CN116667881A - Electric power communication network key link evaluation method based on rapid density clustering - Google Patents

Electric power communication network key link evaluation method based on rapid density clustering Download PDF

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CN116667881A
CN116667881A CN202310303669.7A CN202310303669A CN116667881A CN 116667881 A CN116667881 A CN 116667881A CN 202310303669 A CN202310303669 A CN 202310303669A CN 116667881 A CN116667881 A CN 116667881A
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CN116667881B (en
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庄伟�
李之恒
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Nanjing University of Information Science and Technology
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    • H04ELECTRIC COMMUNICATION TECHNIQUE
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B3/00Line transmission systems
    • H04B3/54Systems for transmission via power distribution lines
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02DCLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00Reducing energy consumption in communication networks
    • Y02D30/70Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a power communication network key link evaluation method based on rapid density clustering, which comprises the following steps: (1) establishing a weighted network data model; according to the physical structure of the region, the network bandwidth and the distance are counted, the distance and the weight of the bandwidth are integrated in a normalized mode, and the network bandwidth and the distance are used as evaluation weights; (2) calculating edge betweenness and link criticality; (3) Inputting the calculated data into a rapid density clustering algorithm, and analyzing and calculating to obtain an importance result of each link; the invention evaluates the key links of the power communication network more accurately, reduces the pressure of overhauling and maintaining the power network links in the power industry, determines the key links, and reduces overhauling and maintaining and labor expenses to the greatest extent; in addition, on the basis that power industry personnel obtain key links, fault preventive measures for the links can be given, instability of a power system is reduced, and the risk of large-scale power failure is reduced.

Description

Electric power communication network key link evaluation method based on rapid density clustering
Technical Field
The invention relates to the technical field of power communication networks and control thereof, in particular to a power communication network key link evaluation method based on rapid density clustering.
Background
With the continuous development of economy and the deep research of information technology, the electric power system increasingly depends on the information communication system to ensure the safe and reliable operation of the electric power system. The reliability research of the power communication network serving as a supporting network of the information communication system is now in a more important position. The power communication network consists of network nodes and transmission links, the network nodes are generally arranged in a transformer substation, the protection measures of the power communication network are mature, but the transmission links are easy to be influenced by the conditions of external force damage, natural disasters, link aging and the like in the actual operation process due to the characteristics of complex operation environment, huge quantity, long distance and the like. Therefore, the key links in the power communication network are identified, corresponding protection measures are adopted for the key links, economical efficiency and reliability are considered, and the method has important practical significance.
The power grid interconnection and the intellectualization are realized, the scale and the operation efficiency of the power system are improved, but the operation uncertainty of the power system is increased at the same time, so that the risk of large-area power failure accidents of the power grid in China is greatly increased. Large-area power failure accidents can seriously affect factory production and machine operation, and unpredictable loss is brought to economic development. The large-area power failure accident is prevented, the power failure influence caused by natural disasters is prevented, and the power failure accident caused by the instability of the operation in the power grid and the loss caused by improper manual operation are prevented. Cascading failures are one of the important causes of blackout accidents. In the power failure accident, the succession of cascading failures has close relation with certain links. When the links are out of operation due to system power flow transfer, the unbalance of the power system is aggravated, and the power system is unstable to cause a blackout accident. Therefore, in the current running state of the power system, it is necessary to accurately identify key links in time, and valuable reference data is provided for safe and stable running of the power grid.
At present, the method for evaluating the key link of the electric power communication gateway at home and abroad can be divided into two types: the first is to identify critical links based on topology; the second category is to characterize the importance of the power traffic carried by links in the power communication network in consideration of it. The existing research results are basically based on the evaluation of unilateral single category. For example, ali et al have proposed using betters on edges in power communication networks to evaluate critical links, passing the test of IEEE39 and 118 power systems. Lin Wenqin and other students define a concept of link importance by themselves, and then the importance of the links is used as an evaluation index of the key links. Both of these can evaluate the critical links, but the single criterion lacks stability and is not generalizable.
According to the early experiments, in the key link evaluation of the power communication network, if only a single category and a single characteristic are adopted for evaluation, the evaluation effect in the current power communication network can be ensured, so that the evaluation indexes of two evaluation types are combined, and more accurate evaluation is performed by using multiple characteristic values. The invention provides a corresponding algorithm and evaluation index in the aspect, and improves the robustness and accuracy of the power communication gateway key link evaluation on the premise of ensuring the complexity as much as possible.
Disclosure of Invention
The invention aims to: the invention aims to provide a power communication network key link evaluation method based on rapid density clustering, which realizes acquisition of a power communication gateway key link.
The technical scheme is as follows: the invention provides a power communication network key link evaluation method based on rapid density clustering, which comprises the following steps:
(1) Establishing a weighted network data model; according to the physical structure of the region, the network bandwidth and the distance are counted, the distance and the weight of the bandwidth are integrated in a normalized mode, and the network bandwidth and the distance are used as evaluation weights;
(2) Calculating edge betweenness and link criticality;
(3) And inputting the calculated data into a rapid density clustering algorithm, and analyzing and calculating to obtain the importance result of each link.
Further, the step (1) of establishing a weighted network data model is specifically as follows:
the mathematical model of the power communication network belt weight with the node number of n and the edge number of m can be represented by an adjacency list; the first column of the adjacency list is an initial node, the second column is a node with an edge connection adjacent to the initial node of the first column, the edge weights are recorded, the third column and the fourth column are of the same structure type as the second column, and the two nodes are not connected and are not counted in the adjacency list, and the table is as follows:
further, the step (1) of normalizing and integrating the distance and the weight of the bandwidth specifically includes: the bandwidth and the distance are normalized by min-max, and the specific formula is as follows:
wherein X is original data, X min Is the minimum value of the feature, X max For maximum value of characteristic, X nom Is normalized data.
Further, the evaluation indexes of the key links of the power communication network are as follows: edge betweenness and link criticality.
Further, the calculating of the edge betweenness in the step (2) is specifically as follows: let the ratio g (e) of the shortest path that link e passes through, the formula is:
wherein sigma st Representing all shortest paths from node s to node t, σ st (e) Representing the number of all shortest paths through link e from node s to node t.
Further, the step (2) of calculating the link criticality includes the steps of:
(21) The expected load of the link is calculated, specifically: provided that there are s network candidate routes between the source and destination node pairs (s, d), y representing the number of routes through the link (i, j) among the x network candidate routesIndicating how often the link may be selected when the setup request arrives; b (s, d) is the historical statistical flow of the traffic flow between the node pair (s, d), M is the request for establishing the traffic flow connectionA set of source and destination node pairs; then
The expected load of the link (i, j) between the source and destination node pairs (s, d) is:
the total expected load of links (i, j) is:
(22) The link criticality is expressed as follows:
further, the step (3) specifically includes the following steps:
(31) Let any data point be i, i=1, 2,3,..n, then the local density value ρ i The formula is as follows:
ρ i =∑ j X(d ij -d c )
wherein d c Is the cut-off distance d ij Is the distance between point i and point j; if d ij -d c < 0, then X (d) ij -d c ) =1, if d ij -d c Not less than 0, then X (d) ij -d c )=0;
(32) Distance delta of point i to point with higher local density i The formula is as follows:
(33) The largest three points are selected as cluster centers, and the formula is as follows:
and->And ρ.delta
Clustering is then performed.
Further, said d ij The horse-type distance representation is adopted; said d c The modified selection rules of (a) are: so that the average number of neighbors of a point is 2% of the total number of points in the data set.
The beneficial effects are that: compared with the prior art, the invention has the following remarkable advantages: the key links of the power communication network are evaluated more accurately, the pressure of overhauling and maintaining the power network links in the power industry is reduced, the key links are clear, and overhauling and maintaining and labor expenses are reduced to the greatest extent; in addition, on the basis that power industry personnel obtain key links, fault preventive measures for the links can be given, instability of a power system is reduced, and the risk of large-scale power failure is reduced.
Drawings
FIG. 1 is a flow chart of a method for evaluating a critical link of a power communication network according to an embodiment of the present invention;
FIG. 2 is a graph showing the result of correlation between edge betweenness and link criticality according to an embodiment of the present invention;
FIG. 3 is a flow chart of acquiring the criticality of a power communication network link in accordance with an embodiment of the present invention;
fig. 4 is a clustering diagram of key links according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present invention provides a method for evaluating a key link of a power communication network based on fast density clustering, including the following steps:
(1) Establishing a weighted network data model; according to the physical structure of the region, the network bandwidth and the distance are counted, the distance and the weight of the bandwidth are integrated in a normalized mode, and the network bandwidth and the distance are used as evaluation weights; the method for establishing the weighted network data model is specifically as follows:
the mathematical model of the power communication network belt weight with the node number of n and the edge number of m can be represented by an adjacency list; the first column of the adjacency list is an initial node, the second column is a node with an edge connection adjacent to the initial node of the first column, the edge weights are recorded, the third column and the fourth column are of the same structure type as the second column, and the two nodes are not connected and are not counted in the adjacency list, and the table is as follows:
the side weights represent the strength or ease of interaction between nodes. If the distance between the nodes is used as the weight, the larger the weight is, the larger the distance between the two points is, and the weaker the effect is. If the bandwidth between nodes is used as the weight, the larger the weight is, the larger the effect is. In the invention, the distance and the weight of the bandwidth are normalized and integrated, and the method specifically comprises the following steps: the bandwidth and the distance are normalized by min-max, and the specific formula is as follows:
wherein X is original data, X min Is the minimum value of the feature, X max For maximum value of characteristic, X nom Is normalized data. The distance and bandwidth weights are 50% each.
The importance of links in a network has a certain relationship with the topology of the network, and also has a certain relationship with the power traffic carried by the links in the network. The invention adopts the edge betweenness and the link criticality as the evaluation index of the key link.
(2) Calculating edge betweenness and link criticality; the edge betweenness is calculated as follows: let the ratio g (e) of the shortest path that link e passes through, the formula is:
wherein sigma st Representing all shortest paths from node s to node t, σ st (e) Representing the number of all shortest paths through link e from node s to node t.
Calculating the link criticality comprises the following steps:
(21) Calculating a link expected load, wherein the link expected load is a link traffic load predicted by using traffic flow statistical information, and specifically comprises the following steps: provided that there are s network candidate routes between the source and destination node pairs (s, d), y representing the number of routes through the link (i, j) among the x network candidate routesIndicating how often the link may be selected when the setup request arrives; b (s, d) is the historical statistical flow of the business flow between node pairs (s, d), M is the source and destination node pair set for establishing the business flow connection request; then
The expected load of the link (i, j) between the source and destination node pairs (s, d) is:
the total expected load of links (i, j) is:
(22) The ratio of the expected load of a link to the overall load of the network, the greater the expected load of the link, the greater the traffic flow through the link and the greater the impact on the overall flow of the network. The link criticality is expressed as follows:
fig. 2 shows the distribution of the edge betweenness and the link criticality, and it can be seen from fig. 2 that the edge betweenness and the link criticality are not completely in positive correlation, so that the edge betweenness and the link criticality factors need to be comprehensively considered in the measurement of the critical links.
(3) And inputting the calculated data into a rapid density clustering algorithm, and analyzing and calculating to obtain the importance result of each link.
As shown in fig. 3, the elements of the fast clustering algorithm in the present invention are two elements, namely edge betweenness and link criticality, so that the classification space is two-dimensional, and in the classification process, the method specifically includes the following steps:
(31) Let any data point be i, i=1, 2,3,..n, then the local density value ρ i The formula is as follows:
ρ i =∑ j X(d ij -d c )
wherein d c Is the cut-off distance d ij Is the distance between point i and point j; if d ij -d c < 0, then X (d) ij -d c ) =1, if d ij -d c Not less than 0, then X (d) ij -d c )=0;
(32) Distance delta of point i to point with higher local density i The formula is as follows:
(33) The largest three points are selected as cluster centers, and the formula is as follows:
and->And ρ.delta
After the cluster center is found, each point remaining is assigned to its nearest neighbor cluster of higher density. The cluster allocation can be completed in one step, and then clustering is performed. The clustering results are shown in fig. 4. The horizontal and vertical axes in fig. 4 represent two clustering factors, namely edge betweenness and link criticality, respectively. As can be seen from fig. 4, the evaluation method of the present invention can well classify the importance of the links. The square in fig. 4 represents that the link is of great importance, the asterisk represents that the link is of relatively great importance, and the triangle represents that the link is of general importance. In practical applications, the square-shaped link is considered to be important enough.

Claims (7)

1. The method for evaluating the key links of the power communication network based on the rapid density clustering is characterized by comprising the following steps of:
(1) Establishing a weighted network data model; according to the physical structure of the region, the network bandwidth and the distance are counted, the distance and the weight of the bandwidth are integrated in a normalized mode, and the network bandwidth and the distance are used as evaluation weights;
(2) Calculating edge betweenness and link criticality;
(3) And inputting the calculated data into a rapid density clustering algorithm, and analyzing and calculating to obtain the importance result of each link.
2. The method for evaluating the key links of the power communication network based on the rapid density clustering according to claim 1, wherein the step (1) of establishing the weighted network data model is specifically as follows:
the mathematical model of the power communication network belt weight with the node number of n and the edge number of m can be represented by an adjacency list; the first column of the adjacency list is an initial node, the second column is a node with an edge connection adjacent to the initial node of the first column, the edge weights are recorded, the third column and the fourth column are of the same structure type as the second column, and the two nodes are not connected and are not counted in the adjacency list, and the table is as follows:
3. the method for evaluating the key links of the power communication network based on the rapid density clustering according to claim 1, wherein the step (1) is characterized in that the normalized integration of the distance and the weight of the bandwidth is specifically as follows: the bandwidth and the distance are normalized by min-max, and the specific formula is as follows:
wherein X is original data, X min Is the minimum value of the feature, X max For maximum value of characteristic, X nom Is normalized data.
4. The method for evaluating the key links of the power communication network based on the rapid density clustering according to claim 1, wherein the evaluation indexes of the key links of the power communication network are as follows: edge betweenness and link criticality.
5. The method for evaluating the key links of the power communication network based on the rapid density clustering according to claim 1, wherein the calculating of the edge betweenness in the step (2) is specifically as follows: let the ratio g (e) of the shortest path that link e passes through, the formula is:
wherein sigma st Representing all shortest paths from node s to node t, σ st (e) Representing the number of all shortest paths through link e from node s to node t.
6. The method for evaluating the critical links of the power communication network based on the rapid density clustering according to claim 1, wherein the step (2) of calculating the link criticality comprises the steps of:
(21) The expected load of the link is calculated, specifically: provided that there are s network candidate routes between the source and destination node pairs (s, d), y representing the number of routes through the link (i, j) among the x network candidate routesIndicating how often the link may be selected when the setup request arrives; b (s, d) is the historical statistical flow of the business flow between node pairs (s, d), M is the source and destination node pair set for establishing the business flow connection request; then
The expected load of the link (i, j) between the source and destination node pairs (s, d) is:
the total expected load of links (i, j) is:
(22) The link criticality is expressed as follows:
7. the method for evaluating the key links of the power communication network based on the rapid density clustering according to claim 1, wherein the step (3) specifically comprises the following steps:
(31) Let any data point be i, i=1, 2,3,..n, then the local density value ρ i The formula is as follows:
ρ i =∑ j X(d ij -d c )
wherein d c Is the cut-off distance d ij Is the distance between point i and point j; if d ij -d c < 0, then X (d) ij -d c ) =1, if d ij -d c Not less than 0, then X (d) ij -d c )=0;d ij The horse-type distance expression is adopted, and the formula is as follows:
wherein x= (x) 1 ,x 2 ,...,x p ) T ,y=(y 1 ,y 2 ,...,y p ) T Sigma is the covariance matrix; d, d c The modified selection rules of (a) are: so that the average number of neighbors of a point is 2% of the total number of points in the data set.
(32) Distance delta of point i to point with higher local density i The formula is as follows:
(33) The largest three points are selected as cluster centers, and the formula is as follows:
and->And ρ.delta
Clustering is then performed.
CN202310303669.7A 2023-03-27 2023-03-27 Electric power communication network key link evaluation method based on rapid density clustering Active CN116667881B (en)

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CN117808175A (en) * 2024-03-01 2024-04-02 南京信息工程大学 Short-term multi-energy load prediction method based on DTformer

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JP2014168119A (en) * 2013-02-28 2014-09-11 Nippon Telegr & Teleph Corp <Ntt> Transmission path division policy determining device and method
CN108880905B (en) * 2018-07-06 2019-06-21 四川大学 Reliability of electric force communication network research method based on node and link different degree
CN109861910B (en) * 2019-03-11 2021-06-22 国网福建省电力有限公司 Power communication network link importance calculation method based on link availability
CN112702107B (en) * 2020-12-21 2021-10-19 北京邮电大学 Method and system for calculating backup route of satellite network based on betweenness centrality

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CN117808175A (en) * 2024-03-01 2024-04-02 南京信息工程大学 Short-term multi-energy load prediction method based on DTformer
CN117808175B (en) * 2024-03-01 2024-05-17 南京信息工程大学 DTformer-based short-term multi-energy load prediction method

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