Power system critical transmission section identification method considering static security stability constraints

Abstract

The application discloses a power system key power transmission section identification method considering static security and stability constraints, first, considering the topological structure characteristics, node electrical coupling relationship, power flow consistency and other factors, a line importance evaluation index is proposed, and an initial power transmission section identification model is constructed in combination with a Girvan-Newman division method; secondly, considering the power flow operation mode and the line load flow, an initial power transmission section weak 'card mouth' identification rule is formulated; then, from the perspective of static security and stability, a massive data generation result and a power transmission section weak 'card mouth' overload evaluation index system are used to establish a data-guided-mechanism-driven static stability key section identification method; finally, based on the static stability key section identification result, corresponding key leading labels are generated, quantitative and classified analysis of the initial power transmission section key degree under multiple power flow modes is realized, and then the key power transmission section identification is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system dispatching operation control, and particularly relates to a power system key power transmission section identification method considering static security and stability constraints. BACKGROUND

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

[0003] In the past power grid operation and dispatching process, the staff usually identifies the key section according to the work experience and administrative work area division principle. With the high proportion of renewable energy system access, the power flow diversification caused by the high proportion of renewable energy system access makes it difficult to meet the actual production and operation requirements by relying on expert experience and traditional offline analysis methods, and it is more and more difficult to accurately and efficiently identify the key power transmission section under different operation modes.

[0004] Overall, although the current method realizes the key section analysis considering the change of operation mode to some extent, there are still some problems to be solved:

[0005] 1) In order to ensure the perfection of the identification result, the initial section is usually screened by using the exhaustive or traversal method, which leads to a large number of sections, and the key section determination process is complicated and inefficient.

[0006] 2) The traditional key section identification method considering static security and stability constraints usually uses time domain simulation method to verify, which has insufficient adaptability to the variable power flow mode, and is usually used to represent the key section under the poor operation mode. SUMMARY

[0007] Based on the problems proposed in the above background technology, the purpose of the present application is to provide a power system key power transmission section identification method considering static security and stability constraints, which cuts into two aspects of qualitative and quantitative, considers the static security and stability constraints, and designs a reasonable framework for the identification of the key power transmission section of the power system, so as to improve the cognition of the operation state of the power grid and enhance the flexibility, openness, interactivity, economy and sharing of the power grid.

[0008] The present application is realized by the following technical scheme:

[0009] The present application provides a power system key power transmission section identification method considering static security and stability constraints, comprising the following steps:

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

[0011] The shortest path of the topological structure importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model are combined to determine the line importance of the power grid;

[0012] The line importance of the power grid is used as a partition principle index, and an initial power transmission section set of the power system is constructed based on a Girvan-Newman splitting algorithm;

[0013] A power transmission section weak notch is identified from the initial power transmission section set of the power system according to the score coefficient and the long-term allowable flow of the line of the power system;

[0014] A data-guided-mechanism-driven static stability key power transmission section identification model is established, and a static stability key section is identified by inputting the power transmission section weak notch into the static stability key power transmission section identification model;

[0015] The key power transmission section identification is completed by deducing the key section corresponding to different power flow modes through the static stability key section. In the above technical solution, first, the line importance evaluation index is proposed by considering the topological structure characteristics, node electrical coupling relationship, power flow consistency and other factors, and an initial power transmission section identification model is constructed by combining the Girvan-Newman division method; second, the initial power transmission section weak "notch" identification rule is formulated by considering the power flow operation mode and the line load flow; then, from the perspective of static security and stability, a result is generated based on massive data and an overload evaluation index system of the power transmission section weak "notch", which can ensure that the power transmission section is not missed and not selected incorrectly, while minimizing the number of initial section identification, reducing the complexity of the determination process, and being used for power transmission section identification of different scenarios of the power system.

[0016] Considering the static security and stability constraint condition, a data-guided-mechanism-driven static stability key section identification method is proposed, based on the static stability key section identification result, a corresponding key leading label is generated, the quantitative and categorical analysis of the initial power transmission section key degree under different power flow modes is realized, and then the key power transmission section identification is realized; it has strong applicability under different power flow modes, and greatly reduces the workload of traditional "N-1" and "N-2" security and stability checking. In addition, compared with the prior art, the method solves the problem that the traditional method is limited by expert experience and historical rules, and further improves the application value of the static stability key section identification in the theoretical field and actual engineering cases under different scenarios.

[0017] In an alternative embodiment, the method for constructing a topology importance evaluation model based on network constraint coefficient comprises:

[0018] calculating the network constraint coefficient of nodes at both ends of power grid line i-j, determining an index C reflecting the topology importance of node i based on the network constraint coefficient L (i), constructing a topology importance index T of line i-j through the index C L (i) i-j ; wherein 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] wherein C(i) is the network constraint coefficient of node i, node q is a neighboring node shared by nodes i and j, p ij represents the proportion of line i-j to all lines connected to node j; Γ i represents the set of neighboring nodes of node i.

[0023] In an alternative embodiment, the method for constructing an electrical coupling importance evaluation model considering spatial electrical characteristics clustering comprises:

[0024] taking the transmission distance of line and the line impedance as input features, normalizing the input features, calculating the normalized input features by using a density peak clustering algorithm to obtain a line clustering label:

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

[0026] calculating the electrical coupling importance according to the line clustering label:

[0027]

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

[0029] In an alternative embodiment, the shortest path of the topology importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model are integrated to determine the importance of the power grid line, which comprises:

[0030]

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

[0032] In an alternative embodiment, the importance of the power grid line is taken as a partition principle index, and an initial power transmission section set of the power system is constructed based on the Girvan-Newman splitting algorithm, which comprises:

[0033] A directed graph is established, the directed graph is partitioned by Girvan-Newman, and the initial Girvan-Newman partition number k is set to 2;

[0034] The edge betweenness in the directed graph is calculated by using the Girvan-Newman splitting algorithm, and a partition tie line set Lk is obtained;

[0035] The power flow consistency of each line in the partition tie line set Lk is determined, and the lines with consistent power flow are stored in the k-th obtained power transmission section set Ck;

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

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

[0038] In an alternative embodiment, the directed graph is established, and the directed graph is partitioned by Girvan-Newman, which comprises:

[0039] The transformers, units and load nodes in the power grid are defined as the vertices of the directed graph, and the power transmission lines between the transformers, units and load nodes are defined as the directed edges, and the direction represents the flow direction of the power;

[0040] wherein the power transmission lines do not include the connecting lines in the substation; when there are multiple identical power transmission lines at the same time, the identical power transmission lines are merged into one edge.

[0041] In an alternative embodiment, identifying the weak bottleneck of transmission section from the initial set of transmission sections of the power system according to the branch coefficient of the lines of the power system and the long-term allowable flow comprises:

[0042] calculating the branch coefficient K of the line i-j in the full connection operation mode i-j and the long-term allowable flow L i-j ;

[0043] calculating the ratio of the branch coefficient of the line and the long-term allowable flow, and constructing a judgment condition based on the ratio;

[0044] defining the transmission section satisfying the judgment condition in the initial set of transmission sections of the power system as the weak bottleneck of transmission section.

[0045] In an alternative embodiment, the calculation process of the branch coefficient K of the line i-j in the full connection operation mode i-j and the long-term allowable flow L i-j is as follows:

[0046]

[0047] |P i-j |≤L i-j

[0048]

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

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

[0051] In an alternative embodiment, establishing a data-guided-mechanism-driven static stability key transmission section identification model according to the active power and the long-term allowable flow, and inputting the weak bottleneck of transmission section into the static stability key transmission section identification model to identify the static stability key section comprises:

[0052] obtaining the active power P i-jand long-term allowable load flow L i-j ;

[0053] using the sum P i-j of active power P g,sum and long-term allowable load flow L i-j establish an initial cross-section static stability security constraint:

[0054] P g,sum > δ·L i-j

[0055] wherein δ is a threshold overload rate;

[0056] using the sum P i-j of active power P g,sum and long-term allowable load flow L i-j of the sum L g,ekl set the cross-section overload rate η:

[0057] η = P g,sum / L g,ekl

[0058] wherein L g,ekl represents the sum of long-term allowable load flows of the transmission cross-section g except for the "bayonet" line;

[0059] Based on the N-1 principle and the N-2 principle, the transmission cross-section g whose cross-section overload rate η exceeds the threshold overload rate is checked, and the transmission cross-section g passing the check is defined as a static stability key cross-section; wherein the checking process is as follows:

[0060] P g,check > λ·L g,ekl

[0061] α = P g,check / L g,ekl

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

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

[0064]

[0065] wherein α' represents the corrected calculation margin, and λ represents the safety margin.

[0066] The second aspect of the present application provides a power system key transmission cross-section identification system considering static security stability constraints, comprising:

[0067] An evaluation model construction module is configured to construct a topological structure importance evaluation model based on a network constraint coefficient, and the topological structure importance evaluation model is used to analyze the topological structure importance of a power system line.

[0068] An importance degree calculation module is configured to determine the importance degree of a 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 power transmission section module is configured to take the importance degree of the power grid line as a partition principle index, and construct an initial power system transmission section set based on a Girvan-Newman splitting algorithm.

[0070] A weak bottleneck identification module is configured to identify a power transmission section weak bottleneck from the initial power system transmission section set according to the score coefficient and the long-term allowable flow of the power system line.

[0071] A static stability key section identification module is configured to establish a data-guided-mechanism-driven static stability key power transmission section identification model, and input the power transmission section weak bottleneck into the static stability key power transmission section identification model to identify a static stability key section.

[0072] A section identification module is configured to deduce the key section corresponding to different power flow modes through the static stability key section, and complete the key power transmission section identification.

[0073] The third aspect of the present application provides an electronic device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the power system key power transmission section identification method considering static security and stability constraints when executing the computer program.

[0074] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the power system key power transmission section identification method considering static security and stability constraints.

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

[0076] (1) The line importance evaluation system considering the topological structure characteristics, node electrical coupling relationship and power flow consistency constructed in the application, combined with the initial power transmission section identification model constructed by the Girvan-Newman partition algorithm, compared with the prior art, can ensure that the power transmission section is not missed and not wrong, while minimizing the number of initial section identification, reducing the complexity of the determination process, and is used for power system power transmission section identification in different scenarios.

[0077] (2) The application considers the static security and stability constraint condition, and proposes a data-guided-mechanism-driven key section identification strategy, which has strong applicability under different power flow modes, and greatly reduces the workload of traditional 'N-1' and 'N-2' security and stability checking. In addition, compared with the prior art, the application better solves the shortcomings of the traditional method limited by expert experience and historical law, and further improves the application value of static stability key section identification in different scenes in the theoretical field and actual engineering cases. BRIEF DESCRIPTION OF DRAWINGS

[0078] In order to more clearly illustrate the technical scheme of the exemplary embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the application, and therefore should not be regarded as a limitation on the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor. In the drawings:

[0079] Figure 1 The overall framework diagram of the power system key power transmission section identification method provided for the embodiment 1 of the application.

[0080] Figure 2 The step flow chart of the power system key power transmission section identification method provided for the embodiment 1 of the application.

[0081] Figure 3 The schematic diagram of the influence of the power transmission section 'card slot' on the system provided for the embodiment 1 of the application.

[0082] Figure 4 The overall structure diagram of the static stability discrimination model based on data guidance-mechanism driving provided for the embodiment 1 of the application.

[0083] Figure 5 The new energy access IEEE 39-node test system provided for the embodiment 2 of the application.

[0084] Figure 6 The initial section overload rate schematic diagram of the new energy access IEEE 39-node system provided for the embodiment 2 of the application.

[0085] Figure 7The static security stability margin calculation result of the initial section of the IEEE 39 system to which the new energy provided by the embodiment 2 of the present application is connected is provided.

[0086] Figure 8 The specific position of the static stability key section of the IEEE 39 system to which the new energy provided by the embodiment 2 of the present application is connected is provided.

[0087] Fig. 9(a) is a schematic diagram of the overload rate of the initial section of the G power grid in the embodiment 3 of the present application.

[0088] Fig. 9(b) is a schematic diagram of the overload rate of the initial section of the L power grid in the embodiment 3 of the present application.

[0089] Fig. 10(a) is a schematic diagram of the static stability security margin of the power transmission section of the G power grid in the embodiment 3 of the present application.

[0090] Fig. 10(b) is a schematic diagram of the static stability security margin of the power transmission section of the L power grid in the embodiment 3 of the present application.

[0091] Fig. 11(a) is a schematic diagram of the identification result of the key power transmission section of the G power grid in the embodiment 3 of the present application.

[0092] Fig. 11(b) is a schematic diagram of the identification result of the key power transmission section of the L power grid in the embodiment 3 of the present application. DETAILED DESCRIPTION

[0093] In order to make the purpose, technical scheme and advantages of the present application more clear and apparent, the present application is further described in detail below with reference to the embodiments and drawings, and the schematic embodiments of the present application and the description thereof are only used to explain the present application, and do not limit the present application.

[0094] Embodiment 1

[0095] As shown in the following Figure 1 , the present application includes two aspects: the first aspect is an initial power transmission section identification method based on line importance and Girvan-Newman partitioning; and the second aspect is a key power transmission section identification method considering static security stability constraints.

[0096] Specifically, as shown in the following Figure 2 , the key power transmission section identification method of the power system considering static security stability constraints includes the following steps:

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

[0098] The shortest path of the topological structure important evaluation model and the edge betweenness of the electrical coupling importance evaluation model are used to determine the importance of the power grid line;

[0099] The importance of the power grid line is used as a partition principle index, and an initial power transmission section set of the power system is constructed based on a Girvan-Newman splitting algorithm;

[0100] A power transmission section weak notch is identified from the initial power transmission section set of the power system according to a score coefficient of a power system line and a long-term allowable flow;

[0101] A data-guided-mechanism-driven static stability key power transmission section identification model is established, and a static stability key section is identified by inputting the power transmission section weak notch into the static stability key power transmission section identification model;

[0102] The static stability key section is used to deduce a key section corresponding to different power flow modes, and key power transmission section identification is completed.

[0103] In the embodiment of the application, a line importance evaluation index is proposed by considering factors such as topological structure characteristics, node electrical coupling relationship and power flow consistency, an initial power transmission section identification model is constructed in combination with a Girvan-Newman division method, then, initial power transmission section weak 'notch' identification rules are formulated by considering power flow operation modes and line load flow, and finally, a massive data generation result and a power transmission section weak 'notch' overload evaluation index system are generated from the perspective of static security and stability, which can ensure that power transmission sections are not missed or misselected, and at the same time, the number of initial section identifications is reduced to the greatest extent, the complexity of the determination process is reduced, and the method is used for power transmission section identification of power systems in different scenarios.

[0104] Considering static security and stability constraints, a data-guided-mechanism-driven static stability key section identification method is proposed, based on the static stability key section identification result, a corresponding key leading label is generated, quantitative and categorical analysis of the key degree of the initial power transmission section under different power flow modes is realized, and then key power transmission section identification is realized; the method has strong applicability under different power flow modes, and greatly reduces the workload of traditional 'N-1' and 'N-2' security and stability checking; in addition, compared with the prior art, the method solves the problem that the traditional method is limited by expert experience and historical rules, and further improves the application value of the static stability key section identification in the theoretical field and actual engineering cases under different scenarios.

[0105] In an optional embodiment, the topological structure important evaluation model based on the network constraint coefficient comprises:

[0106] calculating a network constraint coefficient of nodes at both ends of a power grid line i-j, determining an index C reflecting the importance of topology of node i based on the network constraint coefficient L (i), constructing a topology importance index T of line i-j by the index C L (i) i-j ; wherein the calculation process is as follows:

[0107]

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

[0109] T i-j = C L (i) x C L (j) (3)

[0110] wherein C(i) is a network constraint coefficient of node i, node q is a neighboring node shared by nodes i and j, p ij represents the proportion of line i-j to all lines connected with node j; Γ i represents a set of neighboring nodes of node i.

[0111] It should be noted that p iq , p qj and p ij , by analogy, all represent the proportion of the corresponding line to all lines connected with the node. Based on the network constraint coefficient of the structural hole theory, a topology importance T i-j reflecting the importance of line is proposed from the perspective of the topological characteristics of the power grid. i-j The greater the value of T i-j , the more important the line i-j is in the system, the stronger the role of power flow transmission it bears, and the greater the possibility of causing security and stability problems.

[0112] In addition, the value of the network constraint coefficient C is between 0 and 1, and the smaller the value, the more important the target node is in the topology. To better represent the degree of influence of C, the negative logarithm of C to the base 2 is taken as the node importance index C L . As known from equation (2), when C(i) = 0.5, C L (i) = 1, that is, C(i) is bounded by 0.5, and below 0.5 has a positive effect on T i-j , otherwise it has a negative effect, and C(i) = 1 indicates that the node is a hanging node.

[0113] In an alternative embodiment, the electrical coupling importance evaluation model considering spatial electrical characteristic clustering comprises:

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

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

[0116] The electrical coupling importance is calculated according to the line clustering label:

[0117]

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

[0119] It should be noted that the embodiments of the present application adopt the density peak clustering algorithm (Density Peaks Clustering, DPC) to classify and evaluate the electrical coupling degree of the line, wherein the DPC considers the local density and relative distance of the node to describe the clustering result. The line impedance is used to represent the electrical distance between nodes, the transmission line distance is introduced to represent the electrical coupling degree of the line together with the line impedance, and the scoring principles of different grades of electrical coupling importance E i-j are formulated.

[0120] Further, in the density peak clustering algorithm, considering the data volume, the local density ρ(i) of the node i is calculated by using a Gaussian kernel:

[0121]

[0122] Wherein, d ij represents the Euclidean distance between nodes i and j; d c represents the neighborhood truncation distance, which can be artificially set according to specific requirements.

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

[0124]

[0125] Wherein, the relative distance calculation methods of the density maximum node and other nodes are described respectively. The ρ(i) and δ(i) of the clustering center are relatively high, and the results can be visualized and analyzed by using a decision diagram.

[0126] In addition, the decision value γ can also be defined by i Selecting the cluster center:

[0127] γ(i) = p i × δ i (6)

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

[0129] In an alternative embodiment, determining the line importance of the power grid based on the shortest path of the topology importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model comprises:

[0130]

[0131]

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

[0133] It should be noted that step 3 combines the topology importance degree proposed in step 1, the electrical coupling importance degree proposed in step 2, and the edge betweenness index, comprehensively considers the two types of line importance and the edge betweenness, and jointly constitutes the importance degree evaluation index of the lines of the power grid, so as to determine the line importance of the power grid based on the importance degree evaluation index.

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

[0135] Establishing a directed graph, performing Girvan-Newman partition on the directed graph, and setting the initial Girvan-Newman partition number k = 2;

[0136] Calculating the edge betweenness in the directed graph by using the Girvan-Newman splitting algorithm to obtain a partition tie line set Lk;

[0137] Determining the power flow consistency of each line in the partition tie line set Lk, and storing the line with consistent power flow in the kth obtained power transmission section set Ck;

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

[0139] An initial power transmission section set C={C1, C2, C3, … Ck-1} is constructed according to the iteration result, wherein the order in the initial power transmission section set represents the importance of the section.

[0140] It should be noted that the embodiment of the application takes the line importance degree obtained in step 3 as the partition principle index, and uses the calculation of the line edge betweenness as the basis to establish a directed graph. The Girvan-Newman iterative partition strategy considering the power flow consistency constraint is used to calculate the line importance degree of the power grid, and in the Girvan-Newman partition iteration, the removed edges are recorded, which define the inter-community tie, and finally, the set L k represents the tie lines between communities under the division of k communities, so as to obtain the partition tie set L k .

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

[0142] In an optional embodiment, the directed graph is established, and the Girvan-Newman partition of the directed graph comprises:

[0143] The transformer, unit and load node in the power grid are defined as the vertex of the directed graph, and the power transmission line between the transformer, unit and load node is defined as the directed edge, and the direction represents the flow direction of the power;

[0144] Wherein, the power transmission line does not include the connecting line in the transformer substation; when there are multiple same power transmission lines at the same time, the same power transmission lines are merged into one edge.

[0145] In an optional embodiment, the weak bottleneck of the power transmission section is identified from the initial power transmission section set of the power system according to the score coefficient and the long-term allowed flow of the line of the power system.

[0146] calculating the branch coefficient K of the line i-j under the full connection operation mode i-j and the long-term allowable load flow L i-j ;

[0147] calculating the ratio of the branch coefficient of the line to the long-term allowable load flow, and constructing a determination condition based on the ratio;

[0148] defining the power system initial transmission section set satisfying the determination condition as a transmission section weak notch.

[0149] It should be noted that step 5 is based on the initial transmission section set, and considers the power flow operation mode and line load flow to design the initial transmission section weak “notch” identification rule. Among them, the line with the maximum ratio of K i-j to L i-j is not overloaded, and the remaining lines in the transmission section satisfy the static security constraint, so the line meeting the condition is defined as a transmission section weak “notch”, and a schematic diagram of the influence of the transmission section “notch” on the system is shown in Figure 3 The transmission section is composed of lines L1 and L4, and breaking the transmission section “notch” line L1 has a greater impact on the entire system than breaking the non-“notch” line L4 in the section. When L1 is disconnected, its impact on the system includes but is not limited to line L2 and L4 overload, line L5 power flow reversal, etc. On the contrary, when L4 is disconnected, the system operation does not receive a large impact. Therefore, more attention should be paid to the operation state of the “notch” in the transmission section.

[0150] In an alternative embodiment, the calculation process of the branch coefficient K i-j and the long-term allowable load flow L i-j of the line i-j under the full connection operation mode 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] where K i-j represents the branch coefficient of the line i-j in the transmission section to which it belongs, P i-j represents the active power of the line i-j, Pgi represents the active power of the transmission section g, g belongs to the initial section set C, U N U represents the rated voltage; I max I represents the current limit of the line; T c T represents the temperature correction coefficient; cosφ represents the power factor.

[0156] It should be noted that the power system transmission section is distributed by the line impedance of each line, and the branch coefficient is used to define the ratio of the active power of any line in the transmission section to the section power, so the line branch coefficient under the full wiring operation mode can be approximately constant.

[0157] In the embodiment, the ambient temperature is 25℃, the conductor temperature is 80℃, and the temperature correction coefficient T c is affected by the ambient temperature and the conductor working temperature. In this case, it is taken as 1. The power factor is taken as 0.9 or 0.95 in the embodiment.

[0158] In an alternative embodiment, a data-guided-mechanism-driven static stability key transmission section identification model is established according to the active power and the long-term allowable carrying capacity, and the static stability key section is identified by inputting the transmission section weak bottleneck into the static stability key transmission section identification model, which comprises the following steps:

[0159] The active power P i-j of the line i-j in the transmission section g to which the transmission section weak bottleneck belongs is obtained. i-j The long-term allowable carrying capacity L i-j is obtained.

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

[0161] P g,sum > δ·L i-j (15)

[0162] Wherein, δ is the overload rate threshold;

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

[0164] η = P g,sum / L g,ekl (16)

[0165] Wherein, L g,ekl represents the sum of the long-term allowable carrying capacities of the lines other than the “bottleneck” in the transmission section g.

[0166] The power transmission section g whose overload rate η exceeds the overload rate threshold value is checked based on the N-1 principle and the N-2 principle, and the power transmission section g passing the check is defined as a static stability key section; wherein the checking process is as follows:

[0167] P g,check >λ·L g,ekl (17)

[0168] α=P g,check / L g,ekl (18)

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

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

[0171] It should be noted that step 6: taking the initial power transmission section weak “card slot” as input, considering the static security and stability constraints of the online security and stability operation of the power system, a data-guided-mechanism-driven static stability key power transmission section identification model is established. Based on the massive data generation results and the overload evaluation index system of the section weak “card slot”, the static stability key section identification model is established, and the overall structure of the data-guided-mechanism-driven static stability judgment model is as shown in Figure 4 .

[0172] Based on the weak card slot of the power transmission section obtained in step 5, the overload section is identified, and the initial section η exceeding the set threshold value is set as the overload section based on the historical data, and is included in the consideration category of the key power transmission section. Based on the “N-1” and “N-2” stability checking principles, the overload section is finely identified as a key section.

[0173] Wherein, the “N-1” principle refers to ensuring that the power grid can still operate stably in the case of failure of any single component (such as a line or a transformer); and the “N-2” principle refers to further ensuring that the power grid can still operate stably in the case of failure of any two components.

[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] Wherein α' represents the corrected calculation margin, and λ represents the safety margin.

[0177] It should be noted that generally α≥1 represents that the section is out of limit, in order to improve the visualization effect of α value in [0, 1), it needs to be corrected, and the corrected calculation margin is used for checking, α'≥0 represents that the power transmission section is overloaded, and does not pass the static security and stability check, otherwise it is determined to pass the check. The power transmission section that does not pass the "N-1" or "N-2" check is recorded as a static stability key section.

[0178] Further, the static stability key section is deduced to correspond to the key section of different power flow modes based on the static stability key power transmission section identification model established according to the active power and the long-term allowable flow capacity, the weak keyhole of the power transmission section is input into the static stability key power transmission section identification model to identify the static stability key power transmission section identification model, and the static stability key power transmission section identification model is identified. The power transmission section affected by static stability is found, and the corresponding key leading label is generated, and the key power transmission section identification is completed.

[0179] The key section identified by considering the static stability security constraint is the key power transmission section under the static security perspective, and then it can be deduced to the key section corresponding to different power flow modes.

[0180] After the power system is disturbed, if the power flow distribution changes and presents different operating modes, it is necessary to further determine the corresponding key section of the target system under different power flow modes, and then analyze the operating characteristics of the key power transmission section under the static stability security constraint according to the section key identification method under the static security perspective, and determine the key leading label of different key power transmission sections based on the static stability security theme, that is, whether the key section is easy to be affected by static stability under a specific operating mode.

[0181] Embodiment 2

[0182] In order to verify the effectiveness and adaptability of the key power transmission section identification framework proposed in embodiment 1 of the application, the IEEE 39-node system with new energy access is selected as a test example in embodiment 2 of the application. First, based on the IEEE 39-node system, the traditional units connected between nodes 35 and 37 are replaced by wind power units (W) and photovoltaic units (P), respectively, to test the proposed method and verify the comprehensive performance of the proposed key section identification method. Considering the IEEE 39-node system with new energy access, the system diagram is shown in Figure 5 .

[0183] Result analysis:

[0184] The present application modifies the conventional unit output according to the standard working condition ± 15% fluctuation, and the uncertain wind and light field station generates scenes according to the wind and light characteristics ± 30% disturbance, a total of 5000 power flow samples. The power flow samples of the scenes in which the direction of each line flow is completely consistent are removed, and 349 typical scenes are obtained. The change of the flow direction of each line and the occurrence probability are recorded, which can be seen in Table 1 and Table 2.

[0185] Table 1 Line importance ranking of IEEE 39-node system with new energy access

[0186]

[0187] Table 2 Fixed line of power flow direction of IEEE 39-node system with new energy access in different scenes

[0188]

[0189]

[0190] The line importance and edge betweenness are considered to quantify and rank the importance of the power line, which can be seen in Table 3. Considering the influence of unit output fluctuation on the flow direction of the line, the improved Girvan-Newman partition algorithm based on the present application is used to traverse the initial power transmission section. In addition, the consistency of the power flow of each cut set under different operation modes is analyzed for different scenes, and the probability of power flow consistency is calculated. The cut set with a power flow consistency rate of more than 70% is regarded as the initial power transmission section, so the line with a flow direction change probability of more than 30% is preferentially removed. Therefore, in the verification stage, the section containing 3-4, 8-9, 9-39 and 17-27 lines is removed.

[0191] Table 3 Change and occurrence probability of non-fixed line of power flow direction of IEEE 39-node system with new energy access in different scenes

[0192]

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

[0194] Table 4 Identification results of initial sections of IEEE 39-node system with new energy access

[0195]

[0196] On the basis of the discovered initial power transmission sections, key power transmission sections susceptible to static security and stability are identified, taking into account static security and stability constraints. Since all lines in the IEEE 39-node system are single-circuit lines, the initial sections containing no more than 2 lines will be operated in separate zones 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 containing no more than 2 lines in the IEEE 39-node system with new energy access

[0198]

[0199]

[0200] As shown in Table 5, the key section identification method considering unit output uncertainty and power flow consistency constraints identified 1 "N-1" key section and 11 "N-2" key sections. At the same time, considering the influence of excessive frequency of power flow direction change or power flow direction opposite line limit, the key sections that do not meet the power flow consistency constraint are excluded. Next, based on formula (14), the "card slot" of each initial section containing more than 2 lines is calculated, and based on formula (16), the static stability risk section is identified. The initial sections containing more than 2 lines and the corresponding "card slot" can be seen in Table 6.

[0201] Table 6 Identification results of initial sections and card slots in the improved IEEE 39-node system

[0202]

[0203] The initial section overload rate evaluation index η of the IEEE 39-node system with new energy access is shown in Table 7. Figure 6

[0204] Considering the section overload rate evaluation index η, the initial sections with η not less than the threshold value of 0.7 (marked as orange sections) are subjected to "N-1" or "N-2" static stability fine checking. Based on formulas (18) and (19), the static stability safety margin of the initial out-of-limit section is calculated, and the safety margin of the non-out-of-limit section is calculated as a control group, to compare the reliability and applicability of the proposed static stability determination criterion. According to expert experience, the safety margin λ in formula (17) is set to 0.8, and the static security and stability margin calculation results of the initial sections of the IEEE 39-node system with new energy access are shown in Table 8. Figure 7

[0205] Figure 7 ​​​As shown, the four initial out-of-limit sections identified by the IEEE 39-node system with new energy access all exceed the static security stability margin; therefore, the four initial out-of-limit sections can be summarized as static stability key sections. As a comparison, the static security margins of the remaining 24 initial non-out-of-limit sections are all non-out-of-limit. As above, the static stability determination criterion proposed in the present application has strong applicability, can be used as an important step for identifying static stability key sections, and greatly reduces the workload of traditional "N-1" and "N-2" stability checking. The positions of the static security stability sections in the improved IEEE 39 system are as follows Figure 8 .

[0206] Embodiment 3

[0207] To verify the effectiveness and adaptability of the method proposed in Embodiment 1 of the present application in actual power grid applications, Embodiment 3 of the present application selects the power grids of G City and L City (hereinafter referred to as G power grid and L power grid) in a certain place in Southwest China as the test target. Among them, the G power grid is composed of 30 nodes and 37 transmission lines above 220 kV, with a network supply load of 3286 MW, including 1 thermal power unit, 5 hydropower units, 4 wind power units, a total power supply installed capacity of 5502 MW, and a new energy installed capacity ratio of 18.2%. The L power grid is composed of 51 nodes and 60 transmission lines above 220 kV, with a network supply load of 5835 MW, a total of 11 hydropower units, and a total power supply installed capacity of 3550 MW.

[0208] Result analysis:

[0209] Power grid dispatching and operation personnel usually select the summer large mode with relatively poor scenes in the typical operation mode for security and stability checking, so this paper first identifies the key transmission sections for the summer large operation mode of the G power grid and the L power grid.

[0210] Firstly, the initial transmission section set under the summer large operation mode of the G power grid and the L power grid is obtained, and the initial section "card slot" is obtained, which can be seen in Table 7 and Table 8.

[0211] Table 7 Identification results of initial sections and card slots of G power grid in summer large mode

[0212]

[0213] Table 8 Identification results of initial sections and card slots of L power grid in summer large mode

[0214]

[0215] As shown in Table 7 and Table 8, considering the line importance and community division strategy, the G power grid and the L power grid respectively obtain 10 and 15 initial transmission sections, which to some extent reduces the workload of identifying the key degree of the section.

[0216] The initial cross-section overload rate η of the two regional power grids can be visualized as shown in FIG. 9(a) and FIG. 9(b). Considering the cross-section overload rate η, the initial cross-sections with η not less than the threshold value 0.7 (marked as orange cross-sections) are subjected to “N-1” or “N-2” static stability fine checking.

[0217] The “N-1” or “N-2” stability checking is performed on each initial cross-section “card slot”, the static stability safety margin of the initial out-of-limit cross-section is calculated, and the safety margin of the non-out-of-limit cross-section is calculated as a control group, so as to compare the reliability and applicability of the static stability determination criterion. Among them, the safety margin of the L power grid transmission cross-section 4 is as high as 1.72, that is, no matter how the safety margin λ is set, this cross-section will be out of limit. In addition, according to the expert experience, λ is set to 0.8, and the calculation results of the static safety stability margin of the other cross-sections of the two regional power grids are shown in FIG. 10(a) and FIG. 10(b).

[0218] As can be seen from the figure, except that the calculation margin of the G power grid transmission cross-section 5 is lower than the preset λ, the remaining 9 initial out-of-limit cross-sections of the two power grids do not pass the static safety stability checking; that is, except for the G power grid transmission cross-section 5, the remaining 9 initial out-of-limit cross-sections are summarized as static stability key cross-sections. As a control, the 14 initial non-out-of-limit cross-sections all pass the static safety checking. Therefore, the static stability determination criterion proposed in this paper has strong applicability in the actual power grid, and can be used as a prior step of “N-1” and “N-2” stability checking to reduce the workload of identifying static stability key cross-sections.

[0219] Combined with the static stability determination results, the identification results of the key transmission cross-sections of the two regional power grids are shown in FIG. 11(a) and FIG. 11(b), and are compared with the key transmission cross-sections monitored by experts in the actual operation mode of the two regional power grids. For details, refer to Table 9 and Table 10.

[0220] Table 9 Identification results of static stability key cross-sections of G power grid in summer large mode

[0221]

[0222] As shown in Table 9, there are 2 key transmission cross-sections in the actual operation of the G power grid, and based on the method proposed in this paper, 2 static stability key cross-sections (11-20+11-25+11-6, 20-15+25-15) are newly found in addition to the above 2 key cross-sections (17-24+28-19, 8-26). While avoiding missing selection, the cross-sections with potential risks can also be summarized to obtain better monitoring effect.

[0223] Table 10 Identification results of static stability key cross-sections of L power grid in summer large mode

[0224]

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

[0226] The present application proposes a model framework for static stability key section identification of a power system. Under this framework, first, the line importance evaluation index is proposed by considering the topological structure characteristics, node electrical coupling relationship, power flow consistency and other factors, and an initial power transmission section identification model is constructed by combining the Girvan-Newman division method; second, the initial power transmission section weak “card slot” identification rule is formulated by considering the power flow operation mode and line load flow; third, the data-guided-mechanism-driven static stability key section identification method is established based on the massive data generation results and the power transmission section weak “card slot” overload evaluation index system from the perspective of static security and stability; then, the corresponding key leading labels are generated based on the static stability key section identification results, and the quantitative and categorical analysis of the key degree of the initial power transmission section under multiple power flow modes is realized, and then the key power transmission section identification is realized; finally, the IEEE 39-node system with new energy access and two city-level power grids in southwest China are taken as examples to analyze the identification effect of the static stability key section of the power system. The results verify the effectiveness of the proposed framework and method. The proposed method helps researchers to avoid missing the key power transmission section while summarizing the key sections with potential risks, and to obtain a better monitoring effect.

[0227] (1) The line importance evaluation system constructed by considering the topological structure characteristics, node electrical coupling relationship and power flow consistency can minimize the number of initial section identification and reduce the complexity of the determination process while ensuring that the power transmission section is not missed or misselected, providing protection for identifying the power transmission section in more complex and variable power system operation modes.

[0228] (2) Considering the static security and stability constraints, the data-guided-mechanism-driven key section identification strategy is proposed, which has strong applicability under different power flow modes, and greatly reduces the workload of traditional “N-1” and “N-2” security and stability checking. This model can be used as an important step for static stability key section identification in different scenarios in engineering and theoretical fields and provide certain reference significance.

[0229] Example 4

[0230] The embodiment 4 of the present application provides a power system key power transmission section identification system considering static security and stability constraints, comprising:

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

[0232] An importance degree calculation module is configured to comprehensively determine the importance degree of a power grid line by using the shortest path of the topological structure importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model.

[0233] An initial power transmission section module is configured to use the importance degree of the power grid line as a partition principle index, and construct an initial power transmission section set of a power system based on a Girvan-Newman splitting algorithm.

[0234] A weak keyhole identification module is configured to identify a power transmission section weak keyhole from the initial power transmission section set of the power system according to the score coefficient and long-term allowable flow of a power system line.

[0235] A static stability key section identification module is configured to establish a data-guided-mechanism-driven static stability key power transmission section identification model, and input the power transmission section weak keyhole into the static stability key power transmission section identification model to identify a static stability key section.

[0236] A section identification module is configured to deduce a key section corresponding to different power flow modes through the static stability key section, and complete key power transmission section identification.

[0237] Embodiment 5

[0238] The embodiment 5 of the present application provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the power system key power transmission section identification method considering static security and stability constraints when executing the computer program.

[0239] Embodiment 6

[0240] The embodiment 6 of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the power system key power transmission section identification method considering static security and stability constraints.

[0241] The above detailed description of the specific embodiments of the present application has been given to understand the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for identifying critical transmission sections in a power system considering static security and stability constraints, characterized in that, Includes the following steps: A topology importance evaluation model based on network constraint coefficients is constructed, which is used to analyze the importance of power system line topology; an electrical coupling importance evaluation model considering spatial electrical characteristic clustering is constructed, which is used to analyze the importance of electrical coupling of power system lines. The edge betweenness number of line ij is obtained based on the shortest path between nodes i and j, and the importance of the power grid line is obtained by combining the importance of topology structure and electrical coupling. Using the importance of the power grid lines as the partitioning principle index, an initial set of power transmission sections of the power system is constructed based on the Girvan-Newman splitting algorithm; Weak points in the transmission sections are identified from the initial set of transmission sections of the power system based on the branch coefficient and long-term allowable current carrying capacity of the power system lines. A data-guided, mechanism-driven identification model for statically stable critical transmission sections is established based on active power and long-term allowable current carrying capacity. The weak points of the transmission sections are input into the statically stable critical transmission section identification model to identify the statically stable critical sections. By deriving the key sections corresponding to different power flow modes from the statically stable key sections, the identification of key transmission sections is completed.

2. The method for identifying key transmission sections of a power system considering static security and stability constraints according to claim 1, characterized in that, Key evaluation models for topology structures based on network constraint coefficients include: Calculate power grid lines i - j The network constraint coefficients of the two endpoints are used to determine the reflecting nodes. i Indicators of topological importance C L ( i ), through the aforementioned indicators C L ( i ) Constructing routes i - j Topological importance index T i-j The calculation process is as follows: ； in, C ( i ) is a node i Network constraint coefficients, nodes q For nodes i and j Shared adjacent nodes, p ij Indicates the line i - j Occupy Nodes j The proportion of all connected lines; Γ i Represents a node i The set of adjacent nodes.

3. The method for identifying key transmission sections of a power system considering static security and stability constraints according to claim 1, characterized in that, The construction of an electrical coupling importance evaluation model that considers spatial electrical characteristic clustering includes: Using transmission distance and line impedance as input features, the input features are normalized. Density peak clustering algorithm is then used to calculate the normalized input features to obtain line cluster labels. ； The importance of electrical coupling is calculated based on the line clustering labels: ； in, D ij Indicates the line i - j The transmission distance, r ij and x ij They represent line resistance and reactance, respectively, and DPC(·) represents the density peak clustering algorithm; N DPC This indicates the number of clusters.

4. The method for identifying key transmission sections of a power system considering static security and stability constraints according to claim 1, characterized in that, The determination of the importance of power grid lines by combining the shortest path of the aforementioned topology importance evaluation model and the edge betweenness number of the aforementioned electrical coupling importance evaluation model includes: ； in, For the line i - j The number of edge betweennesses, Represents a node i and j The shortest path between them via the line i - j frequency, σ ij Represents a node i and j The number of shortest paths between them Indicates the line i - j The topological importance index, Indicates the importance of electrical coupling.

5. The method for identifying key transmission sections of a power system considering static security and stability constraints according to claim 1, characterized in that, Using the importance of the power grid lines as the partitioning principle index, the initial set of power system transmission sections is constructed based on the Girvan-Newman splitting algorithm, including: Construct 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 numbers in the directed graph to obtain the set of partitioned connection lines. L k ; For the set of partitioned contact lines L k The power flow consistency of each route is determined, and the routes with consistent power flow are stored in the next step. k The resulting set of power transmission sections C k middle; make k = k +1, and repeat the calculation of edge betweenness numbers in the directed graph using the Girvan-Newman splitting algorithm until the edge betweenness number of the remaining lines is 0 or the number of Girvan-Newman partitions is reached. k The iteration threshold has been reached; Construct an initial set of transmission sections based on the iterative results. C ={ C 1, C 2, C 3,… C k-1 }, where the order in the initial set of transmission sections represents the importance of the sections.

6. The method for identifying key transmission sections of a power system considering static security and stability constraints according to claim 5, characterized in that, Constructing a directed graph and performing Girvan-Newman partitioning on the directed graph includes: Transformers, generating units, and load nodes in the power grid are defined as vertices of a directed graph, and transmission lines between transformers, generating units, and load nodes are defined as directed edges, with the direction indicating the direction of power flow. Transmission lines do not include connecting lines within substations; when multiple identical transmission lines exist simultaneously, the identical transmission lines are merged into one edge.

7. The method for identifying key transmission sections of a power system considering static security and stability constraints according to claim 1, characterized in that, Based on the power system line score coefficients and long-term allowable flow rates, the weak points in the transmission sections identified from the initial set of power system transmission sections include: Calculate the circuit under fully connected operation mode i - j branch coefficient K i-j and long-term allowable carrying capacity L i-j ; Calculate the ratio of the line branch coefficient to the long-term allowable current carrying capacity, and construct the judgment criteria based on the ratio; The set of initial power transmission sections in the power system that meets the above criteria is defined as the weak points of the power transmission section.

8. The method for identifying key transmission sections of a power system considering static security and stability constraints according to claim 7, characterized in that, Calculate the circuit under fully connected operation mode i - j branch coefficient K i-j and long-term allowable carrying capacity L i-j The calculation process is as follows: ； in, K i-j Indicates the line i - j The branching coefficient of its respective transmission section, P i-j Indicates the line i - j active power, Indicates the transmission section g active power, g Belonging to the initial cross section set C , U N Indicates the rated voltage; I max Indicates the current limit of the circuit; T c Indicates the temperature correction factor; cos φ This represents the power factor.

9. The method for identifying key transmission sections of a power system considering static security and stability constraints according to claim 1, characterized in that, A data-guided, mechanism-driven identification model for statically stable critical transmission sections is established based on active power and long-term allowable current carrying capacity. The weak points of the transmission sections are input into the statically stable critical transmission section identification model to identify the following statically stable critical sections: Obtain the transmission section to which the weak point belongs. g Online i - j active power P i-j and long-term allowable carrying capacity L i-j ; Utilizing active power P i-j The sum and long-term allowable carrying capacity L i-j Establish initial cross-sectional static stability safety constraints: ； in, δ The reload rate threshold; Utilizing active power P i-j The sum and long-term allowable carrying capacity L i-j The sum Set the cross-sectional load rate η : ； in, L g,ekl Indicates the transmission section g The sum of the long-term allowable carrying capacity of all lines except for the "checkpoint"; based on N -1 principle and N -2 principle for cross-sectional heavy load rate η Transmission sections exceeding the overload rate threshold g A verification process is performed, and the transmission section g that passes the verification is defined as the statically stable critical section; the verification process is as follows: ； in, λ Indicates safety margin, P g,check express" N -1" or " N -2” Transmission section under planned maintenance mode g active power, α Indicates the calculation margin. α The range of values ​​is [0, +∞].

10. The method for identifying key transmission sections of a power system considering static security and stability constraints according to claim 9, characterized in that, When the calculated margin is in the range [0, 1), the calculated margin is corrected, and the correction process is as follows: ； in, α 'Indicates the adjusted calculation margin, λ Indicates the safety margin.

11. A power system critical transmission section identification system considering static security and stability constraints, characterized in that, A method for identifying critical transmission sections of a power system considering static security and stability constraints as described in any one of claims 1 to 10, comprising: The evaluation model construction module is used to construct a topology importance evaluation model based on network constraint coefficients, which is used to analyze the importance of power system line topology; and to construct an electrical coupling importance evaluation model considering spatial electrical characteristic clustering, which is used to analyze the importance of electrical coupling of power system lines. The importance calculation module is used to determine the importance of power grid lines by combining the shortest path of the topology importance evaluation model and the edge betweenness of the electrical coupling importance evaluation model. The initial transmission section module is used to construct the initial transmission section set of the power system based on the Girvan-Newman splitting algorithm, with the importance of the power grid lines as the partitioning principle index. The weak point identification module is used to identify weak points in the transmission sections from the initial set of transmission sections of the power system based on the score coefficient of the power system line and the long-term allowable flow. The static steady-state critical section identification module is used to establish a data-guided and mechanism-driven static steady-state critical transmission section identification model. The weak points of the transmission section are input into the static steady-state critical transmission section identification model to identify the static steady-state critical section. The section identification module is used to deduce the key sections corresponding to different power flow modes through the static stable key sections, and complete the identification of key transmission sections.

12. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for identifying key transmission sections of a power system that takes into account static security and stability constraints as described in any one of claims 1 to 10.

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

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