Power distribution network voltage anomaly identification method, device, equipment, medium and product

Abstract

This invention discloses a method, device, equipment, medium, and product for identifying voltage anomalies in distribution networks. The method involves: acquiring node voltage data of each key node and branch current data of each branch line in the distribution network within a preset time period; constructing an original voltage matrix and an original current matrix based on these two types of data, and further predicting the global node impedance estimation matrix of the distribution network; using the node voltage data of the current key node as the starting value, and combining the branch current data of each branch line downstream of the current key node and the global node impedance estimation matrix, calculating the voltage estimates of each subsequent node downstream of the current key node step by step; and identifying nodes whose voltage estimates exceed a preset safety range as voltage anomaly nodes. This invention can solve the problem of difficulty in accurately sensing the voltage status of the entire network due to the lack of miniature voltage sensors and the inability to directly measure the voltage of many nodes, thus improving the accuracy of identifying voltage anomalies in distribution networks.

Description

Technical Field

[0001] This invention relates to the field of power distribution network anomaly detection, and more particularly to methods, devices, equipment, media, and products for identifying voltage anomalies in power distribution networks. Background Technology

[0002] As a critical link in the power system directly facing users, the voltage stability of the distribution network directly affects the reliability of power supply and power quality. With the large-scale integration of distributed generation and flexible loads, the power flow distribution of the distribution network is becoming increasingly complex, and the frequency of voltage exceedance (overvoltage or undervoltage) events is significantly increasing. If voltage anomalies are not detected and handled in a timely manner, they may lead to equipment damage, power outages, or even large-scale power blackouts, causing economic losses and safety hazards. Therefore, timely and accurate identification of voltage anomalies in the distribution network, and precise location of voltage anomaly nodes to facilitate appropriate control or protection measures, are of great significance for ensuring the safe and stable operation of the distribution network.

[0003] In existing methods for identifying voltage anomalies in distribution networks, although miniature non-contact current sensors can achieve wide-area acquisition of branch currents, miniature voltage sensors are not yet mature. Traditional voltage transformers, due to their large size, high cost, and complex installation, can only be sparsely deployed at a few key nodes such as substation busbars or feeder heads, forming an asymmetric sensing architecture of "wide-area current measurement, but local voltage measurement." Under these conditions, existing technologies have the following drawbacks: voltage information from a large number of intermediate and terminal nodes is completely missing, making it difficult to accurately perceive the voltage situation of the entire network, unable to effectively identify voltage exceedances at nodes without voltage sensors, and having low accuracy in voltage anomaly identification, making it difficult to meet the requirements of distribution networks for voltage quality monitoring and operational safety. Summary of the Invention

[0004] This invention provides a method, device, equipment, medium, and product for identifying voltage anomalies in distribution networks. It can solve the problem that the lack of miniature voltage sensors and the inability to directly measure the voltage of a large number of nodes in the distribution network make it difficult to accurately perceive the voltage status of the entire network, thereby improving the accuracy of identifying voltage anomalies in the distribution network.

[0005] In a first aspect, an embodiment of the present invention provides a method for identifying voltage anomalies in a power distribution network, comprising: Acquire node voltage data of each key node and branch current data of each branch line in the distribution network within a preset time period. The distribution network includes several nodes, the nodes include the key nodes, and the branch lines are feeder segments between two nodes. Based on the voltage data of each node and the current data of each branch, an original voltage matrix and an original current matrix are constructed respectively. Based on the original current matrix and the original voltage matrix, the global node impedance estimation matrix of the distribution network is predicted. For each key node, the voltage data of the current key node is used as the starting value. The calculation is performed step by step downstream by combining the branch current data of each branch line downstream of the current key node and the global node impedance estimation matrix to obtain the voltage estimation value of each descendant node downstream of the current key node. If any of the voltage estimates exceed a preset safety range, the node corresponding to the voltage estimate that exceeds the preset safety range is identified as a voltage anomaly node.

[0006] This approach, by acquiring node voltage data from a small number of key nodes and branch current data from all branch lines in the distribution network, forms an asymmetric sensing architecture of sparse voltage sampling and network-wide current acquisition. This provides a data foundation for subsequent recursive calculations of the entire network voltage, even under hardware constraints such as the lack of micro-voltage sensors and the inability to directly measure voltage at many nodes, ensuring the accuracy of voltage anomaly identification from the data source perspective. Constructing original voltage and current matrices separately solves the problem of inconsistent processing of multi-time measurement data. Organizing discrete sampled data into a matrix form facilitates direct subsequent calculations, laying the foundation for accurate voltage anomaly identification from the data structure perspective. Based on the original current and voltage matrices, a global node impedance estimation matrix is ​​predicted, solving the problem of inaccurate measurement of line parameters and their variation with operating conditions. Utilizing measured sparse voltage and network-wide current for online impedance identification eliminates the need for preset line parameter values, ensuring the accuracy of voltage anomaly identification from the parameter identification perspective. Starting with the node voltage data of the critical nodes, and combining it with downstream branch current data and the global node impedance estimation matrix, the voltage estimates of each downstream node are obtained through step-by-step recursive calculation. Starting from the critical nodes with known measured voltages, the voltage is calculated along the tree-like topology of the distribution network using branch impedance and branch current, progressively extending downstream. This fills the voltage gaps for general nodes without voltage sensors, solving the problem of many intermediate and terminal nodes being unable to have their voltages directly measured. This achieves complete perception of the entire network's voltage situation from a voltage sensing perspective, improving the accuracy of voltage anomaly identification. If any voltage estimate exceeds a preset safety range, the node exceeding the range is identified as a voltage anomaly node. The estimated voltage value is used to replace the measured blank value for overvoltage and undervoltage judgment, allowing general nodes that were previously unmonitorable due to the lack of miniature voltage sensors to be included in the anomaly identification range, thereby expanding the coverage of voltage anomaly identification and improving the accuracy of distribution network voltage anomaly identification. This application solves the problem of difficulty in accurately perceiving the entire network's voltage situation due to the lack of miniature voltage sensors and the inability to directly measure the voltage of many nodes, thus improving the accuracy of distribution network voltage anomaly identification.

[0007] Furthermore, the prediction of the global node impedance estimation matrix of the distribution network based on the original current matrix and the original voltage matrix specifically includes: Based on the original current matrix, a transformation matrix is ​​constructed. The original current matrix is ​​linearly transformed using the transformation matrix to obtain a current whitening matrix; Based on the current whitening matrix and the original voltage matrix, the global node impedance estimation matrix of the distribution network is predicted.

[0008] This method predicts the global node impedance estimation matrix based on the original current matrix and the original voltage matrix, which solves the problem that line parameters are difficult to measure accurately and change with the operating mode. It uses measured sparse voltage and network current to identify impedance online without the need to preset line parameter values, thus ensuring the accuracy of voltage anomaly identification from the parameter identification level.

[0009] Furthermore, the construction of the transformation matrix based on the original current matrix specifically includes: Based on the original current matrix, the covariance matrix is ​​constructed. The transformation matrix is ​​obtained by performing triangular decomposition on the covariance matrix.

[0010] This process involves constructing a covariance matrix based on the original current matrix and then performing triangular decomposition on the covariance matrix to obtain a transformation matrix. Since the covariance matrix quantifies the correlation between the currents in each branch, triangular decomposition decomposes it into a matrix form that facilitates linear transformation. This provides a transformation tool for eliminating the correlation between current channels, ensuring the feasibility of whitening transformation from the data preprocessing level and guaranteeing the accuracy of subsequent voltage anomaly identification.

[0011] Furthermore, the prediction of the global node impedance estimation matrix of the distribution network based on the current whitening matrix and the original voltage matrix specifically includes: The current whitening matrix and the original voltage matrix are input into a preset least squares model to solve for the mapping matrix; The global node impedance estimation matrix of the distribution network is obtained by restoring the mapping matrix using the transformation matrix.

[0012] This method inputs the current whitening matrix and the original voltage matrix into a preset least squares model to solve for the mapping matrix. Then, the transformation matrix is ​​used to restore the mapping matrix to obtain the global node impedance estimation matrix. The least squares method achieves the optimal estimation of the linear relationship between voltage and current in the whitening space. The transformation matrix is ​​then used to restore it to the physical space, thereby identifying the global node impedance online without relying on preset line parameters. This ensures the accuracy of voltage anomaly identification from the parameter identification level.

[0013] Furthermore, for each of the aforementioned key nodes, starting with the node voltage data of the current key node, and combining the branch current data of each of the downstream branch lines of the current key node with the global node impedance estimation matrix, the voltage estimation values ​​of each downstream successor node of the current key node are calculated step by step downstream, specifically including: Obtain the network topology data of the power distribution network; The branch impedance values ​​corresponding to each branch line are extracted from the global node impedance estimation matrix using the network topology data. For each of the aforementioned key nodes, several descendant nodes of the current key node are determined based on the network topology data, wherein the descendant nodes include child nodes directly connected to the current key node; The voltage data of the current critical node, the branch current data and the branch impedance value of the branch line between the current critical node and the child node are calculated to obtain the voltage estimate of the child node. This process is repeated until all the descendant nodes are traversed to obtain the voltage estimate of each descendant node downstream of the current critical node.

[0014] Starting with the node voltage data of the current key node, and combining it with the downstream branch current data and the global node impedance estimation matrix, the voltage estimates of each downstream node are obtained through step-by-step recursive calculation. Starting from the key node with known measured voltage, the voltage is calculated along the tree-like topology of the distribution network using branch impedance and branch current, and then calculated downstream step by step. This fills the voltage gap of general nodes without voltage sensors and solves the problem that a large number of intermediate and terminal nodes cannot be directly measured. It achieves complete perception of the voltage status of the entire network from the voltage perception level, thereby improving the accuracy of voltage anomaly identification.

[0015] Furthermore, the construction of the original voltage matrix and the original current matrix based on the voltage data of each node and the current data of each branch specifically includes: The node voltage data of each of the key nodes and the branch current data of each of the branch lines are time-synchronized to obtain several voltage synchronization data and several current synchronization data. Anomaly removal processing is performed on each of the voltage synchronization data and each of the current synchronization data to obtain several valid voltage data and several valid current data. Phasor alignment is performed on each of the effective voltage data and each of the effective current data to obtain several voltage-aligned data and several current-aligned data. The voltage alignment data corresponding to each key node are concatenated in chronological order to obtain the voltage vector of each key node, and the voltage vectors are integrated to construct the original voltage matrix. The current alignment data corresponding to each branch line are concatenated in chronological order to obtain the current vector of each branch line, and the current vectors are integrated to construct the original current matrix.

[0016] Constructing the original voltage matrix and the original current matrix separately solves the problem of the difficulty in uniformly processing measurement data at multiple times. Organizing discrete sampling data into a matrix form facilitates subsequent direct calculations, laying the foundation for accurate identification of voltage anomalies from the data structure level.

[0017] Secondly, an embodiment of the present invention provides a voltage anomaly identification device for a power distribution network, comprising a first module, a second module and a third module; The first module is used to acquire node voltage data of each key node and branch current data of each branch line in the distribution network within a preset time period. The distribution network includes a number of nodes, the nodes include the key nodes, and the branch line is a feeder segment between two nodes. The second module is used to construct an original voltage matrix and an original current matrix based on the voltage data of each node and the current data of each branch, respectively. Based on the original current matrix and the original voltage matrix, it predicts the global node impedance estimation matrix of the distribution network. For each key node, it uses the node voltage data of the current key node as the starting value, and combines the branch current data of each branch line downstream of the current key node and the global node impedance estimation matrix to calculate the voltage estimation value of each descendant node downstream of the current key node step by step downstream. The third module is used to determine the node corresponding to the voltage estimate that exceeds the preset safety range as a voltage anomaly node if each of the voltage estimates exceeds the preset safety range.

[0018] This approach, by acquiring node voltage data from a small number of key nodes and branch current data from all branch lines in the distribution network through the first module, forms an asymmetric sensing architecture of sparse voltage sampling and network-wide current acquisition. This provides a data foundation for subsequent recursive calculations of the entire network voltage, even under hardware constraints such as the lack of micro-voltage sensors and the inability to directly measure voltage at many nodes, ensuring the accuracy of voltage anomaly identification from the data source perspective. The second module constructs the original voltage and current matrices respectively, solving the problem of inconsistent processing of multi-time measurement data. Organizing discrete sampled data into a matrix form facilitates direct subsequent calculations, laying the foundation for accurate voltage anomaly identification from the data structure perspective. Based on the original current and voltage matrices, a global node impedance estimation matrix is ​​predicted, solving the problem of inaccurate measurement of line parameters and their variation with operating conditions. By using measured sparse voltage and network-wide current to identify impedance online without pre-setting line parameter values, the accuracy of voltage anomaly identification is ensured from the parameter identification perspective. Starting with the node voltage data of the current key node, and combining it with the downstream branch current data and the global node impedance estimation matrix, the voltage estimates of each downstream node are obtained through step-by-step recursive calculation. Starting from the key node with known measured voltage, the voltage is calculated along the tree-like topology of the distribution network using branch impedance and branch current, and then calculated downstream step by step. This fills the voltage gap of general nodes without voltage sensors and solves the problem that a large number of intermediate and terminal nodes cannot be directly measured. It achieves complete perception of the voltage status of the entire network from the voltage perception level, thereby improving the accuracy of voltage anomaly identification. In the third module, if any voltage estimate exceeds the preset safety range, the node that exceeds the range is identified as a voltage anomaly node. The estimated voltage value is used to replace the measured blank value for overvoltage and undervoltage judgment. This allows general nodes that could not be monitored due to the lack of miniature voltage sensors to be included in the anomaly identification range, thereby expanding the coverage of voltage anomaly identification and improving the accuracy of voltage anomaly identification in the distribution network.

[0019] Thirdly, another embodiment of the present invention provides a terminal device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform the operation of a voltage anomaly identification method for a power distribution network.

[0020] Fourthly, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein the computer program, when running, controls the device or apparatus where the computer-readable storage medium is located to perform a voltage anomaly identification method for a power distribution network.

[0021] Fifthly, another embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements a method for identifying voltage anomalies in a power distribution network. Attached Figure Description

[0022] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating an embodiment of a voltage anomaly identification method for a power distribution network provided in this application; Figure 2 This is a flowchart illustrating steps S201 to S203 provided in this application; Figure 3 This is a schematic diagram of the structure of a voltage anomaly identification device for a power distribution network provided in this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0026] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0029] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0030] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this application according to the specific circumstances.

[0031] This invention relates to the field of distribution network anomaly detection. With the large-scale integration of distributed power sources and flexible loads, voltage exceedance events in distribution networks are frequent. Failure to detect these events in a timely manner may lead to equipment damage or even power outages. While existing identification methods utilize miniature current sensors to achieve wide-area current acquisition from branches, miniature voltage sensors are not yet mature. Traditional voltage transformers are large and costly, and can only be sparsely deployed at a few key nodes, forming an asymmetric architecture where "current can be measured widely, but voltage can only be measured locally." This results in a large number of missing node voltages, making it difficult to accurately perceive the overall network voltage situation and leading to low accuracy in identifying voltage anomalies in the distribution network.

[0032] See Figure 1 In order to solve the problem that the voltage status of the entire distribution network is difficult to accurately perceive due to the lack of micro voltage sensors and the inability to directly measure the voltage of a large number of nodes, and to improve the accuracy of voltage anomaly identification in the distribution network, an embodiment of the present invention provides a voltage anomaly identification method for the distribution network, including steps S101 to S103. Step S101: Obtain node voltage data of each key node and branch current data of each branch line in the distribution network within a preset time period. The distribution network includes several nodes, the nodes include the key nodes, and the branch line is a feeder segment between two nodes. In some embodiments, node voltage data of each key node and branch current data of each branch line in the distribution network are acquired within a preset time period. The distribution network includes a number of nodes, the nodes include the key nodes, and the branch line is a feeder segment between two nodes. Specifically, this includes: arranging synchronous measuring devices at the substation busbar, feeder head end and other key nodes of the distribution network to acquire node voltage data of each key node at each time; and arranging non-contact current sensors at the locations of each branch line of the distribution network to acquire branch current data.

[0033] It should be noted that the synchronous measurement device is a phasor measurement unit or a miniature synchronous phasor measurement device, and the non-contact current sensor is a miniature current sensor based on the magnetoresistive effect.

[0034] Step S102: Based on the voltage data of each node and the current data of each branch, an original voltage matrix and an original current matrix are constructed respectively. Based on the original current matrix and the original voltage matrix, the global node impedance estimation matrix of the distribution network is predicted. For each key node, the voltage data of the current key node is used as the starting value. The voltage estimation value of each downstream node is calculated by combining the branch current data of each branch line downstream of the current key node and the global node impedance estimation matrix. In some embodiments, constructing the original voltage matrix and the original current matrix based on the voltage data of each node and the current data of each branch line specifically includes: performing time synchronization processing on the voltage data of each key node and the current data of each branch line to obtain several voltage synchronization data and several current synchronization data; performing anomaly removal processing on the voltage synchronization data and the current synchronization data to obtain several valid voltage data and several valid current data; performing phasor alignment on the valid voltage data and the valid current data to obtain several aligned voltage data and several aligned current data; concatenating the aligned voltage data corresponding to each key node in time order to obtain the voltage vector of each key node, and integrating the voltage vectors to construct the original voltage matrix; concatenating the aligned current data corresponding to each branch line in time order to obtain the current vector of each branch line, and integrating the current vectors to construct the original current matrix. Specifically, time synchronization processing is performed on the voltage data of each key node and the current data of each branch line. All measurement data are aligned according to a unified time base to obtain several voltage synchronization data and several current synchronization data. Anomaly removal processing is performed on each voltage and current synchronization data to identify and remove abnormal sampled values, obtaining valid voltage and current data. Phasor alignment is performed on each valid voltage and current data, correcting the amplitude of the voltage phasor to the actual value under the standard transformer ratio, and rotating the phase of the current phasor to a common reference phase. Under a unified coordinate system with the quantity as the reference, several voltage-aligned data and several current-aligned data are obtained. The voltage-aligned data corresponding to each key node are arranged in chronological order of sampling time to form the voltage vector of each key node. The voltage vectors of each key node are concatenated in the order of node identification to construct the original voltage matrix. The current-aligned data corresponding to each branch line are arranged in chronological order of sampling time to form the current vector of each branch line. The current vectors of each branch line are concatenated in the order of branch line identification to construct the original current matrix.

[0035] Constructing the original voltage matrix and the original current matrix separately solves the problem of the difficulty in uniformly processing measurement data at multiple times. Organizing discrete sampling data into a matrix form facilitates subsequent direct calculations, laying the foundation for accurate identification of voltage anomalies from the data structure level.

[0036] See Figure 2In some embodiments, the step of predicting the global node impedance estimation matrix of the distribution network based on the original current matrix and the original voltage matrix specifically includes steps S201 to S203. Step S201: Based on the original current matrix, construct the transformation matrix; In some embodiments, constructing the transformation matrix based on the original current matrix specifically includes: constructing a covariance matrix based on the original current matrix; and performing triangular decomposition on the covariance matrix to obtain the transformation matrix. Specifically, the covariance matrix is ​​obtained by calculating the product of the original current matrix and its conjugate transpose; the covariance matrix is ​​then subjected to Cholesky triangular decomposition, decomposing it into the product of an upper triangular matrix and its conjugate transpose, which is the transformation matrix.

[0037] In some embodiments, the formula for constructing the transformation matrix based on the original current matrix specifically includes: The formula for constructing the covariance matrix: ; Cholesky decomposition formula: ; In the formula, Indicates conjugate transpose; This is the original current matrix; It is the covariance matrix; The transformation matrix; It is the inverse of the covariance matrix.

[0038] This process involves constructing a covariance matrix based on the original current matrix and then performing triangular decomposition on the covariance matrix to obtain a transformation matrix. Since the covariance matrix quantifies the correlation between the currents in each branch, triangular decomposition decomposes it into a matrix form that facilitates linear transformation. This provides a transformation tool for eliminating the correlation between current channels, ensuring the feasibility of whitening transformation from the data preprocessing level and guaranteeing the accuracy of subsequent voltage anomaly identification.

[0039] Step S202: Perform a linear transformation on the original current matrix using the transformation matrix to obtain a current whitening matrix; In some embodiments, the original current matrix is ​​linearly transformed using the transformation matrix to obtain a current whitening matrix, specifically including: performing a whitening transformation on the original current matrix using the transformation matrix to obtain a current whitening matrix.

[0040] In some embodiments, the original current matrix is ​​linearly transformed using the transformation matrix to obtain the relevant formula for the current whitening matrix, specifically including: The formula for whitening transformation: ; In the formula, This is the original current matrix; The transformation matrix; This is the current whitening matrix.

[0041] Step S203: Based on the current whitening matrix and the original voltage matrix, the global node impedance estimation matrix of the distribution network is predicted.

[0042] In some embodiments, predicting the global node impedance estimation matrix of the distribution network based on the current whitening matrix and the original voltage matrix specifically includes: inputting the current whitening matrix and the original voltage matrix into a preset least squares model to obtain a mapping matrix; and restoring the mapping matrix using the transformation matrix to obtain the global node impedance estimation matrix of the distribution network. Specifically, the current whitening matrix and the original voltage matrix are used as input data, and a regression equation is constructed using a preset least squares model to solve for the mapping matrix that minimizes the square of the Frobenius norm (i.e., the sum of the squares of all sample errors) between the original voltage matrix and the mapping matrix multiplied by the current whitening matrix; the conjugate transpose of the transformation matrix is ​​multiplied by the obtained mapping matrix to restore the mapping matrix, transforming the mapping relationship in the whitening space back to the original physical space to obtain the global node impedance estimation matrix.

[0043] In some embodiments, the formula for predicting the global node impedance estimation matrix of the distribution network based on the current whitening matrix and the original voltage matrix specifically includes: The formula for the least squares model: ; Formula for calculating the global nodal impedance estimation matrix: ; In the formula, The obtained mapping matrix; These are candidate mapping matrix variables; This is the current whitening matrix; This is the original voltage matrix; It is the Frobenius norm. To make the function The independent variable when the minimum value is obtained The value; This is the global node impedance estimation matrix; Indicates conjugate transpose; Let be the transformation matrix.

[0044] It should be noted that the global node impedance estimation matrix describes the linear mapping relationship between the voltage of key nodes and the branch current in the distribution network. By combining this matrix with the known topology of the distribution network, the cumulative path impedance from the power source node to each observation node, as well as the common path impedance between any two observation nodes, can be extracted. This impedance information is the theoretical basis for subsequent calculation of the branch impedance values ​​of each branch line.

[0045] This method inputs the current whitening matrix and the original voltage matrix into a preset least squares model to solve for the mapping matrix. Then, the transformation matrix is ​​used to restore the mapping matrix to obtain the global node impedance estimation matrix. The least squares method achieves the optimal estimation of the linear relationship between voltage and current in the whitening space. The transformation matrix is ​​then used to restore it to the physical space, thereby identifying the global node impedance online without relying on preset line parameters. This ensures the accuracy of voltage anomaly identification from the parameter identification level.

[0046] This method predicts the global node impedance estimation matrix based on the original current matrix and the original voltage matrix, which solves the problem that line parameters are difficult to measure accurately and change with the operating mode. It uses measured sparse voltage and network current to identify impedance online without the need to preset line parameter values, thus ensuring the accuracy of voltage anomaly identification from the parameter identification level.

[0047] In some embodiments, for each critical node, starting with the node voltage data of the current critical node, and combining the branch current data of each branch line downstream of the current critical node with the global node impedance estimation matrix, the voltage estimation value of each descendant node downstream of the current critical node is obtained by recursively calculating downstream step by step. Specifically, this includes: acquiring the network topology data of the distribution network; extracting the branch impedance value corresponding to each branch line from the global node impedance estimation matrix using the network topology data; for each critical node, determining several descendant nodes of the current critical node based on the network topology data, wherein the descendant nodes include child nodes directly connected to the current critical node; calculating the node voltage data of the current critical node, the branch current data and the branch impedance value corresponding to the branch line between the current critical node and the child node to obtain the voltage estimation value of the child node, until all descendant nodes are traversed to obtain the voltage estimation value corresponding to each descendant node downstream of the current critical node. Specifically, network topology data is exported from the distribution network geographic information system to obtain node connection relationships and feeder branch structures, and to determine the parent-child relationships and upstream-downstream hierarchical relationships between nodes. For each parent-child node pair, the cumulative path impedance from the power source node to the child node and the common upstream path impedance between the parent and child nodes are extracted from the global node impedance estimation matrix. The cumulative path impedance of the child node is subtracted from the common upstream path impedance to obtain the branch impedance value of the branch line between the parent and child nodes. Based on the network topology data, all descendant nodes of the current critical node are determined, among which the nodes directly connected to the current critical node are child nodes. Starting from the current critical node, the measured node voltage data of the current critical node is used as the starting value to obtain the branch impedance value and branch current data corresponding to the branch line between the current critical node and the child node. The voltage value of the current critical node is subtracted from the product of the branch impedance value and the branch current data to calculate the voltage estimate of the child node. This child node is used as the new parent node, and the above calculation process is repeated to continue to the downstream until all descendant nodes of the current critical node have been traversed, and the voltage estimate value of each descendant node downstream of the current critical node is obtained.

[0048] In some embodiments, for each critical node, starting with the node voltage data of the current critical node, and combining the branch current data of each branch line downstream of the current critical node with the global node impedance estimation matrix, the relevant formulas for calculating the voltage estimates of each descendant node downstream of the current critical node are obtained by recursively calculating downstream step by step. Specifically, this includes: Branch impedance value extraction formula: ; Node voltage recursive formula: ; In the formula, This represents a node in the current recursion. The parent node; Indicates the distance from the power node to the node. The cumulative path impedance; Indicates the parent node With nodes Common upstream path impedance; Indicates the parent node With nodes Branch impedance between; Indicates the parent node With nodes The resulting branch lines at time Branch current data; The node voltage data corresponding to the parent node in the current recursion can be the known quantity determined in step S101 or the estimated value determined in the recursion. For nodes The voltage estimate.

[0049] It should be noted that a parent node refers to a directly adjacent node located upstream and closer to the power source in the distribution network topology. When the distribution network has a tree-like radial structure, each child node has one and only one parent node.

[0050] Starting with the node voltage data of the current key node, and combining it with the downstream branch current data and the global node impedance estimation matrix, the voltage estimates of each downstream node are obtained through step-by-step recursive calculation. Starting from the key node with known measured voltage, the voltage is calculated along the tree-like topology of the distribution network using branch impedance and branch current, and then calculated downstream step by step. This fills the voltage gap of general nodes without voltage sensors and solves the problem that a large number of intermediate and terminal nodes cannot be directly measured. It achieves complete perception of the voltage status of the entire network from the voltage perception level, thereby improving the accuracy of voltage anomaly identification.

[0051] Step S103: If each of the voltage estimates exceeds the preset safety range, the node corresponding to the voltage estimate that exceeds the preset safety range is determined as a voltage anomaly node. In some embodiments, if each of the voltage estimates exceeds a preset safety range, the node corresponding to the voltage estimate that exceeds the preset safety range is determined as a voltage abnormal node. Specifically, this includes: comparing the voltage estimates or node voltage data of all nodes with the upper and lower limits of the normal operating voltage of the distribution network (i.e., the safety range, which can be 0.9 to 1.1 times the rated voltage) in real time. If the voltage estimate of a node exceeds the range, it is directly determined that the node has an over / under voltage abnormality, and the node is recorded as a voltage abnormal node.

[0052] For example, since some critical nodes may be descendants of other critical nodes, after identifying the voltage abnormal node, at the critical node equipped with a synchronous measurement device, the estimated voltage can be subtracted from the measured voltage of the critical node to obtain the residual. The residual is then compared with a preset residual threshold. When the residual of the upstream critical node is within the preset residual threshold while the residual of the downstream critical node exceeds the preset residual threshold, the abnormality is determined to occur in the feeder section between the upstream and downstream critical nodes. Combining the current measurement change characteristics of the non-contact current sensors arranged in each branch in this section, the specific faulty branch or equipment can be located, and auxiliary decision-making can be provided for relay protection operation.

[0053] For example, the formula for obtaining the residual by subtracting the corresponding estimated voltage from the measured voltage of the critical node includes: Formula for calculating residuals: ; In the formula, As a key node The measured voltage (i.e., node voltage data); The voltage estimate for the critical node; This represents the residual value.

[0054] This approach, by acquiring node voltage data from a small number of key nodes and branch current data from all branch lines in the distribution network, forms an asymmetric sensing architecture of sparse voltage sampling and network-wide current acquisition. This provides a data foundation for subsequent recursive calculations of the entire network voltage, even under hardware constraints such as the lack of micro-voltage sensors and the inability to directly measure voltage at many nodes, ensuring the accuracy of voltage anomaly identification from the data source perspective. Constructing original voltage and current matrices separately solves the problem of inconsistent processing of multi-time measurement data. Organizing discrete sampled data into a matrix form facilitates direct subsequent calculations, laying the foundation for accurate voltage anomaly identification from the data structure perspective. Based on the original current and voltage matrices, a global node impedance estimation matrix is ​​predicted, solving the problem of inaccurate measurement of line parameters and their variation with operating conditions. Utilizing measured sparse voltage and network-wide current for online impedance identification eliminates the need for preset line parameter values, ensuring the accuracy of voltage anomaly identification from the parameter identification perspective. Starting with the node voltage data of the critical nodes, and combining it with downstream branch current data and the global node impedance estimation matrix, the voltage estimates of each downstream node are obtained through step-by-step recursive calculation. Starting from the critical nodes with known measured voltages, the voltage is calculated along the tree-like topology of the distribution network using branch impedance and branch current, progressively extending downstream. This fills the voltage gaps for general nodes without voltage sensors, solving the problem of many intermediate and terminal nodes being unable to have their voltages directly measured. This achieves complete perception of the entire network's voltage situation from a voltage sensing perspective, improving the accuracy of voltage anomaly identification. If any voltage estimate exceeds a preset safety range, the node exceeding the range is identified as a voltage anomaly node. The estimated voltage value is used to replace the measured blank value for overvoltage and undervoltage judgment, allowing general nodes that were previously unmonitorable due to the lack of miniature voltage sensors to be included in the anomaly identification range, thereby expanding the coverage of voltage anomaly identification and improving the accuracy of distribution network voltage anomaly identification. This application solves the problem of difficulty in accurately perceiving the entire network's voltage situation due to the lack of miniature voltage sensors and the inability to directly measure the voltage of many nodes, thus improving the accuracy of distribution network voltage anomaly identification.

[0055] See Figure 3 Based on the above method embodiments, corresponding device embodiments are provided; One embodiment of the present invention provides a voltage anomaly identification device for a power distribution network, including a first module 100, a second module 200 and a third module 300; The first module 100 is used to acquire node voltage data of each key node and branch current data of each branch line in the distribution network within a preset time period. The distribution network includes a number of nodes, the nodes include the key nodes, and the branch line is a feeder segment between two nodes. The second module 200 is used to construct an original voltage matrix and an original current matrix based on the voltage data of each node and the current data of each branch, respectively. Based on the original current matrix and the original voltage matrix, it predicts the global node impedance estimation matrix of the distribution network. For each key node, it calculates the voltage estimation value of each downstream node by taking the node voltage data of the current key node as the starting value and combining the branch current data of each branch line downstream of the current key node and the global node impedance estimation matrix. The third module 300 is used to determine the node corresponding to the voltage estimate that exceeds the preset safety range as a voltage anomaly node if each of the voltage estimates exceeds the preset safety range.

[0056] This approach, by acquiring node voltage data from a small number of key nodes and branch current data from all branch lines in the distribution network through the first module, forms an asymmetric sensing architecture of sparse voltage sampling and network-wide current acquisition. This provides a data foundation for subsequent recursive calculations of the entire network voltage, even under hardware constraints such as the lack of micro-voltage sensors and the inability to directly measure voltage at many nodes, ensuring the accuracy of voltage anomaly identification from the data source perspective. The second module constructs the original voltage and current matrices respectively, solving the problem of inconsistent processing of multi-time measurement data. Organizing discrete sampled data into a matrix form facilitates direct subsequent calculations, laying the foundation for accurate voltage anomaly identification from the data structure perspective. Based on the original current and voltage matrices, a global node impedance estimation matrix is ​​predicted, solving the problem of inaccurate measurement of line parameters and their variation with operating conditions. By using measured sparse voltage and network-wide current to identify impedance online without pre-setting line parameter values, the accuracy of voltage anomaly identification is ensured from the parameter identification perspective. Starting with the node voltage data of the current key node, and combining it with downstream branch current data and the global node impedance estimation matrix, a step-by-step recursive calculation is performed to obtain the voltage estimates of each downstream descendant node. Starting from the key node with known measured voltage, the voltage is calculated along the tree-like topology of the distribution network using branch impedance and branch current, and this calculation is performed step-by-step downstream. This fills the voltage gap for general nodes without voltage sensors, solving the problem that many intermediate and terminal nodes cannot be directly measured. It achieves complete perception of the entire network's voltage situation from a voltage sensing perspective, thereby improving the accuracy of voltage anomaly identification. In the third module, if any voltage estimate exceeds a preset safety range, the node exceeding the range is identified as a voltage anomaly node. The estimated voltage value is used to replace the measured blank value for overvoltage and undervoltage judgment, allowing general nodes that were previously unmonitorable due to the lack of miniature voltage sensors to be included in the anomaly identification range, thus expanding the coverage of voltage anomaly identification and improving the accuracy of voltage anomaly identification in the distribution network. It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement any of the above-described method embodiments of the present invention to provide a voltage anomaly identification method for a distribution network.

[0057] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0058] Based on the above-described embodiment of a voltage anomaly identification method for a power distribution network, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a voltage anomaly identification method for a power distribution network according to any embodiment of the present invention.

[0059] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0060] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0061] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0062] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the voltage anomaly identification method for a power distribution network described in any of the above-described method embodiments of the present invention.

[0063] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0064] Based on the above-described method embodiments, another embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements a method for identifying voltage anomalies in a power distribution network.

[0065] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for identifying voltage anomalies in a power distribution network, characterized in that, include: Acquire node voltage data of each key node and branch current data of each branch line in the distribution network within a preset time period. The distribution network includes several nodes, the nodes include the key nodes, and the branch lines are feeder segments between two nodes. Based on the voltage data of each node and the current data of each branch, an original voltage matrix and an original current matrix are constructed respectively. Based on the original current matrix and the original voltage matrix, the global node impedance estimation matrix of the distribution network is predicted. For each key node, the voltage data of the current key node is used as the starting value. The calculation is performed step by step downstream by combining the branch current data of each branch line downstream of the current key node and the global node impedance estimation matrix to obtain the voltage estimation value of each descendant node downstream of the current key node. If any of the voltage estimates exceed a preset safety range, the node corresponding to the voltage estimate that exceeds the preset safety range is identified as a voltage anomaly node.

2. The voltage anomaly identification method for a distribution network as described in claim 1, characterized in that, The prediction of the global node impedance estimation matrix of the distribution network based on the original current matrix and the original voltage matrix specifically includes: Based on the original current matrix, a transformation matrix is ​​constructed. The original current matrix is ​​linearly transformed using the transformation matrix to obtain the current whitening matrix; Based on the current whitening matrix and the original voltage matrix, the global node impedance estimation matrix of the distribution network is predicted.

3. The voltage anomaly identification method for a distribution network as described in claim 2, characterized in that, The transformation matrix constructed based on the original current matrix specifically includes: Based on the original current matrix, the covariance matrix is ​​constructed. The transformation matrix is ​​obtained by performing triangular decomposition on the covariance matrix.

4. The voltage anomaly identification method for a distribution network as described in claim 2, characterized in that, The prediction of the global node impedance estimation matrix of the distribution network based on the current whitening matrix and the original voltage matrix specifically includes: The current whitening matrix and the original voltage matrix are input into a preset least squares model to solve for the mapping matrix; The global node impedance estimation matrix of the distribution network is obtained by restoring the mapping matrix using the transformation matrix.

5. The voltage anomaly identification method for a distribution network as described in claim 1, characterized in that, For each of the aforementioned key nodes, starting with the node voltage data of the current key node, and combining the branch current data of each of the downstream branch lines of the current key node with the global node impedance estimation matrix, the voltage estimation values ​​of each downstream descendant node of the current key node are calculated recursively downstream, specifically including: Obtain the network topology data of the power distribution network; The branch impedance values ​​corresponding to each branch line are extracted from the global node impedance estimation matrix using the network topology data. For each of the aforementioned key nodes, several descendant nodes of the current key node are determined based on the network topology data, wherein the descendant nodes include child nodes directly connected to the current key node; The voltage data of the current critical node, the branch current data and the branch impedance value of the branch line between the current critical node and the child node are calculated to obtain the voltage estimate of the child node. This process is repeated until all the descendant nodes are traversed to obtain the voltage estimate of each descendant node downstream of the current critical node.

6. The voltage anomaly identification method for a distribution network as described in claim 1, characterized in that, The process of constructing the original voltage matrix and the original current matrix based on the voltage data of each node and the current data of each branch, specifically includes: The node voltage data of each of the key nodes and the branch current data of each of the branch lines are time-synchronized to obtain several voltage synchronization data and several current synchronization data. Anomaly removal processing is performed on each of the voltage synchronization data and each of the current synchronization data to obtain several valid voltage data and several valid current data. Phasor alignment is performed on each of the effective voltage data and each of the effective current data to obtain several voltage-aligned data and several current-aligned data. The voltage alignment data corresponding to each key node are concatenated in chronological order to obtain the voltage vector of each key node, and the voltage vectors are integrated to construct the original voltage matrix. The current alignment data corresponding to each branch line are concatenated in chronological order to obtain the current vector of each branch line, and the current vectors are integrated to construct the original current matrix.

7. A voltage anomaly identification device for a power distribution network, characterized in that, It includes Module 1, Module 2, and Module 3; The first module is used to acquire node voltage data of each key node and branch current data of each branch line in the distribution network within a preset time period. The distribution network includes a number of nodes, the nodes include the key nodes, and the branch line is a feeder segment between two nodes. The second module is used to construct an original voltage matrix and an original current matrix based on the voltage data of each node and the current data of each branch, respectively. Based on the original current matrix and the original voltage matrix, it predicts the global node impedance estimation matrix of the distribution network. For each key node, it uses the node voltage data of the current key node as the starting value, and combines the branch current data of each branch line downstream of the current key node and the global node impedance estimation matrix to calculate the voltage estimation value of each descendant node downstream of the current key node step by step downstream. The third module is used to determine the node corresponding to the voltage estimate that exceeds the preset safety range as a voltage anomaly node if each of the voltage estimates exceeds the preset safety range.

8. A terminal device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the voltage anomaly identification method for the power distribution network as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus containing the computer-readable storage medium to perform the voltage anomaly identification method for a power distribution network as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the communication device, the voltage anomaly identification method for the power distribution network as described in any one of claims 1 to 6 is implemented.

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