Distribution network overhead line partial discharge integrated live detection system based on ubiquitous Internet of Things architecture

Abstract

The invention is suitable for the technical field of distribution network detection, and provides a distribution network overhead line partial discharge integrated live detection system based on a 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 a partial discharge pulse signal of the overhead line and transmitting the partial discharge pulse signal to the edge gateway; the edge gateway comprises a synchronization pulse trigger module, a time calibration module and an edge analysis module. The generation module is used for generating a positioning distribution diagram of partial discharge points; the cloud monitoring platform comprises a multi-terminal data fusion module, a risk assessment module and a result feedback module; the server is used for fusing the positioning distribution map and the pulse characteristics, outputting a global discharge point correction distribution map, identifying a discharge type based on a discharge characteristic spectrogram, generating an insulation state evaluation result and feeding back the evaluation result to a terminal user; and a high-reliability, high-efficiency and low-cost solution is provided for the management and control of the insulation state of the distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distribution network detection, and particularly relates to a partial discharge integrated live detection system for a distribution network overhead line based on an ubiquitous Internet of Things architecture. BACKGROUND

[0002] As a key carrier for power transmission, the overhead line of the distribution network is long-term exposed to complex environmental stress, and the partial discharge caused by insulation deterioration is the primary cause of line failure. The traditional detection relies on periodic power-off test and manual handheld instrument inspection, which on the one hand causes economic loss due to power interruption, and on the other hand is limited by long inspection period and insufficient blind area coverage, and it is difficult to capture transient discharge defects. Although the pulse current method can detect partial discharge online, the existing sensor has narrow bandwidth and low sensitivity, and cannot effectively couple high-frequency discharge signals, and multi-end detection lacks precise clock synchronization, resulting in large positioning error of the discharge point and lagging response of operation and maintenance.

[0003] In recent years, the Internet of Things technology has been gradually applied in the field of power monitoring, but the existing scheme mainly returns raw data to the cloud for processing, and massive transmission leads to network bandwidth congestion, and the edge side lacks real-time analysis capability. Although the distributed sensing node can improve the coverage density, due to the communication delay and computing capability, the multi-end data is difficult to achieve microsecond-level time alignment, and cannot meet the high-speed characteristics requirement of discharge pulse propagation, resulting in insufficient positioning accuracy and poor real-time fault warning, which is difficult to support preventive operation and maintenance decision. SUMMARY

[0004] The present application provides a partial discharge integrated live detection system for an overhead line of a distribution network based on an ubiquitous Internet of Things architecture, which can solve the problem that the positioning accuracy is insufficient and the real-time fault warning is poor, which is difficult to support preventive operation and maintenance decision.

[0005] The present application provides a partial discharge integrated live detection system for an overhead line 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 edge gateway is in communication connection with the pulse signal detection end and the cloud monitoring platform respectively; The pulse signal detection end is used for detecting the partial discharge pulse signal of the overhead line, and transmitting the partial discharge pulse signal 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 connected with the synchronous pulse trigger module and the time calibration module respectively; The synchronous pulse trigger module is used for injecting a high-voltage synchronous pulse into the line and recording the injection time; The time calibration module is used to calculate the signal propagation speed based on the pulse round-trip delay and trigger multi-terminal time synchronization calibration; The edge analysis module is used to extract the amplitude characteristics, frequency characteristics and time domain characteristics of the partial discharge pulse signal, and generate a positioning distribution map of the partial discharge points; The cloud 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 the multi-terminal data fusion module and the result feedback module respectively; The multi-terminal data fusion module is used to fuse the positioning distribution maps and pulse characteristics 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 an insulation status assessment result; The result feedback module is used to feed back the visual assessment result including the discharge location and risk level to the end user.

[0006] Furthermore, the pulse signal detection end includes a high-frequency inductive sensor and a double-end detection module. The high-frequency inductive sensor is distributedly deployed at multiple monitoring nodes of the overhead line, and is used to collect the local discharge pulse signal generated during the operation of the line in a non-contact manner; the double-end detection module is deployed at the starting and ending points of the line section to be tested, and is used to perform the injection and reception of high-voltage synchronization pulses, and realize the ranging of local discharge based on the double-end signal.

[0007] Furthermore, the two-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 collect the partial discharge pulse signal and the synchronization pulse signal; the control unit is used to achieve two-end time synchronization, and based on the propagation time of the synchronization pulse signal and the time difference between the partial discharge pulse reaching the two ends, calculate the position of the partial discharge occurrence point through a preset algorithm.

[0008] Furthermore, the control unit is used to achieve time synchronization between the two terminals 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 partial discharge pulse arriving at the two terminals, including: The control unit The high voltage pulse injection unit is started at the end to inject high voltage synchronous pulse into the cable and record the injection time. Terminal signal acquisition unit; The terminal signal acquisition unit receives After the synchronization pulse is injected into the Terminal control unit starts The high-voltage pulse injection unit at the end injects high-voltage synchronous pulses into the cable; The terminal signal acquisition unit records the received The moment of pulse injection , based on the formula Calculate the one-way propagation time of the pulse in the cable ,in for The end receives The time to delay the injection of the self pulse after the end pulse; Based on propagation time and wave velocity , the location of the partial discharge occurrence point is calculated through a preset positioning algorithm.

[0009] Furthermore, the calculation of the location of the partial discharge occurrence point by using a preset positioning algorithm includes: extract End in time interval and End in time interval The partial discharge pulses collected in is an integer greater than 1, for times the one-way propagation time; For a pair from Duanhe The arrival time difference of the matching pulses in the time interval of the end is recorded as ; Based on the formula Calculate the distance between partial discharge points Distance between ends ,in is the total length of the cable, is the pulse wave velocity; All matching pulse pairs are traversed to generate a dot map representing the discharge frequency at different positions, and the final position of the local discharge point is determined based on the dot map.

[0010] Furthermore, the high-frequency inductive sensor is a spherical magnetic field sensor based on the principle of an 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 arranged on the sensor head to fix the sensor on the metal surface of the circuit; the filter amplifier circuit integrates a filtering link to expand the sensor frequency band.

[0011] Furthermore, the two-terminal 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 two-terminal detection module; The modeling process of the distribution parameter modeling unit includes: The double-end detection module is divided into a coupling part, a transmission part and a detection part, and a transmission line equation containing unit length mutual inductance, ground capacitance and resistance is established. The influence of the cable intermediate joint on the propagation of the synchronization pulse is considered, and the time-domain waveform expression of the synchronization pulse is modified. The frequency simulation of the transmission line model is performed by the simulation software, so that the amplitude-frequency characteristic and the actual detection characteristic meet the set requirements.

[0012] Further, the pulse signal detection end further comprises a magnetic flux leakage power supply module, which is electrically connected with the high-frequency inductive sensor and used for powering the sensor. The edge gateway integrates a low-power wireless communication unit, which is used for realizing data transmission between the edge gateway and the cloud monitoring platform. When there is no partial discharge signal, the edge gateway enters a sleep mode, and when the pulse signal exceeds the trigger threshold, the pulse signal detection end automatically wakes up.

[0013] Further, the signal acquisition unit comprises a voltage sensor, a current sensor and a signal conditioning circuit. The signal conditioning circuit is used for converting the voltage amplitude of the sensor output signal, adapting the input range of the data processing chip, and integrating a filter capacitor to suppress noise interference. The signal acquisition unit uses a chip to control the data acquisition timing, and configures a cache unit to temporarily store the collected data, and after the detection period ends, the data is transmitted to the edge gateway.

[0014] Further, the cloud monitoring platform further comprises a multi-end data comprehensive analysis module, which has the following functions: Supporting spectrum analysis function, which can identify typical partial discharge types based on spectrum characteristics; Providing multiple detection mode switching, and supporting multiple synchronization modes in multi-end detection mode; Automatically collecting the current environmental noise level and supporting setting the trigger threshold to filter effective partial discharge pulses; Generating a detection report based on the multi-end data fusion result, which includes detection information, discharge information, insulation state evaluation result and operation and maintenance suggestions; When the module judges the partial discharge signal, if the matching degree of the collected signal characteristics and the preset discharge type characteristics meets the set requirements, the corresponding discharge type is determined.

[0015] From the above technical solutions, the present application has the following advantages: The application realizes non-contact pulse signal acquisition of key nodes of distribution network overhead line through a pulse signal detection end, a synchronous pulse trigger module of an edge gateway cooperates with a time calibration module to complete multi-end clock synchronization, an edge analysis module extracts discharge characteristics in real time and generates a positioning distribution map, effectively solving the problems of large synchronization error and high positioning delay of traditional detection; a multi-end data fusion module of a cloud monitoring platform integrates all network data to output a corrected distribution map, a risk assessment module identifies discharge types based on a deep learning method and generates operation and maintenance strategies, and finally a risk visualization map is fed back, improving the real-time performance and positioning accuracy of partial discharge detection. The system realizes live detection through edge-cloud collaborative architecture, accurately guides preventive maintenance while avoiding power loss, and provides a high-reliability, high-efficiency and low-cost solution for distribution network insulation state control. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 It is a structure block diagram of a distribution network overhead line partial discharge integrated live detection system based on a ubiquitous Internet of Things architecture in the application; Figure 2 It is a flowchart for calculating the position of a partial discharge occurrence point in the application; Figure 3 It is a modeling flowchart of a distributed parameter modeling unit in the application. DETAILED DESCRIPTION

[0017] The terms "first", "second", "third", "fourth" and the like used in the description of the application and the above drawings, if any, are used to distinguish similar objects, and do not necessarily have to describe a particular order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "correspond to" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] Embodiment one Please refer to Figure 1The power line partial discharge integrated live detection system based on the ubiquitous Internet of Things architecture provided by the embodiment of the application comprises: a pulse signal detection end 1, an edge gateway 2 and a cloud monitoring platform 3; the edge gateway 2 is in communication connection with the pulse signal detection end 1 and the cloud monitoring platform 3 respectively; the pulse signal detection end 1 is used for detecting a partial discharge pulse signal of the overhead line and transmitting the partial discharge pulse signal to the edge gateway; the edge gateway 2 comprises a synchronous pulse trigger module 201, a time calibration module 202 and an edge analysis module 203; the edge analysis module 203 is connected with the synchronous pulse trigger module 201 and the time calibration module 202 respectively; the synchronous pulse trigger module 201 is used for injecting a high-voltage synchronous pulse into the line and recording the injection time; the time calibration module 202 is used for calculating a signal propagation speed based on a pulse round-trip time delay and triggering multi-end time synchronization calibration; the edge analysis module 203 is used for extracting amplitude features, frequency features and time domain features of the partial discharge pulse signal and generating a positioning distribution map of the partial discharge point; the cloud monitoring platform 3 comprises a multi-end data fusion module 301, a risk assessment module 302 and a result feedback module 303; the risk assessment module 302 is connected with the multi-end data fusion module 301 and the result feedback module 303 respectively; the multi-end data fusion module 301 is used for fusing the positioning distribution map and the pulse features uploaded by all edge gateways and outputting a global discharge point correction distribution map; the risk assessment module 302 identifies a discharge type based on a discharge feature spectrum and generates an insulation state assessment result; and the result feedback module 303 is used for feeding back a visual assessment result containing a discharge position and a risk level to a terminal user.

[0019] The working principle of the system of the application is described in detail in combination with specific scenarios as follows: In the live detection scenario of distribution network overhead line, first, the pulse signal detection end collects the partial discharge pulse signal generated during line operation, the double-end detection module deployed in pairs cooperates with the synchronous pulse trigger module of the edge gateway to inject a high-voltage synchronous pulse into the line and record the injection time; the time calibration module calculates the signal propagation speed based on the round-trip delay of the pulse between the two ends, and simultaneously triggers multi-end time synchronization calibration to ensure that the time references of all monitoring nodes are consistent; after the pulse signal detection end transmits the collected partial discharge pulse signal to the edge gateway, the edge analysis module extracts the amplitude, frequency and time domain features of the pulse signal, and generates a positioning distribution map of the partial discharge point by combining the time mark of the synchronous pulse and the signal propagation speed; then the edge gateway uploads the positioning distribution map and pulse features to the cloud monitoring platform, the multi-end data fusion module fuses all the data uploaded by the edge gateway, and outputs a global discharge point correction distribution map; the risk assessment module identifies the discharge type based on the discharge feature spectrum in the distribution map, and generates an insulation state assessment result; the final result feedback module feeds back the visual assessment result containing the discharge position and risk level to the end user, realizing accurate detection, positioning and insulation state assessment of partial discharge of distribution network overhead line.

[0020] Embodiment Two Referring to Figures 2-3 The pulse signal detection end 1 includes a high-frequency induction sensor 101 and a double-end detection module 102. The high-frequency induction sensor 101 is distributedly deployed at multiple monitoring nodes of the overhead line, and is used to collect partial discharge pulse signals generated during line operation in a non-contact manner. The double-end detection module 102 is deployed at the start and end points of the line segment to be detected, and is used to perform injection and reception of high-voltage synchronous pulses, and to realize partial discharge ranging based on double-end signals. The double-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 is used to inject a high-voltage synchronous pulse signal into the cable. The signal acquisition unit is used to collect partial discharge pulse signals and synchronous pulse signals. The control unit is used to realize double-end time synchronization, and to calculate the position of the partial discharge occurrence point based on the synchronous pulse signal propagation time and the time difference of the partial discharge pulse arriving at the two ends by a preset algorithm.

[0021] The working principle of the pulse signal detection end 1 will be described in detail in combination with specific scenarios as follows: In a specific partial discharge detection scene of the distribution network overhead line, the high-frequency inductive sensor of the pulse signal detection end is used as a sensing unit distributed in multiple monitoring nodes of the overhead line, continuously collects the partial discharge pulse signals generated during the operation of the line in a non-contact manner, and provides original data for subsequent analysis. The double-end detection module deployed at the start and end points of the line section to be detected injects a high-voltage synchronous pulse signal into the cable through the high-voltage pulse injection unit contained therein, and synchronously collects the partial discharge pulse signal and the injected synchronous pulse signal by the signal acquisition unit. In this process, the control unit of the double-end detection module first realizes double-end time synchronization to ensure that the time references of the two ends are consistent, and then accurately calculates the position of the partial discharge occurrence point based on the propagation time of the synchronous pulse signal and the time difference of the partial discharge pulse arriving at the two ends, so as to complete the collection and positioning of the partial discharge pulse signals of the overhead line together with the high-frequency inductive sensor, and realize the effective sensing and preliminary positioning of the line partial discharge by the pulse signal detection end.

[0022] The high-frequency inductive sensor is a spherical magnetic field sensor based on the principle of electrically small antenna, and the sensor includes a spherical electrode, a plate electrode, a filter amplification circuit, a power supply module, and an adsorption structure. The adsorption structure is arranged at the head of the sensor and is used to fix the sensor to the metal surface of the line. The filter amplification circuit integrates a filter link to expand the frequency band of the sensor.

[0023] In this embodiment, the control unit is used to realize double-end time synchronization, and calculate the position of the partial discharge occurrence point based on the propagation time of the synchronous pulse signal and the time difference of the partial discharge pulse arriving at the two ends through a preset algorithm, including the following steps: S11. The control unit starts the high-voltage pulse injection unit at the end to inject a high-voltage synchronous pulse into the cable, and records the injection time, and simultaneously starts the signal acquisition unit at the end. S12. The control unit triggers the high-voltage pulse injection unit at the end to release a high-voltage synchronous pulse into the cable through the built-in time sequence controller, and simultaneously calls the high-precision clock module to record the pulse injection time, and synchronously starts the signal acquisition unit at the end to continuously capture the electrical signals on the cable at a preset sampling frequency, which includes the subsequent feedback of the synchronous pulse and the line partial discharge pulse. S13. After the signal acquisition unit at the end receives the synchronous pulse injected by the control unit at the end, the control unit at the end starts the high-voltage pulse injection unit at the end to inject a high-voltage synchronous pulse into the cable. S14. The control unit at the end triggers the high-voltage pulse injection unit at the end to release a high-voltage synchronous pulse into the cable through the built-in time sequence controller, and simultaneously calls the high-precision clock module to record the pulse injection time, and synchronously starts the signal acquisition unit at the end to continuously capture the electrical signals on the cable at a preset sampling frequency, which includes the subsequent feedback of the synchronous pulse and the line partial discharge pulse.

[0024] S15. The control unit at the end triggers the high-voltage pulse injection unit at the end to release a high-voltage synchronous pulse into the cable through the built-in time sequence controller, and simultaneously calls the high-precision clock module to record the pulse injection time, and synchronously starts the signal acquisition unit at the end to continuously capture the electrical signals on the cable at a preset sampling frequency, which includes the subsequent feedback of the synchronous pulse and the line partial discharge pulse. ​​​​​​​The signal acquisition unit at the end monitors the cable signal in real time with the help of the pulse detection circuit. When the characteristic waveform of the pulse injected at the end, such as the amplitude and rising edge slope, meets the preset template, the trigger The synchronization logic of the terminal control unit starts The high voltage pulse injection unit at the end injects high voltage synchronous pulses into the cable in reverse. The terminal control unit records the moment of its own pulse injection synchronously and communicates with the The two ends calibrate the time reference to ensure the accuracy of time synchronization between the two ends.

[0025] S13. The terminal signal acquisition unit records the received The moment of pulse injection , based on the formula Calculate the one-way propagation time of the pulse in the cable ,in for The end receives The time to delay the injection of the self pulse after the end pulse; The terminal signal acquisition unit continuously samples and when it detects When the characteristic signal of the pulse is injected into the terminal, the clock module is triggered again to record the receiving time. .because The end pulse needs to pass → Spread (time consuming )→ End Delay → End pulse reversal → Spread (time consuming ), so it satisfies , the control unit is peeled off by solving the equation End Delay , divide the remaining time by 2 to get the one-way propagation time of the pulse in the cable .

[0026] S14. Based on propagation time and wave velocity , the location of the partial discharge occurrence point is calculated through a preset positioning algorithm.

[0027] 1. Extraction End in time interval and End in time interval The partial discharge pulses collected in is an integer greater than 1, for times the one-way propagation time; 2. For a pair of Duanhe The arrival time difference of the matching pulses in the time interval of the end is recorded as ; Based on the formula Calculate the distance between partial discharge points Distance between ends ,in is the total length of the cable, is the pulse wave velocity; 3. Traverse all matching pulse pairs to generate a dot map representing the discharge frequency at different positions, and determine the final position of the partial discharge point based on the dot map.

[0028] The process of extracting partial discharge pulses: First, based on the one-way propagation time , determine the time interval rules, End Selection Interval ( is an integer greater than 1, and the pulse propagation periodicity is used to ensure that the pulses in the interval are Same round of transmission on both ends), End-to-end synchronization selection Then, by analyzing the pulse amplitude, frequency, waveform slope and other multi-dimensional characteristics, we can screen 、 The matching pulse pairs within the end interval are determined to be bidirectional propagation pulses of the same discharge event. For each pair of matching pulses, the time difference between their arrival at the two ends is calculated. , substitute into the formula , is the total length of the cable, is the pulse wave velocity, based on And the line parameters are deduced in advance to obtain the discharge point distance Distance between ends Finally, all matching pulse pairs are traversed, and points are placed in the distance-discharge frequency coordinate system. Cluster analysis of densely packed areas is used to determine the final location of the local discharge. The center of the densely packed area is the actual discharge location, completing the complete process from double-ended pulse interaction to precise discharge point location.

[0029] In this embodiment, the two-terminal 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 two-terminal detection module; The modeling process of the distributed parameter modeling unit includes: S21. Divide the two-terminal detection module into a coupling part, a transmission part, and a detection part, and establish a transmission line equation including mutual inductance per unit length, capacitance to ground, and resistance; S22. Considering the influence of the cable intermediate joint on the propagation of the synchronization pulse, the expression of the time-domain waveform of the synchronization pulse is modified; S23. Frequency simulation is performed on the transmission line model by simulation software to ensure that the amplitude-frequency characteristics coincide with the actual detection characteristics to meet the set requirements.

[0030] Specifically, when the distribution parameter modeling unit performs S21, it first divides the double-end detection module into a coupling part (including the coupling components of the high-voltage pulse injection unit and the line), a transmission part (i.e., the body of the overhead cable to be detected), and a detection part (including the sensing components of the signal acquisition unit) according to the physical structure and signal flow of the double-end detection module. Then, based on the distribution parameter theory, transmission line equations are established for each part, which include the unit length mutual inductance (representing the electromagnetic induction effect between lines), the unit length capacitance to ground (reflecting the electric field coupling between the cable and the ground), and the unit length resistance (representing the ohmic loss of the cable) to describe the voltage and current propagation characteristics of the pulse signal in each part. When performing S22, because the intermediate joint of the cable has the characteristic of impedance discontinuity, it will cause reflection, attenuation, and waveform distortion during the propagation of the synchronization pulse. Therefore, the distribution parameter modeling unit analyzes the physical structure and electrical parameters of the joint, deduces the signal transmission characteristics of each intermediate joint, and introduces the characteristics into the original expression of the time-domain waveform of the synchronization pulse to modify the amplitude, rising slope, and oscillation components of the waveform, so as to accurately reflect the actual propagation state of the pulse after passing through the intermediate joint. When performing S23, the distribution parameter modeling unit calls the circuit simulation software, imports the established transmission line equations and the modified waveform expression into the model, performs frequency simulation on the transmission line model in a wide frequency band, obtains the amplitude-frequency characteristic curve at different frequencies, and then compares the simulation curve with the amplitude-frequency characteristics of the pulse signal collected during the actual operation of the double-end detection module. If the coincidence degree does not meet the set requirements, the unit length parameters in the transmission line equation or the transmission characteristic parameters of the intermediate joint are adjusted until the coincidence degree of the simulated amplitude-frequency characteristics and the actual detection characteristics meets the set requirements, thereby completing the precise modeling and analysis of the pulse coupling, propagation, and detection process of the double-end detection module.

[0031] In this embodiment, the pulse signal detection end 1 further includes a magnetic flux leakage energy harvesting power supply module 103 electrically connected with the high-frequency inductive sensor 101 for powering the sensor; an edge gateway integrated low-power wireless communication unit for realizing data transmission between the edge gateway and the cloud monitoring platform; and a sleep mode entered when there is no partial discharge signal, and the pulse signal detection end automatically wakes up when the detected pulse signal exceeds the trigger threshold.

[0032] The signal acquisition unit comprises 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, adapt 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 configures a cache unit to temporarily store the collected data, and after the detection period ends, the data is transmitted to the edge gateway.

[0033] Specifically, the magnetic flux leakage power supply module is based on the magnetic flux leakage generated by the alternating current when the overhead line is running, and the magnetic flux is coupled through the built-in power taking coil and converted into electric energy. After being processed by the rectification and voltage stabilization circuit, it is stored in the energy storage unit to continuously power the high-frequency induction sensor; the low-power wireless communication unit of the edge gateway uses low-power communication technology to reduce energy consumption while ensuring data transmission, and the pulse signal detection end judges the signal state in real time through the monitoring circuit. When there is no partial discharge signal, the sensor, signal acquisition unit and communication unit cooperate to enter the sleep mode, only the low-power wake-up circuit is retained to monitor the pulse signal amplitude, and when the signal exceeds the trigger threshold, the wake-up circuit triggers the system to resume work; in the signal acquisition unit, the voltage sensor and the current sensor collect the line electrical signals in real time, the signal conditioning circuit first filters out noise interference through the integrated filter capacitor, and then amplifies and converts the voltage amplitude of the signal to adapt the input range of the data processing chip. The data processing chip controls data acquisition according to the preset timing, temporarily stores the collected partial discharge pulse and synchronous pulse signal in the cache unit, and after the detection period ends, the data is transmitted to the edge gateway.

[0034] Embodiment three In this embodiment, the cloud monitoring platform 3 further comprises a multi-terminal data comprehensive analysis module, which has the following functions: supports spectrum analysis function, can identify typical partial discharge types based on spectrum characteristics; provides multiple detection mode switching, and supports multiple synchronization modes in multi-terminal detection mode; automatically collects the current environmental noise level, and supports setting the trigger threshold to filter effective partial discharge pulses; automatically generates a detection report based on the multi-terminal data fusion result, the report contains detection information, discharge information, insulation state evaluation result and operation and maintenance suggestions; when the module judges the partial discharge signal, if the matching degree of the collected signal characteristics and the preset discharge type characteristics meets the set requirements, the corresponding discharge type is determined.

[0035] Specifically, when the multi-terminal data comprehensive analysis module implements the spectrum analysis function, it first receives the partial discharge pulse signal characteristic data uploaded by each edge gateway, automatically generates the corresponding spectrum based on the amplitude, phase, frequency and other parameters of the pulse, then calls the built-in typical discharge type characteristic library, which includes spectrum characteristic templates of types such as surface discharge, sharp-end discharge, floating potential discharge, internal cavity discharge, and calculates the similarity of the collected spectrum and each template through a characteristic comparison algorithm. If the similarity meets the set threshold, the current partial discharge type is accurately identified.

[0036] When implementing the multi-detection mode switching function, the module provides a visual operation interface, and the user can select single-end detection, multi-terminal detection (main terminal) or multi-terminal detection (slave terminal) mode according to the detection requirements. After switching to the multi-terminal detection mode, further support is provided for selecting different synchronization methods. If the synchronization method depends on the satellite signal, the time of each monitoring node is calibrated by receiving the satellite time signal. If the synchronization method is combined, the pulse injection synchronization and external time signal double calibration are combined to ensure that the multi-terminal synchronization time error meets the set requirements. When implementing the automatic collection of environmental noise and setting the trigger threshold function, the module controls the pulse signal detection end to collect the line signal under the condition of no discharge interference at the initial stage of the detection period, extracts the amplitude and frequency distribution characteristics of the environmental noise through the noise analysis algorithm, determines the current environmental noise level, and provides a threshold adjustment interface at the same time. The user can set the trigger threshold to a certain multiple of the noise level according to the detection accuracy requirements. The module filters out noise signals with amplitudes below the threshold and retains effective partial discharge pulses based on this. When implementing the automatic generation of detection reports, the module obtains the global discharge point correction distribution map and insulation state evaluation result from the multi-terminal data fusion module, extracts the detection site, line parameters and discharge information from the edge gateway upload data, integrates these information according to the preset report template, generates a report containing basic detection information, detailed discharge data, insulation state level and targeted operation suggestions, and supports exporting report files in specific formats according to user requirements. When implementing the partial discharge signal judgment function, the module first preprocesses the collected pulse signals, then compares each type of characteristics in the preset discharge type characteristic library with these characteristics one by one, calculates the characteristic matching degree, and if the matching degree meets the set requirements, it is determined 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 to further analyze, thereby ensuring the accuracy of the partial discharge type judgment.

[0037] It can be understood that those skilled in the art can combine various embodiments in the above embodiments under the guidance of the above embodiments to obtain technical solutions of various embodiments.

[0038] The above merely describes preferred embodiments of the present application, and is not used to limit the present application, any modification, equivalent replacement and improvement within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A local discharge integrated live detection system for a networked overhead line based on an ubiquitous Internet of Things architecture, characterized in that, The application relates to a pulse signal detection terminal, an edge gateway and a cloud monitoring platform. The edge gateway is in communication connection with the pulse signal detection terminal and the cloud monitoring platform. The pulse signal detection terminal is used for detecting partial discharge pulse signals of an overhead line and transmitting the partial discharge pulse signals to the edge gateway. The edge gateway comprises a synchronous pulse triggering module, a time calibration module and an edge analysis module. The synchronous pulse triggering module is used for injecting a high-voltage synchronous pulse into the line and recording the injection time. The time calibration module is used for calculating a signal propagation speed based on a pulse round-trip time delay and triggering multi-terminal time synchronization calibration. The edge analysis module is used for extracting amplitude features, frequency features and time domain features of the partial discharge pulse signals and generating a positioning distribution map of partial discharge points. The cloud monitoring platform comprises a multi-terminal data fusion module, a risk assessment module and a result feedback module. The multi-terminal data fusion module is used for fusing the positioning distribution maps and pulse features uploaded by all edge gateways and outputting a global partial discharge point correction distribution map. The risk assessment module is used for identifying a discharge type based on a discharge feature spectrum and generating an insulation state assessment result. The result feedback module is used for feeding back a visual assessment result containing a discharge position and a risk level to a terminal user. The pulse signal detection terminal comprises a high-frequency induction sensor and a double-end detection module. 2.The system according to claim 1, wherein, The double-end detection module is arranged at start and end points of a line section to be detected, is used for injecting and receiving a high-voltage synchronous pulse and realizes partial discharge ranging based on double-end signals. The double-end detection module comprises a high-voltage pulse injection unit, a signal acquisition unit and a control unit. 3.The system according to claim 2, wherein, The control unit is used for realizing double-end time synchronization, calculating the position of a partial discharge point based on a synchronous pulse signal propagation time and a time difference between a partial discharge pulse and the double-end and through a preset algorithm.

4. The power line partial discharge integrated live detection system based on the ubiquitous Internet of Things architecture of claim 3, wherein, The control unit is used for realizing double-end time synchronization, calculating the position of a partial discharge point based on a synchronous pulse signal propagation time and a time difference between a partial discharge pulse and the double-end and through a preset algorithm, and comprises the following steps: The control unit The high voltage pulse injection unit is started at the end to inject high voltage synchronous pulse into the cable and record the injection time. Terminal signal acquisition unit; The end signal acquisition unit starts to acquire the signal from the end high-voltage pulse injection unit after receiving the end injected synchronization pulse. The end control unit starts to control the end high-voltage pulse injection unit after receiving the end injected synchronization pulse. The end high-voltage pulse injection unit injects a high-voltage synchronization pulse into the cable under the control of the end control unit. The end high-voltage pulse injection unit injects a high-voltage synchronization pulse into the cable under the control of the end control unit. the end signal acquisition unit records the reception of the time at which the end injects the pulse , based on the formula the one-way propagation time of the pulse in the cable is calculated where is the end receives the time at which the end injects its own pulse after the pulse Based on the propagation time and the wave velocity , the position of the partial discharge occurrence point is calculated by a preset positioning algorithm.

5. The power line partial discharge integrated live detection system based on the ubiquitous Internet of Things architecture of claim 4, wherein, The preset positioning algorithm for calculating the position of a partial discharge point comprises the following steps: extracting end in a time interval and end in a time interval partial discharge pulses collected within the time interval, wherein is an integer greater than 1, is times a single pass propagation time; For a pair of matched pulses from the end and the end time interval, the time difference of arrival is recorded as ; based on the formula , the distance of the partial discharge point from the end is calculated ; and , where is the total length of the cable, is the pulse wave speed; All matched pulse pairs are traversed, a dotting map representing discharge frequencies at different positions is generated, and the final position of a partial discharge point is determined based on the dotting map. 6.The system according to claim 2, wherein, The high-frequency induction sensor is a spherical magnetic field sensor based on the principle of electrically small antenna, and comprises a spherical electrode, a plate electrode, a filter amplification circuit, a power supply module and an adsorption structure. 7.The system according to claim 3, wherein, The double-end detection module further comprises a distributed parameter modeling unit, which is configured to model and analyze the processes of pulse coupling, propagation and detection of the double-end detection module. The modeling process of the distributed parameter modeling unit comprises: The double-end detection module is divided into a coupling part, a transmission part and a detection part, and a transmission line equation containing mutual inductance per unit length, ground capacitance and resistance is established; The influence of the cable intermediate joint on the propagation of the synchronization pulse is considered, and the expression of the time-domain waveform of the synchronization pulse is modified; The transmission line model is simulated by simulation software to ensure that the amplitude-frequency characteristics and the actual detection characteristics meet the set requirements. 8.The IoT-based power line partial discharge integrated live line detection system of claim 1, wherein, The magnetic flux leakage power supply module is electrically connected with the high-frequency induction sensor and is configured to supply power to 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. The pulse signal detection end enters a sleep mode when there is no partial discharge signal, and automatically wakes up when the detected pulse signal exceeds the trigger threshold. 9.The system of claim 3, wherein, The signal acquisition unit comprises a voltage sensor, a current sensor and a signal conditioning circuit. The signal conditioning circuit is configured to convert the voltage amplitude of the sensor output signal and adapt the input range of the data processing chip. The signal acquisition unit uses a chip to control the data acquisition timing and configures a cache unit to temporarily store the collected data. 10.The IoT-based power line partial discharge integrated live line detection system of claim 1, wherein, The cloud monitoring platform further comprises a multi-end data comprehensive analysis module, which has the following functions: Supports spectrum analysis function, which can identify typical partial discharge types based on spectrum characteristics; Provides multiple detection mode switching, and supports multiple synchronization methods in multi-end detection mode; Automatically collects the current environmental noise level and supports setting the trigger threshold to filter effective partial discharge pulses; Generates a detection report based on the multi-end data fusion result, which includes detection information, discharge information, insulation state evaluation result and operation and maintenance suggestions; When the multi-end data comprehensive analysis module judges the partial discharge signal, if the matching degree of the collected signal characteristics and the preset discharge type characteristics meets the set requirements, the corresponding discharge type is determined.

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