An integrated partial discharge live-line detection system for distribution overhead line based on ubiquitous internet of things architecture

Abstract

The application is suitable for the technical field of distribution network detection, and provides a local partial discharge integrated live detection system for overhead lines of a distribution network based on an ubiquitous Internet of Things architecture, which comprises a pulse signal detection end, an edge gateway and a cloud monitoring platform; the pulse signal detection end is used for detecting partial discharge pulse signals of overhead lines and transmitting the partial discharge pulse signals to the edge gateway; the edge gateway comprises a synchronous pulse trigger module, a time calibration module and an edge analysis module; the edge analysis module is used for generating a positioning distribution map of partial discharge points; the cloud monitoring platform comprises a multi-end data fusion module, a risk assessment module and a result feedback module; the cloud monitoring platform is used for fusing the positioning distribution map and pulse characteristics, outputting a global discharge point correction distribution map, identifying a discharge type based on a discharge characteristic spectrum, generating an insulation state assessment result and feeding back the assessment result to a terminal user; and the application provides a high-reliability, high-efficiency and low-cost solution for insulation state control of a distribution network.

Description

Technical Field

[0001] This invention relates to the field of distribution network detection technology, and in particular to an integrated live-line detection system for partial discharge of overhead distribution network lines based on a ubiquitous Internet of Things architecture. Background Technology

[0002] As a critical carrier of power transmission, overhead power distribution lines are exposed to complex environmental stresses for extended periods. Partial discharge caused by insulation degradation is the primary cause of line faults. Traditional detection methods rely on periodic power outage tests and manual handheld instrument inspections. This not only requires power interruptions resulting in economic losses but is also limited by long inspection cycles, insufficient blind zone coverage, and difficulty in capturing transient discharge defects. Although the pulse current method can detect partial discharges online, existing sensors have narrow bandwidth and low sensitivity, making it impossible to effectively couple high-frequency discharge signals. Furthermore, multi-terminal detection lacks precise clock synchronization, leading to large errors in discharge point location and delayed maintenance response.

[0003] In recent years, IoT technology has been gradually applied in the field of power monitoring. However, existing solutions mostly transmit raw data back to the cloud for processing. The massive transmission leads to network bandwidth congestion, and the edge side lacks real-time analysis capabilities. Although distributed sensor nodes can improve coverage density, due to limitations in communication latency and computing power, it is difficult to achieve microsecond-level time alignment of data from multiple terminals. This cannot meet the high-speed characteristics required for discharge pulse propagation, resulting in insufficient positioning accuracy, poor real-time fault warning, and difficulty in supporting preventive operation and maintenance decisions. Summary of the Invention

[0004] This invention provides an integrated live-line detection system for partial discharge of overhead power distribution lines based on a ubiquitous Internet of Things architecture. It is used to solve the problem that the high-speed characteristics of discharge pulse propagation cannot be met, resulting in insufficient positioning accuracy, poor real-time fault warning, and difficulty in supporting preventive operation and maintenance decisions.

[0005] This invention provides an integrated live-line detection system for partial discharge of overhead power distribution lines based on a ubiquitous Internet of Things (IoT) architecture, comprising:

[0006] The system includes a pulse signal detection terminal, an edge gateway, and a cloud monitoring platform; the edge gateway is communicatively connected to both the pulse signal detection terminal and the cloud monitoring platform.

[0007] The pulse signal detection terminal is used to detect the partial discharge pulse signal of the overhead line and transmit the partial discharge pulse signal to the edge gateway;

[0008] The edge gateway includes a synchronization pulse triggering module, a time calibration module, and an edge analysis module; the edge analysis module is connected to the synchronization pulse triggering module and the time calibration module respectively.

[0009] The synchronization pulse triggering module is used to inject high-voltage synchronization pulses into the line and record the injection time.

[0010] The time calibration module is used to calculate the signal propagation speed based on the pulse round-trip time delay and trigger multi-terminal time synchronization calibration;

[0011] The edge analysis module is used to extract the amplitude, frequency, and time domain features of the partial discharge pulse signal, and generate a location distribution map of the partial discharge points.

[0012] The cloud-based monitoring platform includes a multi-terminal data fusion module, a risk assessment module, and a result feedback module; the risk assessment module is connected to both the multi-terminal data fusion module and the result feedback module.

[0013] The multi-terminal data fusion module is used to fuse the location distribution maps and pulse features uploaded by all edge gateways and output a global discharge point correction distribution map.

[0014] The risk assessment module identifies the discharge type based on the discharge characteristic spectrum and generates insulation status assessment results.

[0015] The result feedback module is used to provide end users with visual assessment results that include the discharge location and risk level.

[0016] Furthermore, the pulse signal detection end includes a high-frequency inductive sensor and a dual-end detection module. The high-frequency inductive sensor is distributed and deployed at multiple monitoring nodes of the overhead line to collect partial discharge pulse signals generated during line operation in a non-contact manner. The dual-end detection module is deployed at the start and end points of the line segment under test to perform the injection and reception of high-voltage synchronization pulses and to realize the distance measurement of partial discharge based on the dual-end signal.

[0017] Furthermore, the dual-end detection module includes a high-voltage pulse injection unit, a signal acquisition unit, and a control unit; the high-voltage pulse injection unit is used to inject a high-voltage synchronization pulse signal into the cable; the signal acquisition unit is used to acquire partial discharge pulse signals and synchronization pulse signals; the control unit is used to achieve dual-end time synchronization, and calculate the location of the partial discharge occurrence point through a preset algorithm based on the propagation time of the synchronization pulse signal and the time difference between the arrival of the partial discharge pulse at both ends.

[0018] Furthermore, the control unit is used to achieve time synchronization between the two ends, and calculates the location of the partial discharge occurrence point through a preset algorithm based on the propagation time of the synchronization pulse signal and the time difference between the arrival of the partial discharge pulse at both ends, including:

[0019] Control unit in The high-voltage pulse injection unit is activated to inject high-voltage synchronous pulses into the cable and record the injection time. Simultaneously, it starts... Terminal signal acquisition unit;

[0020] The terminal signal acquisition unit receives After the synchronization pulse is injected, by Terminal control unit start The high-voltage pulse injection unit injects high-voltage synchronous pulses into the cable.

[0021] The terminal signal acquisition unit records the received signal. Timing of end-injection pulse Based on the formula The one-way propagation time of the pulse in the cable was calculated. ,in for End received The time for delaying the injection of its own pulse after the end pulse;

[0022] Based on propagation time and wave velocity The location of the partial discharge point is calculated using a preset positioning algorithm.

[0023] Furthermore, the calculation of the location of the partial discharge occurrence point using a preset positioning algorithm includes:

[0024] extract End in time interval and End in time interval Partial discharge pulses acquired internally, among which It is an integer greater than 1. for The one-way transmission time is twice as long;

[0025] For a pair of respectively from End and The arrival time difference of the matched pulses within the end time interval is denoted as . Based on formula Calculate the distance between partial discharge points Distance between ends ,in Total cable length The pulse wave velocity;

[0026] All matching pulse pairs are traversed to generate a dot map representing the discharge frequency at different locations, and the final location of the partial discharge point is determined based on the dot map.

[0027] Furthermore, the high-frequency inductive sensor is a spherical magnetic field sensor based on the principle of an electrically small antenna. The sensor includes a spherical electrode, a plate electrode, a filtering and amplification circuit, a power supply module, and an adsorption structure. The adsorption structure is disposed at the sensor head and is used to fix the sensor to the metal surface of the circuit. The filtering and amplification circuit integrates a filtering stage to extend the sensor's frequency band.

[0028] Furthermore, the dual-end detection module also includes a distributed parameter modeling unit, which is used to model and analyze the pulse coupling, propagation, and detection process of the dual-end detection module.

[0029] The modeling process of the distributed parameter modeling unit includes:

[0030] The dual-end detection module is divided into a coupling part, a transmission part, and a detection part, and a transmission line equation including mutual inductance per unit length, capacitance to ground, and resistance is established.

[0031] Considering the impact of cable joints on the propagation of synchronization pulses, the time-domain waveform expression of synchronization pulses is modified.

[0032] Frequency simulation of the transmission line model is performed using simulation software to ensure that the amplitude-frequency characteristics match the actual detection characteristics to the set requirements.

[0033] Furthermore, the pulse signal detection terminal also includes a leakage magnetic field power supply module, which is electrically connected to the high-frequency inductive sensor and is used to power the sensor.

[0034] The edge gateway integrates a low-power wireless communication unit to enable data transmission between the edge gateway and the cloud monitoring platform;

[0035] When there is no partial discharge signal, it enters a sleep mode. When a pulse signal is detected that exceeds the trigger threshold, the pulse signal detection terminal automatically wakes up.

[0036] Furthermore, the signal acquisition unit includes a voltage sensor, a current sensor, and a signal conditioning circuit;

[0037] The signal conditioning circuit is used to convert the voltage amplitude of the sensor output signal to match the input range of the data processing chip, and the conditioning circuit integrates a filter capacitor to suppress noise interference.

[0038] The signal acquisition unit uses a chip to control the data acquisition timing and is configured with a cache unit to temporarily store the acquired data. After the detection cycle ends, the data is transmitted to the edge gateway.

[0039] Furthermore, the cloud monitoring platform also includes a multi-terminal data comprehensive analysis module, which has the following functions:

[0040] It supports spectral analysis and can identify typical partial discharge types based on spectral features;

[0041] It offers multiple detection mode switching options and supports multiple synchronization methods in multi-terminal detection mode;

[0042] It automatically collects the current ambient noise level and supports setting trigger thresholds to filter valid partial discharge pulses;

[0043] The test report is automatically generated based on the results of multi-terminal data fusion. The report includes test information, discharge information, insulation status assessment results and operation and maintenance suggestions.

[0044] When the module judges the partial discharge signal, if the matching degree between the collected signal characteristics and the preset discharge type characteristics meets the set requirements, then the corresponding discharge type is determined.

[0045] As can be seen from the above technical solutions, the present invention has the following advantages:

[0046] This invention achieves non-contact pulse signal acquisition of key nodes in overhead power distribution lines through a pulse signal detection terminal. The synchronization pulse triggering module and time calibration module of the edge gateway work together to synchronize clocks across multiple terminals. The edge analysis module extracts discharge characteristics in real time and generates a location distribution map, effectively solving the problems of large synchronization errors and high positioning delays in traditional detection methods. The multi-terminal data fusion module of the cloud monitoring platform integrates data from the entire network to output a corrected distribution map. The risk assessment module identifies discharge types based on deep learning methods and generates operation and maintenance strategies. Finally, a risk visualization map is fed back, improving the real-time performance and positioning accuracy of partial discharge detection. This invention's system achieves live-line detection through an edge-cloud collaborative architecture, accurately guiding preventative maintenance while avoiding power outage losses, providing a highly reliable, efficient, and low-cost solution for distribution network insulation status management. Attached Figure Description

[0047] Figure 1 This is a structural block diagram of an integrated live-line detection system for partial discharge of overhead power distribution lines based on a ubiquitous Internet of Things architecture, as described in this invention.

[0048] Figure 2 This is a schematic diagram of the process for calculating the location of the partial discharge occurrence point in this invention;

[0049] Figure 3 This is a schematic diagram of the modeling process of the distributed parameter modeling unit of the present invention. Detailed Implementation

[0050] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “corresponding to,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0051] Example 1

[0052] Please see Figure 1 The integrated live-line detection system for partial discharge of overhead power distribution lines based on a ubiquitous Internet of Things architecture provided in this application includes: a pulse signal detection terminal 1, an edge gateway 2, and a cloud monitoring platform 3; the edge gateway 2 is communicatively connected to both the pulse signal detection terminal 1 and the cloud monitoring platform 3; the pulse signal detection terminal 1 is used to detect the partial discharge pulse signal of the overhead power line and transmit the partial discharge pulse signal to the edge gateway; the edge gateway 2 includes a synchronization pulse triggering module 201, a time calibration module 202, and an edge analysis module 203; the edge analysis module 203 is connected to both the synchronization pulse triggering module 201 and the time calibration module 202; the synchronization pulse triggering module 201 is used to inject a high-voltage synchronization pulse into the line and record the injection time; the time calibration module 202 is used to calculate the signal based on the pulse round-trip time delay. The propagation speed is measured, and multi-terminal time synchronization calibration is triggered. The edge analysis module 203 is used to extract the amplitude, frequency, and time domain features of the partial discharge pulse signal and generate a location distribution map of the partial discharge point. The cloud monitoring platform 3 includes a multi-terminal data fusion module 301, a risk assessment module 302, and a result feedback module 303. The risk assessment module 302 is connected to the multi-terminal data fusion module 301 and the result feedback module 303 respectively. The multi-terminal data fusion module 301 is used to fuse the location distribution map and pulse features uploaded by all edge gateways and output a global discharge point correction distribution map. The risk assessment module 302 identifies the discharge type based on the discharge feature spectrum and generates an insulation status assessment result. The result feedback module 303 is used to provide end users with a visual assessment result that includes the discharge location and risk level.

[0053] The working principle of the system of the present invention will be described in detail below with reference to specific scenarios:

[0054] In the live-line detection scenario of overhead distribution lines, the pulse signal detection terminal first collects the partial discharge pulse signal generated during line operation. Paired dual-end detection modules, in conjunction with the edge gateway's synchronous pulse triggering module, inject high-voltage synchronous pulses into the line and record the injection time. The time calibration module calculates the signal propagation speed based on the round-trip delay between the two ends of the pulse, simultaneously triggering multi-end time synchronization calibration to ensure consistent time references across monitoring nodes. After the pulse signal detection terminal transmits the collected partial discharge pulse signal to the edge gateway, the edge analysis module extracts the amplitude, frequency, and time-domain characteristics of the pulse signal. Combining the time stamp of the synchronous pulse with the signal propagation speed, it generates a location distribution map of the partial discharge points. Subsequently, the edge gateway uploads this location distribution map and pulse characteristics to the cloud monitoring platform. The multi-end data fusion module merges the data uploaded by all edge gateways and outputs a global discharge point correction distribution map. The risk assessment module identifies the discharge type based on the discharge characteristic spectrum in this distribution map and generates an insulation status assessment result. Finally, the result feedback module provides a visualized assessment result, including discharge location and risk level, to the end user, achieving accurate detection, location, and insulation status assessment of partial discharges in overhead distribution lines.

[0055] Example 2

[0056] See Figures 2-3 The pulse signal detection end 1 includes a high-frequency inductive sensor 101 and a dual-end detection module 102. The high-frequency inductive sensor 101 is distributed across multiple monitoring nodes of the overhead line to collect partial discharge pulse signals generated during line operation in a non-contact manner. The dual-end detection module 102 is deployed at the start and end points of the line segment under test to inject and receive high-voltage synchronization pulses and to measure the distance of partial discharge based on the dual-end signals. The dual-end detection module 102 includes a high-voltage pulse injection unit, a signal acquisition unit, and a control unit. The high-voltage pulse injection unit injects high-voltage synchronization pulse signals into the cable. The signal acquisition unit collects partial discharge pulse signals and synchronization pulse signals. The control unit achieves dual-end time synchronization and calculates the location of the partial discharge occurrence point using a preset algorithm based on the propagation time of the synchronization pulse signal and the time difference between the arrival times of the partial discharge pulses at both ends.

[0057] The working principle of pulse signal detection terminal 1 is described in detail below with reference to specific scenarios:

[0058] In a specific scenario of partial discharge detection on overhead power distribution lines, the high-frequency inductive sensor at the pulse signal detection end serves as a sensing unit distributed across multiple monitoring nodes on the overhead line. It continuously collects partial discharge pulse signals generated during line operation in a non-contact manner, providing raw data for subsequent analysis. Meanwhile, the dual-end detection module deployed at the start and end points of the line segment under test injects high-voltage synchronous pulse signals into the cable through its high-voltage pulse injection unit. Simultaneously, the signal acquisition unit synchronously acquires the partial discharge pulse signals and the injected synchronous pulse signals. During this process, the control unit of the dual-end detection module first achieves time synchronization between the two ends to ensure that the time references at both ends are consistent. Then, based on the propagation time of the synchronous pulse signal and the time difference between the arrival of the partial discharge pulse at both ends, it accurately calculates the location of the partial discharge occurrence point through a preset algorithm. This, in conjunction with the high-frequency inductive sensor, completes the acquisition and positioning distance measurement of the partial discharge pulse signals of the overhead line, enabling the pulse signal detection end to effectively perceive and initially locate the partial discharge on the line.

[0059] Among them, the high-frequency inductive sensor is a spherical magnetic field sensor based on the principle of electric small antenna. The sensor includes a spherical electrode, a plate electrode, a filter amplifier circuit, a power supply module, and an adsorption structure. The adsorption structure is set at the sensor head and is used to fix the sensor to the metal surface of the circuit. The filter amplifier circuit integrates a filter element to extend the sensor's frequency band.

[0060] In this embodiment, the control unit is used to achieve time synchronization between the two ends, and calculates the location of the partial discharge occurrence point through a preset algorithm based on the propagation time of the synchronization pulse signal and the time difference between the arrival of the partial discharge pulse at both ends, including the following steps:

[0061] S11. Control unit in The high-voltage pulse injection unit is activated to inject high-voltage synchronous pulses into the cable and record the injection time. Simultaneously, it starts... Terminal signal acquisition unit;

[0062] The control unit, through its built-in timing controller, The high-voltage pulse injection unit is triggered to release a high-voltage synchronization pulse to the cable, and at the same time, a high-precision clock module is called to record the pulse injection time, thus starting synchronously. The terminal signal acquisition unit continuously captures electrical signals on the cable at a preset sampling frequency, including subsequent... The synchronization pulse and partial discharge pulse of the line feedback.

[0063] S12. The terminal signal acquisition unit receives After the synchronization pulse is injected, by Terminal control unit start The high-voltage pulse injection unit injects high-voltage synchronous pulses into the cable.

[0064] The end signal acquisition unit monitors the cable signal in real time using a pulse detection circuit. When it detects... When the characteristic waveform of the injected pulse, such as amplitude and rise edge slope, matches a preset template, it is triggered. Synchronization logic of the terminal control unit, start The high-voltage pulse injection unit injects high-voltage synchronization pulses in reverse into the cable during this period. The terminal control unit synchronously records its own pulse injection time and communicates with the terminal control unit via a wireless synchronization protocol. The time reference is calibrated at both ends to ensure accurate time synchronization between the two ends.

[0065] S13. The terminal signal acquisition unit records the received signal. Timing of end-injection pulse Based on the formula The one-way propagation time of the pulse in the cable was calculated. ,in for End received The time for delaying the injection of its own pulse after the end pulse;

[0066] The terminal signal acquisition unit continuously samples, and when it detects... When the characteristic signal of the injected pulse is received, the clock module is triggered again to record the receiving time. .because The terminal pulse needs to pass through → Dissemination (time consumption) → End delay → End pulse reverse → Propagation (re-time consumption) Therefore, it satisfies the condition. The control unit strips away the problem by solving equations. End delay Divide the remaining time by 2 to obtain the one-way propagation time of the pulse in the cable. .

[0067] S14. Based on propagation time and wave velocity The location of the partial discharge point is calculated using a preset positioning algorithm.

[0068] 1. Extract End in time interval and End in time interval Partial discharge pulses acquired internally, among which It is an integer greater than 1. for The one-way transmission time is twice as long;

[0069] 2. For a pair of items from... End and The arrival time difference of the matched pulses within the end time interval is denoted as . Based on formula Calculate the distance between partial discharge points Distance between ends ,in Total cable length The pulse wave velocity;

[0070] 3. Traverse all matching pulse pairs to generate a dot map representing the discharge frequency at different locations, and determine the final location of the partial discharge point based on the dot map.

[0071] The process of extracting partial discharge pulses: First, based on the one-way propagation time... Determine the time interval rules. End selection interval ( For integers greater than 1, utilize the periodicity of pulse propagation to ensure that the pulse within the interval is consistent with... (same round of propagation) End-to-end synchronization selection The interval was then selected by analyzing multi-dimensional characteristics such as pulse amplitude, frequency, and waveform slope. , Matching pulse pairs within the terminal interval are identified as bidirectional propagating pulses of the same discharge event. For each pair of matching pulses, the time difference between their arrival at both ends is calculated. Substitute into the formula , Total cable length For pulse wave velocity, based on And by deriving the line parameters in advance, the discharge point distance is obtained. Distance between ends Finally, all matched pulse pairs are traversed and marked on a distance-discharge frequency coordinate system. Cluster analysis of dense regions is used to determine the final location of the partial discharge point. The center of the densely marked area is the actual discharge location, thus realizing the complete process from two-end pulse interaction to precise discharge point localization.

[0072] In this embodiment, the dual-end detection module further includes a distributed parameter modeling unit, which is used to model and analyze the pulse coupling, propagation and detection process of the dual-end detection module.

[0073] The modeling process for the distributed parameter modeling unit includes:

[0074] S21. Divide the double-ended detection module into a coupling part, a transmission part, and a detection part, and establish a transmission line equation that includes mutual inductance per unit length, capacitance to ground, and resistance.

[0075] S22. Considering the impact of cable joints on the propagation of synchronization pulses, the time-domain waveform expression of synchronization pulses is modified.

[0076] S23. Perform frequency simulation on the transmission line model using simulation software to ensure that the amplitude-frequency characteristics match the actual detection characteristics to meet the set requirements.

[0077] Specifically, when the distributed parameter modeling unit executes S21, it first clearly divides the physical structure and signal flow of the double-ended detection module into a coupling part (including the coupling components between the high-voltage pulse injection unit and the line), a transmission part (i.e., the overhead cable under test), and a detection part (including the sensing components of the signal acquisition unit). Then, based on distributed parameter theory, it establishes transmission line equations for each part, incorporating unit-length mutual inductance (characterizing the electromagnetic induction between lines), unit-length capacitance to ground (reflecting the electric field coupling between the cable and the ground), and unit-length resistance (reflecting the ohmic loss of the cable) to describe the voltage and current propagation characteristics of the pulse signal in each part. When executing S22, because the cable intermediate joint has impedance discontinuity characteristics, it will cause reflection, attenuation, and waveform distortion during the propagation of the synchronization pulse. Therefore, the distributed parameter modeling unit analyzes the physical structure and electrical parameters of the joint to derive the signal transmission characteristics of each intermediate joint, and introduces these characteristics into the original expression of the synchronization pulse time-domain waveform to correct the waveform amplitude, rise slope, and oscillation components to accurately reflect the actual propagation state of the pulse after passing through the intermediate joint. When executing S23... At that time, the distributed parameter modeling unit calls the circuit simulation software, imports the established transmission line equation and the corrected waveform expression into the model, performs wideband frequency simulation on the transmission line model, obtains the amplitude-frequency characteristic curves at different frequencies, and then compares the simulation curve with the amplitude-frequency characteristic of the pulse signal collected in the actual operation of the dual-ended detection module. If the degree of agreement between the two does not meet the set requirements, the unit length parameter in the transmission line equation or the transmission characteristic parameter of the intermediate joint is adjusted until the degree of agreement between the simulated amplitude-frequency characteristic and the actual detection characteristic meets the set requirements, thereby completing the accurate modeling and analysis of the pulse coupling, propagation and detection process of the dual-ended detection module.

[0078] In this embodiment, the pulse signal detection terminal 1 further includes a leakage magnetic field power supply module 103, which is electrically connected to the high-frequency inductive sensor 101 and is used to power the sensor; the edge gateway integrates a low-power wireless communication unit to realize data transmission between the edge gateway and the cloud monitoring platform; it enters a sleep mode when there is no partial discharge signal, and automatically wakes up when the pulse signal is detected to exceed the trigger threshold.

[0079] The signal acquisition unit includes a voltage sensor, a current sensor, and a signal conditioning circuit. The signal conditioning circuit is used to convert the voltage amplitude of the sensor output signal to match the input range of the data processing chip. The conditioning circuit also integrates a filter capacitor to suppress noise interference. The signal acquisition unit uses a chip to control the data acquisition timing and is configured with a buffer unit to temporarily store the acquired data. After the detection cycle ends, the data is transmitted to the edge gateway.

[0080] Specifically, the leakage magnetic field power supply module utilizes the leakage magnetic field generated by the alternating current during overhead line operation. It couples this leakage magnetic field with a built-in energy extraction coil and converts it into electrical energy. After rectification and voltage regulation, the energy is stored in the energy storage unit, continuously powering the high-frequency inductive sensor. The low-power wireless communication unit of the edge gateway employs low-power communication technology, reducing energy consumption while ensuring data transmission. The pulse signal detection end uses a monitoring circuit to judge the signal status in real time. When there is no partial discharge signal, the sensor, signal acquisition unit, and communication unit enter a sleep mode together, retaining only the low-power wake-up circuit to monitor the pulse signal amplitude. When the signal exceeds the trigger threshold, the wake-up circuit triggers the system to resume operation. In the signal acquisition unit, voltage and current sensors acquire line electrical signals in real time. The signal conditioning circuit first filters out noise interference through an integrated filter capacitor, then amplifies the signal and converts the voltage amplitude to match the input range of the data processing chip. The data processing chip controls data acquisition according to a preset timing sequence, temporarily storing the acquired partial discharge pulse and synchronization pulse signals in a buffer unit. After the detection cycle ends, the data is transmitted to the edge gateway.

[0081] Example 3

[0082] In this embodiment, the cloud monitoring platform 3 also includes a multi-terminal data comprehensive analysis module, which has the following functions: supports spectrum analysis function, and can identify typical partial discharge types based on spectrum features; provides multiple detection mode switching, and supports multiple synchronization methods in multi-terminal detection mode; automatically collects the current ambient noise level, and supports setting trigger thresholds to filter effective partial discharge pulses; automatically generates detection reports based on multi-terminal data fusion results, and the reports include detection information, discharge information, insulation status assessment results and operation and maintenance suggestions; when the module judges the partial discharge signal, if the matching degree between the collected signal features and the preset discharge type features meets the set requirements, the corresponding discharge type is determined.

[0083] Specifically, when the multi-terminal data comprehensive analysis module implements the spectrum analysis function, it first receives the partial discharge pulse signal feature data uploaded by each edge gateway. Based on parameters such as pulse amplitude, phase, and frequency, it automatically generates the corresponding spectrum. Then, it retrieves the built-in typical discharge type feature library, which includes spectrum feature templates for types such as surface discharge, tip discharge, floating potential discharge, and internal hole discharge. The similarity between the acquired spectrum and each template is calculated through a feature comparison algorithm. If the similarity meets the set threshold, the current partial discharge type is accurately identified.

[0084] When implementing multiple detection mode switching functions, the module provides a visual operation interface. Users can select single-end detection, multi-end detection (master end), or multi-end detection (slave end) modes according to their detection needs. After switching to multi-end detection mode, it further supports the selection of different synchronization methods. If a satellite signal-dependent synchronization method is selected, the time of each monitoring node is calibrated by receiving satellite time signals. If a hybrid synchronization method is selected, pulse injection synchronization and external time signal dual calibration are combined to ensure that the multi-end synchronization time error meets the set requirements. When implementing the automatic acquisition of environmental noise and setting of trigger thresholds, the module controls the pulse signal detection end to acquire line signals under conditions without discharge interference at the initial stage of the detection cycle. The amplitude and frequency distribution characteristics of environmental noise are extracted through noise analysis algorithms to determine the current environmental noise level. At the same time, a threshold adjustment interface is provided. Users can set the trigger threshold to a specific multiple of the noise level according to the detection accuracy requirements. The module then filters the subsequently acquired pulse signals, eliminating noise signals with amplitudes lower than the threshold and retaining valid partial discharge pulses. When implementing the automatic report generation function, the module obtains the global discharge point correction distribution map and insulation status assessment results from the multi-terminal data fusion module, extracts the detection location, line parameters, and discharge information from the data uploaded from the edge gateway, integrates this information according to the preset report template, and generates a report containing basic detection information, detailed discharge data, insulation status level, and targeted operation and maintenance suggestions. It also supports exporting report files in specific formats according to user needs. When implementing the partial discharge signal judgment function, the module first preprocesses the collected pulse signals, then compares these features one by one with the features of each type in the preset discharge type feature library, calculates the feature matching degree, and if the matching degree meets the set requirements, determines that the current partial discharge signal corresponds to the preset discharge type. If the matching degree does not meet the requirements, it is marked as a signal to be confirmed and the user is prompted for further analysis, thereby ensuring the accuracy of the partial discharge type judgment.

[0085] It is understood that those skilled in the art can combine various implementation methods in the above embodiments under the guidance of the above examples to obtain technical solutions with multiple implementation methods.

[0086] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A live-line detection system for partial discharge of overhead power distribution lines based on a ubiquitous Internet of Things (IoT) architecture, characterized in that, include: Pulse signal detection terminal, edge gateway and cloud monitoring platform; The edge gateway is communicatively connected to both the pulse signal detection terminal and the cloud monitoring platform. The pulse signal detection terminal is used to detect the partial discharge pulse signal of the overhead line and transmit the partial discharge pulse signal to the edge gateway; The pulse signal detection end includes a high-frequency inductive sensor and a dual-end detection module. The high-frequency inductive sensor is distributed and deployed at multiple monitoring nodes of the overhead line to collect partial discharge pulse signals generated during line operation in a non-contact manner. The dual-end detection module is deployed at the beginning and end points of the line segment under test. It is used to inject and receive high-voltage synchronization pulses and to measure the distance of partial discharge based on the dual-end signals. The dual-end detection module includes a high-voltage pulse injection unit, a signal acquisition unit, and a control unit. The high-voltage pulse injection unit is used to inject a high-voltage synchronization pulse signal into the cable. The signal acquisition unit is used to acquire partial discharge pulse signals and synchronization pulse signals. The control unit is used to achieve dual-end time synchronization and calculate the location of the partial discharge occurrence point based on the propagation time of the synchronization pulse signal and the time difference between the arrival of the partial discharge pulse at both ends using a preset algorithm. The edge gateway includes a synchronization pulse triggering module, a time calibration module, and an edge analysis module; the edge analysis module is connected to the synchronization pulse triggering module and the time calibration module respectively. The synchronization pulse triggering module is used to inject high-voltage synchronization pulses into the line and record the injection time. The time calibration module is used to calculate the signal propagation speed based on the pulse round-trip time delay and trigger multi-terminal time synchronization calibration; The edge analysis module is used to extract the amplitude, frequency, and time domain features of the partial discharge pulse signal, and generate a location distribution map of the partial discharge points. The cloud-based monitoring platform includes a multi-terminal data fusion module, a risk assessment module, and a result feedback module; the risk assessment module is connected to both the multi-terminal data fusion module and the result feedback module. The multi-terminal data fusion module is used to fuse the location distribution maps and pulse features uploaded by all edge gateways and output a global discharge point correction distribution map. The risk assessment module identifies the discharge type based on the discharge characteristic spectrum and generates insulation status assessment results. The result feedback module is used to provide end users with visual assessment results that include the discharge location and risk level.

2. The integrated live-line detection system for partial discharge of overhead power distribution lines based on a ubiquitous Internet of Things architecture as described in claim 1, characterized in that, The control unit is used to achieve time synchronization between the two ends, and calculates the location of the partial discharge occurrence point through a preset algorithm based on the propagation time of the synchronization pulse signal and the time difference between the arrival of the partial discharge pulse at both ends, including: Control unit in The high-voltage pulse injection unit is activated to inject high-voltage synchronous pulses into the cable and record the injection time. Simultaneously, it starts... Terminal signal acquisition unit; The terminal signal acquisition unit receives After the synchronization pulse is injected, by Terminal control unit start The high-voltage pulse injection unit injects high-voltage synchronous pulses into the cable. The terminal signal acquisition unit records the received signal. Timing of end-injection pulse Based on the formula The one-way propagation time of the pulse in the cable was calculated. ,in for End received The time for delaying the injection of its own pulse after the end pulse; Based on propagation time and wave velocity The location of the partial discharge point is calculated using a preset positioning algorithm.

3. The integrated live-line detection system for partial discharge of overhead power distribution lines based on a ubiquitous Internet of Things architecture as described in claim 2, characterized in that, The calculation of the location of the partial discharge occurrence point using a preset positioning algorithm includes: extract End in time interval and End in time interval Partial discharge pulses acquired internally, among which It is an integer greater than 1. for The one-way transmission time is twice as long; For a pair of respectively from End and The arrival time difference of the matched pulses within the end time interval is denoted as . Based on formula Calculate the distance between partial discharge points Distance between ends ,in Total cable length The pulse wave velocity; All matching pulse pairs are traversed to generate a dot map representing the discharge frequency at different locations, and the final location of the partial discharge point is determined based on the dot map.

4. The integrated live-line detection system for partial discharge of overhead power distribution lines based on a ubiquitous Internet of Things architecture as described in claim 1, characterized in that, The high-frequency inductive sensor is a spherical magnetic field sensor based on the principle of an electrically small antenna. The sensor includes a spherical electrode, a plate electrode, a filtering and amplification circuit, a power supply module, and an adsorption structure. The adsorption structure is located at the sensor head and is used to fix the sensor to the metal surface of the circuit. The filtering and amplification circuit integrates a filtering element to extend the sensor's frequency band.

5. The integrated live-line detection system for partial discharge of overhead power distribution lines based on a ubiquitous Internet of Things architecture as described in claim 1, characterized in that, The dual-end detection module also includes a distributed parameter modeling unit, which is used to model and analyze the pulse coupling, propagation, and detection process of the dual-end detection module. The modeling process of the distributed parameter modeling unit includes: The dual-end detection module is divided into a coupling part, a transmission part, and a detection part, and a transmission line equation including mutual inductance per unit length, capacitance to ground, and resistance is established. Considering the impact of cable joints on the propagation of synchronization pulses, the time-domain waveform expression of synchronization pulses is modified. Frequency simulation of the transmission line model is performed using simulation software to ensure that the amplitude-frequency characteristics match the actual detection characteristics to the set requirements.

6. The integrated live-line detection system for partial discharge of overhead power distribution lines based on a ubiquitous Internet of Things architecture as described in claim 1, characterized in that, The pulse signal detection terminal also includes a leakage magnetic field power supply module, which is electrically connected to the high-frequency inductive sensor and is used to power the sensor. The edge gateway integrates a low-power wireless communication unit to enable data transmission between the edge gateway and the cloud monitoring platform; When there is no partial discharge signal, it enters a sleep mode. When a pulse signal is detected that exceeds the trigger threshold, the pulse signal detection terminal automatically wakes up.

7. The integrated live-line detection system for partial discharge of overhead power distribution lines based on a ubiquitous Internet of Things architecture as described in claim 1, characterized in that, The signal acquisition unit includes a voltage sensor, a current sensor, and a signal conditioning circuit; The signal conditioning circuit is used to convert the voltage amplitude of the sensor output signal to match the input range of the data processing chip, and the conditioning circuit integrates a filter capacitor to suppress noise interference. The signal acquisition unit uses a chip to control the data acquisition timing and is configured with a cache unit to temporarily store the acquired data. After the detection cycle ends, the data is transmitted to the edge gateway.

8. The integrated live-line detection system for partial discharge of overhead power distribution lines based on a ubiquitous Internet of Things architecture as described in claim 1, characterized in that, The cloud-based monitoring platform also includes a multi-terminal data comprehensive analysis module, which has the following functions: It supports spectral analysis and can identify typical partial discharge types based on spectral features; It offers multiple detection mode switching options and supports multiple synchronization methods in multi-terminal detection mode; It automatically collects the current ambient noise level and supports setting trigger thresholds to filter valid partial discharge pulses; The test report is automatically generated based on the results of multi-terminal data fusion. The report includes test information, discharge information, insulation status assessment results and operation and maintenance suggestions. When the multi-terminal data comprehensive analysis module judges the partial discharge signal, if the matching degree between the collected signal characteristics and the preset discharge type characteristics meets the set requirements, then the corresponding discharge type is determined.

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