New energy unit grouping method based on admittance laplacian matrix and multi-dimensional binary coding quadrant division

Abstract

The application provides a new energy unit grouping method based on an admittance Laplacian matrix and a multi-dimensional binary coding quadrant division, and comprises the following steps: reading power grid topology data and element parameters to obtain a branch table of the power grid; constructing an admittance Laplacian matrix according to the branch table of the power grid; calculating eigenvalues and eigenvectors of the admittance Laplacian matrix; sorting n eigenvalues in ascending order to obtain new eigenvalues and a corresponding eigenvector sequence; and dividing nodes in the power grid by using a Laplacian spectrum multi-dimensional binary coding quadrant division method. The network topology is described by the constructed Laplacian matrix, the nodes in the power grid are divided by using the Laplacian spectrum multi-dimensional binary coding quadrant division method, and the new energy unit can be accurately grouped. The application is suitable for grouping and clustering of various new energy stations.

Description

Technical Field

[0001] This invention relates to a method for grouping new energy power plant units, specifically a method for grouping new energy power plant units based on the admittance Laplace matrix and multi-dimensional binary coding quadrant division. Background Technology

[0002] When modeling new energy power plants, due to the large number of generating units within the plant, it is often necessary to perform equivalent transformations to reduce the computational load of the simulation. Before performing the equivalent transformation, the generating units need to be divided into several relatively independent groups based on their electrical connections.

[0003] Existing methods mainly use the k-means clustering method, which has difficulty accurately identifying the electrical connection strength between units, resulting in inaccurate clustering results. Summary of the Invention

[0004] This invention proposes a new energy generator grouping method based on the admittance Laplace matrix and multidimensional binary coding quadrant division. By calculating the spectrum of the admittance Laplace matrix and performing multidimensional quadrant division, the generator can be accurately divided into a specified generator group.

[0005] This invention is achieved using the following technical solution:

[0006] A new energy component grouping method based on admittance Laplace matrix and multidimensional binary coding quadrant partitioning includes the following steps:

[0007] Step 1: Read the power grid topology data and component parameters to obtain the power grid branch table:

[0008] T l ={N i N j Z ij},i=1,2,…,n, j=1,2,…,n;

[0009] Where N i Z is a branch node. ij Let n be the branch impedance between nodes i and j, and n be the number of network nodes.

[0010] Step 2: Construct the admittance Laplace matrix L based on the branch table of the power grid as follows:

[0011]

[0012] Step 3: Calculate the eigenvalues ​​and eigenvectors of the admittance Laplace matrix L:

[0013] D = [λ1, λ2, ... λ] n V = [v1, v2, ... v] n ]

[0014] Where λ1, λ2, ... λ n Let v1, v2, ... v be the eigenvalues ​​of the admittance Laplace matrix. n These are the eigenvectors of the admittance Laplace matrix;

[0015] Step 4: Sort the n eigenvalues ​​in ascending order to obtain a new sequence of eigenvalues ​​and their corresponding eigenvectors.

[0016] D S =[λ S1 ,λ S2 ,…λ Sn ], V S =[v S1 ,v S2 ,…v Sn ]

[0017] Where λ S1 ,λ S2 ,…λ Sn λ is the new eigenvalue of the admittance Laplace matrix. S1 ,λ S2 ,…λ Sn To introduce new eigenvectors of the Laplace matrix;

[0018] Step 5: Based on the new eigenvalues ​​and corresponding eigenvector sequences of the admittance Laplace matrix L obtained in Step 4, the nodes in the power grid are divided using the Laplace spectrum multidimensional binary coding quadrant partitioning method.

[0019] Furthermore, in step five, the nodes in the power grid are divided using the Laplace spectrum multidimensional binary encoding quadrant partitioning method. Specifically, this includes: encoding the feature vectors into binary based on the signs of the first 2 to m+1 feature vectors, determining the quadrant of the node based on the binary values, and assigning the node to different categories based on the quadrant of the node.

[0020] Furthermore, step five specifically includes:

[0021] (5.1) Arrange the sorted eigenvectors from the 2nd to the (m+1)th into an n×m matrix:

[0022] V MS =[v S2 v S2 … v S(m+1) ]

[0023] (5.2) V MS Each row can be viewed as a coordinate in an m-dimensional space, and V is read. MS row data V MSi And define a new quadrant vector matrix C, where each element is calculated as follows:

[0024]

[0025] (5.3) Perform the following calculations on the elements of C to obtain V. MSi Binary quadrant encoding Q Bi :

[0026]

[0027] (5.4) Q Bi Convert to decimal to obtain the decimal quadrant number Q of this node. Di :

[0028] Q Di =DEC(Q Bi )

[0029] Where DEC is the binary-to-decimal conversion function;

[0030] (5.5) Transform vector Q D Non-repeating elements are re-encoded in ascending order:

[0031] Q Di =n, where n is Q Di In Q D The order of non-repeating elements in the text;

[0032] (5.6) Divide node i into block Q Di middle.

[0033] The beneficial effects of this invention are:

[0034] 1. This invention uses an admittance-based Laplace matrix to describe the topological relationship between new energy units, which can better reflect the impact of impedance between actual units on clustering;

[0035] 2. Based on the eigenvalues ​​and eigenvectors of the obtained admittance Laplace matrix L, this invention uses the Laplace spectrum multidimensional binary coding quadrant partitioning method to divide the nodes in the power grid. This method has high discriminative power and can accurately divide the nodes in the power grid into different groups. Attached Figure Description

[0036] Figure 1 This is an electrical wiring diagram of a new energy photovoltaic power station according to an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of the clustering results using an embodiment of the present invention. Detailed Implementation

[0038] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings.

[0039] This invention provides a method for grouping new energy components based on the admittance Laplace matrix and multidimensional binary coding quadrant partitioning, comprising the following steps:

[0040] (1) Read the power grid topology and component parameters to obtain the branch table:

[0041] T l ={N i N j Z ij},i=1,2,…,n, j=1,2,…,n; (5)

[0042] Where N i Z is a branch node. ij Let n be the branch impedance between nodes i and j, and n be the number of network nodes.

[0043] (2) Construct the following admittance Laplace matrix:

[0044]

[0045] (3) Calculate the eigenvalues ​​and eigenvectors of the admittance Laplace matrix:

[0046] D = [λ1, λ2, ... λ] n V = [v1, v2, ... v] n ]

[0047] (4) Sort the n eigenvalues ​​in ascending order to obtain a new sequence of eigenvalues ​​and corresponding eigenvectors:

[0048] D S =[λ S1 ,λ S2 ,…λ Sn ], V S =[v S1 ,v S2 ,…v Sn ]

[0049] (5) Determine the partition coefficient m, and arrange the 2nd to m+1th sorted feature vectors into an n×m matrix:

[0050] V MS =[v S2 v S3 … v S(m+1) ]

[0051] (6) V MSEach row is considered as a coordinate in m-dimensional space. The division is performed based on the quadrant of the coordinates corresponding to each node. The steps are as follows:

[0052] 1) Read V MS row data V MSi And define a new quadrant vector matrix C, where each element is calculated as follows:

[0053]

[0054] 2) Perform the following calculations on the elements of C to obtain V. MSi Binary quadrant encoding Q Bi :

[0055]

[0056] 3) Q Bi Convert to decimal to obtain the decimal quadrant number Q of this node. Di :

[0057] Q Di =DEC(Q Bi )

[0058] DEC is the binary-to-decimal conversion function.

[0059] 4) Transfer vector Q D Non-repeating elements are re-encoded in ascending order:

[0060] Q Di =n, where n is Q Di In Q D The order of non-repeating elements in the text.

[0061] 5) Divide node i into block Q Di middle.

[0062] The method will be further explained below with reference to the accompanying diagram and a specific example:

[0063] A certain new energy photovoltaic power station contains 30 inverters, 30 box-type substations, 1 main transformer, and 32 collector lines. The electrical wiring diagram is as follows. Figure 1 As shown. The division factor m is taken as 3. The parameters of each power grid component are shown in Tables 1, 2, and 3:

[0064] Table 1 Branch Road Table

[0065]

[0066] Table 2 Transformer Table

[0067]

[0068]

[0069] Table 3 Inverter Table

[0070]

[0071]

[0072] Based on the above data, the admittance Laplace matrix L can be calculated as follows, as shown in Table 4:

[0073] Table 4

[0074] 1 2 3 4 5 6 7 8 9 10 ... 63 1 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 2 0.00 0.50 -0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 3 0.00 -0.50 354.49 0.00 -240.55 0.00 0.00 0.00 0.00 0.00 ... 0.00 4 0.00 0.00 0.00 0.50 -0.50 0.00 0.00 0.00 0.00 0.00 ... 0.00 5 0.00 0.00 -240.55 -0.50 320.63 0.00 -79.58 0.00 0.00 0.00 ... 0.00 6 0.00 0.00 0.00 0.00 0.00 0.50 -0.50 0.00 0.00 0.00 ... 0.00 7 0.00 0.00 0.00 0.00 -79.58 -0.50 80.08 0.00 0.00 0.00 ... 0.00 8 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 -0.50 0.00 ... 0.00 9 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.50 68.37 0.00 ... 0.00 10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 ... 0.00 ... ... ... ... ... ... ... ... ... ... ... ... ... 63 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 2.56

[0075] Calculate the eigenvalues ​​and eigenvectors of L, and sort the eigenvectors according to the magnitude of the eigenvalues ​​to obtain a new matrix, as shown in Table 5:

[0076] Table 5

[0077] 1 2 3 4 5 6 7 8 9 10 ... 63 1 0.13 -0.07 -0.05 -0.09 -0.06 -0.52 0.23 0.01 0.07 0.12 ... 0.00 2 0.13 -0.04 -0.02 -0.03 -0.02 -0.04 -0.38 -0.08 0.08 0.09 ... 0.00 3 0.13 -0.01 0.00 0.00 0.00 0.00 -0.01 0.00 0.00 0.00 ... 0.13 4 0.13 -0.04 -0.02 -0.03 -0.02 -0.05 -0.43 -0.10 0.14 0.16 ... 0.00 5 0.13 -0.01 0.00 0.00 0.00 0.00 -0.02 0.00 0.00 0.00 ... -0.06 6 0.13 -0.04 -0.02 -0.04 -0.02 -0.05 -0.52 -0.13 0.24 0.32 ... 0.00 7 0.13 -0.01 0.00 0.00 0.00 0.00 -0.02 0.00 0.00 0.00 ... 0.01 8 0.13 -0.08 -0.07 -0.18 -0.43 0.26 0.17 0.01 0.09 0.17 ... 0.00 9 0.13 -0.01 -0.01 -0.02 -0.03 0.02 0.01 0.00 0.00 0.00 ... 0.00 10 0.13 -0.08 -0.07 -0.17 -0.39 0.23 0.14 0.01 0.05 0.07 ... 0.00 ... ... ... ... ... ... ... ... ... ... ... ... 63 0.13 -0.01 0.00 0.00 0.00 0.00 -0.01 0.00 0.00 0.00 ... 0.00

[0078] Since the partitioning coefficient m is 3, the 2nd to 4th eigenvectors are selected to form a new matrix V. MS See Table 6:

[0079] Table 6

[0080]

[0081]

[0082]

[0083] Based on the above matrix V MS The calculated matrix C is shown in Table 7:

[0084] Table 7

[0085]

[0086]

[0087] The matrix C is encoded in binary to obtain the binary encoded vector Q. B See Table 8:

[0088] Table 8

[0089]

[0090]

[0091]

[0092] QB Converting the elements in the vector to decimal, we get the group vector Q. D See Table 9:

[0093] Table 9

[0094]

[0095]

[0096] Q D The elements in the array are then re-encoded in ascending order, with the non-repeating elements being 0, 1, 3, and 7. 0 is re-encoded as 1, 1 as 2, 3 as 3, and 7 as 4, resulting in the final Q. D See Table 10:

[0097] Table 10

[0098]

[0099]

[0100]

[0101] Based on the above clustering results, the clustering can be obtained as follows: Figure 2 As shown in the figure, nodes of the same color represent a partition. As can be seen from the figure, nodes connected to the same branch are assigned to the same partition, verifying the correctness of the method in this patent.

[0102] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for grouping new energy components based on admittance Laplace matrix and multidimensional binary coding quadrant partitioning, characterized in that... Includes the following steps: Step 1: Read the power grid topology data and component parameters to obtain the power grid branch table: ； in , For branch nodes, Let n be the branch impedance between nodes p and q, and n be the number of network nodes. Step 2: Construct the admittance Laplace matrix L based on the branch table of the power grid as follows: ； Step 3: Calculate the eigenvalues ​​and eigenvectors of the admittance Laplace matrix L: ， ； in For the eigenvalues ​​of the derivative Laplace matrix, These are the eigenvectors of the admittance Laplace matrix; Step 4: Sort the n eigenvalues ​​in ascending order to obtain new eigenvalues. and the corresponding sequence of feature vectors: ， ； in To find the new eigenvalues ​​of the Narplacian matrix, To introduce new eigenvectors of the Laplace matrix; Step 5: New eigenvalues ​​based on the admittance Laplace matrix L obtained in Step 4. The nodes in the power grid are divided using the Laplace spectral multidimensional binary coding quadrant partitioning method, along with the corresponding feature vector sequences. Step five specifically includes: (5.1) Arrange the 2nd to m+1th sorted feature vectors into a single array. The matrix: ； (5.2) will Each row can be viewed as coordinates in an m-dimensional space. Read... row data And define a new quadrant vector matrix. , Each element in the algorithm is calculated as follows: ； (5.3) will The elements in are calculated as follows to obtain Binary quadrant encoding : ； (5.4) will Convert to decimal to obtain the decimal quadrant number of the node. : ； Where DEC is the binary-to-decimal conversion function; (5.5) Transform the vector Non-repeating elements are re-encoded in ascending order: n is exist The order of non-repeating elements in the text; (5.6) Divide node i into blocks middle.

2. The new energy component grouping method based on admittance Laplace matrix and multidimensional binary coding quadrant partitioning as described in claim 1, characterized in that, Step five uses the Laplace spectrum multidimensional binary encoding quadrant partitioning method to partition the nodes in the power grid. Specifically, it includes: encoding the feature vectors into binary form based on the signs of the first 2 to m+1 feature vectors, determining the quadrant of the node based on the binary values, and assigning the node to different categories based on the quadrant of the node.

Images