Method and device for dynamically identifying key power transmission section of power system and electronic equipment

Abstract

The application particularly relates to a dynamic identification method and device for a key power transmission section of a power system and electronic equipment, which comprises the following steps: dynamically partitioning a power system based on electrical characteristic data to obtain a plurality of power transmission sections, calculating a power transmission breadth influence factor and a power transmission depth influence factor of each power transmission section in the power system according to topological characteristics, parameters and an initial power flow direction, calculating a branch vulnerability index of each power transmission section in the power system according to a line load rate of the power system, calculating a branch vulnerability evaluation index of each power transmission section according to the power transmission breadth influence factor, the power transmission depth influence factor and the branch vulnerability index and summing up to obtain a key power transmission section of the power system. Thus, the problems of ignoring the uncertainty of the power system, the change of a power flow topological structure, network parameters and an initial power flow direction of the power system, increasing the uncertainty and complexity of the power system and the like when a complex and changeable power system is processed are solved.

Description

Technical Field

[0001] This application relates to the field of critical section identification in power systems, and in particular to a dynamic identification method, device, and electronic equipment for critical transmission sections in power systems. Background Technology

[0002] With the rapid development of the economy and society, new power systems are facing severe challenges to the safe and stable operation of the system. In order to accurately, quickly and reliably analyze the safety and stability of the power system, the key section identification technology of the power system is fundamental.

[0003] Based on the process of large-scale cascading failures, power system transmission sections can be divided into transmission channel sections and power flow transfer sections, laying the groundwork for subsequent analysis of system section stability. Therefore, the main object of identification is the transmission channel section. Based on domestic and international research on critical transmission section identification, this is further broken down into three sub-problems: vulnerable branch identification, power system partitioning, and transmission section vulnerability assessment, which are considered comprehensively. Considering the current state of new power systems, the identification of critical transmission sections requires comprehensive consideration of the influence of important factors such as wind and solar uncertainties, power flow topology, network parameters, and initial power flow direction.

[0004] In related technologies, the identification of key transmission sections mainly relies on human experience and static analysis methods.

[0005] However, the above methods have limitations when dealing with complex and ever-changing power systems. For example, after the large-scale integration of new energy sources (such as wind and solar power), the uncertainties of the new power system, changes in the power flow topology, network parameters, and the initial power flow direction of the power system are often ignored. Therefore, the uncertainty and complexity of the power system will be significantly increased, which urgently needs to be addressed. Summary of the Invention

[0006] This application provides a method, device, and electronic equipment for dynamic identification of key transmission sections in a power system, in order to solve the problems of neglecting factors such as the uncertainty of the power system, changes in power flow topology, network parameters, and the initial power flow direction when dealing with complex and ever-changing power systems, thereby increasing the uncertainty and complexity of the power system.

[0007] The first aspect of this application provides a method for dynamic identification of critical transmission sections in a power system, comprising the following steps:

[0008] To obtain the topological characteristics, power system parameters, initial power flow direction, and line load rate of the power system;

[0009] Using a preset spectral clustering algorithm, the power system is dynamically partitioned based on its electrical characteristic data to obtain multiple transmission sections of the power system;

[0010] Based on the topological characteristics of the power system, the power system parameters, and the initial power flow direction of the power system, calculate the power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system, and calculate the branch vulnerability index of each transmission section in the power system based on the line load rate of the power system.

[0011] The branch vulnerability assessment index for each transmission section in the power system is calculated based on the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index.

[0012] The vulnerability assessment indices of each transmission section are summed to obtain the critical transmission sections of the power system.

[0013] According to one embodiment of this application, the step of calculating the power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system based on the topological characteristics of the power system, the power system parameters, and the initial power flow direction of the power system includes:

[0014] The connection relationships of multiple branches of each transmission section in the power system are determined based on the topological characteristics and parameters of the power system.

[0015] The influence of the target branch on other branches is obtained based on the connection relationship of multiple branches of each transmission section, and the power distribution factor of each transmission section is calculated based on the influence.

[0016] The power influence factor of the power system is calculated based on the power distribution factor and the initial power flow direction of the power system. The power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system are calculated based on the power influence factor.

[0017] According to one embodiment of this application, the step of calculating the branch vulnerability index of each transmission section in the power system based on the power system line load rate includes:

[0018] The wind and solar power output scenario of the power system is generated using Monte Carlo simulation, wherein the wind speed in the wind and solar power output scenario follows a Weibull distribution and the solar radiation follows a Beta distribution;

[0019] Using a preset probabilistic power flow algorithm, the load factor of the power system line under the wind and solar power output scenario is calculated, so as to calculate the vulnerability index of each branch in each transmission section based on the load factor of the power system line.

[0020] The vulnerability index of each branch is averaged to obtain the branch vulnerability index of each transmission section in the power system.

[0021] According to one embodiment of this application, the step of calculating the branch vulnerability assessment index for each transmission section in the power system based on the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index includes:

[0022] Based on a preset normalization method, the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index are mapped to a preset range.

[0023] Based on the power system requirements, the branch vulnerability assessment index of each transmission section in the power system is calculated by adjusting the weights of the transmission betweenness and vulnerability index of the transmission section according to the preset interval.

[0024] According to one embodiment of this application, the step of summing the branch vulnerability assessment indices of each transmission section to obtain the key transmission sections of the power system includes:

[0025] Based on the electrical characteristic data between each node in the power system, the similarity between each node is calculated, and a node similarity graph of the power system is constructed.

[0026] A Laplacian matrix is ​​constructed based on the node similarity graph, and the Laplacian matrix is ​​subjected to spectral decomposition to obtain multiple eigenvectors and multiple eigenvalues ​​of the power system.

[0027] Cluster analysis is performed on the multiple feature vectors and multiple feature values ​​to obtain the cluster analysis results and node importance index;

[0028] Based on the cluster analysis results and the node importance index, multiple transmission sections of the power system are obtained;

[0029] The vulnerability assessment indices of each branch of the power transmission section are summed to obtain the total vulnerability assessment index of each transmission section in the power system.

[0030] The target transmission section with the highest total vulnerability assessment index among all transmission sections is identified to obtain the key transmission sections of the power system.

[0031] According to one embodiment of this application, the step of performing cluster analysis on the plurality of feature vectors and the plurality of feature values ​​to obtain the cluster analysis results and node importance indicators includes:

[0032] Using a preset spectral clustering algorithm, each node is clustered based on the electrical distance of the power system, and each clustered node is divided into multiple corresponding clusters. The preset spectral clustering algorithm is then used to cluster each cluster until the clustering results meet the preset clustering stopping condition, thereby obtaining the clustering analysis results and the node importance index.

[0033] According to the dynamic identification method for critical transmission sections of a power system according to embodiments of this application, a preset spectral clustering algorithm is used to dynamically partition the power system based on the electrical characteristic data of the power system, resulting in multiple transmission sections of the power system. The power transmission breadth influence factor and power transmission depth influence factor of each transmission section in the power system are calculated based on the topological characteristics, parameters, and initial power flow direction of the power system. The branch vulnerability index of each transmission section in the power system is calculated based on the line load rate. The branch vulnerability assessment index of each transmission section in the power system is calculated based on the power transmission breadth influence factor, power transmission depth influence factor, and branch vulnerability index. The branch vulnerability assessment indices of each transmission section are summed to obtain the critical transmission sections of the power system. This solves the problem of neglecting factors such as power system uncertainty, power flow topology changes, network parameters, and initial power flow direction when dealing with complex and ever-changing power systems, which increases the uncertainty and complexity of the power system. By combining the branch vulnerability assessment index of each transmission section with power system partitioning, the critical transmission sections of the power system can be identified, thereby improving the accuracy and reliability of the identification of critical transmission sections.

[0034] A second aspect of this application provides a dynamic identification device for critical transmission sections in a power system, comprising:

[0035] The acquisition module is used to acquire the topological characteristics of the power system, power system parameters, initial power flow direction, and line load rate of the power system.

[0036] The partitioning module is used to dynamically partition the power system based on the electrical characteristic data of the power system using a preset spectral clustering algorithm, thereby obtaining multiple transmission sections of the power system.

[0037] The first calculation module is used to calculate the power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system based on the topological characteristics of the power system, the power system parameters and the initial power flow direction of the power system, and to calculate the branch vulnerability index of each transmission section in the power system based on the line load rate of the power system.

[0038] The second calculation module is used to calculate the branch vulnerability assessment index for each transmission section in the power system based on the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index.

[0039] The third calculation module is used to sum and calculate the branch vulnerability assessment index of each transmission section to obtain the key transmission sections of the power system.

[0040] According to one embodiment of this application, the first computing module includes:

[0041] A determining unit is used to determine the connection relationship of multiple branches of each transmission section in the power system based on the topological characteristics and parameters of the power system.

[0042] The first calculation unit is used to obtain the degree of influence of the target branch on other branches among the multiple branches according to the connection relationship of the multiple branches of each transmission section, and to calculate the power distribution factor of each transmission section according to the degree of influence.

[0043] The second calculation unit is used to calculate the power influence factor of the power system based on the power distribution factor and the initial power flow direction of the power system, and to calculate the power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system according to the power influence factor.

[0044] According to one embodiment of this application, the first computing module includes:

[0045] The generation unit is used to generate the wind and solar power output scenario of the power system using Monte Carlo simulation, wherein the wind speed in the wind and solar power output scenario follows a Weibull distribution and the illumination follows a Beta distribution;

[0046] The third calculation unit is used to calculate the power system line load rate under the wind and solar power output scenario using a preset probabilistic power flow algorithm, so as to calculate the vulnerability index of each branch in each transmission section based on the power system line load rate.

[0047] The fourth calculation unit is used to calculate the average value of the vulnerability index of each branch to obtain the branch vulnerability index of each transmission section in the power system.

[0048] According to one embodiment of this application, the second computing module includes:

[0049] The mapping unit is used to map the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index to a preset range based on a preset normalization method.

[0050] The fifth calculation unit is used to calculate the branch vulnerability assessment index of each transmission section in the power system based on the power system requirements and by adjusting the weights of the transmission betweenness and vulnerability index of the transmission section according to the preset interval.

[0051] According to one embodiment of this application, the third computing module includes:

[0052] The construction unit is used to calculate the similarity between each node based on the electrical characteristic data between each node in the power system, and to construct a node similarity graph of the power system.

[0053] The spectral decomposition unit is used to construct a Laplacian matrix based on the node similarity graph and perform spectral decomposition on the Laplacian matrix to obtain multiple feature vectors and multiple feature values ​​of the power system.

[0054] A clustering analysis unit is used to perform clustering analysis on the multiple feature vectors and multiple feature values ​​to obtain the clustering analysis results and node importance indicators;

[0055] The acquisition unit is used to obtain multiple transmission sections of the power system based on the clustering analysis results and the node importance index;

[0056] The sixth calculation unit is used to sum and calculate the vulnerability assessment index of each branch of the power transmission section to obtain the total vulnerability assessment index of each power transmission section in the power system.

[0057] The identification unit is used to identify the target transmission section with the highest total vulnerability assessment index among the total vulnerability assessment indices of each transmission section, thereby obtaining the key transmission sections of the power system.

[0058] According to one embodiment of this application, the clustering analysis unit includes:

[0059] The clustering unit is used to cluster each node based on the electrical distance of the power system using a preset spectral clustering algorithm, and divide each clustered node into multiple corresponding clusters. The preset spectral clustering algorithm is then used to cluster each cluster until the clustering results meet the preset clustering stopping conditions, thereby obtaining the clustering analysis results and the node importance index.

[0060] The dynamic identification device for key transmission sections of a power system according to an embodiment of this application utilizes a preset spectral clustering algorithm to dynamically partition the power system based on its electrical characteristic data, thereby obtaining multiple transmission sections of the power system. It calculates the power transmission breadth influence factor and power transmission depth influence factor for each transmission section based on the power system's topological characteristics, power system parameters, and initial power flow direction. It also calculates the branch vulnerability index for each transmission section based on the power system's line load rate. Finally, it calculates the branch vulnerability assessment index for each transmission section based on the power transmission breadth influence factor, power transmission depth influence factor, and branch vulnerability index. The branch vulnerability assessment indices for each transmission section are then summed to obtain the key transmission sections of the power system. This solves the problem of neglecting factors such as power system uncertainty, power flow topology changes, network parameters, and initial power flow direction when dealing with complex and ever-changing power systems, which increases the uncertainty and complexity of the power system. By combining the branch vulnerability assessment index of each transmission section with power system partitioning, the critical transmission sections of the power system can be identified, thereby improving the accuracy and reliability of the identification of critical transmission sections.

[0061] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the dynamic identification method for key power transmission sections of a power system as described in the above embodiments.

[0062] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the dynamic identification method for key transmission sections of a power system as described in the above embodiments.

[0063] A fifth aspect of the present invention provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method described in the above embodiments.

[0064] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0065] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0066] Figure 1 This is a flowchart of a dynamic identification method for key transmission sections in a power system according to an embodiment of this application;

[0067] Figure 2 This is a schematic diagram of the overall steps of this application according to one embodiment;

[0068] Figure 3 This is a schematic diagram of the EEE-39 node New England system according to an embodiment of this application;

[0069] Figure 4 This is a system initial power flow direction topology diagram according to an embodiment of this application;

[0070] Figure 5 This is a schematic diagram of the average vulnerability index of each branch of a 39-node system according to an embodiment of this application;

[0071] Figure 6 This is a schematic diagram of a system partitioning based on a spectral clustering algorithm according to an embodiment of this application;

[0072] Figure 7 A 3D image for key section identification based on spectral clustering recursion according to an embodiment of this application;

[0073] Figure 8 This is a block diagram of a dynamic identification device for critical transmission sections of a power system according to an embodiment of this application;

[0074] Figure 9 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0075] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0076] The following describes, with reference to the accompanying drawings, a method, apparatus, and electronic device for dynamic identification of critical transmission sections in a power system according to embodiments of this application. Addressing the issue mentioned in the background art where the uncertainty of the power system, changes in power flow topology, network parameters, and initial power flow direction are ignored when dealing with complex and ever-changing power systems, thus increasing the uncertainty and complexity of the power system, this application provides a method for dynamic identification of critical transmission sections in a power system. In this method, a preset spectral clustering algorithm is used to dynamically partition the power system based on its electrical characteristic data, resulting in multiple transmission sections. The power transmission breadth influence factor and power transmission depth influence factor for each transmission section are calculated based on the power system's topological characteristics, power system parameters, and initial power flow direction. The branch vulnerability index for each transmission section is calculated based on the power system's line load rate. The branch vulnerability assessment index for each transmission section is calculated based on the power transmission breadth influence factor, power transmission depth influence factor, and branch vulnerability index. The branch vulnerability assessment indices for each transmission section are summed to obtain the critical transmission sections of the power system. This solves the problem of neglecting factors such as power system uncertainty, power flow topology changes, network parameters, and initial power flow direction when dealing with complex and ever-changing power systems, which increases the uncertainty and complexity of the power system. By combining the branch vulnerability assessment index of each transmission section with power system partitioning, the critical transmission sections of the power system can be identified, thereby improving the accuracy and reliability of the identification of critical transmission sections.

[0077] Specifically, Figure 1 This is a flowchart illustrating a dynamic identification method for key transmission sections in a power system, as provided in an embodiment of this application.

[0078] like Figure 1 As shown, the dynamic identification method for key transmission sections of the power system includes the following steps:

[0079] In step S101, the topological characteristics of the power system, power system parameters, initial power flow direction, and line load rate of the power system are obtained.

[0080] In step S102, a preset spectral clustering algorithm is used to dynamically partition the power system based on the electrical characteristic data of the power system, thereby obtaining multiple transmission sections of the power system.

[0081] The preset spectral clustering algorithm can be selected by Benlong and his technical staff according to actual testing needs, and no specific limitations are made here.

[0082] Specifically, in this embodiment of the application, to address the problem in related technologies that increase the uncertainty and complexity of power systems when dealing with complex and ever-changing power systems due to the neglect of factors such as power system uncertainty, power flow topology changes, network parameters, and initial power flow direction, this embodiment of the application, based on the framework of the improved betweenness method in complex graph theory, rewrites the power system transmission betweenness based on power flow influence factors and branch vulnerability indices, obtaining more reliable system branch vulnerability indices that better reflect the current state of the system. Furthermore, it dynamically and rationally partitions the power system using spectral clustering to obtain system transmission sections and their associated branches. Finally, it combines the aforementioned vulnerability indices with the system transmission sections to obtain the key transmission sections of the power system.

[0083] Therefore, as Figure 2 and Figure 3 As shown, this application embodiment requires the use of the topological characteristics of the power system, power system parameters, initial power flow direction and line load rate of the power system, and a preset spectral clustering algorithm to dynamically partition the power system based on electrical characteristic data, such as electrical distance, to obtain multiple transmission sections of the power system. Based on the vulnerability indicators of multiple transmission sections, the critical transmission sections of the power system are identified. This application embodiment uses the IEEE-39 node New England system as an example to verify the proposed method.

[0084] In step S103, the power transmission breadth influence factor and power transmission depth influence factor of each transmission section in the power system are calculated based on the topological characteristics, power system parameters and initial power flow direction of the power system. In addition, the branch vulnerability index of each transmission section in the power system is calculated based on the line load rate of the power system.

[0085] According to one embodiment of this application, the calculation of the power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system based on the topological characteristics, power system parameters, and initial power flow direction of the power system includes: determining the connection relationship of multiple branches of each transmission section in the power system based on the topological characteristics and power system parameters of the power system; obtaining the influence degree of the target branch on other branches among the multiple branches based on the connection relationship of the multiple branches of each transmission section, and calculating the power distribution factor of each transmission section based on the influence degree; calculating the power influence factor of the power system based on the power distribution factor and the initial power flow direction of the power system, and calculating the power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system based on the power influence factor.

[0086] According to one embodiment of this application, calculating the branch vulnerability index of each transmission section in the power system based on the power system line load rate includes: generating a wind and solar power output scenario of the power system using Monte Carlo simulation, wherein the wind speed in the wind and solar power output scenario follows a Weibull distribution, and the solar illumination follows a... Beta Distribution; using a pre-defined probabilistic power flow algorithm, calculate the power system line load rate under wind and solar power output scenarios, and calculate the vulnerability index of each branch in each transmission section based on the power system line load rate; calculate the average value of the vulnerability index of each branch to obtain the branch vulnerability index of each transmission section in the power system.

[0087] The preset probabilistic power flow algorithm can be selected by Benlong and his technical staff according to actual testing needs, and no specific limitations are made here.

[0088] Specifically, in this embodiment, the connection relationships of multiple branches of each transmission section in the power system are first determined based on the topological characteristics and parameters of the power system. Then, the influence degree of the target branch on other branches is obtained based on the connection relationships of multiple branches of each transmission section. The power distribution factor of each transmission section is calculated based on the influence degree. The power distribution factor is defined as the influence degree of power transmission changes between nodes on all other branches, and its expression can be expressed as:

[0089]

[0090] in, The power change over the disconnected branch line km is the amount of power change. In order to be in Influence on the lower branch l ij The change in power on Let be the element in the i-th row and k-th column of the nodal reactance matrix. This is the branch reactance value. Let be the element in the i-th row and m-th column of the nodal reactance matrix. Let be the element in the j-th row and k-th column of the nodal reactance matrix. Let be the element in the j-th row and m-th column of the nodal reactance matrix.

[0091] Secondly, such as Figure 4 As shown, this embodiment of the application combines the power distribution factor calculated above with the initial power flow direction of the power system to calculate the power distribution factor in the power system that has a positive impact on other branches, which is the power influence factor. The main expression is as follows:

[0092]

[0093] in, Indicates a branch l On the linek The positive impact of the power distribution factor under the disconnection condition, where l For the affected branch roads l ij , k For the branch road to be opened l km .

[0094] Finally, based on the power influence factors calculated above, the power transmission breadth influence factor and the power transmission depth influence factor for each transmission section in the power system are calculated respectively. The main expressions are as follows:

[0095]

[0096]

[0097] in, This represents the positive impact of branch l on the power distribution factor when line k is disconnected, where l is the value of the affected branch l. ij k is the disconnected branch l km , For the set of branches l pairs membership degree For the initial branch l km The set of lines with significant impact, categorized by the number of lines disconnected. km A power influence factor greater than 0.2 was used for selection to ensure the sufficiency of lines in the set and to identify all anticipated branches. It represents a set The modulus, whose value is equal to The total number of branches contained therein This reflects the degree of impact on the system after branch k is disconnected. This reflects the range of impact on the system after branch k is disconnected. The larger the value, the greater the impact on the system after the branch is disconnected. The larger the value, the wider the impact range after the branch is broken.

[0098] Furthermore, this embodiment of the application also requires the use of Monte Carlo simulation to generate wind and solar power output scenarios for the power system, resulting in 1000 wind and solar power output scenarios. The wind speed in these scenarios follows a Weibull distribution, and the solar irradiance follows a Beta distribution. The photovoltaic power output model can be expressed as:

[0099]

[0100]

[0101] Among them, GS The simulated light intensity for this scenario is given by P, where CF represents the conversion efficiency of the photovoltaic module in converting solar energy into electrical energy. PVC G represents the total installed capacity of the photovoltaic system (W), and G represents the solar irradiance under standard conditions, G = 1000 W / m². For photovoltaic module conversion efficiency and temperature correction factor, Install azimuth and tilt correction factors for photovoltaic modules. For the efficiency of photovoltaic system inverters, This is the line correction factor for the photovoltaic system.

[0102] The wind power output model can be expressed as:

[0103]

[0104] in, Cut-in wind speed, meaning the minimum wind speed at which the wind turbine starts producing power, v cut Cut-off wind speed, meaning the critical wind speed at which the wind turbine is disconnected from the power grid, where a and b are parameters relating the wind turbine output to the wind speed. This refers to the rated output power of the wind turbine. This refers to the rated wind speed of the wind turbine.

[0105] Furthermore, such as Figure 5 As shown, this embodiment of the application utilizes a preset probabilistic power flow algorithm to obtain the probability distribution of active power on the lines, that is, to calculate the power system line load rate under the wind and solar power output scenario, and to calculate the vulnerability index of each branch in each transmission section based on the power system line load rate, and to calculate the average vulnerability index of each branch to obtain the branch vulnerability index of each transmission section in the power system, that is, the average vulnerability index of each branch of the 39-node system, as shown in Table 1:

[0106] Table 1

[0107]

[0108] The vulnerability index of each transmission section in the power system is assessed by calculating the load factor of each line, i.e., the ratio of line power flow to its rated capacity, to evaluate the vulnerability of the power system lines. The specific formula is as follows:

[0109]

[0110] in, For the active power of the road, The rated capacity of the line is used as the basis for calculating the vulnerability index based on the line load rate, combined with the probabilistic power flow calculation results. The overall vulnerability of the power grid system is then assessed by calculating the average vulnerability index.

[0111] In step S104, the branch vulnerability assessment index for each transmission section in the power system is calculated based on the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index.

[0112] According to one embodiment of this application, the branch vulnerability assessment index of each transmission section in the power system is calculated based on the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index. This includes: mapping the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index to a preset interval based on a preset normalization method; and adjusting the weights of the transmission betweenness and vulnerability index of the transmission section based on the preset interval according to the power system requirements to calculate the branch vulnerability assessment index of each transmission section in the power system.

[0113] The preset normalization method can be selected by those skilled in the art based on actual testing needs, and no specific limitations are made here.

[0114] Specifically, in this embodiment, after calculating the branch vulnerability index for each transmission section, the branch vulnerability assessment index for each transmission section in the power system is calculated based on the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index. First, the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index are mapped to a preset interval based on a preset normalization method. For example, the power transmission depth factor, the power transmission breadth factor, and the branch vulnerability index are mapped to dimensionless decimals between 0 and 1 through feature scaling using a min-max normalization method. The transformation function is:

[0115]

[0116] Secondly, the weights of the normalized parameters are flexibly adjusted according to the needs of the power system, and finally, the weighted sums are obtained to obtain the branch vulnerability assessment index for each transmission section, as shown in Table 2:

[0117] Table 2

[0118]

[0119] The specific formula is as follows:

[0120]

[0121] in, To improve the transmission betweenness index, As a vulnerability indicator, , The values ​​are 0.2 and 0.8 respectively, which are selected in this application example. It is believed that the vulnerability index has a much higher weight than the improved transmission betweenness factor. The principle of setting it is that the vulnerability index can comprehensively consider multiple factors under different operating conditions, dynamically reflect the changes in system state, and provide real-time vulnerability assessment. More importantly, the high-weight vulnerability index enables the assessment model to quickly reflect the increase in vulnerability caused by changes in system state, and supports operators to make rapid decisions to prevent the spread of faults and reduce the impact of accidents.

[0122] In step S105, the vulnerability assessment indices of the branches of each transmission section are summed to obtain the critical transmission sections of the power system.

[0123] According to one embodiment of this application, the branch vulnerability assessment indices of each transmission section are summed to obtain the key transmission sections of the power system. This includes: calculating the similarity between each node based on the electrical characteristic data of each node in the power system, and constructing a node similarity graph of the power system; constructing a Laplacian matrix based on the node similarity graph, and performing spectral decomposition on the Laplacian matrix to obtain multiple eigenvectors and eigenvalues ​​of the power system; performing cluster analysis on the multiple eigenvectors and eigenvalues ​​to obtain cluster analysis results and node importance indices; obtaining multiple transmission sections of the power system based on the cluster analysis results and node importance indices; summing the branch vulnerability assessment indices of each transmission section to obtain the total vulnerability assessment index of each transmission section in the power system; and identifying the target transmission section with the highest total vulnerability assessment index among the total vulnerability assessment indices of each transmission section to obtain the key transmission sections of the power system.

[0124] According to one embodiment of this application, cluster analysis is performed on multiple feature vectors and multiple feature values ​​to obtain cluster analysis results and node importance indicators. The method includes: using a preset spectral clustering algorithm, clustering each node based on the electrical distance of the power system, dividing each clustered node into multiple corresponding clusters, and continuing to use the preset spectral clustering algorithm to cluster each cluster until the clustering results meet the preset clustering stopping condition, thereby obtaining cluster analysis results and node importance indicators.

[0125] The preset clustering stopping conditions can be selected by those skilled in the art based on actual testing needs, and are not specifically limited here.

[0126] Specifically, firstly, based on the electrical characteristic data between each node in the power system, such as the voltage, power, and load data between each node in the power system, the similarity between each node is calculated, and a node similarity graph of the power system is constructed. In the similarity graph, nodes represent different parts of the system, and edges represent the similarity or connection relationship between nodes. Secondly, a Laplacian matrix is ​​constructed based on the node similarity graph. The Laplacian matrix is ​​a commonly used representation in graph theory, used to describe the structure and connection relationship of the graph. It can reflect the mutual relationship between nodes, including the connection relationship and the degree of influence. The calculation of the Laplacian matrix includes the calculation of the degree matrix and the adjacency matrix. Their main characteristics are: (1) Degree matrix: The degree matrix is ​​a diagonal matrix. The elements on its diagonal represent the degree of each node (i.e., the number of edges connected to the node). The degree matrix is ​​usually represented as D, and the element d(i) on the diagonal represents the degree of the i-th node; (2) Adjacency matrix Matrix): The adjacency matrix is ​​a matrix that describes the connection relationship between nodes in a graph. In the power system, the adjacency matrix can be calculated based on the similarity between nodes. The adjacency matrix is ​​usually represented as A, where A(i,j) represents the connection relationship between node i and node j. Its element is 1 to indicate connection and 0 to indicate non-connection. (3) Laplacian matrix: The Laplacian matrix is ​​the difference between the degree matrix and the adjacency matrix. In the case of an undirected graph, the Laplacian matrix can be represented as L = D – A. In the case of a directed graph or a weighted graph, other forms of Laplacian matrix can also be considered, such as the symmetric normalized Laplacian matrix, etc. The formula is as follows:

[0127]

[0128] in, It is the identity matrix. This is the adjacency matrix between nodes. It is the negative square root of the degree matrix.

[0129] Again, such as Figure 6As shown, spectral decomposition of the Laplacian matrix yields multiple eigenvectors and eigenvalues ​​of the power system. Spectral decomposition reduces the dimensionality of the original data and extracts its main features, allowing for the identification of different regions or clusters within the system, which aids in subsequent clustering analysis. Based on the eigenvectors obtained from spectral decomposition, appropriate eigenvectors can be selected as new data representations. Then, a spectral clustering algorithm is used to cluster the nodes, dividing each clustered node into multiple corresponding clusters. Each cluster represents a set of highly correlated nodes, potentially corresponding to critical transmission sections. During the clustering process, a recursive approach can be adopted, applying the spectral clustering algorithm again to each cluster after each clustering until the clustering results meet the preset clustering stopping conditions (such as reaching a predetermined number of clusters), thus obtaining the clustering analysis results and node importance indicators.

[0130] Finally, as Figure 7 As shown, multiple transmission sections of the power system are obtained based on the cluster analysis results and node importance indicators. Transmission sections typically play an important role in the power system in terms of transmission or connection functions, and their failures or malfunctions may have a significant impact on system stability. Therefore, in the clustering results, multiple transmission sections of the power system can be identified based on the characteristics of the clusters and the node importance indicators. Then, the branch vulnerability assessment indicators of each transmission section are summed to obtain the total vulnerability assessment indicator of each transmission section in the power system. Subsequently, the target transmission section with the highest total vulnerability assessment indicator among the total vulnerability assessment indicators of each transmission section is identified, thus obtaining the key transmission sections of the power system.

[0131] Therefore, the vulnerability assessment of the system in this application not only considers the basic network topology and system parameters of the traditional power grid, but also comprehensively considers the initial power flow direction and the branch load volatility after the access of new energy sources. The power system is partitioned based on spectral clustering, which is more reasonable and reliable than the traditional partitioning based on manual experience while conforming to the definition of transmission channel sections. It can also meet the needs of real-time dynamic adjustment of the system, and provide basic technical support for subsequent system stability analysis and maximum transmission capacity range assessment based on key transmission sections of the power system.

[0132] In summary, this application can achieve the following beneficial effects:

[0133] (1) Improve the accuracy of key transmission section identification: By combining spectral clustering algorithm with node importance index, the global structural features of the system are captured, and regions with similar electrical characteristics (i.e. transmission sections) are identified. At the same time, the identification of transmission sections is further refined by combining node importance index, which can more accurately identify key transmission sections in the power system.

[0134] (2) Enhance the dynamic adaptability of the system: Through dynamic partitioning and recursive clustering, it can adapt to the real-time changes of the power system and enhance the dynamic adaptability of the system;

[0135] (3) Improve the stability and reliability of the system: By identifying vulnerable links and optimizing scheduling and maintenance, cascading failures can be effectively prevented, thereby improving the stability and reliability of the power system;

[0136] (4) Reduce computational complexity and improve computational efficiency: By simplifying the model, normalizing the process and using reasonable recursive clustering termination conditions, computational complexity can be significantly reduced and computational efficiency can be improved.

[0137] (5) Wide range of applications: It is applicable to various power systems and different operating scenarios, and can provide effective technical support for the scheduling, planning and operation and maintenance of power systems.

[0138] According to the dynamic identification method for critical transmission sections of a power system according to embodiments of this application, a preset spectral clustering algorithm is used to dynamically partition the power system based on the electrical characteristic data of the power system, resulting in multiple transmission sections of the power system. The power transmission breadth influence factor and power transmission depth influence factor of each transmission section in the power system are calculated based on the topological characteristics, parameters, and initial power flow direction of the power system. The branch vulnerability index of each transmission section in the power system is calculated based on the line load rate. The branch vulnerability assessment index of each transmission section in the power system is calculated based on the power transmission breadth influence factor, power transmission depth influence factor, and branch vulnerability index. The branch vulnerability assessment indices of each transmission section are summed to obtain the critical transmission sections of the power system. This solves the problem of neglecting factors such as power system uncertainty, power flow topology changes, network parameters, and initial power flow direction when dealing with complex and ever-changing power systems, which increases the uncertainty and complexity of the power system. By combining the branch vulnerability assessment index of each transmission section with power system partitioning, the critical transmission sections of the power system can be identified, thereby improving the accuracy and reliability of the identification of critical transmission sections.

[0139] Next, referring to the accompanying drawings, a dynamic identification device for key power transmission sections of a power system is described according to an embodiment of this application.

[0140] Figure 8 This is a block diagram of a dynamic identification device for key power transmission sections in a power system, according to an embodiment of this application.

[0141] like Figure 8 As shown, the dynamic identification device 10 for key transmission sections of the power system includes: an acquisition module 100, a division module 200, a first calculation module 300, a second calculation module 400, and a third calculation module 500.

[0142] Among them, the acquisition module 100 is used to acquire the topological characteristics of the power system, power system parameters, initial power flow direction of the power system, and line load rate of the power system.

[0143] The partitioning module 200 is used to dynamically partition the power system based on the electrical characteristic data of the power system using a preset spectral clustering algorithm to obtain multiple transmission sections of the power system.

[0144] The first calculation module 300 is used to calculate the power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system based on the topological characteristics, parameters and initial power flow direction of the power system, and to calculate the branch vulnerability index of each transmission section in the power system based on the line load rate of the power system.

[0145] The second calculation module 400 is used to calculate the branch vulnerability assessment index for each transmission section in the power system based on the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index.

[0146] The third calculation module 500 is used to sum and calculate the vulnerability assessment index of each transmission section's branches to obtain the key transmission sections of the power system.

[0147] According to one embodiment of this application, the first computing module 300 includes:

[0148] The determination unit is used to determine the connection relationship of multiple branches of each transmission section in the power system based on the topological characteristics and parameters of the power system.

[0149] The first calculation unit is used to obtain the degree of influence of the target branch on other branches in multiple branches according to the connection relationship of multiple branches in each transmission section, and calculate the power distribution factor of each transmission section according to the degree of influence.

[0150] The second calculation unit is used to calculate the power influence factor of the power system based on the power distribution factor and the initial power flow direction of the power system, and to calculate the power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system according to the power influence factor.

[0151] According to one embodiment of this application, the first calculation module 300 includes:

[0152] The generation unit is used to generate wind and solar power output scenarios of the power system using Monte Carlo simulation, wherein the wind speed in the wind and solar power output scenarios follows a Weibull distribution and the illumination follows a Beta distribution.

[0153] The third calculation unit is used to calculate the power system line load rate under the wind and solar power output scenario using a preset probabilistic power flow algorithm, so as to calculate the vulnerability index of each branch in each transmission section based on the power system line load rate.

[0154] The fourth calculation unit is used to calculate the average value of the vulnerability index of each branch, so as to obtain the branch vulnerability index of each transmission section in the power system.

[0155] According to one embodiment of this application, the second computing module 400 includes:

[0156] The mapping unit is used to map the power transmission breadth influence factor, power transmission depth influence factor and branch vulnerability index to a preset range based on a preset normalization method.

[0157] The fifth calculation unit is used to calculate the branch vulnerability assessment index of each transmission section in the power system based on the power system requirements and by adjusting the weights of the transmission betweenness and vulnerability index of the transmission section within a preset interval.

[0158] According to one embodiment of this application, the third computing module 500 includes:

[0159] The construction unit is used to calculate the similarity between each node based on the electrical characteristic data between each node in the power system, and to construct the node similarity graph of the power system.

[0160] The spectral decomposition unit is used to construct the Laplacian matrix based on the node similarity graph and perform spectral decomposition on the Laplacian matrix to obtain multiple eigenvectors and multiple eigenvalues ​​of the power system.

[0161] The clustering analysis unit is used to perform clustering analysis on multiple feature vectors and multiple feature values ​​to obtain clustering analysis results and node importance indicators;

[0162] The acquisition unit is used to obtain multiple transmission sections of the power system based on the cluster analysis results and node importance indicators;

[0163] The sixth calculation unit is used to sum the vulnerability assessment indices of each transmission section to obtain the total vulnerability assessment index of each transmission section in the power system.

[0164] The identification unit is used to identify the target transmission section with the highest total vulnerability assessment index among the total vulnerability assessment indices of each transmission section, thereby obtaining the key transmission sections of the power system.

[0165] According to one embodiment of this application, the clustering analysis unit includes:

[0166] The clustering unit is used to cluster each node based on the electrical distance of the power system using a preset spectral clustering algorithm. Each clustered node is then divided into multiple corresponding clusters. The preset spectral clustering algorithm is then used to cluster each cluster until the clustering results meet the preset clustering stopping conditions, thus obtaining the clustering analysis results and node importance indicators.

[0167] The dynamic identification device for key transmission sections of a power system according to an embodiment of this application utilizes a preset spectral clustering algorithm to dynamically partition the power system based on its electrical characteristic data, thereby obtaining multiple transmission sections of the power system. It calculates the power transmission breadth influence factor and power transmission depth influence factor for each transmission section based on the power system's topological characteristics, power system parameters, and initial power flow direction. It also calculates the branch vulnerability index for each transmission section based on the power system's line load rate. Finally, it calculates the branch vulnerability assessment index for each transmission section based on the power transmission breadth influence factor, power transmission depth influence factor, and branch vulnerability index. The branch vulnerability assessment indices for each transmission section are then summed to obtain the key transmission sections of the power system. This solves the problem of neglecting factors such as power system uncertainty, power flow topology changes, network parameters, and initial power flow direction when dealing with complex and ever-changing power systems, which increases the uncertainty and complexity of the power system. By combining the branch vulnerability assessment index of each transmission section with power system partitioning, the critical transmission sections of the power system can be identified, thereby improving the accuracy and reliability of the identification of critical transmission sections.

[0168] Figure 9 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0169] The memory 901, the processor 902, and the computer program stored on the memory 901 and capable of running on the processor 902.

[0170] When the processor 902 executes the program, it implements the dynamic identification method for key power transmission sections of the power system provided in the above embodiments.

[0171] Furthermore, electronic devices also include:

[0172] Communication interface 903 is used for communication between memory 901 and processor 902.

[0173] The memory 901 is used to store computer programs that can run on the processor 902.

[0174] The memory 901 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0175] If the memory 901, processor 902, and communication interface 903 are implemented independently, then the communication interface 903, memory 901, and processor 902 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0176] Optionally, in a specific implementation, if the memory 901, processor 902, and communication interface 903 are integrated on a single chip, then the memory 901, processor 902, and communication interface 903 can communicate with each other through an internal interface.

[0177] The processor 902 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0178] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for dynamic identification of key power transmission sections in a power system.

[0179] This application also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method as described in the above embodiments.

[0180] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0181] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0182] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for dynamic identification of key transmission sections in a power system, characterized in that, Includes the following steps: To obtain the topological characteristics, power system parameters, initial power flow direction, and line load rate of the power system; Using a preset spectral clustering algorithm, the power system is dynamically partitioned based on its electrical characteristic data to obtain multiple transmission sections of the power system; The connection relationships of multiple branches of each transmission section in the power system are determined based on the topological characteristics and parameters of the power system. The influence of the target branch on other branches is obtained based on the connection relationship of multiple branches of each transmission section, and the power distribution factor of each transmission section is calculated based on the influence. The power influence factor of the power system is calculated based on the power distribution factor and the initial power flow direction of the power system. The power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system are calculated based on the power influence factor. The branch vulnerability index of each transmission section in the power system is calculated based on the line load rate of the power system. The branch vulnerability assessment index for each transmission section in the power system is calculated based on the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index. The vulnerability assessment indices of each transmission section are summed to obtain the critical transmission sections of the power system.

2. The method according to claim 1, characterized in that, The calculation of branch vulnerability indices for each transmission section in the power system based on the power system line load rate includes: The wind and solar power output scenario of the power system is generated using Monte Carlo simulation, wherein the wind speed in the wind and solar power output scenario follows a Weibull distribution and the solar radiation follows a Beta distribution; Using a preset probabilistic power flow algorithm, the load factor of the power system line under the wind and solar power output scenario is calculated, so as to calculate the vulnerability index of each branch in each transmission section based on the load factor of the power system line. The vulnerability index of each branch is averaged to obtain the branch vulnerability index of each transmission section in the power system.

3. The method according to claim 1, characterized in that, The calculation of the branch vulnerability assessment index for each transmission section in the power system based on the power transmission breadth impact factor, the power transmission depth impact factor, and the branch vulnerability index includes: Based on a preset normalization method, the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index are mapped to a preset range. Based on the power system requirements, the branch vulnerability assessment index of each transmission section in the power system is calculated by adjusting the weights of the transmission betweenness and vulnerability index of the transmission section according to the preset interval.

4. The method according to claim 1, characterized in that, The critical transmission sections of the power system are obtained by summing the branch vulnerability assessment indices of each transmission section, including: Based on the electrical characteristic data between each node in the power system, the similarity between each node is calculated, and a node similarity graph of the power system is constructed. A Laplacian matrix is ​​constructed based on the node similarity graph, and the Laplacian matrix is ​​subjected to spectral decomposition to obtain multiple eigenvectors and multiple eigenvalues ​​of the power system. Cluster analysis is performed on the multiple feature vectors and multiple feature values ​​to obtain the cluster analysis results and node importance index; Based on the cluster analysis results and the node importance index, multiple transmission sections of the power system are obtained; The vulnerability assessment indices of each branch of the power transmission section are summed to obtain the total vulnerability assessment index of each transmission section in the power system. The target transmission section with the highest total vulnerability assessment index among all transmission sections is identified to obtain the key transmission sections of the power system.

5. The method according to claim 4, characterized in that, The step of performing cluster analysis on the multiple feature vectors and multiple feature values ​​to obtain the cluster analysis results and node importance indicators includes: Using a preset spectral clustering algorithm, each node is clustered based on the electrical distance of the power system, and each clustered node is divided into multiple corresponding clusters. The preset spectral clustering algorithm is then used to cluster each cluster until the clustering results meet the preset clustering stopping condition, thereby obtaining the clustering analysis results and the node importance index.

6. A dynamic identification device for key transmission sections in a power system, characterized in that, include: The acquisition module is used to acquire the topological characteristics of the power system, power system parameters, initial power flow direction, and line load rate of the power system. The partitioning module is used to dynamically partition the power system based on the electrical characteristic data of the power system using a preset spectral clustering algorithm, thereby obtaining multiple transmission sections of the power system. The first calculation module includes a determination unit, used to determine the connection relationship of multiple branches of each transmission section in the power system based on the topological characteristics of the power system and the parameters of the power system. The first calculation unit is used to obtain the degree of influence of the target branch on other branches among the multiple branches according to the connection relationship of the multiple branches of each transmission section, and calculate the power distribution factor of each transmission section according to the degree of influence; the second calculation unit is used to calculate the power influence factor of the power system based on the power distribution factor and the initial power flow direction of the power system, and calculate the power transmission breadth influence factor and the power transmission depth influence factor of each transmission section of the power system according to the power influence factor, and calculate the branch vulnerability index of each transmission section of the power system according to the line load rate of the power system. The second calculation module is used to calculate the branch vulnerability assessment index for each transmission section in the power system based on the power transmission breadth influence factor, the power transmission depth influence factor, and the branch vulnerability index. The third calculation module is used to sum and calculate the branch vulnerability assessment index of each transmission section to obtain the key transmission sections of the power system.

7. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the dynamic identification method for critical transmission sections of a power system as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the dynamic identification method for critical transmission sections of a power system as described in any one of claims 1-5.

9. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the dynamic identification method for critical transmission sections of a power system as described in any one of claims 1-5.

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