A smart meter low-voltage fault analysis and reporting method and system

Abstract

This invention discloses a method and system for low-voltage fault analysis and reporting of smart meters. The method includes: First, a concentrator initiates monitoring. Each meter parses the source address and physical layer signal characteristics of all data frames in the power line carrier channel to form first monitoring data and reports it. Based on this, the concentrator performs cluster analysis and self-learning to generate a dynamic topology map reflecting the actual connection relationship and quality between meters. Based on this map, each meter continuously monitors its identified neighbor nodes during daily communication, generating second monitoring data. When an anomaly is detected in a target meter, the concentrator triggers its neighbor node set to perform state consensus verification, forming a consensus fault event packet. Finally, the concentrator merges this event packet with the dynamic topology map to analyze and generate a diagnostic report. This invention effectively solves the problem of fault information loss due to single-point communication failure when the channel is busy, significantly improving reporting reliability.

Description

Technical Field

[0001] This invention relates to the field of electrical equipment operation and maintenance, and in particular to a method and system for analyzing and reporting low-voltage faults in smart meters. Background Technology

[0002] With the deepening of smart grid construction, smart meters have become not only electricity metering terminals, but also key nodes for sensing the operational status of the end of the low-voltage distribution network. Their timely detection and reliable reporting of low-voltage faults (such as power outages on the user side, line breaks, equipment malfunctions, etc.) are the core technical links for achieving rapid fault location, power restoration, and improved power supply service quality.

[0003] Currently, low-voltage fault reporting in smart meters primarily employs an "active reporting" mechanism based on backup power. Specifically, smart meters typically integrate backup power units such as supercapacitors. When the meter detects a power outage in the main AC power supply, it immediately switches to backup power and activates its power line carrier communication module, sending an information frame containing its own identifier and the power outage event to its corresponding concentrator. Upon receiving this frame, the concentrator uploads it to the main station system, triggering subsequent fault handling procedures.

[0004] However, this existing technology has a significant drawback in complex real-world network environments: fault reporting is highly prone to failure when the channel is busy, leading to monitoring blind spots in the system. Specifically, the low-voltage power line carrier channel is a shared medium, and during peak electricity consumption periods, service data traffic is high and the channel is congested. Reports from faulty meters are easily confused with or lost due to interference from normal service messages. Limited by finite backup power, the meter's power depletes after multiple failed retries, resulting in the "silent loss" of critical fault information. The success or failure of the entire process depends entirely on the faulty meter's single-point, isolated communication in poor channel conditions, lacking a mechanism for collaborative verification and redundant reporting using other normal nodes in the network.

[0005] Therefore, the core problem with existing technologies lies in the fact that "relying on single-point communication of faulty equipment in busy channels cannot guarantee reliability." This has become the main technical bottleneck restricting the improvement of the proactive fault detection capability of distribution networks, and a new method that can utilize network collaboration to achieve highly reliable reporting is urgently needed. Summary of the Invention

[0006] In view of the aforementioned deficiencies of the prior art, the technical problem to be solved by the present invention is to provide a method and system for analyzing and reporting low-voltage faults in smart meters, aiming to solve the problem of unreliable single-point communication and improve the reliability of reporting.

[0007] To achieve the above objectives, the first aspect of this invention discloses a method for analyzing and reporting low-voltage faults in smart meters, applicable to concentrators and their subordinate smart meters, the method comprising: Step S1: The concentrator sends a monitoring command to each of the subordinate smart meters. Step S2: The smart meter performs a first listening operation on all data frames on the power line carrier channel based on the listening instruction; the smart meter parses and records the source address of each data frame and its corresponding physical layer signal characteristics to obtain the first listening data; the smart meter sends the first listening data to the concentrator; wherein, the physical layer signal characteristics include at least the received signal strength and the signal-to-noise ratio; Step S3: The concentrator obtains a dynamic topology map reflecting the connection relationship and connection quality between smart meters through cluster analysis based on the first monitoring data. Step S4: When each of the smart meters is performing regular data communication services, it continuously monitors the neighboring node meters determined in the dynamic topology map, obtains the second monitoring data, and reports it to the concentrator. Step S5: When an anomaly is detected in the target meter based on the second monitoring data, the concentrator triggers the neighboring node meter set of the target meter to perform state consensus verification according to the dynamic topology map, and obtains the consensus fault event packet generated therefrom. Step S6: The concentrator merges the consensus fault event package with the dynamic topology map to generate a diagnostic report that includes precise fault location and type determination.

[0008] Optionally, step S3 includes: The concentrator uses the received signal strength and the signal-to-noise ratio as core feature vectors to cluster all the smart meters under its jurisdiction; it groups the smart meters that can stably listen to each other and whose signal feature similarity is higher than a preset threshold into the same neighbor set, and assigns an initial health weight to the connection between each pair of meters in the set, thereby forming the dynamic topology map.

[0009] Optionally, step S5 includes: When an anomaly is detected in the target meter based on the second monitoring data, the concentrator sends verification requests to all direct neighboring meters of the target meter according to the dynamic topology map. Upon receiving the request, the neighboring node meter attempts to communicate with the target meter via the power line carrier channel and feeds back its verification result to the concentrator. The concentrator aggregates all verification results. If more than a preset proportion of neighboring node meters confirm that the target meter is unresponsive, consensus is determined to be reached, and the consensus failure event package is generated.

[0010] Optionally, the consensus fault event package includes at least: the target meter identifier, a list of neighboring nodes that have reached a consensus, a fault timestamp, and local electrical quantity fragment data at the verification time reported by each neighboring node.

[0011] Optionally, step S6 includes: The concentrator locates the branch and upstream and downstream related meters in the dynamic topology map based on the target meter identifier in the consensus fault event packet. The system retrieves high-frequency electrical quantity data from the upstream and downstream related meters within the fault time window; wherein the electrical quantity data includes voltage, current waveforms, and phase information. By comparing and analyzing the electrical quantity data of the upstream and downstream related meters, and combining it with the historical connection quality data in the dynamic topology map, the fault type and precise physical location are comprehensively determined.

[0012] The second aspect of the present invention discloses a low-voltage fault analysis and reporting system for smart meters. The system includes: a concentrator and its subordinate smart meters. The concentrator includes a monitoring instruction issuing module, a dynamic topology map construction module, a consensus fault event packet acquisition module, and a diagnostic report generation module. The smart meters include a first monitoring module and a second monitoring module. The monitoring instruction sending module is used to send monitoring instructions to each of the subordinate smart meters; The first monitoring module is configured to perform a first monitoring of all data frames on the power line carrier channel based on the monitoring instruction; parse and record the source address of each data frame and its corresponding physical layer signal characteristics to obtain first monitoring data; and send the first monitoring data to the concentrator; wherein the physical layer signal characteristics include at least the received signal strength and the signal-to-noise ratio. The dynamic topology map construction module is used to obtain a dynamic topology map reflecting the connection relationship and connection quality between smart meters through cluster analysis based on the first monitoring data. The second monitoring module is used to continuously monitor the neighboring node meters determined in the dynamic topology map when the smart meter is performing regular data communication services, obtain the second monitoring data, and report it to the concentrator. The consensus fault event packet acquisition module is used to, when an abnormality of the target meter is detected based on the second monitoring data, trigger the neighbor node meter set of the target meter to perform state consensus verification according to the dynamic topology map, and obtain the consensus fault event packet generated therefrom. The diagnostic report generation module is used to integrate the consensus fault event package with the dynamic topology map to generate a diagnostic report that includes precise fault location and type determination.

[0013] Optionally, the dynamic topology map construction module is specifically used for: Using the received signal strength and the signal-to-noise ratio as core feature vectors, cluster all the smart meters. Smart meters that can stably monitor each other and whose signal feature similarity is higher than a preset threshold are grouped into the same neighbor set, and an initial health weight is assigned to the connection between each pair of meters in the set, thereby forming the dynamic topology map.

[0014] Optionally, the consensus failure event packet acquisition module is specifically used for: When an anomaly is detected in the target meter based on the second monitoring data, the consensus fault event packet acquisition module sends a verification request to all direct neighboring nodes of the target meter according to the dynamic topology map; so that the neighboring nodes that receive the request attempt to communicate with the target meter through the power line carrier channel and feed back their own verification results to the consensus fault event packet acquisition module. The consensus failure event package acquisition module summarizes all verification results. If more than a preset proportion of neighboring node meters confirm that the target meter is not responding, then consensus is determined to be achieved, and the consensus failure event package is generated.

[0015] Optionally, the consensus fault event package includes at least: the target meter identifier, a list of neighboring nodes that have reached a consensus, a fault timestamp, and local electrical quantity fragment data at the verification time reported by each neighboring node.

[0016] Optionally, the diagnostic report generation module is specifically used for: Based on the target meter identifier in the consensus fault event package, locate its branch and upstream and downstream related meters in the dynamic topology map; The system retrieves high-frequency electrical quantity data from the upstream and downstream related meters within the fault time window; wherein the electrical quantity data includes voltage, current waveforms, and phase information. By comparing and analyzing the electrical quantity data of the upstream and downstream related meters, and combining it with the historical connection quality data in the dynamic topology map, the fault type and precise physical location are comprehensively determined.

[0017] The beneficial effects of this invention are as follows: 1. This invention solves the problem of unreliable single-point communication through a dynamic topology "group collaborative verification" mechanism. When the target meter malfunctions, the concentrator organizes multiple normally powered neighboring nodes to perform cross-verification and summarizes them into a consensus event packet for reporting. Even if the target meter itself is completely unable to send a signal due to the depletion of backup power or extreme channel congestion, the system can still reliably perceive the fault through the "other verification" of neighboring nodes, completely avoiding the problem of "silent loss" of information caused by single-point communication failure and improving the fault event perception rate. At the same time, the cross-verification of neighboring nodes further improves the robustness of fault event diagnosis. 2. By fusing consensus fault event packets with dynamic topology maps, this invention can map logical fault events to precise physical connection relationships. By comparing and analyzing multi-dimensional electrical data such as voltage and zero-sequence current of upstream and downstream related meters, it can not only narrow the fault range from "transformer area" to specific meter boxes or branch lines, but also effectively distinguish fault types such as short circuits, grounding, and open circuits, and can combine the historical health trend in the topology to associate hidden dangers. This enables repair personnel to perform precise repairs, significantly shortening fault location and recovery time. 3. This invention, through routine, non-intrusive passive monitoring and cluster analysis, can automatically learn and generate a dynamic topology map that accurately reflects physical connections, replacing the error-prone and outdated manual ledgers. This map not only depicts the connections but also adds quality weights reflecting the health status of the lines. Topology generation requires no additional network overhead and can be continuously verified and updated during operation through consensus events, ensuring high accuracy and providing reliable foundational data for all advanced applications. 4. This invention transforms a large amount of work that previously required manual on-site inspections and centralized calculations at the main station into automatic collaborative sensing and preliminary analysis at the edge. This reduces the continuous pressure on the main station's computing power and communication bandwidth, achieving "cloud-edge" collaboration and thus improving the intelligent operation and maintenance level of the power distribution network.

[0018] In summary, through systematic methodological innovation, this invention not only solves the fatal flaw of unreliable fault reporting in existing technologies, but also achieves a leapfrog progress in fault analysis and localization. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a method for analyzing and reporting low-voltage faults in smart meters according to a specific embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a smart meter low-voltage fault analysis and reporting system provided in a specific embodiment of the present invention. Detailed Implementation

[0020] This invention discloses a method and system for analyzing and reporting low-voltage faults in smart meters. Those skilled in the art can refer to the content of this document and appropriately modify the technical details to implement it. It should be particularly noted that all similar substitutions and modifications are obvious to those skilled in the art and are considered to be included in this invention. The methods and applications of this invention have been described through preferred embodiments. Those skilled in the art can obviously modify or appropriately change and combine the methods and applications described herein without departing from the content, spirit, and scope of this invention to implement and apply the technology of this invention.

[0021] This invention provides a method for analyzing and reporting low-voltage faults in smart meters, applicable to concentrators and their subordinate smart meters, such as... Figure 1 As shown, the method includes: Step S1: The concentrator sends a monitoring command to each of its subordinate smart meters.

[0022] It should be noted that step S1 is the initialization command for the topology discovery process. The concentrator, acting as the control core, sends commands to all subordinate smart meters, causing their communication chips to enter a specific "promiscuous monitoring mode." This command is either periodic or event-triggered, and its purpose is to establish a coordinated working state for subsequent uninterrupted collection of the network's original communication characteristics.

[0023] Step S2: The smart meter performs a first listening operation on all data frames on the power line carrier channel based on the listening command; the smart meter parses and records the source address of each data frame and its corresponding physical layer signal characteristics to obtain the first listening data; the smart meter sends the first listening data to the concentrator.

[0024] Among them, the physical layer signal characteristics include at least the received signal strength and the signal-to-noise ratio.

[0025] It should be noted that this is the specific implementation of "non-intrusive passive monitoring." Each meter, according to instructions, receives all data frames appearing on the power line carrier channel at its communication physical layer, not just frames destined for its own unit. The meter parses the header of each frame, extracts the source address (i.e., the unique ID of the sending meter), and simultaneously measures the physical layer signal characteristics of that frame, the core of which includes received signal strength (RSS) and signal-to-noise ratio (SNR). These characteristic data are encapsulated as "first monitoring data" and reported. Received Signal Strength (RSSI) directly measures the remaining energy intensity of the signal after propagation from the sending meter to the receiving meter. In the power line medium, signal strength attenuates with increasing propagation distance. More importantly, whenever the signal passes through a branch point, switch, or poor connection point, additional reflections and losses occur due to impedance changes. Therefore, RSS is used to determine connectivity. Signal-to-noise ratio (SNR) measures the strength of the useful signal relative to background noise. On low-voltage power lines, noise mainly originates from switches, frequency converters, etc., of various electrical equipment. SNR is used to determine connection quality.

[0026] Step S3: Based on the first monitoring data, the concentrator obtains a dynamic topology map reflecting the connection relationship and connection quality between smart meters through cluster analysis.

[0027] It should be noted that step S3 is the core data processing and knowledge generation step. The concentrator collects the first monitoring data reported by all electricity meters and applies a clustering analysis algorithm. This algorithm uses signal strength and signal-to-noise ratio as feature vectors to cluster electricity meters that can stably and with high quality monitor each other's signals into the same "neighbor set". Based on this, the system automatically draws a topology reflecting the physical connection relationships between electricity meters and assigns an initial health weight representing communication stability to each connection, thus forming a "dynamic topology map". This map replaces the manually maintained static ledger and is the foundation for all subsequent intelligent operations.

[0028] In this specific embodiment, step S3 includes: The concentrator uses the received signal strength and signal-to-noise ratio as core feature vectors to cluster all its subordinate smart meters; it groups smart meters that can stably monitor each other and whose signal feature similarity is higher than a preset threshold into the same neighbor set, and assigns an initial health weight to the connection between each pair of meters in the set, thereby forming a dynamic topology map.

[0029] It should be noted that this embodiment clarifies the core algorithmic basis for constructing the dynamic topology map. It points out that the concentrator uses the two key physical layer indicators reported by the meters, "Received Signal Strength Indicator" (RSSI) and "Signal-to-Noise Ratio" (SNR), as feature vectors for clustering. The technical logic is that a group of meters that can stably and effectively monitor each other's signals must have a tighter electrical connection and are therefore grouped into the same "neighbor set." Simultaneously, the scheme assigns an "initial health weight" to each pair of connections within the set. This allows the map to not only represent connection relationships but also possess preliminary quality assessment capabilities, laying a data foundation for subsequent fault analysis and early warning of hidden hazards.

[0030] Step S4: When performing regular data communication services, each smart meter continuously monitors the neighboring node meters identified in the dynamic topology map, obtains the second monitoring data, and reports it to the concentrator.

[0031] It should be noted that, guided by the topology map, the system enters a normalized intelligent monitoring phase. While performing routine tasks such as meter reading, each meter continuously and selectively monitors the "neighboring node meters" identified in the topology map, generating "secondary monitoring data." This significantly improves monitoring efficiency, enabling the system to detect abnormal states such as "disconnection" of nodes in real time, providing immediate and low-cost input for fault triggering.

[0032] Step S5: When an anomaly is detected in the target meter based on the second monitoring data, the concentrator triggers the neighboring node meter set of the target meter to perform state consensus verification according to the dynamic topology map, and obtains the consensus fault event packet generated therefrom.

[0033] It should be noted that step S5 addresses the unreliability of single-point reporting based on the preparations in the preceding steps. When the concentrator detects an anomaly (such as signal loss) in a target meter through the second monitoring data, it does not wait for the meter itself to report it. Instead, it immediately initiates a "state verification request" to all direct neighbor nodes of the target meter based on the dynamic topology graph. After receiving the request, the neighbor nodes attempt to communicate with the target meter and report the "success" result back to the concentrator. The concentrator aggregates the feedback, and if more than a preset proportion (such as 2 / 3) of the neighbors confirm that the target has not responded, it determines that "consensus has been reached" and packages it into a "consensus failure event packet." This mechanism ensures that even if the faulty meter itself is completely silent, the failure event can still be reliably perceived and reported by the network community.

[0034] In this specific embodiment, step S5 includes: When an anomaly is detected in the target meter based on the second monitoring data, the concentrator sends verification requests to all direct neighboring meters of the target meter according to the dynamic topology map. Upon receiving the request, the neighboring node meter attempts to communicate with the target meter via the power line carrier channel and sends its own verification result back to the concentrator. The concentrator aggregates all verification results. If more than a preset proportion of neighboring nodes confirm that the target meter is unresponsive, consensus is determined to be achieved, and a consensus failure event package is generated.

[0035] It should be noted that this embodiment elaborates on the key process of "state consensus verification" in step S5, which is the core operation of this solution to address the unreliability of single-point reporting. It describes the complete chain from triggering to decision-making: the concentrator first accurately sends verification requests to all direct neighbors of the target meter (based on the dynamic topology map); after receiving the requests, neighbor nodes actively attempt to communicate with the target meter and feed back the verification result of "connectivity" to the concentrator; finally, the concentrator uses a "majority rule" mechanism (i.e., "exceeding a preset proportion") to determine whether consensus has been reached. This process ensures that fault judgment is not based on the partial information of a single node, but on a high-confidence conclusion after cross-verification by multiple independent nodes, greatly improving the reliability of reported events.

[0036] Furthermore, the consensus failure event package includes at least: the target meter identifier, a list of neighboring nodes that have reached a consensus, a failure timestamp, and local electrical quantity fragments reported by each neighboring node at the verification time.

[0037] It should be noted that the consensus fault event package requires the event package to contain at least four types of information: fault subject (target meter identifier), verification evidence (list of neighboring nodes that reached consensus), time stamp (fault timestamp), and on-site electrical snapshot (local electrical quantity fragments of each neighboring node). This structural design endows the event package with rich diagnostic value: the identifier is used for location; the neighbor list provides a spatial correlation evidence chain, enhancing the credibility of the event; the precise timestamp facilitates time series analysis; and the electrical quantity fragments (such as the voltage waveform at the moment of the fault) provide crucial first-hand data for subsequent in-depth fault type analysis at the concentrator side.

[0038] Step S6: The concentrator merges consensus fault event packets with dynamic topology maps to generate a diagnostic report that includes precise fault location and type determination.

[0039] It's important to note that the concentrator doesn't simply forward events; instead, it initiates intelligent diagnostics: it integrates and analyzes "consensus fault event packets" with a "dynamic topology map." First, it locates the target meter in the map and identifies its upstream and downstream connected meters. Next, it retrieves high-frequency electrical quantity data (such as voltage and current waveforms) from the connected meters at the time of the fault. By comparing upstream and downstream voltage differences, analyzing whether there is a sudden increase in zero-sequence current, and combining this with the historical connection quality trends of the path in the map, it comprehensively determines the precise physical location (e.g., the line between A and B meter boxes) and type (e.g., a grounding fault). Finally, it generates a structured diagnostic report to guide precise emergency repairs.

[0040] In this specific embodiment, step S6 includes: Based on the target meter identifier in the consensus fault event packet, the concentrator locates the branch and upstream and downstream related meters in the dynamic topology map; High-frequency electrical quantity data of upstream and downstream related meters within the fault time window is retrieved; the electrical quantity data includes voltage, current waveforms and phase information; By comparing and analyzing the electrical quantity data of upstream and downstream related meters, and combining it with the historical connection quality data in the dynamic topology map, the fault type and precise physical location are comprehensively determined.

[0041] It should be noted that this embodiment refines the specific analytical logic upon which the diagnostic report generation in step S6 relies. Firstly, it clarifies that the data foundation for the analysis is the "high-frequency acquired electrical quantity data" (including voltage, current waveforms, and phase) of the upstream and downstream connected meters at the time of the fault. Its core analytical method is to locate the fault section by comparing whether there is a significant difference in the upstream and downstream voltages, and to assist in determining whether the fault type is grounding by detecting whether there is a sudden increase in zero-sequence current. Simultaneously, it emphasizes the need to combine this with the "historical connection quality data" recorded in the dynamic topology map for comprehensive judgment. This enables the diagnosis not only to determine the fault location and cause, but also to assess the fault's causes by associating historical trends, achieving a comprehensive intelligent diagnosis from phenomenon to cause.

[0042] This invention addresses the unreliability of single-point communication through a dynamic topology-based "group collaborative verification" mechanism. When a target meter malfunctions, the concentrator organizes multiple normally powered neighboring nodes to perform cross-verification, which is then aggregated into a consensus event packet for reporting. Even if the target meter itself is completely unable to send a signal due to backup power depletion or extreme channel congestion, the system can still reliably detect the fault through the "other-verification" of neighboring nodes, completely avoiding the "silent loss" of information caused by single-point communication failure and improving the fault event detection rate.

[0043] This invention, by fusing consensus-based fault event packets with a dynamic topology map, can map logical fault events to precise physical connections. By comparing and analyzing multi-dimensional electrical data such as voltage and zero-sequence current from upstream and downstream connected meters, it can not only narrow down the fault area from a "distribution area" to a specific meter box or branch line, but also effectively distinguish fault types such as short circuits, grounding faults, and open circuits. Furthermore, it can combine historical health trends in the topology to identify hidden hazards. This enables repair personnel to perform precise repairs, significantly shortening fault location and recovery time.

[0044] This invention, through routine, non-intrusive passive monitoring and cluster analysis, can automatically learn and generate a dynamic topology map that accurately reflects physical connectivity, replacing the error-prone and outdated manual ledgers. This map not only depicts connectivity relationships but also includes quality weights reflecting the health of the lines. Topology generation requires no additional network overhead and can be continuously verified and updated during operation through consensus events, ensuring high accuracy and providing reliable foundational data for all advanced applications.

[0045] This invention transforms a significant amount of work that previously required manual on-site inspections and centralized computation at the main station into automated collaborative sensing and preliminary analysis at the edge. This reduces the continuous pressure on the main station's computing power and communication bandwidth, achieves "cloud-edge" collaboration, and thus improves the intelligent operation and maintenance level of the power distribution network.

[0046] In summary, through systematic methodological innovation, this invention not only solves the fatal flaw of unreliable fault reporting in existing technologies, but also achieves a leapfrog progress in fault analysis and localization.

[0047] Based on the aforementioned method for analyzing and reporting low-voltage faults in smart meters, this invention also provides a system for analyzing and reporting low-voltage faults in smart meters, such as... Figure 2 As shown, the system includes: a concentrator 1 and its subordinate smart meters 2. The concentrator 1 includes a monitoring instruction issuing module 11, a dynamic topology map construction module 12, a consensus fault event packet acquisition module 13, and a diagnostic report generation module 14. The smart meters 2 include a first monitoring module 21 and a second monitoring module 22. The monitoring instruction sending module 11 is used to send monitoring instructions to each subordinate smart meter 2; The first monitoring module 21 is used to perform first monitoring on all data frames on the power line carrier channel based on the monitoring command; parse and record the source address of each data frame and its corresponding physical layer signal characteristics to obtain the first monitoring data; and send the first monitoring data to the concentrator 1; wherein, the physical layer signal characteristics include at least the received signal strength and the signal-to-noise ratio. The dynamic topology map construction module 12 is used to obtain a dynamic topology map reflecting the connection relationship and connection quality between smart meters 2 through cluster analysis based on the first monitoring data. The second monitoring module 22 is used to continuously monitor the neighboring node meters determined in the dynamic topology map when the smart meter 2 performs regular data communication services, obtain the second monitoring data and report it to the concentrator 1. The consensus fault event packet acquisition module 13 is used to trigger the neighbor node meter set of the target meter to perform state consensus verification according to the dynamic topology graph when an abnormality of the target meter is detected based on the second monitoring data, and obtain the consensus fault event packet generated therefrom. The diagnostic report generation module 14 is used to integrate consensus fault event packages and dynamic topology maps to generate a diagnostic report that includes precise fault location and type determination.

[0048] Optionally, the dynamic topology graph construction module 12 is specifically used for: Using the received signal strength and signal-to-noise ratio as the core feature vectors, all subordinate smart meters 2 are clustered; smart meters 2 that can stably listen to each other and whose signal feature similarity is higher than a preset threshold are grouped into the same neighbor set, and an initial health weight is assigned to the connection between each pair of meters in the set, thereby forming a dynamic topology map.

[0049] Optionally, the consensus failure event packet acquisition module 13 is specifically used for: When an anomaly is detected in the target meter based on the second monitoring data, the consensus fault event packet acquisition module 13 sends a verification request to all direct neighboring nodes of the target meter according to the dynamic topology map; so that the neighboring nodes that receive the request attempt to communicate with the target meter through the power line carrier channel and feed back their own verification results to the consensus fault event packet acquisition module 13. The consensus failure event package acquisition module 13 summarizes all verification results. If more than a preset proportion of neighboring node meters confirm that the target meter is not responding, then consensus is determined to be achieved, and a consensus failure event package is generated.

[0050] Optionally, the consensus failure event package includes at least: the target meter identifier, a list of neighboring nodes that have reached a consensus, a failure timestamp, and local electrical quantity fragments reported by each neighboring node at the verification time.

[0051] Optionally, the diagnostic report generation module 14 is specifically used for: Based on the target meter identifier in the consensus fault event package, locate the branch and upstream and downstream related meters in the dynamic topology map; High-frequency electrical quantity data of upstream and downstream related meters within the fault time window is retrieved; the electrical quantity data includes voltage, current waveforms and phase information; By comparing and analyzing the electrical quantity data of upstream and downstream related meters, and combining it with the historical connection quality data in the dynamic topology map, the fault type and precise physical location are comprehensively determined.

[0052] This invention, through the introduction of a neighbor node collaborative verification mechanism based on dynamic topology, fundamentally solves the core problem of "silent loss" of fault information caused by single-point communication failure when the channel is busy, thus achieving reliable reporting. At the same time, by integrating multi-source data with a self-learning topology map, the fault location accuracy can be improved from the transformer substation level to the branch or meter box level, and the fault type can be accurately determined. Combined with historical health data, it can also realize early warning of hidden dangers, thereby transforming the distribution network operation and maintenance mode from passive response to proactive and precise intelligent operation and maintenance, significantly improving power supply reliability.

[0053] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0054] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0055] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for analyzing and reporting low-voltage faults in smart meters, characterized in that, The method, applied to a concentrator and its subordinate smart meters, includes: Step S1: The concentrator sends a monitoring command to each of the subordinate smart meters. Step S2: The smart meter performs a first listening operation on all data frames on the power line carrier channel based on the listening instruction; the smart meter parses and records the source address of each data frame and its corresponding physical layer signal characteristics to obtain the first listening data; the smart meter sends the first listening data to the concentrator; wherein, the physical layer signal characteristics include at least the received signal strength and the signal-to-noise ratio; Step S3: The concentrator obtains a dynamic topology map reflecting the connection relationship and connection quality between smart meters through cluster analysis based on the first monitoring data. Step S4: When each of the smart meters is performing regular data communication services, it continuously monitors the neighboring node meters determined in the dynamic topology map, obtains the second monitoring data, and reports it to the concentrator. Step S5: When an anomaly is detected in the target meter based on the second monitoring data, the concentrator triggers the neighboring node meter set of the target meter to perform state consensus verification according to the dynamic topology map, and obtains the consensus fault event packet generated therefrom. Step S6: The concentrator merges the consensus fault event package with the dynamic topology map to generate a diagnostic report that includes precise fault location and type determination.

2. The method for analyzing and reporting low-voltage faults in smart meters according to claim 1, characterized in that, Step S3 includes: The concentrator uses the received signal strength and the signal-to-noise ratio as core feature vectors to cluster all the smart meters under its jurisdiction; it groups the smart meters that can stably listen to each other and whose signal feature similarity is higher than a preset threshold into the same neighbor set, and assigns an initial health weight to the connection between each pair of meters in the set, thereby forming the dynamic topology map.

3. The method for analyzing and reporting low-voltage faults in smart meters according to claim 1, characterized in that, Step S5 includes: When an anomaly is detected in the target meter based on the second monitoring data, the concentrator sends verification requests to all direct neighboring meters of the target meter according to the dynamic topology map. Upon receiving the request, the neighboring node meter attempts to communicate with the target meter via the power line carrier channel and feeds back its verification result to the concentrator. The concentrator aggregates all verification results. If more than a preset proportion of neighboring node meters confirm that the target meter is unresponsive, consensus is determined to be reached, and the consensus failure event package is generated.

4. The method for analyzing and reporting low-voltage faults in smart meters according to claim 3, characterized in that, The consensus failure event package includes at least: the target meter identifier, a list of neighboring nodes that have reached a consensus, a failure timestamp, and local electrical quantity fragment data at the verification time reported by each neighboring node.

5. The method for analyzing and reporting low-voltage faults in smart meters according to claim 1, characterized in that, Step S6 includes: The concentrator locates the branch and upstream and downstream related meters in the dynamic topology map based on the target meter identifier in the consensus fault event packet. The system retrieves high-frequency electrical quantity data from the upstream and downstream related meters within the fault time window; wherein the electrical quantity data includes voltage, current waveforms, and phase information. By comparing and analyzing the electrical quantity data of the upstream and downstream related meters, and combining it with the historical connection quality data in the dynamic topology map, the fault type and precise physical location are comprehensively determined.

6. A low-voltage fault analysis and reporting system for smart meters, characterized in that, The system includes: a concentrator and its subordinate smart meters. The concentrator includes a monitoring instruction issuing module, a dynamic topology map construction module, a consensus fault event packet acquisition module, and a diagnostic report generation module. The smart meters include a first monitoring module and a second monitoring module. The monitoring instruction sending module is used to send monitoring instructions to each of the subordinate smart meters; The first monitoring module is configured to perform a first monitoring of all data frames on the power line carrier channel based on the monitoring instruction; parse and record the source address of each data frame and its corresponding physical layer signal characteristics to obtain first monitoring data; and send the first monitoring data to the concentrator; wherein the physical layer signal characteristics include at least the received signal strength and the signal-to-noise ratio. The dynamic topology map construction module is used to obtain a dynamic topology map reflecting the connection relationship and connection quality between smart meters through cluster analysis based on the first monitoring data. The second monitoring module is used to continuously monitor the neighboring node meters determined in the dynamic topology map when the smart meter is performing regular data communication services, obtain the second monitoring data, and report it to the concentrator. The consensus fault event packet acquisition module is used to, when an abnormality of the target meter is detected based on the second monitoring data, trigger the neighbor node meter set of the target meter to perform state consensus verification according to the dynamic topology map, and obtain the consensus fault event packet generated therefrom. The diagnostic report generation module is used to integrate the consensus fault event package with the dynamic topology map to generate a diagnostic report that includes precise fault location and type determination.

7. The smart meter low-voltage fault analysis and reporting system according to claim 6, characterized in that, The dynamic topology graph construction module is specifically used for: Using the received signal strength and the signal-to-noise ratio as core feature vectors, cluster all the smart meters. Smart meters that can stably monitor each other and whose signal feature similarity is higher than a preset threshold are grouped into the same neighbor set, and an initial health weight is assigned to the connection between each pair of meters in the set, thereby forming the dynamic topology map.

8. The smart meter low-voltage fault analysis and reporting system according to claim 6, characterized in that, The consensus failure event packet acquisition module is specifically used for: When an anomaly is detected in the target meter based on the second monitoring data, the consensus fault event packet acquisition module sends a verification request to all direct neighboring nodes of the target meter according to the dynamic topology map; so that the neighboring nodes that receive the request attempt to communicate with the target meter through the power line carrier channel and feed back their own verification results to the consensus fault event packet acquisition module. The consensus failure event package acquisition module summarizes all verification results. If more than a preset proportion of neighboring node meters confirm that the target meter is not responding, then consensus is determined to be achieved, and the consensus failure event package is generated.

9. The smart meter low-voltage fault analysis and reporting system according to claim 8, characterized in that, The consensus failure event package includes at least: the target meter identifier, a list of neighboring nodes that have reached a consensus, a failure timestamp, and local electrical quantity fragment data at the verification time reported by each neighboring node.

10. The smart meter low-voltage fault analysis and reporting system according to claim 6, characterized in that, The diagnostic report generation module is specifically used for: Based on the target meter identifier in the consensus fault event package, locate its branch and upstream and downstream related meters in the dynamic topology map; The system retrieves high-frequency electrical quantity data from the upstream and downstream related meters within the fault time window; wherein the electrical quantity data includes voltage, current waveforms, and phase information. By comparing and analyzing the electrical quantity data of the upstream and downstream related meters, and combining it with the historical connection quality data in the dynamic topology map, the fault type and precise physical location are comprehensively determined.

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