A method and system for cable fault detection and location

Abstract

This application relates to the field of power cable operation and maintenance testing technology, and provides a cable fault detection and location method and system, including: controlling an autonomous mobile platform to move along the target cable path and simultaneously collecting multi-source physical field data such as pulse reflection, acoustic-magnetic synchronization, and power frequency magnetic field; acquiring prior data on power grid operation; calculating the ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point based on the power frequency magnetic field signal and the single-phase ground fault current using a buried three-phase cable current superposition algorithm, and inferring the cable burial depth based on the ratio of the ground magnetic field strength at the fault point to the ground magnetic field strength at the observation point; and performing multi-source fusion comparison of the multi-source physical field data, cable burial depth, and prior data on power grid operation to determine the fault type and location. This application effectively improves the accuracy of cable fault identification and location in complex environments.

Description

Technical Field

[0001] This application relates to the field of power cable operation and maintenance testing technology, specifically to a cable fault detection and location method and system. Background Technology

[0002] With the increasing cable coverage of urban power grids, accurate cable fault location has become crucial for ensuring power supply reliability. In existing technologies, cable fault location typically employs pulse reflection or electromagnetic measurement methods. For example, Chinese patent application CN117970040A discloses a precise cable fault location system that uses pulse reflection signals to identify suspected fault sections and combines this with the burial depth to correct electromagnetic data and improve location accuracy.

[0003] However, the aforementioned existing technologies still have shortcomings when facing complex underground laying environments: on the one hand, their electromagnetic data correction is mainly based on statistical laws or simple depth deviation analysis, without fully considering the attenuation effect of the cable's metal shielding layer on the magnetic field and the complex electromagnetic field distribution characteristics generated by the superposition of multiphase currents, resulting in large calculation errors in scenarios with deep burial or significant shielding effects; on the other hand, existing solutions mostly rely on single-dimensional detection data from the field, lacking in-depth integration and verification with prior data such as fault waveform recording and tripping information from the power grid dispatching terminal, making it difficult to effectively distinguish between various fault types such as high-resistance grounding, low-resistance grounding, and open circuits, and easily subject to environmental noise interference leading to misjudgments, failing to meet the needs of modern power systems for high-precision and intelligent judgment of cable faults. Summary of the Invention

[0004] To address the problems of low accuracy in cable fault location and insufficient accuracy in fault type identification in complex underground environments in existing technologies, this application proposes a cable fault detection and location method and system that achieves accurate fault detection and location based on multi-source physical field fusion and cable magnetic field physical model.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] The autonomous mobile platform is controlled to move along the path of the target cable, and multi-source physical field data is collected synchronously during the movement; the multi-source physical field data includes pulse reflection signals, acoustic-magnetic synchronization signals, and power frequency magnetic field signals;

[0007] Obtain prior power grid operation data for the target cable;

[0008] The buried three-phase cable current superposition algorithm calculates the ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point based on the power frequency magnetic field signal and the single-phase ground fault current, and inversely infers the cable burial depth based on the ratio of the ground magnetic field strength at the fault point to the ground magnetic field strength at the observation point.

[0009] The multi-source physical field data, the cable burial depth, and the prior data of power grid operation are fused and compared to determine the fault type and fault location of the target cable.

[0010] Optionally, the simultaneous acquisition of multi-source physical field data during movement includes:

[0011] As the autonomous mobile platform moves along the path of the target cable, the pulse reflection signal is collected based on the distance traveled.

[0012] The acoustic-magnetic synchronization signal and the power frequency magnetic field signal are continuously acquired in real time.

[0013] Infrared thermal image data is collected at preset time intervals, and the multi-source physical field data also includes the infrared thermal image data;

[0014] The acquired pulse reflection signal is filtered and its features are extracted, and the acoustic-magnetic synchronization signal is denoised to calculate the time difference between the sound wave and the electromagnetic wave.

[0015] Optionally, the method of calculating the ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point based on the power frequency magnetic field signal and the single-phase ground fault current using the buried three-phase cable current superposition algorithm, and then inferring the cable burial depth based on the ratio of the ground magnetic field strength at the fault point to the ground magnetic field strength at the observation point, includes:

[0016] Based on the buried three-phase cable current superposition algorithm, the theoretical total field strength amplitude after superposition of three-phase unbalanced currents is calculated.

[0017] The theoretical total field strength amplitude is corrected by introducing the single-phase ground fault current to obtain the ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point.

[0018] The cable burial depth can be inferred by using the ratio of the ground magnetic field strength at the fault point to the ground magnetic field strength at the observation point.

[0019] Optionally, the theoretical total field strength amplitude after superimposing the three-phase unbalanced currents is calculated, and the ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point are corrected.

[0020] The algorithm for superimposing the currents of the buried three-phase cable is as follows:

[0021] ; ①

[0022] Introducing the single-phase ground fault current The single-phase ground fault current Equal to three times the zero-sequence current ,Right now Further, at a horizontal distance from the fault point Select an observation point and calculate the spatial distance from the observation point to the center of the target cable using trigonometric functions;

[0023] Substituting the single-phase ground fault current and the spatial distance from the observation point to the cable center into formula ①, the ground magnetic field strength at the observation point is obtained, as follows:

[0024] ;②

[0025] make The ground magnetic field strength at the fault point is calculated by substituting it into formula ②, as shown below:

[0026] ;③

[0027] The distance between the target cable and the ground is calculated using the ratio of Formula ③ to Formula ②, as shown below:

[0028] ④

[0029] Simplifying formula ④, we get:

[0030] ⑤

[0031] in, This represents the theoretical total field strength amplitude. The permeability of free space, The distance between the target cable and the ground, i.e., the cable burial depth. The ground magnetic field strength at the observation point, The ground magnetic field strength at the fault point. This is the single-phase ground fault current. It is the zero-sequence current.

[0032] Optionally, the step of performing multi-source fusion comparison of the multi-source physical field data, the cable burial depth, and the prior power grid operation data to determine the fault type and fault location of the target cable includes:

[0033] Extract the ground magnetic field strength and waveform continuity characteristics of the fault point from the multi-source physical field data;

[0034] The ground magnetic field strength characteristics at the fault point are compared with the magnetic field threshold of a normal cable to obtain the first comparison result;

[0035] The waveform continuity characteristics, the first comparison result, and the prior data of power grid operation are cross-validated.

[0036] The fault type is determined based on the cross-validation results, and the location of the fault point is determined by combining the cable burial depth and the location information of the autonomous mobile platform.

[0037] Optionally, determining the fault type based on the cross-validation results includes:

[0038] If the ground magnetic field strength characteristics at the fault point are greater than the normal cable magnetic field threshold, and the prior data of the power grid operation indicate that the fault phase voltage is reduced and the zero-sequence current is increased, then it is determined to be a low-resistance grounding fault.

[0039] If the ground magnetic field strength characteristic at the fault point is greater than the normal cable magnetic field threshold, and the waveform continuity characteristic exhibits intermittent pulse leakage characteristics, then it is determined to be a high-resistance grounding fault.

[0040] If the multi-source physical field data contains intermittent magnetic induction signals after the injection of alternating current, and the prior data of the power grid operation indicates that the line is de-energized, then it is determined to be an open circuit fault or a phase-to-phase short circuit fault.

[0041] Optionally, the method is applied to an architecture that includes edge computing nodes and a remote data analysis platform. The edge computing nodes are deployed on the autonomous mobile platform and are used to perform data acquisition, preprocessing, and preliminary analysis. The remote data analysis platform is used to receive the preprocessed data, perform multi-source data deep fusion analysis, and generate reports.

[0042] Optionally, the method further includes: generating an electronic cable path map based on the determined fault location and cable path information;

[0043] The portable printing module is controlled to generate field identification labels containing cable number, path information, and status parameters.

[0044] In addition, this application also provides a cable fault detection and location system, including an autonomous mobile detection terminal and a remote data analysis platform; the autonomous mobile detection terminal is configured to perform the data acquisition and edge processing steps in the cable fault detection and location method described above; the remote data analysis platform is configured to perform the fusion analysis and fault determination steps in the cable fault detection and location method described above.

[0045] In addition, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the cable fault detection and location method as described above.

[0046] Beneficial effects:

[0047] The technical solution provided in this application constructs a dynamic and continuous underground cable status perception foundation by synchronously collecting multi-source physical field data such as pulse, acoustic-magnetic, and power frequency magnetic fields along the line using an autonomous mobile platform. It utilizes a buried three-phase cable current superposition algorithm combined with single-phase grounding fault current to calculate the ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point. Finally, by using their ratio, the influence of multi-phase current vector synthesis on magnetic field measurement is eliminated from the physical mechanism level, achieving accurate calculation of ground magnetic field strength and reverse derivation of burial depth, overcoming the limitations of traditional empirical formulas under complex geological and laying conditions. Furthermore, it conducts multi-dimensional cross-verification of on-site measured physical field characteristics with prior data from power grid operation, establishing a dual confirmation mechanism of "on-site measurement + prior data from dispatching," effectively eliminating environmental noise interference and significantly improving the identification accuracy and positioning precision of various fault types such as low-resistance grounding, high-resistance grounding, and line breaks, providing reliable technical support for the intelligent operation and maintenance of power cables. Attached Figure Description

[0048] Figure 1 This is a flowchart of the cable fault detection and location method according to an embodiment of this application;

[0049] Figure 2 This is a schematic diagram of the cable fault detection and location system according to an embodiment of this application. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0052] Example 1

[0053] like Figure 1 As shown in the figure, this embodiment provides a cable fault detection and location method. This method achieves accurate assessment of underground cable faults by constructing a multi-source physical field sensing and physical model inversion mechanism during the mobile detection process. The method mainly includes the following steps:

[0054] Step S100: Control the autonomous mobile platform to move along the path of the target cable, and simultaneously collect multi-source physical field data during the movement; the multi-source physical field data includes pulse reflection signals, acoustic-magnetic synchronization signals, and power frequency magnetic field signals.

[0055] Specifically, an autonomous mobile platform refers to a mobile carrier with autonomous navigation and path tracking capabilities. Its specific form is unrestricted; for example, it can be a wheeled inspection vehicle, a tracked robot, or a quadrupedal bionic robot, as long as it can adapt to the cable laying environment (such as tunnels, trenches, and ground) and travel along a preset or real-time identified cable path. In this embodiment, "synchronous acquisition" does not merely refer to all sensors triggering sampling at the same time, but emphasizes the spatiotemporal alignment of data acquisition with the spatial position of the mobile platform. During movement, the system acquires the platform's position information in real time through positioning modules such as odometers, inertial measurement units, or lidar, and binds pulse reflection signals, acoustic-magnetic synchronization signals, and power frequency magnetic field signals to the corresponding position coordinates, forming a continuous physical field data stream with spatial tags. This dynamic and continuous acquisition method, compared to traditional fixed-point measurement, can completely capture the gradual characteristics and abrupt changes in the electromagnetic environment along the cable, providing a high-resolution data foundation for the subsequent fine division of fault sections. Among them, the pulse reflection signal is used to initially detect impedance mismatch points, the acoustic-magnetic synchronization signal is used to capture transient sound waves and electromagnetic waves generated by fault discharge, and the power frequency magnetic field signal is used to characterize the steady-state electromagnetic field distribution under cable operating conditions.

[0056] Step S200: Obtain prior power grid operation data for the target cable.

[0057] Prior data for power grid operation refers to historical or real-time data from power grid dispatching systems, distribution automation systems, or fault recording devices, reflecting the electrical state of the target cable before and after a fault. For example, this data may include the fault tripping time, fault phase, zero-sequence current amplitude, grounding type (e.g., metallic grounding, arc grounding), line load current, and historical maintenance records. The value of introducing this external prior information lies in providing a reliable "reference benchmark" or "constraint boundary" for on-site physical field detection. In complex underground environments, relying solely on signals acquired on-site is highly susceptible to interference from nearby pipelines, changes in soil medium, or environmental noise, leading to misjudgments. By correlating on-site measured data with authoritative records from the power grid side, false signals can be effectively eliminated, or, in cases of multiple solutions, the system can quickly converge to the true fault mode, thereby significantly improving the robustness and confidence of the detection system.

[0058] Step S300: Using the buried three-phase cable current superposition algorithm, the ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point are calculated based on the power frequency magnetic field signal and the single-phase ground fault current. The cable burial depth is then inferred from the ratio of the ground magnetic field strength at the fault point to the ground magnetic field strength at the observation point.

[0059] This step is the core physical mechanism for achieving high-precision positioning in this embodiment. The buried three-phase cable current superposition algorithm used in this embodiment fully considers the geometric arrangement of the three-phase conductors in space and the synthesis effect of their current vectors, accurately describing the theoretical total field strength amplitude generated outside the cable by the three-phase unbalanced current (such as during a single-phase ground fault). Based on this, the single-phase ground fault current is further introduced to compensate and correct the theoretical field strength, thereby restoring the true magnetic field strength at the fault point and the ground magnetic field strength at the observation point. Then, using the deterministic physical relationship between the corrected ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point, the vertical distance from the fault point to the cable, i.e., the cable burial depth, is dynamically deduced. This dynamic back-calculation method based on a physical model overcomes the limitations of existing technologies that rely on empirical formulas or assume a fixed burial depth, enabling accurate geometric parameters to be obtained under different geological conditions and different laying depths, laying the foundation for three-dimensional spatial positioning of the fault point.

[0060] Step S400: Perform multi-source fusion comparison of the multi-source physical field data, the cable burial depth, and the prior power grid operation data to determine the fault type and fault location of the target cable.

[0061] After completing the above data acquisition and physical quantity calculation, the system performs multi-dimensional cross-verification and fusion analysis on the pulse, acousto-magnetic, and magnetic field characteristics measured on-site, as well as the dynamically calculated burial depth information, with the prior data of power grid operation acquired in step S200. For example, when an abnormal magnetic field enhancement is detected on-site and the waveform exhibits intermittent pulse characteristics, and prior data shows that the line has a high-resistance grounding alarm, the system can comprehensively determine it as a high-resistance grounding fault; if the pulse reflection on-site shows open-circuit characteristics, and prior data confirms that the line has been de-energized and a detection signal has been injected, it can be confirmed as a line break fault. Through this triple verification mechanism of "on-site measurement + physical model + prior knowledge", it can not only accurately distinguish various complex fault types such as low-resistance grounding, high-resistance grounding, short circuit, and line break, but also combine the precise location information of the autonomous mobile platform and the back-calculated burial depth data to output the three-dimensional coordinates of the fault point in the geographic coordinate system, thereby achieving a leap from "qualitative judgment" to "quantitative location", effectively solving the problems of difficult cable fault location and high misjudgment rate in complex underground environments.

[0062] Example 2

[0063] Based on Example 1, this example further details the acquisition strategy of multi-source physical field data and the magnetic field calculation and burial depth inference process based on the physical model.

[0064] During the movement, multi-source physical field data is collected synchronously, specifically including: triggering the acquisition of pulse reflection signals based on the movement distance as the autonomous mobile platform moves along the path of the target cable; continuously acquiring acoustic-magnetic synchronization signals and power frequency magnetic field signals in real time; acquiring infrared thermal image data at preset time intervals, and the multi-source physical field data also includes infrared thermal image data; filtering and feature extraction of the acquired pulse reflection signals, and noise reduction processing of the acoustic-magnetic synchronization signals to calculate the time difference between sound waves and electromagnetic waves.

[0065] Specifically, this differentiated acquisition triggering mechanism is designed based on the characteristics of each physical field signal and the requirements for fault location. Pulse reflection signals are mainly used to detect cable impedance mismatch points, and their effectiveness is directly related to the spatial sampling density. If a fixed time interval is used for acquisition, fluctuations in the speed of the autonomous moving platform will lead to uneven spatial sampling point density, affecting the continuity of fault point distance calculation. Therefore, this embodiment adopts a pulse transmission and reception method triggered based on the moving distance (e.g., every 0.5 meters or 1 meter), ensuring the uniformity of spatial resolution along the line and providing a foundation for constructing a high-precision cable impedance distribution map. In contrast, acoustic-magnetic synchronization signals and power frequency magnetic field signals have transient or continuous change characteristics, and the arrival time of sound waves and electromagnetic waves generated by fault discharge is extremely short; any sampling gap may lead to the loss of key information. Therefore, these two types of signals adopt a real-time continuous acquisition mode and are aligned with the platform's high-frequency position data using microsecond-level timestamps to ensure accurate capture of the transient characteristics and steady-state field distribution of the fault point even during movement. Infrared thermal imaging data serves as an auxiliary verification method to identify abnormal temperature rises at the ground surface or cable joints. Due to its large data volume and relatively slow temperature changes, it is collected at preset time intervals (e.g., 2 frames per second) to balance data integrity and storage / transmission bandwidth. At the data processing level, bandpass filtering and peak feature extraction of the pulse reflection signal effectively suppress clutter interference caused by soil inhomogeneity. Adaptive noise reduction of the acoustic-magnetic synchronization signal and calculation of the propagation time difference between sound and electromagnetic waves, combined with the known speeds of sound and light, allow for the independent calculation of the horizontal distance to the fault point, providing mutual verification with the pulse reflection results.

[0066] Specifically, this includes: calculating the theoretical total field strength amplitude after superimposing the three-phase asymmetrical currents based on the buried three-phase cable current superposition algorithm;

[0067] The theoretical total field strength amplitude is corrected by introducing the single-phase ground fault current to obtain the ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point.

[0068] The cable burial depth can be inferred by using the ratio of the ground magnetic field strength at the fault point to the ground magnetic field strength at the observation point.

[0069] The core of this step lies in establishing a magnetic field calculation model that conforms to the actual physical environment of underground cables. Traditional single-conductor models neglect current vector synthesis, leading to significant calculation errors in deep-buried or highly shielded scenarios. This embodiment employs a buried three-phase cable current superposition algorithm. First, based on the spatial symmetry of the three conductors and the current asymmetry during a fault, the theoretical total field strength amplitude under unshielded conditions is calculated. Then, the single-phase ground fault current is introduced to correct the theoretical total field strength amplitude, yielding the ground magnetic field strength at the fault point and the observation point. By correcting the theoretical field strength of the single-phase ground fault current, the ground magnetic field strength at the fault point and the observation point, which can be measured by sensors, are reconstructed. Finally, using the deterministic physical relationship between the corrected ratio of the ground magnetic field strength at the fault point and the observation point, the vertical distance from the measuring point to the cable is dynamically deduced. This refers to the cable burial depth. This back-calculation method based on physical mechanisms overcomes the limitations of empirical formulas that rely on specific geological conditions, and achieves adaptive calibration of burial depth parameters under different laying environments.

[0070] The theoretical total field strength amplitude after superimposing the above three-phase unbalanced currents is calculated, and the ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point are corrected as follows:

[0071] The algorithm for superimposing the currents of the buried three-phase cable is as follows:

[0072] ; ①

[0073] Introducing the single-phase ground fault current The single-phase ground fault current Equal to three times the zero-sequence current ,Right now Further, at a horizontal distance from the fault point Select an observation point and calculate the spatial distance from the observation point to the center of the target cable using trigonometric functions;

[0074] Substituting the single-phase ground fault current and the spatial distance from the observation point to the cable center into formula ①, the ground magnetic field strength at the observation point is obtained, as follows:

[0075] ;②

[0076] make The ground magnetic field strength at the fault point is calculated by substituting it into formula ②, as shown below:

[0077] ;③

[0078] The distance between the target cable and the ground is calculated using the ratio of Formula ③ to Formula ②, as shown below:

[0079] ④

[0080] Simplifying formula ④, we get:

[0081] ⑤

[0082] in, This represents the theoretical total field strength amplitude. The permeability of free space, The distance between the target cable and the ground, i.e., the cable burial depth. The ground magnetic field strength at the observation point, The ground magnetic field strength at the fault point. This is the single-phase ground fault current. It is the zero-sequence current.

[0083] The above calculation method constitutes a complete logical chain of "theoretical modeling - engineering correction - parameter inversion", which ensures the calculation accuracy and physical interpretability of cable burial depth and fault location in complex underground environments.

[0084] Example 3

[0085] Building upon Example 1, this example further details the multi-source data cross-validation mechanism and the rules for accurate fault type identification. It involves fusing and comparing multi-source physical field data, cable burial depth, and prior power grid operation data to determine the fault type and location of the target cable. Specifically, this includes: extracting the ground magnetic field strength and waveform continuity characteristics of the fault point from the multi-source physical field data; comparing the ground magnetic field strength of the fault point with the magnetic field threshold of a normal cable to obtain a first comparison result; cross-validating the waveform continuity characteristics, the first comparison result, and the prior power grid operation data; determining the fault type based on the cross-validation result; and determining the fault location by combining the cable burial depth and the location information of the autonomous mobile platform.

[0086] Specifically, feature extraction is a crucial step in transforming raw sensor data into structured indicators that can be used for logical judgment. The ground magnetic field strength at the fault point typically refers to the effective or peak value of the power frequency magnetic field after correction using the physical model described in Example 2. It reflects the magnitude of the steady-state electromagnetic field energy generated by the fault current. Waveform continuity characteristics are a statistical description of the time-domain morphology of the magnetic field signal within a certain time window, used to characterize the stability of fault discharge. For example, a continuous sine wave represents a stable metallic connection, while a chaotic or periodically interrupted waveform suggests poor contact or arc discharge. The normal cable magnetic field threshold is the baseline for distinguishing between normal load conditions and abnormal fault conditions. This threshold is not fixed but can be dynamically set according to the voltage level, rated load current, and historical health records of the target cable. For example, for a 10kV three-core armored cable, the comprehensive surface magnetic field during normal operation is typically between 0.5μT and 2μT; exceeding this range is considered abnormal. The core of the cross-validation mechanism lies in constructing a three-dimensional verification space of "on-site measurement + waveform characteristics + scheduling prior," utilizing the complementarity and exclusivity of information from different dimensions to eliminate misjudgments. For example, when an enhanced magnetic field is detected on-site but the waveform remains continuous and stable, and prior data indicates that the line is under overload rather than a ground fault, the system can rule out a ground fault, avoiding misreporting normal temperature rises or enhanced magnetic fields caused by overload as faults. Conversely, if the magnetic field is abnormal and the waveform exhibits typical fault characteristics, even if prior data has not been updated due to communication delays, the system can still provide a high-confidence warning based on on-site evidence. This multi-dimensional fusion strategy effectively overcomes the shortcomings of single signal sources being susceptible to interference and lacking complete information in complex underground environments, significantly improving the robustness of fault assessment.

[0087] The fault type is determined based on the cross-validation results, following specific discrimination rules:

[0088] If the ground magnetic field strength at the fault point is greater than the normal cable magnetic field threshold, and prior data from the power grid operation indicates a decrease in the fault phase voltage and an increase in the zero-sequence current, then it is determined to be a low-resistance grounding fault. This is because a low-resistance or metallic grounding fault will cause a sharp drop in the impedance of the fault phase to ground, generating a large-amplitude continuous grounding current. This current forms a steady-state magnetic field around the cable that is significantly stronger than the normal level, and at the same time, it manifests as a significant zero-sequence component and voltage shift on the power grid side. The simultaneous fulfillment of both conditions constitutes the necessary and sufficient condition for a low-resistance grounding fault, ruling out the possibility of induced current or interference from nearby pipelines.

[0089] If the ground magnetic field strength at the fault point is greater than the normal cable magnetic field threshold, and the waveform exhibits intermittent pulse leakage characteristics, it is determined to be a high-resistance grounding fault. High-resistance grounding is usually caused by insulation aging, moisture, or dendritic discharge. Its fault point resistance is large and unstable, resulting in a small fault current accompanied by repeated reignition and extinction of the arc. This physical process manifests in the magnetic field signal as irregular high-frequency pulses or amplitude modulation superimposed on the power frequency fundamental wave, forming a stark contrast to the stable waveform of low-resistance grounding. By capturing this unique waveform fingerprint, even if the zero-sequence current is not obvious in the prior data or the protection action is not triggered, the system can still accurately identify the highly concealed high-resistance fault.

[0090] If the multi-source physical field data after injecting alternating current contains discontinuous magnetic induction signals, and the prior data of the power grid operation indicates a line outage, then it is determined to be an open-circuit fault or a phase-to-phase short-circuit fault. When such a fault occurs, the line has lost its operating voltage and cannot generate a power frequency magnetic field; therefore, it must rely on an externally injected signal source for detection. When the autonomous mobile platform detects a sudden change, attenuation, or interruption in the magnetic field generated by the injected signal, and this phenomenon matches the outage state, it can be confirmed that the conductor continuity has been disrupted or that a low-resistance phase-to-phase path exists. This rule effectively distinguishes between real faults and normal outages during power outage maintenance, avoiding blindly searching for invalid signals in a powerless state.

[0091] It should be understood that the aforementioned discrimination rules constitute a hierarchical fault diagnosis knowledge base, which not only covers common grounding and open-circuit faults, but also solves the technical problems of easy confusion between low and high resistance and difficulty in confirming power outage faults in traditional methods by introducing a combination of waveform continuity and prior data. In practical applications, this rule base can also adaptively iterate based on field feedback, such as optimizing threshold settings or adding new fault modes through machine learning algorithms, thereby continuously improving the system's adaptability to complex operating conditions and diagnostic accuracy.

[0092] Example 4

[0093] In some embodiments, the cable fault detection and location method is applied in an architecture that includes edge computing nodes and a remote data analysis platform. The edge computing nodes are deployed on an autonomous mobile platform to perform data acquisition, preprocessing, and preliminary analysis; the remote data analysis platform receives the preprocessed data, performs multi-source data deep fusion analysis, and generates reports. Specifically, this edge-cloud collaborative architecture is designed to resolve the contradiction between real-time response and in-depth analysis in underground cable detection scenarios. The edge computing nodes on the autonomous mobile platform are limited by size and power consumption, resulting in relatively limited computing resources, but they possess extremely low data transmission latency. Therefore, the edge side is mainly responsible for cleaning and lightweighting the raw sensor data, such as bandpass filtering the pulse reflection signal to remove environmental noise, denoising the acoustic-magnetic synchronization signal and extracting time difference features, and performing preliminary threshold screening on the power frequency magnetic field data. These preprocessing operations can compress massive amounts of raw waveform data into structured feature vectors, transmitting only key anomaly segments and feature parameters back via wireless network, thereby significantly reducing communication bandwidth usage and improving the system's real-time alarm capabilities. In contrast, remote data analysis platforms possess ample computing resources and a complete historical database, enabling them to perform complex correlation analysis and model inference on uploaded feature data. For example, the platform can perform multi-dimensional fusion and comparison between the magnetic field characteristics extracted on-site and the load curves, fault waveform records, and operating status of other cables in the same area over the past year. Using deep learning models, it can identify subtle high-resistance grounding or complex fault modes that are difficult to determine on the edge side, and ultimately generate a comprehensive analysis report including fault type, precise coordinates, confidence level assessment, and maintenance recommendations. Through this division of labor, the system ensures both real-time perception and rapid response during mobile detection, while also guaranteeing the authority and accuracy of the final diagnostic results.

[0094] As a further extension of this embodiment, the method also includes: generating an electronic cable path map based on the determined fault location and cable path information; and controlling a portable printing module to generate on-site identification labels containing cable number, path information, and status parameters. This step realizes a closed loop of operation and maintenance information from digital space to physical space. In traditional cable inspection operations, there is often a problem of inconsistency between electronic ledgers and actual on-site markings, making it difficult for subsequent repair personnel to quickly locate the target. This embodiment integrates a portable printing module on an autonomous mobile platform, enabling the detection terminal to instantly output physical labels that are strictly consistent with the electronic map data while completing fault location and path mapping. Specifically, after the system determines the three-dimensional coordinates of the fault point and constructs a complete cable path trajectory using the method of the aforementioned embodiment, it automatically generates a standardized electronic cable path map, which includes key geographical information such as cable direction, burial depth changes, joint locations, and fault point markings. At the same time, the system drives the portable printing module to print exclusive identification labels on-site based on the currently detected cable attributes (such as voltage level, model, commissioning date) and real-time status parameters (such as insulation resistance value and partial discharge level). The label is printed with a unique QR code or barcode, which, when scanned, links to an electronic map and detailed inspection report stored in the cloud. This linkage mechanism not only ensures the timeliness and accuracy of on-site labeling information but also provides a reliable physical anchor for subsequent digital operation and maintenance, effectively avoiding management blind spots caused by manual recording errors or lost labels. It should be understood that the specific form of the portable printing module is not limited; it can be a thermal printer, inkjet printing module, or other miniature printing device suitable for mobile platforms, as long as it can achieve real-time generation of on-site labels and data consistency verification.

[0095] Example 5

[0096] like Figure 2 As shown, this embodiment provides a cable fault detection and location system. This system, as the hardware implementation carrier of the aforementioned method embodiment, mainly includes an autonomous mobile detection terminal and a remote data analysis platform. The autonomous mobile detection terminal is a concrete form of the aforementioned autonomous mobile platform, configured to perform data acquisition and edge processing steps. Specifically, the autonomous mobile detection terminal integrates multi-sensor components and an edge computing module. The multi-sensor components include, but are not limited to, a power frequency magnetic field tester, an acoustic-magnetic synchronization sensor, a pulse reflector, an infrared thermal imager, and a high-definition camera, used to simultaneously acquire multi-source physical field data during movement. The edge computing module is typically composed of an embedded processor or AI acceleration chip, internally containing corresponding data processing programs. It can perform signal filtering, noise reduction, feature extraction, and preliminary fault judgment logic locally in real time, thereby reducing the amount of invalid data transmitted and improving the system's real-time response capability. It should be understood that although... Figure 2The example shows various sensors integrated into the same terminal body, but in other embodiments, some sensors can also be connected externally via wired or wireless means, as long as synchronous data acquisition and position alignment can be achieved.

[0097] The remote data analysis platform is configured to perform fusion analysis and fault determination steps. Specifically, the remote data analysis platform is deployed on a cloud server or a local monitoring center and includes a fusion analysis module, a database management module, and a visualization module. The fusion analysis module establishes a communication connection with the autonomous mobile detection terminal via a wireless network, receives structured feature data preprocessed by the edge computing module, and combines it with prior power grid operation data obtained from the power grid dispatching system to perform multi-dimensional cross-validation and deep inference, ultimately determining the fault type and precise location. This collaborative architecture between the terminal and the platform ensures both the flexibility and immediacy of on-site detection while utilizing the powerful computing power and historical data resources of the cloud, achieving an optimal balance between detection accuracy and efficiency.

[0098] Furthermore, this embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the cable fault detection and location method as described in any one of embodiments 1 to 4. Specifically, the computer-readable storage medium can be a tangible non-transitory storage device, such as a read-only memory (ROM), random access memory (RAM), disk, optical disk, or flash memory. When the processor loads and executes the program, it can drive the corresponding hardware units in an autonomous mobile detection terminal or remote data analysis platform to complete a series of technical actions, including multi-source physical field data acquisition, magnetic field model calculation, burial depth back-calculation, and multi-source fusion comparison. In this way, the technical solution of this application can not only be embodied in a specific system device, but also in a software product or firmware update package, facilitating deployment and upgrades on different types of mobile detection devices, further expanding the application scope and protection dimensions of the technical solution.

[0099] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting and locating cable faults, characterized in that, include: Control the autonomous mobile platform to move along the path of the target cable, and simultaneously collect multi-source physical field data during the movement; The multi-source physical field data includes pulse reflection signals, acoustic-magnetic synchronization signals, and power frequency magnetic field signals; Obtain prior power grid operation data for the target cable; The buried three-phase cable current superposition algorithm calculates the ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point based on the power frequency magnetic field signal and the single-phase ground fault current, and inversely infers the cable burial depth based on the ratio of the ground magnetic field strength at the fault point to the ground magnetic field strength at the observation point. The multi-source physical field data, the cable burial depth, and the prior data of power grid operation are fused and compared to determine the fault type and fault location of the target cable.

2. The method according to claim 1, characterized in that, The simultaneous acquisition of multi-source physical field data during movement includes: As the autonomous mobile platform moves along the path of the target cable, the pulse reflection signal is collected based on the distance traveled. The acoustic-magnetic synchronization signal and the power frequency magnetic field signal are continuously acquired in real time. Infrared thermal image data is collected at preset time intervals, and the multi-source physical field data also includes the infrared thermal image data; The acquired pulse reflection signal is filtered and its features are extracted, and the acoustic-magnetic synchronization signal is denoised to calculate the time difference between the sound wave and the electromagnetic wave.

3. The method according to claim 2, characterized in that, The method of using the buried three-phase cable current superposition algorithm to calculate the ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point based on the power frequency magnetic field signal and the single-phase ground fault current, and then using the ratio of the ground magnetic field strength at the fault point to the ground magnetic field strength at the observation point to inversely calculate the cable burial depth, includes: Based on the buried three-phase cable current superposition algorithm, the theoretical total field strength amplitude after superposition of three-phase unbalanced currents is calculated. The theoretical total field strength amplitude is corrected by introducing the single-phase ground fault current to obtain the ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point. The cable burial depth can be inferred by using the ratio of the ground magnetic field strength at the fault point to the ground magnetic field strength at the observation point.

4. The method according to claim 3, characterized in that, The theoretical total field strength amplitude after superposition of three-phase unbalanced currents is calculated, and the ground magnetic field strength at the fault point and the ground magnetic field strength at the observation point are corrected accordingly. The algorithm for superimposing the currents of the buried three-phase cable is as follows: ；① Introducing the single-phase ground fault current The single-phase ground fault current Equal to three times the zero-sequence current ,Right now Further, at a horizontal distance from the fault point Select an observation point and calculate the spatial distance from the observation point to the center of the target cable using trigonometric functions; Substituting the single-phase ground fault current and the spatial distance from the observation point to the cable center into formula ①, the ground magnetic field strength at the observation point is obtained, as follows: ；② make The ground magnetic field strength at the fault point is calculated by substituting it into formula ②, as shown below: ；③ The distance between the target cable and the ground is calculated using the ratio of Formula ③ to Formula ②, as shown below: ；④ Simplifying formula ④, we get: ；⑤ in, This represents the theoretical total field strength amplitude. The permeability of free space, The distance between the target cable and the ground, i.e., the cable burial depth. The ground magnetic field strength at the observation point, The ground magnetic field strength at the fault point. This is the single-phase ground fault current. It is the zero-sequence current.

5. The method according to claim 1, characterized in that, The step of performing multi-source fusion comparison of the multi-source physical field data, the cable burial depth, and the prior power grid operation data to determine the fault type and fault location of the target cable includes: Extract the ground magnetic field strength and waveform continuity characteristics of the fault point from the multi-source physical field data; The ground magnetic field strength characteristics at the fault point are compared with the magnetic field threshold of a normal cable to obtain the first comparison result; The waveform continuity characteristics, the first comparison result, and the prior data of power grid operation are cross-validated. The fault type is determined based on the cross-validation results, and the location of the fault point is determined by combining the cable burial depth and the location information of the autonomous mobile platform.

6. The method according to claim 5, characterized in that, Determining the fault type based on cross-validation results includes: If the ground magnetic field strength characteristics at the fault point are greater than the normal cable magnetic field threshold, and the prior data of the power grid operation indicate that the fault phase voltage is reduced and the zero-sequence current is increased, then it is determined to be a low-resistance grounding fault. If the ground magnetic field strength characteristic at the fault point is greater than the normal cable magnetic field threshold, and the waveform continuity characteristic exhibits intermittent pulse leakage characteristics, then it is determined to be a high-resistance grounding fault. If the multi-source physical field data contains intermittent magnetic induction signals after the injection of alternating current, and the prior data of the power grid operation indicates that the line is de-energized, then it is determined to be an open circuit fault or a phase-to-phase short circuit fault.

7. The method according to claim 1, characterized in that, The method is applied to an architecture that includes edge computing nodes and a remote data analysis platform. The edge computing nodes are deployed on the autonomous mobile platform and are used to perform data acquisition, preprocessing, and preliminary analysis. The remote data analysis platform is used to receive the preprocessed data, perform multi-source data deep fusion analysis, and generate reports.

8. The method according to claim 7, characterized in that, The method further includes: Based on the determined fault location and cable path information, an electronic cable path map is generated. The portable printing module is controlled to generate field identification labels containing cable number, path information, and status parameters.

9. A cable fault detection and location system, characterized in that, include: The autonomous mobile detection terminal is configured to perform data acquisition and edge processing steps; The remote data analysis platform is configured to perform fusion analysis and fault diagnosis steps.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the cable fault detection and location method as described in any one of claims 1 to 8.

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