Dynamic identification method and device for key power transmission section of power system and electronic equipment

Abstract

The invention particularly relates to a dynamic identification method and device for a key power transmission section of a power system and electronic equipment, and the method comprises the steps: carrying out the dynamic partitioning of the power system based on electrical characteristic data, and obtaining 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 the topological characteristics, the parameters and the initial power flow direction, and calculating a branch vulnerability index of each power transmission section in the power system according to a line load rate of the power system, and according to the power transmission breadth influence factor, the power transmission depth influence factor and the branch vulnerability index, calculating a branch vulnerability evaluation index of each power transmission section, and adding the branch vulnerability evaluation indexes to obtain a key power transmission section of the power system. Therefore, the problems that when a complex and changeable power system is processed, factors such as the uncertainty of the power system, the change of a power flow topological structure, network parameters and the initial power flow direction of the power system are ignored, and the uncertainty and complexity of the power system are increased are solved.

Description

Technical Field

[0001] The present application relates to the field of key section identification of power systems, and in particular to a method, device and electronic equipment for dynamically identifying key transmission sections of power systems. Background Art

[0002] With the rapid development of the economy and society and the gradual deepening of the "dual carbon" plan, the new power system is facing severe tests of 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 the basis.

[0003] The power system transmission section can be divided into the transmission channel section and the power flow transfer section according to the process of large-scale cascading failures, which paves the way for the subsequent analysis of the stability of the system section. Therefore, the main identification object is the transmission channel section. According to the research on the identification of key transmission sections at home and abroad, it is divided into three sub-problems: vulnerable branch identification, power system partitioning and transmission section vulnerability assessment for comprehensive consideration. Considering the current status of the new power system, the identification of key transmission sections needs to comprehensively consider the influence of important factors such as wind and solar uncertainty in the new power system, the power system power flow topology, network parameters and the initial power flow direction of the power system.

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

[0005] However, the above methods have limitations when dealing with complex and changeable power systems. For example, after the access of large-scale renewable energy (such as wind power and solar energy), factors such as the uncertainty of the new power system, changes in the power system's power flow topology, network parameters, and the initial power flow direction of the power system are often ignored. This will significantly increase the uncertainty and complexity of the power system, which needs to be solved urgently. Summary of the invention

[0006] The present application provides a method, device and electronic equipment for dynamically identifying key transmission sections of an electric power system, so as to solve the problem that when dealing with a complex and changeable electric power system, factors such as the uncertainty of the electric power system, changes in the power flow topology structure, network parameters and the initial power flow direction of the electric power system are ignored, thereby increasing the uncertainty and complexity of the electric power system.

[0007] The first embodiment of the present application provides a method for dynamically identifying a key transmission section of a power system, comprising the following steps:

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

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

[0010] Calculating the power transmission breadth influence factor of each transmission section in the power system and the power transmission depth influence factor of each transmission section in the power system according to the topological characteristics of the power system, the power system parameters and the initial power flow direction of the power system, and calculating the branch vulnerability index of each transmission section in the power system according to the line load rate of the power system;

[0011] Calculating a branch vulnerability assessment index of each transmission section in the power system according to the power transmission breadth impact factor, the power transmission depth impact factor and the branch vulnerability index;

[0012] The branch vulnerability assessment index of each transmission section is added and calculated to obtain the key transmission section of the power system.

[0013] According to an embodiment of the present application, the calculating, according to the topological characteristics of the power system, the power system parameters and the initial power flow direction of the power system, the power transmission breadth impact factor of each transmission section in the power system and the power transmission depth impact factor of each transmission section in the power system includes:

[0014] Determine the connection relationship of multiple branches of each transmission section in the power system according to the topological characteristics of the power system and the power system parameters;

[0015] Obtaining the influence degree of the target branch among the multiple branches on other branches according to the connection relationship of the multiple branches of each transmission section, and calculating the power distribution factor of each transmission section according to the influence degree;

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

[0017] According to an embodiment of the present application, the calculating of the branch vulnerability index of each transmission section in the power system according to the line load rate of the power system includes:

[0018] Generate a wind-solar output scenario of the power system using Monte Carlo simulation, wherein the wind speed in the wind-solar output scenario obeys Weibull distribution and the light obeys Beta distribution;

[0019] Calculate the load rate of the power system line under the wind and solar power output scenario by using a preset probabilistic power flow algorithm, so as to calculate the vulnerability index of each branch in each transmission section according to the load rate of the power system line;

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

[0021] According to an embodiment of the present application, the calculating of the branch vulnerability assessment index of each transmission section in the power system according to the power transmission breadth impact factor, the power transmission depth impact factor and the branch vulnerability index includes:

[0022] Mapping the power transmission breadth impact factor, the power transmission depth impact factor and the branch vulnerability index to a preset interval based on a preset normalization method;

[0023] According to the power system demand, the transmission medium and the weight of the vulnerability index of the transmission section are adjusted based on the preset interval to calculate the branch vulnerability assessment index of each transmission section in the power system.

[0024] According to an embodiment of the present application, the summing up of the branch vulnerability assessment indexes of each transmission section to obtain the key transmission section of the power system includes:

[0025] Calculating the similarity between each node according to the electrical characteristic data between each node in the power system, and constructing a node similarity graph of the power system;

[0026] Constructing a Laplacian matrix according to the node similarity graph, and performing spectral decomposition on the Laplacian matrix to obtain multiple eigenvectors and multiple eigenvalues ​​of the power system;

[0027] Performing cluster analysis on the multiple eigenvectors and the multiple eigenvalues ​​to obtain the cluster analysis result and the node importance index;

[0028] Obtaining multiple transmission sections of the power system according to the cluster analysis results and the node importance index;

[0029] The branch vulnerability assessment index of each transmission section is summed up to obtain a total vulnerability assessment index of each transmission section in the power system;

[0030] The target transmission section corresponding to the total vulnerability assessment index of each transmission section having the highest total vulnerability assessment index is identified to obtain the key transmission section of the power system.

[0031] According to an embodiment of the present application, performing cluster analysis on the multiple feature vectors and the multiple feature values ​​to obtain the cluster analysis results and the node importance index 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 a corresponding plurality of clusters, and each cluster is clustered continuously using the preset spectral clustering algorithm until the clustering result meets a preset clustering stop condition, thereby obtaining the clustering analysis result and the node importance index.

[0033] According to the dynamic identification method of the key transmission sections of the power system in the embodiment of the present application, the power system is dynamically partitioned 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, and the power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system are calculated according to the topological characteristics of the power system, the power system parameters and the initial power flow direction of the power system, and the branch vulnerability index of each transmission section in the power system is calculated according to the power system line load rate, and the branch vulnerability assessment index of each transmission section in the power system is calculated according to the power transmission breadth influence factor, the power transmission depth influence factor and the branch vulnerability index, and the branch vulnerability assessment index of each transmission section in the power system is added and calculated to obtain the key transmission section of the power system. As a result, the problem of ignoring the uncertainty of the power system, changes in the power flow topology, network parameters and the initial power flow direction of the power system when dealing with complex and changeable power systems, which increases the uncertainty and complexity of the power system, is solved. By combining the branch vulnerability assessment indicators of each transmission section with the power system zoning, the key transmission sections of the power system are identified, thereby improving the identification accuracy and reliability of the key transmission sections.

[0034] The second aspect of the present application provides a device for dynamically identifying a key transmission section of a power system, including:

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

[0036] A partitioning module, 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 a plurality of transmission sections of the power system;

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

[0038] A second calculation module is used to calculate the branch vulnerability assessment index of each transmission section in the power system according to 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 add up the branch vulnerability assessment indicators of each transmission section to obtain the key transmission section of the power system.

[0040] According to one embodiment of the present application, the first calculation module includes:

[0041] A determination unit, configured to determine a connection relationship between multiple branches of each transmission section in the power system according to the topological characteristics of the power system and the power system parameters;

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

[0043] The second calculation unit is used to calculate the power impact 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 impact factor of each transmission section in the power system and the power transmission depth impact factor of each transmission section in the power system according to the power impact factor.

[0044] According to one embodiment of the present application, the first calculation module includes:

[0045] A generating unit, configured to generate a wind-solar power output scenario of the power system by using Monte Carlo simulation, wherein the wind speed in the wind-solar power output scenario obeys Weibull distribution, and the light obeys Beta distribution;

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

[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 the present application, the second calculation module includes:

[0049] A mapping unit, configured to map the power transmission breadth impact factor, the power transmission depth impact factor and the branch vulnerability index to a preset interval 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 according to the power system demand and based on the preset interval to adjust the transmission medium and the weight of the vulnerability index of the transmission section.

[0051] According to one embodiment of the present application, the third calculation module includes:

[0052] A construction unit, configured to calculate the similarity between each node according to the electrical characteristic data between each node in the power system, and to construct a node similarity graph of the power system;

[0053] A spectral decomposition unit, used for constructing a Laplacian matrix according to the node similarity graph, and performing spectral decomposition on the Laplacian matrix to obtain a plurality of eigenvectors and a plurality of eigenvalues ​​of the power system;

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

[0055] An acquisition unit, configured to obtain a plurality of transmission sections of the power system according to the cluster analysis result and the node importance index;

[0056] a sixth calculation unit, configured to sum and calculate the branch vulnerability assessment index of each transmission section to obtain a total vulnerability assessment index of each transmission section in the power system;

[0057] The identification unit is used to identify the target transmission section corresponding to the highest total vulnerability assessment index among the total vulnerability assessment indexes of each transmission section, so as to obtain the key transmission section of the power system.

[0058] According to one embodiment of the present application, the cluster analysis unit includes:

[0059] A 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 a corresponding plurality of clusters, and continue to cluster each cluster using the preset spectral clustering algorithm until the clustering result meets a preset clustering stop condition, thereby obtaining the clustering analysis result and the node importance index.

[0060] According to the dynamic identification device of the key transmission section of the power system of the embodiment of the present application, the power system is dynamically partitioned based on the electrical characteristic data of the power system by using a preset spectral clustering algorithm to obtain multiple transmission sections of the power system, and the power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system are calculated according to the topological characteristics of the power system, the power system parameters and the initial power flow direction of the power system, and the branch vulnerability index of each transmission section in the power system is calculated according to the line load rate of the power system, and the branch vulnerability assessment index of each transmission section in the power system is calculated according to the power transmission breadth influence factor, the power transmission depth influence factor and the branch vulnerability index, and the branch vulnerability assessment index of each transmission section in the power system is added and calculated to obtain the key transmission section of the power system. As a result, the problem of ignoring the uncertainty of the power system, changes in the power flow topology, network parameters and the initial power flow direction of the power system when dealing with complex and changeable power systems, which increases the uncertainty and complexity of the power system, is solved. By combining the branch vulnerability assessment indicators of each transmission section with the power system zoning, the key transmission sections of the power system are identified, thereby improving the identification accuracy and reliability of the key transmission sections.

[0061] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for dynamically identifying key transmission sections of a power system as described in the above embodiment.

[0062] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the method for dynamically identifying key transmission sections of a power system as described in the above embodiment.

[0063] A fifth aspect of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the method described in the above embodiment.

[0064] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0066] Figure 1 A flowchart of a method for dynamically identifying a key transmission section of a power system provided according to an embodiment of the present application;

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

[0068] Figure 3 A structural diagram of the EEE-39 node New England system according to an embodiment of the present application;

[0069] Figure 4 A topological diagram of the initial power flow direction of the system according to an embodiment of the present application;

[0070] Figure 5 A schematic diagram of average vulnerability indicators of each branch of a 39-node system according to an embodiment of the present application;

[0071] Figure 6 A schematic diagram of system partitioning based on a spectral clustering algorithm according to an embodiment of the present application;

[0072] Figure 7 A three-dimensional diagram for critical section identification based on a spectral clustering recursive method according to one embodiment of the present application;

[0073] Figure 8 is a block diagram of an exemplary device for dynamically identifying a key transmission section of a power system according to an embodiment of the present application;

[0074] Fig. 9 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

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

[0076] The following describes a method, device and electronic device for dynamically identifying key transmission sections of an electric power system according to an embodiment of the present application with reference to the accompanying drawings. In view of the problem that when processing a complex and changeable electric power system mentioned in the above background technology, factors such as uncertainty of the electric power system, changes in power flow topology, network parameters and initial power flow direction of the electric power system are ignored, thereby increasing the uncertainty and complexity of the electric power system, the present application provides a method for dynamically identifying key transmission sections of an electric power system. In the method, a preset spectral clustering algorithm is used to dynamically partition the electric power system based on electrical characteristic data of the electric power system to obtain multiple transmission sections of the electric power system. The power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the electric power system are calculated according to the topological characteristics of the electric power system, the parameters of the electric power system and the initial power flow direction of the electric power system, and the branch vulnerability index of each transmission section in the electric power system is calculated according to the line load rate of the electric power system. The branch vulnerability assessment index of each transmission section in the electric power system is calculated according to the power transmission breadth influence factor, the power transmission depth influence factor and the branch vulnerability index. The branch vulnerability assessment index of each transmission section in the electric power system is summed up to obtain the key transmission section of the electric power system. As a result, the problem of ignoring the uncertainty of the power system, changes in the power flow topology, network parameters and the initial power flow direction of the power system when dealing with complex and changeable power systems, which increases the uncertainty and complexity of the power system, is solved. By combining the branch vulnerability assessment indicators of each transmission section with the power system zoning, the key transmission sections of the power system are identified, thereby improving the identification accuracy and reliability of the key transmission sections.

[0077] Specifically, Figure 1 A flowchart of a method for dynamically identifying key transmission sections of a power system provided in an embodiment of the present application.

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

[0079] In step S101, the topological characteristics of the power system, the power system parameters, the initial power flow direction of the power system and the 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 to obtain a plurality of transmission sections of the power system.

[0081] Among them, the preset spectral clustering algorithm can be selected by the present inventor and technical personnel according to actual test requirements, and is not specifically limited here.

[0082] Specifically, in an embodiment of the present application, in order to solve the problem that in the related technology, when dealing with a complex and changeable power system, factors such as the uncertainty of the power system, changes in the power flow topology structure, network parameters and the initial power flow direction of the power system are ignored, thereby increasing the uncertainty and complexity of the power system, the embodiment of the present application is based on the general framework of the improved betweenness method in the complex graph theory, and rewrites the power system transmission betweenness based on the power flow influencing factor and the branch vulnerability index to obtain a more reliable system branch vulnerability index that is more in line with the current status of the system, and dynamically and reasonably partitions the power system in the form of spectral clustering to obtain the system transmission section and its affiliated branches. Finally, the above-mentioned vulnerability index is combined with the system transmission section to obtain the key transmission section of the power system.

[0083] Therefore, if Figure 2 and Figure 3 As shown, the embodiment of the present application needs to dynamically partition the power system based on the electrical characteristic data of the power system, such as electrical distance, based on the topological characteristics of the power system, the power system parameters, the initial power flow direction of the power system and the line load rate of the power system, and utilize a preset spectral clustering algorithm to obtain multiple transmission sections of the power system, thereby identifying the key transmission sections of the power system based on the vulnerability indicators in the multiple transmission sections, and finally obtaining the key transmission sections of the power system. The embodiment of the present application verifies the proposed method based on the IEEE-39 node New England system as an example.

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

[0085] According to an embodiment of the present application, a power transmission breadth influence factor of each transmission section in the power system and a power transmission depth influence factor of each transmission section in the power system are calculated according to the topological characteristics of the power system, the power system parameters and the initial power flow direction of the power system, including: determining the connection relationship of multiple branches in each transmission section in the power system according to the topological characteristics of the power system and the power system parameters; obtaining the degree of influence of a target branch among the multiple branches on other branches according to the connection relationship of the multiple branches of each transmission section, and calculating the power distribution factor of each transmission section according to the degree of influence; 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 respectively calculating the power transmission breadth influence factor of each transmission section in the power system and the power transmission depth influence factor of each transmission section in the power system according to the power influence factor.

[0086] According to one embodiment of the present application, a branch vulnerability index of each transmission section in a power system is calculated according to the load rate of the power system line, including: using Monte Carlo simulation to generate a wind and solar power output scenario of the power system, wherein the wind speed in the wind and solar power output scenario obeys the Weibull distribution, and the light obeys the Beta distribution; using a preset probabilistic power flow algorithm to calculate the load rate of the power system line under the wind and solar power output scenario, so as to calculate the vulnerability index of each branch in each transmission section according to the load rate of the power system line; and calculating 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] Among them, the preset probabilistic power flow algorithm can be selected by the company and technical personnel according to actual test requirements, and is not specifically limited here.

[0088] Specifically, in the embodiment of the present application, the connection relationship of multiple branches of each transmission section in the power system is first determined according to the topological characteristics of the power system and the power system parameters, and then the influence degree of the target branch on other branches in the multiple branches is obtained according to the connection relationship of the multiple branches of each transmission section, and the power distribution factor of each transmission section is calculated according to the influence degree, wherein the power distribution factor is defined as the influence degree of the power transmission change between nodes on all other branches, and its expression can be expressed as:

[0089]

[0090] Among them, ΔP km is the power change on the disconnected branch km, ΔP ij For ΔP km Affecting the lower branch ij The power change on ik is the element in the i-th row and k-th column of the node reactance matrix, x ij is the branch reactance value, x im is the element in the i-th row and m-th column of the node reactance matrix, x jk is the element in the jth row and kth column of the node reactance matrix, x jm is the element in the jth row and mth column of the node reactance matrix.

[0091] Secondly, if Figure 4 As shown, the embodiment of the present application is based on the power distribution factor calculated above and the initial power flow direction of the power system to calculate the power distribution factor that has a positive impact on other branches in the power system, that is, the power impact factor, and the main expression is as follows:

[0092]

[0093] Among them, f k(l) represents the positive impact value of the power distribution factor of branch l when line k is disconnected, where l is the affected branch l ij , k is the disconnected branch l km .

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

[0095]

[0096] Among them, f k (l) represents the positive impact value of the power distribution factor of branch l when line k is disconnected, where l is the affected branch l ij , k is the disconnected branch l km , T km (l) is the aggregate branch l to T km The membership degree, T km For the start branch l km A collection of lines with greater impact, where the lines are disconnected in pairs. km The power impact factor is greater than 0.2 to ensure the sufficiency of the lines in the set and to find all the expected branches. km || represents the set T km The modulus value is equal to T km The total number of branches contained in , a(k) reflects the degree of influence on the system after the branch k is disconnected, and b(k) reflects the scope of influence on the system after the branch k is disconnected. The larger the a(k) value, the greater the impact on the system after the branch is disconnected, and the larger the b(k) value, the wider the scope of influence after the branch is disconnected.

[0097] Furthermore, the embodiment of the present application also needs to use Monte Carlo simulation to generate wind and solar power output scenarios of the power system, and obtain 1000 wind and solar power output scenarios. The wind speed in the wind and solar power output scenarios obeys the Weibull distribution, and the light obeys the Beta distribution. Among them, the photovoltaic output model can be expressed as:

[0098]

[0099] CF=η τ *η t *η n *η l

[0100] Among them, G S is the light intensity in the simulated scene, CF is the conversion efficiency of photovoltaic modules to convert solar energy into electrical energy, P PVCis the total installed capacity of the photovoltaic system (W), G is the sunshine intensity under standard conditions, G = 1000Wm2, η τ is the photovoltaic module conversion efficiency and temperature correction coefficient, η t is the azimuth and inclination correction factor for PV panel installation, η n is the inverter efficiency of the photovoltaic system, η l is the line correction factor of the photovoltaic system.

[0101] The wind power output model can be expressed as:

[0102] 0,v≤v in ,v≥v cut

[0103] P W ={a+b*v,v in <v<v m

[0104] P m , v m <v<v cut

[0105] Among them, v in is the cut-in wind speed, which means the minimum wind speed at which the wind turbine starts to generate power. cut is the cut-out wind speed, which means the critical wind speed that disconnects the wind turbine from the grid. a and b are the parameters of the functional relationship between wind turbine output and wind speed. m is the rated output power of the wind turbine, v m is the rated wind speed of the wind turbine.

[0106] Furthermore, if Figure 5 As shown, the embodiment of the present application uses a preset probability flow algorithm to obtain the probability distribution of line active power, that is, to calculate the load rate of the power system line under the wind and solar power output scenario, so as to calculate the vulnerability index of each branch in each transmission section according to the load rate of the power system line, and 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, that is, the average vulnerability index of each branch of the 39-node system, as shown in Table 1:

[0107] Table 1

[0108]

[0109]

[0110] Among them, the branch vulnerability index of each transmission section in the power system evaluates the vulnerability of the power system line by calculating the load rate of each line, that is, the ratio of the line power flow to its rated capacity. The specific formula is as follows:

[0111]

[0112] Among them, W li is the active power of the road, P li is the rated capacity of the line. Combined with the probabilistic power flow calculation results, the vulnerability index is calculated according to the line load rate, and the overall vulnerability of the power grid system is evaluated by calculating the average vulnerability index.

[0113] In step S104, the branch vulnerability assessment index of each transmission section in the power system is calculated according to the power transmission breadth influence factor, the power transmission depth influence factor and the branch vulnerability index.

[0114] According to one embodiment of the present application, a branch vulnerability assessment index of each transmission section in the power system is calculated based on a power transmission breadth influence factor, a power transmission depth influence factor and a branch vulnerability index, including: 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; according to the power system demand, adjusting the transmission intermediate number and the weight of the vulnerability index of the transmission section based on the preset interval to calculate the branch vulnerability assessment index of each transmission section in the power system.

[0115] Among them, the preset normalization method can be selected by technical personnel in this field according to actual test requirements and is not specifically limited here.

[0116] Specifically, after calculating the branch vulnerability index of each transmission section, the embodiment of the present application calculates the branch vulnerability assessment index of 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. First, 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 interval. For example, the power transmission depth factor, the power transmission breadth factor and the branch vulnerability index are mapped to a dimensionless decimal between 0 and 1 through feature scaling by a min-max normalization method, wherein the transformation function is:

[0117]

[0118] Secondly, the weights of the normalized parameters are flexibly changed according to the needs of the power system, and finally the weighted addition is performed to obtain the branch vulnerability assessment index of each transmission section, as shown in Table 2:

[0119] Table 2

[0120]

[0121]

[0122] The specific formula is as follows:

[0123]

[0124] in, To improve the transmission betweenness index, is the vulnerability index, μ3 and μ4 are the weights corresponding to the index, and in the example of this application, the values ​​are selected as 0.2 and 0.8 respectively. It is believed that the weight of the vulnerability index is much higher than the improved transmission medium. The setting principle is that the vulnerability index can comprehensively consider multiple factors under different operating conditions, dynamically reflect the changes in the system state, and provide real-time vulnerability assessment. More importantly, the high-weighted vulnerability index enables the assessment model to quickly reflect the increase in vulnerability caused by changes in the system state, and support operators to make quick decisions to prevent the spread of faults and reduce the impact of accidents.

[0125] In step S105, the branch vulnerability assessment index of each transmission section is summed up and calculated to obtain the key transmission section of the power system.

[0126] According to an embodiment of the present application, the branch vulnerability assessment index of each transmission section is added and calculated to obtain the key transmission section of the power system, including: calculating the similarity between each node according to the electrical characteristic data between each node in the power system, and constructing a node similarity graph of the power system; constructing a Laplacian matrix according to the node similarity graph, and spectrally decomposing the Laplacian matrix to obtain multiple eigenvectors and multiple eigenvalues ​​of the power system; performing cluster analysis on the multiple eigenvectors and multiple eigenvalues ​​to obtain cluster analysis results and node importance indicators; obtaining multiple transmission sections of the power system according to the cluster analysis results and the node importance indicators; adding and calculating the branch vulnerability assessment index of each transmission section to obtain the total vulnerability assessment index of each transmission section in the power system; identifying the corresponding target transmission section with the highest total vulnerability assessment index among the total vulnerability assessment indicators of each transmission section, and obtaining the key transmission section of the power system.

[0127] According to one embodiment of the present application, a cluster analysis is performed on multiple eigenvectors and multiple eigenvalues ​​to obtain a cluster analysis result and a node importance index, including: using a preset spectral clustering algorithm to cluster each node based on the electrical distance of the power system, and dividing each clustered node into a corresponding plurality of clusters, and continuing to cluster each cluster using the preset spectral clustering algorithm until the clustering result meets a preset clustering stop condition, thereby obtaining a cluster analysis result and a node importance index.

[0128] The preset clustering stop condition may be selected by those skilled in the art according to actual test requirements and is not specifically limited here.

[0129] Specifically, first, the similarity between each node is calculated based on the electrical characteristic data between each node in the power system, such as the voltage, power, load and other data between each node in the power system, 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, which is used to describe the structure and connection relationship of a graph and can reflect the relationship between nodes, including the connection relationship and the degree of influence. The Laplacian matrix calculation includes the calculation of the degree matrix and the adjacency matrix. Their main features are: (1) Degree Matrix: The degree matrix is ​​a diagonal matrix whose elements on the 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: The degree matrix is ​​a diagonal matrix whose elements on the 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. 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 by A, where A(i,j) represents the connection relationship between node i and node j. Its element 1 indicates connection and 0 indicates disconnection. (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 by L = D – A. In the case of a directed graph or a weighted graph, other forms of Laplacian matrices can also be considered, such as the symmetric normalized Laplacian matrix. The formula is as follows:

[0130] L=ID ^ (-1 / 2)*A*D ^ (-1 / 2)

[0131] Among them, I is the identity matrix, A is the adjacency matrix between nodes, and D ∧ (-1 / 2) is the negative square root of the degree matrix.

[0132] Again, if Figure 6As shown in the figure, the Laplacian matrix is ​​spectrally decomposed to obtain multiple eigenvectors and multiple eigenvalues ​​of the power system. The original data is reduced in dimension by spectral decomposition, and the main features of the data are extracted. Different areas or clusters in the system can be determined, which is helpful for subsequent clustering analysis. Then, according to the eigenvectors obtained by spectral decomposition, appropriate eigenvectors can be selected as new data representations. Then, the spectral clustering algorithm is used to cluster the nodes, and each clustered node is divided into multiple corresponding clusters. Each cluster represents a set of nodes with high correlation, which may correspond to key transmission sections. In the clustering process, a recursive method can be adopted, that is, after each clustering, the spectral clustering algorithm is applied to each cluster again until the clustering result meets the preset clustering stop condition (such as reaching the predetermined number of clusters), and the clustering analysis results and node importance indicators are obtained.

[0133] Finally, if Figure 7 As shown in the figure, according to the clustering analysis results and the node importance index, multiple transmission sections of the power system are obtained. The transmission section usually has important transmission function or connection function in the power system, and its fault or failure may have a great impact on the stability of the system. Therefore, in the clustering results, multiple transmission sections of the power system can be identified according to the characteristics of the cluster and the node importance index, and then the branch vulnerability assessment index of each transmission section is added and calculated to obtain the total vulnerability assessment index of each transmission section in the power system, and then the corresponding target transmission section with the highest total vulnerability assessment index in the total vulnerability assessment index of each transmission section is identified to obtain the key transmission section of the power system.

[0134] Therefore, the vulnerability assessment of the system in the embodiment of the present application not only takes into account the basic network topology and system parameters of the traditional power grid, but also comprehensively considers the initial power flow direction of the system and the fluctuation of branch loads 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 artificial experience while complying with the definition of the transmission section of the transmission channel, and can meet the needs of real-time dynamic adjustment of the system, providing basic technical support for the subsequent system stability analysis based on the key transmission sections of the power system and the evaluation of the maximum transmission capacity range.

[0135] In summary, this application can achieve the following beneficial effects:

[0136] (1) Improve the accuracy of identifying key transmission sections: By combining the spectral clustering algorithm with the node importance index, the global structural characteristics of the system are captured, and regions with similar electrical characteristics (i.e., transmission sections) are identified. At the same time, combined with the node importance index, the identification of transmission sections is further refined, and the key transmission sections in the power system can be identified more accurately.

[0137] (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;

[0138] (3) Improve system stability and reliability: By identifying vulnerable links and optimizing scheduling and maintenance, cascading failures can be effectively prevented and the stability and reliability of the power system can be improved;

[0139] (4) Reduce computational complexity and improve computational efficiency: By simplifying the model, normalizing the process, and using reasonable recursive clustering termination conditions, the computational complexity can be significantly reduced and the computational efficiency can be improved.

[0140] (5) Wide range of application scenarios: It is applicable to a variety of power systems and different operating scenarios, and can provide effective technical support for the dispatching, planning and operation and maintenance of power systems.

[0141] According to the dynamic identification method of the key transmission sections of the power system in the embodiment of the present application, the power system is dynamically partitioned 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, and the power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system are calculated according to the topological characteristics of the power system, the power system parameters and the initial power flow direction of the power system, and the branch vulnerability index of each transmission section in the power system is calculated according to the power system line load rate, and the branch vulnerability assessment index of each transmission section in the power system is calculated according to the power transmission breadth influence factor, the power transmission depth influence factor and the branch vulnerability index, and the branch vulnerability assessment index of each transmission section in the power system is added and calculated to obtain the key transmission section of the power system. As a result, the problem of ignoring the uncertainty of the power system, changes in the power flow topology, network parameters and the initial power flow direction of the power system when dealing with complex and changeable power systems, which increases the uncertainty and complexity of the power system, is solved. By combining the branch vulnerability assessment indicators of each transmission section with the power system zoning, the key transmission sections of the power system are identified, thereby improving the identification accuracy and reliability of the key transmission sections.

[0142] Next, a dynamic identification device for key transmission sections of a power system proposed according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0143] Figure 8 It is a block diagram of a device for dynamically identifying key transmission sections of a power system according to an embodiment of the present application.

[0144] 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.

[0145] The acquisition module 100 is used to acquire the topological characteristics of the power system, the power system parameters, the initial power flow direction of the power system and the line load rate of the power system;

[0146] A 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;

[0147] The first calculation module 300 is used to calculate the power transmission breadth influence factor of each transmission section in the power system and the power transmission depth influence factor of each transmission section in the power system according to the topological characteristics of the power system, the power system parameters and the initial power flow direction of the power system, and calculate the branch vulnerability index of each transmission section in the power system according to the line load rate of the power system;

[0148] A second calculation module 400, for calculating a branch vulnerability assessment index of each transmission section in the power system according to the power transmission breadth influence factor, the power transmission depth influence factor and the branch vulnerability index;

[0149] The third calculation module 500 is used to sum up the branch vulnerability assessment index of each transmission section to obtain the key transmission section of the power system.

[0150] According to one embodiment of the present application, the first calculation module 300 includes:

[0151] A determination unit, used to determine the connection relationship of multiple branches of each transmission section in the power system according to the topological characteristics of the power system and the power system parameters;

[0152] A first calculation unit is used to obtain the influence degree of the target branch among the multiple branches on other 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 influence degree;

[0153] 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 of each transmission section in the power system and the power transmission depth influence factor of each transmission section in the power system according to the power influence factor.

[0154] According to one embodiment of the present application, the first calculation module 300 includes:

[0155] A generating unit, used for generating a wind-solar output scenario of a power system by using Monte Carlo simulation, wherein the wind speed in the wind-solar output scenario obeys a Weibull distribution, and the light obeys a Beta distribution;

[0156] A third calculation unit is used to calculate the load rate of the power system line under the wind and solar power output scenario by using a preset probabilistic power flow algorithm, so as to calculate the vulnerability index of each branch in each transmission section according to the load rate of the power system line;

[0157] 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.

[0158] According to one embodiment of the present application, the second calculation module 400 includes:

[0159] A mapping unit, used to map 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;

[0160] The fifth calculation unit is used to calculate the branch vulnerability assessment index of each transmission section in the power system according to the power system demand and based on the preset interval to adjust the transmission medium and the weight of the vulnerability index of the transmission section.

[0161] According to one embodiment of the present application, the third computing module 500 includes:

[0162] A construction unit, used for calculating the similarity between each node according to the electrical characteristic data between each node in the power system, and constructing a node similarity graph of the power system;

[0163] A spectral decomposition unit is used to construct a Laplacian matrix according to the node similarity graph, and to perform spectral decomposition on the Laplacian matrix to obtain multiple eigenvectors and multiple eigenvalues ​​of the power system;

[0164] A cluster analysis unit, used to perform cluster analysis on multiple eigenvectors and multiple eigenvalues ​​to obtain cluster analysis results and node importance indicators;

[0165] An acquisition unit, used for obtaining multiple transmission sections of the power system according to cluster analysis results and node importance indicators;

[0166] A sixth calculation unit, used for summing up the branch vulnerability assessment index of each transmission section to obtain a total vulnerability assessment index of each transmission section in the power system;

[0167] The identification unit is used to identify the target transmission section corresponding to the highest total vulnerability assessment index among the total vulnerability assessment indexes of each transmission section, and obtain the key transmission section of the power system.

[0168] According to one embodiment of the present application, the cluster analysis unit includes:

[0169] 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 a corresponding plurality of clusters, and continue to cluster each cluster using the preset spectral clustering algorithm until the clustering result meets the preset clustering stop condition, thereby obtaining the clustering analysis result and the node importance index.

[0170] According to the dynamic identification device of the key transmission section of the power system of the embodiment of the present application, the power system is dynamically partitioned based on the electrical characteristic data of the power system by using a preset spectral clustering algorithm to obtain multiple transmission sections of the power system, and the power transmission breadth influence factor and the power transmission depth influence factor of each transmission section in the power system are calculated according to the topological characteristics of the power system, the power system parameters and the initial power flow direction of the power system, and the branch vulnerability index of each transmission section in the power system is calculated according to the line load rate of the power system, and the branch vulnerability assessment index of each transmission section in the power system is calculated according to the power transmission breadth influence factor, the power transmission depth influence factor and the branch vulnerability index, and the branch vulnerability assessment index of each transmission section in the power system is added and calculated to obtain the key transmission section of the power system. As a result, the problem of ignoring the uncertainty of the power system, changes in the power flow topology, network parameters and the initial power flow direction of the power system when dealing with complex and changeable power systems, which increases the uncertainty and complexity of the power system, is solved. By combining the branch vulnerability assessment indicators of each transmission section with the power system zoning, the key transmission sections of the power system are identified, thereby improving the identification accuracy and reliability of the key transmission sections.

[0171] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0172] A memory 901 , a processor 902 , and a computer program stored in the memory 901 and executable on the processor 902 .

[0173] When the processor 902 executes the program, the method for dynamically identifying key transmission sections of the power system provided in the above embodiment is implemented.

[0174] Furthermore, the electronic device further comprises:

[0175] The communication interface 903 is used for communication between the memory 901 and the processor 902 .

[0176] The memory 901 is used to store computer programs that can be executed on the processor 902 .

[0177] The memory 901 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0178] If the memory 901, the processor 902 and the communication interface 903 are implemented independently, the communication interface 903, the memory 901 and the processor 902 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0179] Optionally, in a specific implementation, if the memory 901, the processor 902 and the communication interface 903 are integrated on a chip, the memory 901, the processor 902 and the communication interface 903 can communicate with each other through an internal interface.

[0180] The processor 902 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0181] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for dynamically identifying key transmission sections of a power system.

[0182] An embodiment of the present application also provides a computer program product, including a computer program / instruction, which implements the steps of the method in the above embodiment when executed by a processor.

[0183] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0184] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0185] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for dynamically identifying key transmission sections of a power system, characterized in that: The following steps are involved: Obtaining the topological characteristics of the power system, power system parameters, the initial power flow direction of the power system and the line load rate of the power system; Using a preset spectral clustering algorithm, dynamically partitioning the power system based on electrical characteristic data of the power system to obtain multiple transmission sections of the power system; Calculating the power transmission breadth influence factor of each transmission section in the power system and the power transmission depth influence factor of each transmission section in the power system according to the topological characteristics of the power system, the power system parameters and the initial power flow direction of the power system, and calculating the branch vulnerability index of each transmission section in the power system according to the line load rate of the power system; Calculating a branch vulnerability assessment index of each transmission section in the power system according to the power transmission breadth impact factor, the power transmission depth impact factor and the branch vulnerability index; The branch vulnerability assessment index of each transmission section is added and calculated to obtain the key transmission section of the power system.

2. The method according to claim 1, characterized in that The calculating, according to the topological characteristics of the power system, the power system parameters and the initial power flow direction of the power system, the power transmission breadth influence factor of each transmission section in the power system and the power transmission depth influence factor of each transmission section in the power system comprises: Determine the connection relationship of multiple branches of each transmission section in the power system according to the topological characteristics of the power system and the power system parameters; Obtaining the influence degree of the target branch among the multiple branches on other branches according to the connection relationship of the multiple branches of each transmission section, and calculating the power distribution factor of each transmission section according to the influence degree; The power impact factor of the power system is calculated based on the power distribution factor and the initial power flow direction of the power system, and the power transmission breadth impact factor of each transmission section in the power system and the power transmission depth impact factor of each transmission section in the power system are calculated according to the power impact factor.

3. The method according to claim 1, characterized in that The calculating of the branch vulnerability index of each transmission section in the power system according to the line load rate of the power system includes: Generate a wind-solar output scenario of the power system using Monte Carlo simulation, wherein the wind speed in the wind-solar output scenario obeys Weibull distribution and the light obeys Beta distribution; Calculate the load rate of the power system line under the wind and solar power output scenario by using a preset probabilistic power flow algorithm, so as to calculate the vulnerability index of each branch in each transmission section according to the load rate of the power system line; The vulnerability index of each branch is averaged and the branch vulnerability index of each transmission section in the power system is obtained.

4. The method according to claim 1, characterized in that: The calculating the branch vulnerability assessment index of each transmission section in the power system according to the power transmission breadth influence factor, the power transmission depth influence factor and the branch vulnerability index comprises: Mapping the power transmission breadth impact factor, the power transmission depth impact factor and the branch vulnerability index to a preset interval based on a preset normalization method; According to the power system demand, the transmission medium and the weight of the vulnerability index of the transmission section are adjusted based on the preset interval to calculate the branch vulnerability assessment index of each transmission section in the power system.

5. The method according to claim 1, characterized in that The branch vulnerability assessment index of each transmission section is summed up and calculated to obtain the key transmission section of the power system, including: Calculating the similarity between each node according to the electrical characteristic data between each node in the power system, and constructing a node similarity graph of the power system; Constructing a Laplacian matrix according to the node similarity graph, and performing spectral decomposition on the Laplacian matrix to obtain multiple eigenvectors and multiple eigenvalues ​​of the power system; Performing cluster analysis on the multiple eigenvectors and the multiple eigenvalues ​​to obtain the cluster analysis result and the node importance index; Obtaining multiple transmission sections of the power system according to the cluster analysis results and the node importance index; The branch vulnerability assessment index of each transmission section is summed up to obtain a total vulnerability assessment index of each transmission section in the power system; The target transmission section corresponding to the total vulnerability assessment index of each transmission section having the highest total vulnerability assessment index is identified to obtain the key transmission section of the power system.

6. The method according to claim 5, characterized in that The clustering analysis is performed on the multiple feature vectors and the multiple feature values ​​to obtain the clustering analysis result and the node importance index, including: 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 a corresponding plurality of clusters, and each cluster is clustered continuously using the preset spectral clustering algorithm until the clustering result meets a preset clustering stop condition, thereby obtaining the clustering analysis result and the node importance index.

7. A dynamic identification device for key transmission sections of a power system, characterized in that: include: An acquisition module, used to acquire the topological characteristics of the power system, power system parameters, the initial power flow direction of the power system and the line load rate of the power system; A partitioning module, 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 a plurality of transmission sections of the power system; A first calculation module is used to calculate the power transmission breadth influence factor of each transmission section in the power system and the power transmission depth influence factor of each transmission section in the power system according to the topological characteristics of the power system, the power system parameters and the initial power flow direction of the power system, and calculate the branch vulnerability index of each transmission section in the power system according to the line load rate of the power system; A second calculation module is used to calculate the branch vulnerability assessment index of each transmission section in the power system according to the power transmission breadth influence factor, the power transmission depth influence factor and the branch vulnerability index; The third calculation module is used to add up the branch vulnerability assessment indicators of each transmission section to obtain the key transmission section of the power system.

8. An electronic device, characterized in that: include: 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 method for dynamically identifying key transmission sections of a power system as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for dynamically identifying key transmission sections of a power system as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the method for dynamically identifying key transmission sections of a power system as described in any one of claims 1-6.

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