CN112615365A - Smart power grid vulnerability key point identification method and device - Google Patents

Smart power grid vulnerability key point identification method and device Download PDF

Info

Publication number
CN112615365A
CN112615365A CN202011422951.XA CN202011422951A CN112615365A CN 112615365 A CN112615365 A CN 112615365A CN 202011422951 A CN202011422951 A CN 202011422951A CN 112615365 A CN112615365 A CN 112615365A
Authority
CN
China
Prior art keywords
node
influence
nodes
power grid
information network
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011422951.XA
Other languages
Chinese (zh)
Other versions
CN112615365B (en
Inventor
陈一鸣
姚实颖
张全明
侯新安
罗劲瑭
胥威汀
芶继军
祝和春
骆韬锐
高栋梁
马天男
阳小龙
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Economic and Technological Research Institute of State Grid Sichuan Electric Power Co Ltd
Original Assignee
Economic and Technological Research Institute of State Grid Sichuan Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Economic and Technological Research Institute of State Grid Sichuan Electric Power Co Ltd filed Critical Economic and Technological Research Institute of State Grid Sichuan Electric Power Co Ltd
Priority to CN202011422951.XA priority Critical patent/CN112615365B/en
Publication of CN112615365A publication Critical patent/CN112615365A/en
Application granted granted Critical
Publication of CN112615365B publication Critical patent/CN112615365B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Educational Administration (AREA)
  • Health & Medical Sciences (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Development Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Public Health (AREA)
  • Power Engineering (AREA)
  • Primary Health Care (AREA)
  • General Health & Medical Sciences (AREA)
  • Water Supply & Treatment (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a method and a device for identifying vulnerability key points of a smart power grid, wherein the method comprises the steps of obtaining the local influence of all nodes in a power grid and an information network by obtaining a dependent coupling network model corresponding to the smart power grid to be identified; then, correcting the local influence of all nodes in the power grid through a neighbor node contribution matrix of the power grid and a power grid coupling node to obtain the effective influence of each node in the power grid, and correcting the local influence of all nodes in the information grid through the neighbor node contribution matrix of the information grid and the information grid coupling node influence to obtain the effective influence of each node in the information grid; and finally, taking the effective influence of each node of the power grid and the effective influence of each node of the information grid as a set, and sequencing the effective influence in the set to obtain the vulnerability key points of the smart grid to be identified, so as to improve the accuracy and the rapidity of the identification of the vulnerability key nodes of the smart grid under the action of the dependent coupling.

Description

Smart power grid vulnerability key point identification method and device
Technical Field
The invention relates to the field of safe operation of a smart power grid, in particular to a method and a device for identifying vulnerability key points of the smart power grid.
Background
Smart grids have now evolved into power systems that are a deep convergence of information and power grids. With the continuous expansion of the application range of the information communication technology in the power system, the degree of the mutual dependency between the information network and the power network is continuously deepened, which is mainly expressed as follows: the normal operation of the information network requires the power network to provide the power supply, and the normal operation of the power network depends on the 3C functions provided by the information network, such as control, communication, calculation, and the like. This dependent coupling between the information and power grids also makes smart grids more vulnerable and has become one of the biggest causes of power system fault chain reactions. If the key nodes in the smart grid fail, the larger-scale cascading failures among the coupling networks can be caused, so that the method has important significance for identifying the key nodes in the smart grid.
The identification research of key nodes of a single-side network becomes important research content of a smart grid, and a plurality of identification methods are provided based on degree centrality, k-shell, betweenness centrality, PageRank and the like. The method based on the degree centrality mainly analyzes the adjacency relation between the nodes and the neighbor nodes, the influence of one node in the network is related to the number of the neighbor nodes connected with the node, if the number of the neighbor nodes of one node is more, the node has more influence, the method is simple and visual, but the influence of non-neighbor nodes is not considered, and the utilized topological characteristic information is more limited, so the method has certain limitation; the K-shell-based method is a coarse-grained identification method, if a node is located at the core position of a network, even if the number of adjacent nodes connected with the node is small, the node is considered to have more influence, the time complexity of the method is low, but the method is not suitable for certain types of networks such as scale-free networks and the like, and the relative influence of the nodes in the same layer is difficult to distinguish; the method based on betweenness centrality is that the quantity of all shortest paths passing through a certain node in a network is calculated to measure whether the node is in an important position for information propagation in the network, if the betweenness of the node is larger, the node has more influence in the whole network, but the method needs to consider the global network and search the shortest path corresponding to each node when calculating the betweenness, the time complexity is higher, and the algorithm efficiency is lower; the method based on the PageRank evaluates the influence of a certain node by considering the number of neighbor nodes of the node and the importance of the neighbor nodes, so that the key node can be identified more accurately. With the continuous deepening of the coupling degree of the power grid and the information grid, the identification method of the key nodes of the network focuses on researching the single-side network of the smart grid, and the influence of the network coupled with the network and the dependent coupling relation between the power grid and the information grid on the single-side network nodes is not considered, so that the key nodes of the smart grid are not accurately identified.
Disclosure of Invention
The invention aims to solve the technical problem that the key point identification of the intelligent network at present usually adopts single-side network key point identification without considering the coupling relation, so that the key point identification is not accurate enough. Therefore, the invention provides a method and a device for identifying vulnerability key points of a smart power grid, which comprehensively consider the characteristics of an information grid and a power grid and the dependence coupling relationship between the information grid and the power grid, and improve the accuracy of node identification.
The invention is realized by the following technical scheme:
a smart grid vulnerability key point identification method comprises the following steps:
acquiring a dependent coupling network model corresponding to a smart grid to be identified, wherein the smart grid to be identified comprises a power grid and an information grid;
calculating the local influence A of all nodes in the power gridinfo′And calculating the local influence B of all nodes in the information networkinfo′；
Acquiring the contribution of neighbor nodes corresponding to each node in the power grid as a neighbor node contribution matrix H of the power gridPAnd obtaining the contribution of the neighbor nodes corresponding to each node in the information network as the contribution matrix H of the neighbor nodes of the information networkCAn element of (1);
based on the dependent coupling network model, acquiring the influence of the coupling nodes corresponding to the power grid as the influence contribution of the coupling nodes of the power grid
Figure BDA0002823354960000031
And acquiring the influence of the coupling node corresponding to the information network as the influence contribution of the coupling node of the information network
Figure BDA0002823354960000032
A neighbor node contribution matrix H through the power gridPContribution of influence to said power grid coupling node
Figure BDA0002823354960000033
Local influence A on each node of the power gridinfo′Carrying out correction processing to obtain the effective influence V of each node of the power gridA；
A neighbor node contribution matrix H through the information networkCContribution of influence of coupling node with said information network
Figure BDA0002823354960000034
Local influence B on nodes of the information networkinfo′Carrying out correction processing to obtain the effective influence V of each node of the information networkB；
Effective influence V based on each node of power gridAAnd the effective influence V of each node of the information networkBGenerating a node influence set V of a dependent coupling network model, and sorting the effective influences of all nodes in the node influence set V of the dependent coupling network model to obtain a sorting result;
and acquiring vulnerability key points corresponding to the smart grid to be identified based on the sequencing result.
Further, the local influence A of all nodes in the power grid is calculatedinfo′And calculating the local influence B of all nodes in the information networkinfo′The method comprises the following steps:
obtaining the ith node u of the power gridiAt an electrical distance threshold value | ZequThe total number of reachable nodes in |, which is taken as the total number of the first reachable nodes
Figure BDA0002823354960000035
Obtaining the electrical distance threshold value | ZequIn |, i-th node u of power gridiEquivalent electrical distance | Z from other nodesij,equFor the equivalent electrical distance | Z, by a calculation formula of the local influence of the nodes of the power networkij,equI and the first reachable node total number
Figure BDA0002823354960000041
Calculating to obtain the local influence A of each node of the power gridinfo′；
Obtaining ith node v of information networkiThe total number of reachable nodes within the number of L hops is used as the total number of the second reachable nodes
Figure BDA0002823354960000042
Obtaining the ith node v of the information network within the L hop countiShortest path distance d between other nodesijFor said shortest path distance d by means of a formula for calculating the local influence of nodes of the information networkijAnd said second reachable total number of nodes
Figure BDA0002823354960000043
Calculating to obtain the local influence B of each node of the information networkinfo′。
Further, the local influence calculation formula through the power grid node is applied to the equivalent electrical distance | Zij,equI and the first reachable node total number
Figure BDA0002823354960000044
Calculating to obtain the local influence A of each node of the power gridinfo′The method comprises the following steps:
local influence calculation formula on equivalent electrical distance | Z through power grid nodesij,equI and the first reachable node total number
Figure BDA0002823354960000045
Calculating to obtain the ith node u of the power gridiCorresponding local influence De,i；
Taking local influence forces corresponding to all nodes in the power grid as a set Ainfo′Said A isinfo'＝[De,1,De,2,De,3,...,De,n]。
Further, the local influence calculation formula of the power grid node is specifically as follows:
Figure BDA0002823354960000046
wherein the content of the first and second substances,
Figure BDA0002823354960000047
is represented by node uiAs a center, node uiAt an electrical distance threshold value | ZequTotal number of reachable nodes within |; gamma-shapedZ(i) Is represented by node uiAs a center, node uiAt an electrical distance | ZequSet of reachable nodes within |, De,iRepresenting a node uiCorresponding local influence, | Zij,equI represents node uiAnd ujEquivalent electrical distance therebetween.
Further, the calculation formula of local influence through the information network nodes is used for calculating the shortest path distance dijAnd said second reachable total number of nodes
Figure BDA0002823354960000051
Calculating to obtain the local influence B of each node of the information networkinfo′The method comprises the following steps:
calculating the shortest path distance d by the local influence calculation formula of the information network nodeijAnd said second reachable total number of nodes
Figure BDA0002823354960000052
Calculating to obtain the ith node v of the information networkiCorresponding local influence
Figure BDA0002823354960000053
Taking the local influence corresponding to all nodes in the information network as a set Binfo′Said
Figure BDA0002823354960000054
Further, the local influence calculation formula of the information network node is specifically as follows:
Figure BDA0002823354960000055
wherein the content of the first and second substances,
Figure BDA0002823354960000056
is represented by node viAs a center, node viThe total number of reachable nodes within L hops; gamma-shapedL(i) Is represented by node viAs a center, node viA set of nodes within L hops; dijRepresenting a node viTo node vjThe shortest path distance of (a) is,
Figure BDA0002823354960000057
representing a node viCorresponding local influence.
Further, the neighboring nodes through the power grid contribute a force matrix HPContribution of influence to said power grid coupling node
Figure BDA0002823354960000058
Local influence A on each node of the power gridinfo′Carrying out correction processing to obtain the effective influence V of each node of the power gridAThe method comprises the following steps:
a neighbor node contribution matrix H through the power gridPLocal influence A on the nodes of the power networkinfo′Carrying out primary correction processing to obtain the correction influence of each node of the power grid
Figure BDA0002823354960000059
Influencing power contribution via the power grid coupling node
Figure BDA00028233549600000510
Correcting influence on each node of the power grid
Figure BDA00028233549600000511
Carrying out secondary correction to obtain the effective influence V of each node of the power gridA。
Further, the neighbor node contribution force matrix H passing through the information networkCContribution of influence of coupling node with said information network
Figure BDA0002823354960000061
Local influence B on nodes of the information networkinfo′Carrying out correction processing to obtain the effective influence V of each node of the information networkBThe method comprises the following steps:
a neighbor node contribution matrix H through the information networkCLocal influence B on the nodes of the information networkinfo′Performing a correction process to obtain the correction influence of each node of the information network
Figure BDA0002823354960000062
Influencing contribution of coupling nodes through the information network
Figure BDA0002823354960000063
Correction influence on each node of the information network
Figure BDA0002823354960000064
Carrying out secondary correction to obtain the effective influence V of each node of the information networkB。
Further, the obtaining of the vulnerability key point corresponding to the smart grid to be identified based on the sorting result includes:
the system comprises a dependent coupling network model obtaining module, a correlation module and a correlation module, wherein the dependent coupling network model obtaining module is used for obtaining a dependent coupling network model corresponding to a smart grid to be identified, and the smart grid to be identified comprises a power grid and an information grid;
an influence calculation module for calculating the local influence A of all nodes in the power gridinfo′And calculating the local influence B of all nodes in the information networkinfo′；
A contribution matrix obtaining module for obtaining the contribution of the neighbor nodes corresponding to each node in the power grid as the contribution matrix of the neighbor nodes of the power gridHPAnd obtaining the contribution of the neighbor nodes corresponding to each node in the information network as the contribution matrix H of the neighbor nodes of the information networkCAn element of (1);
a coupling node influence obtaining module, configured to obtain, based on the dependent coupling network model, a coupling node influence corresponding to the power grid as an influence contribution of a coupling node of the power grid
Figure BDA0002823354960000071
And acquiring the influence of the coupling node corresponding to the information network as the influence contribution of the coupling node of the information network
Figure BDA0002823354960000072
A power network correction processing module for making contribution to the power matrix H through the neighboring nodes of the power networkPContribution of influence to said power grid coupling node
Figure BDA0002823354960000073
Local influence A on each node of the power gridinfo′Carrying out correction processing to obtain the effective influence V of each node of the power gridA；
An information network correction processing module for passing through the neighbor node contribution force matrix H of the information networkCContribution of influence of coupling node with said information network
Figure BDA0002823354960000074
Local influence B on nodes of the information networkinfo′Carrying out correction processing to obtain the effective influence V of each node of the information networkB；
An influence ranking module for ranking the effective influence V based on each node of the power gridAAnd the effective influence V of each node of the information networkBGenerating a node influence set V of a dependent coupling network model, and sorting the effective influences of all nodes in the node influence set V of the dependent coupling network model to obtain a sorting result;
and the fragile key point identification module is used for acquiring the fragile key points corresponding to the smart grid to be identified based on the sequencing result.
According to the method and the device for identifying the vulnerability key points of the smart grid, the local influence of all nodes in the power grid and the local influence of all nodes in the information grid are obtained by obtaining the dependent coupling network model corresponding to the smart grid to be identified; then, the local influence of all nodes in the power grid is corrected through the contribution matrix of the neighbor nodes of the power grid and the contribution of the influence of the coupling nodes of the power grid to obtain the effective influence of all nodes in the power grid, and meanwhile, the local influence of all nodes in the information grid is corrected through the contribution matrix of the neighbor nodes of the information grid and the contribution of the influence of the coupling nodes of the information grid to obtain the effective influence of all nodes in the information grid; then, the effective influence of each node of the power grid and the effective influence of each node of the information grid are taken as a set, and the effective influences in the set are sorted to obtain a sorting result; and finally, acquiring vulnerability key points corresponding to the smart grid to be identified according to the sequencing result, and improving the accuracy and rapidity of identifying the vulnerability key points of the smart grid under the action of the dependent coupling.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
fig. 1 is a flowchart of a smart grid vulnerability key point identification method of the present invention.
Fig. 2 is a specific flowchart of step S20 in fig. 1.
Fig. 3 is a specific flowchart of step S50 in fig. 1.
Fig. 4 is a specific flowchart of step S60 in fig. 1.
FIG. 5 is a schematic block diagram of a smart grid vulnerability key point identification apparatus of the present invention.
FIG. 6 is a diagram illustrating a dependent coupling network model according to an embodiment of the present invention.
FIG. 7 is a diagram illustrating local influence between nodes of a power grid according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
As shown in fig. 1, the invention provides a method for identifying vulnerability key points of a smart grid, which specifically comprises the following steps:
s10: and acquiring a dependent coupling network model corresponding to the smart grid to be identified, wherein the smart grid to be identified comprises a power grid and an information grid.
The intelligent power grid to be identified refers to the intelligent power grid which needs vulnerability key point identification.
Specifically, a power grid topology G is generated with power plants, substations, and loads in the power grid as nodes and power lines as node connection linesA＝(VA,EA) Wherein V isA＝{u1,u2,u3...,unDenotes a set of nodes of the power grid, ui∈VA(i 1,2.., n) represents a node in the network, EA＝{eijIs the set of edges of the power grid, (u)i,uj)∈EARepresenting a node uiTo node ujThe connecting line of (1).
The wide area measurement system, the data acquisition system, the monitoring control system, the phase measuring device and the dispatching center in the information network are used as nodes, and the communication line is used as a node connecting line to generate an information network topology GB＝(VB,EB) Wherein V isB＝{v1,v2,v3...,vmDenotes a set of nodes of an information network, vi∈VB(i 1,2.., m) denotes a node in the information network, EB＝{eijIs the set of edges of the power grid, (v)i,vj)∈EBRepresenting a node uiTo node ujOne edge of (2).
And establishing connection between the power grid topology and the information network topology through the interdependent coupling relation between the power grid and the information network to generate an interdependent coupling network model. Taking the interdependent coupling relationship between nodes in the power grid and the information grid as a coupling edge, E is adopted in this embodimentA-BRepresents a set of coupled edges, where EA-B＝{(u,v)u∈VA,v∈VB}. If node u in the information network is coupled with node v in the power network dependently, EA-B(u, v) ═ 1, otherwise EA-B(u,v)＝0。
For ease of understanding, the interdependent coupling network model is illustrated by way of example in fig. 6. The solid line refers to an interconnection edge, that is, an edge connecting nodes in a communication information network (i.e., information network) B and an edge connecting nodes in a physical power network (i.e., power network) a, and the interconnection edge connects nodes in each single-layer network to form a network having a certain function, for example: the generation node, the transformation node and the load node in the power grid realize the generation, transmission and consumption of electric energy through the power transmission line. The dotted line refers to a coupling edge, i.e. an edge connecting a node in the power grid and a node in the information grid, and is used for representing a dependent coupling relationship between the power grid and the information grid. Such as P22Representing the edge, P, of the 2 nd node in the physical power network A and the 2 nd node in the communication information network B23Representing the edge of the 2 nd node in the physical power network a and the 3 rd node in the communication information network B.
S20: calculating the local influence A of all nodes in the power gridinfo′And calculating the local influence B of all nodes in the information networkinfo′。
Specifically, for a power grid having n nodes, in order to truly reflect the electrical connection between the nodes, the electrical distance between the nodes is calculated using the impedance value of the line between the nodes of the power grid as a weight. After the topological structure distance under the network global view is corrected by the electrical distance, the total number of reachable nodes of a certain node within a certain electrical distance is transformed to calculate the local influence of the nodes of the power grid. The value of the electrical distance can be determined according to the actual situationThe determination of the condition is not limited herein. For ease of understanding, the present embodiment will be described by taking the nodes of 7 power grids shown in fig. 7 as an example, v1-v7Representing 7 nodes of the power network, e45Representing a node v4And v5Local influence of between e57Representing a node v5And v7Local influence of between e56Representing a node v5And v6Local influence in between.
For an information network with m nodes, considering that information streams in the information network are all transmitted in a shortest path mode, the local influence of the nodes of the information network is calculated by quantifying the total number of the nodes which can be reached by a certain node within a certain hop number. The value of the hop count can be determined according to actual conditions, and is not limited herein.
S30: acquiring the contribution of neighbor nodes corresponding to each node in the power grid as a neighbor node contribution matrix H of the power gridPAnd obtaining the contribution of the neighbor nodes corresponding to each node in the information network as the contribution matrix H of the neighbor nodes of the information networkCOf (2) is used.
Neighbor node contribution matrix H of the power grid in this embodimentPComprises the following steps:
Figure BDA0002823354960000111
wherein D ise,nRepresenting a node unLocal influence in the power grid, δijRepresenting a node ujTo node uiValue of the contribution ratio of (1), δijRepresents the contribution ratio of the node influence, if deltaij0 denotes a node ujNot of node uiOf a direct neighbor node, and node ujTo node uiThe influence contribution ratio of (a) is 0; if deltaijNot equal to 0, indicating node ujIs node uiOf a direct neighbor node, and node ujTo node uiHas an influence contribution ratio of deltaij。
Information network in this embodimentIs given to the neighbor node contribution matrix HCComprises the following steps:
Figure BDA0002823354960000112
wherein the content of the first and second substances,
Figure BDA0002823354960000113
is a node vmLocal influence of the self in the information network, deltaijRepresenting a node vjTo node viThe influence contribution ratio value of (1). DeltaijRepresents the contribution ratio of the node influence, if deltaij0 denotes a node vjIs not node viAnd node v is a direct neighbor node ofjTo node viThe influence contribution ratio of (a) is 0; if deltaijNot equal to 0, indicating node vjIs node viAnd node v is a direct neighbor node ofjTo node viHas an influence contribution ratio of deltaij。
S40: based on a dependent coupling network model, acquiring the influence of coupling nodes corresponding to the power grid as the influence contribution of the coupling nodes of the power grid
Figure BDA0002823354960000114
And acquiring the influence of the coupling node corresponding to the information network as the influence contribution of the coupling node of the information network
Figure BDA0002823354960000115
In particular, the interaction relationship matrix P ═ P between the power network and the information network coupling nodesij]m′×n′Which can be represented as
Figure BDA0002823354960000121
Wherein p isijNode v representing an information networkiNode u to the power gridjValue of the relationship of the interaction betweenM 'represents a node in the information network having a interdependent coupling relationship, and n' represents a node in the power network having a interdependent coupling relationship.
Contribution of influence of coupling nodes of power network
Figure BDA0002823354960000122
Wherein, Pi1Node u representing a power gridiNode v with information network1Value of the relationship between P and Pi2Node u representing a power gridiNode v with information network2Value of the relationship between P and Pin′Node u representing a power gridiNode v with information networkn′The value of the relationship between the two interactions.
Influential contribution of information network coupling nodes
Figure BDA0002823354960000123
Wherein, P1jNode u representing a power grid1Node v with information networkjValue of the relationship between P and P2jNode u representing a power grid2Node v with information networkjValue of the relationship between P and Pm′jNode u representing a power gridm′Node v with information networkjThe value of the relationship between the two interactions.
S50: neighbor node contribution matrix H through power gridPContribution of influence to coupling nodes of power grid
Figure BDA0002823354960000124
Local influence A on each node of the power gridinfo′Carrying out correction processing to obtain the effective influence V of each node of the power gridA。
Specifically, a power grid neighbor node contribution matrix HPLocal influence A with all nodes in the power networkinfo′Adding the result and the contribution of the influence of the coupling node of the power network
Figure BDA0002823354960000125
Adding to obtain the effective influence V of each node of the power gridA。
S60: neighbor node contribution matrix H through information networkCContribution of influence to information network coupling node
Figure BDA0002823354960000131
Local influence on nodes of information network Binfo′Carrying out correction processing to obtain the effective influence V of each node of the information networkB。
Specifically, a neighboring node contribution force matrix H of the information networkCLocal influence B with all nodes in the information networkinfo′Adding the result of the addition to the contribution of the influence of the coupling node of the information network
Figure BDA0002823354960000132
Adding to obtain the effective influence V of each node of the information networkB。
S70: effective influence V based on each node of power gridAAnd the effective influence V of each node of the information networkBAnd generating a node influence set V of the dependent coupling network model, and sorting the effective influences of all nodes in the node influence set V of the dependent coupling network model to obtain a sorting result.
In particular, the effective influence V of each node of the power grid is obtainedAAnd the effective influence V of each node of the information networkBThen, the effective influence V of each node of the power grid is determinedAAnd the effective influence V of each node of the information networkBAnd mixing the data into a set, namely a dependent coupling network model influence set V, and then sorting the effective influences in the dependent coupling network model influence set V to obtain a sorting result.
S80: and acquiring vulnerability key points corresponding to the smart grid to be identified based on the sequencing result.
Specifically, if the sequence of the sorting results is sequentially arranged from large to small, selecting the nodes corresponding to the effective influence at the front of the sorting according to a preset number as vulnerability key points; and if the sequence of the sequencing results is sequentially arranged from small to large, selecting the nodes corresponding to the effective influence at the back of the sequencing according to the preset number as vulnerability key points.
Further, as shown in fig. 2, step S20, the local influence a of all nodes in the power grid is calculatedinfo′And calculating the local influence B of all nodes in the information networkinfo′The method specifically comprises the following steps:
s21: obtaining ith node u of power gridiAt an electrical distance threshold value | ZequThe total number of reachable nodes in |, which is taken as the total number of the first reachable nodes
Figure BDA0002823354960000141
In particular, node u of the power networkiAt an electrical distance threshold value | ZequTotal number of reachable nodes within |
Figure BDA0002823354960000142
Can be expressed as
Figure BDA0002823354960000143
Wherein, | Zij,equI represents node uiAnd ujEquivalent electrical distance between, | ZequI identifies the threshold value of the equivalent electrical distance in the power network, I1(. to) is an indicator function, i.e. when node uiAnd ujThe shortest electrical distance between the two is less than the threshold value | ZequWhen l is equal to I1(. 1) otherwise I1(·)＝0。
Further, due to the hierarchical arrangement of the power dispatching control system in the smart grid and the interconnection structure of the power grid in each link of power generation, power transformation, power transmission, power storage, power distribution, power utilization and the like, the electrical distance threshold | Z in the embodiment isequL can be flexibly configured and adjusted according to the interconnection structure and is not a specific value.
S22: obtaining an electrical distance threshold value | ZequIn |, i-th node u of power gridiEquivalent electrical distance | Z from other nodesij,equI, equivalent electrical distance Z by local influence calculation formula of power grid nodeij,equI and first reachable node total
Figure BDA0002823354960000144
Calculating to obtain the local influence A of each node of the power gridinfo′。
Specifically, the equivalent electrical distance | Z is first calculated by the formula of the local influence of the power grid nodeij,equI and first reachable node total
Figure BDA0002823354960000145
Calculating to obtain the ith node u of the power gridiCorresponding local influence De,i(ii) a Then, local influence corresponding to all nodes in the power grid is taken as a set to obtain local influence A of all nodes in the power gridinof′，Ainfo'＝[De,1,De,2,De,3,...,De,n]。
The calculation formula of the local influence of the power grid nodes is specifically as follows:
Figure BDA0002823354960000151
wherein the content of the first and second substances,
Figure BDA0002823354960000152
is represented by node uiAs a center, node uiAt an electrical distance threshold value | ZequTotal number of reachable nodes within |. Gamma-shapedZ(i) Is represented by node uiAs a center, node uiAt an electrical distance | ZequSet of reachable nodes within |, De,iRepresenting a node uiCorresponding local influence, | Zij,equI represents node uiAnd ujEquivalent electrical distance therebetween.
S23: obtaining ith node v of information networkiThe total number of reachable nodes within the number of L hops is used as the total number of the second reachable nodes
Figure BDA0002823354960000153
In particular, a node v of an information networkiTotal number of reachable nodes within L hops
Figure BDA0002823354960000154
Can be expressed as
Figure BDA0002823354960000155
Wherein m represents the total number of nodes in the information network, L represents the node hop number set by the information network, LijIs a node viAnd vjThe shortest path length between; i is2(. o) is an indicator function, i.e. when node viAnd vjLength of shortest path between lijWhen L is less than or equal to L, I2(. 1) otherwise I2(·)＝0。
S24: obtaining ith node v of information network within L hop countiShortest path distance d between other nodesijFor the shortest path distance d by the calculation formula of local influence of the nodes of the information networkijAnd a second reachable node total number
Figure BDA0002823354960000156
Calculating to obtain the local influence B of each node of the information networkinfo′。
Specifically, firstly, the shortest path distance d is calculated through a local influence calculation formula of the information network nodeijAnd a second reachable node total number
Figure BDA0002823354960000157
Calculating to obtain the ith node v of the information networkiCorresponding local influence
Figure BDA0002823354960000158
Then, the local influence corresponding to all nodes in the information network is taken as a set to obtain the local influence B of all nodes in the information networki nof′，
Figure BDA0002823354960000159
The calculation formula of the local influence of the information network nodes is specifically as follows:
Figure BDA0002823354960000161
wherein the content of the first and second substances,
Figure BDA0002823354960000162
is represented by node viAs a center, node viThe total number of reachable nodes within L hops. Gamma-shapedL(i) Is represented by node viAs a center, node viA set of possible nodes within L hops. dijRepresenting a node viTo node vjThe shortest path distance of (a) is,
Figure BDA0002823354960000163
representing a node viCorresponding local influence.
Further, as shown in fig. 3, step S50, the power matrix H is contributed by the neighboring nodes of the power gridPContribution of influence to coupling nodes of power grid
Figure BDA0002823354960000164
Local influence A on each node of the power gridinfo′Carrying out correction processing to obtain the effective influence V of each node of the power gridAThe method specifically comprises the following steps:
s51: neighbor node contribution matrix H through power gridPLocal influence on nodes of the power network Ainfo′Carrying out primary correction processing to obtain the correction influence of each node of the power grid
Figure BDA0002823354960000165
Specifically, a power grid neighbor node contribution force matrix is formed
Figure BDA0002823354960000166
And all in the power gridLocal influence of node Ainfo'＝[De,1,De,2,De,3,...,De,n]Adding to complete the neighbor node contribution force matrix H of the power gridPLocal influence A on all nodes in the power gridinfo′To obtain the power network correction influence
Figure BDA0002823354960000167
S52: influencing power contribution via power grid coupling nodes
Figure BDA0002823354960000168
Correcting influence on each node of power network
Figure BDA0002823354960000169
Carrying out secondary correction to obtain the effective influence V of each node of the power gridA。
In particular, coupling the power grid to the contribution of influence of the nodes
Figure BDA0002823354960000171
And power network correction influence
Figure BDA0002823354960000172
Adding to complete the power network coupling node influence
Figure BDA0002823354960000173
Correction of influence on the power network
Figure BDA0002823354960000174
Obtaining the effective influence of each node of the power grid
Figure BDA0002823354960000175
Further, as shown in FIG. 4, step S60 is executed by a computerNeighbor node contribution matrix H of information networkCContribution of influence to information network coupling node
Figure BDA0002823354960000176
Local influence on nodes of information network Binfo′Carrying out correction processing to obtain the effective influence V of each node of the information networkBThe method specifically comprises the following steps:
s61: neighbor node contribution matrix H through information networkCLocal influence on nodes of the information network Binfo′Performing a correction process to obtain the correction influence of each node of the information network
Figure BDA0002823354960000177
Specifically, a neighboring node contribution force matrix of the information network
Figure BDA0002823354960000178
And local influence of all nodes in the information network
Figure BDA0002823354960000179
Adding to complete the neighbor node contribution matrix H of the information networkCLocal influence on all nodes in an information network Binof′A correction process of (1) to obtain
Figure BDA00028233549600001710
S62: influencing contribution of coupling nodes through information network
Figure BDA00028233549600001711
Correcting influence on nodes of information network
Figure BDA00028233549600001712
Carrying out secondary correction to obtain the effective influence V of each node of the information networkB。
In particular, coupling an information network to the influence contributions of nodes
Figure BDA00028233549600001713
And the correction influence of each node of the information network
Figure BDA0002823354960000181
Adding to complete the contribution of influence of the coupling nodes of the information network
Figure BDA0002823354960000182
Correcting influence on nodes of information network
Figure BDA0002823354960000183
Obtaining the effective influence of each node of the information network
Figure BDA0002823354960000184
Example 2
As shown in fig. 5, the present embodiment is different from embodiment 1 in that a smart grid vulnerability identification apparatus is provided, and includes:
the dependent coupling network model obtaining module 10 is configured to obtain a dependent coupling network model corresponding to the smart grid to be identified, where the smart grid to be identified includes a power grid and an information grid.
An influence calculation module 20 for calculating the local influence a of all nodes in the power networkinfo′And calculating the local influence B of all nodes in the information networkinfo′。
A contribution matrix obtaining module 30, configured to obtain neighboring node contribution forces corresponding to nodes in the power grid as a neighboring node contribution force matrix H of the power gridPAnd obtaining the contribution of the neighbor nodes corresponding to each node in the information network as the contribution matrix H of the neighbor nodes of the information networkCOf (2) is used.
A coupling node influence obtaining module 40, configured to obtain a coupling node influence corresponding to the power grid as the power based on the dependent coupling network modelContribution of influence of network coupling nodes
Figure BDA0002823354960000185
And acquiring the influence of the coupling node corresponding to the information network as the influence contribution of the coupling node of the information network
Figure BDA0002823354960000186
A power network modification processing module 50 for contributing the force matrix H through the neighboring nodes of the power networkPContribution of influence to coupling nodes of power grid
Figure BDA0002823354960000187
Local influence A on each node of the power gridinfo′Carrying out correction processing to obtain the effective influence V of each node of the power gridA。
An information network modification processing module 60 for making a contribution matrix H of the neighboring nodes through the information networkCContribution of influence to information network coupling node
Figure BDA0002823354960000191
Local influence on nodes of information network Binfo′Carrying out correction processing to obtain the effective influence V of each node of the information networkB。
An influence ranking module 70 for ranking the effective influence V based on the nodes of the power gridAAnd the effective influence V of each node of the information networkBAnd generating a node influence set V of the dependent coupling network model, and sorting the effective influences of all nodes in the node influence set V of the dependent coupling network model to obtain a sorting result.
And the fragile key point identification module 80 is configured to obtain a fragile key point corresponding to the smart grid to be identified based on the sorting result.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A smart grid vulnerability key point identification method is characterized by comprising the following steps:
acquiring a dependent coupling network model corresponding to a smart grid to be identified, wherein the smart grid to be identified comprises a power grid and an information grid;
calculating the local influence A of all nodes in the power gridinfo′And calculating the local influence B of all nodes in the information networkinfo′；
Acquiring the contribution of neighbor nodes corresponding to each node in the power grid as a neighbor node contribution matrix H of the power gridPAnd obtaining the contribution of the neighbor nodes corresponding to each node in the information network as the contribution matrix H of the neighbor nodes of the information networkCAn element of (1);
based on the dependent coupling network model, acquiring the influence of the coupling nodes corresponding to the power grid as the influence contribution of the coupling nodes of the power grid
Figure FDA0002823354950000011
And acquiring the influence of the coupling node corresponding to the information network as the influence contribution of the coupling node of the information network
Figure FDA0002823354950000012
A neighbor node contribution matrix H through the power gridPContribution of influence to said power grid coupling node
Figure FDA0002823354950000013
Local influence A on each node of the power gridinfo′Carrying out correction processing to obtain the effective influence V of each node of the power gridA；
A neighbor node contribution matrix H through the information networkCContribution of influence of coupling node with said information network
Figure FDA0002823354950000014
Local influence B on nodes of the information networkinfo′Carrying out correction processing to obtain the effective influence V of each node of the information networkB；
Effective influence V based on each node of power gridAAnd the effective influence V of each node of the information networkBGenerating a node influence set V of a dependent coupling network model, and sorting the effective influences of all nodes in the node influence set V of the dependent coupling network model to obtain a sorting result;
and acquiring vulnerability key points corresponding to the smart grid to be identified based on the sequencing result.
2. The smart grid vulnerability key point identification method of claim 1, wherein the calculating local influence A of all nodes in the power gridinfo′And calculating the local influence B of all nodes in the information networkinfo′The method comprises the following steps:
obtaining the ith node u of the power gridiAt an electrical distance threshold value | ZequThe total number of reachable nodes in |, which is taken as the total number of the first reachable nodes
Figure FDA0002823354950000021
Obtaining the electrical distance threshold value | ZequIn |, i-th node u of power gridiWith othersEquivalent electrical distance between nodes | Zij,equFor the equivalent electrical distance | Z, by a calculation formula of the local influence of the nodes of the power networkij,equI and the first reachable node total number
Figure FDA0002823354950000022
Calculating to obtain the local influence A of each node of the power gridinfo′；
Obtaining ith node v of information networkiThe total number of reachable nodes within the number of L hops is used as the total number of the second reachable nodes
Figure FDA0002823354950000023
Obtaining the ith node v of the information network within the L hop countiShortest path distance d between other nodesijFor said shortest path distance d by means of a formula for calculating the local influence of nodes of the information networkijAnd said second reachable total number of nodes
Figure FDA0002823354950000024
Calculating to obtain the local influence B of each node of the information networkinfo′。
3. The smart grid vulnerability key point identification method of claim 2, wherein the local impact force calculation formula through power grid nodes is to the equivalent electrical distance | Zij,equI and the first reachable node total number
Figure FDA0002823354950000025
Calculating to obtain the local influence A of each node of the power gridinfo′The method comprises the following steps:
local influence calculation formula on equivalent electrical distance | Z through power grid nodesij,equI and the first reachable node total number
Figure FDA0002823354950000026
Calculating to obtain the ith node u of the power gridiCorresponding local influence De,i；
Taking local influence forces corresponding to all nodes in the power grid as a set Ainfo′Said A isinfo'＝[De,1,De,2,De,3,...,De,n]。
4. The smart grid vulnerability key point identification method according to claim 3, wherein the local influence calculation formula of the power grid nodes is specifically:
Figure FDA0002823354950000031
wherein the content of the first and second substances,
Figure FDA0002823354950000032
is represented by node uiAs a center, node uiAt an electrical distance threshold value | ZequTotal number of reachable nodes within |; gamma-shapedZ(i) Is represented by node uiAs a center, node uiAt an electrical distance | ZequSet of reachable nodes within |, De,iRepresenting a node uiCorresponding local influence, | Zij,equI represents node uiAnd ujEquivalent electrical distance therebetween.
5. The smart grid vulnerability key point identification method of claim 2, wherein the shortest path distance d is calculated by the local influence calculation formula of information grid nodesijAnd said second reachable total number of nodes
Figure FDA0002823354950000033
Calculating to obtain the local influence B of each node of the information networkinfo′The method comprises the following steps:
calculating the shortest path distance by using local influence calculation formula of information network nodedijAnd said second reachable total number of nodes
Figure FDA0002823354950000034
Calculating to obtain the ith node v of the information networkiCorresponding local influence
Figure FDA0002823354950000035
Taking the local influence corresponding to all nodes in the information network as a set Binfo′Said
Figure FDA0002823354950000036
6. The smart grid vulnerability key point identification method according to claim 5, wherein the local influence calculation formula of the information grid nodes is specifically:
Figure FDA0002823354950000041
wherein the content of the first and second substances,
Figure FDA0002823354950000042
is represented by node viAs a center, node viThe total number of reachable nodes within L hops; gamma-shapedL(i) Is represented by node viAs a center, node viA set of nodes is reached within L hops; dijRepresenting a node viTo node vjThe shortest path distance of (a) is,
Figure FDA0002823354950000043
representing a node viCorresponding local influence.
7. The smart grid vulnerability key point identification method of claim 1, wherein the neighbor nodes through the power grid contribute a force matrix HPContribution of influence to said power grid coupling node
Figure FDA0002823354950000044
Local influence A on each node of the power gridinfo′Carrying out correction processing to obtain the effective influence V of each node of the power gridAThe method comprises the following steps:
a neighbor node contribution matrix H through the power gridPLocal influence A on the nodes of the power networkinfo′Carrying out primary correction processing to obtain the correction influence of each node of the power grid
Figure FDA0002823354950000045
Influencing power contribution via the power grid coupling node
Figure FDA0002823354950000046
Correcting influence on each node of the power grid
Figure FDA0002823354950000047
Carrying out secondary correction to obtain the effective influence V of each node of the power gridA。
8. The smart grid vulnerability key point identification method of claim 1, wherein the neighbor node contribution force matrix H through the information gridCContribution of influence of coupling node with said information network
Figure FDA0002823354950000048
Local influence B on nodes of the information networkinfo′Carrying out correction processing to obtain the effective influence V of each node of the information networkBThe method comprises the following steps:
a neighbor node contribution matrix H through the information networkCLocal influence B on the nodes of the information networkinfo′Performing a correction process to obtain each section of the information networkCorrection influence of point
Figure FDA0002823354950000049
Influencing contribution of coupling nodes through the information network
Figure FDA0002823354950000051
Correction influence on each node of the information network
Figure FDA0002823354950000052
Carrying out secondary correction to obtain the effective influence V of each node of the information networkB。
9. The smart grid vulnerability key point identification method according to claim 1, wherein the obtaining vulnerability key points corresponding to the smart grid to be identified based on the sorting result comprises:
if the sequence of the sequencing results is sequentially arranged from big to small, selecting nodes corresponding to the effective influence at the front of the sequencing according to a preset number as vulnerability key points;
and if the sequence of the sequencing results is sequentially arranged from small to large, selecting the nodes corresponding to the effective influence at the back of the sequencing according to a preset number as vulnerability key points.
10. The utility model provides a smart power grids vulnerability key point recognition device which characterized in that includes:
the system comprises a dependent coupling network model obtaining module, a correlation module and a correlation module, wherein the dependent coupling network model obtaining module is used for obtaining a dependent coupling network model corresponding to a smart grid to be identified, and the smart grid to be identified comprises a power grid and an information grid;
an influence calculation module for calculating the local influence A of all nodes in the power gridinfo′And calculating the local influence B of all nodes in the information networkinfo′；
A contribution matrix acquisition module for acquiring the neighbor nodes corresponding to the nodes in the power gridContribution force is a neighbor node contribution force matrix H of the power gridPAnd obtaining the contribution of the neighbor nodes corresponding to each node in the information network as the contribution matrix H of the neighbor nodes of the information networkCAn element of (1);
a coupling node influence obtaining module, configured to obtain, based on the dependent coupling network model, a coupling node influence corresponding to the power grid as an influence contribution of a coupling node of the power grid
Figure FDA0002823354950000053
And acquiring the influence of the coupling node corresponding to the information network as the influence contribution of the coupling node of the information network
Figure FDA0002823354950000061
A power network correction processing module for making contribution to the power matrix H through the neighboring nodes of the power networkPContribution of influence to said power grid coupling node
Figure FDA0002823354950000062
Local influence A on each node of the power gridinfo′Carrying out correction processing to obtain the effective influence V of each node of the power gridA；
An information network correction processing module for passing through the neighbor node contribution force matrix H of the information networkCContribution of influence of coupling node with said information network
Figure FDA0002823354950000063
Local influence B on nodes of the information networkinfo′Carrying out correction processing to obtain the effective influence V of each node of the information networkB；
An influence ranking module for ranking the effective influence V based on each node of the power gridAAnd the effective influence V of each node of the information networkBGenerating a set of node influences V of a interdependent coupling network model for which the nodes areSorting the effective influence of each node in the point influence set V to obtain a sorting result;
and the fragile key point identification module is used for acquiring the fragile key points corresponding to the smart grid to be identified based on the sequencing result.
CN202011422951.XA 2020-12-08 2020-12-08 Smart power grid vulnerability key point identification method and device Active CN112615365B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011422951.XA CN112615365B (en) 2020-12-08 2020-12-08 Smart power grid vulnerability key point identification method and device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011422951.XA CN112615365B (en) 2020-12-08 2020-12-08 Smart power grid vulnerability key point identification method and device

Publications (2)

Publication Number Publication Date
CN112615365A true CN112615365A (en) 2021-04-06
CN112615365B CN112615365B (en) 2022-04-19

Family

ID=75229245

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011422951.XA Active CN112615365B (en) 2020-12-08 2020-12-08 Smart power grid vulnerability key point identification method and device

Country Status (1)

Country Link
CN (1) CN112615365B (en)

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113344743A (en) * 2021-07-26 2021-09-03 国网四川省电力公司经济技术研究院 Fault hazard index calculation and vulnerability assessment method for smart power grid
CN113591937A (en) * 2021-07-09 2021-11-02 国家电网有限公司 Power system key node identification method based on local distance coding
CN113917908A (en) * 2021-10-11 2022-01-11 国网四川省电力公司经济技术研究院 Smart power grid vulnerability analysis method and device for load transfer influence effect
CN114124728A (en) * 2021-11-11 2022-03-01 国网四川省电力公司经济技术研究院 Method for identifying fragile coupling link between power grid control system and power primary equipment
CN114741826A (en) * 2022-04-12 2022-07-12 平顶山学院 Key node identification method of power grid system
CN115801591A (en) * 2023-02-15 2023-03-14 聊城金恒智慧城市运营有限公司 Quantitative calculation method for network security vulnerability assessment
CN113917908B (en) * 2021-10-11 2024-05-28 国网四川省电力公司经济技术研究院 Intelligent power grid vulnerability analysis method and device for load transfer influence effect

Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
SU1171608A1 (en) * 1982-07-02 1985-08-07 Ленинградский Ордена Ленина Политехнический Институт Им.М.И.Калинина Throttle electric-to-hydraulic converter
CN103957032A (en) * 2014-04-17 2014-07-30 华北电力大学 Load redistribution method for electric power coupling network to resist cascade failure
CN104319785A (en) * 2014-09-27 2015-01-28 国家电网公司 Source flow path electrical subdivision-based wind power system key node identification method
CN105429133A (en) * 2015-12-07 2016-03-23 国网智能电网研究院 Information network attack-oriented vulnerability node evaluation method for power grid
CN106100877A (en) * 2016-06-02 2016-11-09 东南大学 A kind of power system reply network attack vulnerability assessment method
CN106789190A (en) * 2016-12-05 2017-05-31 国网河南省电力公司信息通信公司 A kind of power telecom network vulnerability assessment and routing optimization method
US20170302204A1 (en) * 2016-04-14 2017-10-19 General Electric Company Wind Converter Control for Weak Grid
CN107423921A (en) * 2017-09-22 2017-12-01 国网山东省电力公司电力科学研究院 A kind of power networks risk analysis method theoretical based on subdivision
CN107480849A (en) * 2017-06-28 2017-12-15 北京邮电大学 A kind of space dimension reduction method and device applied to power network
CN107622360A (en) * 2017-10-20 2018-01-23 广东电网有限责任公司电力调度控制中心 A kind of critical circuits recognition methods for considering subjective and objective factor
US20180260561A1 (en) * 2017-03-08 2018-09-13 General Electric Company Generic framework to detect cyber threats in electric power grid
CN109919801A (en) * 2019-03-15 2019-06-21 中山大学 Coupling process based on electric system node importance
CN110350522A (en) * 2019-07-10 2019-10-18 西南交通大学 A kind of electric system vulnerable line identifying method based on Weighted H index
CN111697590A (en) * 2020-06-19 2020-09-22 上海交通大学 Entropy weight method-based power system key node identification method and system

Patent Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
SU1171608A1 (en) * 1982-07-02 1985-08-07 Ленинградский Ордена Ленина Политехнический Институт Им.М.И.Калинина Throttle electric-to-hydraulic converter
CN103957032A (en) * 2014-04-17 2014-07-30 华北电力大学 Load redistribution method for electric power coupling network to resist cascade failure
CN104319785A (en) * 2014-09-27 2015-01-28 国家电网公司 Source flow path electrical subdivision-based wind power system key node identification method
CN105429133A (en) * 2015-12-07 2016-03-23 国网智能电网研究院 Information network attack-oriented vulnerability node evaluation method for power grid
US20170302204A1 (en) * 2016-04-14 2017-10-19 General Electric Company Wind Converter Control for Weak Grid
CN106100877A (en) * 2016-06-02 2016-11-09 东南大学 A kind of power system reply network attack vulnerability assessment method
CN106789190A (en) * 2016-12-05 2017-05-31 国网河南省电力公司信息通信公司 A kind of power telecom network vulnerability assessment and routing optimization method
US20180260561A1 (en) * 2017-03-08 2018-09-13 General Electric Company Generic framework to detect cyber threats in electric power grid
CN107480849A (en) * 2017-06-28 2017-12-15 北京邮电大学 A kind of space dimension reduction method and device applied to power network
CN107423921A (en) * 2017-09-22 2017-12-01 国网山东省电力公司电力科学研究院 A kind of power networks risk analysis method theoretical based on subdivision
CN107622360A (en) * 2017-10-20 2018-01-23 广东电网有限责任公司电力调度控制中心 A kind of critical circuits recognition methods for considering subjective and objective factor
CN109919801A (en) * 2019-03-15 2019-06-21 中山大学 Coupling process based on electric system node importance
CN110350522A (en) * 2019-07-10 2019-10-18 西南交通大学 A kind of electric system vulnerable line identifying method based on Weighted H index
CN111697590A (en) * 2020-06-19 2020-09-22 上海交通大学 Entropy weight method-based power system key node identification method and system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
DONGSHENG YANG,等: "General Power Control Methods for Distributed Generation Sources and Its Stability Analysis Based on Unified Power Models", 《2016 IEEE 8TH INTERNATIONAL POWER ELECTRONICS AND MOTION CONTROL CONFERENCE (IPEMC-ECCE ASIA)》 *
谭玉东，等: "基于电气距离的复杂电网关键节点识别", 《中国电机工程学报》 *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113591937A (en) * 2021-07-09 2021-11-02 国家电网有限公司 Power system key node identification method based on local distance coding
CN113591937B (en) * 2021-07-09 2023-09-26 国家电网有限公司 Power system key node identification method based on local distance coding
CN113344743A (en) * 2021-07-26 2021-09-03 国网四川省电力公司经济技术研究院 Fault hazard index calculation and vulnerability assessment method for smart power grid
CN113344743B (en) * 2021-07-26 2022-05-17 国网四川省电力公司经济技术研究院 Fault hazard index calculation and vulnerability assessment method for smart power grid
CN113917908A (en) * 2021-10-11 2022-01-11 国网四川省电力公司经济技术研究院 Smart power grid vulnerability analysis method and device for load transfer influence effect
CN113917908B (en) * 2021-10-11 2024-05-28 国网四川省电力公司经济技术研究院 Intelligent power grid vulnerability analysis method and device for load transfer influence effect
CN114124728A (en) * 2021-11-11 2022-03-01 国网四川省电力公司经济技术研究院 Method for identifying fragile coupling link between power grid control system and power primary equipment
CN114741826A (en) * 2022-04-12 2022-07-12 平顶山学院 Key node identification method of power grid system
CN115801591A (en) * 2023-02-15 2023-03-14 聊城金恒智慧城市运营有限公司 Quantitative calculation method for network security vulnerability assessment

Also Published As

Publication number Publication date
CN112615365B (en) 2022-04-19

Similar Documents

Publication Publication Date Title
CN112615365B (en) Smart power grid vulnerability key point identification method and device
CN100438251C (en) A large electric system vulnerable line identifying method
Das et al. Fuzzy based energy efficient multicast routing for ad-hoc network
Ali et al. A tree based data aggregation scheme for wireless sensor networks using GA
CN112487658B (en) Method, device and system for identifying key nodes of power grid
CN107944705A (en) A kind of all terminal reliability computational methods that communication corporations are divided based on modularity
Puggelli et al. Routing-aware design of indoor wireless sensor networks using an interactive tool
Yueqing et al. A study on topological characteristics of wireless sensor network based on complex network
CN112153715B (en) Hybrid big data transmission topological structure method, system, storage medium and application
CN109033603B (en) Intelligent substation secondary system simulation method based on source flow path chain
Su et al. TrendRank method for evaluating the importance of power grid nodes considering information network
Wang et al. A light-weighted data trust model in WSN
CN115577292A (en) Vulnerable line identification method based on abstract dual network and cascading failure super network
CN101087305A (en) Resource search method in large-scale non-structural P2P network
CN106412935A (en) Network topological structure establishing method based on complex network theory
CN112487590B (en) Method and device for optimizing robustness of smart power grid
Yin et al. Research on an cascade failure model of improved scale-free network
Qingqi et al. Multi-objective mapping for network-on-chip based on bio-inspired optimization algorithms
Li et al. Distributed Fault Section Location for Active Distribution Network Based on Bayesian Complete Analytic Model
CN102256268A (en) Random-walk-based fault tolerance method for wireless sensor network
CN117767250B (en) Direct-current micro-grid coordinated control method and system based on fault monitoring
Alba et al. Optimal wireless sensor network layout with metaheuristics: Solving a large scale instance
US11983472B2 (en) Method for identifying fragile lines in power grids based on electrical betweenness
CN116032727B (en) Electric power internet of things sensing layer self-repairing method based on regional collaboration
CN102542147A (en) Method for measuring topological similarity among factory stations in electric network

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant