A power distribution network power quality treatment zoning method and device

Abstract

The application provides a power distribution network power quality treatment zoning method and device, and belongs to the technical field of power distribution network treatment, which comprises the following steps: calculating the active and reactive voltage sensitivity of each node in the power distribution network according to the network topology and parameters, and obtaining the electrical distance between the nodes; obtaining the historical measurement data of each node harmonic, three-phase imbalance and voltage deviation; obtaining the change trend correlation degree and amplitude correlation degree by using grey correlation analysis; giving different weights to the change trend correlation degree, amplitude correlation degree and electrical distance and adding them together to obtain the comprehensive correlation degree; performing region division by using the spectral clustering algorithm and elbow method; selecting a leading node and installing a treatment device at the leading node to complete the power distribution network power quality treatment zoning. The application constructs a zoning evaluation system from multiple dimensions of power quality, reflects the actual correlation degree of the nodes, and realizes rapid and accurate power distribution network power quality region division in combination with the grey correlation degree method.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution network management, and in particular relates to a method and device for zoning power quality management of a power distribution network. Background Art

[0002] With the increasing number of power electronic devices connected to distribution networks, such as electric vehicles, air conditioners, and distributed photovoltaics, the distribution of power quality pollution sources in distribution networks is becoming more randomized, decentralized, and network-wide. Traditional point-to-point governance strategies for addressing major power quality disturbance sources are no longer sufficient to meet the power quality needs of the entire distribution network. A comprehensive power quality governance strategy that can coordinate governance across the entire network is urgently needed. Partitioning the distribution network and implementing power governance at regionally dominant nodes, a point-to-surface approach, is a popular research direction.

[0003] The existing technology proposes a strategy for power quality zoning based on the electrical distance of nodes. Nodes with close electrical distances are divided into one area. However, zoning based solely on electrical distance cannot accurately reflect the actual power quality changes of nodes in the area. The correlation between different nodes is not only related to the electrical distance but also to many factors such as the distribution of the disturbance source. Zoning based solely on electrical distance cannot reflect the true degree of correlation between nodes.

[0004] Existing technologies have proposed a power quality zoning management strategy based on historical power quality measurement data from existing power grids. This strategy calculates node correlation based on the historical power quality trends of different nodes, assuming that the closer the historical power quality trends of the nodes, the higher the correlation. However, this method relies solely on node power quality trends, ignoring the randomness of node-to-node trends. It is possible that two nodes in a region may have very similar trends due to chance, even though they are actually electrically far apart and have a low correlation. This would result in two nodes with low correlation being grouped into the same region, which is clearly inconsistent with reality. Summary of the Invention

[0005] In response to the above-mentioned deficiencies in the prior art, the present invention provides a distribution network power quality management zoning method and device, which solves the problems of slow zone division speed and low actual correlation of nodes in the divided areas.

[0006] In order to achieve the above objectives, the technical solution adopted by the present invention is as follows: On the one hand, the present invention provides a method for zoning power quality management of a distribution network, comprising the following steps:

[0007] S1. Based on the network topology and parameters of the distribution network, the reactive voltage sensitivity and active voltage sensitivity of each node in the distribution network are calculated using the power flow equation, and the electrical distance between each node is calculated based on the reactive voltage sensitivity and active voltage sensitivity of each node;

[0008] S2. Build a time series measurement data set using historical measurement data of harmonics, three-phase unbalance, and voltage deviation at each node;

[0009] S3. Normalize the time series measurement data set using a normalization method, and analyze the normalized time series measurement data set using gray correlation to obtain the change trend correlation and amplitude correlation of each node;

[0010] S4. Determine the comprehensive correlation between each node based on the change trend correlation, amplitude correlation, and electrical distance of each node, and integrate the comprehensive correlation between each node to obtain a comprehensive correlation matrix;

[0011] S5. Use the spectral clustering algorithm to cluster the comprehensive correlation matrix and combine it with the elbow method to determine the final number of regional divisions. Based on the final number of regional divisions, regional division for harmonic, three-phase unbalance and voltage deviation management is achieved;

[0012] S6. According to the regional division results, select the node with the largest sum of comprehensive correlation with all nodes in any area as the dominant node in the area, and install management equipment on the dominant node to complete the power quality management zoning of the distribution network.

[0013] The beneficial effects of the present invention are as follows: the present invention evaluates the correlation between different nodes from multiple dimensions such as electrical distance, change trend and amplitude, and is more comprehensive than existing partitioning technologies. The regional division results are more reasonable and will not be affected by chance, thus avoiding the unreasonable phenomenon of nodes with low correlation being divided into the same area. The dominant nodes in the area can be accurately identified to guide the installation of relevant governance equipment and improve the overall power quality improvement level in the area. It can also be used to guide the installation of monitoring devices, and use the dominant nodes to represent the overall regional change level, thereby reducing the installation cost of distribution network monitoring equipment.

[0014] Furthermore, the S1 includes the following steps:

[0015] S101. Calculate the reactive voltage sensitivity and active voltage sensitivity of each node based on the Newton-load equation according to the network topology and parameters of the distribution network.

[0016] S102, obtaining the voltage sensitivity between each node using a sensitivity equation based on the reactive voltage sensitivity and active voltage sensitivity of each node;

[0017] S103 , calculating the electrical distance between each node using an electrical distance formula according to the voltage sensitivity between each node.

[0018] Furthermore, the voltage sensitivity equation is as follows:

[0019]

[0020] Among them, A ij represents the voltage sensitivity between node i and node j, Represents the voltage amplitude of node j V j Active voltage sensitivity at node i, Represents the voltage amplitude of node i V i Active voltage sensitivity at node j, Represents the voltage amplitude of node j V j Reactive voltage sensitivity to node i, Represents the voltage amplitude of node i V i The reactive voltage sensitivity of node j is: Represents the voltage amplitude of node i V i The partial derivative of ΔP i represents the transformation of active power at node i, ΔQ i Represents the conversion amount of reactive power at node i.

[0021] Furthermore, the S3 includes the following steps:

[0022] S301. Normalize the historical measurement data of harmonics, three-phase unbalance, and voltage deviation at each node based on the time series measurement data set, and map the historical measurement data to the interval [0, 1].

[0023] S302. Analyze the correlation of the changing trends of harmonics, three-phase imbalance, and voltage deviation at different nodes using a grey correlation analysis method based on the normalized time series measurement data set.

[0024] S303 , based on the normalized time series measurement data set, a grey correlation analysis method is used to analyze the amplitude correlation of harmonics, three-phase imbalance, and voltage deviation at different nodes.

[0025] The beneficial effect of the above further solution is: the present invention adopts the grey correlation analysis method to accurately analyze the similarity correlation of indicators of different dimensions of different nodes, providing a reasonable basis for subsequent regional division.

[0026] Furthermore, the S5 includes the following steps:

[0027] S501, using a spectral clustering algorithm to divide the distribution network into regions, calculating a degree matrix based on the comprehensive correlation matrix of harmonics, three-phase imbalance, and voltage deviation at each node, and symmetric normalizing the degree matrix to obtain a Laplace matrix;

[0028] S502. According to the eigenvalues ​​and eigenvectors of the Laplace matrix, select the eigenvectors corresponding to the first k smallest eigenvalues ​​to form an eigenvector matrix;

[0029] S503, taking each row of the eigenvector matrix as a data point, and clustering the data points using the k-means algorithm in the eigenspace;

[0030] S504. Based on the clustering results, the elbow method is used to determine the number of clusters, and the value changes in the k-means algorithm under different cluster numbers are analyzed to determine the inflection point where the value in the k-means algorithm drops sharply as k increases.

[0031] S505 : According to the inflection point position, the number of clusters corresponding to the inflection point position is selected as the optimal number of clusters, and the optimal number of clusters is used as the final number of region divisions.

[0032] Furthermore, the degree matrix F is a diagonal matrix, and the diagonal elements F in the diagonal matrix F are ii is the sum of the comprehensive associations between all nodes connected to node i, and the diagonal element F ii The formula is as follows:

[0033]

[0034] Among them, Z ij Represents the element in row i and column j of the comprehensive correlation matrix;

[0035] The formula of the Laplace matrix L is as follows:

[0036] L=IF 1 / 2 ZF -1 / 2

[0037] Among them, I represents the identity matrix and Z represents the comprehensive correlation matrix.

[0038] The beneficial effects of the above further scheme are as follows: the present invention adopts the spectral clustering algorithm and the elbow method to achieve region division that does not depend on the initial point, does not require the pre-setting of the number of partitions, more objectively determines the number of partitions based on the actual correlation data, and can perform effective clustering in high-dimensional data, reducing the complexity and noise interference of data in high-dimensional space.

[0039] Furthermore, the calculation formula of the comprehensive correlation is as follows:

[0040]

[0041] Among them, ε represents the comprehensive correlation, Indicates the correlation degree of harmonic amplitude of each node, Indicates the correlation degree of harmonic change trend of each node, D hrepresents electrical distance, b1, b2, and b3 represent the weight coefficients of amplitude correlation, change trend correlation, and electrical distance, and b1+b2+b3=1;

[0042] The comprehensive correlation matrix is ​​as follows:

[0043]

[0044] Among them, ε ij Represents the comprehensive correlation between the i-th node and the j-th node.

[0045] On the other hand, the present invention provides a distribution network power quality management zoning device, characterized by comprising:

[0046] Obtaining electrical distance module: It is used to calculate the reactive voltage sensitivity and active voltage sensitivity of each node in the distribution network using the power flow equation according to the network topology and parameters of the distribution network, and to obtain the electrical distance between each node based on the reactive voltage sensitivity and active voltage sensitivity of each node;

[0047] Dataset Construction Module: used to construct a time series measurement dataset using historical measurement data of harmonics, three-phase unbalance, and voltage deviation at each node;

[0048] Correlation analysis module: used to normalize the time series measurement data set using the normalization method, and analyze the normalized time series measurement data set using the grey correlation degree to obtain the change trend correlation degree and amplitude correlation degree of each node respectively;

[0049] Comprehensive correlation module: used to determine the comprehensive correlation between nodes based on the change trend correlation, amplitude correlation and electrical distance of each node, and integrate the comprehensive correlation between nodes to obtain a comprehensive correlation matrix;

[0050] Spectral clustering module: This module uses a spectral clustering algorithm to cluster the comprehensive correlation matrix and combines it with the elbow method to determine the final number of regional divisions. Based on the final number of regional divisions, regional divisions are implemented to manage harmonics, three-phase imbalance, and voltage deviation.

[0051] Selection module: It is used to select the node with the largest sum of comprehensive correlation with all nodes in any area as the dominant node in the area according to the regional division results, and install management equipment on the dominant node to complete the power quality management zoning of the distribution network.

[0052] Furthermore, the module for obtaining the electrical distance includes:

[0053] Newton-power flow equation submodule: used to calculate the reactive voltage sensitivity and active voltage sensitivity of each node based on the Newton-power flow equation according to the network topology and parameters of the distribution network;

[0054] Voltage sensitivity submodule: used to obtain the voltage sensitivity between nodes based on the reactive voltage sensitivity and active voltage sensitivity of each node using the sensitivity equation;

[0055] Electrical distance submodule: used to calculate the electrical distance between nodes based on the voltage sensitivity between nodes using the electrical distance formula.

[0056] Furthermore, the correlation analysis module includes:

[0057] Normalization submodule: It is used to normalize the historical measurement data of harmonics, three-phase imbalance, and voltage deviation of each node based on the time series measurement data set, and map the historical measurement data to the [0,1] interval;

[0058] Change trend correlation submodule: It is used to analyze the change trend correlation of harmonics, three-phase imbalance and voltage deviation of different nodes based on the normalized time series measurement data set using the grey correlation analysis method;

[0059] Amplitude correlation submodule: It is used to analyze the amplitude correlation of harmonics, three-phase imbalance and voltage deviation at different nodes using the grey correlation analysis method based on the normalized time series measurement data set.

[0060] Furthermore, the spectral clustering module includes:

[0061] Degree matrix submodule: used to calculate the degree matrix based on the comprehensive correlation matrix of harmonics, three-phase imbalance and voltage deviation of each node, and perform symmetric normalization on the degree matrix to obtain the Laplace matrix;

[0062] Eigenvector submodule: used to select the eigenvectors corresponding to the first k smallest eigenvalues ​​according to the eigenvalues ​​and eigenvectors of the Laplace matrix to form an eigenvector matrix;

[0063] K-means submodule: It is used to treat each row of the eigenvector matrix as a data point and cluster the data points using the K-means algorithm in the feature space;

[0064] Elbow method submodule: used to determine the number of clusters based on the clustering results using the elbow method, analyze the value changes in the k-means algorithm under different cluster numbers, and determine the inflection point where the value in the k-means algorithm drops sharply as k increases;

[0065] Optimal number of clusters submodule: It is used to select the number of clusters corresponding to the inflection point position as the optimal number of clusters according to the inflection point position, and use the optimal number of clusters as the final number of region divisions.

[0066] The present invention provides a computer device, characterized by comprising: one or more processors;

[0067] The processor is used to store one or more programs; when the one or more programs are executed by the one or more processors, the distribution network power quality management zoning method as described in any one of the above items is implemented.

[0068] The present invention provides a computer-readable storage medium, characterized in that a computer program is stored thereon, and when the computer program is executed, the distribution network power quality management zoning method as described in any one of the above items is implemented.

[0069] The beneficial effects of the above further scheme are: the present invention provides a distribution network power quality management zoning device, which evaluates the correlation between different nodes from multiple dimensions such as electrical distance, change trend and amplitude. Compared with the existing zoning technology, it is more comprehensive and the regional division results are more reasonable, which reduces the installation cost of distribution network monitoring equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 Flow chart of the method of the present invention.

[0071] Figure 2 This is the IEEE33-node distribution network diagram.

[0072] Figure 3 This is the result diagram of the 5th harmonic content division of the method of the present invention.

[0073] Figure 4 This is a diagram showing the classification results of the 5th harmonic content rate in the prior art.

[0074] Figure 5 This is a comparison diagram before and after installing harmonic control devices at harmonic-dominant nodes.

[0075] Figure 6 This is a comparison diagram before and after installing the three-phase unbalance control device at the three-phase unbalance dominant node.

[0076] Figure 7 This is a comparison diagram before and after installing the voltage management device at the node with dominant voltage deviation.

[0077] Figure 8 Schematic diagram of the device structure of the present invention. DETAILED DESCRIPTION

[0078] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0079] Example 1

[0080] like Figure 1 As shown, the present invention provides a distribution network power quality management zoning method, which is implemented as follows:

[0081] S1. Based on the network topology and parameters of the distribution network, the reactive voltage sensitivity and active voltage sensitivity of each node in the distribution network are calculated using the power flow equation. The electrical distance between each node is then calculated based on the reactive voltage sensitivity and active voltage sensitivity of each node. The implementation method is as follows:

[0082] S101. Calculate the reactive voltage sensitivity and active voltage sensitivity of each node based on the Newton-load equation according to the network topology and parameters of the distribution network.

[0083] S102, obtaining the voltage sensitivity between each node using a sensitivity equation based on the reactive voltage sensitivity and active voltage sensitivity of each node;

[0084] S103 , calculating the electrical distance between each node using an electrical distance formula according to the voltage sensitivity between each node.

[0085] In this embodiment, Figure 2 As shown in FIG, according to the network topology and parameters, the reactive voltage sensitivity and active voltage sensitivity of each node are calculated based on the power flow equation, and the electrical distance d1 between each node is further obtained.

[0086] In this embodiment, electrical distance is the most obvious indicator of the correlation between two nodes. The closer the electrical distance between two nodes, the greater the degree of mutual influence. The electrical distance can be calculated based on the Newton-current equation in polar coordinates. The formula of the Newton-current equation is as follows:

[0087]

[0088] Among them, ΔP represents the transformation vector of node active power, ΔQ represents the transformation vector of node reactive power, Δθ represents the transformation vector of node phase angle, ΔV represents the transformation vector of node voltage, H, N, K and L represent different block matrices. The specific expressions are as follows:

[0089]

[0090] Among them, H ij Represents the element in the i-th row and j-th column of the block matrix H, N ij represents the element in the i-th row and j-th column of the block matrix N, K ij represents the element in the i-th row and j-th column of the block matrix K, L ij represents the element in the i-th row and j-th column of the block matrix L, V j represents the voltage at node j, represents the active power P of node i i The transformation of the phase angle θ of node j j The partial derivative of represents the active power P of node i i The change in the voltage amplitude V at node j is j The partial derivative of Represents the reactive power Q of node i i The transformation of the phase angle θ of node j j The partial derivative of Represents the reactive power Q of node i i The change in the voltage amplitude V at node j is j The partial derivative of .

[0091] Therefore, according to N ij With L ij The node voltage for node i active power P can be obtained i and reactive power Q of node i i Sensitivity and Further, according to the voltage sensitivity formula, the voltage sensitivity A between nodes can be obtained. ij , the voltage sensitivity equation is shown below:

[0092]

[0093] Among them, A ij represents the voltage sensitivity between node i and node j, Represents the voltage amplitude of node j V j Active voltage sensitivity at node i, Represents the voltage amplitude of node i V i Active voltage sensitivity at node j, Represents the voltage amplitude of node j V j Reactive voltage sensitivity to node i, Represents the voltage amplitude of node i V i The reactive voltage sensitivity of node j is: Represents the voltage amplitude of node i V i The partial derivative of ΔP irepresents the transformation of active power at node i, ΔQ i represents the transformation amount of reactive power of node i;

[0094] According to the voltage sensitivity A ij The electrical distance D between nodes can be further obtained ij , the electrical distances are as follows:

[0095] D ij =log(A ij *A ji )

[0096] Wherein, log(·) represents the logarithmic function.

[0097] S2. Build a time series measurement data set using historical measurement data of harmonics, three-phase unbalance, and voltage deviation at each node;

[0098] In this embodiment, historical measurement data of harmonics, three-phase imbalance, and voltage deviation at each node are obtained.

[0099] S3. Use the normalization method to normalize the time series measurement data set, and use the grey correlation degree to analyze the normalized time series measurement data set to obtain the change trend correlation degree and amplitude correlation degree of each node. The implementation method is as follows:

[0100] S301. Normalize the historical measurement data of harmonics, three-phase unbalance, and voltage deviation of each node according to the time series measurement data set, and map the historical measurement data to the interval [0, 1].

[0101] S302. Analyze the correlation of the changing trends of harmonics, three-phase imbalance, and voltage deviation at different nodes using a grey correlation analysis method based on the normalized time series measurement data set.

[0102] S303 , based on the normalized time series measurement data set, a grey correlation analysis method is used to analyze the amplitude correlation of harmonics, three-phase imbalance, and voltage deviation at different nodes.

[0103] In this embodiment, the historical measurement data of harmonics, three-phase imbalance and voltage deviation are normalized, and grey correlation analysis is used to obtain the similarities dH1, dI1 and dV1 of the harmonics, three-phase imbalance and voltage deviation change trends of each node.

[0104] In this embodiment, based on historical measurement data of harmonics, three-phase imbalance, and voltage deviation, the similarities dH2, dI2, and dV2 between different power quality amplitudes are analyzed using grey correlation.

[0105] In this embodiment, when calculating the change trend of the harmonic, three-phase imbalance and voltage deviation of different nodes, the measured data needs to be normalized to map to the interval [0, 1], and the normalization formula is as follows:

[0106]

[0107] Wherein, X represents the time series measurement data set of the harmonic, three-phase imbalance and voltage deviation of the j node, x j (k) and respectively represent the harmonic, three-phase imbalance and voltage deviation measurement data of the j node before and after normalization at the k moment.

[0108] In this embodiment, after obtaining the harmonic, three-phase imbalance and voltage deviation measurement data set X of different nodes and the normalized data set Y, the grey correlation degree analysis method is used to analyze the similarity of the amplitude and the transformation trend of different nodes. Taking the harmonic amplitude data set X of different nodes as an example, the correlation degree of different nodes is analyzed. First, the correlation coefficient of node k and other nodes is calculated, and the formula of the correlation coefficient is as follows:

[0109]

[0110] Wherein, δ k (t) represents the grey correlation coefficient of the kth comparison sequence at the t moment, x0(t) represents the reference sequence, x k (t) represents the comparison sequence, ρ represents the resolution coefficient, which is usually set to 0.5, min k min t |x0(t)-x k (t)| represents the minimum difference of two poles, max k max t |x0(t)-x k (t)| represents the maximum difference of two poles.

[0111] After obtaining the grey correlation coefficient, the grey correlation degree λ k is calculated, and the formula of the grey correlation degree λ k is as follows:

[0112]

[0113] Wherein, n represents the number of nodes.

[0114] The formula of the correlation matrix T is as follows:

[0115]

[0116] Wherein, λ ij represents the grey correlation degree of the i th node and the j th node.

[0117] S4. Determine the comprehensive correlation between each node based on the change trend correlation, amplitude correlation, and electrical distance of each node, and integrate the comprehensive correlation between each node to obtain a comprehensive correlation matrix;

[0118] In this embodiment, different weights are assigned to the similarity indicators, and the comprehensive similarity indicators dH3, dI3, and dV3 of the harmonics, three-phase imbalance, and voltage deviation between the nodes are obtained by adding them up.

[0119] In this embodiment, since the amplitude correlation matrix, change trend correlation matrix, and electrical matrix of the three power quality factors, harmonics, three-phase imbalance, and voltage deviation, have been obtained respectively, a comprehensive similarity index needs to be established to evaluate the correlation of each node. Taking harmonics as an example, the formula for its comprehensive correlation is as follows:

[0120]

[0121] Among them, ε represents the comprehensive correlation, Indicates the correlation degree of harmonic amplitude of each node, Indicates the correlation degree of harmonic change trend of each node, D h represents the electrical distance, b1, b2, and b3 represent different weight coefficients, and b1+b2+b3=1. The comprehensive correlation matrix Z is obtained by calculating the comprehensive correlation degree of each node with other nodes.

[0122] The comprehensive correlation matrix is ​​as follows:

[0123]

[0124] Among them, ε ij Represents the comprehensive correlation between the i-th node and the j-th node.

[0125] S5. Use the spectral clustering algorithm to cluster the comprehensive correlation matrix and combine it with the elbow method to determine the final number of regional divisions. Based on the final number of regional divisions, regional division for harmonic, three-phase unbalance, and voltage deviation management is achieved. The implementation method is as follows:

[0126] S501, using a spectral clustering algorithm to divide the distribution network into regions, calculating a degree matrix based on the comprehensive correlation matrix of harmonics, three-phase imbalance, and voltage deviation at each node, and symmetric normalizing the degree matrix to obtain a Laplace matrix;

[0127] S502. According to the eigenvalues ​​and eigenvectors of the Laplace matrix, select the eigenvectors corresponding to the first k smallest eigenvalues ​​to form an eigenvector matrix;

[0128] S503, taking each row of the eigenvector matrix as a data point, and clustering the data points using the k-means algorithm in the eigenspace;

[0129] S504. Based on the clustering results, the elbow method is used to determine the number of clusters, and the value changes in the k-means algorithm under different cluster numbers are analyzed to determine the inflection point where the value in the k-means algorithm drops sharply as k increases.

[0130] S505 : According to the inflection point position, the number of clusters corresponding to the inflection point position is selected as the optimal number of clusters, and the optimal number of clusters is used as the final number of region divisions.

[0131] In this embodiment, a spectral clustering algorithm is used to set corresponding thresholds according to the comprehensive similarity indicators dH3, dI3, and dV3 of different nodes to divide the harmonic, three-phase unbalance, and voltage deviation management areas.

[0132] In this embodiment, the comprehensive correlation matrix Z reflects the degree of coupling between different nodes. The higher the correlation, the closer the relationship between the two nodes. Managing one node will have a better effect on improving the other nodes, and the power quality of this node can also better represent the power quality indicators of other nodes. Therefore, a clustering algorithm can be used for partitioning, and the spectral clustering algorithm is selected for regional division, as follows:

[0133] First, the degree matrix F is calculated based on the correlation matrix Z. F is a diagonal matrix. The diagonal elements F in the diagonal matrix F are ii is the sum of the comprehensive associations between all nodes connected to node i, and the diagonal element F ii The formula is as follows:

[0134]

[0135] Then the degree matrix is ​​symmetrically normalized to obtain the Laplace matrix L, which is shown as follows:

[0136] L=IF 1 / 2 ZF -1 / 2

[0137] Among them, I represents the identity matrix, Z represents the comprehensive correlation matrix;

[0138] Calculate the eigenvalues ​​and eigenvectors of the Laplace matrix L and select the eigenvectors corresponding to the first k smallest eigenvalues ​​to form the eigenvector matrix U, and then perform clustering. Each row of the eigenvector matrix U is regarded as a data point, and these data points are clustered using the k-means algorithm in the eigenspace. The formula for the k-means algorithm is as follows:

[0139]

[0140] Among them, Ui represents the i-th row element of the eigenvector matrix U, u j represents the centroid of the j-th cluster, k represents the cluster number, and n represents the number of rows of the eigenvector matrix. The cluster number k can be determined by using the elbow method. By analyzing the numerical change in the k-means algorithm formula under different cluster numbers, the inflection point position at which the numerical value in the k-means algorithm formula sharply decreases with the increase of k is determined, and the cluster number corresponding to the inflection point position is selected as the optimal cluster number.

[0141] S6, according to the regional division result, selecting a node with the maximum sum of comprehensive correlation degrees with all nodes in any region as a dominant node in the region, and installing a management device at the dominant node, to complete the power quality management partitioning of the power distribution network.

[0142] In this embodiment, the dominant node in the region is the node with the highest correlation degree with all nodes in the region, and therefore the node with the maximum sum of correlation degrees with other nodes in the region is selected as the dominant node.

[0143] In this embodiment, as shown in Figure 3 and Figure 4 , to verify the effectiveness of the method, the IEEE33 node power distribution network shown in Figure 2 is simulated and verified, b1=0.2, b2=0.3 and b3=0.5 are set, and the prior art is compared, and the partitioning results are shown in Table 1 and Table 2, respectively. Table 1 is the power quality management regional division result of the method, and Table 2 is the power quality management regional division result of the prior art.

[0144] Table 1

[0145]

[0146]

[0147] Table 2

[0148]

[0149] In this embodiment, according to Table 2 and Figure 4 It can be seen that simply according to the change trend to divide the power quality region is easily affected by chance, and two nodes even have no great correlation degree, but because the change trend is similar, they will also be divided into the same region. According to Table 1 and Figure 3 It can be seen that by using the method of the application, the power quality management regional division is performed from multiple dimensions of electrical distance, amplitude and change trend, which can effectively avoid the above situation, and the divided region is more reasonable.

[0150] In this embodiment, as shown in Figure 5 , Figure 6 and Figure 7As shown, the three figures show the changes in harmonics, three-phase imbalance, and voltage deviation at each node after installing a harmonic control device at dominant node 10 in harmonic division area 3, a three-phase imbalance control device at dominant node 11 in three-phase imbalance division area 3, and a voltage deviation control device at dominant node 12 in voltage deviation division area 3. It can also be seen that installing control equipment at dominant nodes within a region has the best effect on improving the power quality of nodes within the same region, while the improvement effect is weaker outside the region, which is consistent with the zoning results and verifies that the method of the present invention can effectively divide different nodes into regions, achieving strong coupling within the region and weak coupling outside the region.

[0151] Example 2

[0152] The embodiment of the present invention provides a distribution network power quality management zoning device, such as Figure 8 As shown, the distribution network power quality management zoning device is characterized by comprising:

[0153] Obtaining electrical distance module: It is used to calculate the reactive voltage sensitivity and active voltage sensitivity of each node in the distribution network using the power flow equation according to the network topology and parameters of the distribution network, and to obtain the electrical distance between each node based on the reactive voltage sensitivity and active voltage sensitivity of each node;

[0154] Dataset Construction Module: used to construct a time series measurement dataset using historical measurement data of harmonics, three-phase unbalance, and voltage deviation at each node;

[0155] Correlation analysis module: used to normalize the time series measurement data set using the normalization method, and analyze the normalized time series measurement data set using the grey correlation degree to obtain the change trend correlation degree and amplitude correlation degree of each node respectively;

[0156] Comprehensive correlation module: used to determine the comprehensive correlation between nodes based on the change trend correlation, amplitude correlation and electrical distance of each node, and integrate the comprehensive correlation between nodes to obtain a comprehensive correlation matrix;

[0157] Spectral clustering module: This module uses a spectral clustering algorithm to cluster the comprehensive correlation matrix and combines it with the elbow method to determine the final number of regional divisions. Based on the final number of regional divisions, regional divisions are implemented for harmonic, three-phase imbalance, and voltage deviation management.

[0158] Selection module: According to the regional division results, the node with the largest sum of comprehensive correlation with all nodes in any area is selected as the dominant node in the area, and management equipment is installed on the dominant node to complete the power quality management zoning of the distribution network.

[0159] The electrical distance acquisition module includes:

[0160] Newton-power flow equation submodule: used to calculate the reactive voltage sensitivity and active voltage sensitivity of each node based on the Newton-power flow equation according to the network topology and parameters of the distribution network;

[0161] Voltage sensitivity submodule: used to obtain the voltage sensitivity between nodes based on the reactive voltage sensitivity and active voltage sensitivity of each node using the sensitivity equation;

[0162] Electrical distance submodule: used to calculate the electrical distance between nodes based on the voltage sensitivity between nodes using the electrical distance formula.

[0163] The association analysis module includes:

[0164] Normalization submodule: It is used to normalize the historical measurement data of harmonics, three-phase imbalance, and voltage deviation of each node based on the time series measurement data set, and map the historical measurement data to the [0,1] interval;

[0165] Change trend correlation submodule: It is used to analyze the change trend correlation of harmonics, three-phase imbalance and voltage deviation of different nodes based on the normalized time series measurement data set using the grey correlation analysis method;

[0166] Amplitude correlation submodule: It is used to analyze the amplitude correlation of harmonics, three-phase imbalance and voltage deviation at different nodes based on the normalized time series measurement data set using the grey correlation analysis method.

[0167] The spectral clustering module includes:

[0168] Degree matrix submodule: used to calculate the degree matrix based on the comprehensive correlation matrix of harmonics, three-phase imbalance and voltage deviation of each node, and perform symmetric normalization on the degree matrix to obtain the Laplace matrix;

[0169] Eigenvector submodule: used to select the eigenvectors corresponding to the first k smallest eigenvalues ​​according to the eigenvalues ​​and eigenvectors of the Laplace matrix to form an eigenvector matrix;

[0170] K-means submodule: It is used to treat each row of the eigenvector matrix as a data point and cluster the data points using the K-means algorithm in the feature space;

[0171] Elbow method submodule: used to determine the number of clusters based on the clustering results using the elbow method, analyze the value changes in the k-means algorithm under different cluster numbers, and determine the inflection point where the value in the k-means algorithm drops sharply as k increases;

[0172] Optimal number of clusters submodule: It is used to select the number of clusters corresponding to the inflection point position as the optimal number of clusters according to the inflection point position, and use the optimal number of clusters as the final number of region divisions.

[0173] In this embodiment, the present application can divide the modules according to the distribution network power quality management zoning method. For example, each function can be divided into each module, or two or more functions can be integrated into one module. The above modules can be implemented in the form of hardware or software. It should be noted that the division of modules in the present invention is schematic and is only a logical division. There may be other division methods in actual implementation.

[0174] In this embodiment, the power quality management zoning device for the distribution network includes hardware structures and / or software parts corresponding to the execution of each function in order to realize the principles and beneficial effects of the power quality management zoning method for the distribution network. It should be easily appreciated by those skilled in the art that, in combination with the schematic modules and method steps described in the embodiments disclosed in the present invention, the present invention can be implemented in the form of hardware and / or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven manner depends on the specific application and design constraints of the technical solution. Different methods can be used for each specific application to implement the described function, but such implementation should not be considered to exceed the scope of this application.

[0175] In this embodiment, a computer device is characterized by comprising: one or more processors;

[0176] The processor is used to store one or more programs; when the one or more programs are executed by the one or more processors, the distribution network power quality management zoning method as described above is implemented.

[0177] In this embodiment, a computer-readable storage medium is provided, characterized in that a computer program is stored thereon, and when the computer program is executed, the distribution network power quality management zoning method as described above is implemented.

Claims

1. A method for zoning power quality management in a distribution network, characterized in that: The following steps are involved: S1. Based on the network topology and parameters of the distribution network, the reactive voltage sensitivity and active voltage sensitivity of each node in the distribution network are calculated using the power flow equation, and the electrical distance between each node is calculated based on the reactive voltage sensitivity and active voltage sensitivity of each node; S2. Build a time series measurement data set using historical measurement data of harmonics, three-phase unbalance, and voltage deviation at each node; S3. Normalize the time series measurement data set using a normalization method, and analyze the normalized time series measurement data set using gray correlation to obtain the change trend correlation and amplitude correlation of each node; S4. Determine the comprehensive correlation between each node based on the change trend correlation, amplitude correlation, and electrical distance of each node, and integrate the comprehensive correlation between each node to obtain a comprehensive correlation matrix; S5. Use the spectral clustering algorithm to cluster the comprehensive correlation matrix and combine it with the elbow method to determine the final number of regional divisions. Based on the final number of regional divisions, regional division for harmonic, three-phase unbalance and voltage deviation management is achieved; S6. According to the regional division results, select the node with the largest sum of comprehensive correlation with all nodes in any area as the dominant node in the area, and install management equipment on the dominant node to complete the power quality management zoning of the distribution network.

2. The power quality management zoning method for distribution network according to claim 1 is characterized in that: The S1 step is as follows: S101. Calculate the reactive voltage sensitivity and active voltage sensitivity of each node based on the Newton-load equation according to the network topology and parameters of the distribution network. S102, according to the reactive voltage sensitivity and active voltage sensitivity of each node, using the sensitivity equation, to obtain the voltage sensitivity between each node; S103 , calculating the electrical distance between the nodes using an electrical distance formula based on the voltage sensitivity between the nodes.

3. The power quality management zoning method for distribution network according to claim 2 is characterized in that: The equation for the voltage sensitivity is as follows: Among them, A ij represents the voltage sensitivity between node i and node j, Represents the voltage amplitude of node j V j Active voltage sensitivity at node i, Represents the voltage amplitude of node i V i Active voltage sensitivity at node j, Represents the voltage amplitude of node j V j Reactive voltage sensitivity to node i, Represents the voltage amplitude of node i V i The reactive voltage sensitivity of node j is: Represents the voltage amplitude of node i V i The partial derivative of ΔP i represents the transformation of active power at node i, ΔQ i Represents the conversion amount of reactive power at node i.

4. The method for zoning power quality management of distribution network according to claim 1, characterized in that: The S3 steps are as follows: S301. Normalize the historical measurement data of harmonics, three-phase unbalance, and voltage deviation at each node based on the time series measurement data set, and map the historical measurement data to the interval [0, 1]. S302. Analyze the correlation of the changing trends of harmonics, three-phase imbalance, and voltage deviation at different nodes using a grey correlation analysis method based on the normalized time series measurement data set. S303 , based on the normalized time series measurement data set, a grey correlation analysis method is used to analyze the amplitude correlation of harmonics, three-phase imbalance, and voltage deviation at different nodes.

5. The method for zoning power quality management of distribution network according to claim 1, characterized in that: In the step S5, the spectral clustering algorithm is used to cluster the comprehensive correlation matrix and the elbow method is combined to determine the final number of regional divisions, including the following: S501, using a spectral clustering algorithm to divide the distribution network into regions, calculating a degree matrix based on the comprehensive correlation matrix of harmonics, three-phase imbalance, and voltage deviation at each node, and symmetric normalizing the degree matrix to obtain a Laplace matrix; S502. According to the eigenvalues ​​and eigenvectors of the Laplace matrix, select the eigenvectors corresponding to the first k smallest eigenvalues ​​to form an eigenvector matrix; S503, taking each row of the eigenvector matrix as a data point, and clustering the data points using the k-means algorithm in the eigenspace; S504. Based on the clustering results, the elbow method is used to determine the number of clusters, and the value changes in the k-means algorithm under different cluster numbers are analyzed to determine the inflection point where the value in the k-means algorithm drops sharply as k increases. S505 : According to the inflection point position, the number of clusters corresponding to the inflection point position is selected as the optimal number of clusters, and the optimal number of clusters is used as the final number of region divisions.

6. The method for zoning power quality management of distribution network according to claim 5, characterized in that: The degree matrix F is a diagonal matrix, and the diagonal elements F in the diagonal matrix F are ii is the sum of the comprehensive associations between all nodes connected to node i, and the diagonal element F ii The formula is as follows: Among them, Z ij Represents the element in row i and column j of the comprehensive correlation matrix; The formula of the Laplace matrix L is as follows: L=I-F 1 / 2 ZF -1 / 2 Among them, I represents the identity matrix and Z represents the comprehensive correlation matrix.

7. The method for zoning power quality management in a distribution network according to claim 1, characterized in that: The calculation formula of the comprehensive correlation is as follows: Among them, ε represents the comprehensive correlation, Indicates the correlation degree of harmonic amplitude of each node, Indicates the correlation degree of harmonic change trend of each node, D h represents electrical distance, b1, b2, and b3 represent the weight coefficients of amplitude correlation, change trend correlation, and electrical distance, and b1+b2+b3=1; The comprehensive correlation matrix is ​​as follows: Among them, ε ij Represents the comprehensive correlation between the i-th node and the j-th node.

8. A power quality management zoning device for a distribution network, characterized in that: include: Obtaining electrical distance module: Based on the network topology and parameters of the distribution network, the power flow equation is used to calculate the reactive voltage sensitivity and active voltage sensitivity of each node in the distribution network, and the electrical distance between each node is calculated based on the reactive voltage sensitivity and active voltage sensitivity of each node; Dataset Construction Module: used to construct a time series measurement dataset using historical measurement data of harmonics, three-phase unbalance, and voltage deviation at each node; Correlation analysis module: normalizes the time series measurement data set using the normalization method, and analyzes the normalized time series measurement data set using the grey correlation method to obtain the change trend correlation and amplitude correlation of each node; Comprehensive correlation module: used to determine the comprehensive correlation between nodes based on the change trend correlation, amplitude correlation and electrical distance of each node, and integrate the comprehensive correlation between nodes to obtain a comprehensive correlation matrix; Spectral clustering module: This module uses a spectral clustering algorithm to cluster the comprehensive correlation matrix and combines it with the elbow method to determine the final number of regional divisions. Based on the final number of regional divisions, regional divisions are implemented to manage harmonics, three-phase imbalance, and voltage deviation. Selection module: According to the regional division results, the node with the largest sum of comprehensive correlation with all nodes in any area is selected as the dominant node in the area, and management equipment is installed on the dominant node to complete the power quality management zoning of the distribution network.

9. A distribution network power quality management zoning device according to claim 8, characterized in that: The electrical distance acquisition module includes: Newton-power flow equation submodule: used to calculate the reactive voltage sensitivity and active voltage sensitivity of each node based on the Newton-power flow equation according to the network topology and parameters of the distribution network; Voltage sensitivity submodule: used to obtain the voltage sensitivity between nodes based on the reactive voltage sensitivity and active voltage sensitivity of each node using the sensitivity equation; Electrical distance submodule: used to calculate the electrical distance between nodes based on the voltage sensitivity between nodes using the electrical distance formula.

10. A distribution network power quality management zoning device according to claim 8, characterized in that: The association analysis module includes: Normalization submodule: It is used to normalize the historical measurement data of harmonics, three-phase imbalance, and voltage deviation of each node based on the time series measurement data set, and map the historical measurement data to the [0,1] interval; Change trend correlation submodule: It is used to analyze the change trend correlation of harmonics, three-phase imbalance and voltage deviation of different nodes based on the normalized time series measurement data set using the grey correlation analysis method; Amplitude correlation submodule: It is used to analyze the amplitude correlation of harmonics, three-phase imbalance and voltage deviation at different nodes based on the normalized time series measurement data set using the grey correlation analysis method.

11. A distribution network power quality management zoning device according to claim 8, characterized in that: The spectral clustering module includes: Degree matrix submodule: used to calculate the degree matrix based on the comprehensive correlation matrix of harmonics, three-phase imbalance and voltage deviation of each node, and perform symmetric normalization on the degree matrix to obtain the Laplace matrix; Eigenvector submodule: used to select the eigenvectors corresponding to the first k smallest eigenvalues ​​according to the eigenvalues ​​and eigenvectors of the Laplace matrix to form an eigenvector matrix; K-means submodule: It is used to treat each row of the eigenvector matrix as a data point and cluster the data points using the K-means algorithm in the feature space; Elbow method submodule: used to determine the number of clusters based on the clustering results using the elbow method, analyze the value changes in the k-means algorithm under different cluster numbers, and determine the inflection point where the value in the k-means algorithm drops sharply as k increases; Optimal number of clusters submodule: It is used to select the number of clusters corresponding to the inflection point position as the optimal number of clusters according to the inflection point position, and use the optimal number of clusters as the final number of region divisions.

12. A computer device, characterized in that: include: one or more processors; The processor is configured to store one or more programs; When the one or more programs are executed by the one or more processors, the distribution network power quality management zoning method as described in any one of claims 1 to 7 is implemented.

13. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed, the distribution network power quality management zoning method as described in any one of claims 1 to 7 is implemented.

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