Method and device for identifying weak area of power grid, electronic equipment and storage medium

Abstract

The invention discloses a power grid weak area identification method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining plant stations and power transmission lines in a power grid as nodes and edges of a power grid graph theory model, and constructing an adjacent matrix of an undirected weighted graph according to the connection relation between the nodes; then, calculating a branch admittance amplitude between the nodes, and taking the branch admittance amplitude as an edge weight between the nodes in the undirected weighted graph; and repeatedly executing the edge contraction operation until a preset number of times is reached, and obtaining the undirected weighted graph after contraction is completed. And partitioning the power grid based on the undirected weighted graph to obtain a plurality of power grid partitions. And calculating a node short-circuit ratio mean value for each subarea, and identifying a weak area of the power grid. By implementing the method, the weak area of the power grid can be effectively identified, a basis is provided for reasonable configuration of the reactive compensation device, and the safety and stability of the power grid are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of identifying weak areas in power grids, and in particular to a method, a device, an electronic device and a storage medium for identifying weak areas in power grids. Background Art

[0002] With the transformation of the global energy structure and the large-scale grid connection of high-penetration renewable energy, the voltage support strength of the power system has decreased, and the safety and stability risks faced by the new power system have increased. In the power system, identifying the weak areas of the power grid and rationally configuring reactive compensation devices are key links to ensure the safe and stable operation of the system. Existing technologies usually rely on steady-state power flow analysis to partition the power grid to identify areas with weak voltage support capabilities in the power grid, that is, weak areas of the power grid. However, in the power grid planning stage, since the distribution of reactive power has not been fully determined, it is difficult to obtain a convergent steady-state power flow, and it is impossible to effectively identify weak areas of the power grid, which affects the overall safety of the power system. Summary of the invention

[0003] The embodiments of the present invention provide a method, device, electronic device and storage medium for identifying weak areas of a power grid. By implementing the present invention, accurate division of power grid areas can be achieved, weak areas of the power grid can be effectively identified, and thus the safety and stability of the power system can be ensured.

[0004] An embodiment of the present invention provides a method for identifying a weak area of ​​a power grid, comprising:

[0005] The power plants and transmission lines in the power grid are obtained as the nodes and edges of the undirected weighted graph corresponding to the power grid graph theory model.

[0006] Get the connection relationship between nodes and construct the adjacency matrix of the undirected weighted graph based on the connection relationship between nodes.

[0007] The branch admittance amplitude between nodes is calculated and used as the edge weight between nodes in the undirected weighted graph.

[0008] The edge contraction operation is repeated until the preset number of times is reached to obtain an undirected weighted graph after contraction.

[0009] The power grid is partitioned according to the undirected weighted graph after the contraction, and a plurality of partitions of the power grid are obtained.

[0010] For each partition, the mean value of the node short circuit ratio of each partition is calculated.

[0011] The node short-circuit ratio mean of each partition is sorted from small to large, and the first M partitions are selected as the weak areas of the power grid.

[0012] The edge shrinking operation includes:

[0013] Traverse all the edges in the current undirected weighted graph from large to small, and sort all the edges according to the size of the edge weights.

[0014] Each edge is extracted one by one according to the above order. When each edge is extracted, it is determined whether there are nodes marked as super nodes on both sides of the current edge. If so, the next edge is extracted; if not, both nodes on both sides of the current edge are marked as super nodes, and the current edge is contracted to merge the two super nodes to obtain a merged super node.

[0015] The edges between two merged super nodes that are connected by multiple edges are merged to obtain an updated undirected weighted graph.

[0016] Furthermore, the adjacency matrix of the undirected weighted graph is constructed by the following steps:

[0017] Get the connection relationship between nodes and determine whether there is an edge connecting the two nodes; if there is an edge connecting the two nodes, set the corresponding element in the adjacency matrix of the undirected weighted graph to 1; if there is no edge connecting the two nodes, set the corresponding element in the adjacency matrix of the undirected weighted graph to 0.

[0018] Furthermore, the edge weights between nodes in the undirected weighted graph are obtained through the following steps:

[0019] Get the non-zero elements in the adjacency matrix, and obtain the nodes on both sides of the edge corresponding to the non-zero elements as non-zero nodes.

[0020] The branch admittance between non-zero nodes and the conductance between nodes are obtained, the square of the branch admittance is added to the square of the conductance, and then the square root operation is performed to obtain the edge weights between nodes in the undirected weighted graph.

[0021] Further, the preset number of times is obtained by the following steps:

[0022] Obtain the number of planned power grid partitions and the number of initial power grid nodes, calculate the ratio of the number of planned power grid partitions to the number of initial power grid nodes, and obtain a first value; use the first value as an independent variable, substitute it into a preset logarithmic function, and obtain a second value; take the opposite of the second value to obtain a preset number of times.

[0023] Furthermore, the mean value of the node short circuit ratio of each partition is obtained through the following steps:

[0024] According to the system rated voltage and the equivalent system impedance of each node, the short-circuit capacity of all nodes in each partition is calculated.

[0025] The node short circuit ratio of each node is calculated according to the ratio of the short circuit capacity of each node to the rated capacity of the node.

[0026] The node short circuit ratios of all nodes in each partition are obtained, and their average value is calculated to obtain the mean value of the node short circuit ratio of each partition.

[0027] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0028] An embodiment of the present invention provides a device for identifying a weak area in a power grid, including: a device for identifying a weak area in a power grid.

[0029] The graph theory model construction module is used to obtain power plants and transmission lines in the power grid as nodes and edges of the undirected weighted graph corresponding to the power grid graph theory model; obtain the connection relationship between the nodes, and construct the adjacency matrix of the undirected weighted graph according to the connection relationship between the nodes; calculate the branch admittance amplitude between the nodes, and use the branch admittance amplitude as the edge weight between the nodes in the undirected weighted graph.

[0030] The edge contraction module is used to repeatedly perform the edge contraction operation until a preset number of times is reached to obtain an undirected weighted graph after the contraction is completed; wherein, the edge contraction operation includes: traversing all edges in the current undirected weighted graph from large to small, and sorting all edges according to the size of the edge weights; extracting each edge one by one according to the sorting, and when each edge is extracted, judging whether there are nodes on both sides of the current edge that are marked as super nodes, if so, extracting the next edge; if not, marking the nodes on both sides of the current edge as super nodes, contracting the current edge to merge the two super nodes to obtain a merged super node; merging the edges between two merged super nodes that are connected by multiple edges to obtain an updated undirected weighted graph.

[0031] The power grid partitioning module is used to partition the power grid according to the undirected weighted graph after the contraction to obtain multiple partitions of the power grid.

[0032] The weak area identification module is used to calculate the node short circuit ratio mean of each partition for each partition; sort the node short circuit ratio mean of each partition from small to large, and select the first M partitions as the weak areas of the power grid.

[0033] Furthermore, the graph theory model building module includes: an adjacency matrix generation unit and an edge weight calculation unit.

[0034] The adjacency matrix unit is used to construct an adjacency matrix of an undirected weighted graph by the following steps:

[0035] Get the connection relationship between nodes and determine whether there is an edge connecting the two nodes; if there is an edge connecting the two nodes, set the corresponding element in the adjacency matrix of the undirected weighted graph to 1; if there is no edge connecting the two nodes, set the corresponding element in the adjacency matrix of the undirected weighted graph to 0.

[0036] The edge weight calculation unit is used to obtain the edge weights between nodes in the undirected weighted graph through the following steps, including:

[0037] Get the non-zero elements in the adjacency matrix and obtain the nodes on both sides of the edge corresponding to the non-zero elements.

[0038] The branch admittance and conductance between nodes are obtained, the square of the branch admittance and the square of the conductance are added together and then the square root operation is performed to obtain the edge weights between nodes in the undirected weighted graph.

[0039] Furthermore, the weak area identification module includes: a node short circuit ratio average value calculation unit.

[0040] The node short circuit ratio mean value calculation unit is used to obtain the node short circuit ratio mean value of each partition through the following steps:

[0041] According to the system rated voltage and the system equivalent impedance of each node to the outside, the short-circuit capacity of all nodes in each partition is calculated; according to the ratio of the short-circuit capacity of each node to the rated capacity of the node, the node short-circuit ratio of each node is calculated; the node short-circuit ratio of all nodes in each partition is obtained, and the average value is calculated to obtain the mean value of the node short-circuit ratio of each partition.

[0042] Based on the above method embodiment, the present invention provides a corresponding electronic device embodiment.

[0043] An embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for identifying weak areas of a power grid as described in any one of the above method embodiments can be implemented.

[0044] Based on the above method embodiment, the present invention provides a corresponding computer-readable storage medium embodiment.

[0045] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for identifying a weak area of ​​a power grid as described in any one of the above method embodiments can be implemented.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The embodiment of the present invention provides a method, device, electronic device and storage medium for identifying weak areas of a power grid. The plant and transmission lines in the power system are obtained as nodes and edges of a power grid graph model; the branch admittance amplitude between nodes is calculated and used as the edge weight between nodes. Based on the edge weight information, the edge contraction method is used to partition the power grid, which can effectively reflect the electrical connection strength between different areas. By calculating the mean short-circuit ratio of the nodes in each partition, the voltage support strength of each partition is quantified. The mean short-circuit ratios of the nodes in these partitions are sorted, and the weak areas of the power grid can be intuitively identified. Compared with the prior art, the present invention does not need to rely on convergent steady-state power flow, and realizes the accurate division of the power grid area through the edge contraction method. Based on the quantitative analysis of the mean short-circuit ratio of the nodes, the weak areas of the power grid can be effectively identified, providing a reliable basis for the reasonable configuration of the reactive power compensation device, and enhancing the safety and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flowchart of a method for identifying a weak area of ​​a power grid provided by an embodiment of the present invention.

[0049] Figure 2 It is a schematic diagram of an edge shrinking operation provided by an embodiment of the present invention.

[0050] Figure 3 It is a schematic diagram of the structure of a device for identifying weak areas of a power grid provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] like Figure 1 As shown, an embodiment of the present invention provides a method for identifying a weak area of ​​a power grid, comprising at least the following steps:

[0053] Step S1: obtaining power plants and transmission lines in the power grid as nodes and edges of an undirected weighted graph corresponding to a power grid graph theory model.

[0054] It should be noted that the plant is an electric power generation site, which may be a thermal power plant, a hydropower plant, a wind power plant, etc.

[0055] It can be understood that by abstracting the power plants and transmission lines in the power grid as nodes and edges in the graph theory model, the topological structure of the power grid can be effectively constructed, which not only simplifies the construction process of the power grid model, but also can intuitively reflect the electrical connections between the various components of the power grid.

[0056] Step S2: Obtain the connection relationship between nodes, and construct an adjacency matrix of the undirected weighted graph according to the connection relationship between nodes.

[0057] Specifically, in a preferred embodiment, the adjacency matrix of the undirected weighted graph is constructed by the following steps:

[0058] Get the connection relationship between nodes and determine whether there is an edge connecting the two nodes; if there is an edge connecting the two nodes, set the corresponding element in the adjacency matrix of the undirected weighted graph to 1; if there is no edge connecting the two nodes, set the corresponding element in the adjacency matrix of the undirected weighted graph to 0.

[0059] In actual operation, since the diagonal elements of the adjacency matrix represent the edges from the node to the node itself, and there is no connecting edge between the node and the node itself, the diagonal elements of the adjacency matrix are all set to 0.

[0060] It can be understood that by judging whether there are edges connecting nodes, a clear power grid topology is constructed to ensure the accurate association between nodes and edges. In addition, the construction steps of the adjacency matrix are simple and computationally efficient, which is suitable for processing large-scale power grid systems, further enhancing the accuracy and timeliness of identifying weak areas in the power grid.

[0061] Step S3: Calculate the branch admittance amplitude between nodes, and use the branch admittance amplitude as the edge weight between nodes in the undirected weighted graph.

[0062] Specifically, in a preferred embodiment, the edge weights between nodes in the undirected weighted graph are obtained by the following steps:

[0063] Get the non-zero elements in the adjacency matrix, and obtain the nodes on both sides of the edge corresponding to the non-zero elements as non-zero nodes.

[0064] The branch admittance between non-zero nodes and the conductance between nodes are obtained, the square of the branch admittance is added to the square of the conductance, and then the square root operation is performed to obtain the edge weights between nodes in the undirected weighted graph.

[0065] It can be understood that by calculating the branch admittance amplitude between nodes and using it as the edge weight in the undirected weighted graph, the electrical connection strength between nodes in the power grid can be more accurately reflected, the accuracy of power grid partitioning can be improved, the quantitative analysis of regional voltage support capacity can be enhanced, and a more accurate data basis can be provided for identifying weak areas of the power grid.

[0066] Step S4: Repeat the edge contraction operation until a preset number of times is reached to obtain an undirected weighted graph after contraction.

[0067] The edge shrinking operation includes:

[0068] Traverse all the edges in the current undirected weighted graph from large to small, and sort all the edges according to the size of the edge weights.

[0069] Each edge is extracted one by one according to the above order. When each edge is extracted, it is determined whether there are nodes marked as super nodes on both sides of the current edge. If so, the next edge is extracted; if not, both nodes on both sides of the current edge are marked as super nodes, and the current edge is contracted to merge the two super nodes to obtain a merged super node.

[0070] Merge the edges between two merged super nodes that are connected by multiple edges to obtain an updated undirected weighted graph. Preferably, the preset number of times is obtained by the following steps:

[0071] Obtain the number of planned power grid partitions and the number of initial power grid nodes, calculate the ratio of the number of planned power grid partitions to the number of initial power grid nodes, and obtain a first value; use the first value as an independent variable, substitute it into a preset logarithmic function, and obtain a second value; take the opposite of the second value to obtain a preset number of times.

[0072] Exemplarily, the preset logarithmic function may be a logarithmic function with base 2.

[0073] For example, Figure 2 The specific process of the edge contraction operation shown is as follows:

[0074] The current undirected weighted graph contains 5 nodes numbered 1-5. The edge weight from node 1 to node 2 (i.e. edge 12) is 0.5, the edge weight of edge 23 is 0.95, the edge weight of edge 24 is 0.75, the edge weight of edge 34 is 1.1, and the edge weight of edge 45 is 0.4. Traverse all the edges in the current undirected weighted graph from large to small, and sort all the edges according to the size of the edge weight. The sorting result is edge 34, edge 23, edge 24, edge 12, and edge 45. According to the above order, edge 34 is first extracted. Since nodes 3 and 4 on both sides of edge 34 are not marked as supernodes, nodes 3 and 4 are marked as supernodes, and edge 34 is contracted. At this time, nodes 3 and 4 are merged supernode 34. Then, according to the edge weight, the next edge 23 is extracted. Since node 3 is marked as a supernode at this time, the next edge 24 is continued to be extracted. Since node 4 is marked as a supernode at this time, the next edge 12 is continued to be extracted. Since nodes 1 and 2 on both sides of edge 12 are not marked as supernodes, nodes 1 and 2 are marked as supernodes, and edge 12 is contracted. At this time, nodes 1 and 2 are merged supernode 12. At this time, there are two edges between merged supernode 12 and merged supernode 34. These two edges are merged, and the edge weights of the two merged edges are summed up as the edge weight of the merged edge to obtain an updated undirected weighted graph. At this time, an edge contraction operation is completed.

[0075] It is determined whether the number of edge contraction operations performed at this time reaches a preset number. If so, the updated undirected weighted graph is the undirected weighted graph after contraction is completed; if not, the edge contraction operation continues to be performed.

[0076] It can be understood that by performing edge contraction operations, the topological structure of the power grid graph model can be effectively simplified. By sorting and traversing edge weights, efficient contraction of the connection relationship between nodes is achieved, and the complex power grid structure is converted into a relatively concise undirected weighted graph. The operation of merging super nodes avoids repeated processing of redundant connections, further improving the execution efficiency of the algorithm. The iterative process of edge contraction is flexible, and the termination of the operation is controlled by a preset number of times, ensuring the rationality and accuracy of the power grid partitioning. This method does not need to solve complex optimization problems, but is directly based on edge weights and preset rules, which simplifies the calculation process and improves the efficiency and accuracy of identifying weak areas in large-scale power grids.

[0077] Step S5: partition the power grid according to the undirected weighted graph after the contraction to obtain a plurality of partitions of the power grid.

[0078] In actual use, the contracted undirected weighted graph contains multiple merged supernodes, and each merged supernode contains multiple nodes. The merged supernode is regarded as a partition, and each partition contains multiple nodes; it is determined whether there are still nodes in the contracted undirected weighted graph. If there are nodes, the node is regarded as a partition of the power grid, thereby obtaining multiple partitions of the power grid.

[0079] It can be understood that by partitioning the undirected weighted graph after contraction, the power grid can be effectively divided into multiple areas with clear electrical connections. By merging super nodes, multiple nodes with strong associations are grouped in one partition, making the electrical connection within each partition closer, which helps to improve the visualization and analysis efficiency of the power grid structure. In addition, independent nodes that are not involved in the merger can also be reasonably assigned to the corresponding partitions to ensure the integrity and accuracy of the power grid partitions.

[0080] Step S6: For each partition, calculate and obtain the average value of the node short circuit ratio of each partition.

[0081] Specifically, in a preferred embodiment, the mean value of the node short circuit ratio of each partition is obtained by the following steps:

[0082] According to the system rated voltage and the equivalent system impedance of each node, the short-circuit capacity of all nodes in each partition is calculated.

[0083] The node short circuit ratio of each node is calculated according to the ratio of the short circuit capacity of each node to the rated capacity of the node.

[0084] The node short circuit ratios of all nodes in each partition are obtained, and their average value is calculated to obtain the mean value of the node short circuit ratio of each partition.

[0085] In actual use, for power supply nodes, such as new energy plants or conventional power plants, the rated capacity of the node is the installed capacity. For DC converter stations, the rated capacity of the node is the DC capacity. For substation nodes, the rated capacity of the node is the total load planned to be supplied by the substation.

[0086] It can be understood that by calculating the mean short-circuit ratio of the nodes in each partition, there is no need to rely on complex steady-state power flow calculations, and the impact of individual node abnormalities on the overall evaluation is reduced through mean processing, ensuring accurate reflection of electrical connections within the partition. It can provide clear quantitative indicators for identifying weak areas of the power grid, help to reasonably configure reactive compensation devices, and improve the safety and stability of the power system.

[0087] Step S7: sort the node short circuit ratio mean values ​​of each partition from small to large, and select the first M partitions as weak areas of the power grid.

[0088] It can be understood that by sorting the mean short-circuit ratio of nodes in each partition, the present invention can intuitively and quickly identify the weak areas of the power grid, avoid complex power flow calculations, and simplify the identification process of weak areas of the power grid. After sorting, the partition with a lower mean short-circuit ratio of nodes is selected as the weak area, which can provide a clear direction for the subsequent configuration of reactive power compensation devices, thereby improving the stability and operational safety of the power system.

[0089] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0090] like Figure 2 As shown, an embodiment of the present invention provides a device for identifying weak areas in a power grid, including: a graph theory model building module 101 , an edge contraction module 103 , a power grid partitioning module 104 and a weak area identification module 104 .

[0091] The graph theory model construction module 101 is used to obtain power plants and transmission lines in the power grid as nodes and edges of the undirected weighted graph corresponding to the power grid graph theory model; obtain the connection relationship between the nodes, and construct an adjacency matrix of the undirected weighted graph according to the connection relationship between the nodes; calculate the branch admittance amplitude between the nodes, and use the branch admittance amplitude as the edge weight between the nodes in the undirected weighted graph.

[0092] The edge contraction module 102 is used to repeatedly perform the edge contraction operation until a preset number of times is reached to obtain an undirected weighted graph after the contraction is completed; wherein, the edge contraction operation includes: traversing all edges in the current undirected weighted graph from large to small, and sorting all edges according to the size of the edge weights; extracting each edge one by one according to the sorting, and when each edge is extracted, judging whether there are nodes on both sides of the current edge that are marked as super nodes, if so, extracting the next edge; if not, marking the nodes on both sides of the current edge as super nodes, contracting the current edge to merge the two super nodes to obtain a merged super node; merging the edges between two merged super nodes that are connected by multiple edges to obtain an updated undirected weighted graph.

[0093] The power grid partitioning module 103 is used to partition the power grid according to the undirected weighted graph after the contraction to obtain multiple partitions of the power grid.

[0094] The weak area identification module 104 is used to calculate the average short circuit ratio of nodes in each partition for each partition; sort the average short circuit ratio of nodes in each partition from small to large, and select the first M partitions as weak areas of the power grid.

[0095] In actual operation, the graph theory model building module includes: an adjacency matrix generation unit and an edge weight calculation unit.

[0096] The adjacency matrix unit is used to construct an adjacency matrix of an undirected weighted graph by the following steps:

[0097] Get the connection relationship between nodes and determine whether there is an edge connecting the two nodes; if there is an edge connecting the two nodes, set the corresponding element in the adjacency matrix of the undirected weighted graph to 1; if there is no edge connecting the two nodes, set the corresponding element in the adjacency matrix of the undirected weighted graph to 0;

[0098] The edge weight calculation unit is used to obtain the edge weights between nodes in the undirected weighted graph through the following steps, including:

[0099] Get the non-zero elements in the adjacency matrix and obtain the nodes on both sides of the edge corresponding to the non-zero elements.

[0100] The branch admittance and conductance between nodes are obtained, the square of the branch admittance and the square of the conductance are added together and then the square root operation is performed to obtain the edge weights between nodes in the undirected weighted graph.

[0101] In actual operation, the weak area identification module includes: a node short circuit ratio average value calculation unit.

[0102] The node short circuit ratio mean value calculation unit is used to obtain the node short circuit ratio mean value of each partition through the following steps:

[0103] According to the system rated voltage and the system equivalent impedance of each node to the outside, the short-circuit capacity of all nodes in each partition is calculated; according to the ratio of the short-circuit capacity of each node to the rated capacity of the node, the node short-circuit ratio of each node is calculated; the node short-circuit ratio of all nodes in each partition is obtained, and the average value is calculated to obtain the mean value of the node short-circuit ratio of each partition.

[0104] It should be noted that the embodiments of the device described above correspond to the above embodiments of the present invention, and can implement any of the methods described above in the present invention. In addition, the embodiments of the above-mentioned device are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0105] Based on the above method embodiment of the present invention, a corresponding electronic device embodiment is provided.

[0106] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for identifying a weak area of ​​a power grid described in any one of the present invention is implemented, or, when the processor executes the computer program, the functions of the modules in the above-mentioned device embodiments are implemented.

[0107] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0108] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0109] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0110] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (SecureDi gita l, SD) card, a flash card (F l ash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0111] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0112] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute any of the above-mentioned methods for identifying weak areas in power grids of the present invention.

[0113] The storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0114] 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. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0115] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for identifying weak areas of a power grid, characterized in that: include: Obtain power plants and transmission lines in the power grid as nodes and edges of the undirected weighted graph corresponding to the power grid graph theory model; Obtain the connection relationship between nodes, and construct the adjacency matrix of the undirected weighted graph based on the connection relationship between nodes; Calculate the branch admittance amplitude between nodes and use the branch admittance amplitude as the edge weight between nodes in the undirected weighted graph; Repeat the edge contraction operation until the preset number of times is reached to obtain an undirected weighted graph after contraction; Partitioning the power grid according to the undirected weighted graph after the contraction to obtain multiple partitions of the power grid; For each partition, the mean value of the node short circuit ratio of each partition is calculated; Sort the node short-circuit ratio averages of each partition from small to large, and select the first M partitions as weak areas of the power grid; The edge shrinking operation includes: Traverse all the edges in the current undirected weighted graph from large to small, and sort all the edges according to the size of the edge weights; Extract each edge one by one according to the above order, and when extracting each edge, determine whether there are nodes marked as super nodes on both sides of the current edge, if yes, extract the next edge; if no, mark both sides of the current edge as super nodes, shrink the current edge, so that two super nodes are merged to obtain a merged super node; The edges between two merged super nodes that are connected by multiple edges are merged to obtain an updated undirected weighted graph.

2. A method for identifying weak areas of a power grid as claimed in claim 1, characterized in that: The adjacency matrix of an undirected weighted graph is constructed by the following steps: Get the connection relationship between nodes and determine whether there is an edge connecting two nodes; if there is an edge connecting two nodes, set the corresponding element in the adjacency matrix of the undirected weighted graph to 1; If there is no edge connecting two nodes, the corresponding element in the adjacency matrix of the undirected weighted graph is set to 0.

3. A method for identifying weak areas of a power grid as claimed in claim 1, characterized in that: The edge weights between nodes in the undirected weighted graph are obtained by the following steps: Get the non-zero elements in the adjacency matrix, and obtain the nodes on both sides of the edge corresponding to the non-zero elements as non-zero nodes; The branch admittance between non-zero nodes and the conductance between nodes are obtained, the square of the branch admittance is added to the square of the conductance, and then the square root operation is performed to obtain the edge weights between nodes in the undirected weighted graph.

4. A method for identifying weak areas of a power grid as claimed in claim 1, characterized in that: Get the preset number by following the steps below: Obtain the number of planned power grid partitions and the number of initial power grid nodes, calculate the ratio of the number of planned power grid partitions to the number of initial power grid nodes, and obtain a first value; use the first value as an independent variable, substitute it into a preset logarithmic function, and obtain a second value; take the opposite of the second value to obtain a preset number of times.

5. A method for identifying weak areas of a power grid as claimed in claim 1, characterized in that: The mean short circuit ratio of nodes in each partition is obtained by the following steps: According to the system rated voltage and the equivalent system impedance of each node, the short-circuit capacity of all nodes in each partition is calculated; According to the ratio of the short-circuit capacity of each node to the rated capacity of the node, the node short-circuit ratio of each node is calculated; The node short circuit ratios of all nodes in each partition are obtained, and their average value is calculated to obtain the mean value of the node short circuit ratio of each partition.

6. A device for identifying weak areas of a power grid, characterized in that: include: Graph model building module, edge contraction module, power grid partitioning module and weak area identification module; The graph theory model building module is used to obtain power plants and transmission lines in the power grid as nodes and edges of the undirected weighted graph corresponding to the power grid graph theory model; Obtain the connection relationship between nodes, and construct the adjacency matrix of the undirected weighted graph according to the connection relationship between nodes; calculate the branch admittance amplitude between nodes, and use the branch admittance amplitude as the edge weight between nodes in the undirected weighted graph; The edge contraction module is used to repeatedly perform the edge contraction operation until a preset number of times is reached to obtain an undirected weighted graph after contraction; wherein the edge contraction operation includes: traversing all edges in the current undirected weighted graph from large to small, and sorting all edges according to the size of the edge weights; extracting each edge one by one according to the sorting, and when each edge is extracted, judging whether there are nodes on both sides of the current edge that are marked as super nodes, if so, extracting the next edge; if not, marking both sides of the current edge as super nodes, contracting the current edge to merge two super nodes to obtain a merged super node; merging the edges between two merged super nodes that are connected by multiple edges to obtain an updated undirected weighted graph; The power grid partitioning module is used to partition the power grid according to the undirected weighted graph after the contraction to obtain multiple partitions of the power grid; The weak area identification module is used to calculate the node short circuit ratio mean of each partition for each partition; sort the node short circuit ratio mean of each partition from small to large, and select the first M partitions as the weak areas of the power grid.

7. The device for identifying weak areas of a power grid according to claim 6, characterized in that: The graph theory model building module includes: an adjacency matrix generation unit and an edge weight calculation unit; The adjacency matrix unit is used to construct an adjacency matrix of an undirected weighted graph by the following steps: Get the connection relationship between nodes and determine whether there is an edge connecting the two nodes; if there is an edge connecting the two nodes, set the corresponding element in the adjacency matrix of the undirected weighted graph to 1; if there is no edge connecting the two nodes, set the corresponding element in the adjacency matrix of the undirected weighted graph to 0; The edge weight calculation unit is used to obtain the edge weights between nodes in the undirected weighted graph through the following steps, including: Get the non-zero elements in the adjacency matrix and obtain the nodes on both sides of the edge corresponding to the non-zero elements; The branch admittance and conductance between nodes are obtained, the square of the branch admittance and the square of the conductance are added together and then the square root operation is performed to obtain the edge weights between nodes in the undirected weighted graph.

8. The device for identifying weak areas of a power grid according to claim 6, characterized in that: The weak area identification module includes: a node short circuit ratio average value calculation unit; The node short circuit ratio mean value calculation unit is used to obtain the node short circuit ratio mean value of each partition through the following steps: According to the system rated voltage and the system equivalent impedance of each node to the outside, the short-circuit capacity of all nodes in each partition is calculated; according to the ratio of the short-circuit capacity of each node to the rated capacity of the node, the node short-circuit ratio of each node is calculated; the node short-circuit ratio of all nodes in each partition is obtained, and the average value is calculated to obtain the mean value of the node short-circuit ratio of each partition.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for identifying a weak area in a power grid as described in any one of claims 1 to 5 can be implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying a weak area in a power grid as described in any one of claims 1 to 5 can be implemented.

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