Grounding loop resistance detection and fault location method and system fused with edge calculation

Abstract

The invention discloses a grounding loop resistance detection and fault location method and system fused with edge calculation, and relates to the technical field of grounding detection, and the method comprises the steps: carrying out the key node analysis of a grounding loop, deploying an edge calculation unit, configuring a mapped monitoring sensor, and carrying out the fault location of the grounding loop; comprising a high-frequency transient traveling wave detector, a broadband impedance spectroscopy measurement sensor and a synchronous clock unit, during the normal operation period of a power grid device, a monitoring sensor is used for monitoring, time synchronization alignment of a monitoring data set is carried out, a continuous situation field of a grounding loop is reconstructed through interpolation of an edge calculation unit, and the continuous situation field is constructed based on three-dimensional fault features; and monitoring the power grid device in real time by using a monitoring sensor, executing fault source inversion based on a real-time monitoring result and a continuous situation field in combination with local height gradient features and extreme region features, and establishing a fault positioning result. Therefore, the technical effects of reducing response delay, optimizing load distribution and improving fault positioning precision are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of grounding detection, and particularly to a grounding loop resistance detection and fault location method and system integrating edge computing. Background Art

[0002] The monitoring and fault location of the grounding loop are key links to ensure the stable operation of the power grid. With the continuous expansion of the distribution network scale and the large-scale access of distributed power sources, the existing technology mainly relies on a centralized data center to uniformly process the monitoring data of power grid devices, transmits the on-site monitoring data to the central server through overhead lines or optical fiber communication, and then uses a mathematical model for fault analysis. There are the following main defects: First, the separation of data acquisition and processing leads to poor time synchronization. Especially in the scenario of high-frequency transient traveling wave detection, the time deviation between the data acquisition end and the processing end may exceed the millisecond level, seriously affecting the accuracy of fault feature extraction. Second, traditional fault location methods are mostly based on two-dimensional impedance spectrum features and cannot comprehensively capture the three-dimensional spatial fault features of the grounding loop, resulting in insufficient accuracy of fault source inversion under complex power grid topologies. Third, the centralized processing architecture faces limited data transmission bandwidth and single-point failure risks when dealing with a large number of distributed monitoring points, and it is difficult to meet the requirements of real-time and reliability of smart grids. Summary of the Invention

[0003] The present invention provides a grounding loop resistance detection and fault location method and system integrating edge computing to solve the technical problems of large data synchronization deviation, excessive calculation and transmission load, and insufficient fault location accuracy in the prior art, and to achieve the technical effects of reducing response delay, optimizing load distribution, and improving fault location accuracy.

[0004] In a first aspect, the present invention provides a grounding loop resistance detection and fault location method integrating edge computing, wherein the grounding loop resistance detection and fault location method integrating edge computing includes: Performing key node analysis of the grounding loop, deploying edge computing units based on the key node analysis results, and configuring monitoring sensors mapped to the edge computing units, where the monitoring sensors include high-frequency transient traveling wave detectors, broadband impedance spectrum measurement sensors, and synchronous clock units.

[0005] When the power grid device is operating normally, using the monitoring sensors to perform monitoring of the power grid device, after time synchronization alignment of the monitoring data set based on the synchronous clock unit, interpolating and reconstructing the continuous situation field of the grounding loop through the edge computing unit, where the continuous situation field is constructed based on three-dimensional fault features.

[0006] Use the monitoring sensor to perform real-time listening on the power grid device, and perform fault source inversion based on local height gradient features and extreme region features through the real-time listening results and the continuous situation field, and establish fault location.

[0007] In a feasible implementation manner, the interpolation and reconstruction of the continuous situation field of the grounding loop by the edge computing unit includes: After synchronizing and aligning the time of the monitoring data set by using the synchronous clock unit, send the time-aligned monitoring data set to the corresponding edge computing unit. After the edge computing units form a computing local area network: Perform the extraction of the spatial propagation characteristics of high-frequency transient traveling waves, and establish spatial propagation characteristics, where the spatial propagation characteristics include the propagation delay characteristics between nodes, attenuation coefficient characteristics, and local reflection characteristics.

[0008] Perform the extraction of the spectral response characteristics of the broadband impedance spectrum, and establish spectral response characteristics, where the spectral response characteristics include amplitude-frequency change characteristics, phase-frequency change characteristics, and frequency-segment feature vectors.

[0009] Perform the extraction of the time-domain evolution characteristics of the monitoring data sets of multiple key nodes, and establish time-domain evolution characteristics, where the time-domain evolution characteristics include synchronous evolution characteristics, node association characteristics, and mutation characteristics.

[0010] Construct three-dimensional fault characteristics with the spatial propagation characteristics, spectral response characteristics, and time-domain evolution characteristics. After interpolating the three-dimensional fault characteristics, reconstruct the continuous situation field of the grounding loop.

[0011] In a feasible implementation manner, the reconstruction of the continuous situation field of the grounding loop after interpolating the three-dimensional fault characteristics includes: Establish connection edges among all key nodes according to the dependency relationship of the key nodes.

[0012] Configure the connection edge weights through the spatial distance and feature difference degree of the connection edges.

[0013] After establishing the strong and weak constraints and direction constraints of feature propagation transition based on the connection edge weights, traverse the three-dimensional fault characteristics to perform feature interpolation, so as to reconstruct the continuous situation field of the grounding loop.

[0014] In a feasible implementation manner, the analysis of the key nodes of the grounding loop and the deployment of edge computing units according to the key node analysis results include: Analyze and construct all deployable nodes of the grounding loop.

[0015] After configuring and fusing multi-dimensional indicators, use the fused multi-dimensional indicators to evaluate the node importance of all deployable nodes. The fused multi-dimensional indicators include electrical contribution degree indicators, propagation sensitivity indicators, spectrum heterogeneity indicators, time-domain evolution coupling degree indicators, and fault evolution potential indicators.

[0016] Use the node importance evaluation results for proportional screening. After performing node distribution compensation based on the proportional screening results, establish key nodes and deploy edge computing units at the key nodes.

[0017] In a feasible implementation manner, the inversion of the fault source based on the local height gradient feature and extreme region feature through the real-time monitoring result and the continuous situation field to establish fault location includes: After synchronizing and aligning the time of the real-time monitoring results, stack the time-synchronized real-time monitoring results into the continuous situation field according to time slices to form a dynamic situation increment graph.

[0018] Calculate the local gradient on the dynamic situation increment graph and establish the gradient modulus.

[0019] Use the gradient modulus to screen high-gradient regions and establish local high-gradient features.

[0020] Use local neighborhood search to identify local maximum points and local minimum points according to the local gradient to establish extreme region features.

[0021] Perform fault source inversion with the local height gradient feature and extreme region feature.

[0022] In a feasible implementation manner, after establishing the gradient modulus, it includes: Obtain the matching fixed decision threshold for the current monitoring scenario.

[0023] Calculate the mean and standard deviation of the global gradient field according to the gradient modulus, and construct a sensitivity adjustment factor using the calculation results of the mean and standard deviation.

[0024] Use the sensitivity adjustment factor to enhance the attention of the matching fixed decision threshold, and screen high-gradient regions with the attention-enhanced matching fixed decision threshold.

[0025] In a feasible implementation manner, the inversion of the fault source with the local height gradient feature and extreme region feature includes: After locating the fault source, use the synchronous clock unit to collect the time-domain data of each key node, and perform fitting of the time evolution and space propagation of the fault based on the time-domain data.

[0026] Use the time evolution and space propagation fitting to perform fault source inversion authentication, and perform fault location based on the fault source inversion authentication result.

[0027] In a feasible implementation, after establishing the fault location, it includes: After establishing the fault level according to the fault location, establish the matching result of the multi-level decision response scheme, and the matching result of the multi-level decision response scheme includes the fault location response decision and the fault path evolution decision.

[0028] Report the fault based on the fault level and the matching result of the multi-level decision response scheme.

[0029] In a feasible implementation, the establishment of the fault location further includes: Execute the development prediction of the fault source, and establish an additional safety level based on the development prediction result.

[0030] Establish the fault location by using the additional safety level and the fault level of the fault source.

[0031] In a second aspect, the present invention also provides a grounding loop resistance detection and fault location system integrating edge computing. Among them, the grounding loop resistance detection and fault location system integrating edge computing includes: The node deployment module is used to perform key node analysis of the grounding loop, deploy edge computing units based on the key node analysis results, and configure monitoring sensors mapped to the edge computing units. The monitoring sensors include high-frequency transient traveling wave detectors, broadband impedance spectrum measurement sensors, and synchronous clock units.

[0032] The field reconstruction module is used to perform grid device monitoring by using the monitoring sensors when the grid device is operating normally. After time synchronization and alignment of the monitoring data set based on the synchronous clock unit, interpolate and reconstruct the continuous situation field of the grounding loop through the edge computing unit. The continuous situation field is constructed based on three-dimensional fault characteristics.

[0033] The fault source inversion and location module is used to perform real-time monitoring of the grid device by using the monitoring sensors, and perform fault source inversion based on local height gradient characteristics and extreme area characteristics through the real-time monitoring results and the continuous situation field, and establish the fault location.

[0034] The present invention discloses a method and system for detecting the grounding loop resistance and fault location by integrating edge computing, including: analyzing key nodes of the grounding loop, deploying edge computing units according to the analysis results, and configuring corresponding monitoring sensors, where the monitoring sensors include high-frequency transient traveling wave detectors, broadband impedance spectrum measurement sensors, and synchronous clock units; during the normal operation of the power grid device, using the monitoring sensors to collect operation data, realizing the time synchronization and alignment of the monitoring data set based on the synchronous clock unit, and then interpolating and reconstructing by the edge computing unit to generate a continuous situation field of the grounding loop based on three-dimensional fault characteristics; implementing real-time monitoring of the power grid device through the monitoring sensors, and combining the real-time monitoring results with the continuous situation field, performing fault source inversion analysis based on local height gradient characteristics and extreme area characteristics, and further realizing fault location. The method and system for detecting the grounding loop resistance and fault location by integrating edge computing disclosed by the present invention solve the technical problems of large data synchronization deviation, overheavy calculation and transmission load, and insufficient fault location accuracy, and achieve the technical effects of reducing response delay, optimizing load distribution, and improving fault location accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic flowchart of the method for detecting the grounding loop resistance and fault location by integrating edge computing according to the present invention.

[0036] Figure 2 It is a schematic structural diagram of the system for detecting the grounding loop resistance and fault location by integrating edge computing according to the present invention.

[0037] Description of reference numerals: Node deployment module 11, field reconstruction module 12, fault source inversion and location module 13. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The following will combine the description of the drawings in the specification and specific embodiments to elaborate on the above technical solutions in detail to better understand the above technical solutions. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments for explaining the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. In addition, it should be noted that for the sake of description, only parts related to the present invention are shown in the drawings rather than all.

[0039] Embodiment 1, as Figure 1 It is a schematic flowchart of the method for detecting the grounding loop resistance and fault location by integrating edge computing according to the present invention, where the method for detecting the grounding loop resistance and fault location by integrating edge computing includes: S100: Perform a critical node analysis of the grounding loop, deploy edge computing units based on the critical node analysis results, and configure monitoring sensors mapped to the edge computing units. The monitoring sensors include high-frequency transient traveling wave detectors, broadband impedance spectrum measurement sensors, and synchronous clock units.

[0040] Specifically, first, based on the topological structure, node electrical characteristics, and historical operation data of the grounding loop, through identification and evaluation, determine the nodes in the power grid grounding loop that have important electrical characteristics and fault propagation impacts, that is, critical nodes. These critical nodes are usually located at key positions in the power grid topology, such as branch points, connection points, or positions where electrical parameters change significantly. Among them, critical node analysis can be determined by technical means such as electrical network analysis, sensitivity analysis, and fault simulation.

[0041] Specifically, deploy edge computing units at the identified critical node positions. Each edge computing unit has the capabilities of local data collection, preliminary analysis, anomaly detection, data preprocessing, and intelligent decision support, and can thus achieve local rapid response, improve the real-time performance and accuracy of detection, and reduce the burden on the central server.

[0042] Specifically, an edge computing unit is a computing resource close to the data source or terminal device, used to process local data and reduce dependence on the central server. The monitoring sensors are devices used to collect power grid operation status data, including high-frequency transient traveling wave detectors (used to capture high-frequency transient signals), broadband impedance spectrum measurement sensors (used to measure impedance changes within a broadband), and synchronous clock units (used to ensure the time synchronization of data collection).

[0043] Through the above steps, through critical node analysis, monitoring resources can be accurately deployed, improving the coverage rate and efficiency of the detection system; at the same time, through the local processing capabilities of the edge computing units, rapid response for fault detection and preliminary location can be achieved, reducing latency.

[0044] In some embodiments, the performing a critical node analysis of the grounding loop and deploying edge computing units based on the critical node analysis results includes: Analyze all deployable nodes for constructing the grounding loop; after configuring and fusing multi-dimensional indicators, use the fused multi-dimensional indicators to evaluate the node importance of all deployable nodes. The fused multi-dimensional indicators include electrical contribution degree indicators, propagation sensitivity indicators, spectral heterogeneity indicators, time-domain evolution coupling degree indicators, and fault evolution potential indicators; perform proportional screening using the node importance evaluation results, perform node distribution compensation based on the proportional screening results, establish critical nodes, and deploy edge computing units at the critical nodes.

[0045] Specifically, first, based on the physical topology of the grounding loop, identify all node positions where monitoring devices can be installed, including nodes that can form a grounding loop such as grounding downlead nodes, grounding electrode nodes, and equipment grounding terminal nodes. Then, for each deployable node, collect the required electrical data and fault data, and calculate or evaluate five fusion multi-dimensional indicators accordingly. Then, based on multi-index fusion algorithms (such as weighted comprehensive scoring, machine learning classification), perform importance scoring on each node.

[0046] Specifically, the fusion multi-dimensional indicators include: Electrical contribution index: It represents the influence degree of the node on the overall electrical characteristics (such as total resistance, grounding potential distribution) of the grounding loop; it is obtained by calculating the sensitivity of the change in node resistance to the change in global electrical parameters.

[0047] Propagation sensitivity index: It represents the sensitivity of the node to the propagation path and intensity of fault signals (such as transient traveling waves), that is, it reflects the criticality of the node in the fault energy propagation link.

[0048] Spectrum heterogeneity index: It represents the degree of difference in the spectral characteristics of the collected signals at the node, and is used to measure the ability of the node to distinguish abnormal signals in different frequency bands.

[0049] Time-domain evolution coupling degree index: It represents the dynamic correlation strength between the node and other nodes in the process of time-domain signal evolution, that is, it reflects the synchrony and synergy of the node in the fault evolution process.

[0050] Fault evolution potential index: It represents the possibility of the node as a starting point or relay point in the process of potential fault development, and can be obtained through historical fault data modeling or simulation deduction.

[0051] Furthermore, perform proportional screening according to the node importance evaluation results, that is, select a certain proportion of the most important nodes, such as setting to select nodes with a scoring ranking in the top 20% - 30%, so as to retain high-importance nodes and eliminate low-importance nodes, and initially form a candidate set of key nodes.

[0052] Finally, analyze the spatial distribution of the initially screened nodes, identify possible monitoring blind spots or node sparse areas among them, and correspondingly supplement and deploy a certain number of nodes or adjust the node distribution to ensure good coverage and redundancy of the key nodes in space. Furthermore, use the finally determined key nodes as the deployment locations of the edge computing units, and configure edge computing units and their corresponding monitoring sensors (such as high-frequency transient traveling wave detectors, broadband impedance spectrum measurement sensors, synchronous clock units) at each key node to form a distributed and intelligent grounding loop monitoring and fault location network.

[0053] S200: When the power grid device is operating normally, use the monitoring sensor to perform monitoring on the power grid device. After time-synchronizing and aligning the monitoring data set based on the synchronous clock unit, interpolate and reconstruct the continuous situation field of the grounding loop through the edge computing unit. The continuous situation field is constructed based on three-dimensional fault characteristics.

[0054] Specifically, the continuous situation field refers to the continuous distribution map of the electrical state (such as potential, impedance, transient characteristics) of the grounding loop in space and time formed by interpolating and reconstructing the data of discrete monitoring nodes during the normal operation of the power grid device; this continuous situation field can dynamically reflect the evolution process of the health state of the grounding loop.

[0055] Specifically, the three-dimensional fault characteristics refer to the three main characteristic dimensions used to describe the state of the grounding loop, including frequency-domain characteristics (such as impedance spectrum change, harmonic component), time-domain characteristics (such as transient waveform, impulse response), and spatial distribution characteristics (such as node potential gradient, energy distribution). Through these three-dimensional fault characteristics, multi-dimensional information about the abnormal evolution of the grounding loop can be comprehensively described, providing sufficient data for subsequent steps.

[0056] In some embodiments, the interpolating and reconstructing the continuous situation field of the grounding loop through the edge computing unit includes: After time-synchronizing and aligning the monitoring data set using the synchronous clock unit, send the time-aligned monitoring data set to the corresponding edge computing unit. After the edge computing units form a computing local area network: Extract the spatial propagation characteristics of high-frequency transient traveling waves and establish spatial propagation characteristics, where the spatial propagation characteristics include inter-node propagation delay characteristics, attenuation coefficient characteristics, and local reflection characteristics; extract the spectral response characteristics of the broadband impedance spectrum and establish spectral response characteristics, where the spectral response characteristics include amplitude-frequency change characteristics, phase-frequency change characteristics, and frequency-segment feature vectors; extract the time-domain evolution characteristics of the monitoring data sets of multiple key nodes and establish time-domain evolution characteristics, where the time-domain evolution characteristics include synchronous evolution characteristics, node association characteristics, and mutation characteristics; form three-dimensional fault characteristics with the spatial propagation characteristics, spectral response characteristics, and time-domain evolution characteristics, and after interpolating the three-dimensional fault characteristics, reconstruct the continuous situation field of the grounding loop.

[0057] Specifically, first, after completing time-synchronizing and aligning the monitoring data sets of each monitoring node using the synchronous clock unit, send the time-aligned monitoring data sets to the corresponding edge computing units. Among them, multiple edge computing units are interconnected through a local area network (LAN) to form a distributed collaborative computing environment.

[0058] Specifically, the spatial propagation characteristics are obtained based on high-frequency transient traveling wave signals and are used to describe the spatio-temporal characteristics of the signal during the propagation between nodes, including: the propagation delay characteristics between nodes (the time delay required for the signal to be transmitted from one node to another), the attenuation coefficient characteristics (the degree of energy attenuation of the signal during propagation, such as the percentage of attenuation or the decibel value of attenuation), and the local reflection characteristics (the phenomenon and characteristics of signal reflection at node or line discontinuities).

[0059] Specifically, the spectral response characteristics are obtained based on broadband impedance spectrum measurement and are used to describe the impedance change law of the node at different frequencies. The spectral response characteristics include: the amplitude-frequency change characteristics (the characteristics of the impedance amplitude changing with frequency), the phase-frequency change characteristics (the characteristics of the impedance phase changing with frequency), and the frequency-segmented feature vector (extracting features by segmenting the frequency interval to form a multi-dimensional vector to describe the response characteristics of the node in different frequency bands).

[0060] Specifically, the time-domain evolution characteristics are obtained based on the time-series data of multiple key nodes and are used to describe the dynamic characteristics of the grounding loop state evolving over time; among them, it includes: the synchronous evolution characteristics (the synchrony of signal changes at different nodes, such as the synchronization coefficient), the node correlation characteristics (the correlation of signal changes between nodes, such as the cross-correlation coefficient), and the mutation characteristics (the sudden changes or anomalies in the node signals, such as the detection of mutation points).

[0061] Specifically, the multi-dimensional feature body composed of the spatial propagation characteristics, the spectral response characteristics, and the time-domain evolution characteristics is the three-dimensional fault feature, which is used to comprehensively describe the state characteristics of the grounding loop in the three dimensions of space, frequency, and time.

[0062] Furthermore, input the three-dimensional fault feature into the edge computing unit and adopt a spatial interpolation algorithm (such as Kriging interpolation, radial basis function interpolation, sparse reconstruction algorithm, etc.) to reconstruct a continuous situation field within the grounding loop range; optionally, fuse the three-dimensional fault feature (frequency domain, time domain, spatial distribution characteristics) during the interpolation process to improve the physical rationality and anomaly sensitivity of the reconstructed situation field.

[0063] The continuous situation field reconstructed by the above steps can truly reflect the spatial distribution and time variation of the electrical state in the grounding loop, and then through the dynamic monitoring of the continuous situation field, potential hazards such as abnormal growth of grounding resistance and small-scale fault germination can be identified in advance.

[0064] In some embodiments, after interpolating the three-dimensional fault feature, reconstructing the continuous situation field of the grounding loop includes: Establish connection edges among all key nodes according to the dependency relationships of key nodes; configure the weights of connection edges based on the spatial distances and feature difference degrees of the connection edges; after establishing the strong and weak constraints and direction constraints for feature propagation and transition based on the weights of the connection edges, traverse the three-dimensional fault features to perform feature interpolation in order to reconstruct the continuous situation field of the grounding loop.

[0065] Specifically, the interpolation process can be guided by a graph structure, enabling the interpolation to consider not only spatial positions but also feature similarity and propagation direction, including: establishing edges based on the dependency relationships of key nodes, configuring the weights of connection edges (based on spatial distances and feature difference degrees), establishing the strong and weak constraints and direction constraints for feature propagation and transition, and performing feature interpolation based on this.

[0066] Specifically, the dependency relationships of key nodes refer to the logical dependency relationships established among different monitoring nodes in the grounding loop based on physical connections, geographical proximities, or electrical property correlations. Connection edges refer to the connecting lines established among key nodes according to the dependency relationships, which are used to represent the feature propagation paths between nodes. The weights of connection edges represent the relational importance or propagation capabilities of the connection edges, and the weights of the connection edges can be determined based on spatial distances and feature difference degrees.

[0067] Exemplarily, the spatial distances include the physical or electrical distances between nodes, and the feature difference degrees involve the similarity degrees of the three-dimensional fault features between nodes, such as Euclidean distance, cosine similarity, etc.

[0068] Specifically, the feature propagation and transition constraints refer to controlling the strength of feature propagation from one node to another according to the weights of connection edges during the feature interpolation process; the direction constraints are the preferred directions or restricted directions of feature propagation set according to the feature change trends between nodes during the feature interpolation process.

[0069] Specifically, to implement the above steps, first, among all key nodes, establish connection edges according to the physical connection relationships, geographical proximity relationships, or electrical property correlations, and configure weights for each connection edge based on spatial distance factors and feature difference degree factors. For example, the farther the distance, the smaller the weight, and the greater the difference, the smaller the weight. Optionally, the weights of connection edges can be calculated by a weighted function that synthesizes spatial distances and feature difference degrees.

[0070] Specifically, then, based on the weights of connection edges, establish the strong and weak control rules for feature propagation, that is, determine the preferred directions or prohibited directions of feature propagation, form a graph structure with weighted directed edges, and then, on the graph structure, according to the weights of connection edges and direction constraints, use weighted average, graph convolution, or interpolation methods based on random walks to expand the discrete three-dimensional fault features into a continuous distribution, and thus complete the reconstruction of the continuous situation field of the grounding loop.

[0071] For the above method steps, by introducing edge weights based on spatial and feature differences, it can ensure that feature propagation follows actual physical laws and avoid meaningless leapfrog interpolation. At the same time, through direction constraints, it can ensure that fault features spread along the actual propagation path (such as the current flow direction), improving the accuracy of fault location. The weighted directed graph structure can effectively suppress the interference of abnormal nodes on the overall interpolation result and enhance the fault tolerance for local anomalies.

[0072] S300: Use the monitoring sensor to perform real-time monitoring of the power grid device, and perform fault source inversion based on local height gradient features and extreme region features through the real-time monitoring result and the continuous situation field, and establish fault location.

[0073] Specifically, after establishing the continuous situation field, continue to monitor and collect the grounding loop state of the power grid device in real time through the monitoring sensor. It also includes the high-frequency signals, impedance signals, and time synchronization conditions collected by the high-frequency transient traveling wave detector, broadband impedance spectrum measurement sensor, and synchronous clock unit, forming a real-time monitoring result. This real-time monitoring result and the continuous situation field are jointly used as the discrimination basis for subsequent fault location.

[0074] In some embodiments, the performing fault source inversion based on local height gradient features and extreme region features through the real-time monitoring result and the continuous situation field, and establishing fault location includes: After time synchronization alignment of the real-time monitoring result, stack the time synchronization aligned real-time monitoring result onto the continuous situation field in time slices to form a dynamic situation increment graph; calculate the local gradient on the dynamic situation increment graph and establish the gradient modulus value; use the gradient modulus value to screen high-gradient regions and establish local high-gradient features; use local neighborhood search to identify local maximum points and local minimum points according to the local gradient to establish extreme region features; perform fault source inversion with the local height gradient features and extreme region features.

[0075] Specifically, the real-time monitoring result refers to the data collected in real time by the monitoring sensor during the operation of the power grid, reflecting the current state change of the power grid. The dynamic situation increment graph is a graph that reflects the dynamic change of fault features formed by stacking the real-time monitoring result into the continuous situation field, showing the incremental change of fault features in the time series.

[0076] Specifically, the local gradient is the change rate of fault features between a point and its surrounding points in the dynamic situation increment graph, reflecting the change trend of fault features in space. Among them, the gradient modulus corresponds to the magnitude of the local gradient, which is used to measure the intensity of the change of fault features. The local high-gradient feature corresponds to the area with a higher gradient modulus in the dynamic situation increment graph, indicating the location where the change of fault features is significant. The extreme value area feature is the local maximum and minimum points identified through local neighborhood search, which can be considered as significant signs of fault features, that is, the extreme value area feature may be related to the fault source.

[0077] Specifically, first, synchronize and align the real-time monitoring results collected by each monitoring node in time. For example, stack the synchronized real-time monitoring results onto the current continuous situation field according to time slices to form a dynamic situation increment graph, reflecting the local change trend after the fault occurs. Then, use numerical differentiation or finite difference methods on the dynamic situation increment graph to calculate the local gradient and the gradient modulus (i.e., the modulus length of the gradient vector). Next, select the area where the gradient modulus exceeds the preset threshold as the local high-gradient area according to the preset threshold, and extract the local high-gradient feature. Then, identify the local maximum and minimum points on the dynamic situation increment graph based on the local neighborhood search algorithm (such as sliding window, maximum and minimum detection), and establish the extreme value area feature correspondingly. Optionally, the extreme value area feature is used to record information such as the position and amplitude of the extreme points.

[0078] Furthermore, comprehensively analyze the distribution of the local high-gradient area and extreme points, and infer the location of the fault source for inversion optimization based on the feature aggregation degree, amplitude size, and spatial distribution pattern to further accurately locate the fault location.

[0079] Through the above process, synchronize and align the real-time monitoring results with the continuous situation field in time and form a dynamic situation increment graph, which can capture the dynamic changes of fault features in real time; calculate the local gradient and establish the gradient modulus, which can effectively identify the area where the change of fault features is significant. Further, through the local high-gradient feature and extreme value area feature for fault source inversion, the accuracy and reliability of fault location can be significantly improved, which helps to quickly respond to fault changes, thus providing strong support for the fault handling and maintenance of the power grid.

[0080] In some implementation manners, after establishing the gradient modulus, it includes: Obtain the matching fixed determination threshold for the current monitoring scenario; calculate the mean and standard deviation of the global gradient field according to the gradient modulus, and construct a sensitivity adjustment factor using the calculation results of the mean and standard deviation; after enhancing the attention of the matching fixed determination threshold using the sensitivity adjustment factor, screen the high-gradient area with the enhanced matching fixed determination threshold.

[0081] Specifically, the matching fixed determination threshold is a fixed threshold preset according to the current monitoring scenario (such as power grid operation status, fault type, etc.), which is used to determine whether the gradient magnitude is large enough to indicate a significant change in fault characteristics. The global gradient field includes all the gradient magnitudes calculated in the entire dynamic situation increment map, reflecting the distribution of the intensity of fault characteristic changes in the entire monitoring area.

[0082] Specifically, by calculating the statistical characteristics (such as mean and standard deviation) of the global gradient field, the corresponding sensitivity adjustment factor can be obtained. This sensitivity adjustment factor is used to adjust the sensitivity of the determination threshold to adapt to different distributions of fault characteristic intensities. Exemplarily, the sensitivity adjustment factor is constructed based on the following formula: ; where k is the sensitivity adjustment factor, γ is the adjustment coefficient used to control the amplitude of sensitivity enhancement, and σ and μ are the mean and standard deviation of the global gradient field respectively.

[0083] Furthermore, the matching fixed determination threshold is adjusted by the sensitivity adjustment factor to obtain a matching fixed determination threshold with enhanced attention, extract local high-gradient regions, and form more accurate and sensitive local high-gradient features.

[0084] The above process, through the combination of the matching fixed determination threshold and the sensitivity adjustment factor, can adaptively adjust the high-gradient recognition standard according to different systems and environments, effectively improve the recognition rate of high-gradient regions, reduce the missed detection rate, and avoid misjudgment or missed detection caused by unreasonable setting of the fixed threshold in the case of weak fault signals or large background noise.

[0085] In some implementation manners, the fault source inversion using the local high-gradient feature and the extreme value region feature includes: After locating the fault source, the time-domain data of each key node is collected by the synchronous clock unit, and the time evolution and spatial propagation of the fault are fitted based on the time-domain data; the fault source inversion authentication is performed using the time evolution and spatial propagation fitting, and the fault location is determined based on the fault source inversion authentication result.

[0086] Specifically, after initially locating the fault source, the time-domain data of the key nodes is collected (ensuring time alignment through the synchronous clock unit), the time evolution and spatial propagation of the fault are fitted based on the time-domain data, and the fault source inversion authentication is performed through the fitting result to determine the fault location. The above process actually adds a spatio-temporal consistency verification link to improve the accuracy and reliability of fault location and avoid misjudgment that may occur based solely on spatial features (gradient, extreme value).

[0087] Specifically, first, based on the local height gradient feature and the extreme value area feature, the preliminary fault source inversion and location are completed; then, the synchronous clock unit is used to collect the high-precision time-domain data of each key node during the fault occurrence in the preliminary fault source inversion and location, including waveform information such as voltage, current, impedance, etc. changing with time; next, based on the time-domain data of each key node, the change of fault characteristics with time is analyzed, such as the dynamic characteristics of the rise, fall, oscillation, etc. of the fault voltage and current signals, and the time evolution curve of the fault characteristics is established (such as using methods such as least squares fitting and spatio-temporal regression analysis) to reflect the dynamic process of the fault spreading outward from the source point, including the propagation path, propagation speed, and propagation direction of the fault signal.

[0088] Furthermore, through the time evolution fitting and space propagation fitting results, the rationality of the preliminary fault source location is verified: the space propagation fitting result is traced back to the starting point, and it is judged whether the starting point is consistent with the preliminary fault source location result; if they are consistent, the preliminary location is confirmed to be correct; if they are inconsistent, the fault source location is corrected according to the fitting result.

[0089] The above steps introduce spatio-temporal fitting certification after the preliminary location, which can greatly reduce the error caused by pure spatial feature location and exclude the influence of accidental noise and local anomalies on the location result.

[0090] In some embodiments, after establishing the fault location, it includes: After establishing the fault level according to the fault location, a multi-level decision response scheme matching result is established, and the multi-level decision response scheme matching result includes a fault location response decision and a fault path evolution decision; a fault report is made based on the fault level and the multi-level decision response scheme matching result.

[0091] Optionally, based on the fault location, first, according to the fault location result, combined with factors such as the severity of the fault, the affected range, and the evolution trend, the fault is classified to establish a fault level, and then according to the fault level, a multi-level decision response scheme is matched in a preset response scheme library. Exemplarily, the multi-level decision response scheme includes: a fault location response decision (such as isolation, switching, load reduction, etc.), a fault path evolution decision (such as predicting the fault expansion trend and warning surrounding nodes). Finally, a fault report is made based on the fault level and the multi-level response scheme.

[0092] Exemplarily, the fault report information includes: the location of the fault source, the fault level, recommended response measures, warning expansion path, etc.

[0093] Through the above process, the fault detection is substantially extended from location to intelligent response and handling, providing the closed-loop ability of fault perception - decision - response, which helps relevant personnel to quickly respond to and handle the fault.

[0094] In some implementations, establishing the fault location further includes: Performing a development prediction of the fault source, establishing an additional safety level based on the development prediction result; and establishing the fault location by using the additional safety level and the fault level of the fault source.

[0095] Furthermore, a corresponding fault prediction model can be constructed through a fitting analysis model, physical modeling, time series prediction, or machine learning regression model, so as to further implement the development prediction of the fault source and obtain the corresponding development prediction result. The development prediction result includes the fault severity, influence range, evolution trend, etc. after a preset time, and is associated with a corresponding additional safety level. Then, strategies such as weighting, taking the maximum value, and priority promotion are adopted to comprehensively combine the current fault level of the fault source with the additional safety level to obtain a comprehensive fault level, and based on the comprehensive fault level as the selection basis, the final fault location result is determined through the same threshold screening and extreme value selection methods as described above.

[0096] The above process not only relies on the current detection results but also takes into account the future development trend, which helps to perceive potential threats in advance, thereby enhancing the safety margin for fault handling, facilitating the transition of fault management from passive response to active prevention, and ultimately achieving the effect of reducing the overall operation risk of the system.

[0097] In summary, the grounding loop resistance detection and fault location method integrating edge computing provided by the present invention has the following technical effects: By analyzing key nodes of the grounding loop, deploying edge computing units according to the analysis results, and configuring corresponding monitoring sensors, the monitoring sensors including high-frequency transient traveling wave detectors, broadband impedance spectrum measurement sensors, and synchronous clock units; during the normal operation of the power grid device, using the monitoring sensors to collect operation data, realizing the time synchronization and alignment of the monitoring data set based on the synchronous clock unit, and then performing interpolation reconstruction by the edge computing unit to generate a continuous situation field of the grounding loop based on three-dimensional fault features; implementing real-time monitoring of the power grid device through the monitoring sensors, and combining the real-time monitoring results with the continuous situation field, performing fault source inversion analysis based on local height gradient features and extreme value area features, and then realizing fault location, thereby achieving the technical effects of reducing response latency, optimizing load distribution, and improving fault location accuracy.

[0098] Embodiment 2, as Figure 2 is a schematic structural diagram of the grounding loop resistance detection and fault location system integrating edge computing of the present invention. For example, Figure 1 the flow schematic diagram of the grounding loop resistance detection and fault location method integrating edge computing of the present invention can be implemented through a structure as Figure 2 shown.

[0099] Based on the same concept as the grounding loop resistance detection and fault location method integrating edge computing in the above embodiments, the grounding loop resistance detection and fault location system integrating edge computing provided by the present invention includes: A node deployment module 11, configured to perform critical node analysis of the grounding loop, deploy edge computing units based on the critical node analysis results, and configure monitoring sensors mapped to the edge computing units. The monitoring sensors include high-frequency transient traveling wave detectors, broadband impedance spectrum measurement sensors, and synchronous clock units.

[0100] A field reconstruction module 12, configured to, when the power grid device is operating normally, use the monitoring sensors to perform power grid device monitoring. After time-synchronizing and aligning the monitoring data set based on the synchronous clock unit, interpolate and reconstruct the continuous situation field of the grounding loop through the edge computing unit. The continuous situation field is constructed based on three-dimensional fault characteristics.

[0101] A fault source inversion and location module 13, configured to use the monitoring sensors to perform real-time monitoring of the power grid device, and perform fault source inversion based on local height gradient characteristics and extreme area characteristics through the real-time monitoring results and the continuous situation field, and establish fault location.

[0102] In some embodiments, the node deployment module 11 includes: A deployable node analysis and construction unit, configured to analyze and construct all deployable nodes of the grounding loop.

[0103] A node importance evaluation unit, configured to, after configuring a multi-dimensional index fusion, use the multi-dimensional index fusion to evaluate the importance of all deployable nodes. The multi-dimensional index fusion includes an electrical contribution index, a propagation sensitivity index, a spectrum heterogeneity index, a time-domain evolution coupling index, and a fault evolution potential index.

[0104] A critical node establishment and edge computing unit deployment unit, configured to perform proportional screening using the node importance evaluation results, perform node distribution compensation based on the proportional screening results, establish critical nodes, and deploy edge computing units at the critical nodes.

[0105] In some embodiments, the field reconstruction module 12 includes: A monitoring data synchronization and edge computing unit transmission unit, configured to, after time-synchronizing and aligning the monitoring data set based on the synchronous clock unit, send the time-aligned monitoring data set to the corresponding edge computing unit to form a computing local area network with the edge computing units.

[0106] A high-frequency transient traveling wave spatial propagation feature extraction unit, configured to perform extraction of the spatial propagation features of high-frequency transient traveling waves and establish spatial propagation features. The spatial propagation features include inter-node propagation delay features, attenuation coefficient features, and local reflection features.

[0107] A broadband impedance spectrum frequency response feature extraction unit, which is used to perform the extraction of the frequency response features of the broadband impedance spectrum and establish the frequency response features, where the frequency response features include amplitude-frequency change features, phase-frequency change features, and frequency-segment feature vectors.

[0108] A time-domain evolution feature extraction and establishment unit, which is used to perform the extraction of time-domain evolution features on the monitoring data sets of multiple key nodes and establish the time-domain evolution features, where the time-domain evolution features include synchronous evolution features, node association features, and mutation features.

[0109] A three-dimensional fault feature formation and continuous situation field reconstruction unit, which is used to form three-dimensional fault features with the spatial propagation features, frequency response features, and time-domain evolution features, and after interpolating the three-dimensional fault features, reconstruct the continuous situation field of the grounding loop.

[0110] In some implementation manners, the three-dimensional fault feature formation and continuous situation field reconstruction unit in the field reconstruction module 12 includes: A key node connection edge establishment unit, which is used to establish connection edges among all the key nodes according to the dependency relationships of the key nodes.

[0111] A connection edge weight configuration unit, which is used to configure the connection edge weights through the spatial distance and feature difference degree of the connection edges.

[0112] A feature propagation constraint and continuous situation field reconstruction unit, which is used to establish strong and weak constraints and direction constraints for feature propagation transition based on the connection edge weights, and then traverse the three-dimensional fault features to perform feature interpolation to reconstruct the continuous situation field of the grounding loop.

[0113] In some embodiments, the fault source inversion and positioning module 13 includes: A real-time monitoring result time synchronization and superposition unit, which is used to synchronize and align the time of the real-time monitoring results, and then superpose the time-synchronized real-time monitoring results onto the continuous situation field according to time slices to form a dynamic situation increment map.

[0114] A local gradient calculation and gradient magnitude establishment unit, which is used to calculate the local gradient on the dynamic situation increment map and establish the gradient magnitude.

[0115] A local high-gradient feature establishment unit, which is used to screen the high-gradient regions by using the gradient magnitude and establish local high-gradient features.

[0116] An extreme value region feature establishment unit, which is used to identify local maximum points and local minimum points according to the local gradient by using local neighborhood search to establish extreme value region features.

[0117] The fault source inversion unit is used to perform fault source inversion based on the local high-gradient feature and the extreme value area feature.

[0118] In some implementation manners, the local gradient calculation and gradient modulus establishment unit in the fault source inversion and location module 13 includes: The fixed determination threshold acquisition subunit is used to acquire a fixed determination threshold that matches the current monitoring scenario.

[0119] The global gradient field statistics and sensitivity adjustment factor construction subunit is used to calculate the mean and standard deviation of the global gradient field according to the gradient modulus, and construct a sensitivity adjustment factor using the calculation results of the mean and standard deviation.

[0120] The high-gradient area screening subunit is used to enhance the attention of the matching fixed determination threshold by using the sensitivity adjustment factor, and screen the high-gradient area with the enhanced matching fixed determination threshold.

[0121] In some implementation manners, the fault source inversion unit in the fault source inversion and location module 13 includes: The time-domain data collection unit after fault source location is used to collect the time-domain data of each key node by using the synchronous clock unit after locating the fault source.

[0122] The fault time evolution and space propagation fitting unit is used to fit the time evolution and space propagation of the fault based on the time-domain data.

[0123] The fault source inversion authentication and location unit is used to perform fault source inversion authentication by using the time evolution and space propagation fitting, and perform fault location based on the fault source inversion authentication result.

[0124] In some embodiments, the fault source inversion and location module 13 further includes: The fault level and decision response scheme matching unit is used to establish a fault level according to the fault location, and establish a multi-level decision response scheme matching result, where the multi-level decision response scheme matching result includes a fault location response decision and a fault path evolution decision.

[0125] The fault reporting unit is used to report the fault based on the fault level and the multi-level decision response scheme matching result.

[0126] In some embodiments, the fault source inversion and location module 13 further includes: The fault development prediction unit is used to perform the development prediction of the fault source, and establish an additional safety level based on the development prediction result.

[0127] The additional fault location unit is used to perform fault location by using the additional safety level and the fault level of the fault source.

[0128] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the foregoing Embodiment 1 are equally applicable to the grounding loop resistance detection and fault location system integrating edge computing described in Embodiment 2. For the sake of brevity of the specification, no further elaboration will be made here.

[0129] It should be understood that the disclosed embodiments of the present invention and the above descriptions enable those skilled in the art to implement the present invention using the present invention. At the same time, the present invention is not limited to the part of the embodiments mentioned above. It should be understood that those of ordinary skill in the art can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for detecting the grounding loop resistance and fault location integrating edge computing, characterized in that The method includes: Performing key node analysis of the grounding loop, deploying edge computing units based on the key node analysis results, and configuring monitoring sensors mapped to the edge computing units. The monitoring sensors include high-frequency transient traveling wave detectors, broadband impedance spectrum measurement sensors, and synchronous clock units; Under the condition of normal operation of the power grid device, using the monitoring sensors to perform power grid device monitoring. After time synchronization and alignment of the monitoring data set based on the synchronous clock unit, the continuous situation field of the grounding loop is interpolated and reconstructed through the edge computing unit. The continuous situation field is constructed based on three-dimensional fault characteristics; Using the monitoring sensors to perform real-time listening of the power grid device, and performing fault source inversion based on local height gradient characteristics and extreme area characteristics through the real-time listening results and the continuous situation field to establish fault location.

2. The method for detecting the grounding loop resistance and fault location integrating edge computing according to claim 1, wherein, The interpolating and reconstructing the continuous situation field of the grounding loop through the edge computing unit includes: After time synchronization and alignment of the monitoring data set using the synchronous clock unit, sending the time-aligned monitoring data set to the corresponding edge computing unit. After forming a computing local area network with the edge computing units: Performing extraction of the spatial propagation characteristics of high-frequency transient traveling waves to establish spatial propagation characteristics, where the spatial propagation characteristics include inter-node propagation delay characteristics, attenuation coefficient characteristics, and local reflection characteristics; Performing extraction of the spectral response characteristics of the broadband impedance spectrum to establish spectral response characteristics, where the spectral response characteristics include amplitude-frequency change characteristics, phase-frequency change characteristics, and frequency-segment feature vectors; Performing extraction of the time-domain evolution characteristics of the monitoring data sets of multiple key nodes to establish time-domain evolution characteristics, where the time-domain evolution characteristics include synchronous evolution characteristics, node association characteristics, and mutation characteristics; Forming three-dimensional fault characteristics with the spatial propagation characteristics, spectral response characteristics, and time-domain evolution characteristics. After interpolating the three-dimensional fault characteristics, the continuous situation field of the grounding loop is reconstructed.

3. The grounding loop resistance detection and fault location method integrating edge computing according to claim 2, characterized in that, The reconstructing the continuous situation field of the grounding loop after interpolating the three-dimensional fault characteristics includes: Establishing connection edges among all key nodes according to the dependency relationship of the key nodes; Configuring the connection edge weights through the spatial distance and feature difference degree of the connection edges; After establishing the strong and weak constraints and direction constraints of feature propagation transition based on the connection edge weights, traversing the three-dimensional fault characteristics to perform feature interpolation to reconstruct the continuous situation field of the grounding loop.

4. The grounding loop resistance detection and fault location method integrating edge computing according to claim 1, characterized in that The performing key node analysis of the grounding loop and deploying edge computing units based on the key node analysis results includes: Analyzing and constructing all deployable nodes of the grounding loop; After configuring and fusing multi-dimensional indicators, using the fused multi-dimensional indicators to perform node importance evaluation of all deployable nodes. The fused multi-dimensional indicators include electrical contribution degree indicators, propagation sensitivity indicators, spectral heterogeneity indicators, time-domain evolution coupling degree indicators, and fault evolution potential indicators; Performing proportional screening using the node importance evaluation results, performing node distribution compensation based on the proportional screening results, establishing key nodes, and deploying edge computing units at the key nodes.

5. The method for detecting the grounding loop resistance and fault location integrating edge computing according to claim 1, characterized in that The performing fault source inversion based on local height gradient characteristics and extreme area characteristics through the real-time listening results and the continuous situation field to establish fault location includes: After synchronizing and aligning the time of the real-time monitoring results, the real-time monitoring results that are time-synchronized and aligned are superimposed on the continuous situation field according to time slices to form a dynamic situation increment map; Calculate the local gradient on the dynamic situation increment map and establish the gradient modulus; Use the gradient modulus to screen high-gradient regions and establish local high-gradient features; Use local neighborhood search to identify local maximum points and local minimum points according to the local gradient to establish extreme region features; Perform fault source inversion with the local high-gradient features and extreme region features.

6. The method for detecting grounding loop resistance and fault location integrating edge computing according to claim 5, characterized in that, After establishing the gradient modulus, it includes: Obtain the matching fixed decision threshold for the current monitoring scenario; Calculate the mean and standard deviation of the global gradient field according to the gradient modulus, and construct a sensitivity adjustment factor using the calculation results of the mean and standard deviation; After enhancing the attention of the matching fixed decision threshold using the sensitivity adjustment factor, screen the high-gradient regions with the attention-enhanced matching fixed decision threshold.

7. The method for detecting grounding loop resistance and fault location integrating edge computing according to claim 5, characterized in that, The performing fault source inversion with the local high-gradient features and extreme region features includes: After locating the fault source, use the synchronous clock unit to collect the time-domain data of each key node, and perform fitting of the time evolution and spatial propagation of the fault based on the time-domain data; Use the fitting of the time evolution and spatial propagation to perform fault source inversion authentication, and perform fault location based on the fault source inversion authentication result.

8. The method for detecting the grounding loop resistance and fault location integrating edge computing according to claim 1, wherein, After establishing the fault location, it includes: After establishing the fault level according to the fault location, establish the matching result of the multi-level decision response plan, and the matching result of the multi-level decision response plan includes the fault location response decision and the fault path evolution decision; Report the fault based on the fault level and the matching result of the multi-level decision response plan.

9. The grounding loop resistance detection and fault location method integrating edge computing according to claim 1, characterized in that The establishing the fault location also includes: Execute the development prediction of the fault source, and establish an additional safety level based on the development prediction result; Use the additional safety level and the fault level of the fault source to establish the fault location.

10. A grounding loop resistance detection and fault location system integrating edge computing, characterized in that The method for detecting and locating the grounding loop resistance and fault of the fusion edge computing according to any one of claims 1 to 9 includes: A node deployment module, which is used to perform analysis of key nodes of the grounding loop, deploy edge computing units according to the key node analysis results, and configure monitoring sensors mapped to the edge computing units. The monitoring sensors include high-frequency transient traveling wave detectors, broadband impedance spectrum measurement sensors, and synchronous clock units; A field reconstruction module, which is used to perform monitoring of the power grid device using the monitoring sensors under the condition that the power grid device is operating normally. After time-synchronizing and aligning the monitoring data set based on the synchronous clock unit, interpolate and reconstruct the continuous situation field of the grounding loop through the edge computing unit. The continuous situation field is constructed based on three-dimensional fault characteristics; A fault source inversion and location module, which is used to perform real-time monitoring of the power grid device using the monitoring sensors, and perform fault source inversion based on local high-gradient features and extreme region features through the real-time monitoring results and the continuous situation field, and establish a fault location.

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