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CN-122022148-A - Power grid key node identification method

CN122022148ACN 122022148 ACN122022148 ACN 122022148ACN-122022148-A

Abstract

The application provides a method for identifying key nodes of a power grid, which comprises the steps of obtaining power grid data, introducing virtual nodes to construct a directional weighted expansion network, further constructing an electric transmission transfer matrix and a self-connection matrix, fusing the electric transmission transfer matrix and the self-connection matrix to obtain an electric Google matrix, carrying out iterative computation on the electric Google matrix by adopting an improved PageRank algorithm to obtain evaluation values of all the nodes, superposing multiple evaluation indexes to evaluate all the nodes, and outputting a power grid key node identification result. The application realizes the adaptability expansion of the PageRank algorithm in the power system topology and running state deep fusion scene by constructing the electric Google matrix based on the non-equal probability transmission characteristic improvement, further, the node evaluation value and the multiple kinds of evaluation indexes are synthesized, the coupling vulnerability of the nodes in multiple aspects can be reflected at the same time, and reliable quantitative basis and decision support are provided for the topology optimization of a power grid at a transmitting end, the construction of a security defense system and the formulation of an operation control strategy.

Inventors

  • YUAN SHAOJUN
  • CHEN DONGYANG
  • YU LIQIANG
  • DUAN MINGHUI
  • SHI SHAOTONG
  • ZHANG LEI
  • ZHAI YANNAN
  • WANG ZUOMIN
  • LIU ZHENYU
  • YIN ZHAOLEI
  • WANG HONGLIANG
  • HAN YU
  • WANG YONG
  • YU BAOXIN
  • YANG MANMAN
  • LIU SICUI

Assignees

  • 国网冀北电力有限公司承德供电公司

Dates

Publication Date
20260512
Application Date
20260127

Claims (10)

  1. 1. The method for identifying the key nodes of the power grid is characterized by comprising the following steps of: Obtaining topological structure data, electrical parameter data and running state data of a power grid; Based on the topological structure data, the electrical parameter data and the running state data, introducing virtual nodes to construct a directional weighted expansion network; constructing an electric transmission transfer matrix based on transmission transfer information of different types of nodes in the directional weighted expansion network; Constructing a self-connection matrix for representing the non-equal probability transmission probability among nodes based on the equivalent impedance association characteristic among the nodes; fusing the electric transmission transfer matrix and the self-connection matrix to obtain an electric Google matrix; performing iterative computation on the electric Google matrix by adopting an improved PageRank algorithm to obtain evaluation values of all nodes; based on the evaluation values of the nodes, superposing multiple types of evaluation indexes to construct node importance evaluation indexes; And evaluating each node based on the node importance evaluation index, and outputting a power grid key node identification result according to the evaluation result.
  2. 2. The method for identifying key nodes of a power grid according to claim 1, wherein the directional weighted expansion network merges node types, tidal current transmission direction association characteristics and line reactance weight characteristics; the node type comprises a power source node, an intermediate node and a terminal node; The power supply node and/or the terminal node complement network connectivity through the virtual node.
  3. 3. The method for identifying key nodes of a power grid according to claim 2, wherein the virtual nodes are used for meeting the connectivity characteristics of the directional weighted expansion network; The virtual nodes comprise a first virtual node and a second virtual node; The first virtual node is connected with the power supply node and is used for providing a link-in path for the power supply node; the second virtual node is connected with the terminal node and is used for providing a chain-out path for the terminal node.
  4. 4. The method for identifying the key nodes of the power grid according to claim 3, wherein the convergence judging conditions of the electric Google matrix comprise a randomness condition, an irreducibility condition and an aperiodic condition when the electric Google matrix is subjected to iterative computation.
  5. 5. The method of claim 1, wherein the improved PageRank algorithm construction comprises: and after the electric Google matrix is normalized, replacing a calculation matrix of the PageRank algorithm.
  6. 6. The grid key node identification method of claim 5, wherein the initial iteration value of the modified PageRank algorithm is set based on generator capacity and load size of the node.
  7. 7. The method for identifying key nodes of a power grid according to claim 5 or 6, wherein the elements of the electrical Google matrix further comprise expanding the actual transmission transition information ratio in the network and the transmission transition probability between nodes.
  8. 8. The method for identifying key nodes of a power grid according to claim 1, wherein the multiple types of evaluation indexes are correspondingly set through multiple safety and stability problems; The multiple safety and stability problems at least comprise short circuit current impact, static voltage instability and frequency inertia support.
  9. 9. The method of claim 8, wherein the multiple types of evaluation metrics include a voltage relative change metric and a node metric; The voltage relative change index is determined by the per unit value of the node voltage, and the upper voltage limit and the lower voltage limit of the node.
  10. 10. The method for identifying key nodes of a power grid according to claim 1, wherein the electrical parameter data comprises line reactance parameters and equivalent impedance parameters between nodes; the running state data comprise node power generation power data, node load power data and tide distribution data; The topological structure data comprises node sets, line sets and connection relation data of the nodes and the lines.

Description

Power grid key node identification method Technical Field The application relates to the technical field of electric power, in particular to a method for identifying key nodes of a power grid. Background In the face of the problems of high-proportion distributed energy access and complicated power grid structure, clean energy such as large-scale wind power, photovoltaic and the like in a power system is sent to a load center through a cross-region power transmission channel, wherein a power grid at a sending end is gradually becoming a key hub for clean energy consumption and cross-region power transmission. However, with the advance of engineering, the power grid at the transmitting end presents new characteristics of 'high-permeability new energy, high-proportion direct current transmission and high-proportion distributed resources'. The structure, operating characteristics and safe and stable forms of the power grid are changing. In this context, safe and stable operation of the power grid at the transmitting end faces serious challenges. On one hand, the problems of power balance and frequency stability of a power grid at a transmitting end are increasingly outstanding due to the strong volatility and randomness of large-scale renewable energy sources, and on the other hand, the inertia characteristics, the short-circuit current level and the voltage stability mechanism of the power grid are obviously changed due to the fact that high-proportion power electronic equipment is connected into and remotely transmitted from large-capacity power, and the traditional stability analysis and weak link identification method based on the synchronous machine-guided power grid is difficult to be applied gradually. In the prior art, the planning operation optimization or single stability problem analysis of the power transmission network cannot systematically identify key nodes and weak links affecting multiple safety and stability problems of the power transmission network. In summary, there is also a need for systematic improvement in the current method for identifying weak locations of a power grid at a transmitting end, so as to quickly and accurately locate key nodes of the power grid. Disclosure of Invention Aiming at the problems existing in the prior art, the application provides the method for identifying the key nodes of the power grid at the transmitting end, which can comprehensively consider the access characteristics of new energy, the topological structure of the power grid and the stability constraint of multiple dimensions, accurately position the weak points of the system, and provide decision-making basis for the construction, the arrangement of the operation mode and the stability control strategy of the power grid at the transmitting end, thereby ensuring the safety, the reliability and the high efficiency of the trans-regional clean energy transportation. In order to achieve the above purpose, the technical scheme adopted by the application is as follows: the application provides a method for identifying key nodes of a power grid, which comprises the following steps: Obtaining topological structure data, electrical parameter data and running state data of a power grid; based on topological structure data, electrical parameter data and running state data, introducing virtual nodes to construct a directional weighted expansion network; Constructing an electric transmission transfer matrix based on transmission transfer information of different types of nodes in the directional weighted expansion network; Constructing a self-connection matrix for representing the non-equal probability transmission probability among nodes based on the equivalent impedance association characteristic among the nodes; Fusing the electric transmission transfer matrix and the self-connection matrix to obtain an electric Google matrix; Performing iterative computation on the electric Google matrix by adopting an improved PageRank algorithm to obtain evaluation values of all nodes; Based on the evaluation values of all the nodes, superposing multiple types of evaluation indexes to construct node importance evaluation indexes; and evaluating each node based on the node importance evaluation index, and outputting a power grid key node identification result according to the evaluation result. Optionally, the network fusion node type, the tidal current transmission direction association characteristic and the line reactance weight characteristic are expanded in a directional weighting manner; The node type comprises a power source node, an intermediate node and a terminal node; The power source node and/or the terminal node complement network connectivity through the virtual node. Optionally, the virtual node is configured to satisfy a connectivity characteristic of the directionally weighted extension network; the virtual nodes comprise a first virtual node and a second virtual node; The first virtual node is connected with t