Wide-area power grid harmonic source positioning method, device and equipment, readable storage medium and program product

Abstract

The invention relates to a wide-area power grid harmonic source positioning method, device and equipment, a readable storage medium and a program product. The method comprises the following steps: obtaining harmonic measurement data of each measurement node in a power system, and constructing a harmonic measurement matrix according to the harmonic measurement data; obtaining a harmonic voltage matrix and at least one harmonic source current according to the harmonic measurement matrix and a pre-obtained power grid admittance matrix; and for each harmonic source current, determining a harmonic source node in the power system based on the correlation between the harmonic source current and the harmonic voltage matrix. By adopting the method, the accuracy of harmonic source positioning can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method, apparatus, device, readable storage medium, and program product for locating harmonic sources in a wide-area power grid. Background Art

[0002] As distribution networks develop towards intelligence, the access of a large number of power electronic equipment and renewable energy sources has led to a surge in the number of harmonic sources, and the problem of harmonic pollution has become increasingly serious.

[0003] Traditional harmonic source location technologies mainly include harmonic state estimation, compressed sensing, and blind source separation. Harmonic state estimation relies on precise network parameters and a large number of measurement points, which limits its engineering application. Compressed sensing performs well with a small number of measurement points, but still requires precise network parameters and a significantly greater number of measurement points than the number of harmonic sources. While blind source separation does not rely on network models, it places high demands on data quality.

[0004] Traditional methods have certain limitations in practice, and there is an urgent need for a low-dependency method for locating harmonic sources to improve the accuracy of harmonic source positioning. Summary of the Invention

[0005] Based on this, it is necessary to provide a wide area power grid harmonic source positioning method, device, equipment, readable storage medium and program product that can improve the accuracy of harmonic source positioning in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for locating harmonic sources in a wide-area power grid, comprising:

[0007] Obtain harmonic measurement data of each measurement node in the power system and construct a harmonic measurement matrix based on the harmonic measurement data;

[0008] Obtaining a harmonic voltage matrix and at least one harmonic source current according to a harmonic measurement matrix and a pre-acquired grid admittance matrix;

[0009] For each harmonic source current, a harmonic source node in the power system is determined based on the correlation between the harmonic source current and the harmonic voltage matrix.

[0010] In one embodiment, obtaining the harmonic voltage matrix and at least one harmonic source current based on the harmonic measurement matrix and the pre-acquired grid admittance matrix includes:

[0011] Perform orthogonal triangular decomposition and extraction processing on the harmonic measurement matrix to determine the number of harmonic sources in the power system;

[0012] Determine the target harmonic voltage of each unmeasured node in the power system based on the harmonic measurement data and the grid admittance matrix;

[0013] Constructing a harmonic voltage matrix based on the voltage data in the harmonic measurement data and the target harmonic voltage;

[0014] A matrix decomposition and screening process is performed on the harmonic voltage matrix according to the number of harmonic sources to obtain at least one harmonic source current.

[0015] In one embodiment, the harmonic measurement data further includes current data, and determining the target harmonic voltage of each unmeasured node in the power system based on the harmonic measurement data and the grid admittance matrix includes:

[0016] Obtain a topological map of the power system and determine each unmeasured node based on the topological map;

[0017] For each unmeasured node, a target harmonic voltage of each unmeasured node in the power system is determined according to adjacent nodes of the unmeasured node.

[0018] In one embodiment, determining the target harmonic voltage of each unmeasured node in the power system based on the adjacent nodes of the unmeasured node includes:

[0019] When the number of adjacent nodes is equal to a preset number, determining a target harmonic voltage of an unmeasured node based on voltage data, current data, and a grid admittance matrix;

[0020] When the number of adjacent nodes is greater than a preset number, for each adjacent node, the initial harmonic voltage is determined based on the voltage data, current data and grid admittance matrix, and a weighted average processing is performed on multiple initial harmonic voltages to determine the target harmonic voltage of the unmeasured node.

[0021] In one embodiment, the above-mentioned matrix decomposition and screening process is performed on the harmonic voltage matrix in sequence according to the number of harmonic sources to obtain at least one harmonic source current, including:

[0022] Performing matrix decomposition processing on the harmonic measurement matrix to obtain an estimated signal matrix; the estimated signal matrix includes multiple current source estimation components;

[0023] For each current source estimation component in the estimation signal matrix, determining a norm of the current source estimation component;

[0024] Sorting the current source estimation components in descending order according to the norm to obtain sorting information;

[0025] According to the ranking information, the current source estimated components ranked in the top N are determined as harmonic source currents, where N is the number of harmonic sources and is a positive integer.

[0026] In one embodiment, performing orthogonal triangular decomposition extraction processing on the harmonic measurement matrix to determine the number of harmonic sources in the power system includes:

[0027] Perform orthogonal triangular decomposition on the harmonic measurement matrix to obtain an upper triangular matrix corresponding to the harmonic measurement matrix;

[0028] Performing extraction processing on the upper triangular matrix to obtain at least one eigenvalue vector corresponding to the upper triangular matrix;

[0029] Inputting at least one eigenvalue vector into a preset quantity determination function to determine a function value;

[0030] When the function value is not less than the preset vector threshold, the quantity corresponding to the function value is determined as the quantity of harmonic sources of the power system.

[0031] In a second aspect, the present application further provides a wide area power grid harmonic source location device, comprising:

[0032] A construction module is used to obtain harmonic measurement data of each measurement node in the power system and construct a harmonic measurement matrix based on the harmonic measurement data;

[0033] a matrix determination module, configured to obtain a harmonic voltage matrix and at least one harmonic source current based on a harmonic measurement matrix and a pre-acquired grid admittance matrix;

[0034] The harmonic source node determination module is used to determine the harmonic source node in the power system based on the correlation between the harmonic source current and the harmonic voltage matrix for each harmonic source current.

[0035] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0036] Obtain harmonic measurement data of each measurement node in the power system and construct a harmonic measurement matrix based on the harmonic measurement data;

[0037] Obtaining a harmonic voltage matrix and at least one harmonic source current according to a harmonic measurement matrix and a pre-acquired grid admittance matrix;

[0038] For each harmonic source current, a harmonic source node in the power system is determined based on the correlation between the harmonic source current and the harmonic voltage matrix.

[0039] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0040] Obtain harmonic measurement data of each measurement node in the power system and construct a harmonic measurement matrix based on the harmonic measurement data;

[0041] Obtaining a harmonic voltage matrix and at least one harmonic source current according to a harmonic measurement matrix and a pre-acquired grid admittance matrix;

[0042] For each harmonic source current, a harmonic source node in the power system is determined based on the correlation between the harmonic source current and the harmonic voltage matrix.

[0043] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0044] Obtain harmonic measurement data of each measurement node in the power system and construct a harmonic measurement matrix based on the harmonic measurement data;

[0045] Obtaining a harmonic voltage matrix and at least one harmonic source current according to a harmonic measurement matrix and a pre-acquired grid admittance matrix;

[0046] For each harmonic source current, a harmonic source node in the power system is determined based on the correlation between the harmonic source current and the harmonic voltage matrix.

[0047] The wide-area power grid harmonic source location method, device, equipment, readable storage medium, and program product described above obtain harmonic measurement data from each measurement node in the power system and construct a harmonic measurement matrix based on the harmonic measurement data. A harmonic voltage matrix and at least one harmonic source current are obtained based on the harmonic measurement matrix and a pre-acquired power grid admittance matrix. For each harmonic source current, the harmonic source node in the power system is determined based on the correlation between the harmonic source current and the harmonic voltage matrix. In this method, by constructing a harmonic measurement matrix and performing calculations in conjunction with the power grid admittance matrix, the harmonic voltage matrix and harmonic source current reflecting the true state of the power grid system can be extracted from the harmonic measurement data. Furthermore, positioning analysis is performed based on the correlation between current and voltage, avoiding the error accumulation problem of traditional methods that rely on precise modeling of the entire network, thereby improving the accuracy of harmonic source node judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 is a diagram of the internal structure of a computer device in one embodiment;

[0050] Figure 21 is a flow chart of a method for locating a harmonic source in a wide area power grid according to an embodiment;

[0051] Figure 3 Schematic diagram of a flow chart of a method for locating a harmonic source in a wide area power grid according to another embodiment;

[0052] Figure 4 1 is a flow chart of a method for locating a harmonic source in a wide area power grid according to another embodiment;

[0053] Figure 5 A schematic diagram of positioning multiple harmonic sources in a power grid system 14 according to an embodiment;

[0054] Figure 6 Schematic diagram of a flow chart of a method for locating a harmonic source in a wide area power grid according to another embodiment;

[0055] Figure 7 Schematic diagram of a flow chart of a method for locating a harmonic source in a wide area power grid according to another embodiment;

[0056] Figure 8 A diagram showing a result of a triangular decomposition eigenvalue vector in a simulation application example provided by one embodiment;

[0057] Figure 9 FIG. 4 is a structural block diagram of a wide area power grid harmonic source locating device in one embodiment. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0059] As distribution networks transition toward smart grids, a vast number of power electronic devices (such as inverters, rectifiers, and electronic switches) and renewable energy sources are being integrated into the grid. This has led to a dramatic increase in the number of harmonic sources, exacerbating harmonic pollution. The waveform distortion caused by harmonics can affect the normal operation of power and electronic equipment, and even threaten the safety and stability of the power grid. To address this harmonic pollution, harmonic source location is an urgent issue. Research on harmonic source location methods for wide-area power grids has important engineering applications.

[0060] Existing multi-harmonic source location technologies include harmonic state estimation, compressed sensing, and blind source separation. Harmonic state estimation is the inverse process of harmonic power flow. It estimates state quantities by establishing a mathematical relationship between network state variables and measured values. Harmonic state estimation was first proposed to locate harmonic sources using harmonic currents as state variables. Building on this foundation, subsequent researchers have conducted extensive research on this topic. Subsequently, Kalman filtering was introduced into harmonic state estimation due to the dynamic characteristics of the system. This method requires precise network parameters and a high number of measurement nodes, which are difficult to meet in engineering practice.

[0061] When the number of measurement nodes is smaller than the number of state variables, the compressed sensing wide-area power grid harmonic source location method transforms the harmonic source estimation problem into a sparse maximization problem that takes into account the sparsity of the state variables. Compressed sensing is then incorporated into the harmonic source location framework. By solving underdetermined equations, the method seeks the most likely combination of harmonic sources for location, achieving excellent results. This type of method requires fewer measurement nodes than harmonic state estimation methods, but it also requires a significantly greater number of measurement nodes than the number of harmonic sources, and similarly requires precise network parameters.

[0062] Both of the above methods rely on precise network parameters, but this is difficult to achieve in complex dynamic power grid systems. Blind source separation methods do not require network parameters, of which the independent component analysis method is the most representative. This method first performs blind source separation on the measured values ​​to obtain harmonic currents with ambiguous order and amplitude, and then locates the harmonic source by calculating the correlation between historical harmonic data and harmonic currents. The advantage of this method is that it is completely independent of network parameters. The disadvantage of this method is that under the condition of multiple harmonic sources, each harmonic current source must be independent of each other, otherwise the independent component analysis method will fail. In addition, the number of measurement nodes must be no less than the number of suspected harmonic source nodes, and its harmonic source location accuracy is also low.

[0063] Based on this, the present application provides a method for locating harmonic sources in a wide-area power grid to solve the above-mentioned problems.

[0064] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 1As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data in the process of locating the harmonic source of the wide area power grid. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for locating the harmonic source of the wide area power grid is implemented.

[0065] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0066] In an exemplary embodiment, Figure 2 As shown in the figure, a method for locating harmonic sources in a wide area power grid is provided. Figure 1 The computer device in the embodiment is used as an example to illustrate the method, including the following steps 201 to 203.

[0067] Step 201: Acquire harmonic measurement data of each measurement node in the power system, and construct a harmonic measurement matrix based on the harmonic measurement data.

[0068] The power system refers to the overall electric energy transmission and distribution network consisting of power generation equipment, transmission lines, substations, distribution facilities, and loads. The power system can include a main grid, distribution network, microgrid, or a combination of these, enabling centralized power supply and dispatch control.

[0069] A measurement node is a node in the power system that has harmonic measurement equipment deployed, capable of collecting harmonic voltage and / or current data in real time. This can be a substation busbar, cable connector, capacitor access point, or other power node where harmonic signals can be obtained.

[0070] Harmonic measurement data refers to the discrete data collected by measurement nodes regarding harmonic voltages and currents. It typically includes amplitude and phase information at specific harmonic orders. This data is used to reflect the propagation characteristics and interference level of harmonics in power systems.

[0071] The harmonic measurement matrix is ​​a matrix-structured data set constructed from the harmonic measurement data collected by each measurement node at multiple sampling moments. This matrix is ​​used to uniformly represent harmonic state information across multiple nodes and time periods, and serves as the fundamental input data structure for subsequent algorithm analysis.

[0072] In an embodiment of the present application, a computer device acquires harmonic measurement data from multiple measurement nodes in a power system, including harmonic voltage data at each measurement node. This harmonic measurement data can be regularly collected by a harmonic monitoring device deployed within the power system. The specific measurement frequency and data format can be set based on the power system configuration.

[0073] After acquiring the aforementioned measurement data, the computer constructs a harmonic measurement matrix based on the harmonic voltage and current amplitude information at each measurement node at multiple sampling times. This matrix can contain multi-dimensional harmonic measurement results for each measurement node for subsequent analysis and processing.

[0074] In a specific embodiment, the harmonic measurement data of the harmonic voltage and current of the power system can be expressed in the following matrix form. The harmonic measurement matrix can be expressed by formula (1).

[0075]

[0076] In equation (1), z is the harmonic measurement matrix, V represents the harmonic measurement voltage data, and I represents the harmonic measurement current data. p represents the number of harmonic voltage measurements, and q represents the number of harmonic current measurements. Each voltage and current measurement data is a vector, and the length of each vector is m. The size of the resulting harmonic measurement matrix z is (p + q) × m.

[0077] Step 202: Obtain a harmonic voltage matrix and at least one harmonic source current according to the harmonic measurement matrix and the pre-acquired grid admittance matrix.

[0078] The grid admittance matrix is ​​a complex matrix used to describe the impedance relationships between nodes in a power system at harmonic frequencies. It reflects the electrical connections between different nodes at harmonic frequencies. It is usually derived based on the power system topology and equipment parameters, and may contain certain errors.

[0079] The harmonic voltage matrix is ​​a collection of harmonic voltage values ​​for all or some nodes in a power system, derived from measured node data and the grid admittance matrix. This matrix reflects the voltage state of each node at harmonic frequencies and is used for subsequent current source inversion and correlation analysis.

[0080] Harmonic source current is an estimate of the harmonic current injected into the power system by a harmonic source, typically calculated through data processing and inversion. This current represents the harmonic pollution caused by certain devices or nodes in the power system and is a key factor in harmonic source location.

[0081] In an embodiment of the present application, a computer device calls a pre-acquired grid admittance matrix. The grid admittance matrix can be estimated based on the grid topology and parameter information during the initial configuration or design phase of the power system. It does not require high accuracy and may have certain structural errors. The grid admittance matrix is ​​combined with the harmonic measurement matrix, and the computer device further derives the voltage value of each measurement node in the power system at the corresponding harmonic order, thereby generating a harmonic voltage matrix. On this basis, the computer device can also infer several harmonic source current estimates based on the measurement data and admittance information. The harmonic source current estimates are used to represent the harmonic currents injected into the power system by the harmonic sources that may exist in the power system.

[0082] Step 203 : For each harmonic source current, determine the harmonic source node in the power system based on the correlation between the harmonic source current and the harmonic voltage matrix.

[0083] Correlation is a measure of the similarity between two variables. In this technical solution, it is used to evaluate the matching degree between each harmonic source current and the harmonic voltage at each node. The stronger the correlation, the more likely the harmonic source current is injected into the node.

[0084] Harmonic source nodes are nodes in the power system where harmonic currents are injected, i.e., the locations of harmonic sources. Harmonic source nodes can be identified and located by analyzing the correlation between harmonic source currents and node voltages.

[0085] In an embodiment of the present application, after obtaining a harmonic voltage matrix and multiple harmonic source currents, the computer device analyzes the correlation between each harmonic source current and the harmonic voltage at each node in the system. This correlation can be measured by calculating the degree of match between the current estimate and the node voltage within a specific sampling interval. Among multiple nodes, if a node has the greatest correlation with a particular harmonic current estimate, it can be inferred that the node is the injection location of the harmonic current.

[0086] Finally, the computer determines the corresponding set of harmonic source nodes in the system based on the node with the highest correlation for each harmonic current. This process does not require full network measurement or high-precision model support and is suitable for power systems with limited measurement points, imprecise grid parameters, or dynamically changing system structures.

[0087] In a specific embodiment, k is calculated separately. min The correlation between the harmonic source current and the harmonic voltage matrix is ​​calculated. For each harmonic source current, the node with the largest correlation is selected and the node is determined to be the node where the harmonic source is located. The correlation between the k harmonic source currents and the harmonic voltage matrix is ​​calculated separately. The calculation formula is shown in Equation (2).

[0088]

[0089] where c k (n) is the sequence c k The nth element in is the kth harmonic source current, u n is the harmonic voltage of the nth node in the harmonic voltage matrix. Select c k The maximum value among them, the n corresponding to the maximum value is identified as the harmonic current Node location is determined, and node n is identified as the location of the harmonic source. Correlation calculation is performed on each harmonic source current to find all harmonic source nodes.

[0090] In the above-mentioned wide-area power grid harmonic source location method, harmonic measurement data is obtained from each measurement node in the power system, and a harmonic measurement matrix is ​​constructed based on the harmonic measurement data. Based on the harmonic measurement matrix and the pre-acquired power grid admittance matrix, a harmonic voltage matrix and at least one harmonic source current are obtained. For each harmonic source current, the harmonic source node in the power system is determined based on the correlation between the harmonic source current and the harmonic voltage matrix. In this method, by constructing the harmonic measurement matrix and combining it with the power grid admittance matrix for calculation, the harmonic voltage matrix and harmonic source current that reflect the true state of the power grid system can be extracted from the harmonic measurement data. On this basis, location analysis is further performed through the correlation between current and voltage, avoiding the error accumulation problem of traditional methods that rely on precise modeling of the entire network, thereby improving the accuracy of harmonic source node judgment.

[0091] In an exemplary embodiment, Figure 3 As shown, the above-mentioned "obtaining a harmonic voltage matrix and at least one harmonic source current according to the harmonic measurement matrix and the pre-acquired grid admittance matrix" includes steps 301 to 304. Among them:

[0092] Step 301 : performing orthogonal triangular decomposition extraction processing on the harmonic measurement matrix to determine the number of harmonic sources in the power system.

[0093] Among them, the orthogonal triangular decomposition extraction processing refers to performing orthogonal triangular decomposition on the harmonic measurement matrix, decomposing the harmonic measurement matrix into the product of an orthogonal matrix and an upper triangular matrix, and further extracting the eigenvalue information in the upper triangular matrix for subsequent analysis method to determine the number of harmonic sources.

[0094] The number of harmonic sources refers to the number of independent harmonic injection sources that exist simultaneously in the power system. By decomposing the harmonic measurement matrix and analyzing its eigenvalues, the number of possible harmonic sources in the power system can be estimated.

[0095] In an embodiment of the present application, a computer device performs orthogonal triangular decomposition extraction processing on a harmonic measurement matrix. Specifically, the computer device decomposes the harmonic measurement matrix into an orthogonal matrix and an upper triangular matrix using an orthogonal triangular decomposition method, and extracts the corresponding diagonal eigenvalues ​​from the upper triangular matrix. Based on the distribution of these eigenvalues, the computer device determines the number of harmonic sources present in the power system.

[0096] Step 302 : Determine the target harmonic voltage of each unmeasured node in the power system based on the harmonic measurement data and the grid admittance matrix.

[0097] Unmeasured nodes are nodes in the power system that lack harmonic measurement devices and therefore cannot obtain real-time harmonic voltage or current data. The harmonic status of these nodes must be estimated using data from connected measurement nodes and the grid admittance matrix.

[0098] The target harmonic voltage is the voltage value at a specific harmonic frequency at an unmeasured node, derived to estimate the state of that node. The target harmonic voltage is not measured but is an estimate calculated based on the measured node data and the grid admittance matrix. This is used to construct the complete harmonic voltage matrix.

[0099] In an embodiment of the present application, after determining the number of harmonic sources, the computer device further uses the harmonic measurement data and the pre-acquired grid admittance matrix to derive the target harmonic voltage of each unmeasured node in the power system.

[0100] In a specific embodiment, the computer device can identify the adjacent measurement nodes of each unmeasured node and calculate the target harmonic voltage of the unmeasured node based on the harmonic voltage data, harmonic current data and the admittance value between these adjacent measurement nodes and the unmeasured node.

[0101] Step 303: construct a harmonic voltage matrix based on the voltage data in the harmonic measurement data and the target harmonic voltage.

[0102] Voltage data refers to the voltage amplitude or phasor data at specific harmonic orders at the measurement node, as contained in the harmonic measurement data. This data is an essential component of constructing the harmonic voltage matrix and can be combined with the estimated target harmonic voltage.

[0103] In this embodiment of the present application, the computer device combines the target harmonic voltage with the voltage data in the harmonic measurement data to construct a harmonic voltage matrix that includes voltage information for both measured and unmeasured nodes. The harmonic voltage matrix, with node dimensions, fully represents the voltage state of each node in the power system at a specific harmonic frequency, supporting subsequent current source estimation.

[0104] Step 304 : performing matrix decomposition and screening processing on the harmonic voltage matrix according to the number of harmonic sources to obtain at least one harmonic source current.

[0105] Among them, matrix decomposition and screening processing refers to the process of processing the harmonic voltage matrix, which specifically includes using the matrix decomposition algorithm to obtain multiple harmonic current source estimates, and based on the number of harmonic sources previously estimated, screening out several of the most representative harmonic source current components for subsequent positioning judgment.

[0106] In an embodiment of the present application, a computer device performs matrix decomposition and screening processing on the harmonic voltage matrix based on the determined number of harmonic sources. This processing includes executing a matrix decomposition algorithm on the harmonic voltage matrix to obtain multiple harmonic current estimation components, and then screening out several most representative harmonic source current estimation results based on the number of harmonic sources. The harmonic source currents serve as input for subsequent location analysis and reflect the characteristics of the interference currents that may be injected by each harmonic source in the power system.

[0107] In an exemplary embodiment, the above harmonic measurement data also includes current data. On this basis, as shown in FIG. Figure 4 As shown, the above-mentioned “determining the target harmonic voltage of each unmeasured node in the power system based on the harmonic measurement data and the grid admittance matrix” includes steps 401 to 402. Among them:

[0108] Step 401: Obtain a topology map of the power system, and determine each unmeasured node according to the topology map.

[0109] A topology map is a graph model that reflects the connectivity between nodes and branches in a power system. It ignores physical distances or electrical parameters, focusing solely on connectivity. It is typically represented in a "node-edge" format from graph theory, where nodes represent substations, busbars, and user terminals, and edges represent transmission lines or equipment connections. Topology maps serve as the fundamental data for determining the locations and adjacencies of unmeasured nodes.

[0110] In an embodiment of the present application, a computer device obtains a topological diagram of a power system. A topological diagram is a structural diagram model that describes the nodes and their connections in a power system. It can be obtained in real time using grid design data, main wiring diagrams, or online topological data. Each node in the topological diagram represents an electrical node in the power system, and each edge represents a physical connection or electrical coupling between nodes.

[0111] The computer extracts the node numbers and adjacency relationships of all nodes from the topology map and compares them with previously acquired harmonic measurement data. It identifies nodes that do not appear in the harmonic measurement data and defines them as the unmeasured node set. Unmeasured nodes are nodes in the power system that do not have harmonic voltage or current measurement devices deployed and therefore cannot directly obtain measurement data.

[0112] Step 402 : For each unmeasured node, determine the target harmonic voltage of each unmeasured node in the power system based on the adjacent nodes of the unmeasured node.

[0113] Among them, adjacent nodes refer to other nodes that are directly connected to a specific node in the topology diagram and have a branch. For example, if node A is connected to node B through a transmission line, then A and B are adjacent nodes to each other. In the embodiment of the present application, the harmonic voltage and current information of the adjacent nodes can be used to estimate the target harmonic voltage of the unmeasured node to which the node is connected. Figure 5 As shown, if the unmeasured node is node 9, the adjacent nodes of node 9 are node 7 and node 14.

[0114] In this embodiment of the present application, for each node in the set of unmeasured nodes, the computer device further determines its set of neighboring nodes based on the node's connection information in the topology graph. The neighboring nodes are measured nodes that are directly connected to the target unmeasured node and have known harmonic voltage and harmonic current data.

[0115] The computer then calculates the target harmonic voltage for the target unmeasured node using the harmonic voltage data of adjacent nodes, the grid admittance information between the adjacent nodes and the target node, and the harmonic current data on the adjacent branches. If the target unmeasured node corresponds to multiple adjacent nodes, the computer may calculate an initial harmonic voltage value for the unmeasured node based on each adjacent node and perform a weighted average of these initial results to obtain the final target harmonic voltage estimate.

[0116] In the above embodiment, the computer device can effectively estimate the harmonic voltages of unmeasured nodes in the power system based on the topological structure and local data under the condition of limited measurement data, and provide the necessary intermediate input for the subsequent construction of the harmonic voltage matrix and harmonic source current estimation.

[0117] In an exemplary embodiment, the above-mentioned “determining the target harmonic voltage of each unmeasured node in the power system based on the adjacent nodes of the unmeasured node” includes:

[0118] Case 1: When the number of adjacent nodes is equal to a preset number, the target harmonic voltage of the unmeasured node is determined based on the voltage data, the current data and the grid admittance matrix.

[0119] In an embodiment of the present application, the computer device obtains the node numbers of all adjacent nodes corresponding to the unmeasured node, and extracts the harmonic voltage data, harmonic current data of each adjacent node and the admittance value between the adjacent node and the unmeasured node.

[0120] The computer then uses the harmonic voltage data, harmonic current data, and admittance value as input parameters and calculates the target harmonic voltage for the unmeasured node based on a preset voltage calculation model. This target harmonic voltage represents an estimate of the harmonic voltage state at the node under the current network structure and measurement data.

[0121] In the second case, when the number of adjacent nodes is greater than the preset number, for each adjacent node, the initial harmonic voltage is determined based on the voltage data, current data and grid admittance matrix, and the weighted average processing is performed on multiple initial harmonic voltages to determine the target harmonic voltage of the unmeasured node.

[0122] In an embodiment of the present application, when the number of neighboring nodes of an unmeasured node is greater than a preset number, the computer device may perform the following processing steps for each neighboring node:

[0123] For each adjacent node, extract its harmonic voltage data and harmonic current data respectively, and obtain the admittance value between it and the target unmeasured node;

[0124] Then, the computer device calculates an initial harmonic voltage based on the voltage data, the current data and corresponding parameters in the grid admittance matrix, which is used to represent the single-point estimation of the harmonic voltage of the target unmeasured node by the adjacent node;

[0125] Once all initial harmonic voltages are calculated, the computer performs a weighted average on them. Weighted averaging can determine weight coefficients based on admittance values, node distances, or other weighting factors to produce the final target harmonic voltage, which serves as an estimate of the harmonic voltage at the unmeasured node.

[0126] In a specific embodiment, the target harmonic voltage of each unmeasured node is derived and calculated based on the harmonic measurement data and an inaccurate grid admittance matrix.

[0127] When the number of adjacent nodes is equal to the preset number, the voltages of the adjacent nodes of the measurement node can be calculated by measuring the current and voltage data using harmonics, as shown in formula (3).

[0128]

[0129] In formula (3), u j is the voltage of the unmeasured node adjacent to the measured node, g ij is the element in the i-th row and j-th column of the grid admittance matrix, u i is the harmonic voltage data of the measured node i, i ij For measuring line i ij Harmonic current data.

[0130] If an unmeasured node has multiple adjacent measured nodes, the voltage of the unmeasured node is calculated using equation (4).

[0131]

[0132] Where n is the number of adjacent measurement nodes of the unmeasured node j, Γ j is the set of adjacent measured nodes of the unmeasured node j.

[0133] Correspondingly, the harmonic voltage matrix of all nodes is obtained, as shown in formula (5):

[0134]

[0135] Where N is the total number of system nodes, u n is the target harmonic voltage of the nth unmeasured node.

[0136] In the above embodiment, different target harmonic voltage estimation strategies can be flexibly selected according to the difference in the number of adjacent nodes, thereby improving the estimation accuracy and providing support for constructing a complete harmonic voltage matrix and subsequent harmonic source current estimation.

[0137] In an exemplary embodiment, Figure 6 As shown, the above-mentioned "performing matrix decomposition and screening processing on the harmonic voltage matrix in sequence according to the number of harmonic sources to obtain at least one harmonic source current" includes steps 501 to 504. Among them:

[0138] Step 501 : performing matrix decomposition processing on the harmonic measurement matrix to obtain an estimated signal matrix; the estimated signal matrix includes a plurality of current source estimation components.

[0139] In an embodiment of the present application, a computer device first receives harmonic measurement data collected from each measurement node in the power system. The measurement data includes harmonic voltage and harmonic current amplitude information at multiple sampling times. The computer device organizes and classifies this data according to the measurement node number and sampling time, constructing a harmonic measurement matrix. The rows of this matrix represent different measurement nodes, and the columns represent different sampling time points. The matrix elements record the harmonic amplitude of the corresponding node at the corresponding time. During the data organization process, the computer device detects and removes outliers to ensure data integrity and accuracy.

[0140] Next, the computer performs matrix decomposition on the constructed harmonic measurement matrix. This decomposition uses an appropriate algorithm, such as eigenvalue decomposition, singular value decomposition, or blind source separation, to break the original complex signal into multiple independent signal components. After the decomposition is complete, the computer obtains an estimated signal matrix containing multiple current source estimation components, each corresponding to the signal of a possible harmonic current source.

[0141] Step 502 : For each current source estimation component in the estimation signal matrix, determine the norm of the current source estimation component.

[0142] In the present embodiment, the computer device performs a norm calculation on each column in the estimated signal matrix, that is, each current source estimated component. The norm is calculated by summing the absolute amplitude values ​​of the signal at all sampling times, i.e., calculating the 1-norm, to quantify the signal strength. The computer device sequentially completes the norm calculation for all current source estimated components, generating a complete norm list.

[0143] Step 503 : sort the current source estimation components in descending order according to the norm to obtain sorting information.

[0144] In this embodiment of the present application, after completing the norm calculation, the computer device sorts all current source estimation components in descending order based on the norm list. The sorting result arranges the signal strength from high to low, generating sorting information. The sorting information details the number and corresponding norm of each current source estimation component to facilitate subsequent screening operations.

[0145] Step 504 : Determine the first N current source estimated components as harmonic source currents according to the ranking information, where N is the number of harmonic sources and is a positive integer.

[0146] In this embodiment of the present application, the computer device selects the top N current source estimated components by norm from the sorted information, based on a predetermined number of harmonic sources, N. These selected signals are identified as the primary harmonic source currents in the power system and are used for subsequent harmonic source location analysis. Finally, the computer device outputs the filtered harmonic source current data for subsequent modules to access. The output format remains consistent with the input format, ensuring data availability and compatibility.

[0147] In a specific embodiment, a blind source separation algorithm can be used to perform blind source separation on the harmonic measurement matrix to obtain a harmonic source current matrix with fuzzy amplitude and order, and the k harmonic source current matrix with the largest 1 norm is selected. min rows, as k min harmonic source current.

[0148] The harmonic voltage matrix U of all nodes is jointly diagonalized by characteristic matrix approximation, so that the harmonic voltage matrix U can be decomposed into formula (6).

[0149] U=Wx (6)

[0150] Where W is the whitening matrix and x is the estimated signal matrix after blind source separation. The estimated signal matrix x is written as Equation (7).

[0151]

[0152] Among them, x n Represents the nth row in x, and calculates x respectively n The 1-norm of (n=1,2...N) is shown in formula (8).

[0153]

[0154] Select k with the largest 1-norm min x n , as the harmonic source current As shown in formula (9).

[0155]

[0156] in, is the estimated value of harmonic source current, x n To estimate the signal matrix x, the first k 1-norms in descending order min OK.

[0157] In an exemplary embodiment, Figure 7 As shown, the above-mentioned "performing orthogonal triangular decomposition and extraction processing on the harmonic measurement matrix to determine the number of harmonic sources in the power system" includes steps 601 to 604. Among them:

[0158] Step 601: Perform orthogonal triangular decomposition on the harmonic measurement matrix to obtain an upper triangular matrix corresponding to the harmonic measurement matrix.

[0159] In this embodiment, a computer device first receives a harmonic measurement matrix constructed in the power system. This matrix consists of harmonic voltage and harmonic current data collected by multiple measurement nodes at different sampling time points. The computer device performs a format check on the matrix to ensure that the data is complete and intact, and establishes a corresponding relationship between the node number and the sampling time.

[0160] The computer then performs an orthogonal triangular decomposition on the harmonic measurement matrix. This decomposition involves decomposing the original harmonic measurement matrix into the product of two matrices: an orthogonal matrix and an upper triangular matrix. The computer performs this decomposition through calculations, obtaining an upper triangular matrix as one of the decomposition results.

[0161] Step 602: extract the upper triangular matrix to obtain at least one eigenvalue vector corresponding to the upper triangular matrix.

[0162] In an embodiment of the present application, a computer device performs eigenvalue extraction on the obtained upper triangular matrix. Specifically, the computer device calculates a set of eigenvalues ​​of the upper triangular matrix and then organizes these eigenvalues ​​into at least one eigenvalue vector. The computer device ensures accurate extraction of the eigenvalues ​​and avoids computational errors.

[0163] Step 603: Input at least one eigenvalue vector into a preset quantity determination function to determine the function value.

[0164] In an embodiment of the present application, the computer device inputs the obtained eigenvalue vector into a pre-set quantity determination function. The function performs a numerical calculation based on the input eigenvalue vector to obtain a function value. The computer device completes the calculation process according to the function algorithm and obtains the function output result.

[0165] Step 604: If the function value is not less than the preset vector threshold, the number corresponding to the function value is determined as the number of harmonic sources in the power system.

[0166] In this embodiment of the present application, the computer device compares the function value with a preset vector threshold. If the function value is greater than or equal to the preset threshold, the computer device determines the number corresponding to the function value as the number of harmonic sources in the power system. If the function value is less than the preset threshold, the computer device can re-perform the decomposition as needed or indicate that the number is uncertain.

[0167] In a specific embodiment, all eigenvalues ​​of the harmonic measurement matrix are calculated using an orthogonal triangular decomposition algorithm, and the numerical distribution of the eigenvalues ​​of the harmonic measurement matrix is ​​analyzed to determine the number of harmonic sources.

[0168] Performing orthogonal triangular decomposition on the above harmonic voltage matrix, we get equation (10).

[0169] z=Q*R (10)

[0170] Where Q is a unitary matrix of size (p+q)×(p+q), and R is an upper triangular matrix of size (p+q)×m. Extracting the elements on the diagonal of matrix R and expressing them as eigenvalue vectors d can be shown in Equation (11).

[0171] d=diag(R) (11)

[0172] in, Indicates the extraction of diagonal elements. According to the eigenvalue vector d, the function of formula (12) is calculated:

[0173]

[0174] Where N is the number of elements in vector d and k is the independent variable of function v(k). Calculate the minimum value k when v(k) is greater than or equal to 0.95. min , with k min As the number of harmonic sources.

[0175] In an exemplary embodiment, the method further includes:

[0176] Step 1: Obtain harmonic measurement data of each measurement node in the power system, and construct a harmonic measurement matrix based on the harmonic measurement data.

[0177] Step 2: Perform orthogonal triangular decomposition on the harmonic measurement matrix to obtain an upper triangular matrix corresponding to the harmonic measurement matrix.

[0178] Step 3: extract the upper triangular matrix to obtain at least one eigenvalue vector corresponding to the upper triangular matrix.

[0179] Step 4: Input at least one eigenvalue vector into a preset quantity determination function to determine the function value.

[0180] Step 5: When the function value is not less than a preset vector threshold, the number corresponding to the function value is determined as the number of harmonic sources in the power system.

[0181] Step 6: Obtain a topological map of the power system and determine each unmeasured node according to the topological map.

[0182] Step 7: For each unmeasured node, when the number of adjacent nodes is equal to a preset number, determine the target harmonic voltage of the unmeasured node based on the voltage data, current data, and grid admittance matrix; when the number of adjacent nodes is greater than the preset number, for each adjacent node, determine the initial harmonic voltage based on the voltage data, current data, and grid admittance matrix, and perform weighted averaging on the multiple initial harmonic voltages to determine the target harmonic voltage of the unmeasured node.

[0183] Step 8: Construct a harmonic voltage matrix based on the voltage data in the harmonic measurement data and the target harmonic voltage.

[0184] Step 9: Perform matrix decomposition processing on the harmonic measurement matrix to obtain an estimated signal matrix; the estimated signal matrix includes multiple current source estimation components.

[0185] Step 10: For each current source estimation component in the estimation signal matrix, determine the norm of the current source estimation component.

[0186] Step 11: sort the current source estimation components in descending order according to the norm to obtain sorting information.

[0187] Step 12: Determine the first N current source estimated components as harmonic source currents according to the ranking information, where N is the number of harmonic sources and is a positive integer.

[0188] Step 13: For each harmonic source current, determine the harmonic source node in the power system based on the correlation between the harmonic source current and the harmonic voltage matrix.

[0189] In one embodiment, this embodiment is a simulation application embodiment of locating a multi-harmonic source by applying the method provided in the above-mentioned embodiment 1.

[0190] according to Figure 5 In the 14-grid system model shown, the harmonic sources are set at system nodes 3, 6, and 12, and the harmonic spectrum settings are shown in Table 1. Table 1 shows the harmonic spectrum settings. Nodes 2, 7, 11, and 13 are set as measurement nodes to obtain the harmonic voltages and branch currents of the measurement nodes. A 5% error is added to the harmonic voltages and currents at the measurement nodes and the grid admittance matrix. The method of the embodiment of the present application is introduced and repeated experiments are performed 1000 times. The characteristic vector d required for the calculation process of the number of harmonic sources in the repeated experiments is as follows: Figure 8 As shown. Figure 8 In the example, nodes 1, 2, and 3 are harmonic source nodes.

[0191] The success rate of multi-harmonic source positioning is shown in Table 2 of the embodiment of the present application, where Table 2 is the accuracy rate of 1000 simulated harmonic positioning.

[0192] Table 1

[0193]

[0194] Table 2

[0195]

[0196] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0197] Based on the same inventive concept, embodiments of the present application also provide a wide-area power grid harmonic source locating device for implementing the wide-area power grid harmonic source locating method described above. The solution provided by this device is similar to the solution described in the method described above. Therefore, the specific limitations of one or more embodiments of the wide-area power grid harmonic source locating device provided below can be found in the above-mentioned limitations of the wide-area power grid harmonic source locating method, and will not be repeated here.

[0198] In an exemplary embodiment, Figure 9 As shown, a wide area power grid harmonic source location device is provided, comprising: a construction module 701, a matrix determination module 702 and a harmonic source node determination module 703, wherein:

[0199] A construction module 701 is used to obtain harmonic measurement data of each measurement node in the power system and construct a harmonic measurement matrix based on the harmonic measurement data;

[0200] A matrix determination module 702 is configured to obtain a harmonic voltage matrix and at least one harmonic source current based on a harmonic measurement matrix and a pre-acquired grid admittance matrix;

[0201] The harmonic source node determination module 703 is configured to determine, for each harmonic source current, a harmonic source node in the power system based on the correlation between the harmonic source current and the harmonic voltage matrix.

[0202] In an exemplary embodiment, the matrix determination module 702 is specifically configured to perform orthogonal triangular decomposition extraction processing on the harmonic measurement matrix to determine the number of harmonic sources in the power system;

[0203] Determine the target harmonic voltage of each unmeasured node in the power system based on the harmonic measurement data and the grid admittance matrix;

[0204] Constructing a harmonic voltage matrix based on the voltage data in the harmonic measurement data and the target harmonic voltage;

[0205] A matrix decomposition and screening process is performed on the harmonic voltage matrix according to the number of harmonic sources to obtain at least one harmonic source current.

[0206] In an exemplary embodiment, the harmonic measurement data also includes current data. The matrix determination module 702 is specifically configured to obtain a topology diagram of the power system and determine each unmeasured node according to the topology diagram.

[0207] For each unmeasured node, a target harmonic voltage of each unmeasured node in the power system is determined according to adjacent nodes of the unmeasured node.

[0208] In an exemplary embodiment, the matrix determination module 702 is specifically configured to determine the target harmonic voltage of the unmeasured node based on the voltage data, the current data, and the grid admittance matrix when the number of adjacent nodes is equal to a preset number;

[0209] When the number of adjacent nodes is greater than a preset number, for each adjacent node, the initial harmonic voltage is determined based on the voltage data, current data and grid admittance matrix, and a weighted average processing is performed on multiple initial harmonic voltages to determine the target harmonic voltage of the unmeasured node.

[0210] In an exemplary embodiment, the matrix determination module 702 is specifically configured to perform matrix decomposition processing on the harmonic measurement matrix to obtain an estimated signal matrix; the estimated signal matrix includes a plurality of current source estimation components;

[0211] For each current source estimation component in the estimation signal matrix, determining a norm of the current source estimation component;

[0212] Sorting the current source estimation components in descending order according to the norm to obtain sorting information;

[0213] According to the ranking information, the current source estimated components ranked in the top N are determined as harmonic source currents, where N is the number of harmonic sources and is a positive integer.

[0214] In an exemplary embodiment, the matrix determination module 702 is specifically configured to perform orthogonal triangular decomposition on the harmonic measurement matrix to obtain an upper triangular matrix corresponding to the harmonic measurement matrix;

[0215] Performing extraction processing on the upper triangular matrix to obtain at least one eigenvalue vector corresponding to the upper triangular matrix;

[0216] Inputting at least one eigenvalue vector into a preset quantity determination function to determine a function value;

[0217] When the function value is not less than the preset vector threshold, the quantity corresponding to the function value is determined as the quantity of harmonic sources of the power system.

[0218] Each module in the wide-area power grid harmonic source locating device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0219] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0220] Obtain harmonic measurement data of each measurement node in the power system and construct a harmonic measurement matrix based on the harmonic measurement data;

[0221] Obtaining a harmonic voltage matrix and at least one harmonic source current according to a harmonic measurement matrix and a pre-acquired grid admittance matrix;

[0222] For each harmonic source current, a harmonic source node in the power system is determined based on the correlation between the harmonic source current and the harmonic voltage matrix.

[0223] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0224] Perform orthogonal triangular decomposition and extraction processing on the harmonic measurement matrix to determine the number of harmonic sources in the power system;

[0225] Determine the target harmonic voltage of each unmeasured node in the power system based on the harmonic measurement data and the grid admittance matrix;

[0226] Constructing a harmonic voltage matrix based on the voltage data in the harmonic measurement data and the target harmonic voltage;

[0227] A matrix decomposition and screening process is performed on the harmonic voltage matrix according to the number of harmonic sources to obtain at least one harmonic source current.

[0228] In one embodiment, the harmonic measurement data also includes current data, and the processor further implements the following steps when executing the computer program:

[0229] Obtain a topological map of the power system and determine each unmeasured node based on the topological map;

[0230] For each unmeasured node, a target harmonic voltage of each unmeasured node in the power system is determined according to adjacent nodes of the unmeasured node.

[0231] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0232] When the number of adjacent nodes is equal to a preset number, determining a target harmonic voltage of an unmeasured node based on voltage data, current data, and a grid admittance matrix;

[0233] When the number of adjacent nodes is greater than a preset number, for each adjacent node, the initial harmonic voltage is determined based on the voltage data, current data and grid admittance matrix, and a weighted average processing is performed on multiple initial harmonic voltages to determine the target harmonic voltage of the unmeasured node.

[0234] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0235] Performing matrix decomposition processing on the harmonic measurement matrix to obtain an estimated signal matrix; the estimated signal matrix includes multiple current source estimation components;

[0236] For each current source estimation component in the estimation signal matrix, determining a norm of the current source estimation component;

[0237] Sorting the current source estimation components in descending order according to the norm to obtain sorting information;

[0238] According to the ranking information, the current source estimated components ranked in the top N are determined as harmonic source currents, where N is the number of harmonic sources and is a positive integer.

[0239] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0240] Perform orthogonal triangular decomposition on the harmonic measurement matrix to obtain an upper triangular matrix corresponding to the harmonic measurement matrix;

[0241] Performing extraction processing on the upper triangular matrix to obtain at least one eigenvalue vector corresponding to the upper triangular matrix;

[0242] Inputting at least one eigenvalue vector into a preset quantity determination function to determine a function value;

[0243] When the function value is not less than the preset vector threshold, the quantity corresponding to the function value is determined as the quantity of harmonic sources of the power system.

[0244] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0245] Obtain harmonic measurement data of each measurement node in the power system and construct a harmonic measurement matrix based on the harmonic measurement data;

[0246] Obtaining a harmonic voltage matrix and at least one harmonic source current according to a harmonic measurement matrix and a pre-acquired grid admittance matrix;

[0247] For each harmonic source current, a harmonic source node in the power system is determined based on the correlation between the harmonic source current and the harmonic voltage matrix.

[0248] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0249] Perform orthogonal triangular decomposition and extraction processing on the harmonic measurement matrix to determine the number of harmonic sources in the power system;

[0250] Determine the target harmonic voltage of each unmeasured node in the power system based on the harmonic measurement data and the grid admittance matrix;

[0251] Constructing a harmonic voltage matrix based on the voltage data in the harmonic measurement data and the target harmonic voltage;

[0252] A matrix decomposition and screening process is performed on the harmonic voltage matrix according to the number of harmonic sources to obtain at least one harmonic source current.

[0253] In one embodiment, the harmonic measurement data further includes current data, and when the computer program is executed by the processor, the following steps are further implemented:

[0254] Obtain a topological map of the power system and determine each unmeasured node based on the topological map;

[0255] For each unmeasured node, a target harmonic voltage of each unmeasured node in the power system is determined according to adjacent nodes of the unmeasured node.

[0256] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0257] When the number of adjacent nodes is equal to a preset number, determining a target harmonic voltage of an unmeasured node based on voltage data, current data, and a grid admittance matrix;

[0258] When the number of adjacent nodes is greater than a preset number, for each adjacent node, the initial harmonic voltage is determined based on the voltage data, current data and grid admittance matrix, and a weighted average processing is performed on multiple initial harmonic voltages to determine the target harmonic voltage of the unmeasured node.

[0259] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0260] Performing matrix decomposition processing on the harmonic measurement matrix to obtain an estimated signal matrix; the estimated signal matrix includes multiple current source estimation components;

[0261] For each current source estimation component in the estimation signal matrix, determining a norm of the current source estimation component;

[0262] Sorting the current source estimation components in descending order according to the norm to obtain sorting information;

[0263] According to the ranking information, the current source estimated components ranked in the top N are determined as harmonic source currents, where N is the number of harmonic sources and is a positive integer.

[0264] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0265] Perform orthogonal triangular decomposition on the harmonic measurement matrix to obtain an upper triangular matrix corresponding to the harmonic measurement matrix;

[0266] Performing extraction processing on the upper triangular matrix to obtain at least one eigenvalue vector corresponding to the upper triangular matrix;

[0267] Inputting at least one eigenvalue vector into a preset quantity determination function to determine a function value;

[0268] When the function value is not less than the preset vector threshold, the quantity corresponding to the function value is determined as the quantity of harmonic sources of the power system.

[0269] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0270] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile memory and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a programmable logic unit (PLC), a data processing logic unit based on quantum computing, an artificial intelligence (AI) processor, and the like.

[0271] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0272] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for locating harmonic sources in a wide area power grid, characterized in that: The method comprises: Acquire harmonic measurement data of each measurement node in the power system, and construct a harmonic measurement matrix based on the harmonic measurement data; Obtaining a harmonic voltage matrix and at least one harmonic source current according to the harmonic measurement matrix and a pre-acquired grid admittance matrix; For each of the harmonic source currents, a harmonic source node in the power system is determined based on a correlation between the harmonic source current and the harmonic voltage matrix.

2. The method according to claim 1, characterized in that The obtaining of a harmonic voltage matrix and at least one harmonic source current according to the harmonic measurement matrix and a pre-acquired grid admittance matrix includes: performing orthogonal triangular decomposition extraction processing on the harmonic measurement matrix to determine the number of harmonic sources in the power system; determining a target harmonic voltage for each unmeasured node in the power system based on the harmonic measurement data and the grid admittance matrix; constructing the harmonic voltage matrix according to the voltage data in the harmonic measurement data and the target harmonic voltage; The harmonic voltage matrix is ​​subjected to matrix decomposition and screening processing according to the number of harmonic sources to obtain at least one harmonic source current.

3. The method according to claim 2, characterized in that The harmonic measurement data further includes current data, and determining the target harmonic voltage of each unmeasured node in the power system based on the harmonic measurement data and the grid admittance matrix includes: Acquire a topological map of the power system, and determine each of the unmeasured nodes according to the topological map; For each of the unmeasured nodes, a target harmonic voltage of each unmeasured node in the power system is determined according to adjacent nodes of the unmeasured node.

4. The method according to claim 3, characterized in that The determining, based on the adjacent nodes of the unmeasured node, the target harmonic voltage of each unmeasured node in the power system includes: When the number of the adjacent nodes is equal to a preset number, determining a target harmonic voltage of the unmeasured node according to the voltage data, the current data, and the grid admittance matrix; When the number of adjacent nodes is greater than the preset number, for each adjacent node, an initial harmonic voltage is determined based on the voltage data, the current data and the grid admittance matrix, and a weighted average processing is performed on the multiple initial harmonic voltages to determine the target harmonic voltage of the unmeasured node.

5. The method according to claim 2, characterized in that The step of sequentially performing matrix decomposition and screening processing on the harmonic voltage matrix according to the number of harmonic sources to obtain at least one harmonic source current includes: Performing matrix decomposition processing on the harmonic measurement matrix to obtain an estimated signal matrix; the estimated signal matrix includes multiple current source estimation components; For each of the current source estimation components in the estimation signal matrix, determining a norm of the current source estimation component; Sorting the current source estimation components in descending order according to the norm to obtain sorting information; According to the ranking information, the current source estimated components ranked first N are determined as the harmonic source currents, where N is the number of the harmonic sources and is a positive integer.

6. The method according to claim 2, characterized in that The performing orthogonal triangular decomposition extraction processing on the harmonic measurement matrix to determine the number of harmonic sources of the power system includes: Performing orthogonal triangular decomposition on the harmonic measurement matrix to obtain an upper triangular matrix corresponding to the harmonic measurement matrix; performing extraction processing on the upper triangular matrix to obtain at least one eigenvalue vector corresponding to the upper triangular matrix; Inputting the at least one eigenvalue vector into a preset quantity determination function to determine a function value; In a case where the function value is not less than a preset vector threshold, the number corresponding to the function value is determined as the number of harmonic sources of the power system.

7. A wide area power grid harmonic source location device, characterized in that: The device comprises: A construction module is used to obtain harmonic measurement data of each measurement node in the power system and construct a harmonic measurement matrix based on the harmonic measurement data; a matrix determination module, configured to obtain a harmonic voltage matrix and at least one harmonic source current based on the harmonic measurement matrix and a pre-acquired grid admittance matrix; The harmonic source node determination module is configured to determine, for each harmonic source current, a harmonic source node in the power system based on a correlation between the harmonic source current and the harmonic voltage matrix.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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