Detection method and device for on-line detection of operation state of drop-out lightning arrester

Abstract

The invention discloses a detection method and device for detecting the running state of a drop-out lightning arrester on line, and relates to a power distribution network equipment detection technology. The device comprises a composite insulation shell, a multi-dimensional sensor group, a data processing module, a Beidou positioning module, a non-explosion type separation module, a communication alarm module and a solar energy-lithium battery power supply module, and full-insulation packaging is realized through a vacuum injection molding process. According to the method, operation electrical parameters, insulation state parameters, environment parameters and equipment attitude parameters are collected in real time through a sensor group, after dynamic calibration of fusion sensor drift, time attenuation and environment temperature, an insulation attenuation comprehensive index and an equipment action reliability index are calculated, and insulation failure, action mechanism faults and short-circuit faults are accurately judged. When a fault occurs, the position is corrected through Beidou positioning and attitude parameters, the risk level is evaluated, a non-explosion type separation threshold value is determined by fusing multiple factors, and non-explosion breaking and multi-level alarm are triggered. And real-time monitoring, accurate diagnosis, safe disposal and efficient operation and maintenance are realized.

Description

Technical Field

[0001] This invention relates to power distribution network equipment testing technology, specifically to an online testing method and device for detecting the operating status of drop-out surge arresters. Background Technology

[0002] Surge arresters, as key protective devices in distribution network systems, are widely used in 10kV-35kV lines. Their core function is to discharge lightning overvoltage and switching overvoltage energy, preventing damage to lines and downstream power equipment due to overvoltage surges and ensuring the continuous and stable operation of the power grid. With the expansion of distribution network coverage and the increasing complexity of operating environments, surge arresters, operating under harsh outdoor conditions for extended periods, are prone to malfunctions such as insulation aging, abnormal leakage current, and jamming of the operating mechanism. Failure to detect and address these issues promptly can lead to line tripping, equipment burnout, or even large-scale power outages, posing a serious threat to the safe operation and maintenance of the power system.

[0003] Currently, surge arrester operational status monitoring is mainly divided into two categories: offline monitoring and online monitoring. Traditional offline monitoring relies on regular manual inspections, using portable instruments to measure parameters such as insulation resistance and leakage current. This method is not only labor-intensive and inefficient, but also suffers from the problem of "missed fault detection during inspection intervals," making it impossible to capture sudden equipment failures in real time. At the same time, manual monitoring is greatly affected by environmental conditions and operator skill, resulting in poor data consistency and making it difficult to accurately reflect the long-term operating status of the equipment.

[0004] While existing online detection technologies achieve real-time parameter acquisition, they still suffer from several technical shortcomings: First, the data calibration mechanism is imperfect. Most solutions only consider the single factor of ambient temperature, ignoring the impact of sensor drift and long-term operating time decay on the acquired data, resulting in large errors in the raw data and affecting the accuracy of subsequent status assessments. Second, the fault diagnosis dimensions are limited, mostly focusing on monitoring explicit faults such as short-circuit faults, lacking effective identification of implicit faults such as insulation attenuation and reliability of moving mechanisms, easily leading to misdiagnosis or missed diagnosis. Third, the fault location accuracy is insufficient, relying solely on the raw coordinate output of the positioning module without combining it with equipment attitude parameters for correction, making it difficult to accurately pinpoint the fault location and increasing the difficulty of operation and maintenance troubleshooting. Fourth, the disconnection and alarm mechanisms are unreasonable. Traditional explosive disconnectors pose an explosion risk, easily causing secondary safety hazards, and the alarm mode is mostly a single prompt, without prioritizing responses according to the fault risk level, resulting in unreasonable allocation of operation and maintenance resources and low fault handling efficiency. Fifth, the power supply mode adaptability is poor. Some online detection devices rely on grid power, resulting in insufficient endurance in remote outdoor scenarios, affecting the long-term stable operation of the equipment.

[0005] In summary, existing detection technologies have shortcomings in terms of data accuracy, comprehensiveness of fault diagnosis, reliability of location, safe disconnection, and efficiency of operation and maintenance response, failing to meet the core requirements of power distribution networks for surge arresters in terms of "real-time monitoring, accurate diagnosis, safe handling, and efficient operation and maintenance." Therefore, developing a multi-dimensional sensing, accurate calibration, comprehensive diagnosis, and safe and reliable online detection system for the operating status of drop-out surge arresters has become an urgent technical problem to be solved in the power operation and maintenance field. Summary of the Invention

[0006] The purpose of this invention is to provide an online detection method and device for detecting the operating status of drop-out surge arresters. This method uses a multi-dimensional sensor group with full insulation to collect relevant parameters in real time. After dynamic calibration, it calculates the comprehensive insulation attenuation index and the equipment operation reliability index to determine the fault type. Simultaneously, it uses a BeiDou positioning module to correct the fault location and assess the fault propagation risk level. Finally, it integrates multiple factors to calculate the non-explosive disconnection trigger threshold. When the threshold is reached, it triggers the thermal fuse disconnector to achieve safe disconnection. At the same time, it pushes multi-level alarm information through an IoT platform according to the risk level.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for online detection of the operating status of a drop-out surge arrester, characterized by the following steps:

[0009] S1: Deploy a multi-dimensional sensor group with full insulation enclosure, including current sensor, insulation resistance sensor, temperature and humidity sensor, and attitude sensor, to collect real-time data on the operating electrical parameters, insulation status parameters, environmental impact parameters, and equipment attitude parameters of the surge arrester.

[0010] S2: The data collected in step S1 is calibrated. The data is calibrated based on the dynamic calibration formula that integrates sensor drift, time decay and the influence of ambient temperature to obtain calibrated data.

[0011] S3: Calculate the comprehensive insulation attenuation index and the equipment operation reliability index based on the calibrated data, and determine the insulation failure and insufficient operation reliability states according to the preset thresholds respectively;

[0012] S4: Based on the insulation and operation reliability assessment results and leakage current threshold, the fault type is determined, the positioning module is activated to collect the original coordinates and combine them with the equipment attitude parameters to complete the positioning correction, and the fault risk level is assessed by the fault propagation risk coefficient.

[0013] S5: Calculates the non-explosive disconnection trigger threshold by integrating rated short-circuit current, insulation status, and fault risk level. When the short-circuit current reaches this threshold, the thermal fuse disconnector is triggered. At the same time, it divides multiple alarm levels according to the fault risk coefficient and links the IoT platform to push relevant information.

[0014] Step S2 performs data calibration based on a dynamic calibration formula that integrates sensor drift, time decay, and the effects of ambient temperature. The calibration formula is as follows:

[0015]

[0016] In the formula, These are the calibrated data values; These are the original values ​​collected by the sensor; This is the sensor drift correction factor; This is the time decay factor, and its value is positively correlated with the cumulative usage time of the sensor; The cumulative operating time of the sensor, in days; This is the ambient temperature correction factor, with a value ranging from 0.005 to 0.02. Real-time ambient temperature; The standard calibration temperature is fixed at 25℃. To ensure the equipment can withstand the maximum temperature, a fixed value of 85℃ is adopted.

[0017] The insulation attenuation comprehensive index mentioned in step S3 The calculation formula is as follows:

[0018]

[0019] In the formula, The comprehensive index of insulation attenuation; This is the insulation resistance value; The initial insulation resistance of the surge arrester; To allow the maximum leakage current; This is an environmental erosion factor, with a value range of 0.01 to 0.05; Surface dirtiness; For ambient humidity; This is the current influence coefficient, with a value ranging from 1.2 to 2.5; This represents the peak value of the leakage current. This is the lifespan loss factor, with a value ranging from 0.8 to 1.0; The design service life of the surge arrester is given in days. This refers to the cumulative operating time of the surge arrester.

[0020] The equipment operation reliability index mentioned in step S3 The calculation formula is as follows:

[0021]

[0022] In the formula, The equipment operation reliability index is defined as follows: an index value of ≥0.7 indicates reliable operation, while an index value of <0.7 indicates insufficient reliability. This is the attitude influence coefficient, with a value ranging from 0.01 to 0.03. This is the temperature sensitivity coefficient, with a value ranging from 0.1 to 0.3. The temperature decay index has a value range of 3 to 5. The tilt angle of the equipment; Real-time ambient temperature; The standard calibration temperature is fixed at 25℃. To ensure the equipment can withstand the maximum temperature, a fixed value of 85℃ is used.

[0023] Step S4, based on the insulation and operational reliability assessment results and leakage current threshold, determines the fault type, specifically including using fault characteristic functions. The fault characteristic function is used to determine the fault type. as follows:

[0024]

[0025] In the formula, The comprehensive index of insulation attenuation; The insulation reliability threshold is fixed at 0.35. When this occurs, it is determined to be an insulation failure; The equipment operation reliability index is defined as follows: an index value of ≥0.7 indicates reliable operation, while an index value of <0.7 indicates insufficient reliability. This represents the peak value of the leakage current. To allow the maximum leakage current.

[0026] When a non-zero fault is identified, the positioning module is activated to collect the original coordinates and combine them with the equipment attitude parameters to complete the positioning correction, and the fault propagation risk coefficient is calculated. The positioning correction formula is as follows:

[0027]

[0028] In the formula, The corrected and accurate longitude coordinates; These are the corrected, precise latitude coordinates; Provide the original longitude coordinates for BeiDou positioning; Provide the original latitude coordinates for BeiDou positioning; This is the positioning error correction coefficient, with a value range of... ; The azimuth angle of the device is collected by the attitude sensor; The tilt angle of the equipment;

[0029] The formula for calculating the fault propagation risk coefficient is as follows:

[0030]

[0031] In the formula, The risk coefficient for fault propagation is defined as follows: >1.2 indicates high risk, 0.8 <≤1.2 indicates medium risk, and ≤0.8 indicates low risk. This is the line correlation coefficient, with a value ranging from 1.0 to 1.8; This is the humidity amplification factor, with a value ranging from 0.02 to 0.05. This represents the peak value of the leakage current. To allow the maximum leakage current; This refers to ambient humidity.

[0032] Step S5 integrates rated short-circuit current, insulation condition, and fault risk level to calculate the non-explosive trip trigger threshold. The calculation formula is as follows:

[0033]

[0034] In the formula, This is the non-explosive disconnection trigger threshold. When the short-circuit current is greater than or equal to this value, the disconnection action is triggered. This refers to the rated short-circuit current of the surge arrester. The weighting is determined by the insulation state, with a value ranging from 0.5 to 1.0.

[0035] This represents the risk level weight, with a value ranging from 0.8 to 1.5. The comprehensive index of insulation attenuation; This represents the risk factor for the spread of the fault.

[0036] When the short-circuit current reaches When the surge arrester is activated, the thermal fuse disconnector is engaged, safely disconnecting the surge arrester from the power grid. Simultaneously, based on the fault risk coefficient, multiple alarm levels are established, and relevant information is pushed to the IoT platform. Specifically, this includes: When a Level 1 alarm is triggered, the platform will notify maintenance personnel in real time via pop-up window, SMS, and telephone, and generate the optimal repair path.

[0037] When a Level 2 alarm is triggered: a platform pop-up window and SMS notification are sent, and emergency repairs are completed within 6 hours;

[0038] When this occurs, a Level 3 alarm is triggered: the platform records the alarm information and completes the inspection and verification within 24 hours.

[0039] An online detection device for the operating status of a drop-out surge arrester, characterized in that it comprises:

[0040] The composite insulating housing contains a built-in sensor group, including a current sensor, an insulation resistance sensor, a temperature and humidity sensor, and an attitude sensor, for acquiring execution parameters.

[0041] Data processing module: integrates IoT card and storage unit, with built-in embedded chip, used to perform dynamic calibration, insulation reliability assessment and fault diagnosis calculation;

[0042] Beidou positioning module: Embedded inside the surge arrester, used to collect the original positioning coordinates and perform positioning correction in conjunction with the data processing module;

[0043] Non-explosive separation module: includes a cryogenic alloy melting plate and a magnetohydrodynamic optimized melting cavity, receives trigger signals from the data processing module, and achieves non-explosive separation;

[0044] Communication alarm module: Supports encrypted data transmission, establishes real-time connection with IoT platform, and executes multi-level alarm linkage steps;

[0045] Power supply module: Powered by solar panels and lithium battery packs.

[0046] The composite insulating shell uses a fully insulating encapsulation, specifically achieved by vacuum injection molding to cover the conductive components.

[0047] The working mechanism of this online monitoring system for the operational status of drop-out surge arresters revolves around multi-dimensional perception, precise calibration, comprehensive evaluation, and intelligent response closed-loop. Through hardware and software collaboration, it achieves real-time monitoring of the arrester's operational status, fault diagnosis, and safe handling, as detailed below:

[0048] The system uses a fully insulated composite housing as its carrier, housing a multi-dimensional sensor array for current, insulation resistance, temperature, humidity, and attitude. It captures real-time operating electrical parameters, insulation status parameters, environmental impact parameters, and equipment attitude parameters of the surge arrester. The fully insulated design also ensures stable operation of the sensor array in complex outdoor environments. After initial reception by the data processing module, the collected raw data undergoes error elimination through a dynamic calibration formula that integrates sensor drift, time decay, and the effects of ambient temperature. This results in accurate and effective calibration data, providing a reliable foundation for subsequent evaluation.

[0049] Based on the calibration data, the data processing module further calculates two core indices: the first is a comprehensive insulation attenuation index that integrates insulation resistance, environmental erosion, leakage current, and lifespan loss, which determines whether the insulation has failed based on a preset threshold; the second is a device operation reliability index that combines the effects of equipment tilt angle and real-time temperature, setting a critical value to determine whether the operating mechanism is reliable. Subsequently, through a fault characteristic function, the two indices are linked with the leakage current threshold to accurately identify three types of faults: insulation failure, operating mechanism failure, and short circuit fault, as well as normal operating conditions.

[0050] When an abnormal operating condition is detected, the BeiDou positioning module collects the original coordinates and combines them with the tilt angle and azimuth angle obtained from the attitude sensor to achieve precise calibration of the fault location through a positioning correction formula. Simultaneously, it calculates and integrates the fault propagation risk coefficient based on the degree of leakage current exceeding the standard, environmental humidity, and line correlation, classifying the risk into high, medium, and low levels. The data processing module further integrates the surge arrester's rated short-circuit current, insulation attenuation comprehensive index, and fault risk level to calculate the non-explosive disconnection trigger threshold. When the actual short-circuit current reaches this threshold, the non-explosive disconnection module, containing a cryogenic alloy fusible link and a magnetohydrodynamic optimized fusion cavity, is triggered, achieving a safe, non-explosive disconnection of the surge arrester from the power grid.

[0051] Simultaneously, the communication alarm module initiates multi-level linkage alarms based on the fault risk level: high risk triggers platform pop-up + SMS + telephone notification and generates the optimal repair path; medium risk triggers platform pop-up + SMS notification and requires repair within 6 hours; low risk only records alarm information and requires inspection and verification within 24 hours. All information is transmitted in real time to the IoT platform through encrypted transmission, forming full-process intelligent operation and maintenance support.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] High accuracy of data monitoring: Compared with existing technologies that often use single-dimensional calibration or ignore the effects of sensor drift and time decay, this invention effectively eliminates the original data error by integrating a dynamic calibration formula that incorporates sensor drift, time decay, and ambient temperature, providing a reliable data foundation for subsequent status assessment and significantly improving the accuracy and stability of operating parameter monitoring.

[0054] By comprehensively collecting electrical parameters, insulation status, environmental impact, and equipment attitude data through a multi-dimensional sensor array, and combining a dual-core evaluation system of insulation attenuation comprehensive index and equipment operation reliability index, along with fault characteristic functions, accurate identification of insulation failure, operating mechanism failure, and short circuit faults is achieved, avoiding missed or false fault identification.

[0055] By fusing and correcting the raw BeiDou positioning data with the equipment attitude parameters, the fault location can be accurately pinpointed. At the same time, a fault propagation risk coefficient is introduced to quantify the risk of fault spread, providing a scientific basis for determining maintenance priorities and solving the problem of the lack of risk classification in existing technologies.

[0056] To address the shortcomings of traditional explosive disconnectors, such as explosion risk and insufficient safety, this invention adopts a non-explosive disconnect module. It achieves safe disconnection without explosion through a low-temperature alloy molten sheet and a magnetic fluid-optimized molten cavity, avoiding secondary safety hazards. Furthermore, the disconnection trigger threshold integrates the rated short-circuit current, insulation status, and fault risk level to achieve "on-demand triggering," ensuring power grid safety while avoiding excessive disconnection.

[0057] A multi-level alarm mechanism based on risk level is established, which is linked with the Internet of Things platform to realize hierarchical notifications via pop-ups, SMS and telephone. With the generation of the optimal repair path and clear repair and inspection time limits, the fault handling cycle is significantly shortened. At the same time, a dual power supply mode of solar panels and lithium battery packs is adopted to adapt to complex outdoor environments, solve the limitations of the traditional power grid supply, and improve the long-term operational stability of the device. Attached Figure Description

[0058] Figure 1 This is a flowchart of an online detection method for the operating status of a drop-out surge arrester according to the present invention. Detailed Implementation

[0059] The technical solutions of the present invention will now be described in detail with reference to the accompanying drawings.

[0060] like Figure 1 As shown, an online detection method for the operating status of a drop-out surge arrester is characterized by the following steps:

[0061] S1: Deploy a multi-dimensional sensor group with full insulation enclosure, including current sensor, insulation resistance sensor, temperature and humidity sensor, and attitude sensor, to collect real-time data on the operating electrical parameters, insulation status parameters, environmental impact parameters, and equipment attitude parameters of the surge arrester.

[0062] S2: The data collected in step S1 is calibrated. The data is calibrated based on the dynamic calibration formula that integrates sensor drift, time decay and the influence of ambient temperature to obtain calibrated data.

[0063] S3: Calculate the comprehensive insulation attenuation index and the equipment operation reliability index based on the calibrated data, and determine the insulation failure and insufficient operation reliability states according to the preset thresholds respectively;

[0064] S4: Based on the insulation and operation reliability assessment results and leakage current threshold, the fault type is determined, the positioning module is activated to collect the original coordinates and combine them with the equipment attitude parameters to complete the positioning correction, and the fault risk level is assessed by the fault propagation risk coefficient.

[0065] S5: Calculates the non-explosive disconnection trigger threshold by integrating rated short-circuit current, insulation status, and fault risk level. When the short-circuit current reaches this threshold, the thermal fuse disconnector is triggered. At the same time, it divides multiple alarm levels according to the fault risk coefficient and links the IoT platform to push relevant information.

[0066] Step S2 performs data calibration based on a dynamic calibration formula that integrates sensor drift, time decay, and the effects of ambient temperature. The calibration formula is as follows:

[0067]

[0068] In the formula, These are the calibrated data values; These are the original values ​​collected by the sensor; This is the sensor drift correction factor; This is the time decay factor, and its value is positively correlated with the cumulative usage time of the sensor; The cumulative operating time of the sensor, in days; This is the ambient temperature correction factor, with a value ranging from 0.005 to 0.02. Real-time ambient temperature; The standard calibration temperature is fixed at 25℃. To ensure the equipment can withstand the maximum temperature, a fixed value of 85℃ is adopted.

[0069] The insulation attenuation comprehensive index mentioned in step S3 The calculation formula is as follows:

[0070]

[0071] In the formula, The comprehensive index of insulation attenuation; This is the insulation resistance value; The initial insulation resistance of the surge arrester; To allow the maximum leakage current; This is an environmental erosion factor, with a value range of 0.01 to 0.05; Surface dirtiness; For ambient humidity; This is the current influence coefficient, with a value ranging from 1.2 to 2.5; This represents the peak value of the leakage current. This is the lifespan loss factor, with a value ranging from 0.8 to 1.0; The design service life of the surge arrester is given in days. This refers to the cumulative operating time of the surge arrester.

[0072] The equipment operation reliability index mentioned in step S3 The calculation formula is as follows:

[0073]

[0074] In the formula, The equipment operation reliability index is defined as follows: an index value of ≥0.7 indicates reliable operation, while an index value of <0.7 indicates insufficient reliability. This is the attitude influence coefficient, with a value ranging from 0.01 to 0.03. This is the temperature sensitivity coefficient, with a value ranging from 0.1 to 0.3. The temperature decay index has a value range of 3 to 5. The tilt angle of the equipment; Real-time ambient temperature; The standard calibration temperature is fixed at 25℃. To ensure the equipment can withstand the maximum temperature, a fixed value of 85℃ is used.

[0075] Step S4, based on the insulation and operational reliability assessment results and leakage current threshold, determines the fault type, specifically including using fault characteristic functions. The fault characteristic function is used to determine the fault type. as follows:

[0076]

[0077] In the formula, The comprehensive index of insulation attenuation; The insulation reliability threshold is fixed at 0.35. When this occurs, it is determined to be an insulation failure; The equipment operation reliability index is defined as follows: an index value of ≥0.7 indicates reliable operation, while an index value of <0.7 indicates insufficient reliability. This represents the peak value of the leakage current. To allow the maximum leakage current.

[0078] When a non-zero fault is identified, the positioning module is activated to collect the original coordinates and combine them with the equipment attitude parameters to complete the positioning correction, and the fault propagation risk coefficient is calculated. The positioning correction formula is as follows:

[0079]

[0080] In the formula, The corrected and accurate longitude coordinates; These are the corrected, precise latitude coordinates; Provide the original longitude coordinates for BeiDou positioning; Provide the original latitude coordinates for BeiDou positioning; This is the positioning error correction coefficient, with a value range of... ; The azimuth angle of the device is collected by the attitude sensor; The tilt angle of the equipment;

[0081] The formula for calculating the fault propagation risk coefficient is as follows:

[0082]

[0083] In the formula, The risk coefficient for fault propagation is defined as follows: >1.2 indicates high risk, 0.8 <≤1.2 indicates medium risk, and ≤0.8 indicates low risk. This is the line correlation coefficient, with a value ranging from 1.0 to 1.8; This is the humidity amplification factor, with a value ranging from 0.02 to 0.05. This represents the peak value of the leakage current. To allow the maximum leakage current; This refers to ambient humidity.

[0084] Step S5 integrates rated short-circuit current, insulation condition, and fault risk level to calculate the non-explosive trip trigger threshold. The calculation formula is as follows:

[0085]

[0086] In the formula, This is the non-explosive disconnection trigger threshold. When the short-circuit current is greater than or equal to this value, the disconnection action is triggered. This refers to the rated short-circuit current of the surge arrester. The weighting is determined by the insulation state, with a value ranging from 0.5 to 1.0.

[0087] This represents the risk level weight, with a value ranging from 0.8 to 1.5. The comprehensive index of insulation attenuation; This represents the risk factor for the spread of the fault.

[0088] When the short-circuit current reaches When the surge arrester is activated, the thermal fuse disconnector is engaged, safely disconnecting the surge arrester from the power grid. Simultaneously, based on the fault risk coefficient, multiple alarm levels are established, and relevant information is pushed to the IoT platform. Specifically, this includes: When a Level 1 alarm is triggered, the platform will notify maintenance personnel in real time via pop-up window, SMS, and telephone, and generate the optimal repair path.

[0089] When a Level 2 alarm is triggered: a platform pop-up window and SMS notification are sent, and emergency repairs are completed within 6 hours;

[0090] When this occurs, a Level 3 alarm is triggered: the platform records the alarm information and completes the inspection and verification within 24 hours.

[0091] An online detection device for the operating status of a drop-out surge arrester, characterized in that it comprises:

[0092] The composite insulating housing contains a built-in sensor group, including a current sensor, an insulation resistance sensor, a temperature and humidity sensor, and an attitude sensor, for acquiring execution parameters.

[0093] Data processing module: integrates IoT card and storage unit, with built-in embedded chip, used to perform dynamic calibration, insulation reliability assessment and fault diagnosis calculation;

[0094] Beidou positioning module: Embedded inside the surge arrester, used to collect the original positioning coordinates and perform positioning correction in conjunction with the data processing module;

[0095] Non-explosive separation module: includes a cryogenic alloy melting plate and a magnetohydrodynamic optimized melting cavity, receives trigger signals from the data processing module, and achieves non-explosive separation;

[0096] Communication alarm module: Supports encrypted data transmission, establishes real-time connection with IoT platform, and executes multi-level alarm linkage steps;

[0097] Power supply module: Powered by solar panels and lithium battery packs.

[0098] The composite insulating shell uses a fully insulating encapsulation, specifically achieved by vacuum injection molding to cover the conductive components.

[0099] This online monitoring device for detecting the operational status of drop-out surge arresters is installed in 10kV outdoor distribution network lines. The composite insulated shell is fully insulated using vacuum injection molding. Built-in current sensors, insulation resistance sensors, temperature and humidity sensors, and attitude sensors continuously collect operational electrical parameters such as peak leakage current and insulation resistance, environmental parameters such as temperature and humidity and surface contamination, and attitude parameters such as tilt angle and azimuth angle. The power supply module uses a solar panel and lithium battery pack to ensure stable outdoor operation. The data processing module dynamically calibrates the raw data, outputting accurate calibration data by considering the effects of sensor drift, time decay, and ambient temperature. Based on the calibration data, it calculates the insulation attenuation comprehensive index and the equipment operation reliability index. An insulation attenuation comprehensive index below 0.35 indicates insulation failure, an equipment operation reliability index below 0.7 indicates insufficient reliability of the operating mechanism, and a peak leakage current exceeding 1.5 times the maximum allowable leakage current indicates a short circuit fault. The absence of any of these conditions indicates normal operation. When an abnormal operating condition is detected, the Beidou positioning module collects the original coordinates and combines them with the equipment tilt angle and azimuth angle to complete the positioning correction. At the same time, it calculates the fault propagation risk coefficient. A coefficient greater than 1.2 indicates high risk, between 0.8 and 1.2 indicates medium risk, and less than or equal to 0.8 indicates low risk. The data processing module integrates the rated short-circuit current, insulation attenuation comprehensive index, and fault risk level to derive the non-explosive disconnection trigger threshold. When the actual short-circuit current reaches this threshold, the non-explosive disconnection module achieves a safe disconnection without explosion through a cryogenic alloy melting sheet and a magnetic fluid optimized melting cavity. The communication alarm module links with the IoT platform to push multi-level alarms. In the case of high risk, the platform will notify the maintenance personnel in real time through pop-up windows, SMS messages, and phone calls, and generate the optimal repair path. In the case of medium risk, the platform will pop-up windows and send SMS messages requiring repair within 6 hours. In the case of low risk, the platform will record the alarm information and complete the inspection and verification within 24 hours.

Claims

1. A method for online detection of the operating status of a drop-out surge arrester, characterized in that, Includes the following steps: S1: Deploy a multi-dimensional sensor group with full insulation enclosure, including current sensor, insulation resistance sensor, temperature and humidity sensor, and attitude sensor, to collect real-time data on the operating electrical parameters, insulation status parameters, environmental impact parameters, and equipment attitude parameters of the surge arrester. S2: The data collected in step S1 is calibrated. The data is calibrated based on the dynamic calibration formula that integrates sensor drift, time decay and the influence of ambient temperature to obtain calibrated data. S3: Calculate the comprehensive insulation attenuation index and the equipment operation reliability index based on the calibrated data, and determine the insulation failure and insufficient operation reliability states according to the preset thresholds respectively; S4: Based on the insulation and operation reliability assessment results and leakage current threshold, the fault type is determined, the positioning module is activated to collect the original coordinates and combine them with the equipment attitude parameters to complete the positioning correction, and the fault risk level is assessed by the fault propagation risk coefficient. S5: Calculates the non-explosive disconnection trigger threshold by integrating rated short-circuit current, insulation status, and fault risk level. When the short-circuit current reaches this threshold, the thermal fuse disconnector is triggered. At the same time, it divides multiple alarm levels according to the fault risk coefficient and links the IoT platform to push relevant information.

2. The method for online detection of the operating status of a drop-out surge arrester according to claim 1, characterized in that, Step S2 performs data calibration based on a dynamic calibration formula that integrates sensor drift, time decay, and the effects of ambient temperature. The calibration formula is as follows: In the formula, These are the calibrated data values; These are the original values ​​collected by the sensor; This is the sensor drift correction factor; This is the time decay factor, and its value is positively correlated with the cumulative usage time of the sensor; The cumulative operating time of the sensor, in days; This is the ambient temperature correction factor, with a value ranging from 0.005 to 0.

02. Real-time ambient temperature; The standard calibration temperature is fixed at 25℃. To ensure the equipment can withstand the maximum temperature, a fixed value of 85℃ is used.

3. The method for online detection of the operating status of a drop-out surge arrester according to claim 1, characterized in that, The insulation attenuation comprehensive index mentioned in step S3 The calculation formula is as follows: In the formula, The comprehensive index of insulation attenuation; This is the insulation resistance value; The initial insulation resistance of the surge arrester; To allow the maximum leakage current; This is an environmental erosion factor, with a value range of 0.01 to 0.05; Surface dirtiness; For ambient humidity; This is the current influence coefficient, with a value ranging from 1.2 to 2.5; This represents the peak value of the leakage current. This is the lifespan loss factor, with a value ranging from 0.8 to 1.0; The design service life of the surge arrester is expressed in days. This refers to the cumulative operating time of the surge arrester.

4. The method for online detection of the operating status of a drop-out surge arrester according to claim 1, characterized in that, The equipment operation reliability index mentioned in step S3 The calculation formula is as follows: In the formula, The equipment operation reliability index is defined as follows: an index value of ≥0.7 indicates reliable operation, while an index value of <0.7 indicates insufficient reliability. This is the attitude influence coefficient, with a value ranging from 0.01 to 0.

03. This is the temperature sensitivity coefficient, with a value ranging from 0.1 to 0.

3. The temperature decay index has a value range of 3 to 5. The tilt angle of the equipment; Real-time ambient temperature; The standard calibration temperature is fixed at 25℃. To ensure the equipment can withstand the maximum temperature, a fixed value of 85℃ is used.

5. The method for online detection of the operating status of a drop-out surge arrester according to claim 1, characterized in that, Step S4, based on the insulation and operational reliability assessment results and leakage current threshold, determines the fault type, specifically including using fault characteristic functions. The fault characteristic function is used to determine the fault type. as follows: In the formula, The comprehensive index of insulation attenuation; The insulation reliability threshold is fixed at 0.

35. When this occurs, it is determined to be an insulation failure; The equipment operation reliability index is defined as follows: an index value of ≥0.7 indicates reliable operation, while an index value of <0.7 indicates insufficient reliability. This represents the peak value of the leakage current. To allow the maximum leakage current.

6. The method for online detection of the operating status of a drop-out surge arrester according to claim 5, characterized in that, When a non-zero fault is identified, the positioning module is activated to collect the original coordinates and combine them with the equipment attitude parameters to complete the positioning correction, and the fault propagation risk coefficient is calculated. The positioning correction formula is as follows: In the formula, The corrected and accurate longitude coordinates; These are the corrected, precise latitude coordinates; Provide the original longitude coordinates for BeiDou positioning; Provide the original latitude coordinates for BeiDou positioning; This is the positioning error correction coefficient, with a value range of... ; The azimuth angle of the device is collected by the attitude sensor; The tilt angle of the equipment; The formula for calculating the fault propagation risk coefficient is as follows: In the formula, The risk coefficient for fault propagation is defined as follows: >1.2 indicates high risk, 0.8 <≤1.2 indicates medium risk, and ≤0.8 indicates low risk. This is the line correlation coefficient, with a value ranging from 1.0 to 1.8; This is the humidity amplification factor, with a value ranging from 0.02 to 0.

05. This represents the peak value of the leakage current. To allow the maximum leakage current; This refers to ambient humidity.

7. The method for online detection of the operating status of a drop-out surge arrester according to claim 1, characterized in that, Step S5 integrates rated short-circuit current, insulation condition, and fault risk level to calculate the non-explosive trip trigger threshold. The calculation formula is as follows: In the formula, This is the non-explosive disconnection trigger threshold. When the short-circuit current is greater than or equal to this value, the disconnection action is triggered. This refers to the rated short-circuit current of the surge arrester. The weighting is determined by the insulation state, with a value ranging from 0.5 to 1.

0. This represents the risk level weight, with a value ranging from 0.8 to 1.

5. The comprehensive index of insulation attenuation; This represents the risk factor for the spread of the fault.

8. The method for online detection of the operating status of a drop-out surge arrester according to claim 7, characterized in that, When the short-circuit current reaches When the surge arrester is activated, the thermal fuse disconnector is engaged, safely disconnecting the surge arrester from the power grid. Simultaneously, based on the fault risk coefficient, multiple alarm levels are established, and relevant information is pushed to the IoT platform. Specifically, this includes: When a Level 1 alarm is triggered, the platform will notify maintenance personnel in real time via pop-up window, SMS, and telephone, and generate the optimal repair path. When a Level 2 alarm is triggered: a platform pop-up window and SMS notification are sent, and emergency repairs are completed within 6 hours; When this occurs, a Level 3 alarm is triggered: the platform records the alarm information and completes the inspection and verification within 24 hours.

9. An online detection device for the operating status of a drop-out surge arrester, characterized in that, include: The composite insulating housing contains a built-in sensor group, including a current sensor, an insulation resistance sensor, a temperature and humidity sensor, and an attitude sensor, for acquiring execution parameters. Data processing module: integrates IoT card and storage unit, with built-in embedded chip, used to perform dynamic calibration, insulation reliability assessment and fault diagnosis calculation; Beidou positioning module: Embedded inside the surge arrester, used to collect the original positioning coordinates and perform positioning correction in conjunction with the data processing module; Non-explosive separation module: includes a cryogenic alloy melting plate and a magnetohydrodynamic optimized melting cavity, receives trigger signals from the data processing module, and achieves non-explosive separation; Communication alarm module: Supports encrypted data transmission, establishes real-time connection with IoT platform, and executes multi-level alarm linkage steps; Power supply module: Powered by solar panels and lithium battery packs.

10. The online detection device for detecting the operating status of a drop-out surge arrester according to claim 9, characterized in that, The composite insulating shell uses a fully insulating encapsulation, specifically achieved by vacuum injection molding to cover the conductive components.

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