Electric power system key node identification method

Abstract

The invention provides a key node identification method for a power system, and the method comprises the steps: obtaining the node information of the power system, the node information comprises the number of nodes, the degree value of each node, and the path information between the nodes, carrying out the calculation according to the number of nodes, the degree value of each node, and the path information between the nodes, and obtaining the comprehensive feature value of each node. By fusing the multi-order neighbor comprehensive feature information, the target feature value of the node is obtained, so that the key degree of the node can be judged according to the size of the target feature value. The target feature values are in one-to-one correspondence with the corresponding nodes, all the nodes are screened, the nodes meeting the preset target feature value range serve as the key nodes, namely the key degree of the screened nodes is high, and therefore the nodes can be accurately recognized according to the target feature values, and the recognition efficiency of the nodes is improved. System operation and maintenance personnel are helped to take effective measures, and system collapse or function damage is prevented.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method for identifying key nodes in a power system. Background Art

[0002] In the prior art, large-scale power systems are described by complex networks. In complex system modeling, the components of the power system are regarded as nodes in the network. When analyzing the power system, key nodes represent the equipment that plays a key role in the power system. Accurate identification of key nodes plays a key role in the maintenance of the power system network. The existing key node identification method cannot accurately identify all the technical characteristics of the node, resulting in low key node identification accuracy, which is not conducive to the maintenance of the power system. If the maintenance is not timely, it may cause the collapse and collapse of the power system, causing greater losses. Summary of the invention

[0003] In view of this, the purpose of this application is to propose a method for identifying key nodes in a power system to solve the technical problem of low accuracy of existing node identification methods.

[0004] Based on the above purpose, the present application provides a method for identifying key nodes in a power system, comprising:

[0005] Acquire node information of the power system, wherein the node information includes the number of nodes, the degree value of each node and the path information between each node;

[0006] Based on the degree centrality algorithm, the target feature value of each node is determined according to the number of nodes, the degree value of each node and the path information between the nodes;

[0007] The key nodes are determined according to the target characteristic value of each node and the preset target characteristic value range.

[0008] Optionally, the degree centrality algorithm is based on the number of nodes, the degree value of each node and the path information between each node to determine the target feature value of all nodes, including:

[0009] Based on the degree centrality algorithm, the degree centrality of each node is determined according to the number of nodes and the degree value of each node;

[0010] Determining the local eigenvalue of each node according to the degree centrality of each node;

[0011] Determine the global eigenvalue of each node based on the path information between the nodes, the degree centrality of each node and the local eigenvalue of each node;

[0012] Determining a comprehensive eigenvalue of each node based on the local eigenvalue of each node and the global eigenvalue of each node;

[0013] Based on the comprehensive characteristic values ​​of the neighboring nodes of each node, the target characteristic value of each node is determined.

[0014] Optionally, the degree centrality algorithm is used to determine the degree centrality of each node according to the number of nodes and the degree value of each node, including:

[0015] The degree centrality of each node is determined by the following formula:

[0016]

[0017] In the formula, Θ i is node v i The degree centrality of k i is node v i The value of v i is the i-th node, and N is the number of nodes.

[0018] Optionally, determining the local eigenvalue of each node according to the degree centrality of each node includes:

[0019] The local eigenvalues ​​of the nodes are determined by the following formula:

[0020] Δ i =(Θ i ) 2 ;

[0021] In the formula, Θ i is node v i The degree centrality of i is node v i The local eigenvalues ​​of .

[0022] Optionally, the path information between the nodes includes the number of shortest paths between the nodes;

[0023] Determining the global eigenvalue of each node based on the path information between the nodes, the degree centrality of each node and the local eigenvalue of each node includes:

[0024] According to the number of shortest paths between the nodes, the degree centrality of each node and the local eigenvalue of each node, the global eigenvalue of each node is determined by the following formula:

[0025]

[0026] In the formula, g ij is node v i and neighbor node v j The number of shortest paths, Θ i is node v i The degree centrality of i is node vi The local eigenvalue of j is node v j Relative to node v i The global eigenvalue of .

[0027] Optionally, determining the comprehensive eigenvalue of each node based on the local eigenvalue of each node and the global eigenvalue of each node includes:

[0028] According to the local eigenvalues ​​of the nodes and the global eigenvalues ​​of the nodes, the comprehensive eigenvalues ​​of the nodes are determined by the following formula:

[0029]

[0030] In the formula, i is node v i The comprehensive characteristic value, g ij is node v i and neighbor node v j The number of shortest paths, Θ i is node v i The degree centrality of i is node v i The local eigenvalue of ; V is the node set.

[0031] Optionally, determining the target feature value of each node based on the comprehensive feature value of the neighboring nodes of each node includes:

[0032] The target eigenvalue of each node is determined by the following formula:

[0033]

[0034] In the formula, j is the neighbor node v j The comprehensive eigenvalue of Ω i is node v i The fused eigenvalues ​​of the neighboring nodes, is node v i The set of neighbor nodes, Ω j is the fused neighbor node v j The fusion feature value of Score i is node v i The target feature value.

[0035] Optionally, determining the key nodes according to the target characteristic value of each node and a preset target characteristic value range includes:

[0036] The node corresponding to the target characteristic value within the preset target characteristic value range is taken as the key node.

[0037] Optionally, the method further includes: generating a key node list based on the key nodes, and evaluating the key node list based on a preset comparison sorting list and an MI algorithm.

[0038] Optionally, the evaluating the key node list based on a preset comparison ranking table and an MI algorithm includes:

[0039] The elements in the key node list are evaluated by the following formula to determine the first evaluation parameter of the key node list:

[0040]

[0041] In the formula, τ(X,Y) is the first evaluation parameter, τ is the Kendall correlation coefficient, X is the preset comparison sort list, Y is the key node list, and N L is the length of the sorted list, then N L ≤N, N is the number of nodes, N c and N d Represent the number of ordered pairs and unordered pairs respectively;

[0042] The elements in the key node list are evaluated by the following formula to determine the second evaluation parameter of the key node list:

[0043]

[0044] Where MI(Y) is the second evaluation parameter, where MI represents the distinguishability value, and N L is the length of the sorted list, L′ is the set of target global eigenvalues, χ is the target global eigenvalue, N χ is the number of nodes with the target global eigenvalue χ;

[0045] In response to the first evaluation parameter being greater than a first preset threshold and the second evaluation parameter being greater than a second preset threshold, it is determined that the key node list meets the list requirement.

[0046] As can be seen from the above, the present application provides a method for identifying key nodes in a power system, by obtaining node information of the power system, the node information includes the number of nodes, the degree value of each node and the path information between each node, and the degree centrality of the node is calculated based on the degree centrality algorithm, and the value of the degree centrality is used as the basis, and the comprehensive characteristic value of each node is calculated according to the number of nodes, the degree value of each node and the path information between each node, and the target characteristic value of each node is obtained by fusing the comprehensive characteristic information of neighbors of each order, so that the criticality of the node can be judged according to the size of the target characteristic value. The node corresponding to the target characteristic value is taken as the key node, and all nodes are screened, and the nodes that meet the preset target characteristic value range are the key nodes, and the nodes with larger target characteristic values ​​are screened, that is, the screened nodes have a higher criticality. In this way, the nodes are identified according to the size of the target characteristic value, and the nodes can be accurately identified, which helps the system operation and maintenance personnel to take effective measures to prevent system crashes or functional damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 A schematic diagram of a method for identifying key nodes in a power system according to an embodiment of the present application;

[0049] Figure 2 A schematic diagram of a process for determining a target characteristic value according to an embodiment of the present application;

[0050] Figure 3a This is a schematic diagram of an exemplary node connection relationship structure of an embodiment of the present application;

[0051] Figure 3b A schematic diagram of the structure of a first embodiment of the connection relationship between nodes in an embodiment of the present application;

[0052] Figure 4 A schematic diagram of the structure of a second embodiment of the connection relationship between nodes in an embodiment of the present application;

[0053] Figure 5 A schematic diagram of a key node identification device for a power system according to an embodiment of the present application;

[0054] Figure 6 A schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0056] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0057] Based on the background technology, a complex network is a structure composed of a large number of interconnected nodes, and the connection relationship between the nodes forms the topological structure of the network. In complex system modeling, the components of the power system can be regarded as nodes in the network, and the relationships between them are represented as edges or connections in the network. The advantage of modeling the power system as a complex network is that it can capture the complex relationships and structural characteristics between the components in the system, providing a powerful tool for the analysis and understanding of the system. The research on the identification of key nodes in complex networks has important academic significance and engineering value. Key nodes are nodes in the network that play a key role in structure and function. The removal or destruction of key nodes may cause the network structure to collapse or the function to be seriously damaged. For example, in the power network, key nodes represent transformers, switchgear, etc. Therefore, accurate identification of key nodes helps prevent the collapse of the network and improve the robustness, stability and anti-interference ability of the network. This provides guidance for the design, optimization and management of the network, and provides support in terms of the sustainability and resilience of the system.

[0058] Exemplary node identification algorithms in the prior art include the following five main types: (1) The first method deeply analyzes the neighborhood structure and local connection pattern of nodes in the network to reveal nodes that are critical to the network structure and function in the local scope. For example, in the degree centrality algorithm, the higher the degree of a node, the more other nodes the node is directly connected to, thereby increasing its criticality in information dissemination and network flow. (2) The second method considers the global connection pattern of nodes in the network to discover nodes that play a key role in the entire network, providing important clues and guidance for understanding the overall structure and function of the network. For example, in the betweenness centrality algorithm, the higher the betweenness value of a node, the more information the node plays in the network. The role of the bridge plays, which is important for evaluating the overall connectivity and anti-interference of the network. (3) The third method considers the dynamic characteristics of the network to accurately identify the criticality of nodes in terms of network dynamic evolution and information dissemination. For example, the VoteRank algorithm takes into account the dynamic changes of nodes during the voting process. An increase in the number of dynamic votes indicates that the node has more significant influence and participation in the neighborhood. Considering this detailed dynamic feature makes the algorithm more accurate and helps to deeply understand the important role of nodes in network dynamics. (4) The fourth method focuses on the position of nodes in the network structure to reveal their criticality in the overall topological structure. For example, the K-Shell algorithm reveals the different levels of shell structure of nodes in the network. The larger the K-Shell value of a node, the larger the k-shell the node is located in, which has an important impact on the overall connectivity and stability of the network. (5) The fifth method deeply analyzes the contractility of nodes to measure the integration ability of nodes in the network topology. For example, the agglomeration algorithm generates a new network by integrating a node with its first-order neighbors. The greater the integration ability of a node, the more critical the node is, providing useful insights for a deeper understanding of the node function of the network.

[0059] However, the above five node identification algorithms have limitations and do not consider the neighbor information of the nodes, resulting in inaccurate identification of key nodes. The specific description of its limitations and shortcomings is as follows: the existing algorithms focus on a certain aspect or a specific type of node features, so they cannot fully consider all the characteristics of the network, making it difficult for the algorithm to accurately capture the overall structure and function of the network. For example, although the betweenness centrality algorithm can capture the global structure of the network, it ignores important information such as the specific location of the node and the local structure of the network. In addition, the existing technology needs to make a trade-off between accuracy and computational efficiency in algorithm design. For example, the betweenness centrality algorithm has moderate accuracy, but the computational complexity is high when processing large-scale networks, resulting in a large amount of computing resources and time; the degree centrality algorithm has high computational efficiency in large-scale networks, but the accuracy of the algorithm is low. The specific description of its shortcomings of not fully considering the neighbor information of the node is as follows: the existing algorithms do not fully consider and utilize the relevance and importance of the node neighbors, resulting in a comprehensive understanding of the relationship between nodes in the network, which limits the algorithm's accurate grasp of the influence and role of the node in the local structure. In some cases, this deficiency may lead to the insufficiency and inaccuracy of the algorithm in capturing network characteristics.

[0060] In order to solve the above technical problems, refer to Figure 1 , the present application provides a method for identifying key nodes in a power system, comprising the following steps:

[0061] Step 102: Obtain node information of the power system, wherein the node information includes the number of nodes, the degree value of each node, and path information between each node.

[0062] In this step, the node information of the power system is obtained from the topological map of the power system, which includes multiple devices and the connection lines between multiple devices. The power system is abstracted into a power network through the complex network method, that is, multiple devices are abstracted into multiple nodes, and the connection lines between multiple devices are abstracted into the connection edges between nodes. Exemplarily, the power system is abstracted into a power network through the following formula: G = (V, W), where V = {v1, v2, ..., v N} represents different equipment in the power system, such as generators, substations, loads, etc., that is, V represents the node set, N represents the number of equipment, E = {e ij |v i ,v j ∈V} represents the actual physical connection of transmission lines, transformer connections, cables, etc. ij Represents node v i With node v jThe number of nodes can be counted based on multiple nodes, and the degree values ​​of nodes in various places and the path information between nodes can be counted based on the connection edges between nodes, where the degree value refers to the number of first-order neighbor nodes of a node. For example, the connection relationship between nodes is as follows: Figure 3a As shown, the first-order neighbor nodes of node A are B and D, and the second-order neighbor node of A is C. Then the degree value of A is 2, the first-order neighbor nodes of B are A and C, and the degree value of B is 2, the first-order neighbors of C are B and D, and the degree value of C is 2, and the first-order neighbors of D are A and C, and the degree value of D is 2.

[0063] Step 104: Based on the degree centrality algorithm, determine the target feature value of each node according to the number of nodes, the degree value of each node and the path information between the nodes.

[0064] In this step, degree centrality is used to indicate the number of connections of a node. The larger the value of degree centrality is, the more connections a node has, which means that the node is more critical. That is, the value of degree centrality can determine the criticality of a node. Therefore, the degree centrality of a node is calculated based on the degree centrality algorithm. Based on the value of degree centrality, the target characteristic value of each node is calculated according to the number of nodes, the degree value of each node, and the path information between each node. That is, by fusing the comprehensive characteristic information of each node, the target characteristic value of each node is obtained, so that the criticality of the node can be judged according to the size of the target characteristic value.

[0065] Step 106: Determine key nodes according to the target characteristic value of each node and a preset target characteristic value range.

[0066] In this step, whether a node is a key node is determined based on the target characteristic value of each node. When the target characteristic value is within the preset target characteristic value range, the node corresponding to the target characteristic value is taken as the key node. That is, all nodes are screened for nodes with larger target characteristic values. Nodes that meet the preset target characteristic value range are key nodes, which plays a vital role in network maintenance and helps system operation and maintenance personnel take effective measures to prevent system crashes or functional damage.

[0067] Specifically, through steps 102-106, by obtaining the node information of the power system, the node information includes the number of nodes, the degree value of each node and the path information between each node, the degree centrality of the node is calculated based on the degree centrality algorithm, and the comprehensive characteristic value of each node is calculated based on the value of the degree centrality according to the number of nodes, the degree value of each node and the path information between each node, and the comprehensive characteristic value of each node is obtained, that is, by fusing the comprehensive characteristic information of neighbors of each order, the node target characteristic value is obtained, so that the criticality of the node can be judged according to the size of the target characteristic value. The node corresponding to the target characteristic value is taken as the key node, and all nodes are screened, and the nodes that meet the preset target characteristic value range are the key nodes, that is, the nodes with larger global characteristic values ​​are screened, that is, the nodes with higher criticality are screened, so that the nodes can be identified according to the size of the target characteristic value, and the nodes can be accurately identified, which helps the system operation and maintenance personnel to take effective measures to prevent the power system from collapsing or functioning.

[0068] In some embodiments, reference Figure 2 The degree centrality-based algorithm determines the comprehensive characteristic values ​​of all nodes according to the number of nodes, the degree value of each node and the path information between the nodes, including the following steps:

[0069] Step 202: Determine the degree centrality of each node based on the degree centrality algorithm according to the number of nodes and the degree value of each node;

[0070] Step 204: Determine the local eigenvalue of each node according to the degree centrality of each node;

[0071] Step 206: Determine the global eigenvalue of each node based on the path information between the nodes, the degree centrality of each node, and the local eigenvalue of each node;

[0072] Step 208: Determine the comprehensive eigenvalue of each node based on the local eigenvalue of each node and the global eigenvalue of each node;

[0073] Step 210: Determine the target feature value of each node based on the comprehensive feature values ​​of the neighboring nodes of each node.

[0074] Specifically, based on the degree centrality algorithm, the degree centrality of each node is obtained by calculating the degree centrality according to the number of nodes and the degree value of each node. The degree centrality is used to indicate the number of connections of the node, which can determine the criticality of the node. The higher the degree centrality value, the higher the criticality of the node. According to the degree centrality of each node, the local eigenvalue of each node is determined, that is, the local eigenvalue is calculated according to the value of the degree centrality of each node. Based on the path information between each node, the degree centrality of each node and the local eigenvalue of each node, the global eigenvalue of each node is calculated by the formula, and the comprehensive eigenvalue is calculated by the formula based on the local eigenvalue and the global eigenvalue. Since the value of the degree centrality represents the criticality of the node, that is, the local feature is assigned a value based on the value of the degree centrality. The number of shortest paths between nodes indicates that the node plays the role of information transmission in the system, which can determine the criticality of the node. The shorter the shortest path, the higher the criticality of the node. Since the value of the shortest path represents the criticality of the node, that is, the global feature is assigned a value based on the value of the shortest path. According to the value of the local feature and the value of the global feature, the comprehensive eigenvalue of each node is calculated. By integrating the comprehensive characteristic information of neighbors of each order, the target characteristic value of each node is determined, so that the criticality of the node can be judged according to the target characteristic value. The global characteristics, local characteristics and node neighbor information of the power system are more comprehensively considered, which improves the accuracy of key node identification.

[0075] In some embodiments, the degree centrality algorithm is used to determine the degree centrality of each node according to the number of nodes and the degree value of each node, including:

[0076] The degree centrality of each node is determined by the following formula:

[0077]

[0078] In the formula, Θ i is node v i The degree centrality of k i is node v i The value of v i is the i-th node, and N is the number of nodes.

[0079] Specifically, the degree of a node refers to the number of first-order neighbor nodes of the node. For example, in the first embodiment, the connection relationship between the nodes is as follows: Figure 3bAs shown, the first-order neighbor nodes of node A are B and D, the second-order neighbor node of A is C, the first-order neighbor nodes of B are A, C and E, the first-order neighbor nodes of D are A and C, N is the number of nodes, that is, N=5, the number of first-order neighbor nodes of A is 2, that is, the degree value of A is 2, and the degree centrality of node A is 50%, the number of first-order neighbor nodes of node B is 3, and the degree centrality of node B is 75%. That is, the more the number of first-order neighbor nodes of B, the higher the value of degree centrality. Exemplarily, in the second embodiment, the connection relationship between the nodes is as follows Figure 4 As shown, the first-order neighbor nodes of node A are B, D, E and F, the second-order neighbor nodes of A are C, F and G, and the first-order neighbor nodes of B are A and C. Then the degree value of A is 4, N=7, so the degree centrality of A is 67%, and the degree value of B is 2, so the degree centrality of B is 33%. That is, the more the number of first-order neighbor nodes of A, the higher the value of degree centrality. It can be seen that degree centrality is calculated based on the node degree value and the number of nodes, that is, degree centrality is used to indicate the number of connections of a node. The larger the value of degree centrality, the more connections the node has, which means that the criticality of the node is higher. If the number of connections between the current node and other nodes is more, it means that the criticality of the current node is higher. Therefore, the feature assignment calculation of each node is based on the degree centrality of each node, so that the node can be identified more accurately.

[0080] In some embodiments, determining the local eigenvalue of each node according to the degree centrality of each node includes:

[0081] The local eigenvalues ​​of the nodes are determined by the following formula:

[0082] Δ i =(Θ i ) 2 ;

[0083] In the formula, Θ i is node v i The degree centrality of i is node v i The local eigenvalues ​​of .

[0084] Specifically, it can be known from the above disclosure that the local eigenvalue of a node is calculated based on degree centrality, which is equivalent to the local eigenvalue being based on degree centrality (degree centrality indicates the criticality of a node). The criticality of a node can be judged according to the number of connections of the node, so as to accurately identify the node, improve the computing efficiency of the network, efficiently process large-scale power system network data, and provide better feasibility for practical applications.

[0085] In some embodiments, the path information between the nodes includes the number of shortest paths between the nodes;

[0086] Determining the global eigenvalue of each node based on the path information between the nodes, the degree centrality of each node and the local eigenvalue of each node includes:

[0087] According to the number of shortest paths between the nodes, the degree centrality of each node and the local eigenvalue of each node, the global eigenvalue of each node is determined by the following formula:

[0088]

[0089] In the formula, g ij is node v i and neighbor node v j The number of shortest paths, Θ i is node v i The degree centrality of i is node v i The local eigenvalue of j is node v j Relative to node v i The global eigenvalue of .

[0090] Specifically, the number of shortest paths between nodes may include multiple ones. For example, the connection relationship between nodes is as follows: Figure 3b As shown, taking the AC path as an example, ADC and ABC, the lengths of the two paths are the same, so it can be determined that the number of shortest paths between AC nodes is two. It can be seen that the global eigenvalue is calculated based on the local characteristics of node A and the path information of A (that is, the number of paths of neighboring nodes), which is equivalent to synthesizing the local characteristics of all paths to obtain the global eigenvalue of the node. In this way, the global eigenvalue combined with the neighboring nodes can more accurately identify the node, making the calculation of the criticality of the node more accurate, that is, the global eigenvalue is more accurate, and can efficiently process large-scale power system networks, providing better feasibility for practical applications. Accurate identification of nodes can provide important guidance for the design, optimization and management of power systems. System designers and managers can carry out targeted system planning and improvement based on the distribution and characteristics of key nodes to improve the overall performance of the system.

[0091] In some embodiments, determining the comprehensive eigenvalue of each node based on the local eigenvalue of each node and the global eigenvalue of each node includes:

[0092] According to the local eigenvalues ​​of the nodes and the global eigenvalues ​​of the nodes, the comprehensive eigenvalues ​​of the nodes are determined by the following formula:

[0093]

[0094] In the formula, Θ i is node v i The comprehensive characteristic value, g ij is node v i and neighbor node v j The number of shortest paths, Θ i is node v i The degree centrality of i is node v i The local eigenvalue of each node; V is a node set. Specifically, after the comprehensive eigenvalue of each node is calculated in the above embodiment, it can be known from the above formula that the comprehensive eigenvalues ​​of the neighboring nodes of the current node are summed. Since the comprehensive eigenvalue of each node indicates the criticality of each node, the comprehensive eigenvalues ​​of each neighboring node are added together. In this application, the above Γ vi Take node v i Take the first-order neighbor node set of as an example. In this embodiment, Ω i Can be node v i The comprehensive eigenvalues ​​of the first-order neighbor nodes. In this way, the global eigenvalue combined with the neighbor nodes can more accurately identify the nodes, making the criticality calculation of the nodes more accurate, that is, the comprehensive eigenvalue is more accurate, and can efficiently handle large-scale power system networks, providing better feasibility for practical applications. Accurate identification of nodes can provide important guidance for the design, optimization and management of power systems. System designers and managers can carry out targeted system planning and improvement based on the distribution and characteristics of key nodes to improve the overall performance of the system.

[0095] In some embodiments, determining the target feature value of each node based on the comprehensive feature value of the neighboring nodes of each node includes:

[0096] The target eigenvalue of each node is determined by the following formula:

[0097]

[0098] In the formula, j is the neighbor node v j The comprehensive eigenvalue of Ω i is node v i The fused eigenvalues ​​of the neighboring nodes, is node v i The set of neighbor nodes, Ω j is the neighbor node v j The fusion feature value of Score i is node v i The target feature value.

[0099] Specifically, in this embodiment, Γvi is node v i A neighbor node set is a set of neighbor nodes, which includes both first-order neighbor nodes and second-order neighbor nodes. Since the characteristic calculation of the first-order neighbor nodes is performed twice in the above embodiment, it can be understood that both the first-order neighbor nodes and the second-order neighbor nodes have been calculated. Therefore, when calculating the target characteristic value, the first-order neighbor nodes and the second-order neighbor nodes are summed. In this way, the target characteristic value of the node can be obtained, the node can be identified more accurately, and the criticality calculation of the node is more accurate, that is, the target characteristic value is more accurate, and large-scale power system networks can be efficiently processed, providing better feasibility for practical applications. Accurate identification of nodes can provide important guidance for the design, optimization and management of power systems. System designers and managers can carry out system planning and improvement in a targeted manner according to the distribution and characteristics of key nodes to improve the overall performance of the system.

[0100] In some embodiments, determining the key nodes according to the target feature value of each node and the preset target feature value range includes:

[0101] The node corresponding to the target characteristic value within the preset target characteristic value range is taken as the key node.

[0102] Specifically, the preset target characteristic value range can be pre-set according to the number of nodes, and can be set according to different devices of the power system network. It is not specifically limited in this application. The nodes corresponding to the target characteristic values ​​within the preset target characteristic value range are taken as key nodes. In this way, key nodes can be found among a large number of nodes, which can provide important guidance for the design, optimization and management of the power system. System designers and managers can carry out targeted system planning and improvement according to the distribution and characteristics of key nodes to improve the overall performance of the system.

[0103] In some embodiments, the method further includes: generating a key node list based on the key nodes, and evaluating the key node list based on a preset comparison sorting list and an MI algorithm.

[0104] Specifically, the target characteristic values ​​of each key node are arranged from large to small to generate a key node list. In this way, the generated key node list is generated according to the order of importance of the key nodes, which can facilitate system designers and managers to carry out targeted system planning and improvement according to the distribution and characteristics of the key nodes, thereby improving the overall performance of the system.

[0105] In this embodiment, in order to ensure that the key point list meets the list requirements, the key point list needs to be evaluated. The specific evaluation process includes: the key point list is evaluated based on the preset comparison sorting table and the MI algorithm, including:

[0106] The elements in the key node list are evaluated by the following formula to determine the first evaluation parameter of the key node list:

[0107]

[0108] In the formula, τ(X,Y) is the first evaluation parameter, τ is the Kendall correlation coefficient, X is the preset comparison sort list, Y is the key node list, and N L is the length of the sorted list, then N L ≤N, N is the number of nodes, N c and N d Represent the number of ordered pairs and unordered pairs respectively;

[0109] The elements in the key node list are evaluated by the following formula to determine the second evaluation parameter of the key node list:

[0110]

[0111] Where MI(Y) is the second evaluation parameter, where MI represents distinguishability, N L is the length of the sorted list, L′ is the set of target eigenvalues, χ is the target eigenvalue, N χ is the number of nodes with the target eigenvalue χ;

[0112] In response to the first evaluation parameter being greater than a first preset threshold and the second evaluation parameter being greater than a second preset threshold, it is determined that the key node list meets the list requirement.

[0113] Specifically, the elements in the key node list are evaluated by the following formula, and the process of determining the first evaluation parameter of the key node list includes: selecting an element pair (x a ,x b ), the element pairs in Y (y a ,y b ), if x a >y a ,x b >y b or x a <y a ,x b <y b , then (x a ,x b ) and (y a ,y b ) is an ordered pair, otherwise it is an unordered pair; Substitute the number of ordered and unordered pairs into In the formula, the first evaluation parameter is obtained. Among them, the X preset control sort list is generated based on the SIR model. SIR (Susceptible Infectious Recovered) model: an epidemiological model in infectious diseases, which simulates the spread of infectious diseases and state changes in the population by considering the three states of susceptible (Susceptible), infected (Infectious), and recovered (Recovered) in the network. In complex networks, node importance measures are derived based on the influence range of nodes in the SIR model and used as a reference for key node identification. These measures reflect the influence and propagation ability of nodes in the propagation process and provide a control group for the key node list.

[0114] Furthermore, in order to prevent uneven arrangement of nodes in the list, the elements in the key node list are calculated using the MI algorithm (Monotonicity index). The formula calculates all elements in the key node list to obtain the second evaluation parameter. When the first evaluation parameter is greater than the first preset threshold, and the second evaluation parameter is greater than the second preset threshold, it means that the key node list meets the list requirements. Through the Kendall correlation coefficient (calculated by the elements of the preset comparison list and the elements of the key node list) and the distinguishability algorithm (MI algorithm), it is possible to accurately verify that the key node list meets the list requirements. In this way, an accurate key node list can be provided to system designers and managers, so that system designers and managers can carry out targeted system planning and improvement based on the distribution and characteristics of the key nodes, thereby improving the overall performance of the system.

[0115] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0116] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0117] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a device for identifying key nodes in a power system.

[0118] refer to Figure 5 , the device for identifying key nodes in a power system comprises:

[0119] An acquisition module 302 is configured to acquire node information of the power system, wherein the node information includes the number of nodes, the degree value of each node, and path information between each node;

[0120] The first determination module 304 is configured to determine the target feature value of each node based on the degree centrality algorithm according to the number of nodes, the degree value of each node and the path information between the nodes;

[0121] The second determination module 306 is configured to determine the key nodes according to the target feature value of each node and a preset target feature value range.

[0122] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0123] The device of the above embodiment is used to implement a corresponding method for identifying key nodes in a power system in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0124] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for identifying key nodes in an electric power system as described in any of the above-mentioned embodiments is implemented.

[0125] Figure 6 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0126] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0127] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0128] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0129] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0130] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0131] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0132] The electronic device of the above embodiment is used to implement a corresponding method for identifying key nodes in a power system in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0133] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute a method for identifying key nodes in an electric power system as described in any of the above embodiments.

[0134] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0135] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute a method for identifying key nodes in a power system as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0136] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0137] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0138] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0139] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0140] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0141] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0142] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0143] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A method for identifying key nodes in a power system, characterized in that: include: Acquire node information of the power system, wherein the node information includes the number of nodes, the degree value of each node and the path information between each node; Based on the degree centrality algorithm, the target feature value of each node is determined according to the number of nodes, the degree value of each node and the path information between the nodes; The key nodes are determined according to the target characteristic value of each node and the preset target characteristic value range.

2. The method according to claim 1, characterized in that The degree centrality-based algorithm determines the target feature values ​​of all nodes according to the number of nodes, the degree value of each node and the path information between the nodes, including: Based on the degree centrality algorithm, the degree centrality of each node is determined according to the number of nodes and the degree value of each node; Determining the local eigenvalue of each node according to the degree centrality of each node; Determine the global eigenvalue of each node based on the path information between the nodes, the degree centrality of each node and the local eigenvalue of each node; Determining a comprehensive eigenvalue of each node based on the local eigenvalue of each node and the global eigenvalue of each node; Based on the comprehensive characteristic values ​​of the neighboring nodes of each node, the target characteristic value of each node is determined.

3. The method according to claim 2, characterized in that The degree centrality algorithm is based on the number of nodes and the degree value of each node to determine the degree centrality of each node, including: The degree centrality of each node is determined by the following formula: In the formula, Θ i is node v i The degree centrality of k i is node v i The value of v i is the i-th node, and N is the number of nodes.

4. The method according to claim 3, characterized in that Determining the local eigenvalue of each node according to the degree centrality of each node includes: The local eigenvalues ​​of the nodes are determined by the following formula: D i =(Θ i ) 2 ; In the formula, Θ i is node v i The degree centrality of i is node v i The local eigenvalues ​​of .

5. The method according to claim 4, characterized in that The path information between the nodes includes the number of shortest paths between the nodes; Determining the global eigenvalue of each node based on the path information between the nodes, the degree centrality of each node and the local eigenvalue of each node includes: According to the number of shortest paths between the nodes, the degree centrality of each node and the local eigenvalue of each node, the global eigenvalue of each node is determined by the following formula: In the formula, g ij is node v i and neighbor node v j The number of shortest paths, Θ i is node v i The degree centrality of i is node v i The local eigenvalue of j is node v j Relative to node v i The global eigenvalue of .

6. The method according to claim 5, characterized in that The determining of the comprehensive eigenvalue of each node based on the local eigenvalue of each node and the global eigenvalue of each node includes: According to the local eigenvalues ​​of the nodes and the global eigenvalues ​​of the nodes, the comprehensive eigenvalues ​​of the nodes are determined by the following formula: In the formula, i is node v i The comprehensive characteristic value, g ij is node v i and neighbor node v j The number of shortest paths, Θ i is node v i The degree centrality of i is node v i The local eigenvalue of , V is the node set.

7. The method according to claim 6, characterized in that The determining the target feature value of each node based on the comprehensive feature value of the neighboring nodes of each node includes: The target eigenvalue of each node is determined by the following formula: In the formula, j is the neighbor node v j The comprehensive eigenvalue of Ω i is node v i The fused eigenvalues ​​of the neighboring nodes, is node v i The set of neighbor nodes, Ω j is the fused neighbor node v j The fusion feature value of Score i is node v i The target feature value.

8. The method according to claim 1, characterized in that The determining of the key nodes according to the target characteristic value of each node and the preset target characteristic value range includes: The node corresponding to the target characteristic value within the preset target characteristic value range is taken as the key node.

9. The method according to claim 1, characterized in that: The method further includes: generating a key node list based on the key nodes, and evaluating the key node list based on a preset comparison sorting list and an MI algorithm.

10. The method according to claim 9, characterized in that The step of evaluating the key node list based on the preset comparison ranking table and the MI algorithm includes: The elements in the key node list are evaluated by the following formula to determine the first evaluation parameter of the key node list: In the formula, τ(X,Y) is the first evaluation parameter, τ is the Kendall correlation coefficient, X is the preset comparison sort list, Y is the key node list, and N L is the length of the sorted list, then N L ≤N, N is the number of nodes, N c and N d Represent the number of ordered pairs and unordered pairs respectively; The elements in the key node list are evaluated by the following formula to determine the second evaluation parameter of the key node list: Where MI(Y) is the second evaluation parameter, where MI represents the distinguishability value, and N L is the length of the sorted list, L′ is the set of target global eigenvalues, χ is the target global eigenvalue, N χ is the number of nodes with the target global eigenvalue χ; In response to the first evaluation parameter being greater than a first preset threshold and the second evaluation parameter being greater than a second preset threshold, it is determined that the key node list meets the list requirement.

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