CN116667881A - A Fast Density Clustering-Based Evaluation Method for Critical Links in Power Communication Networks - Google Patents

Abstract

The invention discloses a method for evaluating key links of electric power communication networks based on rapid density clustering, which includes the following steps: (1) establishing a weighted network data model; according to the physical structure of the region, the network bandwidth and distance are counted, and the distance and The weight of the bandwidth is normalized and integrated, and the network bandwidth and distance are used as the evaluation weight; (2) calculate the edge betweenness and link criticality; (3) input the calculated data into the fast density clustering algorithm, analyze Calculate the importance of each link; the present invention more accurately evaluates the key links of the power communication network, reduces the pressure of the power industry to maintain and maintain the power grid links, defines the key links, and reduces maintenance to the greatest extent Maintenance and labor costs; in addition, based on the key links obtained by the power industry personnel, fault prevention measures for these links can be given to reduce the instability of the power system and reduce the risk of large-scale power outages.

Description

A Fast Density Clustering-Based Evaluation Method for Critical Links in Power Communication Networks

technical field

The invention relates to the technical field of electric power communication network and its control, in particular to a fast density clustering-based key link evaluation method of electric power communication network.

Background technique

With the continuous development of the economy and the in-depth study of information technology, the power system increasingly relies on information and communication systems to ensure its safe and reliable operation. As the supporting network of the information communication system, the reliability research of the electric power communication network is now in a more important position. The power communication network is composed of network nodes and transmission links. The network nodes are generally placed inside the substation, and its protection measures are becoming more and more mature. However, the characteristics of the transmission link operating environment are complex, the number is huge, and the distance is long. Vulnerable to external damage, natural disasters, and link aging. Therefore, it is of great practical significance to identify the key links in the power communication network, and then take corresponding protection measures for the key links, taking into account both economy and reliability.

The interconnection and intelligence of the power grid has improved the scale and operational efficiency of the power system, but at the same time it has also increased the uncertainty of the operation of the power system, thus greatly increasing the risk of large-scale power outages in my country's power grid. Large-scale power outages will seriously affect factory production and machine operation, and bring unpredictable losses to economic development. To prevent large-scale power outages, it is necessary not only to prevent blackouts caused by natural disasters, but also to prevent power outages caused by internal operation instability of the power grid and losses caused by improper human operations. Cascading failures are one of the important causes of blackouts. In blackout accidents, the succession of cascading faults is closely related to certain links. When these links are out of operation due to the transfer of system power flow, the power imbalance of the power system will be aggravated, which will lead to the instability of the power system and cause blackouts. Therefore, in the current operating state of the power system, it is necessary to identify key links in a timely and accurate manner to provide valuable reference data for the safe and stable operation of the power grid.

At present, the evaluation methods of key links in power communication networks at home and abroad can be divided into two categories: the first is to identify key links based on topology; the second is to consider the power services carried by links in power communication networks, and the feature quantification its importance. Existing research results are basically based on one-sided, one-category assessments. For example, scholars such as Ali proposed to use the betweenness of the edges in the power communication network to evaluate the key links, and passed the tests of IEEE39 and 118 power systems. Scholars such as Lin Wenqin defined a concept of link importance, and then used the link importance as the evaluation index of key links. Although these two can evaluate key links, the single evaluation index lacks stability and generalization ability is not strong.

According to our previous experiments, in the evaluation of key links in the power communication network, if only a single category and a single feature are used for evaluation, it can only guarantee the evaluation effect in the current power communication network, so it is necessary to combine the two evaluations type of evaluation index, using multiple eigenvalues for more precise evaluation. In this aspect, the present invention proposes corresponding algorithms and evaluation indexes, and improves the robustness and accuracy of the key link evaluation of the power communication network under the premise of ensuring the complexity as much as possible.

Contents of the invention

Purpose of the invention: The purpose of the present invention is to provide a method for evaluating key links of power communication networks based on fast density clustering, so as to realize the acquisition of key links of power communication networks.

Technical solution: The present invention is to provide a method for evaluating key links of power communication network based on fast density clustering, which includes the following steps:

(1) Establish a weighted network data model; according to the physical structure of the region, count the network bandwidth and distance, normalize and integrate the weights of distance and bandwidth, and use network bandwidth and distance as evaluation weights;

(2) Calculate edge betweenness and link criticality;

(3) Input the calculated data into the fast density clustering algorithm, analyze and calculate the importance results of each link.

Further, the step (1) establishes a weighted network data model as follows:

Assuming that the number of nodes is n and the number of edges is m, the weighted mathematical model of the power communication network can be represented by an adjacency list; the first column of the adjacency list is the initial node, and the second column is adjacent to the initial node in the first column Nodes with edge connections, and record edge weights, the third column and the fourth column are the same structure type as the second column, no connection between two nodes is not included in the adjacency list, as shown in the following table:

Further, the step (1) normalizes and integrates the weights of the distance and the bandwidth as follows: perform min-max normalization on the bandwidth and the distance, and the specific formula is as follows:

Among them, X is the original data, X _min is the minimum value of the feature, X _max is the maximum value of the feature, and X _nom is the normalized data.

Further, the evaluation indexes of the key links of the power communication network are: edge betweenness and link criticality.

Further, the calculation of the edge betweenness in the step (2) is specifically as follows: suppose the proportion g(e) of the shortest path that the link e passes through, the formula is:

Among them, σ _st represents the number of all shortest paths from node s to node t, and σ _st (e) represents the number of all shortest paths from node s to node t through link e.

Further, the step (2) calculating link criticality includes the following steps:

(21) Calculate the expected load of the link, specifically: assuming that there are s network candidate routes between the source and destination node pairs (s, d), y means that the link (i, j ), then Indicates the frequency that the link may be selected when the establishment request arrives; B(s,d) is the historical statistical flow of service flow between the node pair (s,d), and M is the source and target of the establishment request of the service flow connection set of node pairs; then

The expected load of link (i, j) between source and destination node pair (s, d) is:

The total expected load on link (i,j) is:

(22) 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 formula of local density value ρ _i is as follows:

ρ _i =∑ _j X(d _ij -d _c )

Among them, 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 ≥0 , then X(d _ij -d _c )=0;

(32) The distance δ _i from point i to the point with higher local density, the formula is as follows:

(33) Select the largest three points as the cluster center, the formula is as follows:

and/> and ρ·δ

Then perform clustering.

Further, the d _ij is represented by the horse-style distance; the modified selection rule of the d _c is: the average number of neighbors of a point is 2% of the total number of points in the data set.

Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: it can more accurately evaluate the key links of the power communication network, reduce the pressure of the power industry to repair and maintain the power grid links, and clarify the key links, to the greatest extent Reduce maintenance and labor costs; In addition, on the basis of key links obtained by power industry personnel, preventive measures for these links can be given to reduce the instability of the power system and reduce the risk of large-scale blackouts.

Description of drawings

Fig. 1 is the flow chart of the key link evaluation method of electric power communication network according to the embodiment of the present invention;

Fig. 2 is a correlation result diagram between the edge betweenness and the link criticality of the embodiment of the present invention;

FIG. 3 is a flow chart of obtaining the criticality of a link in an electric power communication network according to an embodiment of the present invention;

FIG. 4 is a cluster distribution diagram of key links according to an embodiment of the present invention.

Detailed ways

The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

As shown in Figure 1, an embodiment of the present invention provides a method for assessing critical links in a power communication network based on fast density clustering, including the following steps:

(1) Establish a weighted network data model; according to the physical structure of the region, count the network bandwidth and distance, normalize and integrate the weights of distance and bandwidth, and use network bandwidth and distance as evaluation weights; establish a weighted network data model details as follows:

Assuming that the number of nodes is n and the number of edges is m, the weighted mathematical model of the power communication network can be represented by an adjacency list; the first column of the adjacency list is the initial node, and the second column is adjacent to the initial node in the first column Nodes with edge connections, and record edge weights, the third column and the fourth column are the same structure type as the second column, no connection between two nodes is not included in the adjacency list, as shown in the following table:

The edge weight represents the strength or difficulty of the interaction between nodes. If the distance between nodes is used as the weight, the greater the weight, the greater the distance between the two points, and the weaker the effect. If the bandwidth between nodes is used as the weight, the greater the weight, the greater the effect. In the present invention, the weights of distance and bandwidth are normalized and integrated, specifically: the bandwidth and distance are min-max normalized, and the specific formula is as follows:

Among them, X is the original data, X _min is the minimum value of the feature, X _max is the maximum value of the feature, and X _nom is the normalized data. The weights for distance and bandwidth are 50% each.

The importance of links in the network is not only related to the topology of the network, but also related to the power services carried by the links in the network. The present invention adopts edge betweenness and link criticality as evaluation indexes of key links.

(2) Calculate the edge betweenness and link criticality; the calculation of the edge betweenness is specifically as follows: suppose the proportion g(e) of the shortest path passed by the link e, the formula is:

Among them, σ _st represents the number of all shortest paths from node s to node t, and σ _st (e) represents the number of all shortest paths from node s to node t through link e.

Calculating link criticality includes the following steps:

(21) Calculate the expected load of the link. The expected load of the link is the link traffic load predicted by the service flow statistical information, specifically: assuming that there are s network candidate routes between the source and destination node pairs (s, d) , y represents the number of routes passing link (i, j) among the x network candidate routes, then Indicates the frequency that the link may be selected when the establishment request arrives; B(s,d) is the historical statistical flow of service flow between the node pair (s,d), and M is the source and target of the establishment request of the service flow connection set of node pairs; then

The expected load of link (i, j) between source and destination node pair (s, d) is:

The total expected load on link (i,j) is:

(22) The ratio of the expected load of a link in the network to the overall load of the network. The greater the expected load of a link, the greater the traffic flow through this link, and the greater the impact on the overall traffic of the network. The link criticality is expressed as follows:

Figure 2 shows the distribution of edge betweenness and link criticality. It can be seen from Figure 2 that edge betweenness and link criticality are not completely positively correlated. Therefore, in the measurement of critical links, comprehensive consideration is required. Edge betweenness and link criticality factors.

(3) Input the calculated data into the fast density clustering algorithm, analyze and calculate the importance results of each link.

As shown in Figure 3, among the present invention, the elements of the fast clustering algorithm are these two elements of edge betweenness and link criticality, so the classification space is two-dimensional, and in the classification process, specifically include the following steps:

(31) Let any data point be i, i=1,2,3,...,n, then the formula of local density value ρ _i is as follows:

ρ _i =∑ _j X(d _ij -d _c )

Among them, 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 ≥0 , then X(d _ij -d _c )=0;

(32) The distance δ _i from point i to the point with higher local density, the formula is as follows:

(33) Select the largest three points as the cluster center, the formula is as follows:

and/> and ρ·δ

After the cluster center is found, each remaining point is assigned to the cluster to which its nearest neighbor with higher density belongs. Cluster assignment is done in one step, followed by clustering. The clustering results are shown in Figure 4. The horizontal axis and vertical axis in Figure 4 represent two clustering factors, namely edge betweenness and link criticality, respectively. It can be seen from FIG. 4 that the evaluation method of the present invention can classify the importance of links very well. In Fig. 4, a square represents that the link is very important, an asterisk represents that the link is relatively important, and a triangle represents that the link is of general importance. In practical applications, the links represented by squares should be paid enough attention.

Claims (7)

1. A method for assessing critical links of electric power communication network based on fast density clustering, is characterized in that, comprises the following steps: (1) Establish a weighted network data model; according to the physical structure of the region, count the network bandwidth and distance, normalize and integrate the weights of distance and bandwidth, and use network bandwidth and distance as evaluation weights; (2) Calculate edge betweenness and link criticality; (3) Input the calculated data into the fast density clustering algorithm, analyze and calculate the importance results of each link. 2. a kind of electric power communication network critical link assessment method based on fast density clustering according to claim 1, is characterized in that, described step (1) sets up the network data model with weight and is specifically as follows: Assuming that the number of nodes is n and the number of edges is m, the weighted mathematical model of the power communication network can be represented by an adjacency list; the first column of the adjacency list is the initial node, and the second column is adjacent to the initial node in the first column Nodes with edge connections, and record edge weights, the third column and the fourth column are the same structure type as the second column, no connection between two nodes is not included in the adjacency list, as shown in the following table: 3. a kind of fast density clustering-based evaluation method for the key link of power communication network according to claim 1, is characterized in that, described step (1) carries out the normalization integration of the weight of distance and bandwidth specifically as : Perform min-max normalization on the bandwidth and distance, the specific formula is as follows: Among them, X is the original data, X _min is the minimum value of the feature, X _max is the maximum value of the feature, and X _nom is the normalized data. 4. A kind of fast density clustering-based evaluation method for key links of power communication network according to claim 1, characterized in that, the evaluation index of key links of said power communication network is: edge betweenness, link key Spend. 5. a kind of electric power communication network critical link assessment method based on fast density clustering according to claim 1, is characterized in that, described step (2) calculates edge betweenness and is specifically as follows: Set the shortest that link e passes through The proportion of the path g(e), the formula is: Among them, σ _st represents the number of all shortest paths from node s to node t, and σ _st (e) represents the number of all shortest paths from node s to node t through link e. 6. a kind of electric power communication network critical link assessment method based on fast density clustering according to claim 1, is characterized in that, described step (2) calculates link criticality and comprises the steps: (21) Calculate the expected load of the link, specifically: assuming that there are s network candidate routes between the source and destination node pairs (s, d), y means that the link (i, j ), then Indicates the frequency that the link may be selected when the establishment request arrives; B(s,d) is the historical statistical flow of service flow between the node pair (s,d), and M is the source and target of the establishment request of the service flow connection set of node pairs; then The expected load of link (i, j) between source and destination node pair (s, d) is: The total expected load on link (i,j) is: (22) Link criticality is expressed as follows: 7. a kind of fast density clustering-based electric power communication network critical link assessment method according to claim 1, is characterized in that, described step (3) specifically comprises the following steps: (31) Let any data point be i, i=1,2,3,...,n, then the formula of local density value ρ _i is as follows: ρ _i =∑ _j X(d _ij -d _c ) Among them, 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 ≥0 , then X(d _ij -d _c )=0; d _ij is represented by the horse-style distance, and the formula is as follows: Among them, x=(x ₁ ,x ₂ ,...,x _p ) ^T , y=(y ₁ ,y ₂ ,...,y _p ) ^T , ∑ is the covariance matrix; the modified selection rule of d _c is: such that the average number of neighbors of a point is 2% of the total number of points in the dataset. (32) The distance δ _i from point i to the point with higher local density, the formula is as follows: (33) Select the largest three points as the cluster center, the formula is as follows: and/> and ρ·δ Then perform clustering.