Power grid key node identification method

Abstract

The invention provides a power grid key node identification method, which comprises the steps of obtaining power grid data, introducing virtual nodes to construct a directed weighted expansion network, and further constructing an electrical transmission transfer matrix and a self-connection matrix; fusing the electrical transmission transfer matrix and the self-connection matrix to obtain an electrical Google matrix; carrying out iterative calculation on the electrical Google matrix by adopting an improved PageRank algorithm to obtain an evaluation value of each node; and multiple types of evaluation indexes are superposed to evaluate each node, and a power grid key node identification result is output. According to the method, the electrical Google matrix improved based on the non-equal probability transmission characteristics is constructed, so that the adaptive expansion of the PageRank algorithm in a power system topology and operation state deep fusion scene is realized; furthermore, node evaluation values and various evaluation indexes are integrated, the coupling vulnerability of the nodes in multiple aspects can be reflected at the same time, and a reliable quantitative basis and decision support are provided for topological optimization of a sending-end power grid, construction of a security defense system and formulation of an operation control strategy.

Description

Technical Field

[0001] This application relates to the field of power technology, specifically to a method for identifying key nodes in a power grid. Background Technology

[0002] Faced with the challenges of high-proportion distributed energy access and complex grid structures, large-scale clean energy sources such as wind and solar power in the power system are transmitted to load centers through inter-regional transmission channels; among them, the sending-end grid is gradually becoming a key hub for clean energy consumption and inter-regional power transmission.

[0003] However, as the project progresses, the sending-end power grid is exhibiting new characteristics of "high penetration of new energy sources, high proportion of DC transmission, and high proportion of distributed resources." Its grid structure, operating characteristics, and security and stability are changing.

[0004] Against this backdrop, the safe and stable operation of the sending-end power grid faces severe challenges. On the one hand, the strong volatility and randomness of large-scale renewable energy sources make the power balance and frequency stability issues of the sending-end power grid increasingly prominent. On the other hand, the high proportion of power electronic equipment connected and the long-distance transmission of large-capacity power have significantly altered the inertial characteristics, short-circuit current levels, and voltage stability mechanisms of the power grid, making traditional stability analysis and weak link identification methods based on synchronous machine-dominated power grids increasingly inapplicable. Furthermore, existing technologies for planning and optimizing the operation of the sending-end power grid or analyzing single stability problems cannot systematically identify the key nodes and weak links affecting the multiple safety and stability issues of the sending-end power grid.

[0005] In summary, a systematic improvement is needed to the current methods for identifying weak points in the power grid at the sending end, so as to quickly and accurately locate the critical nodes of the power grid. Summary of the Invention

[0006] This application addresses the problems existing in the prior art by providing a method for identifying key nodes in the sending-end power grid that comprehensively considers the characteristics of new energy access, the grid topology, and multi-dimensional stability constraints. This method accurately locates the weak points in the system and provides a basis for decision-making regarding the architecture construction, operation mode arrangement, and stability control strategy formulation of the sending-end power grid, thereby ensuring the safety, reliability, and efficiency of cross-regional clean energy transmission.

[0007] To achieve the above objectives, the technical solution adopted in this application is as follows: This application provides a method for identifying critical nodes in a power grid, which includes the following steps: Acquire power grid topology data, electrical parameter data, and operating status data; Based on topology data, electrical parameter data, and operational status data, a directed weighted expansion network is constructed by introducing virtual nodes; An electrical transmission transfer matrix is ​​constructed based on the transmission transfer information of different types of nodes in a directed weighted extended network. Based on the equivalent impedance correlation characteristics between nodes, a self-connection matrix is ​​constructed to characterize the non-equal probability transmission probability between nodes. By integrating the electrical transmission transfer matrix and the self-connection matrix, an electrical Google matrix is ​​obtained; An improved PageRank algorithm is used to iteratively calculate the electrical Google matrix to obtain the evaluation value of each node; Based on the evaluation values ​​of each node, multiple evaluation indicators are superimposed to construct a node importance evaluation index. Each node is evaluated based on the node importance assessment index, and the key nodes of the power grid are identified based on the assessment results.

[0008] Optionally, the types of nodes in the directional weighted expansion network can be integrated, the power flow transmission direction correlation characteristics can be considered, and the line reactance weight characteristics can be considered. Node types include power nodes, intermediate nodes, and terminal nodes; Power nodes and / or terminal nodes supplement network connectivity through virtual nodes.

[0009] Optionally, virtual nodes are used to satisfy the connectivity characteristics of a directed weighted expansion network; The virtual node includes a first virtual node and a second virtual node; The first virtual node is connected to the power node and is used to provide the power node with a link path; The second virtual node is connected to the terminal node and is used to provide the outgoing path for the terminal node.

[0010] Optionally, when iteratively calculating the electrical Google matrix, the convergence criteria for the electrical Google matrix include: randomness, irreducibility, and aperiodicity.

[0011] Optionally, improvements to the PageRank algorithm include: After normalizing the electrical Google matrix, it replaces the calculation matrix of the PageRank algorithm.

[0012] Optionally, the initial iteration values ​​of the improved PageRank algorithm can be set based on the node's generator capacity and load size.

[0013] Optionally, elements of the electrical Google matrix also include the ratio of actual transmission transfer information in the extended network and the transmission transfer probability between nodes.

[0014] Optional, multiple evaluation indicators can be set according to various security and stability issues; Multiple safety and stability issues include at least short-circuit current surges, static voltage instability, and frequency inertia support.

[0015] Optional evaluation metrics include relative voltage change metrics and node degree values; The relative voltage change index is determined by the per-unit voltage value of the node, as well as the upper and lower voltage limits of the node.

[0016] Optionally, electrical parameter data includes line reactance parameters and inter-node equivalent impedance parameters; Operational status data includes node power generation data, node load power data, and power flow distribution data; Topology data includes node sets, line sets, and connection relationships between nodes and lines.

[0017] Compared with the prior art, this application has the following advantages: This application constructs an electrical Google matrix based on non-equal probability transmission characteristics by integrating directed weighted grid data from multiple types of power grids. This enables the adaptive extension of the PageRank algorithm in scenarios involving deep integration of power system topology and operating status, providing a unified foundation for structural vulnerability analysis of sending-end power grids with new characteristics. Furthermore, by combining node evaluation values ​​and multiple evaluation indicators, it can simultaneously reflect the coupling vulnerability of nodes in multiple aspects, achieving collaborative identification of multiple security and stability issues and precise location of key nodes. This provides reliable quantitative basis and decision support for topology optimization, security defense system construction, and operation control strategy formulation of sending-end power grids. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the method of an embodiment of this application; Figure 2 This is the static voltage index identification result in Comparative Example 1 of this application; Figure 3 This is the short-circuit current index identification result in Comparative Example 1 of this application; Figure 4 This is the identification result of the frequency inertia support index in Comparative Example 1 of this application; Figure 5 This is the network transmission efficiency value after the removal of the critical node in the IEEE-39 node in Comparative Example 1 of this application; Figure 6 It is the network transmission efficiency value after the removal of the critical node in the IEEE-118 node in Comparative Example 1 of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0022] It is worth noting that, unless otherwise specified, the methods used in this application are all conventional methods; and the raw materials and equipment used are all conventional commercially available products, and their sources are not specifically limited.

[0023] It should also be noted that, for ease of understanding, the method steps in the specific embodiments of this application are described in a certain order, but those skilled in the art can change the order of the steps according to actual needs, so this should not be used as a limiting condition; further, in the description of the following specific embodiments, the superscripts and subscripts of each parameter should be understood as distinguishing marks of similar identifiers in accordance with common interpretations, representing the parameters of the related or corresponding devices, and should not be understood as specific models or special marks.

[0024] Please see Figure 1 As shown in the figure, this embodiment provides a method for identifying key nodes in a power grid, which includes the following steps: 1. Modeling a directed weighted network considering node type and electrical characteristics; First, acquire the power grid's topology data, electrical parameter data, and operational status data. The electrical parameter data includes line reactance parameters and equivalent impedance parameters between nodes; the operational status data includes node power generation data, node load power data, and power flow distribution data; and the topology data includes node sets, line sets, and the connection relationships between nodes and lines.

[0025] Based on the data obtained above, a directed weighted expansion network is constructed by introducing virtual nodes. This directed weighted expansion network integrates node types, power flow direction correlation characteristics, and line reactance weight characteristics. Node types include power generation nodes, intermediate nodes, and terminal nodes; power generation nodes and / or terminal nodes complete network connectivity through virtual nodes. Specifically, the power network can be simplified into a complex network model composed of nodes and lines, where generator nodes and load nodes correspond to nodes in the complex network, and transmission lines correspond to edges. This basic power grid model can be implemented using an undirected, unweighted network. It is indicated that its node set is , The line set is , ;network The connection between the intermediate node and the line is through 3D correlation matrix Description, when When, it indicates a node For the line The endpoints; when When, it indicates a node Not a line The endpoints. Based on this undirected and unweighted network model, considering the power flow direction of the lines and introducing the line reactance value, it is extended into a directed weighted network that is closer to the actual power grid. , The weight set of the line is .

[0026] In order to describe the different transmission characteristics of each node in the actual operation of the power grid, this embodiment divides the power grid nodes into power generation nodes. Intermediate nodes and terminal nodes Furthermore, in the sending-end network, power supply nodes are generator nodes that output active power, terminal nodes are load nodes that only consume power, and intermediate nodes are nodes that transmit power, including tie nodes and some load nodes. However, it should be noted that power supply nodes, as the starting point of the network, only have outgoing lines and no incoming lines, while load nodes, as the ending point of the network, only have incoming lines and no outgoing lines. If subsequent algorithms are directly applied to rank node importance, the importance assessment results for these two types of nodes will be underestimated, which is inconsistent with the actual situation. Therefore, this embodiment also sets up virtual nodes to satisfy the connectivity characteristics of the directed weighted expansion network, specifically including a first virtual node. Second virtual node The first virtual node is connected to the power node to provide an input path for the power node; the second virtual node is connected to the terminal node to provide an output path for the terminal node. By introducing these two types of virtual nodes, the original weighted network is transformed into an extended network, which represents the link relationship matrix between nodes in the extended network. The elements are defined as follows: ; Where 1 indicates that node i and node j have a direct connection relationship, and 0 indicates that node i and node j do not have a direct connection relationship.

[0027] However, the link matrix can only describe the link relationships between nodes in an extended network and cannot characterize the transmission transfer characteristics between nodes. Therefore, based on the differences in transmission information of different types of nodes, the information values ​​characterizing the transmission transfer between a node and its linked nodes can be mapped to the link matrix, thus forming an electrical transmission transfer matrix characterizing the transmission transfer characteristics between nodes. The transmission transfer amount is defined according to the node type as follows: Power Node The amount of transmission transfer is: ; In the formula, It is a line The active power flowing through; It is a power node The set of outgoing lines; It is a power node The power generation capacity; Indicates the first The generating capacity of the generator set; This indicates that the maximum value among the power node capacities is taken.

[0028] The transmission and transfer amount of the intermediate node is: ; In the formula, For generator node set; For load node set; For generator nodes With load nodes The set of all possible paths between them; For generator nodes With load nodes Between The number of nodes in a path.

[0029] The amount of data transferred between the terminal node and the connected virtual load node is: ; In the formula, For terminal nodes The size of the load; This indicates that the maximum load value is selected.

[0030] Therefore, based on the transmission transfer information of different types of nodes in the directed weighted extended network, the aforementioned electrical transmission transfer matrix is ​​constructed. Subsequently, based on the equivalent impedance correlation characteristics between nodes, a self-connectivity matrix is ​​constructed to characterize the non-equal probability transmission probability between nodes. Specifically, in the PageRank algorithm, each node in the network transmits information with each other with equal probability. However, for a power grid with electrical characteristics, information transmission is not equally probable. The probability of information transmission needs to be determined based on the degree of coupling between nodes. Equivalent impedance can reflect the degree of coupling between nodes. Therefore, a self-connection matrix is ​​established based on equivalent impedance, as follows: Establish the link matrix based on equivalent impedance. It can characterize the probability of information transmission between any two nodes in a directed weighted network: ; In the formula, It is a node With nodes The equivalent impedance value between; It is the number of nodes in a directed weighted network model.

[0031] At the same time, by introducing 3D column vector and and combined with the link matrix Form a self-connection matrix that represents the possible probabilities of information transmission between nodes in the extended network. ,Right now: ; in: ; In the formula, It is the transmission probability between virtual nodes, and is related to... Relevant, specifically take Integrated electrical transmission transfer matrix With self-connection matrix Obtain the electrical Google matrix ,Right now: ; in, It is a contribution parameter. The electrical Google matrix contains two parts of information: the actual transmission transfer information ratio in the extended network (derived from the electrical transmission transfer matrix). (characterization) and possible transport transition probabilities (derived from the self-connection matrix) (Characteristics). The above matrix is ​​normalized to form an improved PageRank algorithm that evaluates node importance from the perspective of node transmission and transfer characteristics.

[0032] An improved PageRank algorithm is used to iteratively calculate the electrical Google matrix to obtain the evaluation value of each node. The improved PageRank algorithm is constructed by normalizing the electrical Google matrix and then replacing the calculation matrix of the PageRank algorithm. ; The initial iteration values ​​of this algorithm are set based on the node's generator capacity and load size. The specific iteration formula is as follows: ; In the formula, Indicates the first PageRank values ​​corresponding to the power grid nodes after the next iteration; Indicates the first The PageRank value of the virtual node after the next iteration; For nodes The generator capacity; For nodes The load size. When hour, This indicates the initial importance of a node, and the initial values ​​of the power grid nodes are set based on the type and capacity of the node's connecting elements.

[0033] When iteratively calculating the electrical Google matrix, the convergence criteria include: stochasticity, irreducibility, and aperiodicity. A detailed analysis follows: Property 1 (Randomness): The randomness of a matrix is ​​manifested in the fact that the sum of its rows equals 1, while... The first in row elements satisfy Therefore, the matrix It has randomness.

[0034] Property 2 (Irreducibility): The irreducibility of a matrix is ​​manifested in the fact that the graph formed by the non-zero elements of the matrix is ​​strongly connected, i.e., the matrix is ​​non-negative. Based on the definitions of electrical transmission transfer matrices and self-join matrices, we know that the matrix... The diagonal elements are 0, and the off-diagonal elements are non-negative, i.e., the matrix... Since it is a non-negative matrix, it is irreducible.

[0035] Property 3 (Aperiodicity): The aperiodicity of a matrix is ​​that there exists a positive integer... Make the nonnegative matrix The power is greater than 0. And the matrix... It is a non-negative matrix, and when hour, All elements of the matrix are greater than 0, therefore, the matrix It is non-periodic.

[0036] Based on the above analysis, the established electrical Google matrix satisfies the three properties of randomness, irreducibility, and non-periodicity. This indicates that the improved PageRank algorithm, after being modified from the perspective of node state and topology information transmission and transfer, is convergent and converges to a unique positive vector.

[0037] Based on the evaluation values ​​(i.e., PageRank values) of each node, a node importance evaluation index is constructed by overlaying multiple evaluation metrics. These multiple evaluation metrics are set according to various security and stability issues, including at least short-circuit current surges, static voltage instability, and frequency inertia support. Specifically, the multiple evaluation metrics include a relative voltage change index and a node degree value; the relative voltage change index is determined by the node's per-unit voltage value, as well as the node's upper and lower voltage limits.

[0038] In actual power grid operation, node voltages have upper and lower limits, but node voltages do not always operate at their rated voltages. Therefore, a voltage relative change index is defined. The formula used to measure the relative magnitude between node voltage and limit is as follows: ; In the formula, This is the upper limit of the node voltage, which can be 1.07 pu; This is the lower limit of the node voltage, which can be 0.93 pu; It is a node The per-unit voltage value.

[0039] voltage relative change index Node importance is measured from the perspective of node parameters in the current operating power grid. An improved PageRank algorithm measures node importance from the perspective of node state and topology information transmission and transfer, while the node degree reflects the node's local connectivity characteristics. To more comprehensively evaluate node importance, a new node importance index is proposed, integrating the relative voltage change index, the node's own degree, and the evaluation results of the improved PageRank algorithm. The importance of nodes is assessed using the following formula: ; In the formula, For nodes The degree value; For nodes PageRank value; For nodes The relative magnitude of the voltage.

[0040] Based on node importance assessment indicators Each node is evaluated, and the results of the evaluation are used to identify key nodes in the power grid. In this embodiment, the output is a ranking of the nodes in the network according to the values ​​of their node importance evaluation indicators.

[0041] Furthermore, this embodiment also includes a verification operation. Specifically, to verify the effectiveness of the key node identification results and the superiority of the method, this embodiment also establishes an attack simulation verification framework based on changes in power grid transmission efficiency, as follows: The disconnection of transmission lines disrupts the power transmission path of the power grid, thus affecting the grid's power transmission capacity. Network performance E assesses the ease of information transmission within a network from a global perspective. It is defined as the average of the sum of the reciprocals of the shortest path distances between nodes in an undirected, unweighted network, i.e.: ; In the formula, Indicates the number of nodes in the network; Represents any two nodes and The shortest distance between them.

[0042] For power transmission information of the power grid, drawing on the physical meaning of network efficiency, a directed weighted network is used. The model defines the power grid transmission efficiency index. Assess the power transmission capacity of the power grid, namely: ; In the formula, This indicates that in a directed weighted network model, the generator node... With load nodes Between The electrical distance of each transmission path, i.e., the sum of line weights; , These represent the number of elements in the generator node set and the load node set, respectively.

[0043] Power grid transmission efficiency is a metric that characterizes the power transmission capacity of a power grid from a global topology perspective. To verify how the transmission capacity changes after a power grid failure, network transmission efficiency is defined. This describes the change in the overall transmission capacity of the power grid relative to its normal operating state after it has suffered a fault attack. The network transmission efficiency after the power grid has suffered m fault attacks is as follows: ; In the formula, It is a power grid transmission efficiency indicator under normal power grid operation conditions; It is a power grid transmission efficiency indicator under m fault attacks.

[0044] By simulating the removal process of critical nodes, the effectiveness of the identification results can be verified based on changes in network transmission efficiency, providing reliable quantitative basis and decision support for topology optimization, security defense system construction, and operation control strategy formulation of the sending-end power grid.

[0045] Furthermore, this embodiment considers key nodes and weak points of the sending-end power grid under various security and stability indicators to systematically reveal the key structural nodes and weak links affecting multiple security and stability issues of the sending-end power grid in the context of "high penetration of new energy, high proportion of DC transmission, and high proportion of distributed resources". The proposed identification method integrating electrical characteristics and network topology provides a quantitative analysis tool for characterizing the vulnerability distribution of the sending-end power grid under different operating modes and fault scenarios, facilitating the optimization of power grid structure, construction of defense systems, and coordinated operation and control. Further, this embodiment also accurately identifies key nodes vulnerable to short-circuit current impacts, static voltage instability, and frequency inertia support, providing clear objectives and basis for the planning and construction, operation mode arrangement, stability control strategy formulation, and emergency defense system configuration of the sending-end power grid, thereby improving the grid's carrying capacity and security resilience for large-scale renewable energy access and inter-regional power transmission. In addition, this key node identification method can provide theoretical support and methodological guidance for research directions such as the robust architecture construction of the sending-end power grid, stability analysis of multi-DC feed-in systems, security assessment of new energy grid connection, location of weak links in inter-regional interconnected power grids, and coordinated defense for power system security and stability.

[0046] Comparative Example 1; Based on the above implementation method, the IEEE-39-bus system is used as a test case. By implementing the method of this application, which includes an improved PageRank algorithm, a voltage relative change index, and a combination of node degree values, the node importance index of the system is obtained. The node importance indices are sorted in descending order, and the top 10 non-generator nodes are selected as key nodes. The identification results of the method in this embodiment are compared with those of existing methods. Figure 2 , Figure 3 , Figure 4 As shown.

[0047] The identification results (sorting) of the IEEE-39 key nodes in this embodiment are shown in Table 1 below.

[0048] Table 1 As can be seen from the critical node identification results of the IEEE-39 node system, the critical nodes identified by the node importance index in this embodiment completely overlap with the critical node set identified by existing identification methods. After faults in nodes 6, 10, 19, 31, and 38, the system is more prone to static voltage collapse; after faults in nodes 20, 33, 34, 36, and 38, short-circuit current exceeding limits is more likely; after faults in nodes 4, 14, 26, 34, 36, and 39, the system is more prone to insufficient inertia support and frequency collapse. Among these, node 31 is a balancing node, playing a crucial role in maintaining system power balance; its fault will have a significant impact on the grid's static voltage. Node 19, as the only channel for power transmission from the generators of nodes 33 and 34, will also experience large-scale power flow shifts and voltage surges if it fails; therefore, compared to other nodes with weak safety and stability, these two nodes rank relatively high.

[0049] To verify the effectiveness of the critical node identification method, static deliberate attacks were performed on the IEEE-39 and IEEE-118 node systems. For the IEEE-39 node system, the critical nodes identified using the node importance index, the combination of random matrix theory and entropy theory, node importance, optimal scoring method, and vulnerability assessment index in this embodiment were sequentially removed. The network transmission efficiency changed as follows: Figure 5 As shown.

[0050] Depend on Figure 5 The changes in network transmission efficiency of the IEEE 39-node system show that: After removing key nodes identified by the combination of random matrix theory and entropy theory (based on node voltage data mining) and the weak point judgment index, the network transmission efficiency changes slowly using the method combining random matrix theory and entropy theory, with a final efficiency of 64.23% after all key nodes are removed. The network transmission efficiency of the weak point judgment index changes relatively slowly among the first seven key nodes. However, the removal of the eighth and ninth nodes, which correspond to the endpoints of generator direct-connection lines, causes a faster decrease in network transmission efficiency. After removing key nodes identified by the node importance index (based on node connection relationships) and the optimal scoring method, the corresponding network transmission efficiency changes more sharply, reaching 37.06% and 32.55% respectively after all key nodes are removed. In this embodiment, the network transmission efficiency changes even more significantly after removing key nodes identified by the node importance index (considering node voltage and inter-node transmission characteristics), reaching 27.49% after all key nodes are removed.

[0051] For the IEEE 118-node system, by sequentially removing the key nodes identified using the node importance index, the modified PageRank algorithm, the extended betweenness factor, and the improved MBCC-HITS method, the network transmission efficiency changes as follows: Figure 6 As shown.

[0052] Depend on Figure 6 As shown in the IEEE-118 node system, the network transmission efficiency decreases with the increasing number of critical nodes removed. After removing the first 20 critical nodes identified by the method proposed in this embodiment, the network transmission efficiency drops to 15.48%. In contrast, the network transmission efficiencies identified by the modified PageRank algorithm, improved MBC-HITS, and extended betweenness methods are 45.40%, 29.84%, and 20.6%, respectively, which are higher than the network transmission efficiency of the method in this patent. Compared to the modified PageRank algorithm, which considers node load importance, load capacity, and network topology, the identification method in this application, which incorporates transmission transfer information values ​​and node voltage values ​​for various types of nodes, can better identify the critical nodes of the system.

[0053] In summary, based on the changes in network transmission efficiency and the numerical analysis corresponding to the removal of all critical nodes, it can be seen that the identification method proposed in this application simultaneously considers node voltage and transmission transfer characteristics. The identification results can reflect the nodes that have a significant impact on power grid security and power supply capacity. Therefore, from the perspective of power grid transmission capacity, the effectiveness and superiority of the proposed identification method are further verified.

[0054] Finally, it should be noted that the above content is only used to illustrate the technical solution of this application, and is not intended to limit the scope of protection of this application. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of this application shall not depart from the substance and scope of the technical solution of this application.

Claims

1. A method for identifying key nodes in a power grid, characterized in that, Includes the following steps: Acquire power grid topology data, electrical parameter data, and operating status data; Based on the topology data, electrical parameter data, and operating status data, a directed weighted expansion network is constructed by introducing virtual nodes; Based on the transmission transfer information of different types of nodes in the directed weighted extended network, an electrical transmission transfer matrix is ​​constructed; Based on the equivalent impedance correlation characteristics between nodes, a self-connection matrix is ​​constructed to characterize the non-equal probability transmission probability between nodes. By fusing the electrical transmission transfer matrix and the self-connection matrix, an electrical Google matrix is ​​obtained; An improved PageRank algorithm is used to iteratively calculate the electrical Google matrix to obtain the evaluation value of each node; Based on the evaluation values ​​of each node, multiple evaluation indicators are superimposed to construct a node importance evaluation index; Each node is evaluated based on the aforementioned node importance assessment index, and the key node identification results of the power grid are output based on the assessment results.

2. The method for identifying key nodes in a power grid according to claim 1, characterized in that, The directed weighted extended network integrates node types, power flow transmission direction correlation characteristics, and line reactance weight characteristics. The node types include power nodes, intermediate nodes, and terminal nodes; The power node and / or the terminal node complete the network connectivity through the virtual node.

3. The method for identifying key nodes in a power grid according to claim 2, characterized in that, The virtual nodes are used to satisfy the connectivity characteristics of the directed weighted expansion network; The virtual node includes a first virtual node and a second virtual node; The first virtual node is connected to the power node and is used to provide a link-in path for the power node; The second virtual node is connected to the terminal node and is used to provide an outgoing path for the terminal node.

4. The method for identifying key power grid nodes according to claim 3, characterized in that, When iteratively calculating the electrical Google matrix, the convergence criteria for the electrical Google matrix include: randomness, irreducibility, and aperiodicity.

5. The method for identifying key nodes in a power grid according to claim 1, characterized in that, The improved PageRank algorithm is constructed by: After normalizing the electrical Google matrix, it replaces the calculation matrix of the PageRank algorithm.

6. The method for identifying key nodes in a power grid according to claim 5, characterized in that, The initial iteration values ​​of the improved PageRank algorithm are set based on the node's generator capacity and load size.

7. The method for identifying key nodes in a power grid according to claim 5 or 6, characterized in that, The elements of the electrical Google matrix also include the actual transmission transfer ratio in the extended network and the transmission transfer probability between nodes.

8. The method for identifying key nodes in a power grid according to claim 1, characterized in that, The various evaluation indicators are set according to multiple security and stability issues; The multiple safety and stability issues mentioned include at least short-circuit current surges, static voltage instability, and frequency inertia support.

9. The method for identifying key nodes in a power grid according to claim 8, characterized in that, The various evaluation indicators include relative voltage change indicators and node degree values; The relative voltage change index is determined by the per-unit voltage value of the node, as well as the upper and lower voltage limits of the node.

10. The method for identifying key nodes in a power grid according to claim 1, characterized in that, The electrical parameter data includes line reactance parameters and inter-node equivalent impedance parameters; The operational status data includes node power generation data, node load power data, and power flow distribution data. The topology data includes a set of nodes, a set of lines, and data on the connection relationships between nodes and lines.

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