Oil field power grid key node identification method and device

Abstract

The invention discloses an oil field power grid key node identification method and device, and the method comprises the steps: obtaining an oil field power grid weighted graph according to an obtained oil field power grid node topological graph; calculating the degree, betweenness and power aggregation coefficient of each node in the oilfield power grid weighted graph; according to the oilfield power grid weighted graph, calculating to obtain a structural hole constraint coefficient of each node; calculating a kernel value of each node based on an improved K-Shell algorithm according to the degree, betweenness and power aggregation coefficient of each node; according to the kernel value of each node and the structural hole constraint coefficient, calculating to obtain a Tsallis entropy of each node; according to the Tsallis entropy of each node, calculating to obtain a key evaluation value of each node; and obtaining key nodes of the oil field power grid according to the key evaluation value of each node and a preset proportion. According to the method, key nodes can be efficiently and accurately identified in a power grid, and compared with a traditional method, the method has the advantage that the identification rate precision is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield power, and particularly to a method and device for identifying key nodes of an oilfield power grid. Background Art

[0002] With the advancement of energy transformation, traditional energy enterprises such as oilfields have accelerated the pace of new energy grid connection. However, the grid connection of new energy power generation brings a series of potential safety hazards to the traditional oilfield power grid, and the stability of the oilfield power grid structure becomes increasingly important. Traditional methods for identifying key nodes include Node Degree Centrality, Betweenness Centrality, Closeness Centrality, Current Flow Centrality, structural sensitivity analysis, etc. These methods are based on the topological structure and operating characteristics of the oilfield power system, and identify the key nodes that have the greatest impact on system stability and operation by measuring indicators such as node connection, betweenness, and closeness. These traditional methods provide a basis for evaluating and improving the stability of the oilfield power grid structure. Summary of the Invention

[0003] In order to more accurately identify key nodes in the oilfield power grid, an embodiment of the present invention provides a method and device for identifying key nodes of an oilfield power grid.

[0004] In a first aspect, an embodiment of the present invention provides a method for identifying key nodes of an oilfield power grid, the method comprising:

[0005] Obtaining an oilfield power grid weighted graph according to the obtained oilfield power grid node topology graph;

[0006] Calculating the degree, betweenness, and power clustering coefficient of each node in the oilfield power grid weighted graph;

[0007] Calculating the structural hole constraint coefficient of each node according to the oilfield power grid weighted graph;

[0008] Calculating the core value of each node based on an improved K-Shell algorithm according to the degree, betweenness, and power clustering coefficient of each node;

[0009] Calculating the Tsallis entropy of each node according to the core value and structural hole constraint coefficient of each node;

[0010] Calculating the criticality evaluation value of each node according to the Tsallis entropy of each node;

[0011] Obtaining the key nodes of the oilfield power grid according to the criticality evaluation value of each node and a preset ratio.

[0012] In one or some alternative embodiments of the embodiments of the present application, calculating the core value of each node based on the improved K-Shell algorithm according to the degree, betweenness, and power clustering coefficient of each node includes:

[0013] Calculating the core value of each node based on the following formula (1) according to the degree, betweenness, and power clustering coefficient of each node:

[0014]

[0015] In the formula, is the core value of node i, is the degree of node i, is the betweenness of node i, is the power clustering coefficient of node i, and and are the weight values of the degree, betweenness, and power clustering coefficient respectively;

[0016] Dividing a plurality of interpolation regions based on a preset threshold according to the core values of all nodes in the weighted graph of the oilfield power grid, and taking the interpolation region with the largest core value as the target interpolation region;

[0017] Taking any node in the weighted graph of the oilfield power grid as the current node, and determining whether the current node belongs to the target interpolation region;

[0018] If so, obtaining the next node as the current node;

[0019] If not, deleting the current node from the weighted graph of the oilfield power grid and saving the core value of the current node to obtain a new weighted graph of the oilfield power grid, and calculating the core values of all nodes of the new weighted graph of the oilfield power grid and a new target interpolation region according to the new weighted graph of the oilfield power grid;

[0020] Repeating the process of updating the nodes in the weighted graph of the oilfield power grid and updating the target interpolation region until the core values of all nodes in the updated weighted graph of the oilfield power grid belong to the updated target interpolation region;

[0021] Obtaining the core value of each node in the weighted graph of the oilfield power grid according to the core value of the deleted node and the core values of all nodes in the updated weighted graph of the oilfield power grid; the core value of the deleted node represents the core value saved when the node is deleted.

[0022] In one or some alternative embodiments of the embodiments of the present application, calculating the structural hole constraint coefficient of each node according to the weighted graph of the oilfield power grid includes:

[0023] Calculating the weight ratio of the connection edge between each node and its neighbor nodes based on the following formula (2) according to the weight of each node in the weighted graph of the oilfield power grid:

[0024]

[0025] Where ω(i) represents the weight of node i respectively, and L ij is the weight ratio of the edge connecting node i and node j, and Γ(i) is the set of neighbor nodes of node i;

[0026] According to the weight ratio of the edge connecting each said node and its neighbor nodes, the structural hole constraint coefficient of the corresponding node is calculated based on the following formula 3:

[0027]

[0028] Where R i represents the structural hole constraint coefficient of node i.

[0029] In one or some alternative embodiments of the embodiments of the present application, calculating the Tsallis entropy of each node according to the core value and the structural hole constraint coefficient of each node includes:

[0030] Calculating the Tsallis entropy of each node based on formula 4 according to the core value and the structural hole constraint coefficient of each node:

[0031]

[0032] Where T(i) represents the Tsallis entropy of node i respectively, and P ij is the structural hole coefficient, and q i is the Tsallis entropy parameter;

[0033] Among them, the structural hole coefficient P ij is calculated based on formula 5:

[0034]

[0035] Where R i respectively represent the structural hole end coefficient of node i, and Γ(i) is the set of neighbor nodes of node i;

[0036] Among them, the Tsallis entropy parameter q i is calculated based on formula 6:

[0037]

[0038] Where KS(i) is the core value of node i.

[0039] In one or some alternative embodiments of the embodiments of the present application, calculating the criticality evaluation value of each node according to the Tsallis entropy of each node includes:

[0040] Calculating the neighborhood core value of each node based on the following formula 7 according to the Tsallis entropy of each node:

[0041]

[0042] Based on the neighborhood core value of each node, the criticality evaluation value of each node is calculated according to the following formula 8:

[0043]

[0044] where TSG(i) is the criticality evaluation value of node i.

[0045] In one or some optional implementation manners of the embodiments of the present application, the obtaining the weighted oilfield power grid graph according to the obtained topological graph of the oilfield power grid nodes includes:

[0046] According to the injection power of each node and the system reference power in the topological graph of the oilfield power grid, the weight of the node is calculated based on the following formula 9:

[0047] ω ij = P ij / S B Formula 9;

[0048] In the formula, P ij is the transmission power between node i and neighbor node j, S B is the system reference power, and ω ij is the weight value of the connection between node i and node j;

[0049] According to the transmission power of each edge and the system reference power in the topological graph of the oilfield power grid, the weight of the edge is calculated based on the following formula 10:

[0050] ω i = P i / S B Formula 10;

[0051] In the formula, P i is the injection power of node i, and ω ij is the weight of node i;

[0052] According to the topological graph of the oilfield power grid, the weight of each edge, and the weight of each node, the weighted oilfield power grid graph is obtained.

[0053] In one or some optional implementation manners of the embodiments of the present application, the calculating the degree, betweenness, and power clustering coefficient of each node in the weighted oilfield power grid graph includes:

[0054] According to the weight of each node in the weighted oilfield power grid graph, the degree of each node is calculated based on the following formula 11:

[0055]

[0056] Wherein, D(i) represents the degree of node i, and ω ij is the weight value of the connection between node i and node j, and n represents the total number of nodes;

[0057] Based on the weight of each node in the weighted graph of the oilfield power grid, the betweenness of each node is calculated according to the following formula 12:

[0058]

[0059] Wherein, B(i) represents the betweenness of node i, and ω jk (i) is the sum of the transmission powers passing through node i between node j and node k, and ω jk is the sum of the transmission powers of all the shortest paths between node j and node k.

[0060] Based on the weight of each node in the weighted graph of the oilfield power grid, the power aggregation coefficient of each node is calculated according to the following formula 13:

[0061]

[0062] Wherein, C(i) represents the power aggregation coefficient of node i, and ω ij , ω ik , ω jk respectively represent the weights of node k, node j, and node i.

[0063] In a second aspect, an embodiment of the present invention provides an oilfield power grid key node identification device, which includes:

[0064] A weight calculation module, configured to obtain a weighted graph of the oilfield power grid according to the obtained topological graph of the oilfield power grid nodes;

[0065] A parameter calculation module, configured to calculate the degree, betweenness, and power aggregation coefficient of each node in the weighted graph of the oilfield power grid;

[0066] A structural hole calculation module, configured to calculate the structural hole constraint coefficient of each node according to the weighted graph of the oilfield power grid;

[0067] A core value calculation module, configured to calculate the core value of each node based on the improved K-Shell algorithm according to the degree, betweenness, and power aggregation coefficient of each node;

[0068] An entropy calculation module, configured to calculate the Tsallis entropy of each node according to the core value and the structural hole constraint coefficient of each node;

[0069] An evaluation value calculation module, configured to calculate the criticality evaluation value of each node according to the Tsallis entropy of each node;

[0070] A node acquisition module, configured to obtain key nodes of an oilfield power grid according to the criticality evaluation value of each node and a preset ratio.

[0071] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the oilfield power grid key node recognition method as described above is implemented.

[0072] In a fourth aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the oilfield power grid key node recognition method as described above is implemented.

[0073] In a fifth aspect, an embodiment of the present invention provides a computer program product containing instructions. When the computer program product runs on a computer device, the computer device is enabled to execute the oilfield power grid key node recognition method as described above.

[0074] In a sixth aspect, an embodiment of the present invention provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run a computer program or instructions to implement the oilfield power grid key node recognition method as described above.

[0075] The beneficial effects of the above technical solutions provided by the embodiments of the present invention at least include:

[0076] The oilfield power grid key node recognition method provided by the embodiment of the present invention calculates an oilfield power grid weighted graph by obtaining an oilfield power grid node topology graph, then calculates the degree, betweenness, and power clustering coefficient of each node, then calculates the structural hole constraint coefficient of each node based on the structural hole theory, obtains the core value of each node based on the improved K-shell oilfield power grid weighted graph, calculates the Tsallis entropy according to the core value and the structural hole constraint coefficient, obtains the criticality evaluation value of each node, and divides the key nodes of the oilfield power grid according to a preset ratio. The present invention introduces the structural hole theory to reflect the importance of nodes in a local area, improves the K-shell algorithm based on electrical characteristics to obtain the global characteristics of nodes, uses the Tsallis entropy algorithm to extract the local characteristics presented by the structural hole and the global characteristics obtained by the KS method, comprehensively considers the structural hole characteristics, global characteristics, and electrical characteristics of nodes, can improve the robustness of the key node recognition method in the oilfield power field, provides a more robust and comprehensive solution, and ensures the stable operation and power supply quality of the oilfield power grid.

[0077] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the structure particularly pointed out in the written description, claims, as well as the drawings.

[0078] The technical solution of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0079] The drawings are used to provide a further understanding of the present invention, and constitute a part of the description. Together with the embodiments of the present invention, they are used to explain the present invention, but do not constitute a limitation to the present invention. In the drawings:

[0080] Figure 1 It is a schematic diagram of the steps of the method for identifying key nodes in an oilfield power grid provided by an embodiment of the present invention;

[0081] Figure 2 It is a schematic diagram of the flow of an improved K-Shell algorithm provided by an embodiment of the present invention;

[0082] Figure 3 It is a schematic diagram of a node system diagram provided by an embodiment of the present invention;

[0083] Figure 4 It is a schematic diagram of a node topology diagram provided by an embodiment of the present invention;

[0084] Figure 5 It is a diagram of the experimental results of removing nodes provided by an embodiment of the present invention;

[0085] Figure 6 It is a diagram of the experimental results of a comparative experiment provided by an embodiment of the present invention;

[0086] Figure 7 It is a diagram of the network efficiency results of a network attack experiment provided by an embodiment of the present invention;

[0087] Figure 8 It is a diagram of the relative value of load loss of a network attack experiment provided by an embodiment of the present invention;

[0088] Figure 9 It is a schematic diagram of the structure of a device for identifying key nodes in an oilfield power grid provided by an embodiment of the present application. Detailed Embodiments

[0089] In the following description, specific details such as specific system architectures and technologies are presented for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from obscuring the description of the present application.

[0090] It should be understood that when used in the specification and the appended claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0091] It should also be understood that the term "and / or" used in the specification and the appended claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0092] As used in the specification and the appended claims of the present application, the term "if" can be interpreted as "when" or "once" or "in response to determining" or "in response to detecting" according to the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]" according to the context.

[0093] In addition, in the description of the specification and the appended claims of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0094] The reference to "one embodiment" or "some embodiments" or the like described in the specification of the present application means that a specific feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of the present application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0095] It should be understood that the sequence numbers of the steps in the following embodiments do not imply the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0096] To illustrate the technical solution of the present application, the following specific embodiments are used for illustration.

[0097] The inventors found that in the prior art, the existing key node identification methods mainly rely on local features such as node degree and betweenness, or on the power flow analysis of the power system, ignoring the importance of nodes in the entire network structure and being unable to accurately identify the key nodes of the oilfield power grid. Based on this, the inventors made further research and developed the present invention to provide a method and device for identifying key nodes of an oilfield power grid.

[0098] Embodiment 1

[0099] The embodiment of the present invention provides a method for identifying key nodes of an oilfield power grid. Referring to Figure 1 as shown, the method includes:

[0100] S101: Obtain the weighted graph of the oilfield power grid according to the obtained topological graph of the oilfield power grid nodes;

[0101] In the embodiment of the present application, in the above step S101, obtaining the weighted graph of the oilfield power grid according to the obtained topological graph of the oilfield power grid nodes includes:

[0102] Calculate the weight of the node based on the following formula 9 according to the injection power of each node in the topological graph of the oilfield power grid and the system base power:

[0103] ω ij =P ij / S B Formula 9;

[0104] In the formula, P ij is the transmission power between node i and neighbor node j, S B is the system base power, and ω ij is the weight value of node i connecting to node j;

[0105] Calculate the weight of the edge based on the following formula 10 according to the transmission power of each edge in the topological graph of the oilfield power grid and the system base power:

[0106] ω i =P i / S B Formula 10;

[0107] In the formula, P i is the injection power of node i, and ω ij is the weight of node i;

[0108] According to the topological graph of the oilfield power grid, the weight of each edge, and the weight of each node, a weighted graph of the oilfield power grid is obtained.

[0109] In the embodiments of the present application, from the perspective of complex networks, the oilfield power grid can be regarded as a weighted network, which can be defined as G=(V, E, ω), where V represents the nodes in the graph, E represents the edges, and ω represents the weights. Examples of the obtained node system graph and topological graph of the oilfield power grid are Figure 2 、 Figure 3 shown. The topological graph includes information on nodes and edges, and the weights are calculated by formulas 9 and 10 to obtain the weights of nodes and edges respectively. Among them, S B is the system base power, which is used to eliminate the dimension when substituting electrical values for calculation so as to compare with other indicators. P i is the injection power of node i, and P ij is the transmission power between node i and its neighbor node j, which is obtained through system power flow calculation and can be calculated from the electricity quantity values of smart meters. Thus, the weights corresponding to all nodes are calculated to obtain a weighted graph of the oilfield power grid. The weight of node i can reflect the electrical characteristics. The greater the weight of a node, it means that in the transmission of the oilfield power grid, the power borne by this node is higher. If this node fails, its impact on neighbor nodes will also be more significant.

[0110] S102: Calculate the degree, betweenness, and power clustering coefficient of each node in the weighted graph of the oilfield power grid;

[0111] In the embodiments of the present application, in the above step S102, calculating the degree, betweenness, and power clustering coefficient of each node in the weighted graph of the oilfield power grid includes:

[0112] Based on the weight of each node in the weighted graph of the oilfield power grid, the degree of each node is calculated according to the following formula 11:

[0113]

[0114] In the formula, D(i) represents the degree of node i, ω ij is the weight value of the connection between node i and node j, and n represents the total number of nodes;

[0115] Based on the weight of each node in the weighted graph of the oilfield power grid, the betweenness of each node is calculated according to the following formula 12:

[0116]

[0117] In the formula, B(i) represents the betweenness of node i, ω jk (i) is the sum of the transmission powers passing through node i between node j and node k, and ω jkIt is the sum of the transmission powers of all the shortest paths between node j and node k.

[0118] Based on the weights of each node in the weighted graph of the oilfield power grid, the power aggregation coefficient of each node is calculated according to the following formula 13:

[0119]

[0120] In the formula, C(i) represents the power aggregation coefficient of node i, and ω ij 、ω ik 、ω jk respectively represent the weights of node k, node j, and node i.

[0121] In the embodiment of the present application, first, the degree of the node is calculated according to the weighted graph of the oilfield power grid. The degree is a simple method to measure the importance of the node. In the topological graph, the degree can intuitively represent the number of neighbor nodes of the node. However, in the oilfield power grid, due to the existence of weights on the edges of the nodes, the number of neighbor nodes of the node can no longer be intuitively used to represent the degree of the node in the weighted graph. Therefore, the calculation formula of the node degree is as shown in formula 11, and the degree of node i is the sum of the weights of node i and all its neighbor nodes.

[0122] Then, the betweenness of the node is calculated according to the weighted graph of the oilfield power grid. In a complex network, the betweenness can reflect the importance of the node to a certain extent and measure the number of paths from the starting node to the target node. However, in the oilfield power grid, the definition of the betweenness needs to consider both the number of paths from the starting node to the target node and the influence of the transmission power and current on the stable state of the node and the line. Therefore, the calculation formula of the node betweenness is as shown in formula 12.

[0123] Finally, the power aggregation coefficient of the node is calculated according to the weighted graph of the oilfield power grid. The power aggregation coefficient reflects the connection relationship between the neighbor nodes around a node and embodies the clustering of the graph. Correspondingly, in the oilfield power grid, the definition of the power aggregation coefficient also reflects the relationship between the oilfield power grid node and its surrounding nodes. When a node with high clustering fails, it may have a ripple effect on the surrounding nodes. Therefore, the clustering index of the nodes in the weighted graph of the oilfield power grid can also reflect its importance. Therefore, the calculation formula of the node betweenness is as shown in formula 13.

[0124] Above, the degrees, betweennesses, and power aggregation parameters of all the nodes in the weighted graph of the oilfield power grid are obtained for subsequent calculations.

[0125] S103: Calculate the structural hole constraint coefficient of each node according to the weighted graph of the oilfield power grid;

[0126] In the embodiment of the present application, the above step S103, calculating the structural hole constraint coefficient of each node according to the weighted graph of the oilfield power grid, includes:

[0127] Based on the weights of each node in the weighted graph of the oilfield power grid, the weight ratio of the connection edge between each node and its neighbor nodes is calculated according to the following formula 2:

[0128]

[0129] In the formula, ω(i) represents the weight of node i respectively, and L ij is the weight ratio of the connection edge between node i and node j, and Γ(i) is the set of neighbor nodes of node i;

[0130] Based on the weight ratio of the connection edge between each node and its neighbor nodes, the structural hole constraint coefficient of the corresponding node is calculated according to the following formula 3:

[0131]

[0132] In the formula, R i represents the structural hole constraint coefficient of node i.

[0133] In the embodiments of the present application, the structural hole theory is introduced as a key node search algorithm. This algorithm reflects the importance of a node in the local area by calculating the relationship between the neighbor nodes and the common neighbor nodes of the node. In the weighted graph of the oilfield power grid, the structural hole theory needs to be redefined considering the influence of the electrical characteristics of the nodes on its definition. The calculation formula of the structural hole constraint coefficient of the nodes in the weighted graph of the oilfield power grid is as shown in formula 3, where R i represents the structural hole constraint coefficient of node i, and L ij is the weight ratio of the connection edge between node i and node j, where L ij The calculation formula is as shown in formula 2, and the weight ratio of the edge between node i and node j in all the connection edges of node i is calculated.

[0134] S104: Based on the degree, betweenness, and power clustering coefficient of each node, the core value of each node is calculated based on the improved K-Shell algorithm;

[0135] In the embodiments of the present application, the above step S104, based on the degree, betweenness, and power clustering coefficient of each node, calculating the core value of each node based on the improved K-Shell algorithm includes:

[0136] Based on the degree, betweenness, and power clustering coefficient of each node, the core value of each node is calculated according to the following formula 1:

[0137]

[0138] Wherein, KS(i) is the core value of node i, D(i) is the degree of node i, B(i) is the betweenness centrality of node i, and C(i) is the power clustering coefficient of node i. and are the weight values of degree, betweenness centrality, and power clustering coefficient respectively;

[0139] Based on the core values of all nodes in the weighted graph of the oilfield power grid, multiple interpolation regions are divided based on a preset threshold, and the interpolation region with the largest core value is used as the target interpolation region.

[0140] Taking any node in the weighted graph of the oilfield power grid as the current node, determine whether the current node belongs to the target interpolation region.

[0141] If so, obtain the next node as the current node.

[0142] If not, delete the current node from the weighted graph of the oilfield power grid and save the core value of the current node to obtain a new weighted graph of the oilfield power grid, and based on the new weighted graph of the oilfield power grid, calculate the core values of all nodes of the new weighted graph of the oilfield power grid and the new target interpolation region.

[0143] Repeat the process of updating the nodes in the weighted graph of the oilfield power grid and updating the target interpolation region until the core values of all nodes in the updated weighted graph of the oilfield power grid belong to the updated target interpolation region.

[0144] Based on the core values of the deleted nodes and the core values of all nodes in the updated weighted graph of the oilfield power grid, obtain the core value of each node in the weighted graph of the oilfield power grid; the core value of the deleted node represents the core value saved when the node is deleted.

[0145] In the embodiments of the present application, the K-Shell algorithm is improved based on the oilfield power grid data. The flow chart of the improved K-Shell algorithm is as Figure 4 shown, and the steps include: First step, calculate the core value of each node according to the degree, betweenness centrality, and power clustering coefficient of each node obtained in step S102, and the core value is Figure 4The KS value in it, the calculation formula of the core value is shown in Formula 1. After obtaining the core values of all nodes in the weighted graph of the oilfield power grid, according to the preset threshold, the value range of the core value is divided into multiple interpolation regions, and the interpolation region with the largest value range among the multiple interpolation regions is used as the target interpolation region; in the second step, any node in the weighted graph is taken as the current node, and it is judged whether the current node belongs to the target interpolation region. If so, the next node is taken as the current node and it is continued to judge whether it belongs to the target interpolation region. If not, the current node is deleted from the weighted graph of the oilfield power grid to obtain a new weighted graph of the oilfield power grid, and the first step is returned to recalculate the degree, betweenness, power clustering coefficient and core value of each node, and the target interpolation region is re-divided according to the preset threshold; the above two steps are repeated, the nodes are deleted to update the weighted graph of the oilfield power grid and the node core values until the core values of all nodes in the updated weighted graph of the oilfield power grid belong to the updated target interpolation region, and the betweenness loops. Combining the core values of all nodes in the updated weighted graph of the oilfield power grid with the core values of the deleted nodes, the core values of all nodes in the weighted graph of the oilfield power grid are obtained. Among them, the core value of the deleted node is the core value when the node is deleted.

[0146] In a specific embodiment, when calculating the core value of a node, the weight values of the degree, betweenness and power clustering coefficient of the node can be determined by the entropy weight method.

[0147] S105: Calculate the Tsallis entropy of each node according to the core value of each node and the structural hole constraint coefficient;

[0148] In the embodiment of the present application, in the above step S105, calculating the Tsallis entropy of each node according to the core value of each node and the structural hole constraint coefficient includes:

[0149] Calculating the Tsallis entropy of each node based on Formula 4 according to the core value of each node and the structural hole constraint coefficient:

[0150]

[0151] In the formula, T(i) respectively represents the Tsallis entropy of node i, P ij is the structural hole coefficient, q i is the Tsallis entropy parameter;

[0152] Among them, the structural hole coefficient P ij is calculated based on Formula 5:

[0153]

[0154] In the formula, R i respectively represent the structural hole ending coefficient of node i, and Γ(i) is the set of neighbor nodes of node i;

[0155] Among them, the Tsallis entropy parameter q i is calculated based on Equation 6:

[0156]

[0157] In the formula, KS(i) is the kernel value of node i.

[0158] In the embodiments of the present application, the Tsallis entropy will be used to extract the local features presented by the structural hole constraint coefficients obtained in step S103 and the global features presented by the kernel values obtained in step S104. The Tsallis entropy calculation formula is as shown in Formula 4, where P ij and q i are the structural hole coefficient and the Tsallis entropy parameter respectively, and the calculation formulas are as shown in Formulas 5 and 6, which are calculated based on the structural hole constraint coefficient and the kernel value respectively, and respectively consider the influence of the structural hole characteristics of neighbor nodes on the importance of nodes, the relationship between nodes and the weighted graph of the entire oilfield power grid, and the relationships between different nodes.

[0159] S106: Calculate the criticality evaluation value of each node according to the Tsallis entropy of each node;

[0160] S107: Obtain the critical nodes of the oilfield power grid according to the criticality evaluation value of each node and a preset ratio.

[0161] In the embodiments of the present application, in the above step S106, calculating the criticality evaluation value of each node according to the Tsallis entropy of each node includes:

[0162] Calculate the neighborhood core value of each node based on the following Formula 7 according to the Tsallis entropy of each node:

[0163]

[0164] Calculate the criticality evaluation value of each node based on the following Formula 8 according to the neighborhood core value of each node:

[0165]

[0166] Among them, TSG(i) is the criticality evaluation value of node i.

[0167] In the embodiments of the present application, the Tsallis entropy of all neighbor nodes of each node obtained in step S105 is accumulated to obtain the neighborhood core value of the node, and the calculation formula is as shown in Formula 7. Based on the neighborhood core value of each node, the neighborhood core values of all neighbor nodes of each node are summed to calculate the criticality evaluation value of the node, and the calculation formula is as shown in Formula 8.

[0168] In an embodiment of the present application, critical evaluation values ​​of all nodes in the oilfield power grid weighted graph are obtained, and the nodes are sorted according to their critical evaluation values, and multiple nodes with larger critical evaluation values ​​are selected as key nodes of the oilfield power grid according to a preset ratio.

[0169] In a specific embodiment, Figure 2 and Figure 3 The node system diagram and node topology diagram of the IEEE-39 feeder system, with a total of 39 nodes, is used as the experimental data for the oilfield power grid topology diagram. After the key node identification method of the oilfield power grid is implemented, the key node identification results are obtained, and 10 key nodes are obtained. The oilfield power grid is analyzed from multiple angles based on the key node identification results, including node removal experiments, comparison experiments, and network attack experiments.

[0170] In the node removal experiment, the oilfield power grid was subjected to a node removal experiment to interfere with the network. The ways of attacking the oilfield power grid include intentional attack and random failure. The intentional attack is to attack the key nodes, while the random failure is not specifically aimed at the key nodes. The experimental results are as follows: Figure 5 As shown in the figure, the impact of two attack modes on the connectivity of the oilfield power grid is shown. As the attack intensity increases, both intentional attacks and random failures have an important impact on the robustness of the oilfield power grid. When the attack intensity coefficient is 1, the capacity of the oilfield power grid will be completely destroyed. However, it can be seen from the figure that the oilfield power network has a certain robustness to random failures, but is vulnerable to intentional attacks, indicating the importance of key nodes in network connectivity.

[0171] In the comparative experiment, the traditional methods (degree sorting, betweenness sorting and clustering coefficient sorting) and the key node identification methods using structural holes and K-shell algorithm alone were compared. The experimental results are shown in the figure below. Figure 6 As shown, Tsallis entropy is the method. Compared with other methods, the robustness curve of this method is below the experimental curves of other methods, which shows that the key nodes found by this method are more important in the oilfield power grid network. When the key nodes identified based on this method are removed, the connectivity and network efficiency of the oilfield power grid will be quickly destroyed. This is because the top-ranked nodes found by this method (such as node 19, node 6, and node 16) are both structural hole nodes in the local neighborhood index and have a higher global index core value. By comparing the experimental results, it can be seen that the key nodes identified by this method have a greater impact on the robustness and load loss of the network, so this method is more accurate in identifying key nodes.

[0172] The network attack experiment compared the relative values ​​of network efficiency and load loss after network attacks under different methods. The experimental results are as follows: Figure 7 , 8As shown: According to Figure 7 the results, when a critical node is deliberately attacked in the oilfield power grid, the network efficiency of all methods gradually approaches 0, but the curve of this method is significantly lower than the other five methods. This indicates that when the critical nodes identified by this method fail, the network efficiency drops significantly. Similarly, in Figure 8 , after deliberately attacking the 10 critical nodes identified by this method, the overall network load loss is significantly higher than that of other methods.

[0173] In the embodiments of the present application, the problem that the existing critical node identification methods mainly rely on local features such as node degree and betweenness, or on the power flow analysis of the power system, ignoring the importance of nodes in the entire network structure and being unable to accurately identify the critical nodes of the oilfield power grid is solved. By adopting the Tsallis entropy oilfield power grid critical node identification method that combines the structure hole and K-shell algorithms, the accuracy and efficiency of identifying critical nodes in the oilfield power grid are improved, so as to better meet the requirements of identifying critical nodes in the oilfield power grid. Compared with traditional methods, the recognition rate accuracy is significantly improved. This solution has good robustness. The structure hole theory is used to identify local critical nodes, and the improved K-shell algorithm reflects the global node positioning. By synthesizing these two indicators through Tsallis entropy, the critical nodes can be sorted more accurately to effectively resist the impact of various unknown factors on the oilfield power grid. At the same time, this method simplifies the data processing steps, avoids the cumbersome data processing and model establishment in traditional methods, and makes the identification of power grid critical nodes more simple and efficient. In addition, this solution has wide applicability and can be used for various types of oilfield power grids, including DC and AC power grids, and can adapt to changes in different power grid loads and impact behaviors. Finally, the positive effects of this method include improving the safety and stability of the power grid, reducing the impact of various sudden unstable behaviors on the power grid, reducing the operation cost of the power grid, and improving the economic and social benefits of the power grid.

[0174] Embodiment 2

[0175] Based on the same inventive concept, the embodiments of the present invention also provide an oilfield power grid critical node identification device. Referring to Figure 9 as shown, the device includes:

[0176] A weight calculation module 101, configured to obtain a weighted graph of the oilfield power grid according to the obtained topological graph of the oilfield power grid nodes;

[0177] A parameter calculation module 102, configured to calculate the degree, betweenness, and power clustering coefficient of each node in the weighted graph of the oilfield power grid;

[0178] A structure hole calculation module 103, configured to calculate the structure hole constraint coefficient of each node according to the weighted graph of the oilfield power grid;

[0179] The core value calculation module 104 is configured to calculate the core value of each node based on the degree, betweenness, and power aggregation coefficient of each node according to an improved K-Shell algorithm;

[0180] The entropy calculation module 105 is configured to calculate the Tsallis entropy of each node according to the core value of each node and the structural hole constraint coefficient;

[0181] The evaluation value calculation module 106 is configured to calculate the criticality evaluation value of each node according to the Tsallis entropy of each node;

[0182] The node acquisition module 107 is configured to obtain the critical nodes of the oilfield power grid according to the criticality evaluation value of each node and a preset ratio.

[0183] Embodiment III

[0184] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the method for identifying critical nodes of an oilfield power grid as described in Embodiment I above.

[0185] Embodiment IV

[0186] Based on the same inventive concept, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the method for identifying critical nodes of an oilfield power grid as described in Embodiment I above.

[0187] Embodiment V

[0188] Based on the same inventive concept, an embodiment of the present invention further provides a computer program product including instructions. When the computer program product runs on a computer device, it causes the computer device to execute the method for identifying critical nodes of an oilfield power grid as described in Embodiment I above.

[0189] Embodiment VI

[0190] Based on the same inventive concept, an embodiment of the present invention further provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor, and the processor is configured to run a computer program or instructions to implement the method for identifying critical nodes of an oilfield power grid as described in Embodiment I above.

[0191] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0192] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure One one flow or multiple flows and / or blocks Figure One one block or multiple blocks.

[0193] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that realizes the functions specified in Figure One one flow or multiple flows and / or blocks Figure One one block or multiple blocks.

[0194] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure One one flow or multiple flows and / or blocks Figure One one block or multiple blocks.

[0195] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for identifying key nodes in an oilfield power grid, characterized in that, it includes: According to the obtained node topology diagram of the oilfield power grid, obtain the weighted graph of the oilfield power grid; Calculate the degree, betweenness, and power clustering coefficient of each node in the weighted graph of the oilfield power grid; According to the weighted graph of the oilfield power grid, calculate the structural hole constraint coefficient of each node; Based on the improved K-Shell algorithm, calculate the core value of each node according to the degree, betweenness, and power clustering coefficient of each node; Calculate the Tsallis entropy of each node according to the core value and structural hole constraint coefficient of each node; Calculate the criticality evaluation value of each node according to the Tsallis entropy of each node; According to the criticality evaluation value of each node and the preset ratio, obtain the key nodes of the oilfield power grid.

2. The method according to claim 1, characterized in that, The step of calculating the core value of each node based on the improved K-Shell algorithm according to the degree, betweenness, and power clustering coefficient of each node includes: Based on the following formula 1, calculate the core value of each node according to the degree, betweenness, and power clustering coefficient of each node: Wherein, KS(i) is the core value of node i, D(i) is the degree of node i, B(i) is the betweenness of node i, and C(i) is the power clustering coefficient of node i, and are the weight values of degree, betweenness, and power clustering coefficient respectively; Based on the core values of all nodes in the weighted graph of the oilfield power grid, divide multiple interpolation regions based on a preset threshold, and take the interpolation region with the largest core value as the target interpolation region; Take any node in the weighted graph of the oilfield power grid as the current node, and determine whether the current node belongs to the target interpolation region; If so, obtain the next node as the current node; If not, delete the current node from the weighted graph of the oilfield power grid and save the core value of the current node to obtain a new weighted graph of the oilfield power grid, and calculate the core values of all nodes of the new weighted graph of the oilfield power grid and the new target interpolation region according to the new weighted graph of the oilfield power grid; Repeat the above process of updating the nodes in the weighted graph of the oilfield power grid and updating the target interpolation region until the core values of all nodes in the updated weighted graph of the oilfield power grid belong to the updated target interpolation region; According to the core values of the deleted nodes and the core values of all nodes in the updated weighted graph of the oilfield power grid, obtain the core values of each node in the weighted graph of the oilfield power grid; the core value of the deleted node represents the core value saved when the node is deleted.

3. The method according to claim 1, characterized in that, The step of calculating the structural hole constraint coefficient of each node according to the weighted graph of the oilfield power grid includes: Based on the weight of each node in the weighted graph of the oilfield power grid, calculate the weight ratio of the connection edge between each node and its neighbor nodes based on the following formula 2: where ω(i) represents the weight of node i respectively, and L ij is the weight ratio of the edge connecting node i and node j, and Γ(i) is the set of neighbor nodes of node i; Based on the weight ratio of the connection edge between each node and its neighbor nodes, calculate the structural hole constraint coefficient of the corresponding node based on the following formula 3: where R i represents the structural hole constraint coefficient of node i.

4. The method according to claim 1, characterized in that, The step of calculating the Tsallis entropy of each node according to the core value and structural hole constraint coefficient of each node includes: Based on formula 4, calculate the Tsallis entropy of each node according to the core value and structural hole constraint coefficient of each node: where T(i) represents the Tsallis entropy of node i, respectively, and P ij is the structural hole coefficient, and q i is the Tsallis entropy parameter; Among them, the structural hole coefficient P ij is calculated based on Formula 5: where R i respectively represent the structural hole ending coefficient of node i, and Γ(i) is the set of neighbor nodes of node i; Among them, the Tsallis entropy parameter q i is calculated based on Equation 6: In the formula, KS(i) is the core value of node i.

5. The method according to claim 1, wherein, the step of calculating the criticality evaluation value of each node according to the Tsallis entropy of each node includes: calculating the neighborhood core value of each node according to the Tsallis entropy of each node based on the following formula 7: calculating the criticality evaluation value of each node according to the neighborhood core value of each node based on the following formula 8: where TSG(i) is the criticality evaluation value of node i.

6. The method according to claim 1, wherein, the step of obtaining the weighted oilfield power grid graph according to the obtained topological graph of the oilfield power grid nodes includes: calculating the weight of each node according to the injection power and the system reference power of each node in the oilfield power grid topological graph based on the following formula 9: ω ij = P ij / S B Formula 9; Wherein, P ij is the transmission power between node i and neighbor node j, S B is the system reference power, and ω ij is the weight value of node i connecting to node j; calculating the weight of each edge according to the transmission power and the system reference power of each edge in the oilfield power grid topological graph based on the following formula 10: ω i = P i / S B Formula 10; where P i is the power injected into node i, and ω ij is the weight of node i; obtaining the weighted oilfield power grid graph according to the oilfield power grid topological graph, the weight of each edge, and the weight of each node.

7. The method according to claim 1, wherein, the step of calculating the degree, betweenness, and power clustering coefficient of each node in the weighted oilfield power grid graph according to the weighted oilfield power grid graph includes: calculating the degree of each node according to the weight of each node in the weighted oilfield power grid graph based on the following formula 11: where D(i) represents the degree of node i, ω ij is the weight value of the connection between node i and node j, and n represents the total number of nodes; calculating the betweenness of each node according to the weight of each node in the weighted oilfield power grid graph based on the following formula 12: where B(i) represents the betweenness of node i, and ω jk (i) is the sum of the transmission powers passing through node i between node j and node k, and ω jk is the sum of the transmission powers of all the shortest paths between node j and node k; calculating the power clustering coefficient of each node according to the weight of each node in the weighted oilfield power grid graph based on the following formula 13: Where C(i) represents the power aggregation coefficient of node i, ω ij , ω ik , ω jk represent the weights of node k, node j, and node i respectively.

8. An apparatus for identifying critical nodes in an oilfield power grid, wherein, it includes: a weight calculation module for obtaining a weighted oilfield power grid graph according to the obtained topological graph of the oilfield power grid nodes; a parameter calculation module for calculating the degree, betweenness, and power clustering coefficient of each node in the weighted oilfield power grid graph; a structural hole calculation module for calculating the structural hole constraint coefficient of each node according to the weighted oilfield power grid graph; a core value calculation module for calculating the core value of each node based on an improved K-Shell algorithm according to the degree, betweenness, and power clustering coefficient of each node; an entropy calculation module for calculating the Tsallis entropy of each node according to the core value and the structural hole constraint coefficient of each node; an evaluation value calculation module for calculating the criticality evaluation value of each node according to the Tsallis entropy of each node; a node acquisition module for obtaining the critical nodes of the oilfield power grid according to the criticality evaluation value of each node and a preset ratio.

9. A computer-readable storage medium storing instructions that, when run on a terminal, cause the terminal to execute the method for identifying critical nodes in an oilfield power grid according to any one of claims 1-7.

10. A computer device, wherein, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the method for identifying key nodes of an oilfield power grid according to any one of claims 1-7.

11. A computer program product containing instructions, which causes a computer device to execute the method for identifying key nodes of an oilfield power grid according to any one of claims 1-7 when the computer program product runs on the computer device.

12. A chip, which includes a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run a computer program or instructions to implement the method for identifying key nodes of an oilfield power grid according to any one of claims 1-7.

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