Frequency adaptive spectral clustering spatial clustering method and device for power system

Abstract

The invention discloses a power system frequency adaptive spectral clustering spatial clustering method and device. The method comprises the following steps: acquiring frequency measurement data of different observation nodes after disturbance of a power system, and constructing a measurement space of the power system; extracting corresponding frequency measurement data from the measurement space according to the time scale of the adaptively selected frequency measurement data; constructing an undirected graph of similar information of the frequency measurement data of the power system according to the frequency measurement data; and performing clustering analysis on the undirected graph, and determining a frequency space group of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and more specifically, to a method and device for frequency adaptive spectral clustering space grouping of a power system. Background Art

[0002] In recent years, on the one hand, the proportion of new energy units integrated into the power system through power electronic interfaces has continued to increase, resulting in a continuous decrease in the overall inertia of the system; on the other hand, the spatial distribution differences between traditional frequency regulation resources and power electronic frequency regulation resources in the power system are increasing, resulting in increasing differences in the spatial distribution of the system's inertia and power regulation capabilities, and in the process of power grid frequency response after a fault, the spatial distribution differences of the power system frequency response in the inertia response stage are becoming more and more obvious. The wide-area frequency measurement data of the actual power system show that in the inertia response stage after an active disturbance fault, the power system frequency response presents increasingly significant spatial distribution characteristics, 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 the system frequency response and study the method of spatial clustering of system node frequencies. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method and device for frequency adaptive spectral clustering space grouping in an electric power system.

[0004] According to one aspect of the present invention, there is provided a method for frequency adaptive spectral clustering space grouping of a power system, comprising:

[0005] Obtain frequency measurement data of different observation nodes after power system disturbance and construct the measurement space of power system;

[0006] extracting corresponding frequency measurement data from the measurement space according to the time scale of the adaptively selected frequency measurement data;

[0007] Constructing an undirected graph of similar information of frequency measurement data of the power system according to the frequency measurement data;

[0008] Cluster analysis is performed on undirected graphs to determine the frequency space clustering of the power system.

[0009] Optionally, extracting corresponding frequency measurement data from the measurement space according to the time scale of the adaptively selected frequency measurement data includes:

[0010] Calculate the first extreme point of the frequency deviation value sequence of each node in the measurement space and its corresponding frequency deviation amplitude in turn, and construct the extreme point vector F c and the frequency first swing extreme value deviation amplitude vector ΔF;

[0011] Take the minimum value of the time when the frequency deviation amplitude of each node is greater than min(ΔF) / 100 as the starting time t of the measurement data0 ;

[0012] Take the time corresponding to the maximum value of the data sequence in the frequency first swing extreme value deviation amplitude vector ΔF as the measurement data termination time t d ;

[0013] According to the starting time t 0 and the end time t d , adaptively determine the time scale of the measured data T = [t 0 ,t d ];

[0014] The corresponding frequency measurement data is extracted from the measurement space according to the time scale.

[0015] Optionally, the extreme point vector F c for:

[0016] F c =[f c1 ,f c2 , ..., f cn ] T

[0017] Among them, f cn is the first swing extreme value of the nth measurement node;

[0018] The frequency first swing extreme value deviation amplitude vector ΔF is:

[0019] ΔF=F c -F N =[Δf 1 ,Δf 2 , ..., Δf n ] T

[0020] Among them, F N is the system rated frequency, Δf n is the first swing extreme value of the nth measurement node. If there is no extreme value point at node i within the measurement range, let f ci =max(f 1 ,f 2 ...f i-1 ), Δf i =min(Δf 1 ,Δf 2 ,...,Δf i-1 );

[0021] Optionally, an undirected graph of similar information of frequency measurement data of the power chest is constructed according to the frequency measurement data, including:

[0022] averaging the frequency measurement data to determine average frequency measurement data;

[0023] Calculating a differential mode component of the frequency measurement data based on the average frequency measurement data;

[0024] Calculate the correlation coefficient of the similarity of the frequency response process between nodes of the power system based on the average frequency measurement data and the differential mode component;

[0025] According to the correlation coefficient, a correlation coefficient matrix of the similarity of frequency transient curves between any two measurement nodes of the power system is constructed;

[0026] An undirected graph of similar information of frequency measurement data of power system is constructed according to the correlation coefficient matrix.

[0027] Optionally, the calculation formula for the average frequency measurement data and the differential mode component is:

[0028]

[0029] In the formula, is the node average frequency measurement data; f′ is the frequency deviation value of the measurement node relative to the average frequency, that is, the differential mode component.

[0030] Optionally, the correlation coefficient r ij The calculation formula is:

[0031]

[0032] In the formula, r ij is the Pearson correlation coefficient of the frequency measurement data sequence of measurement nodes i and j; f ik ′ is the kth data of measurement node i in the measurement space; f j ' k is the kth data of measurement node j in the measurement space; is the average frequency of measurement node i in the measurement space; is the average frequency of measurement node j in the measurement space; m is the number of rows in the measurement space.

[0033] Optionally, the adjacency matrix A(G) of the undirected graph G is:

[0034]

[0035] In the formula, r ij is the correlation coefficient, a ij is the adjacency matrix coefficient.

[0036] Optionally, cluster analysis is performed on the undirected graph to determine frequency space clustering of the power system, including:

[0037] Compute the normalized Laplacian matrix of an undirected graph;

[0038] The normalized Laplace matrix is ​​segmented based on spectral clustering, and the frequency space clustering of the power system is determined according to the segmentation results.

[0039] Optionally, compute the normalized Laplacian matrix of an undirected graph, including:

[0040] Compute the unnormalized Laplacian matrix of an undirected graph;

[0041] Construct the normalized Laplacian matrix of an undirected graph from the unnormalized Laplacian matrix.

[0042] Optionally, the unnormalized Laplacian matrix L is:

[0043]

[0044] in, n is the total number of vertices of the undirected graph G;

[0045] Normalized Laplace matrix L N for:

[0046]

[0047] Where D = diag (d 1 ,d 2 ,...d n ), is d i The diagonal matrix formed.

[0048] Optionally, the normalized Laplace matrix is ​​segmented based on spectral clustering, and the frequency space grouping of the power system is determined according to the segmentation result, including:

[0049] Calculate the normalized Laplace matrix L N The first k eigenvalues ​​diag(λ 1 ,λ 2 ,...,λ k ) and the eigenvector matrix T = [t 1 ,t 2 ,…,t k ] n×k ;

[0050] Transpose the eigenvector matrix T to get the transposed matrix T T =[t 1 ,t 2 ,…,t k ] T k×n , where the transposed matrix T T The i-th column vector in the matrix is ​​the coordinate of the observation node i in the k-dimensional mapping space, that is, v i =[t 1i , t2i ,...t ki ] T ;

[0051] In the k-dimensional mapping space, a clustering algorithm is used to divide the nodes in the vertex set V into k categories, so as to segment the undirected graph G of frequency similarity information and determine the frequency space clustering of the power system.

[0052] According to another aspect of the present invention, there is provided a power system frequency adaptive spectral clustering spatial grouping device, comprising:

[0053] The first construction module is used to obtain frequency measurement data of different observation nodes after the power system is disturbed and construct the measurement space of the power system;

[0054] An extraction module, used for extracting corresponding frequency measurement data from the measurement space according to the time scale of the adaptively selected frequency measurement data;

[0055] The second construction module is used to construct an undirected graph of similar information of frequency measurement data of the power system according to the frequency measurement data;

[0056] The determination module is used to perform cluster analysis on the undirected graph and determine the frequency space clustering of the power system.

[0057] 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.

[0058] 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.

[0059] Therefore, the present invention provides a method for frequency adaptive spectral clustering space clustering of power system. First, the power grid frequency observation space is constructed and the data window is adaptively determined; secondly, the Pearson correlation coefficient is introduced to measure the similarity of the frequency response of each observation node of the power grid in the inertia response stage after the disturbance; then, the power grid node frequency space clustering method is studied based on the spectral clustering theory to characterize the frequency space distribution phenomenon after the power grid disturbance. The power system frequency space clustering realized by the present invention based on the proposed power system frequency adaptive spectral clustering space clustering method is helpful for researchers and staff in related fields to clarify the spatial distribution characteristics of the power system frequency response and provide assistance for the wide-area coordinated control of the power system frequency. Nodes with high similarity in frequency response after the disturbance can be divided into the same group. Therefore, the frequency dynamic response curves of nodes in the same group after the power shortage disturbance are more similar, and the frequency dynamic response curves between different node groups are less similar, and there are significant differences in key indicators such as transient frequency drop rate and lowest point. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0061] Figure 1 It is a flow chart of a method for frequency adaptive spectral clustering space grouping of a power system provided by an exemplary embodiment of the present invention;

[0062] Figure 2 is a schematic diagram of an improved IEEE 39 system provided by an exemplary embodiment of the present invention;

[0063] Figure 3 is a schematic diagram of grouping results of an improved IEEE 39 system provided by an exemplary embodiment of the present invention;

[0064] Figure 4 is a schematic diagram of a node frequency response curve of an improved IEEE 39 system provided by an exemplary embodiment of the present invention;

[0065] Figure 5 is a schematic diagram of a real power grid model provided by an exemplary embodiment of the present invention;

[0066] Figure 6 It is a schematic diagram of grouping results of setting a power shortage of 10% of the total load at a node PS in a certain actual power grid model provided by an exemplary embodiment of the present invention;

[0067] Figure 7 It is a schematic diagram of a node frequency response curve of a certain actual power grid model node PS setting a power shortage of 10% of the total load provided by an exemplary embodiment of the present invention;

[0068] Figure 8It is a schematic diagram of grouping results of setting a power shortage of 10% of the total load at the node RT of a certain actual power grid model provided by an exemplary embodiment of the present invention;

[0069] Fig. 9 It is a schematic diagram of a node frequency response curve of a certain actual power grid model provided by an exemplary embodiment of the present invention, in which a power shortage of 10% of the total load is set at the node RT;

[0070] Fig.10 It is a structural schematic diagram of a frequency adaptive spectrum clustering spatial grouping device for a power system provided by an exemplary embodiment of the present invention;

[0071] Fig.11 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] Exemplary Methods

[0086] Figure 1 FIG. 1 is a flow chart of a method for frequency adaptive spectral clustering space grouping of a power system provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the power system frequency adaptive spectral clustering spatial grouping method 100 includes the following steps:

[0087] Step 101, obtaining frequency measurement data of different observation nodes after the power system is disturbed, and constructing a measurement space of the power system;

[0088] Step 102, extracting corresponding frequency measurement data from the measurement space according to the time scale of the adaptively selected frequency measurement data;

[0089] Step 103, constructing an undirected graph of similar information of frequency measurement data of the power system according to the frequency measurement data;

[0090] Step 104, cluster analysis is performed on the undirected graph to determine the frequency space grouping of the power system. Specifically, in response to the deficiencies of the prior art, the present invention provides a method for studying the frequency space distribution characteristics driven by wide-area massive measurement data of the power system. First, the power grid frequency observation space is constructed and the data window is adaptively determined; secondly, the Pearson correlation coefficient is introduced to measure the similarity of the frequency responses of each observation node of the power grid in the inertia response stage after the disturbance; then, the power grid node frequency space clustering method is studied based on the spectral clustering theory to characterize the frequency space distribution phenomenon after the power grid disturbance; finally, based on the proposed node frequency space clustering method, the effectiveness and feasibility of the proposed method are verified with the improved IEEE39 node system model and the actual power grid model of a certain area. The method comprises the following steps:

[0091] (1) Obtain the frequency measurement data of different observation nodes of the system after the disturbance, the power system frequency measurement space F;

[0092] (2) Adaptive processing of power system frequency measurement data

[0093] 1) Adaptive selection of the time scale of frequency measurement data

[0094] a Calculate the first extreme point of the frequency deviation sequence of each node and its corresponding frequency deviation amplitude in turn, and construct the extreme point vector F c and frequency first swing extreme value deviation amplitude vector ΔF

[0095] F c =[f c1 ,f c2 , ..., f cn ] T (1)

[0096] Among them, f cn is the first swing extreme value of the nth measurement node.

[0097] ΔF=F c -F N =[Δf 1 ,Δf 2 , ..., Δf n ] T (2)

[0098] Among them, F N is the system rated frequency, Δf n is the first swing extreme value of the nth measurement node. If there is no extreme value point at node i within the measurement range, let f ci =max(f 1 ,f 2 ...f i-1 ), Δf i =min(Δf 1 ,Δf 2 ,...,Δf i-1 );

[0099] b Take the minimum time corresponding to the frequency deviation amplitude of each node greater than min(ΔF) / 100 as the measurement data starting time t 0 ;

[0100] c Take the time corresponding to the maximum value of the data sequence in the extreme point vector ΔF as the measurement data termination time t d .

[0101] In summary, the time scale of the measured data T = [t 0 ,t d ], and then extract the corresponding frequency measurement data from the power grid frequency measurement space F.

[0102] 2) Extraction of differential mode component of frequency measurement data:

[0103] In order to further highlight the difference characteristics of transient frequency spatial distribution after disturbance, weaken the convergence trend of frequency response of each node, and reduce the influence of common components on the analysis of frequency fluctuation spatial distribution characteristics, the specific processing process is as follows:

[0104]

[0105] In the formula, is the node average frequency measurement data; f′ is the frequency deviation value of the measurement node relative to the average frequency, that is, the differential mode component.

[0106] (3) Calculate the correlation coefficient matrix R between frequency measurement data

[0107] The correlation coefficient calculation formula for measuring the similarity of the frequency response process between nodes is:

[0108]

[0109] Among them, r ij is the Pearson correlation coefficient of the frequency measurement data sequence of measurement nodes i and j. The closer its value is to 1, the higher the similarity of the two sets of frequency dynamic responses is. The closer its value is to 0, the smaller the similarity is.ik is the kth data of measurement node i in the measurement space; f′ jk is the kth data of measurement node j in the measurement space; is the average frequency of measurement node i in the measurement space; is the average frequency of measurement node j in the measurement space; m is the number of rows in the measurement space.

[0110] The correlation coefficient matrix R of the similarity of frequency transient curves between any two measurement nodes is:

[0111]

[0112] (4) Construct the frequency measurement data similarity information graph G(V,E). The adjacency matrix A(G) of graph G is as follows:

[0113]

[0114] (5) Calculate the normalized Laplacian matrix L of the undirected graph G N

[0115] a Establish the unnormalized Laplacian matrix L of graph G, where the element L ij for

[0116]

[0117] in, n is the total number of vertices in graph G.

[0118] b Non-normalized Laplacian matrix L Normalized Laplacian matrix L N , the specific steps are as follows:

[0119]

[0120] Where D = diag (d 1 ,d 2 ,…d n ), is d i The diagonal matrix formed.

[0121] (6) Frequency Similarity Information Graph Segmentation Based on Spectral Clustering

[0122] aCalculate L N The first k eigenvalues ​​diag(λ 1 ,λ 2 ,…,λ k ) and the eigenvector matrix T = [t 1 ,t 2 ,…,t k ] n×k ;

[0123] b matrix T transpose, get TT =[t 1 ,t 2 ,…,t k ] T k×n , the i-th column vector in the matrix is ​​the coordinate of the observation node i in the k-dimensional mapping space, that is, v i =[t 1i , t 2i ,...t ki ] T .

[0124] c In the k-dimensional mapping space, the commonly used K-means clustering algorithm is used

[22] The nodes in the vertex set V are divided into k categories to segment the frequency similarity information graph G, thereby realizing frequency space clustering of the power system.

[0125] In one embodiment of the present application, the technical solution proposed in the present application is verified by the following specific examples, as follows:

[0126] (1) Improved IEEE 39 node model, such as Figure 2 As shown:

[0127] Based on PSD Power Tools software to create Figure 2 The improved IEEE 39 system model shown in the figure has a grid topology structure that refers to the IEEE 39 node, unit speed regulation parameters that refer to actual unit speed regulator parameters, a load level of 5740MW, and an installed capacity of 6400MW.

[0128] based on Figure 2 In the system shown in the figure, a 100MW load shortage is set at node 24 at 1 second. The frequency response data of each bus node is used as the frequency measurement space F. According to the method proposed in this paper, the node frequency space is grouped. The time range of the frequency measurement data determined adaptively is 1.002 to 1.354 seconds. The node frequency grouping results and the frequency response curves of each node are as follows Figure 3 and Figure 4 As shown. Among them, Figure 3 This is the result of node frequency space clustering when node 24 experiences load disturbance. Figure 4 Frequency response curves of each node under load disturbance at node 24. Analysis shows that after load disturbance at node 24, the frequency response space is clustered into three groups, each of which constitutes a cut set in topology, and has significant differences in key indicators such as transient frequency drop rate and lowest point.

[0129] (2) Actual power grid model of a certain region

[0130] Based on PSD Power Tools software and actual power grid data, Figure 5The actual power grid model is shown in Figure 2. Based on the method proposed in this paper, the frequency space clustering characteristics of the system after power disturbances occur on different buses are analyzed.

[0131] Example 1: A power shortage of 10% of the total load is set at the node PS. The frequency response data of each bus node is used as the frequency measurement space F. The node frequency space is grouped according to the method proposed in this paper. The time range of the frequency measurement data determined adaptively is 2.002 to 2.980 seconds. The node frequency grouping results and the frequency response curves of each node are as follows Figure 6 and Figure 7 As shown. Among them, Figure 6 is the result of frequency space clustering of nodes under load disturbance of node PS, Figure 7 After the load disturbance, the frequency response space is clustered into three groups, each of which constitutes a cut set in topology, and has significant differences in key indicators such as transient frequency drop rate and lowest point.

[0132] (2) Example 2: A power shortage of 10% of the total load is set at node RT. The frequency response data of each bus node is used as the frequency measurement space F. The node frequency space is grouped according to the method proposed in this paper. The time range of the frequency measurement data determined adaptively is 2.002 to 2.960 seconds. The node frequency grouping results and the frequency response curves of each node are as follows Figure 8 and Fig. 9 As shown. Among them, Figure 8 is the result of node frequency space clustering under load disturbance of node RT, Fig. 9 The frequency response curves of each node under the load disturbance of node RT are shown in Figure 2. The analysis shows that after the load disturbance of node RT occurs, the frequency response space is clustered into two groups, each of which constitutes a cut set in topology, and has significant differences in key indicators such as transient frequency drop rate and lowest point.

[0133] In the present invention, after obtaining the frequency measurement data of different observation nodes of the system after the disturbance and constructing the power system frequency measurement space, attention should be paid to the adaptive processing process of the power system frequency measurement data, in which the selection of the measurement data time scale and the extraction of the data differential mode component abnormalities will affect the accuracy and rationality of the grouping.

[0134] In the present invention, when constructing the frequency measurement data similarity information graph G(V, E) based on the correlation coefficient matrix R, attention should be paid to the relationship between the elements of the undirected graph adjacency matrix and the elements of the correlation coefficient matrix, otherwise it will affect the subsequent steps and even cause calculation failures of the method.

[0135] In the present invention, when frequency similarity information graph segmentation is performed based on spectral clustering, it should be noted that the undirected graph corresponds to the normalized Laplace matrix L NThe maximum feature gap and the number of clusters k must be accurately calculated, and the nodes must be accurately mapped to the k-dimensional space. Otherwise, the clustering effect of the proposed method will be affected, and even unreasonable clustering will occur.

[0136] Therefore, the proposed power system frequency adaptive spectral clustering spatial clustering method realizes the power system frequency spatial clustering, which helps researchers and staff in related fields to clarify the spatial distribution characteristics of power system frequency response and provide support for wide-area coordinated control of power system frequency. Nodes with high similarity in frequency response after disturbance can be divided into the same group, so the frequency dynamic response curves of nodes in the same group after power shortage disturbance are more similar, and the frequency dynamic response curves between different node groups are less similar, with significant differences in key indicators such as transient frequency drop rate and lowest point.

[0137] Exemplary Devices

[0138] Fig.10 FIG. 1 is a schematic diagram of a structure of a frequency adaptive spectrum clustering space grouping device for a power system provided by an exemplary embodiment of the present invention. Fig.10 As shown, the device 1000 includes:

[0139] The first construction module 1010 is used to obtain frequency measurement data of different observation nodes after the power system is disturbed, and to construct a measurement space of the power system;

[0140] An extraction module 1020, configured to extract corresponding frequency measurement data from the measurement space according to the time scale of the adaptively selected frequency measurement data;

[0141] The second construction module 1030 is used to construct an undirected graph of similar information of frequency measurement data of the power system according to the frequency measurement data;

[0142] The determination module 1040 is used to perform cluster analysis on the undirected graph to determine the frequency space clustering of the power system.

[0143] Optionally, the extraction module 1020 includes:

[0144] The construction submodule is used to calculate the first swing extreme point of the frequency deviation value sequence of each node in the measurement space and its corresponding frequency deviation amplitude, and construct the extreme point vector F c and the frequency first swing extreme value deviation amplitude vector ΔF;

[0145] The first submodule is used to take the minimum value of the time corresponding to the frequency deviation amplitude of each node being greater than min(ΔF) / 100 as the starting time t of the measurement data. 0 ;

[0146] The second submodule is used to take the time corresponding to the maximum value of the data sequence in the frequency first swing extreme value deviation amplitude vector ΔF as the measurement data termination time t d ;

[0147] Determine the submodule for determining the starting time t 0 and the end time t d , adaptively determine the time scale of the measured data T = [t 0 ,t d ];

[0148] The extraction submodule is used to extract the corresponding frequency measurement data from the measurement space according to the time scale.

[0149] Optionally, the extreme point vector F c for:

[0150] F c =[f c1 ,f c2 , ..., f cn ] T

[0151] Among them, f cn is the first swing extreme value of the nth measurement node;

[0152] The frequency first swing extreme value deviation amplitude vector ΔF is:

[0153] ΔF=F c -F N =[Δf 1 ,Δf 2 , ..., Δf n ] T

[0154] Among them, F N is the system rated frequency, Δf n is the first swing extreme value of the nth measurement node. If there is no extreme value point at node i within the measurement range, let f ci =max(f 1 ,f 2 ...f i-1 ), Δf i =min(Δf 1 ,Δf 2 ,...,Δf i-1 );

[0155] Optionally, the second building block 1030 includes:

[0156] averaging the frequency measurement data to determine average frequency measurement data;

[0157] Calculating a differential mode component of the frequency measurement data based on the average frequency measurement data;

[0158] Calculate the correlation coefficient of the similarity of the frequency response process between nodes of the power system based on the average frequency measurement data and the differential mode component;

[0159] According to the correlation coefficient, a correlation coefficient matrix of the similarity of frequency transient curves between any two measurement nodes of the power system is constructed;

[0160] An undirected graph of similar information of frequency measurement data of power system is constructed according to the correlation coefficient matrix.

[0161] Optionally, the calculation formula for the average frequency measurement data and the differential mode component is:

[0162]

[0163] In the formula, is the node average frequency measurement data; f′ is the frequency deviation value of the measurement node relative to the average frequency, that is, the differential mode component.

[0164] Optionally, the correlation coefficient r ij The calculation formula is:

[0165]

[0166] In the formula, r ij is the Pearson correlation coefficient of the frequency measurement data sequence of measurement nodes i and j; f ik ′ is the kth data of measurement node i in the measurement space; f j ' k is the kth data of measurement node j in the measurement space; is the average frequency of measurement node i in the measurement space; is the average frequency of measurement node j in the measurement space; m is the number of rows in the measurement space.

[0167] Optionally, the adjacency matrix A(G) of the undirected graph G is:

[0168]

[0169] In the formula, r ij is the correlation coefficient, a ij is the adjacency matrix coefficient.

[0170] Optionally, the determination module 1040 includes:

[0171] Compute the normalized Laplacian matrix of an undirected graph;

[0172] The normalized Laplace matrix is ​​segmented based on spectral clustering, and the frequency space clustering of the power system is determined according to the segmentation results.

[0173] Optionally, compute the normalized Laplacian matrix of an undirected graph, including:

[0174] Compute the unnormalized Laplacian matrix of an undirected graph;

[0175] Construct the normalized Laplacian matrix of an undirected graph from the unnormalized Laplacian matrix.

[0176] Optionally, the unnormalized Laplacian matrix L is:

[0177]

[0178] in, n is the total number of vertices of the undirected graph G;

[0179] Normalized Laplace matrix L N for:

[0180]

[0181] Where D = diag (d 1 ,d 2 ,...d n ), is d i The diagonal matrix formed.

[0182] Optionally, the normalized Laplace matrix is ​​segmented based on spectral clustering, and the frequency space grouping of the power system is determined according to the segmentation result, including:

[0183] Calculate the normalized Laplacian matrix L N The first k eigenvalues ​​diag(λ 1 ,λ 2 ,...,λ k ) and the eigenvector matrix T = [t 1 ,t 2 ,...,t k ] n×k ;

[0184] Transpose the eigenvector matrix T to get the transposed matrix T T =[t 1 ,t 2 ,...,t k ] T k×n , where the transposed matrix T T The i-th column vector in the matrix is ​​the coordinate of the observation node i in the k-dimensional mapping space, that is, v i =[t 1i , t 2i ,...t ki ] T ;

[0185] In the k-dimensional mapping space, a clustering algorithm is used to divide the nodes in the vertex set V into k categories, so as to segment the undirected graph G of frequency similarity information and determine the frequency space clustering of the power system.

[0186] Exemplary Electronic Devices

[0187] Fig.11 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Fig.11 As shown, the electronic device 110 includes one or more processors 111 and a memory 112 .

[0188] The processor 111 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.

[0189] The memory 112 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 111 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 113 and an output device 114, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0190] In addition, the input device 113 may also include, for example, a keyboard, a mouse, and the like.

[0191] The output device 114 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.

[0192] Of course, to simplify, Fig.11 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.

[0193] Exemplary computer program products and computer-readable storage media

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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 frequency adaptive spectral clustering spatial clustering method for power system, characterized in that: include: Acquire frequency measurement data of different observation nodes after the power system is disturbed, and construct a measurement space of the power system; extracting corresponding frequency measurement data from the measurement space according to the time scale of the adaptively selected frequency measurement data; Constructing an undirected graph of similar information of the frequency measurement data of the power system according to the frequency measurement data; A cluster analysis is performed on the undirected graph to determine frequency space clustering of the power system.

2. The method according to claim 1, characterized in that: Extracting corresponding frequency measurement data from the measurement space according to the time scale of the adaptively selected frequency measurement data includes: Calculate the first extreme point of the frequency deviation value sequence of each node in the measurement space and its corresponding frequency deviation amplitude in turn, and construct the extreme point vector F c and the frequency first swing extreme value deviation amplitude vector ΔF; Take the minimum value of the time when the frequency deviation amplitude of each node is greater than min(ΔF) / 100 as the starting time t0 of the measurement data; Take the time corresponding to the maximum value of the data sequence in the frequency first swing extreme value deviation amplitude vector ΔF as the measurement data termination time t d ; According to the starting time t0 and the ending time td, the time scale T of the measurement data is adaptively determined as T=[t0,t d ]; The corresponding frequency measurement data is extracted from the measurement space according to the time scale.

3. The method according to claim 2, characterized in that The extreme point vector F c for: F c =[f c1 ,f c2 ,...,f cn ] T Among them, f cn is the first swing extreme value of the nth measurement node; The frequency first swing extreme value deviation amplitude vector ΔF is: ΔF=F c -F N =[Δf1,Δf2,...,Δf n ] T Among them, F N is the system rated frequency, Δf n is the first swing extreme value of the nth measurement node. If there is no extreme value point at node i within the measurement range, let f ci =max(f1,f2...f i-1 ), Δf i =min(Δf1,Δf2,...,Δf i-1 ).

4. The method according to claim 1, characterized in that: Constructing an undirected graph of similar information of the frequency measurement data of the power chest according to the frequency measurement data, including: averaging the frequency measurement data to determine average frequency measurement data; Calculating a differential mode component of the frequency measurement data according to the average frequency measurement data; Calculate the correlation coefficient of the similarity of the frequency response process between nodes of the power system according to the average frequency measurement data and the differential mode component; According to the correlation coefficient, a correlation coefficient matrix of the similarity of frequency transient curves between any two measurement nodes of the power system is constructed; The undirected graph of similarity information of the frequency measurement data of the power system is constructed according to the correlation coefficient matrix.

5. The method according to claim 4, characterized in that The calculation formula of the average frequency measurement data and the differential mode component is: In the formula, is the node average frequency measurement data; f′ is the frequency deviation value of the measurement node relative to the average frequency, that is, the differential mode component.

6. The method according to claim 4, characterized in that The correlation coefficient r ij The calculation formula is: In the formula, r ij is the Pearson correlation coefficient of the frequency measurement data sequence of measurement nodes i and j; f ik ′ is the kth data of measurement node i in the measurement space; f j ' k is the kth data of measurement node j in the measurement space; is the average frequency of measurement node i in the measurement space; is the average frequency of measurement node j in the measurement space; m is the number of rows in the measurement space.

7. The method according to claim 4, characterized in that The adjacency matrix A(G) of the undirected graph G is: In the formula, r ij is the correlation coefficient, a ij is the adjacency matrix coefficient.

8. The method according to claim 1, characterized in that Performing cluster analysis on the undirected graph to determine the frequency space grouping of the power system includes: Calculating a normalized Laplacian matrix of the undirected graph; The normalized Laplace matrix is ​​graph segmented based on spectral clustering, and the frequency space grouping of the power system is determined according to the graph segmentation result.

9. The method according to claim 8, characterized in that Calculating a normalized Laplacian matrix of the undirected graph includes: Calculating a non-normalized Laplacian matrix of the undirected graph; The normalized Laplacian matrix of the undirected graph is constructed according to the non-normalized Laplacian matrix.

10. The method according to claim 9, characterized in that The non-normalized Laplace matrix L is: in, n is the total number of vertices of the undirected graph G; The normalized Laplace matrix L N for: Where D = diag(d1, d2, ...d n ), is d i The diagonal matrix formed.

11. The method according to claim 8, characterized in that Performing graph segmentation on the normalized Laplace matrix based on spectral clustering, and determining the frequency space grouping of the power system according to the graph segmentation result, including: Calculate the normalized Laplace matrix L N The first k eigenvalues ​​diag(λ1,λ2,...,λ k ) and the eigenvector matrix T = [t1, t2, ..., t k ] n×k ; Transpose the eigenvector matrix T to obtain the transposed matrix T T =[t1,t2,...,t k ] T k×n , where the transposed matrix T T The i-th column vector in the matrix is ​​the coordinate of the observation node i in the k-dimensional mapping space, that is, v i =[t 1i , t 2i ,...t ki ] T ; In the k-dimensional mapping space, a clustering algorithm is used to divide the nodes in the vertex set V into k categories, so as to segment the undirected graph G of the frequency similarity information and determine the frequency space clustering of the power system.

12. A frequency adaptive spectrum clustering spatial grouping device for power system, characterized in that: include: A first construction module is used to obtain frequency measurement data of different observation nodes after the power system is disturbed, and to construct a measurement space of the power system; An extraction module, configured to extract corresponding frequency measurement data from the measurement space according to the time scale of the adaptively selected frequency measurement data; A second construction module is used to construct an undirected graph of similar information of the frequency measurement data of the power system according to the frequency measurement data; A determination module is used to perform cluster analysis on the undirected graph to determine the frequency space clustering of the power system.

13. The device according to claim 12, characterized in that Extraction modules, including: The construction submodule is used to sequentially calculate the first swing extreme point of the frequency deviation value sequence of each node in the measurement space and its corresponding frequency deviation amplitude, and construct the extreme point vector F c and the frequency first swing extreme value deviation amplitude vector ΔF; The first submodule is used to take the minimum value of the time corresponding to the frequency deviation amplitude of each node being greater than min(ΔF) / 100 as the starting time t0 of the measurement data; The second submodule is used to take the time corresponding to the maximum value of the data sequence in the frequency first swing extreme value deviation amplitude vector ΔF as the measurement data termination time t d ; A determination submodule is used to determine the starting time t0 and the ending time t d , adaptively determine the time scale T of the measurement data = [t0, t d ]; The extraction submodule is used to extract the corresponding frequency measurement data from the measurement space at the time scale.

14. 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 11.

15. 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 1-11.

Images