set L k The power flow consistency of each line in the kth transmission section set C is determined, and the lines with the power flow consistency are stored in the kth transmission section set C. k middle; Let k=k+1, and repeatedly perform the calculation of the edge betweenness in the directed graph using the Girvan-Newman splitting algorithm until the edge betweenness of the remaining lines is 0 or the number of Girvan-Newman partitions k reaches an iteration threshold; The initial transmission section set C = {C1, C2, C3, ... C k-1 }, where the order in the initial transmission section set represents the importance of the section.

6. The method for identifying key transmission sections of a power system taking into account static safety and stability constraints according to claim 5, characterized in that: Establishing a directed graph, performing Girvan-Newman partitioning on the directed graph includes: The transformers, generators and load nodes in the power grid are defined as vertices of a directed graph, and the transmission lines between the transformers, generators and load nodes are defined as directed edges, where the direction indicates the flow direction of electricity; Among them, the transmission line does not include the connecting lines within the substation; when there are multiple identical transmission lines at the same time, the identical transmission lines are merged into one edge.

7. The method for identifying key transmission sections of a power system taking into account static safety and stability constraints according to claim 1, characterized in that: According to the score coefficient and long-term allowable flow of the power system line, the weak points of the transmission section are identified from the initial transmission section set of the power system, including: Calculate the branching coefficient K of line ij under full wiring operation mode i-j and long-term allowable current carrying capacity L i-j ; Calculating the ratio of the line branching factor to the long-term allowable current carrying capacity, and constructing a determination condition based on the ratio; The sections in the initial transmission section set of the power system that meet the determination condition are defined as weak transmission section points.

8. The method for identifying key transmission sections of a power system taking into account static safety and stability constraints according to claim 7, characterized in that: Calculate the branching coefficient K of line ij under full wiring operation mode i-j and long-term allowable current carrying capacity L i-j The calculation process is as follows: |P i-j |≤L i-j |K i-j / L i-j |≤1 / |P g,sum | Among them, K i-j It represents the branch coefficient of line ij in the transmission section to which it belongs, P i-j represents the active power of line ij, represents the active power of transmission section g, which belongs to the initial section set C, U N Indicates rated voltage; I max Indicates the current limit of the line; T c represents the temperature correction factor; Indicates the power factor.

9. The method for identifying key transmission sections of a power system taking into account static safety and stability constraints according to claim 1, characterized in that: A data-guided and mechanism-driven static stability key transmission section identification model is established according to active power and long-term allowable current carrying capacity, and the weak points of the transmission section are input into the static stability key transmission section identification model to identify the static stability key sections, including: Obtain the active power P of the line ij on the transmission section g to which the weak gate of the transmission section belongs i-j and long-term allowable current carrying capacity L i-j ; Using active power P i-j The sum of P g,sum and long-term allowable current carrying capacity L i-j Establish the initial section static stability safety constraint: P g,sum >δ·L i-j Among them, δ is the overload rate threshold; Using active power P i-j The sum of P g,sum and long-term allowable current carrying capacity L i-j The sum of L g,ekl Set the section overload rate η: n=P g,sum / L g,ekl Among them, L g,ekl It indicates the sum of the long-term allowable current carrying capacity of the remaining lines in the transmission section g except the "checkpoint"; Based on the N-1 principle and the N-2 principle, the transmission section g whose section overload rate η exceeds the overload rate threshold is verified, and the transmission section g that passes the verification is defined as a static stability critical section; wherein, the verification process is as follows: P g,check >λ·L g,ekl α=P g,check / L g,ekl Among them, λ represents the safety margin, P g,check It represents the active power of transmission section g under the "N-1" or "N-2" planned maintenance mode, α represents the calculation margin, and the value range of α is [0, +∞].

10. The method for identifying key transmission sections of a power system taking into account static safety and stability constraints according to claim 9, characterized in that: When the value of the calculation margin is in [0,1), the calculation margin is corrected, and the correction process is as follows: Among them, α' represents the modified calculation margin, and λ represents the safety margin.

11. A power system key transmission section identification system considering static safety and stability constraints, characterized in that: include: An evaluation model building module is used to build a topology structure importance evaluation model based on a network constraint coefficient, wherein the topology structure importance evaluation model is used to analyze the importance of the topology structure of power system lines; An electrical coupling importance evaluation model considering spatial electrical characteristic clustering is constructed, wherein the electrical coupling importance evaluation model is used to analyze the importance of electrical coupling of power system lines; An importance calculation module, used to determine the importance of the power grid line by comprehensively considering the shortest path of the topological structure importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model; An initial transmission section module, used to construct an initial transmission section set of the power system based on the Girvan-Newman splitting algorithm, taking the importance of the power grid line as a partition principle indicator; A weak checkpoint identification module is used to identify the weak checkpoints of the power transmission section from the initial power transmission section set of the power system according to the score coefficient of the power system line and the long-term allowable flow; A static stable critical section identification module is used to establish a data-guided and mechanism-driven static stable critical transmission section identification model, and input the weak points of the transmission section into the static stable critical transmission section identification model to identify the static stable critical section; The section identification module is used to deduce the key sections corresponding to different power flow modes through the static stability key sections to complete the identification of key transmission sections.

12. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for identifying key transmission sections of an electric power system taking into account static safety and stability constraints as described in any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying key transmission sections of a power system taking into account static safety and stability constraints as described in any one of claims 1 to 10 is implemented.

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