Virtual simulation analysis method and system for global perception of low-voltage distribution network

Abstract

The invention discloses a virtual simulation analysis method and system for global perception of a low-voltage power distribution network, and relates to the technical field of power grid simulation, and the method comprises the steps: constructing a dynamically updated adjacency matrix through linear weighted fusion and an exponential decay function, and representing the correlation strength between nodes; screening a high-correlation path as an initial fault diffusion path according to the dynamic adjacency matrix, verifying logic rationality of a phase abrupt change time sequence and circuit breaker action time in combination with recording data, reserving an effective propagation channel after a time sequence conflict path is eliminated, and calculating a theoretical harmonic amplitude by distributing a harmonic transfer coefficient, so as to realize fault diagnosis. And comparing with actual data layer by layer to generate an error vector, iteratively correcting time-frequency domain weight parameters until errors converge, positioning a fault source and generating a simulation report containing a path matching degree. According to the invention, high-precision positioning of the fault source of the low-voltage power distribution network and dynamic simulation matching of the propagation path are realized, and the accuracy of fault diagnosis and the reliability of power grid state analysis are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid simulation, and in particular to a virtual simulation analysis method and system for global perception of a low-voltage distribution network. Background Art

[0002] A low-voltage distribution network refers to an electric power network with a voltage level typically below 1kV. It is responsible for transmitting electricity from distribution transformers to end users (such as residents, industrial and commercial users) and is a key link in the "last mile" of the power system. Its structure includes distribution transformers, feeders, switchgear, protection devices, and user access points. It has the characteristics of dense nodes, diverse load types, and complex operating conditions. It needs to support the grid connection of new equipment such as distributed photovoltaics, energy storage, and electric vehicle charging piles. With the intelligent transformation of distribution networks, traditional analysis methods based on static topology and single electrical quantity monitoring are difficult to adapt to its dynamic and highly interactive operation requirements. In particular, in scenarios such as harmonic propagation and fault diffusion path tracing, there are bottlenecks such as distorted node correlation representation, lack of timing logic verification, and rough modeling of harmonic transmission mechanisms. It is urgent to improve the observability of its operating status and the accuracy of fault diagnosis through multi-dimensional perception and dynamic simulation technology.

[0003] With the widespread access to distributed energy and diversified loads, the topological complexity and dynamic uncertainty of low-voltage distribution networks have increased significantly, and traditional fault analysis methods face severe challenges. Existing technologies usually perform fault simulation based on static topology models, relying on fixed impedance parameters and preset association rules, and it is difficult to accurately characterize the dynamic coupling relationship between nodes. For example, in the harmonic source location scenario, existing methods often use a node association matrix with fixed weights, ignoring the dynamic changes in the timing characteristics of voltage phase fluctuations and the impedance-harmonic coupling mechanism, resulting in a high misjudgment rate of fault diffusion paths. In the fault propagation timing verification link, most schemes only focus on voltage amplitude mutations, and fail to align the circuit breaker action time constraints with the phase mutation sequence in time and space. They are easily interfered by electromagnetic transient processes, resulting in timing logic distortion. Summary of the Invention

[0004] In view of the above-mentioned existing problems, the present invention provides a virtual simulation analysis method and system for full-domain perception of low-voltage distribution networks, which is used to solve the problems in the existing technology that it is difficult to accurately characterize the dynamic coupling relationship between nodes, the fault diffusion path misjudgment rate is high, and it is susceptible to interference from electromagnetic transient processes, resulting in timing logic distortion.

[0005] In order to solve the above technical problems, a virtual simulation analysis method for global perception of low-voltage distribution network is proposed, including:

[0006] The node impedance weight vector is calculated based on the low-voltage distribution network topology parameters, the voltage phase sequence and historical load data of each node are collected in real time, and the time domain similarity and frequency domain coupling are calculated. A dynamic adjacency matrix is ​​generated based on the time domain and frequency domain indicators. When the node voltage deviation is detected to exceed the preset threshold, the adjacent node path with the highest correlation strength is selected according to the dynamic adjacency matrix as the initial fault diffusion path, and paths with timing conflicts and time anomalies are eliminated. The harmonic transfer coefficient is allocated based on the correlation strength ratio of each node in the effective fault propagation channel, and the theoretical harmonic amplitude of the fault source is calculated to generate an error vector. The time domain and frequency domain weight parameters of the dynamic adjacency matrix are iteratively corrected according to the error vector until the error converges to the target threshold, and a simulation report is generated.

[0007] As a preferred solution of the virtual simulation analysis method for global perception of the low-voltage distribution network described in the present invention, the generation of the dynamic adjacency matrix includes real-time collection of voltage phase sequence and load data of each node based on the low-voltage distribution network topology parameters, calculation of time domain similarity and frequency domain coupling, linear fusion through preset weight coefficients, and smooth update of the fusion results using a dynamic attenuation function to generate an adjacency matrix that characterizes the dynamic correlation strength between nodes.

[0008] As a preferred solution of the virtual simulation analysis method for global perception of the low-voltage distribution network according to the present invention, the generating of the dynamic adjacency matrix further includes weighted calculation of the correlation strength by a dynamic attenuation function based on the linear fusion result of the time domain similarity and the frequency domain coupling degree, combined with the historical data in the sliding time window;

[0009] After each data collection cycle, the current association strength is compared with the historical value. When the difference exceeds the preset threshold, the association strength value in the adjacency matrix is ​​updated. When the difference is less than or equal to the preset threshold, the original value is retained.

[0010] As a preferred solution of the virtual simulation analysis method for global perception of the low-voltage distribution network described in the present invention, wherein: the smooth updating of the fusion result includes performing interpolation and smoothing processing on the node pairs whose association strength changes, eliminating step mutations, and generating a continuously changing dynamic association strength curve;

[0011] The initial fault diffusion path includes traversing the association strength data in the adjacency matrix, expanding the path layer by layer along the electrical topology connection relationship according to the breadth-first search algorithm, and preferentially selecting adjacent nodes with association strength higher than a preset benchmark threshold as the path starting point.

[0012] As a preferred solution of the virtual simulation analysis method for global perception of the low-voltage distribution network described in the present invention, wherein: the elimination of paths with timing conflicts and time anomalies includes aligning the theoretical fault propagation timing of each node in the initial fault diffusion path with the voltage phase mutation moment in the actual recorded data, verifying whether the timing logic of the phase mutation and the time interval of the mutation of adjacent nodes meet the preset response time range, eliminating abnormal paths, and retaining effective fault propagation channels;

[0013] Verifying the timing logic involves extracting the theoretical propagation time intervals of adjacent nodes in the effective fault propagation channel, matching them with the mutation time intervals in the actual recorded data, and eliminating paths with time intervals exceeding the preset response range and reverse propagation paths.

[0014] As a preferred solution of the virtual simulation analysis method for global perception of a low-voltage distribution network according to the present invention, the allocating harmonic transfer coefficient includes normalizing the correlation strength values ​​of the nodes in the effective fault propagation channel to generate an initial transfer coefficient;

[0015] Based on the physical connection order of the power grid topology, the initial transfer coefficient is distributed to the downstream nodes according to the preset attenuation gradient to form a hierarchical set of transfer coefficients;

[0016] The calculation of the theoretical harmonic amplitude of the fault source includes: reversely distributing the deviation value between the theoretical harmonic amplitude and the actual data to the transfer coefficient according to the node level, and weightedly correcting the deviation value through the initial transfer coefficient until the total error meets the convergence condition.

[0017] As a preferred solution of the virtual simulation analysis method for global perception of the low-voltage distribution network described in the present invention, the generating of the simulation report includes correcting the time domain correlation and frequency domain coupling weights of the dynamic adjacency matrix based on the error vector until the harmonic transfer coefficient error converges to the target threshold, locking the final fault path and outputting the simulation report;

[0018] The final fault path is superimposed on the power grid geographic information system to generate a visual path map containing the latitude and longitude coordinates of the fault source, and the confidence interval of the path matching degree is marked with a color gradient.

[0019] As a preferred solution of the virtual simulation analysis system for global perception of a low-voltage distribution network described in the present invention, it is characterized by including a dynamic adjacency matrix generation module, an initial fault path screening module, a path correction module and a fault simulation positioning module.

[0020] The dynamic adjacency matrix generation module is used to calculate the node impedance weight vector based on the low-voltage distribution network topology parameters, and combine the real-time collected voltage phase sequence and historical load data to dynamically update the adjacency matrix by calculating the time domain similarity and frequency domain coupling, using linear weighted fusion and exponential decay function.

[0021] The initial fault path screening module is used to screen out the adjacent node path with the strongest correlation as the initial fault diffusion path according to the dynamic adjacency matrix when the node voltage deviation exceeds a preset threshold.

[0022] The path correction module is used to align the theoretical propagation timing of each node in the initial fault diffusion path with the voltage phase mutation moment of the actual recorded data, verify the consistency of the mutation sequence and path direction, and check whether the mutation intervals between adjacent nodes meet the circuit breaker operation time requirements. After eliminating abnormal paths, effective fault propagation channels that comply with the propagation laws of the power grid are retained.

[0023] The fault simulation location module is used to allocate the harmonic transfer coefficient based on the correlation strength ratio of each node in the effective fault propagation channel, calculate the theoretical harmonic amplitude of the fault source and compare it with the actual data to generate an error vector, iteratively correct the time-frequency domain weights of the dynamic adjacency matrix until the error converges, determine the fault path and output a simulation report.

[0024] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a method described in a virtual simulation analysis of global perception of a low-voltage distribution network are implemented.

[0025] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method described in a virtual simulation analysis of global perception of a low-voltage distribution network.

[0026] The beneficial effects of the present invention are as follows: the present invention collects node voltage phase sequence and historical load data, extracts frequency domain coupling based on the dot product operation of the node impedance weight vector and the voltage harmonic distribution, calculates time domain similarity by the standard deviation of the phase difference between adjacent time points, and constructs a dynamically updated adjacency matrix using linear weighted fusion and exponential decay function to characterize the correlation strength between nodes; when the voltage is abnormal, the high-correlation path is screened as the initial fault diffusion path based on the dynamic adjacency matrix, and the logical rationality of the phase mutation timing and the circuit breaker action time is verified in combination with the recorded data, and the effective propagation channel is retained after the timing conflict path is eliminated; the theoretical harmonic amplitude is further calculated by allocating the harmonic transfer coefficient, and the error vector is generated by comparing it with the actual data layer by layer, and the time-frequency domain weight parameters are iteratively corrected until the error converges, and finally the fault source is accurately located and a simulation report containing the path matching degree is generated, thereby realizing high-precision positioning of the fault source of the low-voltage distribution network and dynamic simulation matching of the propagation path, effectively improving the accuracy of fault diagnosis and the reliability of power grid status analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 An overall flow chart of a virtual simulation analysis method for global perception of a low-voltage distribution network provided by an embodiment of the present invention.

[0029] Figure 2 A system solution flow chart of a virtual simulation analysis system for global perception of a low-voltage distribution network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0031] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0032] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it individually or selectively refer to an embodiment that is mutually exclusive of other embodiments.

[0033] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0034] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0035] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0036] Example 1, with reference to Figure 1 , which is the first embodiment of the present invention, provides a virtual simulation analysis method for global perception of a low-voltage distribution network, comprising:

[0037] S1: Calculate the node impedance weight vector based on the low-voltage distribution network topology parameters, collect the voltage phase sequence and historical load data of each node in real time, calculate the time domain similarity and frequency domain coupling, and generate a dynamic adjacency matrix based on the time domain and frequency domain indicators.

[0038] Furthermore, the impedance weight vector of each node is calculated based on the topological parameters of the low-voltage distribution network, and the voltage phase sequence and historical load data of each node are collected in real time. The standard deviation of the phase difference between adjacent time points of each node is calculated as the time domain similarity, and the impedance weight vector is multiplied by the node voltage harmonic distribution to obtain the frequency domain coupling degree. The time domain similarity and frequency domain coupling degree are fused using linear weighting, and the fusion result is dynamically attenuated by an exponential decay function to generate a dynamic adjacency matrix that is smoothly updated over time. The dynamic adjacency matrix includes the correlation strength.

[0039] Calculate the impedance weight vector of each node, the formula is expressed as:

[0040]

[0041] in, is the impedance weight vector of node i, z iN is the impedance weight between node i and node N, N is the total number of nodes in the low-voltage distribution network, is the basic impedance value between node i and node N, represents the sum of the basic impedance values ​​between node i and all remaining nodes, n represents the total number of nodes in the low-voltage distribution network, and k is an index variable used to sum the basic impedance values ​​between node i and all other nodes.

[0042] It should be noted that the impedance weight vector of each node represents the inherent impedance relationship between nodes under the physical connection state of the power grid; for example, between the end node of the branch line and the trunk node, due to the difference in line impedance, the weight vector can quantify their electrical distance; the voltage phase sequence and historical load data of each node are collected in real time, and abnormal conditions are identified by monitoring the phase fluctuation pattern. The standard deviation of the phase difference between adjacent time points is calculated as the time domain similarity index. This index can effectively capture the temporal fluctuation characteristics of the node voltage - when the voltage phase fluctuation pattern of a node is highly synchronized with that of the adjacent node, the standard deviation difference decreases, indicating that the time domain correlation is enhanced; for example, when a voltage drop occurs at a user-side node due to the startup of high-power equipment, the standard deviation of the phase difference of the upstream node will increase synchronously. At this time, the time domain similarity calculation can reveal the abnormal propagation path.

[0043] Furthermore, the frequency-domain coupling calculation uses the dot product of the impedance weight vector and the node voltage harmonic distribution, enabling a fusion analysis of grid topology characteristics and harmonic pollution propagation. The impedance weight vector reflects the basic electrical characteristics between nodes, while the voltage harmonic distribution characterizes the propagation characteristics of high-frequency interference signals. For example, when a nonlinear load injects the fifth harmonic, the dot product operation can identify trunk line nodes with lower impedance as more likely to become critical paths for harmonic coupling. Based on preset time-domain and frequency-domain weight coefficients, the time-domain similarity and frequency-domain coupling are linearly superimposed to obtain an initial fusion value. The sum of the time-domain and frequency-domain weight coefficients is 1. This not only retains the temporal correlation characteristics of voltage fluctuations but also incorporates the spatial coupling effects of harmonic propagation, forming a multi-dimensional correlation assessment system. The preset time-domain and frequency-domain weight coefficients can be adjusted according to specific scenarios. For example, in areas with severe harmonic pollution, the frequency-domain weight can be increased to 0.7, while in areas with frequent load fluctuations, the time-domain weight can be increased to 0.6.

[0044] After the initial fusion value is input into the exponential decay function, a sliding time window is used to decay-weight the historical fusion data to ensure that the adjacency matrix can quickly respond to changes in the grid state. For example, when the power of a photovoltaic grid-connected node suddenly changes due to cloud obstruction, the data of the most recent 10 sampling periods within the sliding window will be given a higher weight, while the weight of the historical data from 30 seconds ago will decay to 30% of the initial value. The difference comparison mechanism sets the dynamic update trigger condition: when the difference between the new association strength and the historical value exceeds a preset difference threshold (such as 15%), the association strength value of the corresponding node in the original adjacency matrix is ​​overwritten with the current decayed association strength, generating an update mark to avoid matrix oscillation caused by frequent small fluctuations. For example, if the association strength of a node drops by 25% due to a circuit breaker tripping, the system will trigger the update mark and overwrite the old value, while the adjacent nodes that only change by 8% will maintain the original association value.

[0045] In the embodiment of the present application, generating the dynamic adjacency matrix includes fusing the time domain similarity and the frequency domain coupling by linear weighting, and dynamically updating the adjacency matrix using an exponential decay function.

[0046] In an optional embodiment, generating a dynamic adjacency matrix includes calculating time domain similarity (phase difference standard deviation) and frequency domain coupling (impedance weight and harmonic distribution dot product), linearly superimposing time domain and frequency domain indicators with a weight of 0.6, calculating the arithmetic mean of historical fusion values ​​using a sliding time window (such as 10 cycles), updating the adjacency matrix according to a preset period, and overwriting the original value when the difference between the current value and the historical mean exceeds a threshold (such as 15%).

[0047] In another optional embodiment, the generation of a dynamic adjacency matrix includes calculating time domain and frequency domain indicators, linearly superimposing them according to preset weights (such as α=0.5), setting an association strength threshold (such as 0.7), retaining only node associations above the threshold, and fully updating the adjacency matrix every 5 minutes.

[0048] It should be noted that after the update mark is generated, the node pairs with changed association strength are identified according to the update mark, and the adjacency matrix data of the node pairs are smoothed by the cubic spline interpolation algorithm to eliminate the step mutation caused by the data collection interval, and generate a dynamic adjacency matrix containing a continuous change curve of the association strength between nodes; wherein, the cubic spline interpolation algorithm smoothes the step mutation caused by the data collection interval. When the node pairs with changed association strength are identified, the system constructs a cubic spline curve within the data collection interval to generate a continuous association strength change trajectory; for example, under a 30-second collection cycle, the association strength of a node suddenly changes from 0.6 to 0.8, and the interpolation algorithm will reconstruct a smooth transition curve of 0.6→0.68→0.76→0.8, eliminating the step error caused by discrete sampling, so that the dynamic adjacency matrix can accurately reflect the gradual characteristics of the fault propagation process, for example, the slow change process of the association strength in the early stage of the arc fault can be accurately captured.

[0049] The time domain similarity calculation formula is expressed as:

[0050]

[0051] Δθ i (t l )=θ i (t l )-θ i (t l-1 )

[0052]

[0053] Among them, S T (i, t) is the time domain similarity of node i at time t, Δθ i (t l ) is the phase difference between adjacent time points, θ i (t l ) is t l Phase at a time point, θ i (t l-1 ) is t l-1 Phase at a time point, μ i is the mean phase difference, L is the number of sampling points in the sliding window, i and l are variable indices;

[0054] The calculation formula of frequency domain coupling degree is expressed as:

[0055]

[0056] Among them, C F (i, j, t) is the frequency domain coupling degree between node i and node j at time t, is the impedance weight vector of node i, is the harmonic distribution vector of node j, M is the upper limit of harmonic order, m is the harmonic order index, Z i,m H is the equivalent impedance weight of node i under the mth harmonic. j,m (t) represents the amplitude of the mth harmonic voltage at node j at time t.

[0057] The linear weighted fusion calculation formula is expressed as:

[0058] F(i,j,t)=α·S T (i,t)+(1-α)·C F (i,j,t)

[0059] Among them, F(i,j,t) is the initial fusion value of node i and node j at the historical moment, α is the time domain weight coefficient, C F (i, j, t) is the frequency domain coupling degree between node i and node j at time t, S T (i, t) is the time domain similarity of node i at time t;

[0060] The calculation formula of the exponential decay function is expressed as:

[0061]

[0062] Where D(i,j,t) is the association strength between node i and node j at time t, λ is the decay coefficient, Δt is the sampling interval, W is the sliding window size (such as 10 periods), τ is the historical moment index within the time sliding window, and F(i,j,t-τ) is the initial fusion value of node i and node j at historical time t-τ.

[0063] S2: When it is detected that the node voltage deviation exceeds the preset threshold, the adjacent node path with the highest correlation strength is selected as the initial fault diffusion path according to the dynamic adjacency matrix, and the paths with timing conflicts and time anomalies are eliminated.

[0064] Furthermore, when the real-time voltage deviation of a node in the system exceeds a preset threshold (e.g., ±10% of the rated voltage), the system triggers the fault location mechanism. At this point, the correlation strength data stored in the dynamic adjacency matrix becomes the key basis for determining the direction of fault propagation. The correlation strength is generated by the dynamic fusion of time-domain similarity and frequency-domain coupling, encompassing both the temporal synchronization characteristics of voltage phase fluctuations between nodes and the spatial coupling characteristics of harmonic propagation. For example, when the voltage deviation of a secondary-side node of a distribution transformer in a residential area reaches 12% due to three-phase load imbalance, the system first extracts the correlation strength values ​​of the node and all adjacent nodes from the dynamic adjacency matrix. If the correlation strength of the node with the upstream trunk node A is 0.85, the correlation strength with the downstream branch node B is 0.62, and the correlation strength with the same-level parallel node C is 0.45, the system preferentially selects the path with the highest correlation strength (i.e., node to node A) as the starting point of the initial fault diffusion path.

[0065] It should be noted that during the screening process, the system uses a breadth-first search algorithm to traverse the correlation strength data in the dynamic adjacency matrix and expand the path layer by layer along the electrical connection topology; that is, the dynamic adjacency matrix is ​​traversed by accessing the nodes layer by layer to ensure that the path is expanded in hierarchical order; for example, when the correlation strength of node A is the highest, the node with the second highest correlation strength among the adjacent nodes of node A is further retrieved to form the path of "fault node → node A → node D" in sequence. The real-time update characteristics of the dynamic adjacency matrix ensure that the path screening can reflect the instantaneous changes in the power grid status; for example, when the voltage of the photovoltaic inverter node suddenly rises due to the islanding effect, the correlation strength with the upper grid-connected node may drop from 0.7 to 0.3 within a few seconds. At this time, the system will automatically eliminate the attenuated path and instead select a backup path with higher correlation strength (such as the adjacent energy storage node).

[0066] The generation of the initial fault diffusion path relies not only on static topological connections but also on quantifying the degree of real-time interaction between nodes through the association strength in the dynamic adjacency matrix. For example, in an intermittent ground fault scenario caused by cable insulation aging, the association strength between the fault point and adjacent nodes will change periodically with the fluctuation of the leakage current. The dynamic adjacency matrix can accurately capture this gradual process through exponential decay functions and cubic spline interpolation. For example, if an arc is generated due to insulation breakdown at a middle node of a cable, the association strength with the nodes at both ends gradually decreases from 0.9 to 0.4 within 1 minute. The system uses attenuation weighted calculations within a sliding time window to identify the direction with the fastest rate of association strength decline (for example, toward the load-side node rather than the power-side node), thereby correctly constructing the initial path from "fault point → load-side node → end user".

[0067] Furthermore, the theoretical fault propagation timing of each node in the initial fault diffusion path is aligned with the voltage phase mutation moment in the actual recorded data to verify whether the sequence of phase mutations is consistent with the direction of the initial fault diffusion path, and to determine whether the time interval between mutations at adjacent nodes is within the response time range of the circuit breaker tripping action; paths with inconsistent timing logic or abnormal response time are eliminated, and channels that conform to the dynamic propagation laws of the power grid are retained as effective fault propagation channels.

[0068] It should be noted that the theoretical fault propagation timing of each node in the initial fault diffusion path is extracted; the timing is calculated and generated based on the electrical connection relationship of the power grid topology and the signal propagation speed. For example, between adjacent nodes with a cable length of 100 meters, the fault signal propagation time is approximately 0.33 microseconds; the system derives the theoretical propagation timing based on the topological connection sequence. For example, in the path of "node A→node B→node C", when node A fails at t=0, the theoretical fault arrival time of node B is t+0.33 microseconds, and that of node C is t+0.66 microseconds.

[0069] The system retrieves the voltage phase mutation moments from the actual recorded data and aligns them with the theoretical timing. This alignment process combines timestamp matching with waveform feature extraction. For example, for a voltage sag fault, the recorded data shows that node A experiences a phase mutation at 10:00:00.000000, node B at 10:00:00.000330, and node C at 10:00:00.000660. At this point, the theoretical propagation timing fully matches the actual data, verifying the correctness of the path. If the mutation moment of node C in the actual data is earlier than that of node B, it indicates a timing logic contradiction and the system automatically flags the anomaly. During the timing alignment process, the system pays special attention to whether the time interval between adjacent node mutations meets the response time range of the circuit breaker opening action. Taking the typical opening time of a miniature circuit breaker as an example, if the recorded data shows that the mutation interval between adjacent nodes is 15 milliseconds, which is lower than the time required for circuit breaker operation, the system determines that there is an anomaly in the path.

[0070] To eliminate misjudgments, the system adopts a multi-level verification mechanism. For timing logic contradictions, the system detects whether there is a reverse propagation or a circular path. For example, in the "node F → node G → node H" path, when the actual recording shows that the node H mutation time is earlier than the node G, a reverse propagation alarm is triggered. The system will perform a secondary verification based on the change direction of the correlation strength of the dynamic adjacency matrix. For time interval anomalies, the system calculates the maximum reasonable propagation time threshold based on the line parameters. For example, when the overhead line node spacing is 200 meters, the maximum allowable propagation time is 0.67 microseconds. When the actual interval reaches 1 microsecond, it indicates that there is a detour or a hidden fault point. The system automatically eliminates the path. After completing the verification, the system retains the effective channel that conforms to the dynamic propagation law of the power grid.

[0071] In the embodiment of the present application, the fault path timing verification includes aligning the theoretical propagation timing with the phase mutation moment of the recorded data, and verifying the path logic in combination with the circuit breaker action time.

[0072] In an optional embodiment, the fault path timing verification includes verifying whether the phase mutation time difference between the first and last nodes of the fault path conforms to the theoretical propagation delay. If the mutation time difference is within a preset range (such as 0.1-1ms), it is considered a valid path.

[0073] In another optional embodiment, the fault path timing verification includes presetting a typical fault propagation delay template (such as 0.5ms / node for the trunk line and 0.8ms / node for the branch line), matching the phase mutation sequence of the recorded data with the template, allowing an error of ±20%, and not verifying the specific circuit breaker action time.

[0074] S3: Based on the correlation strength ratio of each node in the effective fault propagation channel, the harmonic transfer coefficient is allocated, the theoretical harmonic amplitude of the fault source is calculated, and an error vector is generated. The time domain and frequency domain weight parameters of the dynamic adjacency matrix are iteratively corrected according to the error vector until the error converges to the target threshold, and a simulation report is generated.

[0075] Furthermore, the dynamic adjacency matrix is ​​used to extract the correlation strength values ​​of each node in the effective fault propagation channel. This strength value integrates the dynamic calculation results of time-domain similarity and frequency-domain coupling, and can reflect the real-time state of electrical interaction between nodes. Based on the dynamic adjacency matrix, the correlation strength values ​​of each node in the effective fault propagation channel are extracted, and nodes with strengths above a preset benchmark threshold are selected as the main transfer path benchmark nodes. That is, the system sets a preset benchmark threshold (for example, 0.65) to perform a preliminary screening of the correlation strengths, retaining only nodes with strengths above the threshold as the main transfer path benchmark nodes, effectively eliminating interference from noise nodes. For example, in scenarios with frequent photovoltaic switching, instantaneous correlation signals caused by power fluctuations can be filtered out. The correlation strengths of the selected benchmark nodes are normalized to generate an initial set of harmonic transfer coefficients. The correlation strengths of different dimensions are converted into dimensionless coefficient ratios. For example, if the correlation strengths of three benchmark nodes are 0.8, 0.7, and 0.6, respectively, the normalized transfer coefficients are 0.38, 0.33, and 0.29, ensuring that the contributions of each node are comparable in subsequent calculations.

[0076] Based on the initial set of harmonic transfer coefficients, the system assigns transfer coefficients to the adjacent nodes downstream of each reference node according to the physical connection order of the nodes in the propagation path according to a preset attenuation gradient. The attenuation gradient is pre-set based on the line type and topology level. For example, in a cable-overhead line hybrid line, the downstream nodes of the cable segment adopt an attenuation rate of 5% per level, while the overhead line segment adopts an attenuation rate of 8% per level due to its higher impedance. Considering the harmonic attenuation characteristics of different lines in the actual power grid, for example, when fault harmonics propagate along the cable, the skin effect causes the high-frequency components to attenuate faster, requiring a higher attenuation gradient to be matched. The transfer coefficients assigned to the downstream nodes decrease in a step-by-step manner with increasing levels, forming a tree-like attenuation structure. For example, after the trunk node is assigned a coefficient of 0.38, the coefficient of the first-level sub-node is reduced to 0.36, and the second-level sub-node is 0.34. And so on. This can accurately simulate the actual propagation loss of harmonics in the power grid and avoid the amplitude calculation deviation caused by the uniform distribution of coefficients in the traditional method.

[0077] When calculating the theoretical harmonic amplitude of the fault source based on the harmonic distortion rate collected in real time according to the harmonic transfer coefficient, the system divides the measured harmonic distortion rate of each node by the transfer coefficient and reversely infers the theoretical emission level of the fault source. For example, if the 5th harmonic distortion rate of a node is 4% and its transfer coefficient is 0.8, then the theoretical harmonic amplitude of the fault source is 5%. Assuming that the harmonic components of each node mainly come from the transfer contribution of the fault source, the theoretical harmonic amplitude is compared with the actual recorded data layer by layer to generate an error vector. The comparison process uses a waveform similarity algorithm, focusing on analyzing the differences in harmonic amplitude, phase and spectral distribution. For example, when theoretical calculations show that the 5th harmonic of the fault source should be 6%, but the measured value of the upstream node in the recorded data is 5.8%, the generated node error vector is -0.2%.

[0078] When correcting the time-domain correlation and frequency-domain coupling weights of the dynamic adjacency matrix based on the error vector, the system reversely distributes the error values ​​to the transfer coefficients of each node according to the node hierarchy. The errors of downstream nodes are preferentially fed back to the directly upstream node. For example, 60% of the error of the terminal node is allocated to the parent node, and 40% is retained for self-correction. At the same time, the deviation values ​​are weighted and corrected using the benchmark node coefficients in the initial harmonic transfer coefficient set. For example, nodes with a benchmark node coefficient of 0.38 receive a higher correction weight, which has a greater impact on the overall error. During the correction process, the time-domain correlation weight is adjusted according to the timing deviation component in the error vector. When the time delay error between the theoretical value and the actual value of a node is large, the weight coefficient of the time-domain similarity is increased. The frequency-domain coupling weight is adjusted according to the harmonic spectrum matching error. For example, when the calculation error of the seventh harmonic is significant, the frequency-domain weight is increased accordingly. This enables the model to adapt to the characteristics of different fault types. For example, transient faults focus on time-domain weights, while harmonic resonance faults focus on frequency-domain weights.

[0079] The iterative correction process continues until the total harmonic amplitude error converges to a preset threshold (such as 1%). Convergence is determined using a sliding window variance calculation method. When the error variance of three consecutive iterations is less than 0.05%, the correction is terminated and the system locks the final fault path. The path matching degree is determined by the error convergence rate of each node and the stability of the correlation strength. For example, if the error of each node on a path is less than 0.8% after correction and the correlation strength fluctuation is less than 2%, the matching degree is marked as level A.

[0080] In the embodiment of the present application, the allocation of harmonic transfer coefficients includes allocating coefficients according to correlation strength ratios and iteratively correcting time-frequency domain weights through error vectors.

[0081] In an optional embodiment, the harmonic transfer coefficient allocation includes allocating 5% attenuation per level according to the topological level (such as the number of hops from the fault source), and calculating the theoretical harmonic amplitude according to the level coefficient.

[0082] In another optional embodiment, the harmonic transfer coefficient allocation includes dividing the effective propagation channel nodes into two groups with strong and weak correlation (strength > 0.6 is the strong group), allocating a uniform coefficient of 0.8 to the strong group nodes, and allocating another coefficient of 0.3 to the weak group nodes.

[0083] The generated simulation report includes the fault source location coordinates and path matching. The location coordinates convert electrical nodes into physical coordinates in the geographic information system through topological mapping. For example, the circuit breaker nodes in a distribution cabinet are mapped to coordinate points in the GIS map. The path matching quantifies the credibility of the fault propagation path and provides a basis for decision-making for operation and maintenance personnel.

[0084] The final fault path is overlaid and rendered with the power grid GIS geographic information map to generate a visual path map containing the latitude and longitude coordinates of the fault source, and the confidence interval of the path matching degree is marked with gradient color levels, making it easier for operation and maintenance personnel to obtain information more intuitively.

[0085] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0086] Example 2, reference Figure 2 , which is the second embodiment of the present invention, provides a virtual simulation analysis system with global perception of a low-voltage distribution network, including a dynamic adjacency matrix generation module 201, an initial fault path screening module 202, a path correction module 203 and a fault simulation positioning module 204.

[0087] The dynamic adjacency matrix generation module 201 is used to calculate the node impedance weight vector based on the low-voltage distribution network topology parameters, and combine the real-time collected voltage phase sequence and historical load data to dynamically update the adjacency matrix by calculating the time domain similarity and frequency domain coupling, using linear weighted fusion and exponential decay function.

[0088] The initial fault path screening module 202 is configured to screen the adjacent node path with the strongest correlation as the initial fault diffusion path according to the dynamic adjacency matrix when the node voltage deviation exceeds a preset threshold.

[0089] The path correction module 203 is used to align the theoretical propagation timing of each node in the initial fault diffusion path with the voltage phase mutation moment of the actual recorded data, verify the consistency of the mutation sequence and the path direction, and check whether the mutation interval between adjacent nodes meets the circuit breaker operation time requirements. After eliminating abnormal paths, effective fault propagation channels that comply with the propagation laws of the power grid are retained.

[0090] The fault simulation location module 204 is used to allocate the harmonic transfer coefficient based on the correlation strength ratio of each node in the effective fault propagation channel, calculate the theoretical harmonic amplitude of the fault source and compare it with the actual data to generate an error vector, iteratively correct the time-frequency domain weights of the dynamic adjacency matrix until the error converges, determine the fault path and output a simulation report.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0092] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0093] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0094] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0095] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0096] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A virtual simulation analysis method for global perception of a low-voltage distribution network, characterized by: include, The node impedance weight vector is calculated based on the low-voltage distribution network topology parameters. The voltage phase sequence and historical load data of each node are collected in real time. The time domain similarity and frequency domain coupling are calculated, and a dynamic adjacency matrix is ​​generated based on the time domain and frequency domain indicators. When it is detected that the node voltage deviation exceeds the preset threshold, the adjacent node path with the highest correlation strength is selected according to the dynamic adjacency matrix as the initial fault diffusion path, and the paths with timing conflicts and time anomalies are eliminated; The harmonic transfer coefficient is allocated based on the correlation strength ratio of each node in the effective fault propagation channel, the theoretical harmonic amplitude of the fault source is calculated, and an error vector is generated. The time domain and frequency domain weight parameters of the dynamic adjacency matrix are iteratively corrected according to the error vector until the error converges to the target threshold, and a simulation report is generated.

2. A virtual simulation analysis method for global perception of a low-voltage distribution network according to claim 1, characterized in that: The generation of the dynamic adjacency matrix includes collecting the voltage phase sequence and load data of each node in real time based on the low-voltage distribution network topology parameters, calculating the time domain similarity and frequency domain coupling, performing linear fusion using preset weight coefficients, and smoothly updating the fusion results using a dynamic attenuation function to generate an adjacency matrix that represents the dynamic association strength between nodes.

3. A virtual simulation analysis method for global perception of a low-voltage distribution network according to claim 2, characterized in that: Generating the dynamic adjacency matrix further includes performing weighted calculation of the association strength using a dynamic attenuation function based on the linear fusion result of the time domain similarity and the frequency domain coupling degree in combination with the historical data in the sliding time window; After each data collection cycle, the current association strength is compared with the historical value. When the difference exceeds the preset threshold, the association strength value in the adjacency matrix is ​​updated. When the difference is less than or equal to the preset threshold, the original value is retained.

4. A virtual simulation analysis method for global perception of a low-voltage distribution network according to claim 3, characterized in that: The smooth updating of the fusion result includes performing interpolation smoothing processing on the node pairs whose association strength has changed, eliminating step mutations, and generating a continuously changing dynamic association strength curve; The initial fault diffusion path includes traversing the association strength data in the adjacency matrix, expanding the path layer by layer along the electrical topology connection relationship according to the breadth-first search algorithm, and preferentially selecting adjacent nodes with association strength higher than a preset benchmark threshold as the path starting point.

5. A virtual simulation analysis method for global perception of a low-voltage distribution network according to claim 4, characterized in that: Eliminating paths with timing conflicts and time anomalies includes aligning the theoretical fault propagation timing of each node in the initial fault diffusion path with the voltage phase mutation moment in the actual recorded data, verifying whether the timing logic of the phase mutation and the time interval between adjacent node mutations meet the preset response time range, eliminating abnormal paths, and retaining valid fault propagation channels; Verifying the timing logic involves extracting the theoretical propagation time intervals of adjacent nodes in the effective fault propagation channel, matching them with the mutation time intervals in the actual recorded data, and eliminating paths with time intervals exceeding the preset response range and reverse propagation paths.

6. A virtual simulation analysis method for global perception of a low-voltage distribution network according to claim 5, characterized in that: The allocating harmonic transfer coefficient includes performing normalization processing according to the correlation strength values ​​of the nodes in the effective fault propagation channel to generate an initial transfer coefficient; Based on the physical connection order of the power grid topology, the initial transfer coefficient is distributed to the downstream nodes according to the preset attenuation gradient to form a hierarchical set of transfer coefficients; The calculation of the theoretical harmonic amplitude of the fault source includes: reversely distributing the deviation value between the theoretical harmonic amplitude and the actual data to the transfer coefficient according to the node level, and weightedly correcting the deviation value through the initial transfer coefficient until the total error meets the convergence condition.

7. A virtual simulation analysis method for global perception of a low-voltage distribution network according to claim 6, characterized in that: Generating the simulation report includes correcting the time domain correlation and frequency domain coupling weights of the dynamic adjacency matrix based on the error vector until the harmonic transfer coefficient error converges to a target threshold, locking the final fault path and outputting the simulation report; The final fault path is superimposed on the power grid geographic information system to generate a visual path map containing the latitude and longitude coordinates of the fault source, and the confidence interval of the path matching degree is marked with a color gradient.

8. A system using the virtual simulation analysis method for global perception of a low-voltage distribution network according to any one of claims 1 to 7, characterized in that: It includes a dynamic adjacency matrix generation module, an initial fault path screening module, a path correction module, and a fault simulation location module; The dynamic adjacency matrix generation module is used to calculate the node impedance weight vector based on the low-voltage distribution network topology parameters, and combine the real-time collected voltage phase sequence and historical load data to dynamically update the adjacency matrix by calculating the time domain similarity and frequency domain coupling degree using linear weighted fusion and exponential decay function; The initial fault path screening module is used to screen the adjacent node path with the strongest correlation as the initial fault diffusion path according to the dynamic adjacency matrix when the node voltage deviation exceeds a preset threshold; The path correction module is used to align the theoretical propagation timing of each node in the initial fault diffusion path with the voltage phase mutation moment of the actual recorded data, verify the consistency of the mutation sequence and path direction, and check whether the mutation interval between adjacent nodes meets the circuit breaker operation time requirements. After eliminating abnormal paths, it retains the effective fault propagation channel that conforms to the propagation law of the power grid; The fault simulation location module is used to allocate the harmonic transfer coefficient based on the correlation strength ratio of each node in the effective fault propagation channel, calculate the theoretical harmonic amplitude of the fault source and compare it with the actual data to generate an error vector, iteratively correct the time-frequency domain weights of the dynamic adjacency matrix until the error converges, determine the fault path and output a simulation report.

9. 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 a virtual simulation analysis method for global perception of a low-voltage distribution network according to any one of claims 1 to 7 are implemented.

10. 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 a virtual simulation analysis method for global perception of a low-voltage distribution network according to any one of claims 1 to 7 are implemented.

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