Method for identifying key nodes of power system

Abstract

The application provides a power system key node identification method, node information of a power system is acquired, the node information includes the number of nodes, the degree value of each node and the path information between nodes, the comprehensive characteristic value of each node is obtained by calculation according to the number of nodes, the degree value of each node and the path information between nodes. By fusing the multi-order neighbor comprehensive feature information, the target characteristic value of the node is obtained, so that the key degree of the node can be judged according to the size of the target characteristic value. By one-to-one correspondence between the target characteristic value and the corresponding node, all nodes are screened, and the nodes meeting the preset target characteristic value range are used as key nodes, that is, the key degree of the screened nodes is high. In this way, the nodes are identified according to the size of the target characteristic value, the nodes can be accurately identified, which helps the system operation and maintenance personnel to take effective measures to prevent system collapse or function damage.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system, and particularly relates to a power system key node identification method. BACKGROUND

[0002] In the prior art, a large-scale power system is described by using a complex network, in complex system modeling, components of the power system are regarded as nodes in the network, in analyzing the power system, a key node represents a device playing a key role in the power system, and accurate identification of the key node plays a key role in maintenance of the power system network. The existing key node identification method cannot accurately identify all technical features of the node, resulting in low accuracy of key node identification, which is not conducive to maintenance of the power system, and if the maintenance is not timely, the power system may collapse and collapse, causing greater losses. SUMMARY

[0003] Therefore, the present application aims to provide a power system key node identification method to solve the technical problem of low accuracy of the existing node identification method.

[0004] To achieve the above purpose, the present application provides a power system key node identification method, comprising:

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

[0006] determining a target feature value of each node based on the number of nodes, the degree value of each node and the path information between nodes by using a degree centrality algorithm;

[0007] determining a key node according to the target feature value of each node and a preset target feature value range.

[0008] Optionally, the target feature value of all nodes is determined based on the number of nodes, the degree value of each node and the path information between nodes by using the degree centrality algorithm, comprising:

[0009] determining a degree centrality of each node based on the number of nodes and the degree value of each node by using the degree centrality algorithm;

[0010] determining a local feature value of each node according to the degree centrality of each node;

[0011] determining a global feature value of each node based on the path information between nodes, the degree centrality of each node and the local feature value of each node;

[0012] determining a comprehensive feature value of each node based on the local feature value of each node and the global feature value of each node;

[0013] determine a target feature value of each node based on a comprehensive feature value of neighbor nodes of the node.

[0014] Optionally, the degree centrality algorithm determines the degree centrality of each node according to the number of nodes and the degree value of each node, and includes:

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

[0016]

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

[0018] Optionally, the determination of the local feature value of each node according to the degree centrality of each node includes:

[0019] The local feature value of each node is determined by the following formula:

[0020] Δ i = (Θ i ) 2 ;

[0021] wherein Θ i is the degree centrality of node v i , and Δ i is the local feature value of node v i .

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

[0023] The determination of the global feature value of each node based on the path information between the nodes, the degree centrality of each node, and the local feature value of each node includes:

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

[0025]

[0026] wherein g ij is the number of shortest paths between node v i and neighbor node v j , Θ i is the degree centrality of node v i , and Δ i is the local feature value of node vi local feature value of node v; V is a node set, Φ j is a global feature value of node v j relative to node v i .

[0027] Optionally, the determining of the comprehensive feature value of each node based on the local feature value of each node and the global feature value of each node comprises:

[0028] the comprehensive feature value of each node is determined according to the local feature value of each node and the global feature value of each node by the following formula:

[0029]

[0030] in the formula, Ξ i is a comprehensive feature value of node v i , g ij is a shortest path number of node v i and neighbor node v j , Θ i is a degree centrality of node v i , Δ i is a local feature value of node v i ; V is a node set.

[0031] Optionally, the determining of the target feature value of each node based on the comprehensive feature value of the neighbor node of each node comprises:

[0032] the target feature value of each node is determined by the following formula:

[0033]

[0034] in the formula, Ξ j is a comprehensive feature value of neighbor node v j , Ω i is a fusion feature value of neighbor node of node v i , is a neighbor node set of node v i , Ω j is a fusion feature value of fusion neighbor node v j , Score i is a target feature value of node v i .

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

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

[0037] Optionally, the method further comprises: generating a key node list based on the key nodes; and evaluating the key node list based on a preset reference ranking list and an MI algorithm.

[0038] Optionally, the evaluating the key node list based on the preset reference ranking list and the MI algorithm comprises:

[0039] evaluating elements in the key node list by the following formula to determine a first evaluation parameter of the key node list:

[0040]

[0041] wherein τ(X, Y) is the first evaluation parameter, τ is a Kendall correlation coefficient, X is the preset reference ranking list, Y is the key node list, N L is a length of the ranking list, N L ≤ N, N is a number of nodes, and N c and N d respectively represent numbers of ordered pairs and unordered pairs;

[0042] evaluating elements in the key node list by the following formula to determine a second evaluation parameter of the key node list:

[0043]

[0044] wherein MI(Y) is the second evaluation parameter, MI represents a distinguishability value, N L is a length of the ranking list, L' is a set of target global feature values, χ is a target global feature value, and N χ is a number of nodes having the target global feature value 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, determining that the key node list meets a list requirement.

[0046] From the above, it can be seen that the power system key node identification method provided by the application, by acquiring the node information of the power system, the node information including the number of nodes, the degree value of each node and the path information between each node, calculating the degree centrality of the node based on the degree centrality algorithm, and based on the value of the degree centrality, calculating according to the number of nodes, the degree value of each node and the path information between each node, obtaining the comprehensive characteristic value of each node, obtaining the target characteristic value of each node by fusing the comprehensive characteristic information of each order neighbor, so as to be able to judge the key degree of the node according to the size of the target characteristic value. The node corresponding to the target characteristic value is regarded as a key node, so that all nodes are screened, the nodes meeting the preset target characteristic value range are key nodes, and the nodes with larger target characteristic values are screened, that is, the key degree of the screened nodes is higher. In this way, the nodes are identified according to the size of the target characteristic value, the nodes can be accurately identified, which helps the system operation and maintenance personnel to take effective measures to prevent system collapse or function damage. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the application or related art, the drawings needed to be used in the embodiments or related art description will be briefly introduced. Obviously, the drawings in the following description are only embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0048] Figure 1 A flowchart of a power system key node identification method according to an embodiment of the application is shown in the figure.

[0049] Figure 2 A flowchart of determining a target characteristic value according to an embodiment of the application is shown in the figure.

[0050] Figure 3a An exemplary structure diagram of the connection relationship of each node according to an embodiment of the application is shown in the figure.

[0051] Figure 3b A first embodiment structure diagram of the connection relationship of each node according to an embodiment of the application is shown in the figure.

[0052] Figure 4 A second embodiment structure diagram of the connection relationship of each node according to an embodiment of the application is shown in the figure.

[0053] Figure 5 A framework diagram of a power system key node identification device according to an embodiment of the application is shown in the figure.

[0054] Figure 6 An electronic device hardware structure diagram according to an embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0055] For the purposes of the present application, the technical solutions and advantages thereof are more clearly apparent, the following further describes the present application in detail with reference to the 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 understood as the common meanings understood by those with ordinary skills in the field to which the present application pertains. The terms "first", "second", and similar terms used in the embodiments of the present application do not represent any order, number, or importance, but are only used to distinguish different components. The terms "include", "contain", and similar terms mean that the elements or objects before the terms encompass the elements or objects listed after the terms and their equivalents, and do not exclude other elements or objects. The terms "connect" or "connected" and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right", and the like are only used to represent relative positional relationships, and when the absolute positions of the described objects change, the relative positional relationships can also change accordingly.

[0057] Based on the background art, 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 a power system can be regarded as nodes in the network, and their mutual relationship is represented as edges or connections in the network. The advantage of modeling a power system as a complex network is that it can capture the complex mutual relationship and structural characteristics between the components in the system, providing a powerful tool for 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, and the removal or destruction of key nodes can lead to the collapse of the network structure or serious damage to the function. For example, in a power network, key nodes represent transformers, switch devices, etc. Therefore, accurate identification of key nodes helps to 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 for the sustainability and resilience of the system.

[0058] The existing node identification algorithms in the prior art exemplarily include the following five main types, (1) the first method deeply analyzes the neighborhood structure and local connection mode of the nodes in the network to reveal the nodes that are crucial to the network structure and function in the local range. For example, in the degree centrality algorithm, the higher the degree of a node, the more other nodes it is directly connected to, thereby improving its criticality in information dissemination and network flow. (2) The second method considers the global connection mode of the nodes in the network to find 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 it plays the role of a bridge for information transmission in the network, 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 the nodes in network dynamic evolution and information dissemination, etc. For example, the VoteRank algorithm considers the dynamic changes of nodes in the voting process, and the increase in dynamic voting number indicates that the node has more significant influence and participation in the neighborhood. Considering such detailed dynamic characteristics 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 the nodes in the network structure to reveal their criticality in the overall topology. For example, the K-Shell algorithm reveals the different shell structures of nodes in the network, and the larger the K-Shell value of a node, the larger the k-shell it is located in, which has an important influence on the overall connectivity and stability of the network. (5) The fifth method deeply analyzes the shrinkage of the nodes to measure the integration ability of the nodes in the network topology. For example, the condensation algorithm integrates a node with its first-order neighbors into a node to generate a new network. The larger the integration ability of a node, the more critical it is, providing useful insights into 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, leading to inaccurate key node identification. The specific description of the limitations of the existing algorithms is as follows: the existing algorithms focus on a certain aspect or specific type of node features, so they cannot fully consider all characteristics of the network, making it difficult for the algorithm to accurately capture the overall structure and function of the network. For example, the betweenness centrality algorithm can capture the global structure of the network, but it ignores important information such as the specific location of the node and the local structure of the network. In addition, existing technologies need to balance between accuracy and computational efficiency in algorithm design. For example, the betweenness centrality algorithm has moderate accuracy, but has high computational complexity when dealing with large-scale networks, requiring a large amount of computational resources and time; the degree centrality algorithm has high computational efficiency in large-scale networks, but has low accuracy. The specific description of the shortcomings of not fully considering the neighbor information of the nodes is as follows: the existing algorithms do not fully consider and utilize the relevance and importance of node neighbors, leading to an incomplete understanding of the relationships between nodes in the network, limiting the algorithm's accurate grasp of the influence and role of nodes in the local structure. In some cases, this deficiency may result in insufficient and inaccurate capture of network characteristics.

[0060] To solve the above technical problems, with reference to Figure 1 The application provides a key node identification method for a power system, comprising the following steps:

[0061] Step 102, obtaining 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 nodes.

[0062] In this step, the node information of the power system is obtained from the topology graph of the power system, which includes multiple devices and connection lines between the devices. The power system is abstracted into a power network through a complex network method, that is, the multiple devices are abstracted into multiple nodes, and the connection lines between the multiple devices are abstracted into connection edges between the nodes. For example, the power system is abstracted into a power network by the following formula: G=(V,W), wherein V={v1,v2,…,vN} represents different devices in the power system, such as generators, substations, loads, etc., that is, V represents a node set, N represents the number of devices, E={e1,e2,…,eM} represents the actual physical connections such as transmission lines, transformer connections, cables, etc., and eij represents the connection between node v N and node v ij . i j ij i j ​​​​The edges between the nodes. Among them, the number of nodes can be counted according to the plurality of nodes, the degree value of each node and the path information between each node can be counted according to the connection edges between each node, wherein the degree value refers to the number of first-order neighbor nodes of the node. For example, the connection relationship between each node is as shown in Figure 3a FIG. 1, the first-order neighbor nodes of node A are B and D, the second-order neighbor nodes of A are C, and the degree value of A is 2. The first-order neighbor nodes of B are A and C, the degree value of B is 2. The first-order neighbor nodes of C are B and D, the degree value of C is 2. The first-order neighbor nodes of D are A and C, and the degree value of D is 2.

[0063] Step 104, 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 each node.

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

[0065] Step 106, according to the target feature value of each node and the preset target feature value range, determining the critical node.

[0066] In this step, whether a node is a critical node is determined according to the target feature value of each node. When the target feature value is within the preset target feature value range, the node corresponding to the target feature value is regarded as a critical node. That is, all nodes with a large target feature value are screened, and the nodes within the preset target feature value range are regarded as critical nodes. The critical nodes play a crucial role in network maintenance, which helps system maintenance personnel to take effective measures to prevent system collapse or function damage.

[0067] Specifically, by steps 102-106, by acquiring node information of the power system, the node information including 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 based on the value of the degree centrality, the comprehensive characteristic value of each node is obtained according to the number of nodes, the degree value of each node and the path information between each node, that is, the target characteristic value of each node is obtained by fusing the comprehensive characteristic information of each order neighbor, so that the key degree of the node can be judged according to the size of the target characteristic value. The node corresponding to the target characteristic value is regarded as a key node, that is, all nodes are screened, and the nodes meeting the preset target characteristic value range are key nodes, that is, the nodes with larger global characteristic values are screened, that is, the nodes with higher key degree, 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 the power system from collapsing or being damaged.

[0068] In some embodiments, with reference to Figure 2 , the degree centrality algorithm is used to determine the comprehensive characteristic value of all nodes according to the number of nodes, the degree value of each node and the path information between each node, including the following steps:

[0069] Step 202, 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.

[0070] Step 204, according to the degree centrality of each node, the local characteristic value of each node is determined.

[0071] Step 206, based on the path information between each node, the degree centrality of each node and the local characteristic value of each node, the global characteristic value of each node is determined.

[0072] Step 208, based on the local characteristic value of each node and the global characteristic value of each node, the comprehensive characteristic value of each node is determined.

[0073] Step 210, based on the comprehensive characteristic value of the neighbor node of each node, the target characteristic value of each node is determined.

[0074] Specifically, based on the degree centrality algorithm, the degree centrality of each node is calculated using a formula based on the number of nodes and their degree values. Degree centrality represents the number of connections a node has and can determine its criticality; a higher degree centrality value indicates a higher criticality. Based on the degree centrality of each node, local feature values ​​are determined, meaning local feature values ​​are calculated using the degree centrality values. Based on the path information between nodes, the degree centrality of each node, and its local feature values, the global feature value of each node is calculated using a formula. A comprehensive feature value is then calculated based on the local and global feature values ​​using a formula. Since the degree centrality value represents the criticality of a node, it is used as the basis for assigning values ​​to local features. The number of shortest paths between nodes indicates the information transmission role a node plays in the system and can determine its criticality; a shorter shortest path indicates a higher criticality. Since the shortest path value represents the criticality of a node, it is used as the basis for assigning values ​​to global features. Finally, based on the local and global feature values, the comprehensive feature value of each node is calculated. By integrating comprehensive feature information from neighbors at various levels, the target feature value of each node is determined, thereby judging the criticality of the node based on the target feature value. This approach more comprehensively considers the global features, local features, and node neighbor information of the power system, improving the accuracy of critical node identification.

[0075] In some embodiments, the degree centrality algorithm, which determines the degree centrality of each node based on the number of nodes and the degree value of each node, includes:

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

[0077]

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

[0079] Specifically, the degree value of a node refers to the number of its first-order neighbor nodes. For example, in the first embodiment, the connection relationships between nodes are 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, i.e. N=5, the number of the first-order neighbor nodes of A is 2, i.e. the degree value of A is 2, the degree centrality of A node is 50%, the number of the first-order neighbor nodes of B is 3, and the degree centrality of B node is 75%. That is, the more the first-order neighbor nodes of B, the higher the value of the degree centrality. For example, in the second embodiment, the connection relationship between nodes is as shown in 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, the first-order neighbor nodes of B are A and C, the degree value of A is 4, N=7, the degree centrality of A is 67%, the degree value of B is 2, and the degree centrality of B is 33%. That is, the more the first-order neighbor nodes of A, the higher the value of the degree centrality. It can be seen that the degree centrality is calculated based on the degree value of a node and the number of nodes, i.e. the degree centrality is used to represent how many connections a node has, the larger the value of the degree centrality, the more connections a node has, which means that the node is more critical. If a current node has more connections with other nodes, it means that the current node is more critical. Therefore, the degree centrality of each node is used to calculate the feature value of each node, so that the node can be more accurately identified.

[0080] In some embodiments, the local feature value of each node is determined according to the degree centrality of each node, including:

[0081] The local feature value of each node is determined by the following formula:

[0082] Δ i = (Θ i ) 2 ;

[0083] In the formula, Θ i is the degree centrality of node v i , and Δ i is the local feature value of node v i .

[0084] Specifically, as disclosed above, the local feature value of a node is calculated based on the degree centrality, which means that the local feature value is based on the degree centrality (the degree centrality represents the criticality of a node), and the criticality of a node can be determined according to the number of connections of the node, so that the node can be accurately identified, the calculation efficiency of the network can be improved, and the network data of a large-scale power system can be efficiently processed, which provides 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 characteristic value of each node based on the path information between the nodes, the degree centrality of each node, and the local characteristic value of each node, comprises:

[0087] determining the global characteristic value of each node based on the path information between the nodes, the degree centrality of each node, and the local characteristic value of each node, comprises:

[0088]

[0089] wherein g is the number of shortest paths between node v and neighbor node v, Θ is the degree centrality of node v, Δ is the local characteristic value of node v, V is the node set, Φ is the global characteristic value of node v with respect to node v. ij is the number of shortest paths between node v i and neighbor node v j , Θ i is the degree centrality of node v i , Δ i is the local characteristic value of node v i ; V is the node set, Φ j is the global characteristic value of node v j with respect to node v i .

[0090] Specifically, the number of shortest paths between nodes can include multiple, such as the first example described above, the connection relationship between nodes is as shown in Figure 3b Taking the A-C path as an example, A-D-C and A-B-C, wherein the lengths of the two paths are consistent, and the number of shortest paths between A-C nodes can be determined as two. As can be seen, the global characteristic value is calculated based on the local characteristic of node A and the path information of A (i.e., the number of paths of neighbor nodes), which is equivalent to comprehensively integrating the local characteristics of all paths to obtain the global characteristic value of the node. In this way, the global characteristic value in combination with the neighbor nodes can more accurately identify the node, so that the calculation of the key degree of the node is more accurate, that is, the global characteristic value is more accurate, which can efficiently process large-scale power system networks and provide better feasibility for actual application. Accurate identification of nodes can provide important guidance for the design, optimization and management of power systems. System designers and managers can plan and improve the system according to the distribution and characteristics of the key nodes to improve the overall performance of the system.

[0091] In some embodiments, determining the comprehensive characteristic value of each node based on the local characteristic value of each node and the global characteristic value of each node comprises:

[0092] determining the comprehensive characteristic value of each node based on the local characteristic value of each node and the global characteristic value of each node comprises:

[0093]

[0094] where Θ i is the integrated eigenvalue of node v i , g ij is the number of shortest paths from node v i to its neighbor nodes v j , Θ i is the degree centrality of node v i , Δ i is the local eigenvalue of node v i ; and V is the set of nodes. Specifically, after the integrated eigenvalue of each node is calculated in the above embodiment, it can be known from the above formula that the integrated eigenvalues of the neighbor nodes of the current node are summed up. Since the integrated eigenvalue of each node represents the criticality of each node, the integrated eigenvalues of the neighbor nodes are added up, and in the present application, the above Γ vi is the integrated eigenvalue of the first-order neighbor nodes of node v i . In the present embodiment, Ω i may be the integrated eigenvalue of the first-order neighbor nodes of node v i . In this way, the global eigenvalue in combination with the neighbor nodes can more accurately identify the nodes, so that the criticality calculation of the nodes is more accurate, that is, the integrated eigenvalue is more accurate, which can efficiently process large-scale power system networks and provide 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 plan and improve the system according to the distribution and characteristics of the key nodes, so as to improve the overall performance of the system.

[0095] In some embodiments, determining the target eigenvalue of each node based on the integrated eigenvalue of the neighbor nodes of each node comprises:

[0096] determining the target eigenvalue of each node by the following formula:

[0097]

[0098] where Ξ j is the integrated eigenvalue of neighbor node v j , Ω i is the fusion eigenvalue of the neighbor nodes of node v i , is the set of neighbor nodes of node v i , Ω j is the fusion eigenvalue of neighbor node v j , Score i is the target eigenvalue of node v i .

[0099] Specifically, in the present embodiment, Γvi is a neighbor node set of the node v i , which includes both first-order neighbor nodes and second-order neighbor nodes. Since the feature 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 the target feature value is calculated, the first-order neighbor nodes and the second-order neighbor nodes are both summed up, so that the target feature value of the node can be obtained, the node can be more accurately identified, the key degree calculation of the node is more accurate, that is, the target feature value is more accurate, and large-scale power system networks can be efficiently processed, thereby providing better feasibility for actual application. Accurate identification of nodes can provide important guidance for design, optimization and management of power systems. System designers and managers can perform system planning and improvement according to the distribution and characteristics of key nodes, thereby improving the overall performance of the system.

[0100] In some embodiments, the key nodes are determined according to the target feature values of the nodes and a preset target feature value range.

[0101] The nodes corresponding to the target feature values within the preset target feature value range are taken as the key nodes.

[0102] Specifically, the preset target feature value range can be preset according to the number of nodes, and can be set according to different devices of the power system network. In this application, the nodes corresponding to the target feature values within the preset target feature value range are taken as the key nodes, so that the key nodes can be found in a large number of nodes, and important guidance can be provided for design, optimization and management of power systems. System designers and managers can perform system planning and improvement according to the distribution and characteristics of key nodes, thereby improving the overall performance of the system.

[0103] In some embodiments, the method further comprises 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 key node list is generated by arranging the target feature values of the key nodes from large to small. In this way, the generated key node list is sorted according to the importance of the key nodes, which can facilitate system designers and managers to perform system planning and improvement according to the distribution and characteristics of key nodes, thereby improving the overall performance of the system.

[0105] In this embodiment, in order to ensure that the key node list meets the list requirements, the key node list needs to be evaluated. The specific evaluation process comprises: evaluating the key node list based on a preset comparison sorting list and an MI algorithm, which comprises:

[0106] A first evaluation parameter of the key node list is determined by evaluating elements in the key node list according to the following formula:

[0107]

[0108] wherein τ (X, Y) is the first evaluation parameter, τ is the Kendall correlation coefficient, X is the preset control ranking list, Y is the key node list, N L is the length of the ranking list, N L is the number of nodes, N c and N d represent the number of ordered pairs and unordered pairs, respectively;

[0109] A second evaluation parameter of the key node list is determined by evaluating elements in the key node list according to the following formula:

[0110]

[0111] wherein MI (Y) is the second evaluation parameter, MI represents distinguishability, N L is the length of the ranking list, L' is a set of target feature values, χ is a target feature value, N χ is the number of nodes with the target feature value 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 requirements.

[0113] Specifically, the process of determining the first evaluation parameter of the key node list by evaluating elements in the key node list according to the following formula includes: selecting an element pair (x a , x b ) in the preset control ranking list X, an element pair (y a , y b ) in Y, 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 ) are ordered pairs, otherwise unordered pairs; the number of ordered pairs and unordered pairs is brought into In the formula, a first evaluation parameter is obtained. Wherein, the X preset control ranking list is generated based on a SIR model. The SIR (Susceptible Infectious Recovered) model is an epidemiological model in epidemiology, which simulates the spread of infectious diseases and the state change in the population by considering the three states of susceptible, infectious and recovered in the network. In a complex network, the node importance metrics are derived according to the influence range of the nodes in the SIR model, which are used as references for key node identification. These metrics reflect the influence and propagation ability of the nodes in the propagation process, and provide a control group for the key node list.

[0114] Further, in order to prevent uneven arrangement of nodes in the list, the elements in the key node list are calculated by a MI algorithm (Monotonicity index), that is, the formula is used to calculate all elements in the key node list to obtain a second evaluation parameter. The first evaluation parameter is greater than a first preset threshold, and the second evaluation parameter is greater than a second preset threshold, which indicates that the key node list meets the list requirements. By calculating the Kendall correlation coefficient (between the elements of the preset control list and the elements of the key node list) and the distinguishability algorithm (MI algorithm), it can be accurately verified that the key node list meets the list requirements, so that the system designer and manager can provide an accurate key node list for the system designer and manager to plan and improve the system according to the distribution and characteristics of the key nodes, and improve the overall performance of the system.

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

[0116] It should be noted that some embodiments of the present application have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than the order described above and still achieve desirable results. In addition, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0117] Based on the same inventive concept, the application also provides an electric power system key node identification device corresponding to the method of any of the above embodiments.

[0118] Reference Figure 5 The electric power system key node identification device comprises:

[0119] The acquisition module 302 is configured to acquire node information of an electric power system, wherein the node information comprises a node quantity, a degree value of each node and path information between nodes.

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

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

[0122] For the convenience of description, the above device is described as various modules respectively described in terms of functions. Of course, in the implementation of the present application, the functions of each module can be implemented in one or more software and / or hardware.

[0123] The device of the above embodiment is used to implement the electric power system key node identification method of any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be described here.

[0124] Based on the same inventive concept, the application also provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the electric power system key node identification method of any of the above embodiments.

[0125] Figure 6 A more specific hardware structure schematic diagram of an electronic device provided by the present embodiment is shown, which can comprise 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 for communication within the device.

[0126] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., for executing relevant programs to implement the technical solutions provided by the embodiments of the present specification.

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

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

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

[0130] The bus 1050 includes a channel for transmitting information between various components (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040) of the device.

[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 can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device can also only include the components necessary to implement the solutions of the embodiments of the present specification, and does not have to include all the components shown in the figure.

[0132] The electronic device of the above-mentioned embodiments is used to implement the power system key node identification method of any one of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0133] Based on the same inventive concept, the present application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the power system key node identification method of any one of the above-mentioned embodiments.

[0134] The computer-readable medium of the present embodiment includes permanent and non-permanent, removable and non-removable media, which can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules 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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium 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-mentioned embodiments are used to cause the computer to perform the power system key node identification method of any one of the above-mentioned embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0136] It can be understood that before using the technical solutions of various embodiments in the present disclosure, the user will be informed of the type, use range, use scenario, etc. of the personal information involved by appropriate means, and the authorization of the user will be obtained.

[0137] For example, in response to receiving the user's active request, prompt information is sent to the user to explicitly prompt the user that the operation requested to be performed will require the acquisition and use of the user's personal information. Thus, the user can voluntarily choose whether to provide personal information to the software or hardware such as electronic devices, application programs, servers or storage media that perform the operation of the technical solutions of the present disclosure according to the prompt information.

[0138] As an optional but not limited implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

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

[0140] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0141] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0142] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, 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 be used with the embodiments discussed.

[0143] The embodiments of this 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 this application should be included within the protection scope of this application.

Claims

1. A method for identifying key nodes in a power system, characterized in that, include: Obtain 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 each node; Based on the target feature values ​​of each node and the preset target feature value range, key nodes are determined; The degree centrality-based algorithm determines the target feature values ​​of all nodes based on the number of nodes, the degree value of each node, and the path information between 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; Based on the degree centrality of each node, determine the local feature value of each node; Based on the path information between the nodes, the degree centrality of each node, and the local feature values ​​of each node, the global feature values ​​of each node are determined. Based on the local feature values ​​and global feature values ​​of each node, the comprehensive feature value of each node is determined. Based on the comprehensive feature values ​​of the neighboring nodes of each node, the target feature value of each node is determined. The degree centrality-based algorithm determines the degree centrality of each node based on the number of nodes and the degree value of each node, including: The degree centrality of each node is determined by the following formula; ； In the formula, It is a node Degree centrality, It is a node The degree value, It is the i-th node, and N is the number of nodes; The step of determining the local feature values ​​of each node based on the degree centrality of each node includes: The local eigenvalues ​​of each node are determined by the following formula: ； In the formula, It is a node Degree centrality, It is a node Local eigenvalues; The path information between each node includes the number of shortest paths between each node; The determination of global feature values ​​for each node based on path information between nodes, degree centrality of each node, and local feature values ​​of each node includes: Based on the number of shortest paths between the nodes, the degree centrality of each node, and the local feature values ​​of each node, the global feature values ​​of each node are determined by the following formula: ； In the formula, It is a node and neighboring nodes The number of shortest paths, It is a node Degree centrality, It is a node Local eigenvalues; It is a set of nodes. It is a node Relative to node Global eigenvalues; The determination of the comprehensive feature value of each node based on the local feature values ​​and the global feature values ​​of each node includes: Based on the local and global eigenvalues ​​of each node, the comprehensive eigenvalue of each node is determined using the following formula: ； In the formula, It is a node The comprehensive eigenvalues, It is a node and neighboring nodes The number of shortest paths, It is a node Degree centrality, It is a node Local eigenvalues, It is a set of nodes; The determination of the target feature value of each node based on the comprehensive feature values ​​of its neighboring nodes includes: The target feature value of each node is determined by the following formula: ； ； In the formula, Neighboring nodes The comprehensive eigenvalues, It is a node The fusion feature values ​​of the neighboring nodes, , It is a node The set of neighboring nodes, It is to merge neighbor nodes The fusion feature value, It is a node The target feature value.

2. The method according to claim 1, characterized in that, The step of determining key nodes based on the target feature values ​​of each node and a preset range of target feature values ​​includes: The nodes corresponding to the target feature values ​​that are within the preset target feature value range are designated as the key nodes.

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

4. The method according to claim 3, characterized in that, The evaluation of the list of key nodes based on a preset comparison sorting table and the MI algorithm includes: The first evaluation parameter of the key node list is determined by evaluating the elements in the key node list using the following formula: ； In the formula, It is the first evaluation parameter, where, This is the Kendall correlation coefficient, where X is the pre-defined control sort list and Y is the list of key nodes. This is the length of the sorted list. , It is the number of nodes. and These represent the number of ordered pairs and the number of unordered pairs, respectively. The second evaluation parameter of the key node list is determined by evaluating the elements in the key node list using the following formula: ； In the formula, It is the second evaluation parameter, where MI represents the distinguishability value. It is the length of the sorted list. It is a set of global feature values ​​of the target. It is the target's global feature value. It has the target global feature value The number of nodes; 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, the key node list is determined to meet the list requirements.

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