Node frequency space-time difference grouping method based on electromechanical wave transmission coefficient matrix

Abstract

The invention discloses a node frequency space-time difference grouping method based on an electromechanical wave transmission coefficient matrix. The method comprises the following steps: establishing a projective difference matrix and a system node admittance matrix of a power system; performing normalization processing on the transmission difference matrix to obtain a normalized transmission difference matrix; and based on the normalized transmission difference matrix and the system node admittance matrix, determining a node frequency space-time difference grouping result of the power system by adopting a topological connectivity test method.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and more specifically, to a node frequency spatiotemporal difference clustering method based on an electromechanical wave transmission coefficient matrix. Background Art

[0002] Under the condition of large-scale grid connection of new energy, conventional synchronous power sources are gradually being replaced by non-synchronous power sources such as wind and light, which reduces the system inertia level and increases the unevenness of inertia spatial distribution, increases the spatial difference of system anti-interference ability, and makes the spatial and temporal differences of frequency response more obvious, which brings challenges to the stable operation of the system. In this regard, it is of great significance to accurately describe the spatial difference characteristics of system inertia level and system node disturbance resistance ability, and then realize the differentiated zoning and grouping of system inertia distribution and node anti-interference ability. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a node frequency spatiotemporal difference clustering method based on an electromechanical wave transmission coefficient matrix.

[0004] According to one aspect of the present invention, a method for node frequency-space difference clustering based on an electromechanical wave transmission coefficient matrix is ​​provided, comprising:

[0005] Establish the projective difference matrix and system node admittance matrix of the power system;

[0006] Normalizing the transmission difference matrix to obtain a normalized transmission difference matrix;

[0007] Based on the normalized transmission difference matrix and the system node admittance matrix, the topological connectivity test method is used to determine the node frequency spatiotemporal difference clustering results of the power system.

[0008] Optionally, the transmission difference matrix is ​​normalized to obtain a normalized transmission difference matrix ΔT LN The process is:

[0009]

[0010] Where ΔT is the projective difference matrix; |ΔT| min is the minimum modulus of the projective difference matrix ΔT elements; |ΔT| max is the maximum modulus of the elements of the matrix ΔT, ΔT ij is the transmission difference coefficient from node i to node j; ΔT LNij is the normalized transmission difference coefficient.

[0011] Optionally, based on the normalized transmission difference matrix and the system node admittance matrix, a topological connectivity verification method is used to determine the node frequency spatiotemporal difference clustering result of the power system, including:

[0012] Record the positions of the elements of the normalized transmission difference matrix that are greater than a preset transmission difference coefficient threshold, and construct a coordinate set;

[0013] The positions corresponding to the coordinate set in the system node admittance matrix are set to zero to obtain an updated system node admittance matrix;

[0014] Use the updated system node admittance matrix as the adjacency matrix and draw the system admittance undirected graph;

[0015] The connectivity check of the system admittance undirected graph is carried out to determine the clustering results of the node frequency spatiotemporal differences of the power system.

[0016] Optionally, a connectivity check is performed on the system admittance undirected graph to determine the node frequency spatiotemporal difference clustering result of the power system, including:

[0017] The depth-first search algorithm is used to check the connectivity of the system admittance undirected graph, count the number of subgraphs and the subgraph node sets, and determine the node frequency spatiotemporal difference clustering results, where the node frequency spatiotemporal difference clustering results include: the number of node clusters and the nodes contained in the node clusters.

[0018] Optionally, a depth-first search algorithm is used to perform a connectivity check on the system admittance undirected graph, count the number of subgraphs and subgraph node sets, and determine the node frequency spatiotemporal difference clustering results, including:

[0019] Step 1: Obtain the adjacency matrix array of the system admittance undirected graph;

[0020] Step 2: Initialize the adjacency matrix array visited = [0 0…0] 1×n , n is the number of vertices;

[0021] Step 3: Starting from vertex v in the adjacency matrix array, visit vertex v and set visited[v] = 1;

[0022] Step 4: Find all the adjacent points w of vertex v in turn. If visited[w]≠1, start from the adjacent point w, traverse the system admittance undirected graph in depth first, and repeat step 2;

[0023] Step 5: After the depth-first traversal is completed once, traverse the visited array. The vertex corresponding to the element with a value of 1 in the array is a node that constitutes a connected subgraph i;

[0024] Step 6: Traverse the visited array. If there is a value in the visited array that is not 1, it means that the point is not connected to the previous subgraph. Start from this point to traverse the graph in depth first and count the new subgraph i+1.

[0025] Step 7: Traverse the visited array. If all the visited array values ​​are 1, count the number of subgraphs N and the vertices contained in the subgraphs to determine the node frequency and spatiotemporal difference clustering results.

[0026] According to another aspect of the present invention, a device for node frequency-space difference clustering based on an electromechanical wave transmission coefficient matrix is ​​provided, comprising:

[0027] Establishing a module for establishing a projected difference matrix of a power system and a system node admittance matrix;

[0028] An acquisition module, used for normalizing the transmission difference matrix to obtain a normalized transmission difference matrix;

[0029] The determination module is used to determine the node frequency spatiotemporal difference clustering results of the power system based on the normalized transmission difference matrix and the system node admittance matrix using a topological connectivity verification method.

[0030] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0031] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of the above aspects of the present invention.

[0032] Thus, the present invention can divide nodes with similar inertia distribution and frequency anti-interference ability into the same group, so the frequency dynamic response curves of nodes in the same group after power shortage disturbance are highly similar, including the initial decline slope and the first node frequency minimum are approximately equal; when power shortage occurs in generator nodes in different node groups, the initial decline slope of the node and the first node frequency minimum are quite different; considering the connectivity constraints between nodes, the unreasonable grouping of nodes with similar frequency response curves but no electrical connection into the same group is avoided, and the grouping results are more meaningful. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0034] Figure 1 It is a flow chart of a node frequency spatiotemporal difference clustering method based on an electromechanical wave transmission coefficient matrix provided by an exemplary embodiment of the present invention;

[0035] Figure 2It is another flow chart of a node frequency-space difference clustering method based on an electromechanical wave transmission coefficient matrix provided by an exemplary embodiment of the present invention;

[0036] Figure 3 is a schematic diagram of a mesh 8×8 node system model provided by an exemplary embodiment of the present invention;

[0037] Figure 4 is a system admittance undirected graph of a mesh 8×8 node system model provided by an exemplary embodiment of the present invention;

[0038] Figure 5 It is a clustering result diagram of a mesh 8×8 node system model provided by an exemplary embodiment of the present invention;

[0039] Figure 6 is a frequency dynamic response curve diagram of a mesh 8×8 node system model provided by an exemplary embodiment of the present invention;

[0040] Figure 7 is a diagram of system inertia variation provided by an exemplary embodiment of the present invention;

[0041] Figure 8 is a grouping result diagram of a system inertia variation diagram provided by an exemplary embodiment of the present invention;

[0042] Fig. 9 is a schematic diagram of a frequency dynamic response curve diagram of a system inertia variation diagram provided by an exemplary embodiment of the present invention;

[0043] Fig.10 is a branch susceptance variation diagram provided by an exemplary embodiment of the present invention;

[0044] Fig.11 is a grouping result diagram of a branch susceptance variation diagram provided by an exemplary embodiment of the present invention;

[0045] Fig.12 It is a frequency dynamic response curve diagram of branch susceptance variation diagram provided by an exemplary embodiment of the present invention;

[0046] Fig.13 It is a structural schematic diagram of a node frequency spatiotemporal difference clustering device based on an electromechanical wave transmission coefficient matrix provided by an exemplary embodiment of the present invention;

[0047] Fig.14 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0048] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0049] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0050] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0051] It should also be understood that, in the embodiments of the present invention, “plurality” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0052] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0053] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0054] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0055] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0056] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0057] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0058] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0059] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0060] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0061] Exemplary Methods

[0062] Figure 1 FIG. 1 is a flow chart of a method for node frequency spatiotemporal difference clustering based on an electromechanical wave transmission coefficient matrix provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the node frequency spatiotemporal difference clustering method 100 based on the electromechanical wave transmission coefficient matrix includes the following steps:

[0063] Step 101, establishing a projected difference matrix and a system node admittance matrix of a power system;

[0064] Step 102, normalizing the transmission difference matrix to obtain a normalized transmission difference matrix;

[0065] Step 103, based on the normalized transmission difference matrix and the system node admittance matrix, a topological connectivity verification method is used to determine the node frequency spatiotemporal difference clustering result of the power system.

[0066] Specifically, in response to the shortcomings of the prior art, the present invention provides a method for node frequency spatiotemporal difference clustering based on electromechanical wave transmission coefficient. First, a transmission difference coefficient index and a transmission difference coefficient matrix are established to reflect the degree of connection and difference between system nodes, and describe the spatial distribution level of inertia of the system; secondly, the transmission difference coefficient matrix is ​​deformed and normalized; finally, the system node frequency spatiotemporal difference clustering is realized based on the normalized transmission difference matrix, the system branch admittance matrix and the topological connectivity verification method.

[0067] The following steps are involved:

[0068] (1) Obtain the system transmission difference matrix ΔT and the node admittance matrix Y;

[0069] (2) Transmission difference matrix deformation and normalization;

[0070] The transmission difference matrix ΔT is deformed and normalized using the maximum normalization method to obtain ΔT LN :

[0071]

[0072] Where |ΔT| min is the minimum modulus of the elements of the matrix ΔT; |ΔT| max is the maximum modulus of the elements of the matrix ΔT.

[0073] (3) Given the transmission difference coefficient threshold λ:

[0074] Given the transmission difference coefficient threshold λ, the matrix ΔT LN The mean of the non-zero elements in , that is:

[0075] λ=average(ΔT LNij ), ΔT LNij ≠0 (2)

[0076] (4) Lock the position of the element λ greater than the threshold:

[0077] The normalized transmission difference matrix ΔT LN The elements are compared with the threshold λ, the positions of the elements greater than the threshold λ are recorded, and the coordinate set Coord is constructed, that is:

[0078] Coord={(i,j)|ΔT LNij ≥λ} (3)

[0079] (5) Matrix zeroing:

[0080] Set the elements at the corresponding positions of the coordinate set Coord in the system node admittance matrix Y to zero, that is,

[0081] Y(i,j)=0(i,j)∈Coord (4)

[0082] (6) Modify the system topology diagram:

[0083] With the node admittance matrix Y as the adjacency matrix A, draw the system admittance undirected graph G, that is,

[0084] A(G)=Y (5)

[0085] (7) Connectivity test grouping:

[0086] The Depth First Search (DFS) algorithm is used to check the connectivity of the undirected graph G. The number of subgraphs N and the subgraph node set are counted, which are the number of node clusters N and the nodes contained in the node clusters.

[0087] Algorithm steps:

[0088] 1) Get the adjacency matrix array;

[0089] 2) Initialize visited = [0 0…0] 1×n , n is the number of vertices;

[0090] 3) Starting from vertex v, visit vertex v and set visited[v] = 1;

[0091] 4) Search for all adjacent points w of v in turn. If visited[w]≠1, start from w and traverse the graph depth-first, that is, repeat step 2;

[0092] 5) After completing the depth-first traversal once, traverse the visited array. The vertex corresponding to the element with a value of 1 in the array is a node that constitutes a connected subgraph i.

[0093] 6) Make a judgment and traverse the visited array. If there is a value in the visited array that is not 1, it means that the point is not connected to the previous subgraph. The subgraph starts to traverse the graph in depth first and counts the new subgraph i+1;

[0094] 7) Make a judgment and traverse the visited array. If the visited array is all 1, count the number of subgraphs N and the vertices contained in the subgraphs.

[0095] (8) The flow chart of the node frequency difference clustering method based on transmission coefficient is as follows: Figure 2 As shown:

[0096] Step 1: Obtain the system transmission difference matrix ΔT;

[0097] Step 2: Transmission difference matrix ΔT LN Normalization;

[0098] Step 3: Specify the transmission difference coefficient threshold λ;

[0099] Step 4: Lock ΔT LN The elements greater than the threshold value are constructed, and the coordinate set Coord={(i,j)|ΔT ij >λ};

[0100] Step 5: Set the corresponding element of Coord in the matrix Y to zero, that is, Y(i,j)=0, (i,j)∈Coord;

[0101] Step 6: Perform connectivity check based on the DFS algorithm and count the number N and composition of node groups.

[0102] In one embodiment, an ideal 8×8 grid system is taken as an example to illustrate the evaluation effect of an inertia distribution evaluation index based on the electromechanical wave transmission coefficient. In the ideal grid system, each node is connected to a generator, and the inertia, line length, and unit length susceptance of each generator are equal. The line resistance and parallel capacitance are ignored. The system topology is as follows: Figure 3 The system structure parameters are shown in Table 1.

[0103] Table 1 System structure parameters

[0104]

[0105] (1) Obtain the system transmission difference matrix ΔT and the node admittance matrix Y:

[0106]

[0107] (2) Transmission difference matrix deformation and normalization

[0108] In the matrix |ΔT| min =0, |ΔT| max = 0.2667, the normalized transmission difference matrix ΔT is calculated LN .

[0109]

[0110] (3) Given a transmission difference coefficient threshold λ

[0111]

[0112] (4) Lock the position of the element λ greater than the threshold

[0113] The normalized transmission difference matrix ΔT LN The elements are compared with the threshold λ, the positions of the elements greater than the threshold λ are recorded, and the coordinate set Coord is constructed:

[0114] Coord={(1,2),(1,9),(2,1),(2,10),(3,11),(4,12)…}

[0115] (5) Matrix zeroing

[0116] Set the elements at the corresponding positions of the coordinate set Coord in the system node admittance matrix Y to zero, that is,

[0117]

[0118] (6) Modify the system topology diagram

[0119] Take the modified node admittance matrix Y as the adjacency matrix A and draw the system admittance undirected graph G, that is,

[0120] A(G)=Y

[0121] The undirected graph G is Figure 4 shown.

[0122] (6) Connectivity test grouping

[0123] The Depth First Search (DFS) algorithm is used to check the connectivity of the undirected graph G. The number of subgraphs N and the subgraph node set are counted, which are the number of node clusters N and the nodes contained in the node clusters. Figure 5 The node clustering results are shown.

[0124] Even when the system structure parameters change, this calculation method can still cluster nodes based on their spatiotemporal differences in frequency. Figure 7 and Figure 8 The topological structure diagram after adding the nine-node inertia (28-30, 36-38, 44-46) and its corresponding node grouping result diagram; Fig.10 and Fig.11 This is the topological structure diagram after adding the branch susceptance in the middle area and its corresponding node grouping result diagram.

[0125] (7) Example 1 of the effect analysis of the node frequency spatiotemporal difference clustering method based on the electromechanical wave transmission coefficient matrix: uniform parameter model;

[0126] Figure 2 The corresponding clustering results of the mesh 8×8 node system model are shown as follows Figure 5 As shown in the figure, the frequency dynamic response curves of nodes in different groups under the same disturbance are as follows: Figure 6 shown.

[0127] A. Figure 5 It shows that the structure of the subsystem in the middle area is more symmetrical, the motor inertia and branch susceptance are more evenly distributed, while the structural differences and parameter distribution unevenness between the four vertices, four edge areas and the middle area are relatively large. Figure 6 It can be seen that the node frequency dynamic response curves of the nodes in the same node group after the power shortage disturbance are highly similar, including the initial decline slope and the first node frequency minimum value are approximately equal.

[0128] (2) Example 2: Changing the inertia distribution;

[0129] Figure 7 The inertia time constant of the generator marked in red is increased to 15s, and other parameters remain unchanged. The results are as follows Figure 8 As shown in the figure, the frequency dynamic response curves of nodes in different groups under the same disturbance are as follows: Fig. 9 shown.

[0130] A. Figure 8 It shows that the proposed method divides the 64 nodes of the system into 6 groups, including a single node group consisting of 4 vertices, a central area node group consisting of an inertia increase area, and a ring area node group between the two groups; B. The clustering method realizes the separation of nodes in the middle area and the peripheral area, that is, the separation of the area with larger inertia and the area with smaller inertia; C. Simulation results Fig. 9 It shows that the node frequency dynamic response curves of the generator nodes in the same node group are highly similar after the power shortage disturbance occurs; D. The node frequency dynamic response curves between the middle area and the peripheral area are quite different. At the same time, the initial decline slope of the middle area with larger inertia density is smaller and the first node frequency minimum value is larger.

[0131] (3) Calculation benefit three: Changing branch reactance affects the electrical distance of nodes;

[0132] Fig.10 The branch impedance in the red area is reduced to 0.04pu, keeping other parameters unchanged, the results are as follows Fig.11 As shown in the figure, the frequency dynamic response curves of nodes in different groups under the same disturbance are as follows: Fig.12 shown.

[0133] A. Fig.11 It shows that the proposed method divides the 64 nodes of the system into 10 groups, including a single node group consisting of 4 vertices, 4 internal single node groups consisting of four vertices in the branch reactance reduction area, 1 central area node group for reducing branch reactance and 1 ring area node group; B. The clustering method realizes the separation of nodes in the middle area and the peripheral area, and the separation of the area with smaller node electrical distance and the area with larger node electrical distance; C. Simulation results Fig.12It shows that the dynamic response curves of the nodes in the same node group are highly similar after a power shortage disturbance occurs at the generator nodes, including the initial decline slope and the first minimum value of the node frequency, which are approximately equal; D. There is a large difference between the dynamic response curves of the node frequency between the middle area and the outer area. At the same time, the initial decline slope of the middle area with a smaller electrical distance is smaller and the first node frequency minimum value is larger.

[0134] Thus, the present invention can divide nodes with similar inertia distribution and frequency anti-interference ability into the same group, so the frequency dynamic response curves of nodes in the same group after power shortage disturbance are highly similar, including the initial decline slope and the first node frequency minimum are approximately equal; when power shortage occurs in generator nodes in different node groups, the initial decline slope of the node and the first node frequency minimum are quite different; considering the connectivity constraints between nodes, the unreasonable grouping of nodes with similar frequency response curves but no electrical connection into the same group is avoided, and the grouping results are more meaningful.

[0135] Exemplary Devices

[0136] Fig.13 FIG. 1 is a schematic diagram of a node frequency-time-space difference clustering device based on an electromechanical wave transmission coefficient matrix provided by an exemplary embodiment of the present invention. Fig.13 As shown, the apparatus 1300 includes:

[0137] Establishing module 1310, for establishing a projective difference matrix and a system node admittance matrix of the power system;

[0138] An acquisition module 1320 is used to normalize the transmission difference matrix to obtain a normalized transmission difference matrix;

[0139] The determination module 1330 is used to determine the node frequency spatiotemporal difference clustering result of the power system based on the normalized transmission difference matrix and the system node admittance matrix by adopting a topological connectivity verification method.

[0140] Optionally, the transmission difference matrix is ​​normalized to obtain a normalized transmission difference matrix ΔT LN The process is:

[0141]

[0142] Where ΔT is the projective difference matrix; |ΔT| min is the minimum modulus of the projective difference matrix ΔT elements; |ΔT| max is the maximum modulus of the elements of the matrix ΔT, ΔT ij is the transmission difference coefficient from node i to node j; ΔT LNij is the normalized transmission difference coefficient.

[0143] Optionally, the determination module 1330 includes:

[0144] A construction submodule is used to record the positions of the elements of the normalized transmission difference matrix that are greater than a preset transmission difference coefficient threshold value, and to construct a coordinate set;

[0145] An acquisition submodule is used to set the position corresponding to the coordinate set in the system node admittance matrix to zero, and obtain an updated system node admittance matrix;

[0146] A drawing submodule is used to draw the system admittance undirected graph by taking the updated system node admittance matrix as the adjacency matrix;

[0147] The submodule is determined to perform connectivity check on the system admittance undirected graph and determine the node frequency spatiotemporal difference clustering results of the power system.

[0148] Optionally, submodules are determined, including:

[0149] The determination unit is used to use a depth-first search algorithm to perform a connectivity check on the system admittance undirected graph, count the number of subgraphs and the subgraph node set, and determine the node frequency and time-space difference clustering results, wherein the node frequency and time-space difference clustering results include: the number of node clusters and the nodes contained in the node clusters.

[0150] Optionally, the determining unit includes:

[0151] Step 1: Obtain the adjacency matrix array of the system admittance undirected graph;

[0152] Step 2: Initialize the adjacency matrix array visited = [0 0…0] 1×n , n is the number of vertices;

[0153] Step 3: Starting from vertex v in the adjacency matrix array, visit vertex v and set visited[v] = 1;

[0154] Step 4: Find all the adjacent points w of vertex v in turn. If visited[w]≠1, start from the adjacent point w, traverse the system admittance undirected graph in depth first, and repeat step 2;

[0155] Step 5: After the depth-first traversal is completed once, traverse the visited array. The vertex corresponding to the element with a value of 1 in the array is a node that constitutes a connected subgraph i;

[0156] Step 6: Traverse the visited array. If there is a value in the visited array that is not 1, it means that the point is not connected to the previous subgraph. Start from this point to traverse the graph in depth first and count the new subgraph i+1.

[0157] Step 7: Traverse the visited array. If all the visited array values ​​are 1, count the number of subgraphs N and the vertices contained in the subgraphs to determine the node frequency and spatiotemporal difference clustering results.

[0158] Exemplary Electronic Devices

[0159] Fig.14 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Fig.14 As shown, the electronic device 140 includes one or more processors 141 and a memory 142 .

[0160] The processor 141 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0161] The memory 142 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 141 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 143 and an output device 144, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0162] In addition, the input device 143 may also include, for example, a keyboard, a mouse, etc.

[0163] The output device 144 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0164] Of course, to simplify, Fig.14 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0165] Exemplary computer program products and computer-readable storage media

[0166] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above-mentioned "Exemplary Method" section of this specification.

[0167] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0168] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0169] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0170] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0171] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0172] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0173] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0174] It should also be noted that in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but in accordance with the widest range consistent with the principles and novel features disclosed here.

[0175] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for node frequency spatiotemporal difference clustering based on electromechanical wave transmission coefficient matrix, characterized in that: include: Establish the projective difference matrix and system node admittance matrix of the power system; Normalizing the transmission difference matrix to obtain a normalized transmission difference matrix; Based on the normalized transmission difference matrix and the system node admittance matrix, a topological connectivity verification method is adopted to determine the node frequency spatiotemporal difference clustering result of the power system.

2. The method according to claim 1, characterized in that The transmission difference matrix is ​​normalized to obtain a normalized transmission difference matrix ΔT LN The process is: Where ΔT is the projective difference matrix; |ΔT| min is the minimum modulus of the projective difference matrix ΔT elements; |ΔT| max is the maximum modulus of the elements of the matrix ΔT, ΔT ij is the transmission difference coefficient from node i to node j; ΔT LNij is the normalized transmission difference coefficient.

3. The method according to claim 1, characterized in that: Based on the normalized transmission difference matrix and the system node admittance matrix, a topological connectivity verification method is used to determine the node frequency spatiotemporal difference clustering result of the power system, including: Recording the positions of the elements of the normalized transmission difference matrix that are greater than a preset transmission difference coefficient threshold, and constructing a coordinate set; Setting the positions corresponding to the coordinate set in the system node admittance matrix to zero to obtain an updated system node admittance matrix; Using the updated system node admittance matrix as an adjacency matrix, and drawing a system admittance undirected graph; A connectivity check is performed on the system admittance undirected graph to determine the node frequency spatiotemporal difference clustering result of the power system.

4. The method according to claim 1, characterized in that: Performing a connectivity check on the system admittance undirected graph to determine the node frequency spatiotemporal difference clustering result of the power system includes: A depth-first search algorithm is used to perform connectivity check on the system admittance undirected graph, count the number of subgraphs and subgraph node sets, and determine the node frequency spatiotemporal difference clustering results, wherein the node frequency spatiotemporal difference clustering results include: the number of node clusters and the nodes contained in the node clusters.

5. The method according to claim 4, characterized in that A depth-first search algorithm is used to perform a connectivity check on the system admittance undirected graph, the number of subgraphs and the subgraph node set are counted, and the result of the node frequency-time-space difference clustering is determined, including: Step 1: Obtain an adjacency matrix array of the system admittance undirected graph; Step 2: Initialize the adjacency matrix array visited = [0 0...0] 1×n , n is the number of vertices; Step 3: Starting from the vertex v of the adjacency matrix array, visit the vertex v and set visited[v]=1; Step 4: Search for all the adjacent points w of vertex v in turn. If visited[w]≠1, start from the adjacent point w, traverse the system admittance undirected graph in depth-first order, and repeat step 2. Step 5: After the depth-first traversal is completed once, traverse the visited array. The vertex corresponding to the element with a value of 1 in the array is a node that constitutes a connected subgraph i; Step 6: Traverse the visited array. If there is a value in the visited array that is not 1, it means that the point is not connected to the previous subgraph. Start from this point to traverse the graph in depth first and count the new subgraph i+1. Step 7: traverse the visited array. If all the visited array values ​​are 1, count the number of subgraphs N and the vertices contained in the subgraphs to determine the node frequency-space difference clustering result.

6. A node frequency spatiotemporal difference clustering device based on electromechanical wave transmission coefficient matrix, characterized in that: include: Establishing a module for establishing a projected difference matrix of a power system and a system node admittance matrix; An acquisition module, used for normalizing the transmission difference matrix to acquire a normalized transmission difference matrix; A determination module is used to determine the node frequency spatiotemporal difference clustering result of the power system based on the normalized transmission difference matrix and the system node admittance matrix by adopting a topological connectivity verification method.

7. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 5.

8. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 5.

Images