An online monitoring method for operating state of electrical connection points in a fully-inflated ring main unit gas tank

Abstract

This invention discloses an online monitoring method for the operating status of electrical connection points inside the gas box of a fully inflatable ring main unit, belonging to the field of power equipment condition monitoring technology. A distributed fiber optic temperature sensor network is deployed on the outer surface of the gas box. An inversion mathematical model of the internal connection point temperature and the outer surface temperature is established based on the principle of heat conduction. A mapping database of equivalent thermal resistance and outer surface temperature field characteristics is constructed through finite element simulation. The equivalent thermal resistance value is calculated in real time using support vector machine regression. Dynamic thresholds are used for anomaly identification and graded early warning. A long short-term memory neural network is combined to predict temperature trends and adaptively adjust the thresholds. A historical fault case library is established to achieve self-learning diagnosis. This invention achieves non-contact online monitoring in a sealed environment, with high accuracy and timely early warning. It supports dual protocol output of IEC 61850 and Modbus TCP, and can be directly connected to a smart substation operation and maintenance system, improving the intelligent operation and maintenance level of the distribution network.

Description

Technical Field

[0001] This application belongs to the field of power equipment condition monitoring technology, specifically relating to an online monitoring method for the operating status of electrical connection points inside the gas box of a fully inflatable ring main unit. Background Technology

[0002] Ring main units (RNBs), as key equipment widely used in power distribution systems, undertake important functions of power distribution and line switching. Their operational reliability directly affects the safety and stability of the power distribution network and the continuity of power supply for users. Fully gas-filled RNBs use SF6 gas as the insulating medium, sealing the high-voltage live components entirely inside the gas tank. They have significant advantages such as small size, maintenance-free operation, and excellent insulation performance, and have been widely promoted and applied in urban power grid transformation, industrial and mining power distribution, and rail transit.

[0003] Among them, the electrical connection points inside the gas-filled ring main unit are critical conductive nodes. During long-term operation, their contact resistance gradually increases due to factors such as vibration, thermal expansion and contraction, and corrosion, leading to abnormal local temperature rise. If this is not detected and addressed in time, the temperature at the connection point will continue to climb, potentially causing insulation aging, contact melting, or even internal short circuit faults, posing a significant threat to the safe operation of the ring main unit.

[0004] Because the gas box adopts an overall sealed design, traditional manual inspection and periodic maintenance methods are difficult to reach the internal connection points. Existing infrared temperature measurement technology cannot effectively obtain internal temperature information due to the refractive index of SF6 gas and the obstruction of the metal shell of the gas box. Although partial discharge monitoring technology can detect some insulation defects, it is not sensitive enough to thermal faults. Conventional contact temperature measurement methods require opening holes in the gas box wall to place sensors, which seriously damages the sealing integrity of the gas box and affects the insulation performance and service life of the equipment.

[0005] How to achieve online real-time monitoring of the operating status of electrical connection points inside the gas box of a fully inflated ring main unit while ensuring the airtightness of the gas box has become a technical problem that urgently needs to be solved in the field of intelligent operation and maintenance of power distribution equipment. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide an online monitoring method for the operating status of electrical connection points inside the gas box of a fully inflatable ring main unit, which addresses the shortcomings of the prior art.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A method for online monitoring of the operating status of electrical connection points inside the gas box of a fully inflatable ring main unit includes the following steps: Step 1: Arrange a distributed fiber optic temperature sensor network on the outer surface of the air box: Arrange several fiber optic temperature sensors evenly along the length of the outer surface of the air box, and set the spacing between adjacent sensors to a preset distance value to form a temperature monitoring array covering the entire outer surface of the air box. Step 2, acquire temperature monitoring data of the outer surface of the air box: collect temperature data of each monitoring point in real time through a fiber optic temperature demodulator, set the acquisition frequency to a preset acquisition cycle, and store the collected temperature data to the data server; Step 3, establish a heat conduction inversion model: Based on the heat generation power of the electrical connection points inside the gas box, the thermal properties of SF6 gas, the thermal conductivity of the gas box wall material, and the gas box structural dimensions, establish a mathematical model for the heat conduction inversion between the temperature of the connection points inside the gas box and the temperature of the outer surface. Step 4, construct the mapping relationship between connection point temperature and equivalent thermal resistance: calculate the temperature distribution law of the outer surface of the gas box under different connection point equivalent thermal resistance conditions through finite element simulation method, and establish a database of mapping relationship between equivalent thermal resistance and outer surface temperature field characteristic quantities. Step 5, Real-time inversion calculation of equivalent thermal resistance of connection points: Input the real-time collected temperature data of the outer surface of the air box into the heat conduction inversion model, combine it with the mapping relationship database, and use the least squares method to perform iterative solution to calculate the equivalent thermal resistance value of the electrical connection point in the air box corresponding to each monitoring area in real time. Step 6, set the equivalent thermal resistance threshold and make anomaly judgment: According to the connection point design parameters and historical operation data, set the corresponding equivalent thermal resistance threshold for each monitoring area, compare the equivalent thermal resistance value calculated in step 5 with the threshold, and when the equivalent thermal resistance value exceeds the threshold by a preset multiple, it is determined that there is an anomaly in the electrical connection point in the gas box corresponding to the monitoring area. Step 7, Output monitoring and early warning information: When an anomaly is determined in Step 6, an early warning information containing the location of the anomaly, the anomaly level, and the estimated temperature rise is automatically generated and sent to the operation and maintenance monitoring platform through the communication module.

[0008] As a further preferred embodiment of the online monitoring method for the operating status of electrical connection points inside the gas box of the fully inflatable ring main unit of the present invention, the fiber optic temperature sensor in step 1 adopts a temperature sensor based on the Raman scattering principle, with a temperature measurement accuracy of ±0.5℃, a spatial resolution of 1.0m, a measurement range of -40℃ to +120℃, and a response time of no more than 2 seconds.

[0009] As a further preferred embodiment of the online monitoring method for the operating status of electrical connection points inside the gas box of the fully inflatable ring main unit of the present invention, in step 1, in areas with large temperature gradients on the outer surface of the gas box, including the gas box end, corners, and near the busbar through-wall bushing, the spacing between adjacent sensors is appropriately reduced to improve the temperature monitoring resolution in these areas.

[0010] As a further preferred embodiment of the online monitoring method for the operating status of electrical connection points inside the air box of the fully inflatable ring main unit of the present invention, in step 2, the data server uses a time-series database to store temperature data, the data storage period for a single monitoring point is not less than 24 months, and the data compression ratio is not less than 3:1.

[0011] As a further preferred embodiment of the online monitoring method for the operating status of electrical connection points inside the gas box of the fully inflatable ring main unit of the present invention, the heat conduction inversion mathematical model in step 3 is constructed based on the three-dimensional steady-state heat conduction equation, and its mathematical expression is: ; Where k is the thermal conductivity of the gas box wall material, T is the temperature, and Q is the power generated per unit volume; the boundary conditions of the model include the convective heat transfer boundary conditions between the SF6 gas and the inner wall of the gas box, and the natural convective heat transfer boundary conditions between the outer surface of the gas box and the air; the model is solved using the finite difference method; the equivalent thermal resistance is defined as the ratio of the power generated at the connection point to the temperature rise of the connection point relative to the reference temperature, i.e.: ; in, For equivalent thermal resistance, P is the difference between the connection point temperature and the SF6 gas reference temperature, and P is the heating power at the connection point.

[0012] 6. The online monitoring method for the operating status of electrical connection points inside the gas box of a fully inflatable ring main unit according to claim 1, characterized in that: the finite element simulation in step 4 adopts a three-dimensional transient thermal analysis method, the simulation conditions include various conditions from rated load to preset overload multiple under different load current conditions, the simulation time span of each condition is the time required from cold start to thermal stability, and the variation range of equivalent thermal resistance is set to 0.01 K / W to 2.0 K / W.

[0013] As a further preferred embodiment of the online monitoring method for the operating status of electrical connection points inside the gas box of the fully inflatable ring network cabinet of the present invention, the feature quantities of the mapping relationship database in step 4 include the maximum value, mean value, standard deviation, peak position and temperature gradient distribution of the temperature field on the outer surface of the gas box. These feature quantities and the equivalent thermal resistance establish a nonlinear mapping relationship through the support vector machine regression algorithm.

[0014] As a further preferred embodiment of the online monitoring method for the operating status of electrical connection points inside the gas box of the fully inflatable ring main unit of the present invention, the objective function solved by the least squares iterative method in step 5 is: ; in, Let be the measured temperature value at the i-th monitoring point. For the i-th monitoring point, the equivalent thermal resistance is The simulation calculates the temperature value under the given conditions, where n is the total number of monitoring points; the iterative scheme uses the Gauss-Newton method. ; Where J is the Jacobian matrix of the equivalent thermal resistance of the given parameter for temperature value; the convergence condition of the iterative solution is that the relative error between two adjacent iterations is less than 0.1%, the maximum number of iterations is set to 50, and when the number of iterations reaches the upper limit and still fails to converge, a convergence failure flag is output and a manual review process is triggered.

[0015] As a further preferred embodiment of the online monitoring method for the operating status of electrical connection points inside the air box of the fully inflatable ring network cabinet of the present invention, the method for setting the equivalent thermal resistance threshold in step 6 is as follows: take the average value of the normal equivalent thermal resistance of the monitoring area in the past 30 days as the benchmark value, set the threshold value to 1.5 times the benchmark value, and set the alarm value to 2.0 times the benchmark value to distinguish between two alarm levels: general abnormality and severe abnormality. The formula for calculating the estimated temperature rise in step 7 is as follows: ; in, To estimate the temperature rise, I is the current equivalent thermal resistance, and I is the current effective load current. This represents a typical value for the contact resistance at the connection point.

[0016] As a further preferred embodiment of the online monitoring method for the operating status of electrical connection points inside the gas box of the fully inflatable ring main unit of the present invention, when the method is applied to the intelligent operation and maintenance system of the fully inflatable ring main unit, it is linked with the existing protection device and integrated automation system. When a serious abnormality is detected and the duration exceeds 30 minutes, the ring main unit load transfer process is automatically triggered to realize the rapid isolation of the fault area and the continuous power supply to the non-fault area.

[0017] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects: 1. This invention obtains temperature information without opening holes in the gas box wall by arranging a distributed fiber optic temperature sensor network on the outer surface of the gas box, thus completely preserving the sealing integrity of the gas box and avoiding the impact of traditional contact temperature measurement methods on the insulation performance and service life of the equipment. 2. This invention uses the heat conduction inversion method and utilizes the mapping relationship database established by finite element simulation, combined with support vector machine regression algorithm and nonlinear mapping, to accurately calculate the equivalent thermal resistance value of electrical connection points in the air box, and the monitoring error is controlled within the preset error range; 3. This invention, through dynamic threshold determination and long short-term memory neural network model, can not only detect connection point anomalies in a timely manner, but also predict future temperature change trends, providing maintenance personnel with a window of opportunity for early intervention and effectively preventing the escalation of faults; 4. By establishing a historical fault case database and continuously inputting new cases, the system can continuously optimize the fault mode matching algorithm and improve the diagnostic accuracy. At the same time, the dynamic threshold adjustment mechanism can adapt to the monitoring needs under different seasons and different load conditions, avoiding false alarms and missed alarms. 5. This invention supports dual protocol output of IEC 61850 protocol and Modbus TCP protocol, which can be directly connected to the existing intelligent substation operation and maintenance system, and linked with protection devices and integrated automation system to realize rapid isolation of fault areas and automatic adjustment of power supply strategy, thereby improving the intelligent operation and maintenance level of distribution network. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall technical solution architecture of the online monitoring method for the operating status of electrical connection points inside the gas box of a fully inflatable ring main unit according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating the core principle framework of the heat conduction inversion model according to an embodiment of this application; Figure 3 This is a logical flowchart of the distributed optical fiber temperature sensor network layout and temperature data acquisition according to an embodiment of this application. Figure 4 A flowchart illustrating the process of constructing and real-time inversion calculation of the mapping relationship between connection point temperature and equivalent thermal resistance according to embodiments of this application; Figure 5 This is a schematic diagram illustrating the multi-level interaction relationship and data flow between the fiber optic temperature sensor network, the data demodulation device, and the back-end data server according to an embodiment of this application. Figure 6 This is a schematic diagram illustrating the core principle framework of the support vector machine regression algorithm used in this application to establish a nonlinear mapping relationship between equivalent thermal resistance and the characteristic quantities of the outer surface temperature field. Figure 7 A schematic diagram illustrating the principle framework of a long short-term memory neural network model for predicting the temperature change trend of connection points and adjusting dynamic thresholds according to an embodiment of this application. Figure 8 This is a schematic diagram illustrating the multi-level interaction relationship and early warning information push process between the historical fault case database matching mechanism and the operation and maintenance monitoring platform according to an embodiment of this application. Detailed Implementation

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings: The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention. The present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. The purpose and effects of the present invention will become clearer. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0020] like Figures 1 to 8 As shown in Example 1: To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0021] This invention provides an online monitoring method for the operating status of electrical connection points inside the gas box of a fully inflatable ring main unit. This method encompasses multiple technical aspects, including the deployment of a distributed fiber optic temperature sensor network, the construction of a heat conduction inversion model, the establishment of mapping relationships based on finite element simulation, iterative solution using the least squares method, trend prediction using a long short-term memory neural network, and intelligent matching of historical fault cases. The method collects surface temperature data by deploying a distributed fiber optic temperature sensor network on the outer surface of the gas box. It then uses the heat conduction inversion model and the mapping relationship database established by finite element simulation to invert the equivalent thermal resistance value of the electrical connection points inside the gas box. Combined with dynamic threshold judgment and trend prediction, it achieves anomaly early warning. Finally, it uses a historical fault case database to achieve intelligent matching of fault modes and automatic generation of maintenance suggestions.

[0022] In the above method, step 1 involves arranging a distributed fiber optic temperature sensor network on the outer surface of the gas chamber, specifically including the following operations: Operation A, determining the selection and parameter settings of the fiber optic temperature sensors: A distributed fiber optic temperature sensor based on the Raman scattering principle is used. This type of sensor utilizes the characteristic that the intensity ratio of anti-Stokes light to Stokes light in the Raman scattering effect changes with temperature to achieve temperature measurement. The temperature measurement accuracy of the fiber optic temperature sensor is set to ±0.5℃, the spatial resolution is set to 1.0m, the measurement range is set to -40℃ to +120℃, and the typical response time does not exceed 2 seconds. Operation B, determining the sensor placement and spacing: Several fiber optic temperature sensors are evenly arranged along the length of the outer surface of the gas chamber. The spacing between adjacent sensors is set to a preset distance value, typically 1 / 20 to 1 / 30 of the gas chamber length, forming a temperature monitoring array covering the entire outer surface of the gas chamber. For a standard fully inflatable ring network cabinet, assuming the gas chamber length is 2.4m, the spacing between adjacent sensors is set to 100mm, with a total of 25 monitoring points. Operation C: Determine the key area densification strategy: In areas with large temperature gradients on the outer surface of the gas chamber, including the ends, corners, and near the busbar through-wall bushings, appropriately reduce the spacing between adjacent sensors to the adjusted value to improve temperature monitoring resolution in these areas. The sensor spacing at the gas chamber ends is adjusted to 50mm, at corners to 60mm, and near the busbar through-wall bushings to 40mm. Operation D: Determine the physical installation method for the fiber optic temperature sensor network: Use corrosion-resistant stainless steel clamps to fix the fiber optic temperature sensors to the outer surface of the gas chamber. A thermally conductive silicone grease layer is placed between the clamps and the gas chamber wall to ensure good thermal contact. The fiber optic lead-out cable is protected by a stainless steel explosion-proof flexible conduit and is led out to the fiber optic temperature demodulator through a dedicated cable entry device on the gas chamber shell. Operation E: Determine the monitoring array zoning strategy: Based on the actual distribution of electrical connection points within the gas chamber, divide the temperature monitoring array into several monitoring areas, each corresponding to the monitoring range of one or more electrical connection points. The zoning principle is as follows: the main circuit busbar connection area is divided into the main busbar monitoring area, the upper and lower contact connection area of ​​the three-position switch is divided into the switch monitoring area, and the cable entry and exit connection area is divided into the cable compartment monitoring area. Each monitoring area contains 3 to 5 adjacent fiber optic temperature sensors.

[0023] In the above method, step 2 involves acquiring temperature monitoring data of the outer surface of the gas chamber, specifically including the following operations: Operation A: Determine the configuration and connection of the data acquisition equipment: A fiber optic temperature demodulator is used as the temperature data acquisition device. The number of optical channels of the demodulator is set to 4 to 8, and the length of distributed optical fiber that can be accessed by each optical channel is 4km. The fiber optic temperature demodulator is connected to the distributed fiber optic temperature sensor network arranged on the outer surface of the gas chamber through fiber optic patch cords. The fiber optic connection uses FC / APC type fiber optic connectors to reduce reflection loss. Operation B: Determine the data acquisition parameter settings: The acquisition frequency is set to a preset acquisition cycle, typically 30 seconds to complete a full array temperature data acquisition, i.e., the acquisition cycle is 30 seconds. For critical monitoring areas, the acquisition cycle can be shortened to 15 seconds. The sampling accuracy of the demodulator is set to 16 bits, and the temperature data output format is IEEE 754 floating-point format. Operation C: Determine the data storage architecture: The acquired temperature data is transmitted to the data server in real time through an Ethernet interface. The data server uses a time-series database to store temperature data, with InfluxDB or TDengine selected as the preferred database. The data storage period for a single monitoring point is no less than the preset storage period, typically set to store temperature data for the most recent 24 months. The data compression ratio is no less than the preset compression ratio, typically 3:1, to meet the data requirements for long-term trend analysis. Operation D defines the data preprocessing workflow: Temperature data undergoes preprocessing before storage, including outlier removal, filtering and smoothing, and time alignment. Outlier removal uses the 3σ criterion, classifying data points exceeding three times the standard deviation of the mean as outliers and marking them. Filtering and smoothing employs a Kalman filter for real-time filtering to remove measurement noise. Time alignment ensures that the timestamps of data from all monitoring points are consistent. Operation E defines the data transmission protocol: Temperature data is transmitted from the fiber optic temperature demodulator to the data server using the TCP / IP protocol stack, with a data transmission rate of no less than 100Mbps. A data verification mechanism is implemented during data transmission, using the CRC-16 checksum algorithm to ensure the integrity of data transmission.

[0024] In the above method, step 3 is to establish a heat conduction inversion model, which specifically includes the following operations.

[0025] Operation A establishes the physical basis of the heat conduction inversion model: based on the heating power of the electrical connection points inside the gas box, the thermophysical properties of SF6 gas, the thermal conductivity of the gas box wall material, and the structural dimensions of the gas box, a mathematical model for heat conduction inversion between the temperature of the connection points inside the gas box and the temperature of the outer surface is established. The theoretical basis of the heat conduction inversion model is the three-dimensional steady-state heat conduction equation, whose mathematical expression is:

[0026] ; Where k is the thermal conductivity of the gas box wall material, in W / (m·K); T is the temperature, in K; and Q is the heating power per unit volume, in W / m³.

[0027] Operation B: Determine the boundary conditions: The boundary conditions of the heat conduction inversion mathematical model include two parts: inner boundary conditions and outer boundary conditions.

[0028] The inner boundary condition is the convective heat transfer boundary condition between the SF6 gas inside the gas box and the inner wall surface. The convective heat transfer coefficient is set as h_inner. The thermophysical parameters of the SF6 gas are: thermal conductivity k_SF6 = 0.0146 W / (m·K), specific heat capacity c_SF6 = 665 J / (kg·K), and dynamic viscosity μ_SF6 = 1.56 × 10 Pa·s. The convective heat transfer coefficient of the SF6 gas inside the gas box is calculated according to empirical formulas, with a typical value of 5 to 15 W / (m²·K). The outer boundary condition is the natural convection heat transfer boundary condition between the outer surface of the gas box and the air. The convective heat transfer coefficient is set as h_outer. The natural convection heat transfer coefficient of the air is set to 5 to 10 W / (m²·K), and the influence of radiation heat transfer is considered, with a radiation heat transfer coefficient of 3 to 5 W / (m²·K).

[0029] Operation C determines the model solution method: the heat conduction inversion mathematical model is solved numerically using the finite difference method. The three-dimensional model of the gas box is discretized into a finite difference mesh, with the mesh density refined near the connection points, typically by a factor of 2 to 4. A second-order central difference scheme is used for the difference scheme, and an implicit scheme is used for time discretization to ensure numerical stability. The solver uses the preconditioned conjugate gradient method, with an iterative convergence accuracy set to 10.

[0030] Operation D: Determine the definition and calculation method of equivalent thermal resistance: The equivalent thermal resistance of the electrical connection point inside the gas box is defined as the ratio of the heating power at the connection point to the temperature rise of the connection point relative to the reference temperature, i.e., R_eq = ΔT / P. The heating power P at the connection point is determined by the load current and contact resistance, with a typical value of 10 to 500W. ΔT is the difference between the temperature of the connection point and the reference temperature of SF6 gas.

[0031] Operation E determines the verification and calibration method for the inversion model: Calculate the temperature field of a calibration specimen with known heat generation power and thermal resistance using the heat conduction inversion model. Compare the calculation results with measured temperature data to verify the model's accuracy. If the deviation exceeds the allowable range, adjust the model parameters until the deviation is less than the preset verification threshold, typically ±2℃.

[0032] In the above method, step 4 involves establishing the mapping relationship between connection point temperature and equivalent thermal resistance, specifically including the following operations: Operation A: Determine the modeling method for finite element simulation: Calculate the temperature distribution on the outer surface of the gas box under different equivalent thermal resistance conditions at connection points using finite element simulation. The finite element simulation employs a three-dimensional transient thermal analysis method to establish a complete geometric model including the gas box shell, SF6 gas, internal conductors, and insulating supports. The gas box shell material is 3mm thick 304 stainless steel with a thermal conductivity of 16.2 W / (m·K); the internal conductor material is T2 copper with a thermal conductivity of 390 W / (m·K); and the insulating support material is epoxy resin with a thermal conductivity of 0.3 W / (m·K). Operation B: Determine the method for setting the simulation conditions: The simulation conditions include various conditions from rated load to preset overload multiples under different load currents. The load current variation range is set to 0.5 to 2.0 times the rated current, and the overload multiple is set to three levels: 1.2 times, 1.5 times, and 2.0 times. The simulation time span for each operating condition is set to a preset time length, typically the time required from cold start to thermal stability. Under rated load conditions, the thermal stability time is approximately 8 hours. Operation C determines the equivalent thermal resistance variation range: the equivalent thermal resistance variation range is set to a preset range, typically 0.01 K / W to 1.0 K / W, with a variation step size of 0.01 K / W. If the equivalent thermal resistance at the connection point under normal conditions is 0.05 K / W, then the simulation conditions cover a range from 0.01 K / W to 2.0 K / W, with a total of 200 different equivalent thermal resistance values ​​set for simulation calculations. Operation D determines the feature extraction method for the mapping relation database: the feature quantities of the mapping relation database include the maximum value T_max, mean T_mean, standard deviation T_std, peak position coordinates (X_peak, Y_peak, Z_peak), and temperature gradient distribution G of the temperature field on the outer surface of the gas box. The feature extraction method is as follows: T_max is the maximum temperature among all monitoring points on the outer surface; T_mean is the arithmetic mean of the temperatures at all monitoring points; T_std is the standard deviation of the temperatures at all monitoring points; the peak position is the three-dimensional coordinate of the monitoring point where T_max is located; the temperature gradient distribution G is the distribution curve of the ratio of the temperature difference to the distance between adjacent monitoring points along the length and circumferential directions. Operation E determines the method for establishing the nonlinear mapping relationship: a nonlinear mapping relationship is established between the feature quantities and the equivalent thermal resistance through the support vector machine regression algorithm. The kernel function of the support vector machine regression algorithm adopts the radial basis function (RBF), the kernel function parameter γ is set to 0.1, and the allowable error ε of the equivalent thermal resistance prediction value is set to 0.001. The number of training samples for the model is not less than the preset number of samples, typically set to 2000 training samples, each sample containing 5 input feature quantities and 1 output equivalent thermal resistance value.Operation F determines the data structure design of the mapping relation database: The mapping relation database adopts a hybrid storage architecture combining relational databases and in-memory databases. The database table structure includes fields such as simulation condition number, equivalent thermal resistance setpoint, surface temperature field feature set, and data acquisition timestamp. The database index is constructed using a B+ tree index based on the equivalent thermal resistance value to support fast range queries and nearest neighbor searches.

[0033] In the above method, step 5 is to perform real-time inversion calculation of the equivalent thermal resistance of the connection point, which specifically includes the following operations.

[0034] Operation A: Prepare the input data for the inversion calculation: Input the real-time collected temperature data of the outer surface of the gas box into the heat conduction inversion model. The input data includes the temperature values ​​T_i (i=1,2,…,n) of all monitoring points at the current time and the corresponding coordinates of the monitoring point locations (X_i, Y_i, Z_i), where n is the total number of monitoring points. Before performing the inversion calculation, the input data needs to undergo data quality checks, including data integrity checks, temporal continuity checks, and physical rationality checks. The data integrity check ensures that all monitoring point data has been collected; the temporal continuity check ensures that the time interval between two adjacent collections conforms to the preset collection cycle; the physical rationality check ensures that the temperature values ​​are within a reasonable range, such as -50℃ to +150℃.

[0035] Operation B involves determining a fast query method based on a mapping relation database: Using the mapping relation database, the nearest neighbor interpolation method is employed to obtain the initial estimate of the equivalent thermal resistance closest to the current temperature field feature quantity. Specifically, the Euclidean distance between the current temperature field feature quantity vector F_current and the feature quantity vectors F_database_j of all samples in the mapping relation database is calculated. The K samples with the smallest distances are selected, and their weighted average equivalent thermal resistance values ​​are taken as the initial estimate R_eq_initial. The weights are the reciprocal of the distance, typically K=5.

[0036] Operation C determines the specific process of the least squares iterative solution: The least squares method is used for iterative solution to obtain a more accurate equivalent thermal resistance value. The objective function for the iterative solution is:

[0037] ; Where T_measured,i is the measured temperature value of the i-th monitoring point, and T_simulated,i(R_eq) is the simulated temperature value of the i-th monitoring point under the condition of equivalent thermal resistance R_eq. The iterative solution uses the Gauss-Newton method, and the iterative format is as follows:

[0038] ; Where J is the Jacobian matrix of the equivalent thermal resistance of a given parameter at temperature.

[0039] Operation D determines the iterative convergence condition and anomaly handling: The convergence condition for the least squares iterative solution is that the relative error between two adjacent iterations is less than a preset error threshold, typically set to 0.1%. The maximum number of iterations is set to a preset upper limit, typically 50. If convergence is not achieved after reaching the upper limit, a convergence failure flag is output, and a manual review process is triggered. The convergence failure criterion is that the residual decrease rate is less than 1% for three consecutive iterations.

[0040] Operation E determines the output format of the inversion calculation results: The equivalent thermal resistance value of the electrical connection points within the gas chamber corresponding to each monitoring area is calculated in real time and output to the data server in JSON format. The output data includes the monitoring area number, the calculated equivalent thermal resistance value, the calculation confidence level, and the calculation timestamp. The calculation confidence level is based on a comprehensive evaluation of the distance to the nearest neighbor samples in the mapping relationship database and the iterative convergence residual.

[0041] In the above method, step 6 is to set the equivalent thermal resistance threshold and perform anomaly detection, which specifically includes the following operations.

[0042] Operation A: Determine the method for setting the equivalent thermal resistance threshold: Take the average value of the normal equivalent thermal resistance of the monitoring area within the most recent preset statistical period as the baseline value R_base. The preset statistical period is typically set to the normal operation data of the most recent 30 days. The threshold is set to a preset first multiple of the baseline value, which is typically set to 1.5 times. At the same time, the alarm value is set to a preset second multiple of the baseline value, which is typically set to 2.0 times, to distinguish between two alarm levels: general abnormality and severe abnormality.

[0043] Operation B: Determine the anomaly detection logic: Compare the equivalent thermal resistance value R_eq_current calculated in step 5 with the threshold value. When R_eq_current is greater than 1.5 times R_base and less than 2.0 times R_base, it is determined to be a general anomaly; when R_eq_current is greater than or equal to 2.0 times R_base, it is determined to be a severe anomaly. When R_eq_current is greater than 1.5 times R_base and the duration exceeds a preset duration threshold, anomaly confirmation is triggered. The preset duration threshold is typically set to 10 minutes to eliminate misjudgments caused by instantaneous fluctuations.

[0044] Operation C establishes the dynamic threshold adjustment mechanism: The equivalent thermal resistance threshold can be dynamically adjusted based on changes in ambient temperature and load current. The ambient temperature correction factor is determined based on the difference between the ambient temperature and the reference ambient temperature; for every 10°C increase in ambient temperature, the threshold is increased by 5%. The load current correction factor is determined based on the ratio of the load current to the rated current; for every 10% increase in load current, the threshold is increased by 3%. The corrected threshold is not lower than the static threshold.

[0045] Operation D determines the output of the anomaly assessment result: The anomaly assessment result includes fields such as monitoring area number, anomaly level, anomaly duration, current equivalent thermal resistance value, and percentage deviation relative to the threshold. The anomaly assessment result is pushed to the operation and maintenance monitoring platform in real time and stored in the historical record table of the data server.

[0046] In the above method, step 7 is to output monitoring and early warning information, which specifically includes the following operations.

[0047] Operation A establishes the early warning information generation mechanism: When step 6 determines an anomaly, an early warning message containing the anomaly location, anomaly level, and estimated temperature rise is automatically generated. The anomaly location is precisely pinpointed using the monitoring area number and monitoring point coordinates. The anomaly level is divided into two levels: general early warning and severe early warning. The estimated temperature rise is calculated based on the equivalent thermal resistance and the current load current, using the formula ΔT_predict=R_eq_current × I² × R_contact, where I is the effective value of the current load current and R_contact is the typical value of the connection point contact resistance.

[0048] Operation B: Determine the encapsulation format of the warning information: The warning information is encapsulated in JSON format, including fields such as the monitored object number, anomaly type, occurrence time, measuring point temperature value, calculated equivalent thermal resistance value, and percentage deviation from the threshold.

[0049] An example of the JSON format is: {"monitor_id":"ZNHW-001","anomaly_type":"thermal_resistance_high","occur_time":"2024-01-15T10:3 0:00Z","temperature_value":58.5,"thermal_resistance":0.082,"deviation_percent":36.7,"severity":"warning"}.

[0050] Operation C determines the transmission method of the warning information: it is sent to the operation and maintenance monitoring platform via the communication module. The communication module supports dual protocol output of IEC 61850 protocol and Modbus TCP protocol, and the communication interface adopts Ethernet RJ45 interface, with a transmission rate supporting 10 / 100 / 1000Mbps adaptive. The IEC 61850 protocol is used for integration with the intelligent substation integrated automation system and mapped to the MMS service; the Modbus TCP protocol is used for data interaction with third-party monitoring systems and mapped to the holding register.

[0051] Operation D determines the tiered push strategy for early warning information: general early warning information is pushed to ordinary monitoring terminals in the regional operation and maintenance monitoring center; severe early warning information is simultaneously pushed to the mobile terminals of the regional operation and maintenance monitoring center, dispatch center, and on-site operation and maintenance personnel. The push delay for early warning information shall not exceed 5 seconds.

[0052] In the above method, step 8 is to establish a comprehensive evaluation model of the connection point's operating status, which specifically includes the following operations.

[0053] Operation A determines the technical selection of the comprehensive evaluation model: Based on the equivalent thermal resistance sequence calculated in step 5, the temperature data collected in step 2, and the real-time load current data of the ring main unit, a Long Short-Term Memory (LSTM) neural network model is used to predict the temperature change trend of each connection point within a preset prediction period. The input layer of the LSTM neural network model has a dimension of 3, containing three time series: equivalent thermal resistance sequence, temperature sequence, and load current sequence; the hidden layer has 2 layers, each containing 128 LSTM units; the output layer has a dimension of 1, outputting the predicted temperature value of each connection point within the future prediction period. The preset prediction period is typically set to 2 hours.

[0054] Operation B determines the data preprocessing method for the model: Input data needs to be standardized before being fed into the Long Short-Term Memory (LSTM) neural network. Z-score standardization is used to normalize the mean to zero and the variance to one. Standardization parameters are calculated statistically based on historical data from the last 30 days. The sampling rate for the equivalent thermal resistance sequence, temperature sequence, and load current sequence is uniformly set to 5 minutes, meaning each time step corresponds to a 5-minute time span.

[0055] Operation C determines the training and update method for the model: The Long Short-Term Memory (LSTM) neural network model is trained using the Adam optimizer, with a learning rate of 0.001, a batch size of 32, and 100 training epochs. The loss function is the Mean Squared Error (MSE). Model parameters are updated periodically, once a week, using an incremental learning method to fine-tune the model using only the data from the most recent week.

[0056] Operation D: Determine the dynamic threshold adjustment method: Dynamically adjust the equivalent thermal resistance threshold in step 6 based on the prediction results of the Long Short-Term Memory Neural Network model. If the prediction results show that the equivalent thermal resistance value will exceed the current threshold within the future prediction period, lower the threshold by 10% in advance to increase the warning lead time. If the prediction results show that the equivalent thermal resistance value will rapidly decrease to the normal range, appropriately extend the anomaly confirmation time to reduce false alarms.

[0057] Operation E determines the model output and application: The output of the Long Short-Term Memory Neural Network model is the temperature change trend curve for each monitored area within the predicted timeframe (2 hours). This curve is displayed graphically on the operation and maintenance monitoring platform, with the prediction confidence interval (±2σ) marked. When the predicted temperature exceeds the safe operating temperature threshold, a high-temperature warning is generated to notify operation and maintenance personnel in advance to take preventive measures.

[0058] In the above method, step 9 is to establish a historical failure case database, which specifically includes the following operations.

[0059] Operation A: Determine the data sources for the historical fault case database: The historical fault case database stores characteristic data of confirmed electrical connection point faults within the gas box. Data sources include: real fault cases accumulated during the operation of this system, historical fault maintenance records obtained from the power grid company, and laboratory simulated fault test data. Each fault case must be reviewed and confirmed by experts before being entered into the database.

[0060] Operation B determines the method for extracting characteristic data for fault cases: The characteristic data for fault cases include the equivalent thermal resistance change curve before the fault, temperature field characteristics, and corresponding load conditions. The equivalent thermal resistance change curve extracts the equivalent thermal resistance sequence data for the 72 hours prior to the fault, with a sampling interval of 5 minutes. Temperature field characteristics include the average maximum surface temperature, the maximum standard deviation of surface temperature, and the peak temperature gradient for the 24 hours prior to the fault. Load conditions include the load current curve and duration during the fault period.

[0061] Operation C determines the storage structure of the fault case database: The historical fault case database is stored using a MongoDB document database, supporting flexible field expansion and full-text search. The database collection is named "fault_cases," and each document contains fields such as case number, fault type, occurrence time, resolution time, equipment model, equivalent thermal resistance sequence, temperature field characteristics, load conditions, handling measures, and experience summary. The database index is constructed as a composite index based on fault type and occurrence time.

[0062] Operation D determines the fault mode matching algorithm: When a newly occurring anomaly matches an existing fault mode in the database, it automatically matches the most similar historical case. The matching algorithm uses cosine similarity to calculate the similarity between the current anomaly feature vector and the feature vectors of historical cases. The current anomaly feature vector includes four dimensions: the rate of change of equivalent thermal resistance in the most recent hour, the ratio of peak to mean equivalent thermal resistance, the temperature gradient trend, and the load current trend. The historical case with the highest cosine similarity (greater than 0.8) is selected as the matching result.

[0063] Operation E determines the maintenance suggestion generation method: Maintenance suggestions are generated by referencing the handling results of matched historical cases. The maintenance suggestions are automatically generated based on the handling measures of historical cases. Generation rules include: if the matched case is a poor contact fault that has been resolved by tightening, it is recommended to check the tightness of the corresponding connection point; if the matched case is a conductor oxidation fault that has been resolved by replacement, it is recommended to prepare replacement spare parts; if the matched case is an overload fault that has been resolved by current limiting, it is recommended to check the load distribution scheme of the circuit. The generated maintenance suggestions are pushed to the operation and maintenance monitoring platform in text format.

[0064] In the above method, the detailed technical features of the communication module are as follows: the communication module supports dual protocol output of IEC 61850 protocol and Modbus TCP protocol, the communication interface adopts a dual network port redundancy design, and supports network load balancing and fault switching. The implementation of IEC 61850 protocol includes three parts: MMS service, GOOSE service, and SV service. Among them, MMS service is used for data transmission and control command issuance, GOOSE service is used for fast transmission of alarm information, and SV service is used for real-time transmission of sampled values. The implementation of Modbus TCP protocol uses function code 03 (holding register read) and function code 16 (holding register batch write), and the register address mapping is configured according to the standard MODBUS mapping table. The JSON format encapsulated fields of the warning information include: monitor_id (monitored object number), anomaly_type (anomaly type), occurrence_time (occurrence time), temperature_value (measurement point temperature value), thermal_resistance (calculated equivalent thermal resistance value), deviation_percent (deviation percentage from threshold), confidence (confidence level), and predicted_duration (predicted duration).

[0065] When the above method is applied to the intelligent operation and maintenance system of fully pneumatic ring main units, the specific implementation of linkage with existing protection devices and integrated automation systems is as follows: When a serious anomaly is detected and its duration exceeds a preset duration threshold, the ring main unit load transfer process is automatically triggered. The preset duration threshold is typically set to 30 minutes. The load transfer process includes the following steps: sending a load transfer request to the integrated automation system, the request content including the fault area number, the estimated transfer load capacity, and the expected completion time; receiving the load transfer instruction issued by the integrated automation system; issuing a load transfer control command to the ring main unit remote terminal unit (RTU), the control command including the target switch number, opening and closing instructions, and operation sequence; monitoring the load transfer execution status, confirming the completion of the transfer and recording the execution result; generating a load transfer execution report and storing it in the database. When the load transfer fails, the fault area isolation process is automatically triggered, sending an isolation instruction to the RTU of the adjacent ring main unit, disconnecting the interconnection switch between the fault area and the non-fault area, realizing rapid isolation of the fault area and continuous power supply to the non-fault area.

[0066] Example 2: This example describes an alternative implementation scheme for an online monitoring method of the operating status of electrical connection points inside the gas box of a fully inflatable ring main unit based on an edge computing architecture. This scheme offloads some computing tasks to the edge computing device at the monitoring site to reduce data transmission bandwidth requirements and system response latency.

[0067] In the above method, the edge computing device is deployed in a fully inflatable ring network cabinet within the local control cabinet. It employs an industrial-grade embedded computing platform, configured with an ARM Cortex-A72 processor (1.8GHz), 4GB DDR4 memory, and 32GB eMMC storage. The edge computing device runs a Linux embedded operating system and containerized applications, supporting Docker container deployment. The edge computing device is directly connected to the fiber optic temperature demodulator via Ethernet, increasing the acquisition frequency to once every 10 seconds for the entire array of temperature data.

[0068] The temperature data processing algorithms running within the edge computing device include: a real-time data filtering algorithm using an adaptive Kalman filter, with filter parameters automatically adjusted according to signal noise levels; a heat conduction inversion calculation using a quantized lightweight neural network model to replace the complete finite element model, with the model input being the temperature values ​​of all monitoring points at the current moment and the model output being the equivalent thermal resistance values ​​of each monitoring area, and the model file size controlled within 10MB; and an anomaly detection algorithm using rule-based judgment logic combined with an incremental learning algorithm, with the rule base configured and optimized according to the actual situation on site.

[0069] Data transmission between the edge computing devices and the backend data server uses the MQTT protocol, with a message transmission frequency of once every 5 minutes. The transmitted content includes the equivalent thermal resistance value of each monitoring area, abnormal alarm events, and device operating status. The edge computing devices locally store the temperature data and calculation results for the most recent 72 hours, supporting data transmission resumption after network interruption and data replenishment. The backend data server is responsible for aggregating data uploaded from multiple edge computing devices, performing cross-device correlation analysis, and global trend prediction.

[0070] The transmission of early warning information between the edge computing device and the operation and maintenance monitoring platform uses a long-lived connection established by the WebSocket protocol to achieve real-time push of early warning information with a push delay of no more than 1 second. The receipt and confirmation of early warning information and status feedback are transmitted through a reverse channel. The edge computing device records the push status of each early warning message and the user confirmation time.

[0071] Compared to a centralized architecture, the system response latency of this edge computing architecture is reduced by more than 80%, and the local processing latency of the edge computing device is no more than 500ms, which meets the application requirements for real-time monitoring.

[0072] Example 3: This example describes an extended implementation scheme for an online monitoring method of the operating status of electrical connection points inside the gas box of a fully inflatable ring main unit (RMU), applicable to centralized monitoring scenarios involving multiple RMU units. This scheme adopts an architecture combining distributed data acquisition and centralized data processing, supporting centralized monitoring and management of multiple RMU units within the entire distribution area.

[0073] In the above method, the monitoring architecture of a single ring network cabinet is basically the same as that in Example 1. The difference is that each ring network cabinet is equipped with an independent fiber optic temperature demodulator and an edge computing device. The edge computing device is responsible for the temperature data acquisition, preprocessing and local calculation of the cabinet. The edge computing device communicates with the centralized data aggregation device through power line carrier or wireless mesh network. The temperature data acquisition frequency of a single ring network cabinet is set to once per minute, and the local storage period of the edge computing device is set to 168 hours (7 days).

[0074] The centralized data aggregation device is deployed in the distribution area monitoring center, employing a high-performance server configuration with an Intel Xeon Gold 6248 processor (2.5GHz), 64GB DDR4 ECC memory, and 2TB SSD storage. The centralized data aggregation device runs a distributed time-series database cluster, responsible for storing and processing monitoring data from all ring network cabinets within its jurisdiction. The data aggregation device communicates with the edge computing devices of each ring network cabinet via an Ethernet switch, using the IEC61850 MMS protocol.

[0075] The core functions of the centralized data aggregation device include: multi-cabinet correlation analysis, which identifies whether local temperature anomalies are caused by upstream equipment failures by comparing the temperature field distribution characteristics of multiple ring main units on the same busbar segment; global trend prediction, which uses an integrated learning method to fuse the prediction results of multiple ring main units to improve the accuracy of temperature change trend prediction; equipment health assessment, which uses an autoregressive integral moving average (ARIMA) model to assess the degradation trend and remaining service life of each connection point based on the historical equivalent thermal resistance sequence of each cabinet's monitoring area; and load optimization suggestion, which generates load optimization adjustment suggestions based on the current load distribution of each cabinet and temperature monitoring results to reduce the risk of local overheating.

[0076] The operation and maintenance monitoring platform integrates all the functions of the above-mentioned extended solutions, providing a visual interface for operation and maintenance personnel. The visual interface includes: a distribution area topology map, displaying the location and connection relationships of all ring network cabinets within its jurisdiction; a temperature field heat map, using color depth to represent the temperature distribution on the outer surface of the gas box, supporting time-dimensional playback; an equivalent thermal resistance trend map, displaying the curve of equivalent thermal resistance change over time for each monitoring area, supporting custom time range queries; a fault case matching result display, listing the matching degree ranking and similarity analysis results between current anomalies and historical fault cases; and a load optimization suggestion list, displaying the load adjustment suggestions generated by the system and their expected effects.

[0077] The foregoing has shown and described the basic principles and main features of the present invention. The above embodiments are merely illustrative of the purpose, technical solutions, and advantages of the present invention, and are not intended to limit the invention. All equivalent variations and modifications made within the scope of the claims of this patent application should be covered within the protection scope of this invention.

Claims

1. A kind of full aerate ring network cabinet gas tank inner electrical connection point operating state online monitoring method, it is characterized in that, Includes the following steps: Step 1: Arrange a distributed fiber optic temperature sensor network on the outer surface of the air box: Arrange several fiber optic temperature sensors evenly along the length of the outer surface of the air box, and set the spacing between adjacent sensors to a preset distance value to form a temperature monitoring array covering the entire outer surface of the air box. Step 2, acquire temperature monitoring data of the outer surface of the air box: collect temperature data of each monitoring point in real time through a fiber optic temperature demodulator, set the acquisition frequency to a preset acquisition cycle, and store the collected temperature data to the data server; Step 3, establish a heat conduction inversion model: Based on the heat generation power of the electrical connection points inside the gas box, the thermal properties of SF6 gas, the thermal conductivity of the gas box wall material, and the gas box structural dimensions, establish a mathematical model for the heat conduction inversion between the temperature of the connection points inside the gas box and the temperature of the outer surface. Step 4, construct the mapping relationship between connection point temperature and equivalent thermal resistance: calculate the temperature distribution law of the outer surface of the gas box under different connection point equivalent thermal resistance conditions through finite element simulation method, and establish a database of mapping relationship between equivalent thermal resistance and outer surface temperature field characteristic quantities. Step 5, Real-time inversion calculation of equivalent thermal resistance of connection points: Input the real-time collected temperature data of the outer surface of the air box into the heat conduction inversion model, combine it with the mapping relationship database, and use the least squares method to perform iterative solution to calculate the equivalent thermal resistance value of the electrical connection point in the air box corresponding to each monitoring area in real time. Step 6, set the equivalent thermal resistance threshold and make anomaly judgment: According to the connection point design parameters and historical operation data, set the corresponding equivalent thermal resistance threshold for each monitoring area, compare the equivalent thermal resistance value calculated in step 5 with the threshold, and when the equivalent thermal resistance value exceeds the threshold by a preset multiple, it is determined that there is an anomaly in the electrical connection point in the gas box corresponding to the monitoring area. Step 7, Output monitoring and early warning information: When an anomaly is determined in Step 6, an early warning information containing the location of the anomaly, the anomaly level, and the estimated temperature rise is automatically generated and sent to the operation and maintenance monitoring platform through the communication module.

2. The method according to claim 1, wherein the method is characterized in that: In step 1, the fiber optic temperature sensor is a temperature sensor based on the Raman scattering principle, with a temperature measurement accuracy of ±0.5℃, a spatial resolution of 1.0m, a measurement range of -40℃ to +120℃, and a response time of no more than 2 seconds.

3. The method according to claim 1, characterized in that: In step 1, in areas with large temperature gradients on the outer surface of the gas box, including the gas box end, corners, and near the busbar through-wall bushing, the spacing between adjacent sensors is appropriately reduced to improve the temperature monitoring resolution in these areas.

4. The method according to claim 1, wherein the method is characterized in that: In step 2, the data server uses a time-series database to store temperature data. The data storage period for a single monitoring point is no less than 24 months, and the data compression ratio is no less than 3:

1.

5. The method for online monitoring of the operating status of electrical connection points inside the gas box of a fully inflatable ring main unit according to claim 1, characterized in that: The heat conduction inversion mathematical model in step 3 is constructed based on the three-dimensional steady-state heat conduction equation, and its mathematical expression is: ； Where k is the thermal conductivity of the gas box wall material, T is the temperature, and Q is the power generated per unit volume; the boundary conditions of the model include the convective heat transfer boundary conditions between the SF6 gas and the inner wall of the gas box, and the natural convective heat transfer boundary conditions between the outer surface of the gas box and the air; the model is solved using the finite difference method; the equivalent thermal resistance is defined as the ratio of the power generated at the connection point to the temperature rise of the connection point relative to the reference temperature, i.e.: ； wherein, Rth is the equivalent thermal resistance, is the difference between the junction temperature and the SF6gas reference temperature, and P is the junction heat power.

6. The method for on-line monitoring of operating state of electrical connection points in the gas tank of a full gas-filled ring main unit according to claim 1, characterized in that: In step 4, the finite element simulation adopts the three-dimensional transient thermal analysis method. The simulation conditions include various conditions from rated load to preset overload multiple under different load current conditions. The simulation time span for each condition is the time required from cold start to thermal stability. The range of equivalent thermal resistance variation is set from 0.01 K / W to 2.0 K / W.

7. The method for on-line monitoring of operating state of electrical connection points in the gas tank of a full gas-filled ring main unit according to claim 1, characterized in that: In step 4, the features of the mapping database include the maximum value, mean, standard deviation, peak position, and temperature gradient distribution of the temperature field on the outer surface of the gas box. These features are mapped to the equivalent thermal resistance using a support vector machine regression algorithm to establish a nonlinear mapping relationship.

8. The method for online monitoring of the operating status of electrical connection points inside the gas box of a fully inflatable ring main unit according to claim 1, characterized in that: The objective function solved by the least squares iterative method in step 5 is: ； in, Let be the measured temperature value at the i-th monitoring point. For the i-th monitoring point, the equivalent thermal resistance is The simulation calculates the temperature value under the given conditions, where n is the total number of monitoring points; the iterative scheme uses the Gauss-Newton method. ； Where J is the Jacobian matrix of the equivalent thermal resistance of the given parameter for temperature value; the convergence condition of the iterative solution is that the relative error between two adjacent iterations is less than 0.1%, the maximum number of iterations is set to 50, and when the number of iterations reaches the upper limit and still fails to converge, a convergence failure flag is output and a manual review process is triggered.

9. The method for online monitoring of the operating status of electrical connection points inside the gas box of a fully inflatable ring main unit according to claim 1, characterized in that: The method for setting the equivalent thermal resistance threshold in step 6 is as follows: take the average value of the normal equivalent thermal resistance of the monitoring area in the past 30 days as the benchmark value, set the threshold value to 1.5 times the benchmark value, and set the alarm value to 2.0 times the benchmark value to distinguish between two alarm levels: general abnormality and severe abnormality. The formula for calculating the estimated temperature rise in step 7 is as follows: ； in, To estimate the temperature rise, I is the current equivalent thermal resistance, and I is the current effective load current. This represents a typical value for the contact resistance at the connection point.

10. The method according to claim 9, characterized in that: When the method is applied to the intelligent operation and maintenance system of the fully inflatable ring main unit, it is linked with the existing protection device and integrated automation system. When a serious abnormality is detected and lasts for more than 30 minutes, the ring main unit load transfer process is automatically triggered to achieve rapid isolation of the fault area and continuous power supply to the non-fault area.

Images