Distribution line passive real-time fault positioning system

Abstract

The invention discloses a power distribution line passive real-time fault positioning system, which belongs to the technical field of power distribution line fault detection and comprises a power distribution line fault positioning device management system and a plurality of fault positioning terminals distributed along a power distribution line. The distribution line fault positioning device management system establishes two-way communication with each fault positioning terminal through a 4G / 5G wireless public network; the distribution line fault positioning device management system comprises a data center, a client and a plurality of mobile terminals. The fault positioning terminal comprises an ARM processor, a time synchronization unit, a signal acquisition unit, a signal processing unit, a passive self-powered unit, a communication unit and an auxiliary unit. According to the invention, the distributed fault positioning terminal is combined with an intelligent algorithm, so that high-precision fault recognition and real-time positioning under a complex network are realized, and the fault handling efficiency of the power distribution network is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution line fault detection technology, and in particular to a passive real-time fault location system for power distribution lines. Background Technology

[0002] With the gradual development of new power grid construction, the year-on-year increase in user load, and the integration of multiple power sources, the power grid is exhibiting new characteristics in terms of power supply structure, grid form, load characteristics, and operating characteristics. Fault location plays a crucial role in ensuring power supply security, preventing major accidents, reducing power outage time, improving operational efficiency, and lowering maintenance costs.

[0003] Due to their relatively simple structure, transmission lines are widely used for fault location due to the traveling wave method's advantages of fast location speed and high accuracy. However, for distribution lines, the application of the relatively mature impedance method and traveling wave method for transmission lines is limited due to their complex network structure and diverse fault types. These methods often suffer from problems such as being time-consuming and labor-intensive, inaccurate fault type identification, low location accuracy, and long response time.

[0004] Therefore, there is an urgent need to research and develop a new type of efficient and accurate passive real-time fault location system for power distribution lines. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a passive real-time fault location system for power distribution lines. By rationally arranging detection points along the line and employing a dual-end B-type traveling wave ranging algorithm and a multi-mode communication architecture, high-precision fault location can be achieved in complex power distribution networks.

[0006] The technical solution adopted in this invention is: A passive real-time fault location system for power distribution lines includes a power distribution line fault location device management system and multiple fault location terminals distributed along the power distribution lines. The power distribution line fault location device management system establishes two-way communication with each fault location terminal through a 4G / 5G wireless public network. The power distribution line fault location device management system includes a data center, a client and multiple mobile terminals, which are used for hierarchical permission management, line topology relationship maintenance, remote terminal configuration, fault identification and location and alarm information push. The fault location terminal includes an ARM processor, a time synchronization unit, a signal acquisition unit, a signal processing unit, a self-powered unit, a communication unit, and an auxiliary unit. It is used to perform real-time signal acquisition and processing, and to trigger the identification and location of power distribution line faults through the uploaded fault signals. When a line fault occurs, the fault location terminal located on the branch closest to the fault point detects the traveling wave sudden change signal through the signal acquisition unit and triggers the power distribution line fault identification and location. Each fault location terminal records the arrival time stamp of the traveling wave synchronously through the time synchronization unit. The signal processing unit performs wavelet decomposition on the traveling wave signal to extract fault features. The communication unit uploads the fault features and timestamp to the power distribution line fault location device management system. After receiving the fault characteristics and timestamp, the data center of the power distribution line fault location device management system performs double-ended B-type traveling wave ranging to calculate the fault location based on the power distribution line topology. At the same time, it matches the fault type through a lightweight convolutional neural network model. The client dynamically marks the fault location in the three-dimensional digital twin model and generates a fault location report by associating it with historical fault handling plans. Alarm information and navigation paths are pushed to the mobile devices of maintenance personnel simultaneously.

[0007] Furthermore, the data center is configured with a database, an application server, and a front-end communication management unit to store the topology of the power distribution lines, terminal files, and historical fault records, and to perform fault location and identification through the application server; The client is equipped with a terminal management module, a fault notification module, and a line topology visualization module, which supports multi-level user permission control, remote terminal data transmission, and dynamic map display of line fault status. The mobile terminal integrates a mobile app, providing real-time alarm push notifications, fault details queries, and location result navigation functions.

[0008] Furthermore, the application server includes a fault identification module and a fault location module; The fault identification module has a built-in lightweight convolutional neural network model that classifies fault types based on fault characteristics. The fault location module is equipped with a dual-end B-type traveling wave ranging calculation unit, which calculates the fault location in combination with the line topology.

[0009] Furthermore, the dual-ended B-type traveling wave ranging calculation unit includes: The spatiotemporal coordinate system construction module uses the absolute timestamp obtained from the BeiDou / GPS timing signal and combines it with the line topology to construct a spatiotemporal coordinate system with the fault location terminal at the beginning as the origin. The traveling wave velocity calibration module constructs a traveling wave propagation model based on line topology characteristic parameters. The propagation model is as follows: (1) In formula (1), For traveling wave speed, This refers to the total length of the line between two adjacent fault location terminals. The time difference between the arrival of the traveling wave at two adjacent fault location terminals; The least squares method is used to eliminate the traveling wave velocity deviation caused by line parameter fluctuations, and the calibrated wave velocity is output. ; The fault traveling wave energy analysis module performs wavelet energy spectrum analysis on the initial traveling wave detected by two adjacent fault location terminals and calculates the energy ratio of the reflected wave to the transmitted wave at the fault point. The topology path compensation module performs dynamic compensation based on line topology data. It combines the fault traveling wave arrival time difference and the corrected traveling wave propagation velocity with a traveling wave ranging algorithm to locate the fault. The calculation formula is as follows: (2) In formula (2), This represents the distance from the fault location point to the first-end fault location terminal. The calibrated traveling wave velocity, This represents the energy ratio of the reflected wave to the transmitted wave at the fault point. The line attenuation coefficient is... This is the compensation factor for the length of the nearest branch line.

[0010] Furthermore, the ARM processor uses a Cortex-A72 high-speed processor, which is responsible for coordinating the task scheduling and data processing of each functional module; The time synchronization unit includes a BeiDou / GPS timing module and a clock crystal module, which are used for time synchronization and provide a reference signal and time standard for the fault location terminal. The signal acquisition unit is equipped with a wideband Rogowski coil, which couples the current signal of the circuit through electromagnetic induction. The signal processing unit includes an analog-to-digital conversion module and a signal processing module. Through the parallel processing pipeline of the FPGA processor, the acquired current signal is decomposed by wavelet and fault features are extracted by energy integration. The self-powered unit includes an inductive power extraction module, a charging management module, and a lithium battery. It supplies power through electromagnetic induction power extraction coil coupled with line current, and switches to lithium battery power supply when the inductive power is insufficient through the charging and discharging management module. The communication unit includes a multi-mode communication module and a local debugging interface. The multi-mode communication module supports 5G, LoRa and RS485 multi-mode communication. The local debugging interface includes a USB3.0 and a Type-C interface for OTA firmware upgrades and local parameter configuration. The auxiliary unit is equipped with a magnetic base and has a built-in acceleration sensor. When abnormal vibration is detected, it triggers a local audible and visual alarm and reports the abnormal status through the NB-IoT network.

[0011] Furthermore, the signal processing module includes: The wavelet decomposition module, based on the high-frequency characteristics of the fault traveling wave signal, performs a 4-level decomposition using the db4 wavelet basis to capture the abrupt change features of the traveling wave. The energy entropy calculation module performs energy integration on the wavelet coefficients of each decomposed layer, calculates the energy proportion of each frequency band, and constructs the energy entropy index. (3) In formula (3), For the first Layer energy entropy, For the first Layer The normalized energy value of each wavelet coefficient. This represents the total number of coefficients in the current layer. The fault feature extraction module selects the frequency band containing the maximum energy entropy as the fault feature frequency band by comparing the energy entropy distribution difference between the faulty line and the non-faulty line.

[0012] Furthermore, the multi-mode communication module adopts a layered communication architecture, including: The upward communication unit includes a 5G communication module that supports the Sub-6GHz frequency band and SA / NSA networking mode, and transmits fault alarm information and location results through a dynamic spectrum allocation algorithm; The downward communication unit includes a LoRa spread spectrum communication module, an RS485 bus interface, and a communication protocol conversion module. The LoRa spread spectrum communication module supports adaptive adjustment of the spread spectrum factor from SF7 to SF12 and establishes a wireless communication link with the fault location terminal through the LoRaWAN protocol. The RS485 bus interface transmits data with the fault location terminal through the Modbus-RTU communication protocol. The communication protocol conversion module integrates a dual-protocol parsing engine for bidirectional conversion between the LoRaWAN protocol and the Modbus-RTU communication protocol.

[0013] The beneficial effects of this invention are: By combining inductive power supply with lithium battery energy storage, the equipment is freed from dependence on external power sources, solving the problem of monitoring blind spots caused by unstable power supply in traditional monitoring equipment. At the same time, the charging and discharging management module enables intelligent energy switching, significantly improving the equipment's continuous operation capability in complex environments and reducing operation and maintenance costs. By capturing the traveling waves generated during a fault and combining the time-frequency localization performance of wavelet transform, the sensitivity to interference signals such as normal load current and voltage fluctuations is reduced, resulting in strong anti-interference capability and accurate identification of fault points. By combining the traveling wave ranging algorithm with the path compensation mechanism, and through high-speed data acquisition and processing, the location of the fault point can be quickly calculated, which greatly shortens the time for fault diagnosis and handling. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the structure of a passive real-time fault location system for power distribution lines provided in the embodiment; Figure 2 This is a schematic diagram of the structure of the dual-ended B-type traveling wave ranging calculation unit provided in the embodiment; Figure 3 This is a schematic diagram of the fault location terminal provided in the embodiment. Detailed Implementation

[0015] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0016] Example 1 Reference Figure 1 A passive real-time fault location system for power distribution lines includes a power distribution line fault location device management system and multiple fault location terminals distributed along the power distribution lines. The power distribution line fault location device management system establishes two-way communication with each fault location terminal through a 4G / 5G wireless public network. The power distribution line fault location device management system includes a data center, a client and multiple mobile terminals, which are used for hierarchical permission management, line topology relationship maintenance, remote terminal configuration, fault identification and location and alarm information push. The fault location terminal includes an ARM processor, a time synchronization unit, a signal acquisition unit, a signal processing unit, a self-powered unit, a communication unit, and an auxiliary unit. It is used to perform real-time signal acquisition and processing, and to trigger the identification and location of power distribution line faults through the uploaded fault signals. When a line fault occurs, the fault location terminal located on the branch closest to the fault point detects the traveling wave sudden change signal through the signal acquisition unit and triggers the power distribution line fault identification and location. Each fault location terminal records the arrival time stamp of the traveling wave synchronously through the time synchronization unit. The signal processing unit performs wavelet decomposition on the traveling wave signal to extract fault features. The communication unit uploads the fault features and timestamp to the power distribution line fault location device management system. After receiving the fault characteristics and timestamp, the data center of the power distribution line fault location device management system performs double-ended B-type traveling wave ranging to calculate the fault location based on the power distribution line topology. At the same time, it matches the fault type through a lightweight convolutional neural network model. The client dynamically marks the fault location in the three-dimensional digital twin model and generates a fault location report by associating it with historical fault handling plans. Alarm information and navigation paths are pushed to the mobile devices of maintenance personnel simultaneously.

[0017] Specifically, when deploying fault location terminals, they are deployed along segment points and connection points to cover risk nodes on the main line. Then, monitoring points are increased in areas with high load and frequent faults, while redundant configurations are reduced in areas with low load density and fewer faults to reduce deployment costs.

[0018] In this embodiment, the data center is configured with a database, an application server, and a front-end communication management unit to store the topology of the power distribution lines, terminal files, and historical fault records, and to perform fault location and identification through the application server. The client is equipped with a terminal management module, a fault notification module, and a line topology visualization module, which supports multi-level user permission control, remote terminal data transmission, and dynamic map display of line fault status. The mobile terminal integrates a mobile app, providing real-time alarm push notifications, fault details queries, and location result navigation functions.

[0019] In this embodiment, the application server includes a fault identification module and a fault location module; The fault identification module has a built-in lightweight convolutional neural network model that classifies fault types based on fault characteristics. The fault location module is equipped with a dual-end B-type traveling wave ranging calculation unit, which calculates the fault location in combination with the line topology.

[0020] Specifically, the database includes a MySQL database and a Neo4j graph database. The MySQL database stores device metadata and historical fault data, while the Neo4j graph database dynamically maintains the distribution network topology and supports real-time updates of key nodes such as segment points and tie points. The application server has a built-in lightweight convolutional neural network model and a dual-ended B-type traveling wave ranging calculation unit to stream-process fault characteristic waveforms, locate fault locations based on line topology, and identify fault types. The front-end communication management unit uses multi-protocol interfaces to support Ethernet, RS485, LoRa, and 4G / 5G communication, and uses the national cryptographic SM4 algorithm to achieve two-way authentication and communication encryption to prevent unauthorized terminal access.

[0021] Specifically, the terminal management module implements three-level permission management based on the RBAC permission model, with permission levels divided into system administrator, regional maintenance personnel, and inspection personnel. System administrators can add or delete terminals and configure all line topologies, regional maintenance personnel can only manage terminals bound to their region, and inspection personnel can only view terminal status and have no configuration permissions. When a user initiates an operation request, their identity is verified through a JWT token, and the topology operation permissions in the Neo4j graph database are checked in real time. Terminal configuration data adopts a differential update mechanism, which compares the hash values ​​of the old and new configuration files and only sends the difference data packets to the target terminal. The fault notification module adopts a multi-level alarm push engine and automatically associates the responsible maintenance personnel based on the topology attribution relationship. The line topology visualization module integrates a WebGL rendering engine, overlays a fault probability cloud map on a 3D map, and pops up a fault diagnosis report card when a fault point is clicked.

[0022] Specifically, the mobile fault location navigation uses GPS / BeiDou positioning data from the mobile phone and an electronic compass, overlaying the coordinates of the power distribution towers to construct a spatial coordinate system, and dynamically generates directional arrows and distance markers pointing to the fault point in the three-dimensional view.

[0023] Please refer to Figure 2 The dual-ended B-type traveling wave ranging calculation unit described in this embodiment includes: The dual-ended B-type traveling wave ranging calculation unit includes: The spatiotemporal coordinate system construction module uses the absolute timestamp obtained from the BeiDou / GPS timing signal and combines it with the line topology to construct a spatiotemporal coordinate system with the fault location terminal at the beginning as the origin. The traveling wave velocity calibration module constructs a traveling wave propagation model based on line topology characteristic parameters. The propagation model is as follows: (1) In formula (1), For traveling wave speed, This refers to the total length of the line between two adjacent fault location terminals. The time difference between the arrival of the traveling wave at two adjacent fault location terminals; The least squares method is used to eliminate the traveling wave velocity deviation caused by line parameter fluctuations, and the calibrated wave velocity is output. ; The fault traveling wave energy analysis module performs wavelet energy spectrum analysis on the initial traveling wave detected by two adjacent fault location terminals and calculates the energy ratio of the reflected wave to the transmitted wave at the fault point. The topology path compensation module performs dynamic compensation based on line topology data. It combines the fault traveling wave arrival time difference and the corrected traveling wave propagation velocity with a traveling wave ranging algorithm to locate the fault. The calculation formula is as follows: (2) In formula (2), This represents the distance from the fault location point to the first-end fault location terminal. The calibrated traveling wave velocity, This represents the energy ratio of the reflected wave to the transmitted wave at the fault point. The line attenuation coefficient is... This is the compensation factor for the length of the nearest branch line.

[0024] Specifically, this invention uses a fault location terminal combined with line parameters integrated in real time from a three-dimensional digital twin distribution network model to locate faults in the distribution network. The timestamp of the first traveling wave arriving at the location terminal at each detection point is synchronized via BeiDou / GPS time synchronization to reduce measurement time difference errors. The distance from the fault point to the beginning of the line is calculated by the round-trip time of the fault traveling wave and the line length, and it can determine whether the fault point is located on the main line. When the fault point is located on a branch line, the distance from the fault point to the branch connection point is calculated by the time difference between the beginning of the main line and the end of the branch line, combined with topology parameters, thus achieving accurate location in complex branch networks of the distribution network.

[0025] Reference Figure 3 The ARM processor described in this embodiment uses a Cortex-A72 high-speed processor, which is responsible for coordinating the task scheduling and data processing of each functional module; The time synchronization unit includes a BeiDou / GPS timing module and a clock crystal module, which are used for time synchronization and provide a reference signal and time standard for the fault location terminal. The signal acquisition unit is equipped with a wideband Rogowski coil, which couples the current signal of the circuit through electromagnetic induction. The signal processing unit includes an analog-to-digital conversion module and a signal processing module. Through the parallel processing pipeline of the FPGA processor, the acquired current signal is decomposed by wavelet and fault features are extracted by energy integration. The self-powered unit includes an inductive power extraction module, a charging management module, and a lithium battery. It supplies power through electromagnetic induction power extraction coil coupled with line current, and switches to lithium battery power supply when the inductive power is insufficient through the charging and discharging management module. The communication unit includes a multi-mode communication module and a local debugging interface. The multi-mode communication module supports 5G, LoRa and RS485 multi-mode communication. The local debugging interface includes a USB3.0 and a Type-C interface for OTA firmware upgrades and local parameter configuration. The auxiliary unit is equipped with a magnetic base and has a built-in acceleration sensor. When abnormal vibration is detected, it triggers a local audible and visual alarm and reports the abnormal status through the NB-IoT network.

[0026] Specifically, the clock crystal oscillator module uses the AiP8025T chip, which integrates a 32.768KHz crystal and an RTC real-time clock chip into a single package. It is a TCXO that can be precisely temperature compensated and corrected within a full temperature range of -40 to 85℃. It features high precision, wide temperature range, support for IIC bus (400K), timed alarm, automatic leap year adjustment, fixed-period timed interrupt, time update interrupt, and second pulse output, achieving high-precision time synchronization. The BeiDou / GPS timing module provides a nanosecond-level clock reference for traveling wave ranging through BeiDou timing with a high-precision atomic clock.

[0027] Rogowski coils are composed of a toroidal coil uniformly wound on a non-ferromagnetic material. They can be directly placed on the conductor being measured to perform non-contact current signal acquisition. They are suitable for AC current measurement with a wide frequency bandwidth and linearity range, and can measure a large current range. They are suitable for high-frequency and high-current measurements, and have no special requirements for conductors or dimensions.

[0028] The analog-to-digital conversion module converts the acquired current signal into a digital signal, uses wavelet transform technology to denoise the acquired data, extracts feature information from the signal, and determines steady-state and transient signals. It identifies fault transient signals by detecting transient abrupt changes, and accurately detects fault transient signals with singularity and transientity by selecting appropriate wavelet basis functions and decomposition levels. It captures weak changes in current and voltage signals and extracts feature information closely related to distinguishing fault types.

[0029] The inductive power module is based on the principle of electromagnetic induction. It induces electrical energy from the magnetic field generated by the current. After rectification, filtering, voltage regulation and DC-DC conversion, it provides a stable and reliable power supply for electronic devices. The charge and discharge management module switches to lithium battery power supply when the induced power is insufficient, providing a stable and reliable backup power supply for the device.

[0030] It supports 5G / LoRa / RS485 multi-mode communication and ensures the stability of data backhaul by adaptively switching communication protocols.

[0031] The magnetic base makes installation easy. When the accelerometer detects abnormal vibration, it triggers an audible and visual alarm and reports the status via the NB-IoT network, helping maintenance personnel to promptly detect physical abnormalities in the terminal.

[0032] The signal processing module described in this embodiment includes: The wavelet decomposition module, based on the high-frequency characteristics of the fault traveling wave signal, performs a 4-level decomposition using the db4 wavelet basis to capture the abrupt change features of the traveling wave. The energy entropy calculation module performs energy integration on the wavelet coefficients of each decomposed layer, calculates the energy proportion of each frequency band, and constructs the energy entropy index. (3) In formula (3), For the first Layer energy entropy, For the first Layer The normalized energy value of each wavelet coefficient. This represents the total number of coefficients in the current layer. The fault feature extraction module selects the frequency band containing the maximum energy entropy as the fault feature frequency band by comparing the energy entropy distribution difference between the faulty line and the non-faulty line.

[0033] Specifically, the signal processing module performs wavelet decomposition on the acquired current signal through the parallel processing pipeline of the FPGA processor, and extracts the fault transient signal through energy integration. The signal processing module is built on a Xilinx Kintex-7 FPGA to construct a parallel processing pipeline. It uses Verilog to implement a four-level decomposition algorithm for the db4 wavelet basis and employs a ping-pong buffer structure to achieve uninterrupted data stream processing, completing a 128-point wavelet transform in a single cycle. The energy integration module uses fixed-point arithmetic to accumulate the sum of squares of the high-frequency coefficients at each level. It also features dynamic threshold detection to automatically adapt to the transient energy characteristics of different fault types, accurately identifying and preserving fault features.

[0034] The multi-mode communication module described in this embodiment adopts a layered communication architecture, including: The upward communication unit includes a 5G communication module that supports the Sub-6GHz frequency band and SA / NSA networking mode, and transmits fault alarm information and location results through a dynamic spectrum allocation algorithm; The downward communication unit includes a LoRa spread spectrum communication module, an RS485 bus interface, and a communication protocol conversion module. The LoRa spread spectrum communication module supports adaptive adjustment of the spread spectrum factor from SF7 to SF12 and establishes a wireless communication link with the fault location terminal through the LoRaWAN protocol. The RS485 bus interface transmits data with the fault location terminal through the Modbus-RTU communication protocol. The communication protocol conversion module integrates a dual-protocol parsing engine for bidirectional conversion between the LoRaWAN protocol and the Modbus-RTU communication protocol.

[0035] Example 2 A certain 10kV distribution network in the city includes over 200 ring main units and a total cable line length of 150km. The load mainly consists of commercial complexes and residential areas, with a daily fault frequency of 0.8 times. Traditional manual line inspection takes an average of 2.5 hours. Based on the passive real-time fault location system for distribution lines of this invention, accurate fault location and rapid handling are achieved. The specific implementation process is as follows: 120 fault location terminals are distributed and deployed on the urban 10kV distribution network, and are densely installed at line segment points (ring network cabinet intervals), connection points (busbar connection switches) and load-dense areas (every 300 meters). The terminals are equipped with wideband Rogowski coils, collect current waveforms every 5μs, and are equipped with Beidou / GPS timing modules to synchronize traveling wave arrival timestamps.

[0036] During a power distribution network fault, the fault location terminal located on the fault branch collects three-phase current, zero-sequence current, and transient traveling wave signals in real time. After preprocessing with an anti-aliasing filter, these signals are converted into digital signals by a MAX11040 high-speed ADC. Sunny / cloudy scenarios correspond to peak / off-peak load periods, respectively. For example, during the midday period from 11:00 to 14:00, when the commercial load peaks at 4500A, the terminal automatically switches to high-speed sampling mode to capture transient signals as fault transient signals. The fault location terminal's data processing module performs db4 wavelet 4-level decomposition on the fault transient signal through the parallel pipeline of the FPGA processor, extracting features such as traveling wavefront steepness and transient energy. Noise is filtered out using a dynamic threshold method, retaining fault-related features. Simultaneously, an absolute timestamp is obtained using BeiDou / GPS timing signals. The fault location terminal then uploads the extracted fault features and timestamp to the data center of the power distribution line fault location device management system.

[0037] The power distribution line fault location device management system constructs a three-dimensional spatiotemporal coordinate system with the first-end fault location terminal as the origin based on the data uploaded by each fault location terminal and the line topology data. It builds a traveling wave propagation model based on the line topology characteristic parameters and uses the least squares method to eliminate deviations caused by line parameter fluctuations. Wavelet energy spectrum analysis is performed on the initial traveling waves detected by two adjacent fault location terminals to calculate the ratio of reflected wave energy to transmitted wave energy, which is used to correct the fault location and improve the location accuracy of complex branch lines. Based on the line topology data, the system dynamically compensates for the branch influence and calculates the distance from the fault point to the first-end fault location terminal using a traveling wave ranging algorithm, thus achieving fault location. A lightweight convolutional neural network model classifies fault types based on the fault characteristics after wavelet decomposition and the output fault type probability distribution. Fault types include overcurrent faults, single-phase grounding faults, phase-to-phase short-circuit faults, grounding-phase-to-phase composite faults, and open / closed circuit faults.

[0038] The power distribution line fault location device management system constructs a 3D topology model of the power distribution line based on fault identification and location results, combined with the Neo4j graph database. It integrates the Spark real-time computing engine for multi-dimensional analysis and dynamically displays the line topology map and fault points on the client's visual interface. Clicking on a fault point pops up a fault diagnosis report card, allowing users to view detailed fault information. Maintenance personnel obtain fault point navigation via a mobile app, and upon arriving at the site, connect to a terminal via Bluetooth to read real-time waveforms. After confirming the fault type, they remotely reset the equipment.

[0039] Maintenance personnel upload fault handling results to the power distribution line fault location device management system in real time and update the historical fault mode database; the power distribution line fault location device management system performs health checks on the clock synchronization error and Rogowski coil sensitivity of all line terminals every hour, and automatically triggers remote calibration when an abnormality is detected.

[0040] In this embodiment, high-frequency signal acquisition and feature extraction are completed through the fault location terminal, and the power distribution line fault location device management system realizes global topology modeling and strategy generation, achieving a closed-loop management of "monitoring-analysis-handling-optimization".

[0041] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0042] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0043] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0044] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features of the invention herein.

[0045] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A passive real-time fault location system for power distribution lines, characterized in that, This includes a power distribution line fault location device management system and multiple fault location terminals deployed in a distributed manner along the power distribution line. The power distribution line fault location device management system establishes two-way communication with each fault location terminal through a 4G / 5G wireless public network. The power distribution line fault location device management system includes a data center, a client and multiple mobile terminals, which are used for hierarchical permission management, line topology relationship maintenance, remote terminal configuration, fault identification and location and alarm information push. The fault location terminal includes an ARM processor, a time synchronization unit, a signal acquisition unit, a signal processing unit, a self-powered unit, a communication unit, and an auxiliary unit. It is used to perform real-time signal acquisition and processing, and to trigger the identification and location of power distribution line faults through the uploaded fault signals. When a line fault occurs, the fault location terminal located on the branch closest to the fault point detects the traveling wave sudden change signal through the signal acquisition unit and triggers the power distribution line fault identification and location. Each fault location terminal records the arrival time stamp of the traveling wave synchronously through the time synchronization unit. The signal processing unit performs wavelet decomposition on the traveling wave signal to extract fault features. The communication unit uploads the fault features and timestamp to the power distribution line fault location device management system. After receiving the fault characteristics and timestamp, the data center of the power distribution line fault location device management system performs double-ended B-type traveling wave ranging to calculate the fault location based on the power distribution line topology. At the same time, it matches the fault type through a lightweight convolutional neural network model. The client dynamically marks the fault location in the three-dimensional digital twin model and generates a fault location report by associating it with historical fault handling plans. Alarm information and navigation paths are pushed to the mobile devices of maintenance personnel simultaneously.

2. The passive real-time fault location system for power distribution lines according to claim 1, characterized in that: The data center is equipped with a database, application server, and front-end communication management unit to store the topology of power distribution lines, terminal files, and historical fault records, and to perform fault location and identification through the application server. The client is equipped with a terminal management module, a fault notification module, and a line topology visualization module, which supports multi-level user permission control, remote terminal data transmission, and dynamic map display of line fault status. The mobile terminal integrates a mobile app, providing real-time alarm push notifications, fault details queries, and location result navigation functions.

3. The passive real-time fault location system for power distribution lines according to claim 2, characterized in that: The application server includes a fault identification module and a fault location module; The fault identification module has a built-in lightweight convolutional neural network model that classifies fault types based on fault characteristics. The fault location module is equipped with a dual-end B-type traveling wave ranging calculation unit, which calculates the fault location in combination with the line topology.

4. The passive real-time fault location system for power distribution lines according to claim 3, characterized in that: The dual-ended B-type traveling wave ranging calculation unit includes: The spatiotemporal coordinate system construction module uses the absolute timestamp obtained from the BeiDou / GPS timing signal and combines it with the line topology to construct a spatiotemporal coordinate system with the fault location terminal at the beginning as the origin. The traveling wave velocity calibration module constructs a traveling wave propagation model based on line topology characteristic parameters. The propagation model is as follows: （1） In formula (1), For traveling wave speed, This refers to the total length of the line between two adjacent fault location terminals. The time difference between the arrival of the traveling wave at two adjacent fault location terminals; The least squares method is used to eliminate the traveling wave velocity deviation caused by line parameter fluctuations, and the calibrated wave velocity is output. ; The fault traveling wave energy analysis module performs wavelet energy spectrum analysis on the initial traveling wave detected by two adjacent fault location terminals and calculates the energy ratio of the reflected wave to the transmitted wave at the fault point. The topology path compensation module performs dynamic compensation based on line topology data. It combines the fault traveling wave arrival time difference and the corrected traveling wave propagation velocity with a traveling wave ranging algorithm to locate the fault. The calculation formula is as follows: （2） In formula (2), This represents the distance from the fault location point to the first-end fault location terminal. The calibrated traveling wave velocity, This represents the energy ratio of the reflected wave to the transmitted wave at the fault point. The line attenuation coefficient is... This is the compensation factor for the length of the nearest branch line.

5. The passive real-time fault location system for power distribution lines according to claim 1, characterized in that: The ARM processor uses a Cortex-A72 high-speed processor, which is responsible for coordinating the task scheduling and data processing of various functional modules. The time synchronization unit includes a BeiDou / GPS timing module and a clock crystal module, which are used for time synchronization and provide a reference signal and time standard for the fault location terminal. The signal acquisition unit is equipped with a wideband Rogowski coil, which couples the current signal of the circuit through electromagnetic induction. The signal processing unit includes an analog-to-digital conversion module and a signal processing module. Through the parallel processing pipeline of the FPGA processor, the acquired current signal is decomposed by wavelet and fault features are extracted by energy integration. The self-powered unit includes an inductive power extraction module, a charging management module, and a lithium battery. It supplies power through electromagnetic induction power extraction coil coupled with line current, and switches to lithium battery power supply when the inductive power is insufficient through the charging and discharging management module. The communication unit includes a multi-mode communication module and a local debugging interface. The multi-mode communication module supports 5G, LoRa and RS485 multi-mode communication. The local debugging interface includes a USB3.0 and a Type-C interface for OTA firmware upgrades and local parameter configuration. The auxiliary unit is equipped with a magnetic base and has a built-in acceleration sensor. When abnormal vibration is detected, it triggers a local audible and visual alarm and reports the abnormal status through the NB-IoT network.

6. The passive real-time fault location system for power distribution lines according to claim 5, characterized in that: The signal processing module includes: The wavelet decomposition module, based on the high-frequency characteristics of the fault traveling wave signal, performs a 4-level decomposition using the db4 wavelet basis to capture the abrupt change features of the traveling wave. The energy entropy calculation module performs energy integration on the wavelet coefficients of each decomposed layer, calculates the energy proportion of each frequency band, and constructs the energy entropy index. （3） In formula (3), For the first Layer energy entropy, For the first Layer The normalized energy value of each wavelet coefficient. This represents the total number of coefficients in the current layer. The fault feature extraction module selects the frequency band containing the maximum energy entropy as the fault feature frequency band by comparing the energy entropy distribution difference between the faulty line and the non-faulty line.

7. A passive real-time fault location system for power distribution lines according to claim 5, characterized in that: The multi-mode communication module adopts a layered communication architecture, including: The upward communication unit includes a 5G communication module that supports the Sub-6GHz frequency band and SA / NSA networking mode, and transmits fault alarm information and location results through a dynamic spectrum allocation algorithm; The downward communication unit includes a LoRa spread spectrum communication module, an RS485 bus interface, and a communication protocol conversion module. The LoRa spread spectrum communication module supports adaptive adjustment of the spread spectrum factor from SF7 to SF12 and establishes a wireless communication link with the fault location terminal through the LoRaWAN protocol. The RS485 bus interface transmits data with the fault location terminal through the Modbus-RTU communication protocol. The communication protocol conversion module integrates a dual-protocol parsing engine for bidirectional conversion between the LoRaWAN protocol and the Modbus-RTU communication protocol.

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