CN115545479A - Method and device for determining important nodes or important lines of power distribution network - Google Patents
Method and device for determining important nodes or important lines of power distribution network Download PDFInfo
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Abstract
The invention discloses a method and a device for determining important nodes or important lines of a power distribution network, which are used for preprocessing basic data of a target power distribution network to obtain first data and calculating identification degree to obtain a multi-dimensional identification index; based on the multi-dimensional identification indexes, the importance degree corresponding to the target power distribution network topological structure is calculated and obtained through an entropy weight method and a weighted TOPSIS algorithm, and therefore important nodes or important lines in the power distribution network are determined according to the obtained importance degree. Compared with the prior art that the importance degree of the nodes or lines of the important target power distribution network is judged through expert evaluation, the importance degree comparison of the nodes or lines in each target power distribution network is carried out based on the multi-dimensional identification index, the entropy weight method and the TOPSIS algorithm, the identification accuracy of the comprehensive important nodes or important lines of the power distribution network is improved, protection can be carried out according to the confirmation results of the important nodes or important lines under typhoon disasters, and the protection efficiency of the power distribution network is improved.
Description
Technical Field
The invention relates to the field of power distribution systems, in particular to a method and a device for determining important nodes or important lines of a power distribution network.
Background
For the discovery after the large-scale wind disaster research, currently, some scholars use a complex network theory and a hierarchical analysis/entropy weight method to perform importance analysis on a trip circuit, judge weak links of a power distribution network exposed to the wind disaster, and measure the vulnerability by using specific evaluation indexes. In a real power distribution network, a power plant, a substation and a load in the power grid can be regarded as nodes in the network, and a power transmission line is regarded as an edge in the network. And judging the importance of the nodes according to the aggregation degree after the network nodes are shrunk. The influence of the edge weight difference is also considered in the weighting network, the main work is to adopt a complex network theory to weight the line in the network, and then a main/objective weighting method is used for judging the importance of the tripping circuit, so that reference is provided for the optimization of the follow-up power distribution network post-disaster recovery emergency repair strategy. The selection of the electrical medium/degree (power grid topology model edge weight parameter) usually needs to select node voltage, line load power or current, line impedance value, etc. as key node identification parameters to establish node or line evaluation indexes, so that the electrical connection between the node and the line can be embodied.
However, the existing complex network theory-based method for identifying the comprehensive importance of the nodes and the lines of the power distribution network under the typhoon disaster has defects, the comprehensive importance identification depends on expert scoring, the result is subjective, and the accuracy of the comprehensive importance identification of the nodes and the lines in the power distribution network is low.
Therefore, a strategy for determining important nodes or important lines of a power distribution network is needed to solve the problem that the important nodes or important lines of the power distribution network are determined inaccurately under typhoon disasters.
Disclosure of Invention
The embodiment of the invention provides an identification control method for comprehensive importance of nodes and lines of a power distribution network, which aims to improve the accuracy of determining important nodes or important lines of the power distribution network under typhoon disasters.
In order to solve the above problem, an embodiment of the present invention provides a method for determining an important node or an important line of a power distribution network, including:
acquiring basic data of a target power distribution network, and preprocessing the basic data to acquire first data; wherein the first data comprises: topological data and per-unit system data;
calculating according to the topological data and the per-unit system data to obtain a multi-dimensional identification index;
according to the index value of the multi-dimensional identification index, obtaining the importance corresponding to the topological structure of the target power distribution network through an entropy weight method and a weighted TOPSIS algorithm; the importance corresponding to the topological structure of the target power distribution network comprises any one or more of the following items: importance of a node of the target distribution network or importance of a line of the target distribution network;
and determining important nodes or important lines according to the importance corresponding to the topological structure of the target power distribution network and a preset importance threshold.
Therefore, the invention has the following beneficial effects:
the invention provides a method for determining important nodes or important lines of a power distribution network, which comprises the steps of preprocessing basic data of a target power distribution network to obtain first data, and calculating identification degree to obtain a multi-dimensional identification index; based on the multi-dimensional identification indexes, the importance degree corresponding to the target power distribution network topological structure is calculated and obtained through an entropy weight method and a weighted TOPSIS algorithm, and therefore important nodes or important lines in the power distribution network are determined according to the obtained importance degree. Compared with the prior art, the importance degree of the nodes or lines of the important target power distribution network is judged through expert evaluation, the importance degree comparison of the nodes or lines in each target power distribution network is carried out based on the multidimensional identification index, the entropy weight method and the TOPSIS algorithm, the identification accuracy of the comprehensive important nodes or important lines of the power distribution network is improved, protection can be carried out according to the confirmation results of the important nodes or important lines under typhoon disasters, and the protection efficiency of the power distribution network is improved.
As an improvement of the above scheme, the obtaining of the importance corresponding to the topological structure of the target power distribution network according to the index value of the multidimensional identification index by an entropy weight method and a weighted TOPSIS algorithm specifically includes:
establishing a judgment matrix corresponding to the topological structure of the target power distribution network according to the multi-dimensional identification index; wherein the judgment matrix corresponds to the topological structure and includes any one or more of the following items: a node judgment matrix and a line judgment matrix; and the multi-dimensional identification index comprises: improving a node degree index, a node cohesion degree index, a node betweenness index, a weighted line betweenness index, a line transfer power flow entropy index and a line fault rate index under the wind disaster;
acquiring information entropy weighting according to a judgment matrix corresponding to the topological structure of the target power distribution network: if the judgment matrix is a node judgment matrix, calculating and obtaining first information entropy weighting of a plurality of nodes through an entropy weighting method according to the improved node degree index, the node cohesion degree index and the node betweenness index; if the judgment matrix is a line judgment matrix, calculating and obtaining second information entropy weighting of a plurality of lines through an entropy weighting method according to the weighted line betweenness index, the line transfer power flow entropy index and the line fault rate index under the wind disaster; wherein the information entropy weighting comprises: the first information entropy weighting and the second information entropy weighting;
calculating and obtaining the importance corresponding to the topological structure of the target power distribution network through a weighted TOPSIS algorithm according to the information entropy weighting;
wherein, the formula of the entropy weight method is as follows:
in the formula, z ij Is to judge each element, z 'in the matrix' ij Is an element z ij Normalized matrix element, p ij Is each element in the probability matrix, e j The information entropy is weighted, i is a node serial number or a line serial number, and j is a type label of a multi-dimensional identification index; wherein, when the judgment matrix is a node judgment matrix, each element is: an index value of an improved node degree index of each node or an index value of a node cohesion degree index of each node or an index value of a node betweenness index of each node; when the judgment matrix is a line judgment matrix, each element is: and the index value of the weighted line betweenness index of each line or the index value of the line transfer tidal current entropy index of each line or the index value of the line fault rate index of each line under wind disaster.
By implementing the improved scheme of the embodiment, the embodiment separately judges the nodes and the lines, establishes a judgment matrix (a node judgment matrix or a line judgment matrix) of the target distribution network topology structure through the multi-dimensional identification indexes corresponding to the nodes and the multi-dimensional identification indexes corresponding to the lines, and calculates information entropy weighting through an entropy weighting method, so that the weighted TOPSIS algorithm is calculated according to the information entropy weighting to obtain the importance corresponding to the target distribution network topology structure, and the calculation precision of the TOPSIS algorithm is improved through the calculation of the entropy weighting method.
As an improvement of the above scheme, the importance corresponding to the topological structure of the target power distribution network is obtained through calculation by a weighted TOPSIS algorithm according to the entropy weighting, specifically:
substituting the weighted information entropy and the judgment matrix into a TOPSIS algorithm to carry out importance corresponding to the topological structure of the target power distribution network;
when the judgment matrix is a node judgment matrix, substituting the information entropy weighting of each node, the index value of the improved node degree index of each node, the index value of the node agglomeration degree index of each node and the index value of the node betweenness index of each node into a TOPSIS algorithm to calculate and obtain the importance degree of each node of the target power distribution network;
when the judgment matrix is a line judgment matrix, substituting the information entropy empowerment of each line, the index value of the weighted line betweenness index of each line, the index value of the line transfer power flow entropy index of each line and the index value of the line fault rate index of each line under the wind disaster into a TOPSIS algorithm to calculate and obtain the importance of each line of the target power distribution network;
wherein the formula of the TOPSIS algorithm is as follows:
in the formula, z ij For each element in the decision matrix, T i And the importance degree corresponding to the topological structure of the target power distribution network.
By implementing the improved scheme of the embodiment, the information entropy weighting and judgment matrix is substituted into the TOPSIS algorithm for calculation, and the nodes and the lines in the target power distribution network are considered separately, so that the importance of the nodes and the importance of the lines are obtained, and the calculation accuracy of the importance of the nodes and the importance of the lines is improved.
As an improvement of the above scheme, the obtaining of the improved node degree index specifically includes:
calculating to obtain impedance data according to the per-unit system data;
obtaining an electrical coupling distance through an electrical coupling distance formula according to the impedance data; wherein, the electrical coupling distance formula is specifically as follows:
d ij ＝U ij /I i ＝(Z ii -Z ij )+(Z jj -Z ij )＝Z ii +Z ij -2Z ij
in the formula Z ii And Z jj Node i, j self impedance respectively; z is a linear or branched member ij Is the node i, j mutual impedance; d is a radical of ij Is the electrical coupling distance;
calculating to obtain an improved node degree index through a first index formula according to the electrical coupling distance; wherein the first index formula is specifically as follows:
wherein n is the total number of nodes in the topology network, Z 1i To improve the node degree index.
By implementing the improvement scheme of the embodiment, the embodiment can well make up for the defect of insufficient description of the electrical connection between the nodes by the traditional node degree by improving the node degree index. The traditional node degree index is defined as the number of nodes directly adjacent to the node, and the more the number of adjacent nodes is, the stronger the association between the node and the network is, and the heavier the association is in the network. While the present embodiment uses an electrical coupling distance d ij The electrical distance can be used for displaying the electrical connection between any two nodes, the larger the numerical value is, the more the tidal current transmission electric energy between the two nodes is, the more accurate the node degree is compared with the traditional node degree, and a foundation is laid for improving the accuracy of the importance degree corresponding to the topological structure of the target power distribution network.
As an improvement of the above scheme, the obtaining of the node aggregation degree index specifically includes:
according to the topological data, calculating by a Dijkstra algorithm to obtain the shortest electrical distance;
calculating to obtain the node condensation degree through a second index formula according to the shortest electrical distance; wherein, the second index formula is specifically as follows:
in the formula, l is the shortest average path between any two nodes; d min,ij The shortest electrical distance between any nodes i and j in the power distribution network is obtained; n is a radical of hydrogen 2i Is an index of the degree of node agglomeration.
By implementing the improvement scheme of the embodiment, the embodiment can well depict the actual importance of any node in the network through the node aggregation degree index. According to the definition of node degree, although the more the number of adjacent points of a node, the higher the importance of the node may be. However, in many real networks, the degree of some important nodes is not necessarily large, so a node polycondensation method needs to be adopted, and the node condensation degree after the nodes are subjected to polycondensation is used as an original node importance evaluation index, so that a foundation is laid for improving the accuracy of the importance corresponding to the topological structure of the target power distribution network.
As an improvement of the above scheme, the obtaining of the node betweenness index specifically includes:
according to the topological data, calculating through an adjacency matrix to obtain the shortest first path number and the shortest second path number;
calculating to obtain a node betweenness index through a third index formula according to the shortest first path number and the shortest second path number; wherein, the third index formula is specifically as follows:
in the formula, D jl (i) Indicates the shortest first path number, D, between nodes i, j passing through node i jl Indicates the shortest distance between nodes i and jA second number of paths; n is a radical of 3i Is the node betweenness index.
By implementing the improvement scheme of the embodiment, the embodiment judges the criticality of the node in the network through the node betweenness index, and if a large number of nodes are passed by the shortest path between other node pairs, the node betweenness value is large, and the influence force in the network is high. The node betweenness index obtained by calculation in the embodiment lays a foundation for improving the accuracy of the importance degree corresponding to the topological structure of the target power distribution network.
As an improvement of the above scheme, the obtaining of the weighted line betweenness index specifically includes:
according to the per unit system data, calculating through an adjacency matrix to obtain the shortest third path number and the shortest fourth path number;
calculating to obtain a weighted line betweenness index through a fourth index formula according to the shortest third path number and the shortest fourth path number; wherein, the fourth index formula is specifically as follows:
in the formula (E) i≠j∈V Z ij (k) Represents the number of times the shortest third path (i.e. path having the smallest total reactance value) between nodes i, j passes through line k, Σ i≠j∈V Z ij Representing the shortest fourth path number between the sections i and j; l is 1k Is a weighted channel index.
By implementing the improvement scheme of the embodiment, the embodiment considers that the self electrical quantity of the high-voltage transmission line possibly affects the system power flow distribution, and converts the original topological network into an undirected weighted network for analysis, so that a weighted line betweenness index is obtained, wherein the larger the index value of the betweenness index of the weighted line is, the more the influence of the line fault on the power system is, and the more important the line is; the topological network used in the calculation of the traditional line betweenness is an undirected and unweighted network, and the electrical characteristics of the transmission line can not be reflected only by considering the distribution condition of the line in a physical space. Therefore, the weighted line betweenness index obtained by calculation in the embodiment lays a foundation for improving the accuracy of the importance degree corresponding to the topological structure of the target power distribution network.
As an improvement of the above scheme, the obtaining of the line transfer power flow entropy index specifically includes:
calculating to obtain a transfer power flow entropy through a power flow entropy calculation formula according to the per-unit system data; the load flow entropy calculation formula is as follows:
in the formula, P αk Is the power, P, flowing through the line alpha after the line k is open α0 Is the power flowing through line a before line k is open; s is a topological network line set; delta P αk Is a uniform line L α Bearer line L k A transferred power flow increment; beta is a beta αk The load flow transfer impact rate of the branch to the branch is obtained; h T (k) The transfer power flow entropy of the line k is obtained;
calculating to obtain a line transfer power flow entropy index through a fifth index calculation formula according to the transfer power flow entropy; wherein, the fifth index calculation formula is specifically as follows:
in the formula, P k K is the branch tidal power; l is 2k And the index is the entropy index of the line transfer power flow.
By implementing the improved scheme of the embodiment, the line transfer power flow entropy index is calculated through per unit system data, so that a foundation is laid for obtaining the multi-dimensional identification index, the importance corresponding to the topological structure of the target power distribution network can be carried out by substituting the obtained multi-dimensional identification index into an entropy weight method and a weighted TOPSIS algorithm, and the calculation accuracy of the importance can be improved.
As an improvement of the above scheme, the obtaining of the line fault rate index in wind disaster specifically includes: determining the fault rate of the line by a Monte Carlo sampling method; and the fault rate of the line is the line fault rate index under the wind disaster.
By implementing the improved scheme of the embodiment, the embodiment calculates the line fault rate index under the wind disaster through the monte carlo sampling method, and the fault rate of each line in the power grid can be calculated more accurately only by comprehensively considering various factors. Because fault damage caused by typhoon is highly random, fault rate is simulated by a Monte Carlo sampling method to show fault randomness of the distribution network under the typhoon, thereby laying a foundation for improving accuracy of importance corresponding to a topological structure of a target distribution network.
Correspondingly, an embodiment of the present invention further provides a device for determining an important node or an important line of a power distribution network, including: the device comprises a data preprocessing module, a first calculating module, a second calculating module and a result generating module;
the data preprocessing module is used for acquiring basic data of a target power distribution network and preprocessing the basic data to acquire first data; wherein the first data comprises: topological data and per-unit system data;
the first calculation module is used for calculating and obtaining a multi-dimensional identification index according to the topological data and the per-unit system data;
the second calculation module is used for obtaining the importance corresponding to the topological structure of the target power distribution network through an entropy weight method and a weighted TOPSIS algorithm according to the index value of the multi-dimensional identification index; the importance corresponding to the topological structure of the target power distribution network comprises any one or more of the following items: importance of a node of the target distribution network or importance of a line of the target distribution network;
and the result generation module is used for determining important nodes or important lines according to the importance corresponding to the topological structure of the target power distribution network and a preset importance threshold.
Drawings
Fig. 1 is a schematic flow chart of a method for determining an important node or an important line of a power distribution network according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a device for determining an important node or an important line of a power distribution network according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a topology of an IEEE-39 node system provided by an embodiment of the present invention;
FIG. 4 is a diagram illustrating the result of importance of important nodes according to an embodiment of the present invention;
FIG. 5 is a graph illustrating the results of importance of important links provided by an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for determining an important node or an important line of a power distribution network according to an embodiment of the present invention, as shown in fig. 1, the embodiment includes steps 101 to 104, and each step specifically includes the following steps:
step 101: acquiring basic data of a target power distribution network, and preprocessing the basic data to acquire first data; wherein the first data comprises: topology data and per unit system data.
In a specific embodiment, the basic data of the target distribution network includes: generator parameters, transformer parameters, transmission line parameters, and distribution network physical structure.
In a specific embodiment, the pre-processing comprises: constructing a topological network, manufacturing a network adjacent matrix and performing per unit on the electrical parameters.
In a specific embodiment, after obtaining generator parameters, transformer parameters, transmission line parameters and a physical structure of a power distribution network, a topological network is constructed through a complex network theory, the connection condition between each node and each line is reflected by using the form of an adjacent matrix for the node and line labels in the topological network, and geometric information is expressed by using a mathematical matrix, so that the subsequent data processing is facilitated, and topological data are obtained; performing per-unit system processing according to the generator parameters, the transformer parameters, the transmission line parameters and the physical structure of the power distribution network to obtain per-unit system data; wherein, the per-unit system formula is as follows:
X * ＝X/X B
in the formula X * Is a per unit value of a physical quantity X, X B Are the corresponding reference values.
In one embodiment, for better illustration, refer to FIG. 3, which is a schematic diagram of a topology of an IEEE (Institute of Electrical and Electronics Engineers) 39 node system.
Step 102: and calculating to obtain a multi-dimensional identification index according to the topological data and the per-unit system data.
In this embodiment, the obtaining of the improved node degree index specifically includes:
calculating to obtain impedance data according to the per-unit system data;
obtaining an electrical coupling distance through an electrical coupling distance formula according to the impedance data; wherein, the electrical coupling distance formula is specifically as follows:
d ij ＝U ij /I i ＝(Z ii -Z ij )+(Z jj -Z ij )＝Z ii +Z ij -2Z ij
in the formula Z ii And Z jj Node i, j self impedance respectively; z ij Is the node i, j mutual impedance; d ij Is the electrical coupling distance;
calculating to obtain an improved node degree index through a first index formula according to the electrical coupling distance; wherein the first index formula is specifically as follows:
wherein N is the total number of nodes in the topology network, N 1i To improve the node degree index.
In this embodiment, the obtaining of the node aggregation level index specifically includes:
according to the topological data, calculating by a Dijkstra algorithm to obtain the shortest electrical distance;
calculating to obtain the node condensation degree through a second index formula according to the shortest electrical distance; wherein, the second index formula is specifically as follows:
in the formula, l is the shortest average path between any two nodes; d is a radical of min,ij The shortest electrical distance between any nodes i and j in the power distribution network; n is a radical of hydrogen 2i Is an index of the degree of node agglomeration.
In a specific embodiment, the shortest path between two nodes in the network is calculated using Dijkstra algorithm with the calculated electrical coupling distance as the line weight.
In this embodiment, the obtaining of the node betweenness index specifically includes:
according to the topological data, calculating through an adjacency matrix to obtain the shortest first path number and the shortest second path number;
calculating to obtain a node betweenness index through a third index formula according to the shortest first path number and the shortest second path number; wherein, the third index formula is specifically as follows:
in the formula, D jl (i) Represents the shortest first path number between nodes i and j passing through node i, D jl Representing the shortest second path number between the nodes i and j; n is a radical of 3i Is the node betweenness index.
In a specific embodiment, the shortest path number is calculated based on the topology, and is calculated in matlab by programming the adjacency matrix. All the line weights are 1, the number of lines passing through the nodes i to j is the minimum, namely the weight and the minimum path are the shortest paths, and the obtained result is the number of the shortest paths.
In this embodiment, the obtaining of the weighted line number index specifically includes:
according to the per-unit system data, calculating through an adjacency matrix to obtain the shortest third path number and the shortest fourth path number;
calculating to obtain a weighted line betweenness index through a fourth index formula according to the shortest third path number and the shortest fourth path number; wherein, the fourth index formula is specifically as follows:
in the formula (E) i≠j∈V Z ij (k) Represents the number of times the shortest third path (i.e. path having the smallest total reactance value) between nodes i, j passes through line k, Σ i≠j∈V Z ij Representing the shortest fourth path number between the sections i and j; l is 1k Is a weighted channel index.
In a specific embodiment, because the reactance value in the alternating current transmission line is far larger than the resistance value, the reactance value of the line is used as a weight, the original topological network is converted into a non-directional weighted network for analysis, and the obtained weight and the path with the least weight are the shortest electrical path. Weighted line betweenness indicator L ik The larger the value, the more significant the line represents the impact of the line fault on the power system.
In this embodiment, the obtaining of the line transfer power flow entropy index specifically includes:
calculating to obtain a transfer power flow entropy through a power flow entropy calculation formula according to the per-unit system data; the load flow entropy calculation formula is as follows:
in the formula, P αk Is the power, P, flowing through the line alpha after the line k is open α0 Is the power flowing through line a before line k opens; s is a topological network line set; delta P αk Is a uniform line L α Bearer line L k A transferred power flow increment; beta is a beta αk The load flow transfer impact rate of the branch to the branch is obtained; h T (k) The transfer power flow entropy of the line k is obtained;
calculating to obtain a line transfer power flow entropy index through a fifth index calculation formula according to the transfer power flow entropy; wherein, the fifth index calculation formula is specifically as follows:
in the formula, P k K is the branch tidal power; l is 2k And transferring the load flow entropy index for the line.
In a specific embodiment, if a branch L between nodes i, j in the distribution network is provided k Tripping to break circuit, another line L in the system α Bearer line L k The transferred tidal current increment is: delta P αk ＝P αk -P α0 (ii) a Wherein, P αk And P α0 And load flow calculation is carried out on the basis of the parameters of the power distribution network to obtain the power flowing before and after the circuit is disconnected. In case of tripping the line k, the power flow P k The larger the fault is, the larger the residual line bears the load flow impact after the fault is broken, and the transferred load flow entropy is H T (k) The smaller the power flow transition of the system is, the more intensively the power flow transition is distributed on a few branches, so the importance index L of the line k 2k The larger.
In a specific embodiment, the tidal current power is obtained by matpower tool calculation.
In this embodiment, the obtaining of the line fault rate index in the wind disaster specifically includes: determining the fault rate of the line by a Monte Carlo sampling method; and the fault rate of the line is the line fault rate index under the wind disaster.
In a specific embodiment, the power grid in the coastal region of southeast of China is researched to be impacted by typhoon, and most fault reasons are concentrated on damage of a pole tower, pole falling and tower falling, line breakage, windage yaw flashover, foreign matter hanging and the like. The damage condition of the power distribution network is closely related to the typhoon strength, and is also related to the toughness, aging condition, typhoon resistance and the like of various power elements, and the fault rate of each line in the power distribution network can be accurately calculated by comprehensively considering various factors. Considering that failure damage caused by typhoon is highly random from a certain point of view, a Monte Carlo sampling method is adopted to determine the failure rate of each line in the node network, and the sampling range is between 0 and 1 as representing the line fragility under the wind disaster, so as to show the failure randomness of the distribution network under the typhoon.
Step 103: according to the index value of the multi-dimensional identification index, obtaining the importance corresponding to the topological structure of the target power distribution network through an entropy weight method and a weighted TOPSIS algorithm; the importance corresponding to the topological structure of the target power distribution network comprises any one or more of the following items: the importance of the nodes of the target distribution network or the importance of the lines of the target distribution network.
In this embodiment, the obtaining, according to the index value of the multidimensional identification index, the importance corresponding to the topological structure of the target power distribution network through an entropy weight method and a weighted TOPSIS algorithm specifically includes:
establishing a judgment matrix corresponding to the topological structure of the target power distribution network according to the multi-dimensional identification index; wherein the judgment matrix corresponds to the topological structure and includes any one or more of the following items: a node judgment matrix and a line judgment matrix; and the multi-dimensional identification index comprises: improving a node degree index, a node cohesion degree index, a node betweenness index, a weighted line betweenness index, a line transfer power flow entropy index and a line fault rate index under the wind disaster;
acquiring information entropy weighting according to a judgment matrix corresponding to the topological structure of the target power distribution network: if the judgment matrix is a node judgment matrix, calculating and obtaining first information entropy weighting of a plurality of nodes through an entropy weighting method according to the improved node degree index, the node agglomeration degree index and the node betweenness index; if the judgment matrix is a line judgment matrix, calculating and obtaining second information entropy weighting of a plurality of lines through an entropy weighting method according to the weighted line betweenness index, the line transfer power flow entropy index and the line fault rate index under the wind disaster; wherein the information entropy weighting comprises: the first information entropy weighting and the second information entropy weighting;
calculating and obtaining the importance corresponding to the topological structure of the target power distribution network through a weighted TOPSIS algorithm according to the information entropy weighting;
wherein, the formula of the entropy weight method is as follows:
in the formula, z ij To determine each element, z 'in the matrix' ij Is an element z ij Normalized matrix element, p ij Is each element of the probability matrix, e j The information entropy is weighted, i is a node serial number or a line serial number, and j is a type label of a multi-dimensional identification index; wherein, when the judgment matrix is a node judgment matrix, each element is: an index value of an improved node degree index of each node or an index value of a node cohesion degree index of each node or an index value of a node betweenness index of each node; when the judgment matrix is a line judgment matrix, each element is: index value of weighted line betweenness index of each line or index value of line transfer power flow entropy index of each line or each lineAnd (4) an index value of the line fault rate index under the wind disaster of the road.
In a specific embodiment, the normalized matrix elements are specifically:
in a specific embodiment, a decision matrix is established for each of the node metrics and the line metrics. The judgment matrix is specifically as follows: the node/line numbers are arranged in rows, the multi-dimensional identification indexes are arranged in rows, and the internal elements are corresponding identification scores. For example, in the node judgment matrix, the 3 rd row and 2 nd column element Z 32 The method comprises the following steps: the score of the node No. 3 under the N2 index; and the 1 st row and 2 nd column elements Z in the route judgment matrix 12 The method comprises the following steps: line No. 1 scores under the N2 index.
In this embodiment, the obtaining of the importance corresponding to the topological structure of the target power distribution network through the weighted TOPSIS algorithm according to the information entropy weighting specifically includes:
substituting the weighted information entropy and the judgment matrix into a TOPSIS algorithm to carry out importance corresponding to the topological structure of the target power distribution network;
when the judgment matrix is a node judgment matrix, substituting the information entropy weighting of each node, the index value of the improved node degree index of each node, the index value of the node agglomeration degree index of each node and the index value of the node betweenness index of each node into a TOPSIS algorithm to calculate and obtain the importance degree of each node of the target power distribution network;
when the judgment matrix is a line judgment matrix, substituting the information entropy weighting of each line, the index value of the weighted line betweenness index of each line, the index value of the line transfer power flow entropy index of each line and the index value of the line fault rate index under the wind disaster of each line into a TOPSIS algorithm to calculate and obtain the importance of each line of the target power distribution network;
wherein, the formula of the TOPSIS algorithm is as follows:
in the formula, z ij For each element in the decision matrix, T i And the importance degree corresponding to the topological structure of the target power distribution network.
In the present embodiment, T i The score is higher for the value of the importance of the ith node, the larger the value is.
Step 104: and determining important nodes or important lines according to the importance corresponding to the topological structure of the target power distribution network and a preset importance threshold.
In a specific embodiment, the predetermined importance threshold may be: and ranking the importance values corresponding to the top 10% nodes/lines according to the importance.
In a specific embodiment, after determining the important nodes or the important lines, the expert may perform subjective consideration in combination with the actual situation of the target distribution network.
In a specific embodiment, after determining the important node or the important line, the important node or the important line is prepared for a safeguard measure.
In one embodiment, for better illustration, please refer to fig. 4, and the key nodes with the top ten degrees of importance are listed in fig. 4, and compared with other single index identification results, as can be seen from fig. 4: the first ten nodes are mostly in the hub position in the topology network. The combined index identification result (i.e. the importance level of the present invention) overlaps more than half of the other three single index identification results, but the importance level sequence is greatly different, mainly because the combined index is considered more comprehensively. If only a certain index is adopted for evaluation, the evaluation result has certain one-sidedness. For example, the local electrical connection between the node 4 and other nodes may be insufficient, but the impact on the system may be large after the node fails. Therefore, the improvement value of the node 4 is low, the medium value is higher, and the comprehensive evaluation result is higher. Therefore, the embodiment can better identify the key nodes in the power system.
In one embodiment, for better illustration, please refer to fig. 5, and key lines with top ten importance are listed in fig. 5, as can be seen from fig. 5: the combined index identification result (i.e., the importance of the present invention) and the single index identification result are also greatly overlapped. Among the critical lines identified herein are 7 lines that are directly connected to or in close proximity to the generator (share the same bus with the generator). In addition, the lines 21-22, 16-21 and 15-16 are all in the key positions of the topological network, and are connected with the node 16 with the maximum condensation degree, so that the key lines for ensuring the power supply of the generator nodes 35 and 36 are out. If a single index is used to evaluate the importance of the line, for example, the line 35 has a lower value of the line number, i.e., the lines 21-22 have a lower position in the topology. However, the shortest path for transmitting electric energy between the generator nodes 35 and 36 is the path with which the system has a large impact on power flow after a fault trip occurs, and the power flow is likely to be ignored when identifying an important line. Therefore, the present embodiment can more reasonably and comprehensively consider the system key characteristic parameters.
In the embodiment, the basic data of the target power distribution network is preprocessed to obtain first data, and identification degree calculation is performed to obtain a multi-dimensional identification index; based on the multi-dimensional identification indexes, the importance degree corresponding to the target power distribution network topological structure is calculated and obtained through an entropy weight method and a weighted TOPSIS algorithm, and therefore important nodes or important lines in the power distribution network are determined according to the obtained importance degree. According to the method, the geographical position distribution of the generator, the bus and the line in the distribution network geographical information distribution system is fully considered, the operation and fault characteristics of the distribution network under the typhoon disaster are combined, the multidimensional identification index and the objective weighting method are selected to identify the comprehensive importance of the node and the line, the influence of the node and the line on the network after the fault is considered, and the accuracy of determining the important node or the important line of the distribution network under the typhoon disaster is improved.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a device for determining an important node or an important line of a power distribution network according to an embodiment of the present invention, including: a data preprocessing module 201, a first calculating module 202, a second calculating module 203 and a result generating module 204;
the data preprocessing module 201 is configured to acquire basic data of a target power distribution network, and preprocess the basic data to acquire first data; wherein the first data comprises: topological data and per unit system data;
the first calculating module 202 is configured to calculate to obtain a multidimensional identification index according to the topology data and the per unit system data;
the second calculating module 203 is configured to obtain an importance corresponding to the topological structure of the target power distribution network through an entropy weight method and a weighted TOPSIS algorithm according to the index value of the multidimensional identification index; the importance corresponding to the topological structure of the target power distribution network comprises any one or more of the following items: importance of a node of the target distribution network or importance of a line of the target distribution network;
the result generating module 204 is configured to determine an important node or an important line according to the importance corresponding to the topological structure of the target power distribution network and a preset importance threshold.
It can be clearly understood by those skilled in the art that, for convenience and brevity, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
According to the embodiment, the basic data of the target power distribution network is acquired through the data preprocessing module, the first data are acquired through preprocessing, the first data are calculated through the first calculating module according to the multidimensional identification index, the acquired multidimensional identification index is substituted into the second calculating module to acquire the importance degree corresponding to the topological structure of the target power distribution network, the important node or the important line is determined through the result generating module according to the importance degree corresponding to the topological structure of the target power distribution network, the identification accuracy of the comprehensive important node or the important line of the power distribution network is improved, protection can be performed according to the confirmation result of the important node or the important line under typhoon disasters, and the protection efficiency of the power distribution network is improved.
EXAMPLE III
Referring to fig. 6, fig. 6 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
A terminal device of this embodiment includes: a processor 601, a memory 602, and computer programs stored in said memory 602 and executable on said processor 601. The processor 601, when executing the computer program, implements the steps of the above-mentioned method for determining important nodes or important lines of the power distribution network in an embodiment, for example, all the steps of the method for determining important nodes or important lines of the power distribution network shown in fig. 1. Alternatively, the processor, when executing the computer program, implements the functions of the modules in the device embodiments, for example: all the modules of the determination device of the important nodes or important lines of the power distribution network shown in fig. 2.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where when the computer program runs, a device in which the computer-readable storage medium is located is controlled to execute the method for determining an important node or an important line of a power distribution network according to any of the above embodiments.
It will be appreciated by those skilled in the art that the schematic diagram is merely an example of a terminal device and does not constitute a limitation of the terminal device, and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input output devices, network access devices, buses, etc.
Processor 601 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor 601 is a control center of the terminal device and connects various parts of the whole terminal device by using various interfaces and lines.
The memory 602 can be used for storing the computer programs and/or modules, and the processor 601 can implement various functions of the terminal device by running or executing the computer programs and/or modules stored in the memory and calling the data stored in the memory 602. The memory 602 may mainly include a program storage area and a data storage area, where the program storage area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required by at least one function, and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein, the terminal device integrated module/unit can be stored in a computer readable storage medium if it is implemented in the form of software functional unit and sold or used as a stand-alone product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, and software distribution medium, etc.
It should be noted that the above-described embodiments of the apparatus are merely illustrative, where the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A method for determining important nodes or important lines of a power distribution network is characterized by comprising the following steps:
acquiring basic data of a target power distribution network, and preprocessing the basic data to acquire first data; wherein the first data comprises: topological data and per-unit system data;
calculating according to the topological data and the per-unit system data to obtain a multi-dimensional identification index;
according to the index value of the multi-dimensional identification index, obtaining the importance corresponding to the topological structure of the target power distribution network through an entropy weight method and a weighted TOPSIS algorithm; the importance corresponding to the topological structure of the target power distribution network comprises any one or more of the following items: importance of a node of the target distribution network or importance of a line of the target distribution network;
and determining important nodes or important lines according to the importance corresponding to the topological structure of the target power distribution network and a preset importance threshold.
2. The method for determining important nodes or important lines of a power distribution network according to claim 1, wherein the importance corresponding to the topological structure of the target power distribution network is obtained by an entropy weight method and a weighted TOPSIS algorithm according to the index values of the multidimensional identification indexes, and specifically comprises:
establishing a judgment matrix corresponding to the topological structure of the target power distribution network according to the multi-dimensional identification index; wherein the judgment matrix corresponds to the topological structure and includes any one or more of the following items: a node judgment matrix and a line judgment matrix; and the multi-dimensional identification index comprises: improving a node degree index, a node cohesion index, a node betweenness index, a weighted line betweenness index, a line transfer power flow entropy index and a line fault rate index under the wind disaster;
acquiring information entropy weighting according to a judgment matrix corresponding to the topological structure of the target power distribution network: if the judgment matrix is a node judgment matrix, calculating and obtaining first information entropy weighting of a plurality of nodes through an entropy weighting method according to the improved node degree index, the node agglomeration degree index and the node betweenness index; if the judgment matrix is a line judgment matrix, calculating and obtaining second information entropy weighting of a plurality of lines through an entropy weighting method according to the weighting line betweenness index, the line transfer power flow entropy index and the line fault rate index under the wind disaster; wherein the information entropy weighting comprises: the first information entropy weighting and the second information entropy weighting;
calculating and obtaining the importance corresponding to the topological structure of the target power distribution network through a weighted TOPSIS algorithm according to the information entropy weighting;
wherein, the formula of the entropy weight method is as follows:
in the formula, z ij Is to judge each element, z 'in the matrix' ij Is an element z ij Normalized matrix element, p ij Is each element of the probability matrix, e j The information entropy is weighted, i is a node serial number or a line serial number, and j is a type label of a multi-dimensional identification index; wherein, when the judgment matrix is a node judgment matrix, each element is: an index value of an improved node degree index of each node or an index value of a node cohesion degree index of each node or an index value of a node betweenness index of each node; when the judgment matrix is a line judgment matrix, each element is: and the index value of the weighted line betweenness index of each line or the index value of the line transfer tidal current entropy index of each line or the index value of the line fault rate index of each line under wind disaster.
3. The method for determining important nodes or important lines of a power distribution network according to claim 2, wherein the importance degree corresponding to the topology structure of the target power distribution network is obtained through weighted TOPSIS algorithm calculation according to the entropy weighting, specifically:
substituting the weighted information entropy and the judgment matrix into a TOPSIS algorithm to carry out importance corresponding to the topological structure of the target power distribution network;
when the judgment matrix is a node judgment matrix, substituting the information entropy weighting of each node, the index value of the improved node degree index of each node, the index value of the node agglomeration degree index of each node and the index value of the node betweenness index of each node into a TOPSIS algorithm to calculate and obtain the importance degree of each node of the target power distribution network;
when the judgment matrix is a line judgment matrix, substituting the information entropy empowerment of each line, the index value of the weighted line betweenness index of each line, the index value of the line transfer power flow entropy index of each line and the index value of the line fault rate index of each line under the wind disaster into a TOPSIS algorithm to calculate and obtain the importance of each line of the target power distribution network;
wherein, the formula of the TOPSIS algorithm is as follows:
in the formula, z ij For each element in the decision matrix, T i And the importance degree corresponding to the topological structure of the target power distribution network.
4. The method for determining important nodes or important lines of the power distribution network according to claim 2, wherein the obtaining of the improved node degree index specifically comprises:
calculating to obtain impedance data according to the per-unit system data;
obtaining an electrical coupling distance through an electrical coupling distance formula according to the impedance data; wherein, the electrical coupling distance formula is specifically as follows:
d ij ＝U ij /I i ＝(Z ii -Z ij )+(Z jj -Z ij )＝Z ii +Z ij -2Z ij
in the formula Z ii And Z jj Node i, j self impedance respectively; z ij Is the node i, j mutual impedance; d ij Is the electrical coupling distance;
calculating an improved node degree index through a first index formula according to the electrical coupling distance; the first index formula is specifically as follows:
wherein N is the total number of nodes in the topology network, N 1i To improve the node degree index.
5. The method for determining the important node or the important line of the power distribution network according to claim 2, wherein the obtaining of the node aggregation degree index specifically comprises:
according to the topological data, calculating by a Dijkstra algorithm to obtain the shortest electrical distance;
calculating to obtain the node condensation degree through a second index formula according to the shortest electrical distance; wherein, the second index formula is specifically as follows:
in the formula, l is the shortest average path between any two nodes; d is a radical of min,ij The shortest electrical distance between any nodes i and j in the power distribution network is obtained; n is a radical of 2i Is an index of the degree of node agglomeration.
6. The method for determining the important nodes or the important lines of the power distribution network according to claim 2, wherein the obtaining of the node betweenness index specifically comprises:
according to the topological data, calculating through an adjacency matrix to obtain the shortest first path number and the shortest second path number;
calculating to obtain a node betweenness index through a third index formula according to the shortest first path number and the shortest second path number; wherein, the third index formula is specifically as follows:
in the formula, D jl (i) Represents the shortest first path number between nodes i and j passing through node i, D jl Representing the shortest second path number between the nodes i and j; n is a radical of hydrogen 3i Is the node betweenness index.
7. The method for determining the important node or the important line of the power distribution network according to claim 2, wherein the obtaining of the weighted line betweenness index specifically comprises:
according to the per unit system data, calculating through an adjacency matrix to obtain the shortest third path number and the shortest fourth path number;
calculating to obtain a weighted line betweenness index through a fourth index formula according to the shortest third path number and the shortest fourth path number; wherein, the fourth index formula is specifically as follows:
in the formula, sigma i≠j∈V Z ij (k) Represents the number of times the shortest third path (i.e. path having the lowest total reactance) between nodes i, j passes through line k, Σ i≠j∈V Z ij Representing the shortest fourth path number between the sections i and j; l is 1k Is a weighted channel index.
8. The method for determining the important nodes or the important lines of the power distribution network according to claim 2, wherein the obtaining of the line transfer power flow entropy index specifically comprises:
calculating to obtain a transfer power flow entropy through a power flow entropy calculation formula according to the per-unit system data; the load flow entropy calculation formula is as follows:
in the formula, P αk Is the power, P, flowing through the line alpha after the line k is open α0 Is the power flowing through line a before line k is open; s is a topological network line set; delta P αk Is a uniform line L α Bearer line L k A transferred tidal current increment; beta is a αk The load flow transfer impact rate of the branch to the branch is obtained; h T (k) The transfer power flow entropy of the line k is obtained;
calculating to obtain a line transfer power flow entropy index through a fifth index calculation formula according to the transfer power flow entropy; wherein, the fifth index calculation formula is specifically as follows:
in the formula, P k K is the branch tidal power; l is 2k And the index is the entropy index of the line transfer power flow.
9. The method for determining important nodes or important lines of a power distribution network according to claim 2, wherein the obtaining of the line fault rate index in the wind disaster specifically includes: determining the fault rate of the line by a Monte Carlo sampling method; and the fault rate of the line is the line fault rate index under the wind disaster.
10. An apparatus for determining important nodes or important lines of a power distribution network, comprising: the device comprises a data preprocessing module, a first calculating module, a second calculating module and a result generating module;
the data preprocessing module is used for acquiring basic data of a target power distribution network and preprocessing the basic data to acquire first data; wherein the first data comprises: topological data and per-unit system data;
the first calculation module is used for calculating and obtaining a multi-dimensional identification index according to the topological data and the per unit system data;
the second calculation module is used for obtaining the importance corresponding to the topological structure of the target power distribution network through an entropy weight method and a weighted TOPSIS algorithm according to the index value of the multi-dimensional identification index; the importance corresponding to the topological structure of the target power distribution network comprises any one or more of the following items: importance of a node of the target distribution network or importance of a line of the target distribution network;
and the result generation module is used for determining important nodes or important lines according to the importance corresponding to the topological structure of the target power distribution network and a preset importance threshold.
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| CN117592871A (en) * | 2024-01-19 | 2024-02-23 | 中铁四局集团有限公司 | Concrete quality safety tracing and tracking management system based on big data |
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| CN115800272A (en) * | 2023-02-06 | 2023-03-14 | 国网山东省电力公司东营供电公司 | Power grid fault analysis method, system, terminal and medium based on topology identification |
| CN117592871A (en) * | 2024-01-19 | 2024-02-23 | 中铁四局集团有限公司 | Concrete quality safety tracing and tracking management system based on big data |
| CN117592871B (en) * | 2024-01-19 | 2024-04-12 | 中铁四局集团有限公司 | Concrete quality safety tracing and tracking management system based on big data |
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