Analysis method and system based on cooperative influence between lightning arrester fault and operation condition

Abstract

The invention discloses an analysis method and system based on cooperative influence between lightning arrester faults and operation conditions, and relates to the technical field of energy power, and the main scheme is as follows: obtaining lightning arrester operation data sets such as lightning arrester working environment parameters, physical parameters, electrical parameters, appearance images and the like in real time; analyzing a lightning arrester operation data set through a digital graph, and calculating a lightning arrester feature set comprising a working environment feature set, a thermal collapse feature set, a damp feature set, a degradation acceleration feature set, a mechanical defect feature set, a sealing failure feature set and the like; performing risk analysis on the lightning arrester working environment condition and the lightning arrester fault condition including the thermal collapse risk, the damp risk, the degradation acceleration risk, the mechanical defect risk and the sealing failure risk on the lightning arrester feature set to obtain lightning arrester risk comprehensive assessment; and carrying out operation condition early warning fusion grading and collaborative mapping grading on lightning arrester risk comprehensive assessment. Various information is displayed on the terminal, so that an operation unit can fully and comprehensively master the operation condition of the lightning arrester, various resources are accurately linked and coordinated, and deployment work is guided.

Description

Technical Field

[0001] The present invention relates to the field of energy and power technology, and in particular to an analysis method and system based on the synergistic influence between arrester faults and operating conditions. Background Art

[0002] Lightning arresters play a vital role in power systems, effectively dissipating the energy of lightning or operational overvoltages, thereby protecting electrical equipment from transient overvoltage damage. They also interrupt continuous current flow, preventing system ground shorts. Once the overvoltage reaches the specified operating voltage, the arrester rapidly activates, limiting the overvoltage amplitude by passing a charge through it, thereby protecting the equipment's insulation from breakdown damage. Once the voltage returns to normal, the arrester immediately returns to its original state, ensuring the normal power supply of the power system. Its operational reliability is directly related to the safety of the power system.

[0003] After the arrester is put into use, it needs to be regularly inspected and maintained to detect its faults. However, there are a large number of arresters in the entire line and the entire area, and the inspection content is complicated. The operation and maintenance unit needs to invest a lot of manpower and material resources. Due to factors such as the inconsistent technical level and focus of the operation and maintenance personnel, it is easy to miss inspections and wrong inspections. The potential danger of lightning arrester failure cannot be discovered in time, which easily leads to an increase in the probability of accidents. At the same time, because inspections have a certain periodicity, it is impossible to grasp the changing status of the lightning arrester in real time. There is a lack of comprehensive analysis and evaluation and real-time and targeted data basis for emergency warning and collaborative work, resulting in a lack of prominent focus in collaborative work and a reduction in early warning and emergency response capabilities. Summary of the Invention

[0004] In view of the deficiencies in existing technologies, the present invention provides an analysis method and system based on the synergistic influence between arrester faults and operating conditions, which collects, pre-processes and integrates the arrester operation data set including multiple categories and multiple sources of data such as arrester working environment, physical parameters, electrical parameters and arrester images in real time through the data acquisition module; the digital graph analysis module performs digital graph analysis on the arrester operation data set, analyzes the working environment feature set, thermal collapse feature set, moisture feature set, degradation acceleration feature set, mechanical defect feature set and sealing failure feature set, including arrester surface temperature, temperature rise rate, resistive current, insulation resistance, internal humidity, arrester deformation rate, arrester tilt angle, equalizing ring variable, equalizing ring corrosion rate, sealing structure crack length, sealing structure crack density, sealing structure contamination area rate and other arrester feature values, and integrates them into an arrester feature set; the risk analysis module performs analysis based on the arrester feature set, including the arrester working environment, The various risk factors of thermal breakdown risk, moisture risk, accelerated deterioration risk, mechanical defect risk, and sealing failure risk are analyzed to obtain a comprehensive assessment of the arrester risk, which includes the arrester working environment conditions and arrester fault conditions, and realizes multi-dimensional and multi-level risk analysis of the arrester; the early warning collaborative grading module integrates and grades the arrester working environment conditions and arrester fault conditions in the arrester risk comprehensive assessment to obtain the operating condition early warning level, and maps the operating condition early warning level to the operating condition collaborative level, realizing the simultaneous update of the operating condition early warning level and collaborative level, which is more conducive to the operation and maintenance units and personnel to fully understand the operating status of the arresters on the entire line and in the entire region. The system can accurately coordinate various resources, deploy various tasks, enhance the real-time and targeted nature of the arrester work, and is conducive to the rational allocation of manpower and material resources to realize the monitoring of the power system operation of the entire line and the entire area to ensure its safe and stable operation.

[0005] In order to achieve the above objectives, the present invention is implemented through the following technical solutions: an analysis method based on the synergistic influence between arrester faults and operating conditions, comprising:

[0006] Real-time acquisition of arrester operation data, pre-processing of the arrester operation data, including arrester working environment, physical parameters, electrical parameters and arrester images, and integration of the arrester operation data set;

[0007] Perform digital graph analysis on the arrester operation data set. This includes calculating and analyzing the characteristic values ​​of the working environment feature set, thermal collapse feature set, moisture feature set, accelerated degradation feature set, mechanical defect feature set, and seal failure feature set, and integrating them to obtain the arrester feature set.

[0008] Perform risk analysis on the arrester feature set based on the arrester working environment and arrester fault conditions to obtain a comprehensive arrester risk assessment. The risk factors for the arrester fault condition include the arrester working environment, thermal breakdown risk, moisture risk, accelerated degradation risk, mechanical defect risk, and sealing failure risk.

[0009] The comprehensive risk assessment of lightning arresters is carried out by integrating the grading of the operating condition warning level and mapping the grading of the operating condition coordination level.

[0010] In the preferred embodiment of the above analysis method based on the synergistic effect between arrester faults and operating conditions:

[0011] The working environment of the lightning arrester includes: ambient temperature and ambient humidity;

[0012] Physical parameters include: arrester surface temperature, internal humidity;

[0013] Electrical parameters include: resistive current, DC reference voltage, insulation resistance;

[0014] Arrester images include: arrester appearance image, equalizing ring appearance image, sealing structure appearance image, and arrester infrared thermal image.

[0015] In the preferred embodiment of the above analysis method based on the synergistic effect between arrester faults and operating conditions:

[0016] The working environment characteristic set includes the ambient temperature RH w , ambient humidity T w , rainfall probability P r ;

[0017] The thermal collapse feature set includes the arrester surface temperature T surface , temperature rise rate v △T ;

[0018] The damp feature set includes the arrester resistive current I r , insulation resistance R ins , internal humidity RH in ;

[0019] The degradation acceleration feature set includes the arrester resistive current I r , DC reference voltage drop rate Maximum surface temperature difference △T s ;

[0020] The mechanical defect feature set includes the arrester deformation rate δ, the arrester tilt angle θ, the equalizing ring variable D and the equalizing ring corrosion rate η jyh ;

[0021] The sealing failure feature set includes the sealing structure crack length L, the sealing structure crack density ρ, and the sealing structure contamination area ratio η mf ;

[0022] In the preferred embodiment of the above analysis method based on the synergistic effect between arrester faults and operating conditions:

[0023] Rainfall probability P r The calculation formula is:

[0024] P r =RH w +(T w -25)·1.5

[0025] Among them, RH w is the ambient humidity, T w is the ambient temperature.

[0026] Temperature rise rate v △T The calculation formula is:

[0027] △T=R th ·U COV I leakage

[0028]

[0029] Among them, △T is the temperature difference between the arrester surface and the environment, R th is the total thermal resistance including the material thermal resistance and the surface heat dissipation resistance, U COV is the continuous operating voltage, I leakage is the leakage current under the corresponding voltage, t is the time period;

[0030] Arrester surface temperature T surface The calculation formula is:

[0031] T surface =T w +△T

[0032] Among them, T w is the ambient temperature.

[0033] Insulation resistance R ins The calculation formula is:

[0034] R ins (t) = R0·e -k·t

[0035] Where R0 is the insulation resistance of the latest test, and k is the moisture absorption rate constant;

[0036] DC reference voltage drop rate The calculation formula is:

[0037]

[0038] Among them, I r is the arrester resistive current, I r0 is the initial resistive current recorded when the arrester is put into operation, n is the current acceleration index, E a is the activation energy, T f is the valve plate temperature, △U 1mA is the DC reference voltage change value, U 1mA It is the initial DC reference voltage when it is put into operation;

[0039] Maximum surface temperature difference △T s The calculation formula is:

[0040] △T s =T smax -T smin

[0041] Among them, T smax is the maximum surface temperature, T smin is the minimum surface temperature.

[0042] The calculation formula of the arrester deformation rate δ is:

[0043]

[0044] Among them, D max is the maximum deflection of the arrester, H is the height of the arrester;

[0045] Corrosion rate of equalizing ring η jyh The calculation formula is:

[0046]

[0047] Among them, S jyh-xs is the corrosion area of ​​the pressure equalizing ring, S jyh is the total surface area of ​​the pressure equalizing ring;

[0048] Sealing structure contamination area rate η mf The calculation formula is:

[0049]

[0050] Among them, S mfjg-wh is the contamination area of ​​the sealing structure, S mfjg is the total surface area of ​​the sealing structure.

[0051] In the preferred embodiment of the above analysis method based on the synergistic effect between arrester faults and operating conditions:

[0052] The comprehensive assessment of lightning arrester risk includes: the working environment of the lightning arrester and the lightning arrester fault condition;

[0053] The working environment of the lightning arrester is based on the rainfall probability P r Make a judgment, the judgment method is:

[0054] When P r When the risk is less than 30%, the arrester working environment risk is sunny;

[0055] When 30%≤P r When the risk is less than 60%, the arrester working environment risk is cloudy;

[0056] When 60%≤P r When , the working environment risk of the lightning arrester is thunderstorm;

[0057] Risk factors for arrester failure conditions include: thermal breakdown risk, moisture risk, accelerated degradation risk, mechanical defect risk, and seal failure risk. Each risk factor is scored from 0 to 2, and is classified as low risk, medium risk, or high risk. A risk factor of 0 indicates low risk, 1 indicates medium risk, and 2 indicates high risk.

[0058] The arrester fault condition is a comprehensive evaluation score Sum based on the weighted sum of various risk factors gz The judgment method is:

[0059] When 0≤Sum gz When ≤3, the arrester fault status is healthy;

[0060] When 4≤Sum gz When ≤6, the arrester fault status is good;

[0061] When 7≤Sum gz When ≤10, the arrester fault status is faulty.

[0062] In the preferred embodiment of the above analysis method based on the synergistic effect between arrester faults and operating conditions:

[0063] Operating condition warning level L yj The classification is based on the arrester working environment and arrester fault conditions of the comprehensive assessment of the arrester risk. The fusion classification method is as follows:

[0064] When the arrester working environment is assessed as sunny and the arrester fault is assessed as healthy, L yj is level three;

[0065] When the arrester working environment is assessed as sunny and the arrester fault is assessed as faulty, L yj is level three;

[0066] When the arrester working environment is evaluated as cloudy and the arrester fault is evaluated as healthy, Lyj is level three;

[0067] When the arrester working environment is assessed as sunny and the arrester fault is assessed as faulty, L yj is level 2;

[0068] When the arrester working environment is evaluated as cloudy and the arrester fault is evaluated as good, L yj is level 2;

[0069] When the arrester working environment is assessed as thunderstorm and the arrester fault is assessed as normal, L yj is level 2;

[0070] When the arrester working environment is evaluated as cloudy and the arrester fault is evaluated as fault, L yj is level one;

[0071] When the arrester working environment is assessed as thunderstorm and the arrester fault is assessed as good, L yj is level one;

[0072] When the arrester working environment is assessed as thunderstorm and the arrester fault is assessed as fault, L yj is level one;

[0073] Operation condition coordination level L xt Classification is based on the operating condition warning level L yj Perform mapping grading, and the mapping grading method is:

[0074] When L yj For level three, L xt is level three;

[0075] When L yj For the second level, L xt is level 2;

[0076] When L yj For the first level, L xt For the first level.

[0077] The present invention also discloses a system based on the synergistic influence between arrester faults and operating conditions, comprising the following steps:

[0078] The data acquisition module is used to collect and pre-process the arrester operation data in real time. The arrester operation data includes the arrester working environment, physical parameters, electrical parameters, and arrester images. The data is integrated into an arrester operation data set and uploaded to the historical database of the data and image analysis module and the terminal display module in real time.

[0079] The digital graph analysis module is used to perform digital graph analysis on the arrester operation data set. It uses numerical calculation and image recognition methods to obtain the characteristic values ​​of the arrester, including the working environment feature set, thermal collapse feature set, moisture feature set, accelerated degradation feature set, mechanical defect feature set, and seal failure feature set. These values ​​are integrated into the arrester feature set and uploaded to the historical database of the risk analysis module and the terminal display module in real time.

[0080] The risk analysis module is used to analyze the arrester's working environment and arrester fault conditions based on the arrester feature set, and to obtain a comprehensive assessment of the arrester's risk. The risk factor analysis of the arrester's fault condition includes analysis of the arrester's working environment, thermal breakdown risk, moisture risk, accelerated degradation risk, mechanical defect risk, and sealing failure risk. The risk is uploaded to the historical database of the early warning collaborative classification module and the terminal display module in real time.

[0081] Early warning collaborative grading module: This module is used to integrate and grade the arrester working environment and arrester fault conditions in the comprehensive arrester risk assessment to derive the operating condition early warning level. The operating condition collaborative level is then derived based on the operating condition early warning level mapping and grading, and uploaded to the historical database of the terminal display module in real time.

[0082] The terminal display module is used to visualize the distribution of lightning arresters, various data, warning and collaboration levels, and work push. It has the contents of operation data dynamics, risk situation distribution, operation condition monitoring and warning, collaborative work push, human resources and material library, and historical database.

[0083] The present invention provides an analysis method and system based on the synergistic influence between arrester faults and operating conditions, which has the following beneficial effects:

[0084] (1) Real-time collection, pre-processing and integration of multiple arrester data from multiple lines and multiple areas provide a rich and accurate data source for subsequent data analysis and risk assessment, ensuring the real-time and accuracy of the data, enabling the system to quickly respond to changes in the operating status of the arrester and discover potential problems in a timely manner, thus greatly avoiding problems such as human errors and low timing in manual inspections.

[0085] (2) Digital graph analysis and risk analysis can analyze the characteristic values ​​of the arrester in multiple dimensions and levels. The integrated arrester feature set can ensure a more comprehensive risk analysis, including analysis of risk factors such as the arrester working environment, thermal breakdown risk, moisture risk, accelerated degradation risk, mechanical defect risk, and sealing failure risk. It can analyze various risks from multiple angles and determine the risk level to obtain a comprehensive assessment of the arrester risk that includes the arrester working environment and arrester fault conditions, providing sufficient basis for early warning and collaborative classification.

[0086] (3) Operating condition warning and coordinated classification: Based on the comprehensive assessment of the arrester risk, the arrester can be given a real-time operating condition warning and coordinated classification. This can assist the operating unit to fully grasp the focus of the arrester operation on the entire line and the entire area, accurately link and coordinate various resources, reasonably deploy various tasks, enhance the pertinence and timeliness of the arrester work, and facilitate more efficient allocation of human and material resources, improve the efficiency of inspection and maintenance work, and achieve safe and stable operation of the power system on the entire line and the entire area.

[0087] (4) The terminal visualizes the distribution of lightning arresters, various data, warning and coordination levels, work push, etc., breaking the constraints of time and space, allowing operating units and personnel to view various information anytime and anywhere, and has operation data dynamics, risk situation distribution, operation condition monitoring and warning, collaborative work push, human resources and material library, historical database and other contents, providing support and strong help for operation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 Schematic diagram of a flow chart of an analysis method based on the synergistic influence between arrester faults and operating conditions of the present invention;

[0089] Figure 2 The figure is a schematic diagram of a system solution based on the synergistic influence between arrester faults and operating conditions of the present invention. DETAILED DESCRIPTION

[0090] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present invention without creative work shall fall within the scope of protection of the present invention.

[0091] Example 1

[0092] See Figure 1 The present invention provides an analysis method based on the synergistic influence between arrester faults and operating conditions, including:

[0093] The arrester operation data is collected in real time and pre-processed. The arrester operation data includes: arrester working environment, physical parameters, electrical parameters and arrester images, and is integrated into the arrester operation data set.

[0094] Specifically, ensure that all lightning arrester data within the jurisdiction are collected, ensure the accuracy and real-time nature of the collected data, pre-process the collected data to improve data quality, reduce errors, and make subsequent analysis more accurate and effective, and package and integrate the operating data of each lightning arrester into a lightning arrester operating data set to facilitate digital graph analysis and subsequent database storage.

[0095] A digital graph analysis is performed on the arrester operation data set, which includes the characteristic value calculation and analysis of the working environment feature set, thermal collapse feature set, moisture feature set, degradation acceleration feature set, mechanical defect feature set, and sealing failure feature set, and the arrester feature set is obtained by integration.

[0096] Specifically, a digital graph analysis is performed on the arrester operation data set, including the calculation and analysis of the characteristic values ​​of the working environment feature set, thermal collapse feature set, moisture feature set, degradation acceleration feature set, mechanical defect feature set, and sealing failure feature set. The analysis methods mainly include numerical calculation and image recognition analysis. The numerical calculation analysis mainly calculates the numerical formula of the data, and the image recognition analysis mainly uses computer vision, deep learning, geometric measurement algorithms, etc. in the image. In the analysis of the working environment feature set, numerical calculation is used to analyze the ambient temperature RH w , ambient humidity T w , rainfall probability P r Equal eigenvalues; in the thermal collapse characteristic set analysis, the arrester surface temperature T is analyzed by numerical calculation surface , temperature rise rate v △T Equal characteristic values; in the analysis of moisture characteristic set, numerical calculation is used to analyze the arrester resistive current I r , insulation resistance R ins , internal humidity RH in Equal characteristic values; in the degradation acceleration characteristic set analysis, the arrester resistive current resistive current I is analyzed by numerical calculation r , DC reference voltage drop rate Image recognition is used to analyze the maximum surface temperature difference △T; in the mechanical defect feature set analysis, numerical calculation analysis and image recognition are used to analyze the arrester deformation rate δ, the arrester tilt angle θ, the equalizing ring variable D, and the equalizing ring corrosion rate η. jyh In the analysis of seal failure feature set, numerical calculation and image recognition are used to analyze the seal structure crack length L, seal structure crack density ρ, seal structure contamination area rate η mf , providing a sufficient and comprehensive feature set for subsequent risk factor analysis.

[0097] The arrester feature set is subjected to risk analysis of the arrester working environment and arrester fault conditions to obtain a comprehensive assessment of the arrester risk. The risk factors of the arrester fault condition include the arrester working environment, thermal breakdown risk, moisture risk, accelerated degradation risk, mechanical defect risk, and sealing failure risk.

[0098] Specifically, the arrester working environment risk factor is evaluated by the working environment feature set; the thermal collapse risk factor is evaluated by the thermal collapse feature set; the moisture risk factor is evaluated by the moisture feature set, the degradation acceleration risk factor is evaluated by the degradation acceleration feature set, the mechanical defect risk factor is evaluated by the mechanical defect feature set, and the sealing failure risk factor is evaluated by the sealing failure feature set. Each risk factor has a value of 0-2 points, where a risk factor with a value of 0 is low risk, a risk factor with a value of 1 is medium risk, and a risk factor with a value of 2 is high risk. The comprehensive assessment score Sum of the arrester fault condition is obtained by weighted summing of each risk factor. gz , integrating the arrester working environment conditions and arrester fault conditions into a comprehensive arrester risk assessment, providing a multi-dimensional and multi-level risk basis for subsequent operating condition warning and operating condition classification.

[0099] The comprehensive risk assessment of lightning arresters is carried out by integrating the grading of the operating condition warning level and mapping the grading of the operating condition coordination level.

[0100] Specifically, both the operating condition warning level and the operating condition coordination level are divided into three levels. The operating condition warning level is integrated and graded based on the arrester risk assessment, while the operating condition coordination level is mapped and graded based on the operating condition warning level. This classification of operating condition warning and coordination levels helps operators and personnel better understand the severity, probability, and impact of potential risks. It also provides reliable and powerful guidance for subsequent work deployment, enabling rapid response to various situations and ensuring the stable operation of the arresters in the power system.

[0101] In the preferred embodiment of the above analysis method based on the synergistic effect between arrester faults and operating conditions:

[0102] Rainfall probability P r The calculation formula is:

[0103] P r =RH w +(T w -25)·1.5

[0104] Among them, RH w is the ambient humidity, T w is the ambient temperature;

[0105] Specifically, the ambient temperature RH w , ambient humidity T w Real-time data collection is performed through temperature sensors and humidity sensors respectively.

[0106] Temperature rise rate v △T The calculation formula is:

[0107] △T=R th ·U COV I leakage

[0108]

[0109] Among them, △T is the temperature difference between the arrester surface and the environment, R th is the total thermal resistance including the material thermal resistance and the surface heat dissipation resistance, U COV is the continuous operating voltage, I leakage is the leakage current under the corresponding voltage, t is the time period;

[0110] Arrester surface temperature T surface The calculation formula is:

[0111] T surface =T w +△T

[0112] Among them, T w is the ambient temperature.

[0113] Specifically, U COV Through real-time acquisition of voltage transformer or capacitor divider, I leakage Real-time data collection is performed by connecting a current sensor in series at the grounding terminal of the lightning arrester.

[0114] Insulation resistance R ins The calculation formula is:

[0115] R ins (t) = R0·e -k·t

[0116] Where R0 is the insulation resistance of the latest test, and k is the moisture absorption rate constant;

[0117] DC reference voltage drop rate The calculation formula is:

[0118]

[0119] Among them, I r is the arrester resistive current, I r0 is the initial resistive current recorded when the arrester is put into operation, n is the current acceleration index, E a is the activation energy, T f is the valve plate temperature. 1mA is the DC reference voltage change value, U 1mA It is the initial DC reference voltage when the device is put into operation.

[0120] Maximum surface temperature difference △T s The calculation formula is:

[0121] △T s =T smax -T smin

[0122] Among them, T smax is the maximum surface temperature, T smin is the minimum surface temperature.

[0123] Specifically, the arrester resistive current I r JSH-7 monitor can be used for real-time collection; internal humidity RH in Real-time collection is performed through a preset moisture-proof humidity sensor; insulation resistance R ins Since the arrester needs to be shut down for accurate measurement, there is a certain periodicity. Therefore, the present invention uses a method of calculating the attenuation of the ambient temperature in combination with the latest detection results of each cycle to grasp the insulation resistance R ins Time-varying estimation to enhance the insulation resistance R ins The value of k is related to the ambient temperature. When T<0℃, k is 0.08~0.12; when 0℃<T≤40℃, k is 0.05~0.07; when T<0℃, k is 0.03~0.05. Limited by the real-time DC reference voltage U cs The arrester must be tested offline to obtain the voltage drop rate. Therefore, the present invention indirectly evaluates the DC reference voltage drop rate through the attenuation equation. Among them, the current acceleration index n is 1.5 to 2.0, and the activation energy E a Take 0.8~1.2, valve temperature T f The maximum surface temperature T in the calculation of the maximum surface temperature difference can be measured by a fiber Bragg grating sensor. smax , minimum surface temperature T smin The infrared thermal image of the arrester captured by the infrared thermal imager is processed by temperature matrix processing and spatial positioning algorithm to perform temperature extreme value analysis, and the maximum surface temperature T of the arrester can be obtained. smax and the minimum surface temperature T smin .

[0124] The calculation formula of the arrester deformation rate δ is:

[0125]

[0126] Among them, D max is the maximum deflection of the arrester, H is the height of the arrester;

[0127] Corrosion rate of equalizing ring η jyh The calculation formula is:

[0128]

[0129] Among them, S jyh-xs is the corrosion area of ​​the pressure equalizing ring, S jyh is the total surface area of ​​the pressure equalizing ring;

[0130] Sealing structure contamination area rate η mf The calculation formula is:

[0131]

[0132] Among them, S mfjg-wh is the contamination area of ​​the sealing structure, S mfjg is the total surface area of ​​the sealing structure.

[0133] Specifically, the maximum deflection D of the arrester in the arrester deformation rate δ max And the arrester height H, the arrester tilt angle θ, the equalizing ring variable D, the equalizing ring corrosion rate η jyh The corrosion area S jyh-xs , sealing structure crack length L, sealing structure crack density ρ, sealing structure contamination area rate η mf The contamination area S of the sealing structure mfjg-wh It is mainly obtained through image recognition and detection of the appearance images of lightning arresters, equalizing rings, and sealing structures, combined with computer vision, deep learning, and geometric measurement algorithms.

[0134] Arrester maximum offset D max The height H of the lightning arrester is detected by target detection and key point positioning. The overall outline of the lightning arrester is detected by YOLOv8 or Mask R-CNN on the lightning arrester image, and the minimum external rectangular frame is output. The positioning key points are defined at the top / bottom of the rectangular frame. The vertical direction of the equipment is set as the reference axis. The horizontal projection distance between the key point at the top of the lightning arrester and the base point at the bottom is calculated to identify and detect the maximum offset D of the lightning arrester. max ; Set the ratio coefficient between the calibration pixels and the actual size, and then calculate the pixel distance between the key points in the image to identify and detect the height H of the lightning arrester.

[0135] The inclination angle θ of the lightning arrester is determined by linear detection and angle calculation. The image camera is installed perpendicular to the ground. The center axis of the lightning arrester is extracted from the lightning arrester appearance image using Canny edge detection and Hough transform. The angle θ between the axis and the vertical direction is calculated, and the inclination angle θ of the lightning arrester can be identified and detected.

[0136] The pressure-equalizing ring variable D is obtained by ring detection and diameter measurement. The pressure-equalizing ring image is segmented using Hough circle detection or U-Net. The best circular contour is fitted and the diameter of the fitted circle is calculated to identify and detect the pressure-equalizing ring variable D.

[0137] Corrosion area of ​​equalizing ring S jyh-xs By using semantic segmentation and pixel statistics, a large number of corroded equalizing ring images are segmented into corroded areas and labeled with corroded pixels. The segmented corroded areas and labeled corroded pixels are used as input features, and the corroded areas are used as output results. The corroded area recognition model is obtained by training the U-Net model. The newly collected equalizing ring images are input into the corroded area recognition model to identify the corroded areas, and then the corroded area S of the equalizing ring is obtained by pixel statistics. jyh-xs .

[0138] The crack length L and crack density ρ of the sealing structure are determined through skeleton extraction, curve integration, and area calculation. By segmenting the crack region using a U-Net on the seal structure's appearance image, morphological refinement is performed to obtain the skeleton lines, and the skeleton pixel chain length is calculated and converted into actual length. The crack length L of the sealing structure can be detected and obtained. The crack density ρ is calculated by defining the detection window area and calculating the area, and then calculating the ratio of the crack length L of the sealing structure to the window area.

[0139] Sealing structure contamination area S mfjg-wh The contamination area segmentation and area calculation method is adopted. Since contamination is usually dark in color and low in saturation, the contamination area can be detected by performing HSV color space threshold segmentation on the sealing structure appearance image. Then, the actual contamination area S of the sealing structure is calculated based on the pixel area occupied by the contamination area. mfjg-wh .

[0140] In the preferred embodiment of the above analysis method based on the synergistic effect between arrester faults and operating conditions:

[0141] The specific steps for analyzing the risk factors of the arrester feature set and deriving the risk assessment are as follows:

[0142] The arrester feature set is analyzed for various risk factors, including: arrester working environment, thermal breakdown risk, moisture risk, accelerated degradation risk, mechanical defect risk, and sealing failure risk.

[0143] Analyze the working environment of the lightning arrester. The specific steps are as follows:

[0144] According to the rainfall probability P r Perform analytical judgment, the judgment method is:

[0145] When P r When the risk is less than 30%, the arrester working environment risk is sunny;

[0146] When 30%≤P r When the risk is less than 60%, the arrester working environment risk is cloudy;

[0147] When 60%≤P r When , the working environment risk of the lightning arrester is thunderstorm;

[0148] Specifically, by analyzing the working environment of the lightning arrester, the relationship between the probability of rainfall and the type of lightning arrester working environment is revealed, providing consideration of the lightning arrester working environment dimension for subsequent comprehensive assessment of lightning arrester risks.

[0149] To analyze the thermal crash risk, the specific steps are as follows:

[0150] According to the arrester surface temperature T surface , temperature rise rate v △T Perform analytical judgment, the judgment method is:

[0151] When T surface <0.8T limit or when the risk of thermal collapse is low;

[0152] When 0.8T limit ≤T surface ≤0.9T limit or 2℃ / h≤v △T When the temperature is ≤5℃ / h, the risk of thermal collapse is medium;

[0153] When T surface >0.9T limit or v △T When the temperature is >5℃ / h, the risk of thermal collapse is high;

[0154] Among them, T limit is the material limit temperature;

[0155] Specifically, by analyzing the thermal collapse risk, the arrester surface temperature T surface , temperature rise rate v △T The relationship between the thermal collapse risk level and the thermal collapse risk level provides a consideration of the thermal collapse risk dimension for the subsequent comprehensive assessment of arrester risks.

[0156] To analyze the risk of moisture, the specific steps are as follows:

[0157] According to the arrester surface temperature T surface , temperature rise rate v △T , insulation resistance R ins Perform analytical judgment, the judgment method is:

[0158] When R ins >1000MΩ and RH <50% and I r <1.2I r0 When , the risk of moisture is low;

[0159] When 100MΩ≤R ins ≤1000MΩ or 50% ≤RH ≤70% or 1.2I r0 ≤I r ≤1.5I r0 When , the risk of moisture is medium;

[0160] When R ins <100MΩ or 70%<RH or 1.5I r0 <I r When , the risk of moisture is high;

[0161] Specifically, by analyzing the moisture risk, the arrester surface temperature T surface , temperature rise rate v △T , insulation resistance R ins The relationship between the moisture risk level and the moisture risk level provides a consideration of the moisture risk dimension for the subsequent comprehensive assessment of lightning arrester risks.

[0162] To analyze the risk of accelerated degradation, the specific steps are as follows:

[0163] According to the arrester DC reference voltage drop rate The maximum surface temperature difference △T is analyzed and determined by the following method:

[0164] When I r <1.2I r0 and and △T≤3℃, the risk of accelerated degradation is low;

[0165] When 1.2I r0 ≤I r <1.5I r0 or Or when 3℃<△T≤5℃, the risk of accelerated degradation is medium risk;

[0166] When 1.5I r0 ≤I r or Or when 5℃<△T, the risk of accelerated degradation is high.

[0167] Specifically, by analyzing the accelerated degradation risk, the arrester DC reference voltage drop rate is revealed. The relationship between the maximum surface temperature difference △T and the degradation acceleration risk level provides a consideration of the degradation acceleration risk dimension for subsequent comprehensive assessment of arrester risks.

[0168] Analyze the risk of mechanical defects. The specific steps are as follows:

[0169] According to the arrester deformation rate δ and the equalizing ring corrosion rate η jyh Perform analytical judgment, the judgment method is:

[0170] When δ≤1% or θ≤2° and D≤5mm and η jyh When ≤10%, the risk of mechanical defects is low;

[0171] When 1%<δ≤3% or 2°<θ≤5° or 5mm<D≤10mm or 10%<η jyh When ≤30%, the risk of mechanical defects is medium risk;

[0172] When 3%<δ or 5°<θ or 10mm<D or 30%<η jyh When , the mechanical defect risk is high;

[0173] Specifically, by analyzing the risk of mechanical defects, the deformation rate δ of the arrester and the corrosion rate η of the equalizing ring are revealed. jyh The relationship between the risk level of mechanical defects and the mechanical defect risk level provides a consideration of the mechanical defect risk dimension for the subsequent comprehensive assessment of arrester risks.

[0174] To analyze the risk of seal failure, the specific steps are as follows:

[0175] According to the crack length L of the sealing structure and the contamination area rate η mf Perform analytical judgment, the judgment method is:

[0176] When L≤10mm and ρ≤2 and η mf When ≤20%, the risk of seal failure is low;

[0177] When 10mm<L≤30mm or 2<ρ≤5 or 20%<η mf ≤50%, the risk of seal failure is medium risk;

[0178] When 30mm<L or 5<ρ or 50%<η mf , the risk of seal failure is high.

[0179] Specifically, by analyzing the seal failure risk, the crack length L of the sealing structure and the contamination area ratio η are revealed. mf The relationship between the sealing failure risk level and the sealing failure risk level provides a consideration of the sealing failure risk dimension for the subsequent comprehensive assessment of lightning arrester risks.

[0180] Analyze the comprehensive risk assessment of lightning arresters. The specific steps are as follows:

[0181] The comprehensive evaluation score Sum of the arrester fault condition is obtained by weighted summation of various risk factors. gz Each risk factor is scored on a scale of 0 to 2, with a low risk factor value of 0, a medium risk factor value of 1, and a high risk factor value of 2.

[0182] According to the comprehensive evaluation score Sum gzAnalyze and determine the arrester fault condition. The determination method is:

[0183] When 0≤Sum gz When ≤3, the arrester fault status is healthy;

[0184] When 4≤Sum gz When ≤6, the arrester fault status is good;

[0185] When 7≤Sum gz When ≤10, the arrester fault status is faulty.

[0186] Integrate the arrester working environment and arrester fault conditions to obtain a comprehensive assessment of arrester risk;

[0187] Specifically, the comprehensive risk assessment of lightning arresters takes into account multiple risk factors, including the operating environment, thermal breakdown risk, moisture risk, accelerated degradation risk, mechanical defect risk, and seal failure risk. This allows for a multi-dimensional and multi-level assessment of potential risks associated with lightning arresters. The risk levels of different risk factors have varying impacts on the operation of lightning arresters. Multi-risk factor monitoring and assessment can capture risk evolution trends in real time, enabling better prevention and providing a more comprehensive basis for subsequent operational condition warnings, collaborative grading, collaborative work planning, and material allocation.

[0188] In the preferred embodiment of the above analysis method based on the synergistic effect between arrester faults and operating conditions:

[0189] The comprehensive risk assessment of the arrester is carried out by integrating the grading of the operating condition warning level and mapping the grading of the operating condition coordination level. The specific steps are as follows:

[0190] According to the arrester working environment and arrester fault in the arrester comprehensive risk assessment, the operating condition warning level L is determined. yj Fusion grading, the fusion grading rules are:

[0191] When the arrester working environment is assessed as clear and the arrester fault is assessed as healthy, L yj is level three;

[0192] When the arrester working environment is assessed as sunny and the arrester fault is assessed as faulty, L yj is level three;

[0193] When the arrester working environment is evaluated as cloudy and the arrester fault is evaluated as healthy, L yj is level three;

[0194] When the arrester working environment is assessed as sunny and the arrester fault is assessed as faulty, L yj is level 2;

[0195] When the arrester working environment is evaluated as cloudy and the arrester fault is evaluated as good, L yj is level 2;

[0196] When the arrester working environment is assessed as thunderstorm and the arrester fault is assessed as normal, L yj is level 2;

[0197] When the arrester working environment is evaluated as cloudy and the arrester fault is evaluated as fault, L yj is level one;

[0198] When the arrester working environment is assessed as thunderstorm and the arrester fault is assessed as good, L yj is level one;

[0199] When the arrester working environment is assessed as thunderstorm and the arrester fault is assessed as fault, L yj is level one;

[0200] Specifically, Level 3 of the operating condition warning is the lowest level, and Level 1 is the highest. The higher the level, the more attention the arrester should receive, and the more attention the operator and personnel need. For example, in a sunny working environment, whether normal or faulty, the arrester will basically not need to work, so attention can be reduced. However, if a thunderstorm is expected, both normal and faulty arresters will need to work to a large extent, and more attention should be paid.

[0201] According to the operating condition warning level L yj For the operating condition coordination level L xt Perform mapping grading. The mapping grading rules are as follows:

[0202] When L yj For level three, L xt is level three;

[0203] When L yj For the second level, L xt is level 2;

[0204] When L yj For the first level, L xt is level one;

[0205] Specifically, different levels of operation coordination will provide different instructions, work plans, and material allocation. Operation coordination level three is the lowest level, with flexible execution of work plans and on-demand allocation of materials; operation coordination level one is the highest level, with mandatory priority execution of work plans and emergency allocation of materials. For example, when the work content of coordination level three, "periodic testing of lightning arresters in sunny conditions," is compared with the work content of coordination level one, "lightning arresters that are about to face thunderstorms and need to be repaired," the priorities of the two are different, and it is necessary to first concentrate manpower and materials to handle the work content of coordination level one, "lightning arresters that are about to face thunderstorms and need to be repaired."

[0206] When the operating condition warning level L is triggered yj Level 3 and operating condition coordination level L xt At level three, the work deployment characteristics are routine monitoring and preventive maintenance; the early warning method is no active alarm; the collaborative work content includes executing standard inspection cycles, regular inventory of spare parts warehouses, and regular rotation of operation centers.

[0207] When the operating condition warning level L is triggered yj Level 2 and operating condition coordination level L xt At level two, the work deployment features are risk prevention and enhanced monitoring; the early warning method is pop-up reminders when there are abnormalities; the collaborative work content includes increasing the frequency of on-site inspections, pre-purchasing consumable parts in the spare parts warehouse, updating emergency plans and conducting desktop exercises, etc.

[0208] When the operating condition warning level L is triggered yj Level 1 and operating condition coordination level L xt At level one, the work deployment characteristics are emergency intervention and emergency preparedness; the early warning method is sound and light alarm; the collaborative work content is to immediately suspend the maintenance of other non-critical equipment, allocate manpower and material resources to focus on the maintenance of lightning arresters within the high warning level range, and arrange emergency duty personnel.

[0209] Example 2

[0210] See Figure 2 The present invention also discloses a system based on the collaborative influence between lightning arrester faults and operating conditions, including: a data acquisition module, a digital graph analysis module, a risk analysis module, an early warning collaborative classification module, and a terminal display module.

[0211] The data acquisition module is used to collect and pre-process the arrester operation data in real time. The arrester operation data includes the arrester working environment, physical parameters, electrical parameters, and arrester images. The data is integrated into an arrester operation data set and uploaded to the historical database of the data and image analysis module and the terminal display module in real time.

[0212] The digital graph analysis module is used to perform digital graph analysis on the arrester operation data set. It uses numerical calculation and image recognition methods to obtain the characteristic values ​​of the arrester, including the working environment feature set, thermal collapse feature set, moisture feature set, accelerated degradation feature set, mechanical defect feature set, and seal failure feature set. These values ​​are integrated into the arrester feature set and uploaded to the historical database of the risk analysis module and the terminal display module in real time.

[0213] The risk analysis module is used to analyze the arrester's working environment and arrester fault conditions based on the arrester feature set, and to obtain a comprehensive assessment of the arrester's risk. The risk factor analysis of the arrester's fault condition includes analysis of the arrester's working environment, thermal breakdown risk, moisture risk, accelerated degradation risk, mechanical defect risk, and sealing failure risk. The risk is uploaded to the historical database of the early warning collaborative classification module and the terminal display module in real time.

[0214] Early warning collaborative grading module: This module is used to integrate and grade the arrester working environment and arrester fault conditions in the comprehensive arrester risk assessment to derive the operating condition early warning level. The operating condition collaborative level is then derived based on the operating condition early warning level mapping and grading, and uploaded to the historical database of the terminal display module in real time.

[0215] The terminal display module is used to visualize the distribution of lightning arresters, various data, warning and collaboration levels, and work push. It has the contents of operation data dynamics, risk situation distribution, operation condition monitoring and warning, collaborative work push, human resources and material library, and historical database.

[0216] The present invention can realize real-time data collection, preprocessing, sorting and uploading by collecting lightning arrester operation data sets including lightning arrester working environment, physical parameters and electrical parameters, lightning arrester images, etc.; digital graph analysis of lightning arrester operation data sets is used to obtain lightning arrester feature sets that integrate various characteristic values ​​such as working environment feature set, thermal collapse feature set, moisture feature set, degradation acceleration feature set, mechanical defect feature set, and sealing failure feature set. Multi-dimensional and multi-level mining can reflect the comprehensive characteristic values ​​of the lightning arrester as a whole, providing sufficient characteristic value basis for risk analysis; risk factor analysis of lightning arrester feature sets is carried out to obtain the characteristics of lightning arrester working environment, thermal collapse risk, moisture risk, degradation acceleration, mechanical defect feature set, and sealing failure feature set. The comprehensive risk assessment of lightning arresters is based on risk factors such as chemical acceleration risk, mechanical defect risk, and sealing failure risk, including the working environment conditions and fault conditions of the lightning arresters. It reveals the relationship between various feature sets of lightning arresters and various risk levels, and between various risk levels and the comprehensive risk assessment of lightning arresters. The comprehensive risk assessment of lightning arresters is graded by integrating the fusion of operating condition warning levels and mapping of operating condition coordination levels, and provides corresponding work responses, material deployment, etc. according to the levels. It reveals the relationship between the comprehensive risk assessment of lightning arresters and the operating condition warning and coordination levels, and better guides the operating units and personnel to carry out their work, so as to achieve safe and stable operation of lightning arresters in the entire power system.

[0217] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0218] The foregoing description is intended only to provide specific embodiments of the present invention, which are intended to enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest manner consistent with the principles and novel features disclosed herein.

Claims

1. An analysis method based on the synergistic influence between arrester faults and operating conditions, characterized in that: S1: real-time acquisition of arrester operation data, pre-processing of the arrester operation data, the arrester operation data including: arrester working environment, physical parameters, electrical parameters and arrester images, and integration into an arrester operation data set; S2: performing a datagram analysis on the arrester operation data set, the datagram analysis including calculating and analyzing characteristic values ​​of a working environment feature set, a thermal collapse feature set, a moisture feature set, a degradation acceleration feature set, a mechanical defect feature set, and a sealing failure feature set, and integrating the features to obtain an arrester feature set; S3: performing risk analysis on the arrester feature set based on the arrester working environment and arrester fault conditions to obtain a comprehensive arrester risk assessment, wherein the risk factors of the arrester fault condition include the arrester working environment, thermal breakdown risk, moisture risk, accelerated degradation risk, mechanical defect risk, and sealing failure risk; S4: performing a fusion classification of the operating condition warning level and a mapping classification of the operating condition coordination level on the comprehensive risk assessment of the arrester.

2. The analysis method based on the synergistic influence between arrester faults and operating conditions according to claim 1 is characterized in that: The working environment of the arrester includes: ambient temperature and ambient humidity; The physical parameters include: arrester surface temperature and internal humidity; The electrical parameters include: resistive current, DC reference voltage, and insulation resistance; The arrester image includes: an arrester appearance image, a grading ring appearance image, a sealing structure appearance image, and an arrester infrared thermal image.

3. The analysis method based on the synergistic influence between arrester faults and operating conditions according to claim 1 is characterized in that: The working environment feature set includes the ambient temperature RH w , ambient humidity T w , rainfall probability P r ; The thermal collapse feature set includes the arrester surface temperature T surface , temperature rise rate v △T ; The damp feature set includes the arrester resistive current I r , insulation resistance R ins , internal humidity RH in ; The degradation acceleration feature set includes the arrester resistive current I r , DC reference voltage drop rate Maximum surface temperature difference △T s ; The mechanical defect feature set includes the arrester deformation rate δ, the arrester tilt angle θ, the equalizing ring variable D and the equalizing ring corrosion rate η jyh ; The sealing failure feature set includes the sealing structure crack length L, the sealing structure crack density ρ, the sealing structure contamination area rate η mf .

4. The analysis method based on the synergistic influence between arrester faults and operating conditions according to claim 3 is characterized in that: The rainfall probability P r , the calculation formula is: P r =HR w +(T w -25)·1.5 Among them, RH w is the ambient humidity, T w is the ambient temperature.

5. The analysis method based on the synergistic influence between arrester faults and operating conditions according to claim 3 is characterized in that: The temperature rise rate v △T , the calculation formula is: △T=R th ·U COV ·I leakage Among them, △T is the temperature difference between the arrester surface and the environment, R th is the total thermal resistance including the material thermal resistance and the surface heat dissipation resistance, U COV is the continuous operating voltage, I leakage is the leakage current under the corresponding voltage, t is the time period; The arrester surface temperature T surface , the calculation formula is: T surface =T w +△T Among them, T w is the ambient temperature.

6. The analysis method based on the synergistic influence between arrester faults and operating conditions according to claim 3 is characterized in that: The insulation resistance R ins , the calculation formula is: R ins (t)=R0·e -k·t Where R0 is the insulation resistance of the latest test, and k is the moisture absorption rate constant; The DC reference voltage drop rate The calculation formula is: Among them, I r is the arrester resistive current, I r0 is the initial resistive current recorded when the arrester is put into operation, n is the current acceleration index, E a is the activation energy, T f is the valve temperature, △U 1mA is the DC reference voltage change value, U 1mA It is the initial DC reference voltage when it is put into operation; The maximum surface temperature difference ΔT s , the calculation formula is: △T s =T smax -T smin Among them, T smax is the maximum surface temperature, T smin is the minimum surface temperature.

7. The analysis method based on the synergistic influence between arrester faults and operating conditions according to claim 3 is characterized in that: The calculation formula of the arrester deformation rate δ is: Among them, D max is the maximum deflection of the arrester, H is the height of the arrester; The rust rate η of the pressure equalizing ring jyh , the calculation formula is: Among them, S jyh-xs is the corrosion area of ​​the pressure equalizing ring, S jyh is the total surface area of ​​the pressure equalizing ring; The contamination area rate η of the sealing structure mf , the calculation formula is: Among them, S mfjg-wh is the contamination area of ​​the sealing structure, S mfjg is the total surface area of ​​the sealing structure.

8. The analysis method based on the synergistic influence between arrester faults and operating conditions according to claim 1 is characterized in that: The comprehensive assessment of the arrester risk includes: the arrester working environment and the arrester fault condition; The working environment of the lightning arrester is determined by the rainfall probability P r Make a judgment, the judgment method is: When P r When the risk is less than 30%, the arrester working environment risk is sunny; When 30%≤P r When the risk is less than 60%, the arrester working environment risk is cloudy; When 60%≤P r When , the working environment risk of the lightning arrester is thunderstorm; The risk factors of the arrester fault condition include: thermal breakdown risk, moisture risk, accelerated degradation risk, mechanical defect risk, and seal failure risk; each risk factor is scored from 0 to 2, and is divided into low risk, medium risk, and high risk. A risk factor of 0 indicates low risk, a risk factor of 1 indicates medium risk, and a risk factor of 2 indicates high risk. The arrester fault condition is a comprehensive evaluation score Sum calculated by weighting various risk factors. gz The judgment method is: When 0≤Sum gz When ≤3, the arrester fault status is healthy; When 4≤Sum gz When ≤6, the arrester fault status is good; When 7≤Sum gz When ≤10, the arrester fault status is faulty.

9. An analysis method based on the synergistic influence between arrester faults and operating conditions according to claim 1, characterized in that: The operating condition warning level L yj The classification is based on the arrester working environment and arrester fault conditions of the comprehensive assessment of the arrester risk. The fusion classification method is as follows: When the arrester working environment is assessed as sunny and the arrester fault is assessed as healthy, L yj is level three; When the arrester working environment is assessed as sunny and the arrester fault is assessed as faulty, L yj is level three; When the arrester working environment is evaluated as cloudy and the arrester fault is evaluated as healthy, L yj is level three; When the arrester working environment is assessed as sunny and the arrester fault is assessed as faulty, L yj is level 2; When the arrester working environment is evaluated as cloudy and the arrester fault is evaluated as good, L yj is level 2; When the arrester working environment is assessed as thunderstorm and the arrester fault is assessed as normal, L yj is level 2; When the arrester working environment is evaluated as cloudy and the arrester fault is evaluated as fault, L yj is level one; When the arrester working environment is assessed as thunderstorm and the arrester fault is assessed as good, L yj is level one; When the arrester working environment is assessed as thunderstorm and the arrester fault is assessed as fault, L yj is level one; The operating condition coordination level L xt Classification according to the operating condition warning level L yj Perform mapping grading, and the mapping grading method is: When L yj For level three, L xt is level three; When L yj For the second level, L xt is level 2; When L yj For the first level, L xt For the first level.

10. An analysis system based on the synergistic impact between arrester faults and operating conditions, characterized in that: The system is used to implement an analysis method based on the synergistic influence between arrester faults and operating conditions as described in any one of claims 1 to 9, and the system includes: The data acquisition module is used to collect and pre-process the arrester operation data in real time. The arrester operation data includes the arrester working environment, physical parameters, electrical parameters, and arrester images. The data is integrated into an arrester operation data set and uploaded to the historical database of the data and image analysis module and the terminal display module in real time. The digital graph analysis module is used to perform digital graph analysis on the arrester operation data set. It uses numerical calculation and image recognition methods to obtain the characteristic values ​​of the arrester, including the working environment feature set, thermal collapse feature set, moisture feature set, accelerated degradation feature set, mechanical defect feature set, and seal failure feature set. These values ​​are integrated into the arrester feature set and uploaded to the historical database of the risk analysis module and the terminal display module in real time. The risk analysis module is used to analyze the arrester's working environment and arrester fault conditions based on the arrester feature set, and to obtain a comprehensive assessment of the arrester's risk. The risk factor analysis of the arrester's fault condition includes analysis of the arrester's working environment, thermal breakdown risk, moisture risk, accelerated degradation risk, mechanical defect risk, and sealing failure risk. The risk is uploaded to the historical database of the early warning collaborative classification module and the terminal display module in real time. Early warning collaborative grading module: This module is used to integrate and grade the arrester working environment and arrester fault conditions in the comprehensive arrester risk assessment to derive the operating condition early warning level. The operating condition collaborative level is then derived based on the operating condition early warning level mapping and grading, and uploaded to the historical database of the terminal display module in real time. The terminal display module is used to visualize the distribution of lightning arresters, various data, warning and collaboration levels, and work push. It has the contents of operation data dynamics, risk situation distribution, operation condition monitoring and warning, collaborative work push, human resources and material library, and historical database.

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