A power system fragile line evaluation method and device based on comprehensive betweenness

Abstract

The application provides a kind of fragile line evaluation method and device based on comprehensive betweenness.The method comprises: obtaining the complex network model of target power system;Wherein, the complex network model includes nodes and lines;Determine whether the complex network model has complex network characteristics;If the complex network model has complex network characteristics, then calculate the improved electrical betweenness index and reliability index of each line in the target power system;Based on the improved electrical betweenness index and reliability index of each line, calculate the comprehensive betweenness index of each line;The line with the maximum comprehensive betweenness index is determined as the fragile line in the target power system.The application can avoid unnecessary calculation when the complex network model of target power system has complex network characteristics;And the reliability index of line is integrated for fragile line evaluation, considering the possibility of line failure itself, using more comprehensive index, can get better evaluation effect.

Description

Technical Field

[0001] This invention relates to the field of power network technology, and in particular to a method and apparatus for assessing vulnerable lines based on comprehensive betweenness. Background Technology

[0002] With the rapid development of the power industry, modern power systems are large-scale and complex in operation, and many large power grids both domestically and internationally exhibit significant complex network characteristics. A complex network is a network with significant "small-world" and "scale-free" characteristics. In power systems with complex network characteristics, cascading faults and power outages are often caused by certain vulnerable lines. To control the risk of power outages, electrical betweenness indices are typically used to assess vulnerable lines.

[0003] Currently, the assessment of vulnerable lines based on traditional electrical betweenness has the following problems: First, no triggering conditions are set for the assessment of vulnerable lines, making it meaningless to conduct assessments of vulnerable lines in power systems without the risk of power outages, thus consuming excessive computing resources without improving the assessment effect; Second, the electrical betweenness index only considers impedance and power flow, without fully taking into account operational indicators that may affect the vulnerability of the line; Third, the assessment index is only characterized by electrical betweenness, without considering the possibility of line failure itself. Summary of the Invention

[0004] This invention provides a method and apparatus for assessing vulnerable lines based on comprehensive betweenness, in order to solve the problem of excessive computational resources being consumed by meaningless assessments of vulnerable lines.

[0005] In a first aspect, embodiments of the present invention provide a method for evaluating vulnerable circuits based on comprehensive betweenness, including:

[0006] Obtain a complex network model of the target power system; the complex network model includes nodes and lines.

[0007] Determine whether a complex network model possesses the characteristics of a complex network;

[0008] If the complex network model has complex network characteristics, then calculate the improved electrical betweenness index and reliability index of each line in the target power system.

[0009] The comprehensive betweenness index of each line is calculated based on the improved electrical betweenness index and reliability index of each line.

[0010] The line with the highest comprehensive betweenness index is identified as a vulnerable line in the target power system.

[0011] In one possible implementation, the calculation of the improved electrical betweenness indices for each line in the target power system includes:

[0012] Obtain the degree of each node and the weight of each line in the target power system;

[0013] The number of transmission paths containing line ij between any two nodes in the target power system is calculated based on the weight of line ij; where line ij is any line, and i and j are the nodes at both ends of line ij, respectively.

[0014] The improved electrical betweenness index of line ij is calculated based on the degree of each node, the number of lines ij in the transmission path between any two nodes in the target power system, and the electrical betweenness calculation formula; whereby the electrical betweenness calculation formula is:

[0015]

[0016] Among them, B ij k represents the improved electrical betweenness index of line ij. i k j and k a Let i, j, and a represent the degrees of nodes i, j, and a, respectively; N represents the total number of nodes in the target power system; and a represents any node. The traditional electrical betweenness index for line ij.

[0017] In one possible implementation, obtaining the weights of each line includes:

[0018] By running simulations, the actual power flow of each line in the target power system is calculated;

[0019] Obtain the node impedance matrix of the target power system; wherein, the node impedance matrix includes the electrical distance between each node in the target power system;

[0020] For any line ab, calculate the product of the actual power flow of line ab and the electrical distance between nodes ab to obtain the weight of line ab.

[0021] In one possible implementation, the reliability indices of each line in the target power system are calculated as follows:

[0022] For each line, calculate its historical failure rate and use it as a reliability indicator for that line.

[0023] In one possible implementation, the comprehensive betweenness index of each line is calculated based on the improved electrical betweenness index and reliability index of each line, including:

[0024] For each line, the comprehensive betweenness index is calculated based on the improved electrical betweenness index, reliability index, and comprehensive betweenness calculation formula for that line; wherein, the comprehensive betweenness calculation formula is:

[0025]

[0026] Among them, E ij br represents the comprehensive betweenness index of line ij. ij B represents the reliability index of line ij. ij P represents the improved electrical betweenness index of line ij. ij This represents the actual power flow of line ij. This indicates the maximum power flow of line ij.

[0027] In one possible implementation, determining whether a complex network model possesses complex network characteristics includes:

[0028] Calculate the average path length and average clustering coefficient of the complex network model to obtain the first average path length and the first average clustering coefficient;

[0029] Based on the number of nodes and average degree of the complex network model, a random network model with the same number of nodes and average degree as the complex network model is generated in a random manner.

[0030] Calculate the average path length and average clustering coefficient of the random network model to obtain the second average path length and the second average clustering coefficient;

[0031] If the first average path length is greater than the second average path length, and the first average clustering coefficient is greater than the second average clustering coefficient, then the complex network model is determined to have complex network characteristics.

[0032] In one possible implementation, calculating the average path length of a complex network model includes:

[0033] The average path length of a complex network model is calculated using the average path length formula. The average path length formula is:

[0034]

[0035] Where N represents the number of nodes in the complex network model, d ij This represents the electrical distance between node i and node j.

[0036] In one possible implementation, calculating the average clustering coefficient of a complex network model includes:

[0037] The average clustering coefficient of a complex network model is calculated using the formula for the average clustering coefficient. The formula for the average clustering coefficient is:

[0038]

[0039] Where, k i e represents the degree of node i. iThis represents the number of branches in the subnet formed by nodes directly connected to node i in a complex network model.

[0040] Secondly, embodiments of the present invention provide a vulnerable circuit evaluation device based on comprehensive betweenness, comprising:

[0041] The acquisition module is used to acquire a complex network model of the target power system; the complex network model includes nodes and lines.

[0042] The judgment module is used to determine whether a complex network model has the characteristics of a complex network.

[0043] The calculation module is used to calculate the improved electrical betweenness index and reliability index of each line in the target power system if the complex network model has complex network characteristics.

[0044] The integration module is used to calculate the integrated betweenness index of each line based on the improved electrical betweenness index and reliability index of each line;

[0045] The evaluation module is used to identify the line with the highest comprehensive betweenness index as a vulnerable line in the target power system.

[0046] Thirdly, embodiments of the present invention provide a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect or any possible implementation of the first aspect.

[0047] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation thereof.

[0048] The beneficial effects of the vulnerable circuit evaluation method and apparatus based on comprehensive betweenness provided in this invention are as follows:

[0049] Before assessing vulnerable lines in a power system, this invention first determines whether the target power system's complex network model possesses complex network characteristics. If it does not, meaning the target power system has no vulnerable lines, then vulnerability line assessment is unnecessary. If the target power system's complex network model does possess complex network characteristics, then vulnerability line assessment is required, and performing this assessment avoids unnecessary calculations. Furthermore, this invention integrates line reliability indicators for vulnerability line assessment, considering the possibility of line failures and using more comprehensive indicators, thus achieving better vulnerability line assessment results. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart illustrating the implementation of a vulnerable circuit evaluation method based on comprehensive betweenness, according to an embodiment of the present invention.

[0052] Figure 2 This is the evaluation result of a vulnerable circuit evaluation method based on comprehensive betweenness provided in an embodiment of the present invention;

[0053] Figure 3 This is an assessment result of a vulnerable line based on conventional electrical betweenness provided by an embodiment of the present invention;

[0054] Figure 4 This is a real evaluation result obtained based on Monte Carlo simulation provided by an embodiment of the present invention;

[0055] Figure 5 This is a schematic diagram of a vulnerable circuit evaluation device based on comprehensive betweenness, provided in an embodiment of the present invention.

[0056] Figure 6 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Detailed Implementation

[0057] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0059] With the rapid development of the power industry, modern power systems are large-scale and complex in operation, and many large power grids both domestically and internationally exhibit significant complex network characteristics. Complex networks are networks with prominent "small-world" and "scale-free" characteristics. Research on the application of complex networks in power systems has attracted increasing attention, as their theory effectively explains the causes of large-scale blackouts and solves blackout risk problems that traditional simulation methods cannot explain. Currently, power systems in central and eastern my country have exhibited significant complex network characteristics. Among the two main characteristics of complex networks, the "small-world" characteristic is considered an intrinsic cause of large-scale blackouts, while the "scale-free" characteristic is the root cause of the coexistence of robustness and vulnerability in power systems.

[0060] In power systems with complex network characteristics, cascading failures and power outages are often caused by certain vulnerable lines. In complex networks, vulnerable lines are evaluated using the "betweenness" metric, but this only considers network structural attributes and ignores the characteristics of the power system. Subsequently, "electrical betweenness" was proposed and became an important indicator for evaluating vulnerable lines in power systems. The betweenness of a line refers to the number of times that line appears in all shortest paths in the network; while the electrical betweenness of a line refers to the number of paths containing that line among all shortest electrical transmission paths between any two nodes in the power system. The shortest electrical transmission path is the combination of lines with the minimum sum of all line weights connecting two nodes. In other words, the electrical betweenness is related to the weights of each line in the power system's network model, and the electrical betweenness uses line impedance as the line weight. With the deepening research on electrical betweenness, power flow has been incorporated into line weights.

[0061] However, the assessment of vulnerable lines based on electrical betweenness still has the following problems: First, no triggering conditions are set when assessing vulnerable lines, so it is meaningless to conduct assessments on power systems that do not have the risk of power outages; second, traditional electrical betweenness indices only consider impedance and power flow, and do not fully account for operational indices that may affect the vulnerability of the lines; third, the assessment indices are only characterized by electrical betweenness and do not consider the possibility of line failures.

[0062] To address the aforementioned problems, this application proposes a method for evaluating vulnerable circuits based on comprehensive betweenness, the details of which are as follows:

[0063] See Figure 1 The document illustrates a flowchart of a vulnerable circuit evaluation method based on comprehensive betweenness, provided by an embodiment of the present invention, which is described in detail below:

[0064] Step 101: Obtain a complex network model of the target power system; wherein, the complex network model includes nodes and lines.

[0065] In this embodiment, the structural characteristics of the target power system can be effectively characterized using complex network theory, and potential cascading failure risks can be discovered by mining the complex network characteristics of the network structure. For analysis purposes, this embodiment abstracts the target power system into a topology graph composed of nodes and lines, i.e., a complex network model.

[0066] Specifically, complex network modeling of the receiving-end main network structure includes two aspects: "model abstraction method" and "key model elements." The former is the process of abstracting the receiving-end main network structure into a topology graph composed of nodes and branches, while the latter is the process of establishing relevant information about nodes or branches in the topology graph. These two aspects are explained in detail below:

[0067] 1) Model Abstraction Method: The receiving-end main grid is abstracted as an undirected graph with nodes and branches as its core. The receiving-end main grid is a network with a large number of nodes and complex connections between them. It has the general characteristics of a complex network. Abstracting the receiving-end main grid into a complex network model means abstracting the structure of the main grid. Specifically, based on the existing information of buses, transformers, and transmission lines, the way generator nodes, intermediate nodes, and load nodes are connected is one of the key factors determining the safe and stable operation of the power system. The complex network model of the receiving-end main grid includes buses, transformers, generators, loads, transmission lines, switches, series lines, etc. The abstraction process involves abstracting the device endpoints and switch endpoints into nodes, and abstracting the devices between nodes into edges.

[0068] Abstracting the receiving-end main network structure into an undirected graph G(V,E,W) centered on nodes and branches is the first step in complex network modeling. Here, G represents an undirected weighted graph composed of nodes and branches, and V, E, and W represent the set of nodes, the set of branches, and the set of weights for each branch, respectively. In fact, complex network modeling of the receiving-end main network structure involves abstracting it into a weighted undirected graph.

[0069] The main objective of this step is to abstract the receiving-end main network structure into an undirected graph centered on nodes and branches, i.e., to construct the nodes and branches in the graph. The weights of each branch will be determined in the next step (establishing the adjacency matrix in the undirected weighted graph model) through the adjacency matrix of graph G. In this step, the set of weights W for each branch can be ignored, or the weights of each branch can be temporarily set to 1 as in an unweighted graph. The structural analysis of the receiving-end main network structure based on complex networks focuses more on inherent topological relationships. Therefore, when modeling the complex network of the receiving-end main network, it is necessary to simplify each device to generate the elements in the undirected weighted graph G(V,E,W). The specific generation method is as follows.

[0070] a) Busbar treatment. The busbars in the main grid are all located within the plant and are devices that can connect multiple lines in the system. Therefore, various types of busbars are the carriers of various types of nodes in the model. Consistent with power supply busbars, load busbars and intermediate busbars, the nodes in the model are divided into power supply nodes, intermediate nodes and load nodes.

[0071] b) Treatment of transmission lines. Transmission lines refer to the power transmission lines connecting power plants and substations. Lines within the power plants and substations are not considered. When modeling, the transmission lines connecting the power plants and substations can be abstracted as branches in the model. For lines within the power plants and substations, they are usually integrated into the equipment (transformers, switches, etc.) within the power plants and substations and are regarded as additional connection lines of the equipment within the power plants and substations for unified modeling.

[0072] c) Transformer treatment. Transformers are important equipment located in power plants, especially substations. Transformers are connected between two busbars, so transformers inside the power plant are modeled as branches. For three-winding transformers, since they are connected to three busbars, they are modeled as three two-winding transformers.

[0073] d) External Equivalence Treatment. External equivalence treatment refers to treating the power grid outside the analysis area as equivalent nodes. For example, when analyzing the 500kV and 220kV grids in the receiving-end main grid, the AC / DC UHV landing points within the receiving-end main grid area can be treated as equivalent power source nodes.

[0074] e) Treatment of parallel branches. In the general modeling of complex networks, parallel transmission lines and parallel transformers are usually treated as single-circuit branches with parameters for simplification.

[0075] f) Handling of generators and load equipment. Handling of generators and load equipment refers to the process of integrating generators and loads into their respective output buses under normal circumstances. That is, the generator and generator bus are abstracted together as a power supply node, and the load and load bus are abstracted together as a load node.

[0076] g) Handling of switches and other devices. The handling of switches and other devices refers to the fact that various switches in the main grid are usually located within the plant, so they have little impact on the structure and can be ignored or abstracted as branches; as for other devices, since they do not affect the topology of the main grid, they are usually ignored during modeling.

[0077] 2) Key elements of the model: Establishing the adjacency matrix in the undirected weighted graph model. The receiving-end main network is abstracted as a weighted undirected graph model G(V,E,W). Besides abstracting devices as nodes or branches, it is also necessary to establish the weights of each branch in the model, i.e., the weight set W. In traditional models, branch length is also important information representing a branch and is often used as its weight. However, in complex networks, multiple weighting methods are used to determine branch weights, which represent branch information. Furthermore, the length of a branch is usually one of the factors influencing its weight.

[0078] Generally, the branch information required in the model includes the branch weights and the nodes they connect to. This can be represented by the adjacency matrix of a weighted undirected graph. The core of this step in modeling is determining the adjacency matrix A(G) of the weighted undirected graph model G(V,E,W) of the receiving-end main network. The adjacency matrix A(G) represents the node connection relationships in the undirected weighted graph model G(V,E,W) through the weights of the branches between nodes. That is, the elements in matrix A(G) are the weights of the corresponding branches in model G(V,E,W). The specific meaning of the adjacency matrix is ​​as follows:

[0079] Suppose that the weighted undirected graph model G(V,E,W) of the receiving end main network structure contains N nodes and B branches. Then the adjacency matrix A(G) is an N×N matrix. In the matrix, the element aij in the i-th row and j-th column represents the weight of the branch between the i-th node and the j-th node. Obviously, in the adjacency matrix, aij = aji. When there is no connection between node i and node j, the element aij is 0. For the diagonal elements of matrix A(G), the diagonal elements aii are also all 0.

[0080] As can be seen, the adjacency matrix not only represents the weights of each branch but also the connection relationships between nodes, reflecting all the information of the model G(V,E,W). Using the adjacency matrix to represent a network makes it easy to determine whether any two nodes are connected by a branch, and the matrix can be analyzed to study many network properties. For calculating the adjacency matrix, determining the weights of each branch is a crucial step. In early complex network models, the number of network hops or branch length, as used in general network models, were still used as the weights of a branch. For the receiving-end main network, in addition to considering the number of hops and length in traditional networks, the electrical characteristics of the equipment should also be considered when assigning weights. Currently, in complex network models of power systems, the most common methods for assigning branch weights include two categories:

[0081] a) Weighting based on equipment electrical parameters. This method uses the electrical parameters of the equipment as branch weights, such as equipment impedance, line length, or new indicators calculated from equipment parameters. Currently, the most common approach in this type of weighting is to set the weight of a branch based on its impedance, i.e., using the impedance of transmission lines, transformers, or other equipment equivalent to branches as the weight of the corresponding branch.

[0082] b) Weighting based on system operating parameters. This method uses the operating parameters of branches during system operation as weights, such as the active power flow or reactive power of lines or transformers, and the reactive power-voltage sensitivity between nodes. Currently, the most common approach in this type of weighting is to set the weight of a branch based on its power flow, i.e., using the power flow of transmission lines, transformers, or other equipment as the weight of the corresponding branch.

[0083] Using branch impedance to characterize the connection strength between nodes is a reasonable approach and one of the main methods for weighting complex networks in the power system field.

[0084] Step 102: Determine whether the complex network model has complex network characteristics.

[0085] In this embodiment, the target power system is a network with a large number of nodes and complex connections between them, exhibiting the general characteristics of a complex network. After abstracting the target power system into a complex network model, a series of structural indicators from complex network theory can be calculated for the complex network model, such as average distance, degree distribution, and clustering coefficient. Based on the calculation results of these indicators, it can be determined whether the complex network model possesses the characteristics of a complex network.

[0086] In one possible implementation, step 102 includes:

[0087] Step 1021: Calculate the average path length and average clustering coefficient of the complex network model to obtain the first average path length and the first average clustering coefficient;

[0088] Step 1022: Based on the number of nodes and average degree of the complex network model, generate a random network model with the same number of nodes and average degree as the complex network model in a random manner.

[0089] Step 1023: Calculate the average path length and average clustering coefficient of the random network model to obtain the second average path length and the second average clustering coefficient;

[0090] Step 1024: If the first average path length is greater than the second average path length and the first average clustering coefficient is greater than the second average clustering coefficient, then the complex network model is determined to have complex network characteristics.

[0091] In this embodiment, the random network model has the same number of nodes and average degree as the target power system, meaning the random network model and the target power system are of the same size. Average path length and average clustering coefficient are key characteristics of complex networks. If these two metrics of a network are significantly greater than the corresponding metrics of a random network of the same size, then the network exhibits characteristics of a complex network.

[0092] In one possible implementation, calculating the average path length of a complex network model includes:

[0093] The average path length of a complex network model is calculated using the average path length formula. The average path length formula is:

[0094]

[0095] Where N represents the number of nodes in the complex network model, d ij This represents the electrical distance between node i and node j.

[0096] In this embodiment, the average path length refers to the average electrical distance of the path between any two nodes in a power system. In a power system, current and voltage follow Ohm's law and Kirchhoff's laws; therefore, the strength of the electrical connection between nodes in the network exists not only between directly connected nodes but also between indirectly connected nodes. The strength of the connection between nodes can be characterized by electrical distance; that is, the electrical distance between nodes is a parameter characterizing the electrical relationship between two nodes. A larger electrical distance between two nodes indicates a weaker electrical connection, while a smaller electrical distance represents a stronger electrical connection. In power systems, line impedance is typically used as the line weight to calculate the average path length, which differs somewhat from actual power system operation. Therefore, this application utilizes the node impedance matrix to calculate the average path length.

[0097] In one possible implementation, calculating the average clustering coefficient of a complex network model includes:

[0098] The average clustering coefficient of a complex network model is calculated using the formula for the average clustering coefficient. The formula for the average clustering coefficient is:

[0099]

[0100] Where, k i e represents the degree of node i. i This represents the number of branches in the subnet formed by nodes directly connected to node i in a complex network model.

[0101] In this embodiment, the average clustering coefficient refers to the average of the clustering coefficients of all nodes in the power system; the clustering coefficient C of a certain node i is... i Defined as the ratio of the number of edges in the subnet formed by nodes directly connected to node i to the maximum possible number of edges in that subnet. The larger the clustering coefficient of a power system, the more local connections are in the network.

[0102] Step 103: If the complex network model has complex network characteristics, then calculate the improved electrical betweenness index and reliability index of each line in the target power system.

[0103] In this embodiment, in power systems with complex network characteristics, cascading failures and power outages are often caused by certain vulnerable lines. However, power systems without complex network characteristics do not pose a risk of power outages, and therefore, vulnerability line assessments are unnecessary. If the complex network model exhibits complex network characteristics, it indicates that the target power system may contain vulnerable lines that could lead to power outages, necessitating vulnerability line assessments for the target power system.

[0104] In one possible implementation, step 103 includes:

[0105] Step 1031: Obtain the degree of each node and the weight of each line in the target power system;

[0106] Step 1032: Calculate the number of transmission paths containing line ij between any two nodes in the target power system based on the weight of line ij; where line ij is any line, and i and j are the nodes at both ends of line ij, respectively.

[0107] Step 1033: Calculate the improved electrical betweenness index of line ij based on the degree of each node, the number of lines ij in the transmission path between any two nodes in the target power system, and the electrical betweenness calculation formula; wherein, the electrical betweenness calculation formula is:

[0108]

[0109] Among them, B ij k represents the improved electrical betweenness index of line ij. i k j and k a Let i, j, and a represent the degrees of nodes i, j, and a, respectively; N represents the total number of nodes in the target power system; and a represents any node. The traditional electrical betweenness index for line ij.

[0110] In this embodiment, existing electrical dielectric constants only consider impedance and power flow, ignoring other operating factors affecting line vulnerability. Therefore, this application improves the calculation formula for the electrical dielectric constant index, wherein... The existing electrical betweenness index requires obtaining the values ​​from the weights of each line in the system. The improved electrical betweenness index in this embodiment, based on line impedance and power flow, takes into account the importance of the nodes at both ends of the line, and uses a nodal impedance matrix in the line impedance calculation to better characterize the electrical characteristics of the power system operation. Therefore, the improved electrical betweenness index can more comprehensively assess the importance of the lines.

[0111] In one possible implementation, step 1031 includes:

[0112] By running simulations, the actual power flow of each line in the target power system is calculated;

[0113] Obtain the node impedance matrix of the target power system; wherein, the node impedance matrix includes the electrical distance between each node in the target power system;

[0114] For any line ab, calculate the product of the actual power flow of line ab and the electrical distance between nodes ab to obtain the weight of line ab.

[0115] In this embodiment, the actual power flow represents the power transmitted by the line during actual operation, which varies depending on the power load. A certain element value in the node impedance matrix Z is used as the equivalent electrical distance between corresponding nodes in the target power system, thus more closely resembling the actual operating state. That is, for any line ab, its equivalent electrical distance is the element value in the a-th row and b-th column of Z, and Z is a symmetric matrix. To adapt to the electrical operating state, this application also improves the traditional line weighting by assigning weights to lines through the node impedance matrix and line power flow. The weight W of a certain line ab in the power system is... ab The calculation formula is as follows:

[0116] W ab =Z ab ×P ab

[0117] Among them, Z ab P represents the element in the a-th row and b-th column of the nodal impedance matrix Z, which is also the electrical distance between nodes ab. ab This represents the actual power flow of line ab. In this embodiment, the electrical distance is represented by the elements in the nodal impedance matrix Z, d ij Defined as the element in the i-th row and j-th column of Z, i.e.: d ij =Z ij Z is obtained through the power flow equations of the power system, satisfying the following equation, where U and I are vectors composed of the voltage phasor values ​​and current phasor values ​​of each node, respectively:

[0118]

[0119] In one possible implementation, step 103 includes:

[0120] Step 1034: For each line, calculate the historical failure rate of that line and use the historical failure rate as the reliability index of that line.

[0121] In this embodiment, a high historical failure rate for a certain line indicates that the line has experienced frequent failures in the past. The causes of these failures may be the line's location within the power system or inherent characteristics such as the line's material composition. Consequently, the probability of the line failing in the future will also be affected by these factors. Therefore, the historical failure rate of a line can be used as a reliability indicator.

[0122] Step 104: Calculate the comprehensive betweenness index of each line based on the improved electrical betweenness index and reliability index of each line.

[0123] In this embodiment, the reliability index of the line reflects the probability of the line itself failing. In addition to the traditional electrical betweenness index, this embodiment also incorporates the line reliability index, combining the two to obtain the comprehensive betweenness index for each line, thus taking into account the probability of the line itself failing.

[0124] In one possible implementation, step 104 includes:

[0125] For each line, the comprehensive betweenness index is calculated based on the improved electrical betweenness index, reliability index, and comprehensive betweenness calculation formula for that line; wherein, the comprehensive betweenness calculation formula is:

[0126]

[0127] Among them, E ij br represents the comprehensive betweenness index of line ij. ij B represents the reliability index of line ij. ij P represents the improved electrical betweenness index of line ij. ij This represents the actual power flow of line ij. This indicates the maximum power flow of line ij.

[0128] In this embodiment, the maximum power flow is the maximum active power that the line can carry. For example, a 2*400 line can transmit a maximum of 530MW of power, meaning the maximum power flow is 530MW. The maximum power flow is only related to the equipment model. Considering that the electrical betweenness cannot take into account the operational reliability of the line itself, this application proposes a comprehensive betweenness index that can scientifically characterize vulnerable lines, based on an improved electrical betweenness. The comprehensive betweenness, while improving the electrical betweenness to characterize the importance of the line, takes into account the possibility of line failure, and can more scientifically reflect the vulnerability of the line.

[0129] Step 105: The line with the largest comprehensive betweenness index is identified as a vulnerable line in the target power system.

[0130] In this embodiment, the larger the comprehensive betweenness index of a certain line, the greater the probability of the line having an improved electrical betweenness index and / or a fault, and the more likely it is to be a vulnerable line in the target power system.

[0131] In one specific embodiment, based on the above steps, taking a provincial power system grid as an example, vulnerable lines are assessed according to the method proposed in this application. This power grid includes 1914 nodes and 2579 branches. The assessment process is as follows:

[0132] First, the complex network characteristics of the power system are analyzed, and the calculation results are shown in Table 1. As can be seen from the table, this power system has a higher clustering coefficient and average path length compared to random networks, thus indicating the presence of vulnerable lines.

[0133] Table 1

[0134]

[0135] Then, the above steps were used to assess vulnerable lines. A comparative analysis was performed on the top 40 lines with the highest comprehensive betweenness factors, and the results are as follows: Figures 2-4 As shown, where Figure 2 The vulnerability assessment results of the method proposed in this application are as follows. Figure 3 Based on the evaluation results of traditional electrical betweenness, Figure 4 Based on the realistic evaluation results obtained from Monte Carlo simulation, each point in the figure represents an identified vulnerable line, and the horizontal and vertical coordinates of each point represent the node numbers at both ends of the line. For example, if the horizontal coordinate of a point in the figure is 15 and the vertical coordinate is 68, then this point represents line "15-68". As can be seen from the figure, the evaluation results obtained by the method of this application are almost identical to the results of Monte Carlo simulation. Compared with the traditional electrical betweenness method, the results of this application can greatly improve the evaluation accuracy and ensure the validity of the vulnerable line evaluation results. At the same time, the vulnerable line evaluation time of the method of this application is about 10s to 20s, while the vulnerable line evaluation time of traditional simulation is 9 to 10 hours. Therefore, compared with the traditional electrical betweenness method, the method of this application not only has higher evaluation accuracy, but also greatly reduces the evaluation time while ensuring the accuracy of vulnerable line evaluation.

[0136] Before assessing vulnerable lines in a power system, this invention first determines whether the target power system's complex network model possesses complex network characteristics. If it does not possess complex network characteristics, meaning the target power system has no vulnerable lines, then vulnerability line assessment is unnecessary. If the target power system's complex network model possesses complex network characteristics, then vulnerability line assessment is required, and performing this assessment avoids unnecessary calculations. Furthermore, this invention integrates line reliability indicators for vulnerability line assessment, considering the possibility of line failures and using more comprehensive indicators, thereby achieving better vulnerability line assessment results.

[0137] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0138] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0139] Figure 5 The diagram shows a structural schematic of a vulnerable circuit evaluation device based on synthetic betweenness according to an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0140] like Figure 5 As shown, a vulnerable circuit evaluation device 5 based on comprehensive betweenness includes:

[0141] The acquisition module 51 is used to acquire a complex network model of the target power system; wherein the complex network model includes nodes and lines;

[0142] Module 52 is used to determine whether a complex network model has complex network characteristics;

[0143] The calculation module 53 is used to calculate the improved electrical betweenness index and reliability index of each line in the target power system if the complex network model has complex network characteristics.

[0144] The comprehensive module 54 is used to calculate the comprehensive betweenness index of each line based on the improved electrical betweenness index and reliability index of each line.

[0145] Evaluation module 55 is used to identify the line with the largest comprehensive betweenness index as a vulnerable line in the target power system.

[0146] In one possible implementation, the computing module 53 includes:

[0147] The acquisition unit is used to acquire the degree of each node and the weight of each line in the target power system;

[0148] The quantity calculation unit is used to calculate the number of transmission paths containing line ij between any two nodes in the target power system based on the weight of line ij; where line ij is any line, and i and j are the nodes at both ends of line ij, respectively.

[0149] The betweenness calculation unit is used to calculate the improved electrical betweenness index of line ij based on the degree of each node, the number of lines ij in the transmission path between any two nodes in the target power system, and the electrical betweenness calculation formula; wherein, the electrical betweenness calculation formula is:

[0150]

[0151] Among them, B ij k represents the improved electrical betweenness index of line ij. i k j and k a Let i, j, and a represent the degrees of nodes i, j, and a, respectively; N represents the total number of nodes in the target power system; and a represents any node. The traditional electrical betweenness index for line ij.

[0152] In one possible implementation, the acquisition unit is specifically used for:

[0153] By running simulations, the actual power flow of each line in the target power system is calculated;

[0154] Obtain the node impedance matrix of the target power system; wherein, the node impedance matrix includes the electrical distance between each node in the target power system;

[0155] For any line ab, calculate the product of the actual power flow of line ab and the electrical distance between nodes ab to obtain the weight of line ab.

[0156] In one possible implementation, the computation module 53 is specifically used for:

[0157] For each line, calculate its historical failure rate and use it as a reliability indicator for that line.

[0158] In one possible implementation, synthesis module 54 is specifically used for:

[0159] For each line, the comprehensive betweenness index is calculated based on the improved electrical betweenness index, reliability index, and comprehensive betweenness calculation formula for that line; wherein, the comprehensive betweenness calculation formula is:

[0160]

[0161] Among them, E ijbr represents the comprehensive betweenness index of line ij. ij B represents the reliability index of line ij. ij P represents the improved electrical betweenness index of line ij. ij This represents the actual power flow of line ij. This indicates the maximum power flow of line ij.

[0162] In one possible implementation, the judgment module 52 is specifically used for:

[0163] Calculate the average path length and average clustering coefficient of the complex network model to obtain the first average path length and the first average clustering coefficient;

[0164] Based on the number of nodes and average degree of the complex network model, a random network model with the same number of nodes and average degree as the complex network model is generated in a random manner.

[0165] Calculate the average path length and average clustering coefficient of the random network model to obtain the second average path length and the second average clustering coefficient;

[0166] If the first average path length is greater than the second average path length, and the first average clustering coefficient is greater than the second average clustering coefficient, then the complex network model is determined to have complex network characteristics.

[0167] In one possible implementation, the judgment module 52 is specifically used for:

[0168] The average path length of a complex network model is calculated using the average path length formula. The average path length formula is:

[0169]

[0170] Where N represents the number of nodes in the complex network model, d ij This represents the electrical distance between node i and node j.

[0171] In one possible implementation, the judgment module 52 is specifically used for:

[0172] The average clustering coefficient of a complex network model is calculated using the formula for the average clustering coefficient. The formula for the average clustering coefficient is:

[0173]

[0174] Where, k i e represents the degree of node i. i This represents the number of branches in the subnet formed by nodes directly connected to node i in a complex network model.

[0175] Before assessing vulnerable lines in a power system, this invention first determines whether the target power system's complex network model possesses complex network characteristics. If it does not possess complex network characteristics, meaning the target power system has no vulnerable lines, then vulnerability line assessment is unnecessary. If the target power system's complex network model possesses complex network characteristics, then vulnerability line assessment is required, and performing this assessment avoids unnecessary calculations. Furthermore, this invention integrates line reliability indicators for vulnerability line assessment, considering the possibility of line failures and using more comprehensive indicators, thereby achieving better vulnerability line assessment results.

[0176] Figure 6 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Figure 6 As shown, the terminal 6 in this embodiment includes: a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60. When the processor 60 executes the computer program 62, it implements the steps described in each of the above embodiments of a fragile circuit evaluation method based on synthetic betweenness, for example... Figure 1 Steps 101 to 105 are shown. Alternatively, when the processor 60 executes the computer program 62, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 5 The functions of modules / units 51 to 55 shown.

[0177] For example, the computer program 62 can be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 62 in the terminal 6. For example, the computer program 62 can be divided into... Figure 5 Modules / units 51 to 55 are shown.

[0178] The terminal 6 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The terminal 6 may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 6 This is merely an example of terminal 6 and does not constitute a limitation on terminal 6. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0179] The processor 60 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0180] The memory 61 can be an internal storage unit of the terminal 6, such as a hard disk or memory of the terminal 6. The memory 61 can also be an external storage device of the terminal 6, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the terminal 6. Furthermore, the memory 61 can include both internal storage units and external storage devices of the terminal 6. The memory 61 is used to store the computer program and other programs and data required by the terminal. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0181] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0182] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0183] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0184] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0185] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0186] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0187] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of each of the above embodiments of the vulnerable circuit evaluation method based on comprehensive betweenness. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0188] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for assessing vulnerable circuits based on comprehensive betweenness, characterized in that, include: Obtain a complex network model of the target power system; wherein the complex network model includes nodes and lines; Determine whether the complex network model possesses complex network characteristics; If the complex network model has complex network characteristics, then the improved electrical betweenness index and reliability index of each line in the target power system are calculated. The comprehensive betweenness index of each line is calculated based on the improved electrical betweenness index and reliability index of each line. The line with the highest comprehensive betweenness index is identified as a vulnerable line in the target power system. The calculation of the comprehensive betweenness index of each line based on the improved electrical betweenness index and reliability index of each line includes: For each line, the comprehensive betweenness index is calculated based on the improved electrical betweenness index, reliability index, and comprehensive betweenness calculation formula for that line; wherein, the comprehensive betweenness calculation formula is: in, Indicates the line The comprehensive betweenness index, Indicates the line Reliability indicators Indicates the line Improved electrical dielectric constant, Indicates the line The actual trend Indicates the line The biggest trend; The calculation of the improved electrical betweenness index of each line in the target power system includes: Obtain the degree of each node and the weight of each line in the target power system; Based on the line The weight calculation of the transmission path between any two nodes in the target power system includes the line. The number; among which, lines For any line, The lines are respectively The nodes at both ends; Based on the degree of each node, the transmission path between any two nodes in the target power system includes the line. The quantity and improved electrical dielectric constant calculation formula are used to calculate the circuit. An improved electrical betweenness index; wherein the improved electrical betweenness calculation formula is: in, Indicates the line Improved electrical dielectric constant, , and Representing nodes respectively The degree, This represents the total number of nodes in the target power system. Represents any node, This refers to the traditional electrical betweenness index.

2. The method for assessing vulnerable lines based on comprehensive betweenness as described in claim 1, characterized in that, Obtaining the weights of each line includes: By running simulations, the actual power flow of each line in the target power system is calculated; Obtain the node impedance matrix of the target power system; wherein, the node impedance matrix includes the electrical distances between nodes in the target power system. Z ab Represents the node impedance matrix Z The Middle Line number The elements of the column, as nodes Electrical distance between them; For any line Calculate the line Actual trends and key points The product of the electrical distances between them yields the line. The weight.

3. The method for assessing vulnerable lines based on comprehensive betweenness as described in claim 1, characterized in that, The calculation of reliability indices for each line in the target power system includes: For each line, calculate its historical failure rate and use the historical failure rate as a reliability indicator for that line.

4. A method for assessing vulnerable lines based on comprehensive betweenness as described in any one of claims 1 to 3, characterized in that, The determination of whether the complex network model possesses complex network characteristics includes: Calculate the average path length and average clustering coefficient of the complex network model to obtain the first average path length and the first average clustering coefficient; Based on the number of nodes and average degree of the complex network model, a random network model with the same number of nodes and average degree as the complex network model is generated in a random manner. Calculate the average path length and average clustering coefficient of the random network model to obtain the second average path length and the second average clustering coefficient; If the first average path length is greater than the second average path length, and the first average clustering coefficient is greater than the second average clustering coefficient, then the complex network model is determined to have complex network characteristics.

5. The method for assessing vulnerable lines based on comprehensive betweenness as described in claim 4, characterized in that, Calculating the average path length of the complex network model includes: The average path length of the complex network model is calculated based on the average path length formula; the average path length formula is: in, N This represents the number of nodes in the complex network model. d ij Represents a node i and nodes j The electrical distance between them.

6. The method for assessing vulnerable lines based on comprehensive betweenness as described in claim 4, characterized in that, Calculating the average clustering coefficient of the complex network model includes: The average clustering coefficient of the complex network model is calculated based on the formula for the average clustering coefficient; the formula for the average clustering coefficient is: in, Represents a node i The degree, This indicates that in the complex network model, nodes... i The number of branches in a subnet formed by directly connected nodes.

7. A vulnerable circuit evaluation device based on comprehensive betweenness, characterized in that, include: An acquisition module is used to acquire a complex network model of the target power system; wherein the complex network model includes nodes and lines; The judgment module is used to determine whether the complex network model has complex network characteristics; The calculation module is used to calculate the improved electrical betweenness index and reliability index of each line in the target power system if the complex network model has complex network characteristics. The integration module is used to calculate the integrated betweenness index of each line based on the improved electrical betweenness index and reliability index of each line; The evaluation module is used to identify the line with the largest comprehensive betweenness index as a vulnerable line in the target power system. The integrated module is specifically used for: For each line, the comprehensive betweenness index is calculated based on the improved electrical betweenness index, reliability index, and comprehensive betweenness calculation formula for that line; wherein, the comprehensive betweenness calculation formula is: in, Indicates the line The comprehensive betweenness index, Indicates the line Reliability indicators Indicates the line Improved electrical dielectric constant, Indicates the line The actual trend Indicates the line The biggest trend; The calculation module is specifically used for: Obtain the degree of each node and the weight of each line in the target power system; Based on the line The weight calculation of the transmission path between any two nodes in the target power system includes the line. The number; among which, lines For any line, The lines are respectively The nodes at both ends; Based on the degree of each node, the transmission path between any two nodes in the target power system includes the line. The quantity and improved electrical dielectric constant calculation formula are used to calculate the circuit. An improved electrical betweenness index; wherein the improved electrical betweenness calculation formula is: in, Indicates the line Improved electrical dielectric constant, , and Representing nodes respectively The degree, This represents the total number of nodes in the target power system. Represents any node, This refers to the traditional electrical betweenness index.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6 above.

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