Gas leak fault monitoring system and method for gil ducts

Abstract

The present application relates to the technical field of gas leakage monitoring, and discloses a gas leakage fault monitoring system and method for a GIL pipe gallery, which generates a dynamic debris source term based on operating state parameters, combines real-time flow field simulation of the debris transport process, analyzes the dynamic erosion risk of the pipe wall in real time, couples the dynamic erosion risk with external environmental load for analysis, evaluates the dynamic leakage risk probability, and screens out high-risk areas, matches the leakage suspicious areas located by the leakage monitoring signal with the screened high-risk areas in space, and locates the leakage point, the present application dynamically updates the debris source term, the erosion risk, and the leakage probability under the coupling of the dynamic erosion risk and the external environmental load, can reflect the immediate impact of circuit state mutation on the safety of the pipe wall, matches the positioning result of the sensor with the screened high-risk areas in space, and improves the confidence of the leakage point positioning in a complex pipe gallery environment.

Description

Technical Field

[0001] This invention relates to the field of gas leak monitoring technology, and in particular to a gas leak fault monitoring system and method for GIL (Gas Intake Line) tunnels. Background Technology

[0002] In the operation and maintenance management of underground integrated facilities such as utility tunnels, circuit system failures and the resulting derivative disasters are key factors affecting structural safety.

[0003] Existing technologies for gas leak fault monitoring in GIL (Gas Intake Line) tunnels typically employ discrete sensors to monitor the operating parameters of circuit nodes, focusing primarily on over-limit alarms. Each parameter is analyzed independently, lacking the ability to integrate and identify multi-source precursors of faults. This makes it difficult to determine the evolution trend of abnormal types and their potential physical derivatives, and ignores the dynamic corrosive effects of electrical faults on the tunnel's physical environment. Consequently, it is impossible to analyze the leakage risk of the tunnel under the coupled effects of structural strength degradation and external environmental loads. Furthermore, existing erosion and wear analyses mostly focus on fluid erosion within the pipes, lacking consideration for the transport and erosion behavior of debris generated by circuit faults in the confined space of the tunnel under the airflow field.

[0004] To address the aforementioned problems, this invention provides a gas leak fault monitoring system and method for GIL (Gas Inlet and Outer Limit) pipe corridors. Summary of the Invention

[0005] In view of this, the present invention provides a gas leak fault monitoring system and method for GIL pipe corridors. The present invention dynamically updates debris source terms, erosion risk, and leakage probability under the coupling of erosion risk and external environmental load, reflects the immediate impact of sudden circuit state changes on pipe wall safety, and spatially matches the sensor positioning results with the selected high-risk areas, thereby improving the confidence of leak point location in complex pipe corridor environments.

[0006] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for monitoring gas leak faults in GIL (Gas Intake Line) tunnels, comprising the following specific steps: S1. Real-time monitoring of the operating status parameters of key circuit nodes in the utility tunnel, including partial discharge signals, abnormal temperature rise, harmonic distortion rate, and the concentration of characteristic gases from the pyrolysis of insulating materials. S2. Based on the operating status parameters, quantify the debris type, particle size distribution and generation rate derived from circuit anomalies, and generate dynamic debris source terms; S3. Obtain dynamic debris source terms, combine with real-time flow field simulation of debris transport process, and analyze the dynamic erosion risk of pipe wall in real time; S4. Couple the dynamic erosion risk with the external environmental load to assess the probability of dynamic leakage risk and screen out high-risk areas. S5. Collect leakage monitoring signals, spatially match the suspected leakage areas located by the leakage monitoring signals with the screened high-risk areas, and locate the leakage point.

[0007] Preferably, step S1 includes the following specific steps: S11. Real-time monitoring of the operating status parameters of key circuit nodes in the pipe gallery, including partial discharge, abnormal temperature rise, current harmonic distortion rate, and concentration of characteristic gases from the pyrolysis of insulating materials. S12. Filter out the operating status parameters that exceed the corresponding preset threshold, and set the node where the operating status parameter is located as the coordinate of the fault node.

[0008] Preferably, step S2 includes the following specific steps: S21. Obtain the quality score of each type of debris by the proportion of the weighted association strength of each type of debris to the total weighted association strength of all debris types, and filter out the debris type corresponding to the maximum quality score. S22. Construct a particle size probability density function and obtain the probability density value of particle size occurrence through the particle size probability density function. S23. Construct a mass generation rate model, which includes a mass generation rate calculation formula, and obtain the total mass generation rate of debris through the mass generation rate calculation formula. S24. Obtain the coordinates of the fault node, debris type, total debris mass generation rate, and probability density value of particle size occurrence, and construct a dynamic debris source term vector.

[0009] Preferably, step S3 includes the following specific steps: S31. Collect the gas pressure and temperature inside the pipe gallery, and obtain the gas density under high pressure using the gas density calculation formula; S32. Construct a simulation model of the debris transport process. The simulation model of the debris transport process includes the debris particle motion equation and the debris particle heat balance equation. The velocity of the particle impacting the pipe wall is obtained by integrating the debris particle motion equation to the impact time. The temperature of the particle impacting the pipe wall is obtained by integrating the debris particle heat balance equation to the impact time. S33. Construct a thermo-coupled erosion rate model, which includes a thermo-coupled erosion rate calculation formula. Obtain the pipe wall erosion risk rate through the thermo-coupled erosion rate calculation formula.

[0010] Preferably, step S4 includes the following specific steps: S41. Construct a pipe wall remaining thickness model, the pipe wall remaining thickness model includes a pipe wall remaining thickness calculation formula, and obtain the effective remaining thickness of the pipe wall through the pipe wall remaining thickness calculation formula; S42. Obtain external environmental load data, which includes static membrane stress caused by soil pressure and thermal stress caused by temperature changes. Obtain the total stress of the pipe wall under the coupling of dynamic erosion risk and external environmental load through the environmental coupling stress calculation formula. S43. Construct a leakage risk probability model, which includes a leakage risk probability calculation formula. The leakage risk probability of the pipe wall under the influence of the total stress of the pipe wall is obtained through the leakage risk probability calculation formula. S44. Obtain the preset leakage risk probability threshold, filter the leakage risk probabilities that exceed the leakage risk probability threshold, and set the corresponding areas as high-risk areas.

[0011] Preferably, step S5 includes the following specific steps: S51. Collect leakage monitoring signals and obtain the suspected leakage area located in real time by the leakage monitoring signals; S52. Set the overlapping area between the suspected leakage area and the high-risk area as the leakage point.

[0012] Secondly, the present invention provides a gas leak fault monitoring system for GIL (Gas Intake System) tunnels, comprising: The operation parameter monitoring module is used to monitor the operation status parameters of key circuit nodes in the utility tunnel in real time, including partial discharge signals, abnormal temperature rise, harmonic distortion rate, and the concentration of characteristic gases from the pyrolysis of insulating materials. The dynamic debris generation module is used to quantify the debris type, particle size distribution and generation rate derived from circuit anomalies based on operating status parameters, and generate dynamic debris source terms. The erosion risk analysis module is used to obtain dynamic debris source terms, combine real-time flow field simulation of debris transport process, and analyze the dynamic erosion risk of pipe wall in real time. The risk area screening module is used to couple the dynamic erosion risk with the external environmental load to assess the probability of dynamic leakage risk and screen out high-risk areas. The leak point location module is used to collect leak monitoring signals, spatially match the suspected leak areas located by the leak monitoring signals with the screened high-risk areas, and locate the leak point.

[0013] Thirdly, the present invention provides a storage medium comprising stored instructions, wherein, when the instructions are executed, the device in which the storage medium is located is controlled to perform the gas leak fault monitoring method for GIL tunnels as described above.

[0014] Fourthly, the present invention provides an electronic device including a memory and one or more instructions, wherein one or more instructions are stored in the memory and configured to be executed by one or more processors as described above for gas leak fault monitoring method for GIL pipe corridor.

[0015] Compared with the prior art, the beneficial effects of the present invention are: dynamically updating debris source terms, erosion risk, and leakage probability under the coupling of dynamic erosion risk and external environmental load, which can reflect the immediate impact of sudden circuit state changes on pipe wall safety, spatially matching the sensor positioning results with the screened high-risk areas, improving the confidence of leak point location in complex pipe gallery environments, effectively eliminating environmental interference, and achieving accurate locking of leak points. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic diagram of the gas leak fault monitoring method for GIL pipe corridors provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the dynamic debris source item generation process provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the high-risk area screening process provided in an embodiment of the present invention; Figure 4 A schematic diagram of a gas leak fault monitoring system for GIL (Gas Intake System) pipe racks provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of an electronic device structure provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] In this invention, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element.

[0020] Please see Figure 1 This invention provides a method for monitoring gas leak faults in GIL (Gas Infrared Lever) tunnels, comprising the following specific steps: S1. Real-time monitoring of the operating status parameters of key circuit nodes in the utility tunnel, including partial discharge signals, abnormal temperature rise, harmonic distortion rate, and the concentration of characteristic gases from the pyrolysis of insulating materials. In this embodiment, S1 includes the following specific steps: S11. By deploying an integrated sensor network in key parts such as cable joints, switch cabinets, and transformers, the operating status parameters of key circuit nodes in the pipe gallery are monitored in real time. The operating status parameters include partial discharge, abnormal temperature rise, current harmonic distortion rate, and concentration of characteristic gases from the pyrolysis of insulating materials. In this embodiment, an ultra-high frequency (UHF) sensor is used to capture partial discharge signals caused by insulation defects. The collected pulse signals are denoised and feature extracted to calculate the apparent charge of the partial discharge, reflecting the cumulative energy of the insulation damage. Using fiber optic grating (FBG) temperature sensors, the node temperature is measured in real time, and the difference between the measured temperature and the reference temperature is calculated to obtain the abnormal temperature rise value of the node. Current waveforms are acquired using current transformers (CTs), and the spectral components are analyzed using Fast Fourier Transform (FFT) to calculate the current harmonic distortion rate. An electrochemical gas sensor is installed inside the switchgear or cable junction box to monitor the volume fraction of characteristic gases produced by the pyrolysis of insulating materials. This is achieved by dividing the volume of the measured characteristic gas by the total volume of the sampled gases and then multiplying by 10. 6 Obtain the concentration of characteristic gases from the pyrolysis of insulating materials, 10 6 It is a conversion factor for ppm units, and the characteristic gases include acetylene (C2H2), ethylene (C2H4), carbon monoxide (CO), etc., which are used to help identify the chemical composition of debris. S12. Filter out the operating status parameters that exceed the corresponding preset threshold, and set the node where the operating status parameter is located as the coordinate of the fault node.

[0021] In this embodiment, the operating status parameters reflect the health status of the circuit nodes. When the parameters exceed the corresponding preset thresholds, it indicates that there is an abnormality in the node, such as insulation aging, poor contact, overload, etc. The preset thresholds are set according to the alarm thresholds of key parameters in the equipment's technical manual, such as the design upper limit of partial discharge and the allowable range of temperature rise.

[0022] S2. Based on the operating status parameters, quantify the debris type, particle size distribution and generation rate derived from circuit anomalies, and generate dynamic debris source terms; Please see Figure 2 In this embodiment, S2 includes the following specific steps: S21. Obtain the quality score of each type of debris by using the proportion of its weighted correlation strength to the total weighted correlation strength of all debris types. The formula for calculating the quality score of debris can be expressed as:

[0023] In the formula, This represents the mass fraction of the i-th type of debris; for example, i=1 represents metal particles, i=2 represents carbonized insulating material particles, and so on. The weighting coefficients represent the failure modes. This represents a type mapping characteristic function used to establish the mapping relationship between pyrolysis characteristic gases or partial discharge quantities and debris types. This indicates the concentration of characteristic gases from the pyrolysis of insulating materials. Indicates the amount of partial discharge. This indicates that the indexes of all debris types are being traversed. This represents the total number of all debris types, and filters out the debris type corresponding to the maximum quality score. In this embodiment, the degree of pyrolysis and discharge properties of the insulating material are determined by the concentration of characteristic gases and the amount of partial discharge of the insulating material, thereby determining the material composition ratio of the debris. The weighting coefficients of failure modes reflect the importance of different failure modes in debris type identification. The steps for obtaining the weighting coefficients of failure modes are as follows: Obtain all modes of insulation material failure, such as arcing, insulation aging, and metal wear. Define the assessment dimensions of failure severity, including equipment damage degree, failure propagation, and repair difficulty. Equipment damage degree is judged by whether it leads to short circuits, insulation breakdown, or equipment shutdown. Fault propagation is judged by whether it triggers cascading failures; for example, arcing may cause phase-to-phase short circuits and has strong propagation. Repair difficulty is judged by whether component replacement or shutdown for maintenance is required; for example, metal wear only requires adjustment and is simple to repair, while arcing may require replacement of the entire component and is complex to repair. Each fault mode is divided into three levels: high, medium, and low, and assigned a baseline weight. High hazard is set to 0.8, medium hazard to 0.5, and low hazard to 0.3. Arcing faults can cause short circuits and equipment burnout, have strong propagation, and are difficult to repair, so they are assessed as high hazard and can be weighted at 0.8. Insulation aging can cause a decrease in insulation performance and may cause partial discharge, with medium propagation, requiring replacement of insulation materials for repair, so it is assessed as medium hazard and can be weighted at 0.5. Metal wear can cause poor contact, with weak propagation, requiring only adjustment or replacement of worn parts for repair, so it is assessed as low hazard and can be weighted at 0.3. The above weights are normalized, and the normalized weights are substituted into historical fault cases to verify their rationality. The steps for obtaining the type mapping feature function are as follows: Obtain several insulation material fault cases, record the pyrolysis characteristic gas concentration and partial discharge amount of each case, and analyze the corresponding debris types. Statistically, the correspondence between different gas / discharge characteristics and debris types is calculated. For example, high acetylene (C2H2) concentration corresponds to arc-ablated metal particles, high carbon monoxide (CO) concentration corresponds to carbonized insulating paper particles, and high ethylene (C2H4) concentration corresponds to carbonized particles generated by the decomposition of insulation materials. Define the membership function of the insulation material pyrolysis characteristic gas concentration / partial discharge amount. For example, the membership of high acetylene concentration. Output the association strength of debris types through fuzzy rules. For example, high acetylene concentration points to metal particles. S22. Construct a particle size probability density function and obtain the probability density value of particle size occurrence through the particle size probability density function. In this embodiment, the particle size distribution parameters are dynamically adjusted by the apparent charge of partial discharge and abnormal temperature rise. High-energy faults usually produce finer particles, such as electric arcs caused by high current harmonic distortion rate, while mechanical vibration or low-temperature aging produces larger particles. The particle size probability density function can be expressed as:

[0024] In the formula, Indicates particle size The probability density value of occurrence, The geometric standard deviation (logarithmic scale) representing the particle size distribution reflects the degree of particle size dispersion. Indicates the reference particle size parameter. Indicates the particle size refinement factor. The geometric mean (logarithmic scale) of the particle size distribution decreases as the partial discharge increases, reflecting that high-energy discharge produces finer debris. It should be noted that in actual calculations... and All are first standardized by dividing by the reference unit; Steps for obtaining the geometric standard deviation of particle size distribution: Collect debris samples, measure the number / mass of particles of different sizes using a laser particle size analyzer, take the natural logarithm of the particle size to obtain the logarithmic particle size sequence, and calculate the standard deviation of the logarithmic particle size. The reference particle size parameter is the geometric mean (logarithmic scale) of the initial state, reflecting the central tendency of the reference particle size distribution. The acquisition steps are as follows: collect debris before the equipment discharges (or at the initial moment), measure its particle size distribution, take the natural logarithm of the particle size, and calculate the mean of the logarithmic particle size. The particle size refinement factor reflects the rate of change of the geometric mean of the particle size distribution with the amount of discharge charge. High-energy discharge reduces the geometric mean of the particle size distribution, i.e., increases the refinement degree. The acquisition steps are: at different discharge energies, debris is repeatedly generated and the particle size distribution is measured to obtain multiple sets of data on the geometric mean of the particle size distribution and the amount of discharge charge. and Perform linear regression (since the two are linearly related), and the absolute value of the slope of the fitted line is the particle size refinement coefficient. S23. Construct a mass generation rate model, which includes a mass generation rate calculation formula. Obtain the total mass generation rate of debris through the mass generation rate calculation formula. In this embodiment, a multi-physics coupled erosion rate model is constructed by using the apparent charge of partial discharge, abnormal temperature rise, and harmonic distortion rate. In this model, electrical strength (discharge, harmonics) leads to microscopic peeling of the material, while thermal stress (temperature rise) accelerates the aging and peeling of the material. The formula for calculating the mass generation rate can be expressed as:

[0025] In the formula, This represents the rate of total debris mass generation at time t. This represents the electrical erosion coefficient, expressed in mg / (pC·s). This represents the partial discharge amount at time t, expressed in pC. This represents the harmonic distortion rate of the current at time t. This represents the thermal aging peel coefficient, expressed in mg / s. Indicates the activation energy of the material. This represents the ideal gas constant, with a value of 8.314 J / (mol·K). This represents the reference temperature, which is the long-term stable temperature under normal operating conditions of the equipment. It can be the average value of long-term monitored temperatures. This represents the abnormal temperature rise at time t. This indicates the rate of debris generation caused by electrical erosion. This indicates the rate of debris generation caused by thermal aging and spalling. This represents an exponential function with the real number e as its base. Steps for obtaining the electrical erosion coefficient: Simulate electrical faults (partial discharge) in the laboratory, control the discharge charge and current harmonic distortion rate, and perform the simulation under constant temperature conditions. The electrically induced debris rate is obtained by measuring the total debris generation rate (i.e., without the influence of thermal aging) and subtracting the thermal aging rate obtained through thermal aging peeling. This rate is then divided by the product of partial discharge and current harmonic distortion rate to obtain the electrical erosion coefficient. The thermal aging peeling coefficient is also obtained experimentally by setting different abnormal temperature rises under isothermal conditions, measuring the corresponding debris generation rates, and then taking the natural logarithm of the rates and multiplying them by the electrical erosion coefficient. Perform linear regression, and take the exponent of the intercept of the fitted line as the thermal aging peel coefficient. The steps for obtaining the material activation energy are as follows: linearly fit the thermal aging experimental data using the Arrhenius equation, take the natural logarithm of the debris generation rate at different temperatures and perform a linear regression with the reciprocal of the corresponding temperature, and multiply the slope by the negative value of the ideal gas constant to obtain the material activation energy. S24. Obtain the coordinates of the fault node, debris type, total debris mass generation rate, and probability density value of particle size occurrence, and construct a dynamic debris source term vector.

[0026] In this embodiment, the coordinates of the fault node serve as the spatial source location for debris generation, which is the starting point of the particle movement trajectory and determines the initial distribution area of ​​debris within the pipe gallery.

[0027] S3. Obtain dynamic debris source terms, combine with real-time flow field simulation of debris transport process, and analyze the dynamic erosion risk of pipe wall in real time; In this embodiment, S3 includes the following specific steps: S31 and GIL pipe racks are usually filled with high-pressure SF6 or SF6 / N2 mixed gas, which has a density much higher than that of normal air. The gas pressure and temperature inside the pipe rack are collected. The gas pressure inside the pipe rack is collected by a high-pressure resistant capacitive sensor, and the gas density under high pressure is obtained by the gas density calculation formula. In this embodiment, the formula for calculating gas density can be expressed as:

[0028] In the formula, This indicates the gas density under high pressure. High pressure reduces the distance between gas molecules, significantly increasing the density and thus increasing the aerodynamic drag force on debris. This indicates the molar mass of an insulating gas, representing the mass of 1 mol of that gas. For example, the molar mass of SF6 is approximately 0.146 kg / mol, and the molar mass of N2 is approximately 0.028 kg / mol. It reflects the molecular weight of the gas. Indicates the gas pressure inside the utility tunnel. It indicates the temperature of the gas inside the pipe gallery, reflecting the average kinetic energy of the gas molecules inside the pipe gallery; S32. When debris is generated by an electric arc or violent discharge, the initial temperature is extremely high. During the transport process, the particles are dragged by aerodynamics and undergo violent convective heat exchange with the high-pressure gas. A simulation model of the debris transport process is constructed. The debris transport process simulation model includes the debris particle motion equation and the debris particle heat balance equation. The velocity of the particle at the moment of impact is obtained by integrating the debris particle motion equation to the impact moment. The temperature of the particle at the moment of impact is obtained by integrating the debris particle heat balance equation to the impact moment. In this embodiment, the particle motion equation is used to calculate the motion state of particles in the gas within the pipe gallery, thereby predicting the particle trajectory, determining the specific location of the particle impacting the pipe wall, and the velocity at the moment of impact. The greater the velocity, the more concentrated the energy upon impact with the pipe wall, and the more severe the mechanical damage to the pipe wall. The particle motion equation can be expressed as:

[0029] In the formula, The mass of the particles is expressed as the product of their density and volume. Particle density can be obtained from material handbooks; for example, the density of metal particles is approximately 7.8 g / cm³. 3 The density of silicon carbide particles is approximately 3.2 g / cm³. 3 , Indicates particle velocity. This represents the integral of particle motion over time. This represents the drag coefficient, describing the magnitude of the resistance experienced by a particle moving in a fluid. It is obtained by looking up the drag coefficient curve based on the particle's Reynolds number. This represents the windward area of ​​a particle, that is, the projected area of ​​the particle in the direction of motion. This represents the flow velocity of gas within the pipe gallery, reflecting the macroscopic flow state of the gas. The velocity field is obtained by simulating the gas flow within the pipe gallery using computational fluid dynamics (CFD) software or by measuring it using particle image velocimetry (PIV). This represents the acceleration due to gravity, with a value of 9.81 m / s². 2 ; The heat balance equation for debris particles can be expressed as:

[0030] In the formula, The specific heat capacity of particles can be obtained from material handbooks. For example, the specific heat capacity of metal particles is approximately 500 J / (kg·K), and that of silicon carbide particles is approximately 670 J / (kg·K). Indicates particle temperature. The thermal conductivity of insulating gases can be obtained from gas property handbooks, such as the NIST database. The Nusselt number represents the intensity of convective heat transfer at the particle surface, and is calculated using an empirical formula combining the particle Reynolds number and the Prandtl number of the gas. This represents the heat transfer through particle radiation, calculated using the Stefan-Boltzmann law. S33. When high-temperature debris impacts the pipe wall, the wear mechanism is no longer limited to mechanical cutting. High-temperature particles may cause local softening of the pipe wall, or the particles themselves may be in a molten state and cause adhesive erosion. A thermo-coupled erosion rate model is constructed, which includes a thermo-coupled erosion rate calculation formula. The pipe wall erosion risk rate is obtained through the thermo-coupled erosion rate calculation formula.

[0031] In this embodiment, the formula for calculating the thermal coupling erosion rate can be expressed as:

[0032] In the formula, This represents the pipe wall erosion risk rate at time t (the time of impact) at spatial location x (the specific location of the pipe wall), expressed in kg / (m²). 2 ·s), The particle number flow rate represents the number of particles per unit area per unit time for the i-th type of debris. It is obtained by multiplying the mass fraction of debris and the rate of generation of total debris mass by the average mass of debris. The average mass of debris is obtained by combining the average particle size distribution with the debris density, reflecting the spatial distribution density of particles. The reference erosion coefficient is the mass contribution of the number of particles per unit velocity squared per unit time to pipe wall erosion, expressed in kg·s. 2 / m 4 This reflects the inherent properties (hardness, density) of different debris types. For example, metal particles have high hardness, while silicon carbide particles are brittle and have different erosion capabilities. This represents the erosion mass of each particle. The baseline erosion coefficient is obtained by first acquiring the material type, density, average particle size, and average particle mass of the debris. Standard erosion tests are then conducted on different debris. A pipe wall material with consistent surface roughness, such as stainless steel, is selected. An airflow acceleration device is used to accelerate the particles to a preset speed, such as 10 m / s, 20 m / s, or 30 m / s. The number of particles per unit area per unit time (1000 particles / (m²·s)) is controlled by a particle supply device (vibrating screen). The assumption is vertical impact (…). In a low-temperature environment (temperature softening factor of 1), the model is simplified to an erosion rate proportional to the square of the velocity. The pipe wall mass is weighed using an electronic balance before and after impact to calculate the mass loss. The impact time and pipe wall area are recorded to obtain the mass loss per unit time per unit area, i.e., the erosion rate. The square of each experimental velocity is calculated, and a scatter plot is drawn with the square of the velocity as the x-axis and the erosion rate as the y-axis. A linear fit is performed on the scatter plot to obtain the slope. The baseline erosion coefficient is obtained by dividing the slope by the controlled particle number flow rate in the experiment. The R-value of the fitted curve is then calculated. 2 Value (R) 2 >0.95), to ensure that the erosion rate and the square of the velocity have a linear relationship, 5 independent experiments were conducted, and the average value of the baseline erosion coefficient was taken. Represents the incident angle function. This represents the incident angle (0°-90°), which is the angle between the particle velocity vector and the pipe wall normal. It reflects the influence of the particle impact angle on the erosion rate. An empirical model is selected based on the type of debris (brittle / ductile). For example, the empirical model for brittle materials (metal particles, ceramics) is: The empirical model for plastic materials (polymers, soft metals) is as follows: , The velocity of the i-th type of debris particle at the moment of impact with the pipe wall is represented. Erosion is the damage caused by particles impacting the material surface, and its destructive power depends on the kinetic energy of the particle (the square of the velocity). The thermal damage coefficient is represented by data obtained through high-temperature erosion experiments (particles are heated to different temperatures, impacted against the pipe wall, and the erosion rate is measured). An exponential function of the temperature softening factor is fitted, and the coefficient is extracted. For example, the thermal damage coefficient of metal particles to the metal pipe wall is approximately 0.3-0.8. This indicates the temperature at the moment the particles collide with the tube wall. This indicates the softening point temperature of the pipe wall material, used to determine whether particle temperature causes thermal damage. This represents the temperature softening factor, which reflects the effect of particle impact temperature on the erosion rate. When the temperature exceeds the softening point of the pipe wall, the erosion rate increases sharply. In this embodiment, the flow field parameters are modified for the high-voltage insulating gas environment inside the GIL tube gallery. At the same time, the heat loss of high-temperature debris during transportation and the thermal coupling erosion damage to the tube wall are simulated. The particle impact position is the result of the movement after the source position of the debris is generated.

[0033] S4. Couple the dynamic erosion risk with the external environmental load to assess the probability of dynamic leakage risk and screen out high-risk areas. Please see Figure 3 In this embodiment, S4 includes the following specific steps: S41. Construct a pipe wall remaining thickness model, which includes a pipe wall remaining thickness calculation formula. Obtain the effective remaining thickness of the pipe wall through the pipe wall remaining thickness calculation formula. In the specific implementation of this embodiment, the formula for calculating the remaining pipe wall thickness can be expressed as:

[0034] In the formula, This represents the effective remaining thickness of the pipe wall at spatial location x at time t. This indicates the initial design thickness of the pipe wall. This represents the integral over the erosion time. Indicates the density of the wall material of the utility tunnel; S42. When the pipe wall thickness is reduced, the stress generated by the external environmental load in the pipe wall will be redistributed. The stress level increases non-linearly with the decrease of thickness. Obtain the external environmental load data, which includes the static membrane stress caused by soil pressure and the thermal stress caused by temperature change. Obtain the total stress of the pipe wall under the coupling of dynamic erosion risk and external environmental load through the environmental coupling stress calculation formula. Steps for obtaining static membrane stress caused by soil pressure: Use soil pressure sensors to measure the soil pressure distribution on the pipe wall or calculate it according to soil mechanics formulas. For example, for buried pipelines, the static membrane stress caused by soil pressure is obtained by multiplying the soil unit weight (about 18 kN / m³ for sand and about 20 kN / m³ for clay), the pipeline burial depth (distance from the ground to the pipe wall), and the static earth pressure coefficient (about 0.4-0.5 for sand and about 0.5-0.7 for clay). Steps for obtaining thermal stress caused by temperature changes: Collect the temperature change of the pipe wall with position and time, and measure the pipe's elastic modulus (approximately 200 GPa for steel pipes) and thermal expansion coefficient (approximately 12 × 10⁻⁶ for steel pipes). -6 The thermal stress caused by the temperature change is obtained by multiplying the temperature (°C) and the temperature change. In the specific implementation of this embodiment, the formula for calculating environmental coupling stress can be expressed as:

[0035] In the formula, This represents the total stress on the pipe wall at time t, spatially located at position x, under the coupling of dynamic erosion risk and external environmental load. This represents the static membrane stress caused by soil pressure. This represents the thermal stress caused by temperature changes. The stress concentration index reflects the stress amplification effect caused by wall thickness reduction. It is calibrated experimentally: stress tests are conducted on the pipe wall at different thicknesses, keeping other conditions constant, and the amplification factor of static membrane stress caused by soil pressure is measured by changing the wall thickness. The result is then fitted to the desired index. The relationship with the stress amplification factor is used to obtain the stress concentration index. In specific implementation, when... When the value is 1, it is suitable for pure membrane stress (axial stress in thin-walled pipes). At this time, the stress distribution is relatively uniform, and the amplification of stress due to wall thickness reduction is linear. However, when the pipe is subjected to bending stress (radial bending of buried pipes), plastic deformation (stress concentration caused by local corrosion), or complex loads (combination of internal and external pressure), The value can be 1.5 or 2; S43. Construct a leakage risk probability model. The leakage risk probability model includes a leakage risk probability calculation formula. The leakage risk probability of the pipe wall under the influence of the total stress of the pipe wall is obtained through the leakage risk probability calculation formula. In this embodiment, the formula for calculating the probability of leakage risk can be expressed as:

[0036] In the formula, This represents the probability of leakage risk at time t, where the pipe wall is at location x in space, under the influence of the total stress on the pipe wall. This represents the mean yield strength of a material, reflecting the central tendency of its properties. The yield strength of a pipe is measured through a tensile test, and the average value is taken. For example, a tensile test on a steel pipe sample yields a mean yield strength of approximately 250 MPa. The standard deviation of a material's yield strength reflects the dispersion of that yield strength. The cumulative distribution function represents the standard normal distribution; S44. Obtain the preset leakage risk probability threshold. GIL is a key piece of equipment for high-voltage power transmission. Its characteristics include high operating pressure (internal SF6 gas insulation, operating pressure is about 0.4-0.6MPa, leakage will cause insulation failure), environmental sensitivity (internal SF6 is a strong greenhouse gas, leakage will cause serious environmental pollution), and operational importance (long-distance, large-capacity power transmission, leakage will cause power transmission interruption and affect grid security). Therefore, the leakage risk probability threshold is set to 0.8 based on experience. Screen the leakage risk probability exceeding the leakage risk probability threshold and set the corresponding area as a high-risk area.

[0037] S5. Collect leakage monitoring signals, spatially match the suspected leakage areas located by the leakage monitoring signals with the screened high-risk areas, and locate the leakage point.

[0038] In this embodiment, S5 includes the following specific steps: S51. When the pressure sensor in the pipe gallery detects an abnormal pressure drop or the gas sensor detects that the concentration of dangerous gas exceeds the standard, the leak location program is triggered to collect the leak monitoring signal and locate the suspected leak area in real time. S52. Set the overlapping area between the suspected leakage area and the high-risk area as the leakage point.

[0039] For pressure sensor monitoring, underground utility tunnels are usually equipped with mechanical ventilation systems. The sudden start or stop of the fans and the adjustment of the opening of the air valves can cause drastic and instantaneous fluctuations in the air pressure inside the tunnel. The pressure drop signal generated by such normal operating condition changes can easily lead to false alarms. Changes in the ambient temperature inside the tunnel (cable heating, seasonal changes) can cause thermal expansion and contraction of the gas, resulting in pressure fluctuations. Such slow pressure changes may mask the pressure drop caused by minor leaks, or cause abnormal pressure fluctuations due to sudden temperature changes, interfering with the judgment. Ground traffic vibrations or geological subsidence may cause micro-deformation of the tunnel structure, thereby changing the internal volume and causing micro-pressure disturbances. These environmental noises can be captured by high-sensitivity pressure sensors, leading to false alarms. For gas sensor monitoring, the internal structure of the pipe gallery is complex, with a large number of cable trays, supports and fireproof partitions. During the diffusion process, the leaked gas will encounter obstacles, generating eddies and stagnation, resulting in extremely uneven gas concentration distribution. The concentration peak position detected by the sensor is often not the actual location of the leak point. Triggering the positioning program based on this will lead to huge positioning deviation. Moreover, gas sensors have cross-sensitivity to humidity, temperature or other non-target gases. The humid and dusty environment inside the pipe gallery has a large background noise, which can easily cause sensor baseline drift or false alarms. Therefore, spatial matching of the sensor's positioning results with the selected high-risk areas improves the confidence level of locating leak points in complex utility tunnel environments, effectively eliminates environmental interference, and achieves precise location of leak points.

[0040] Please see Figure 4 This invention also provides a gas leak fault monitoring system for GIL (Gas Infrared) tunnels, comprising: The operation parameter monitoring module is used to monitor the operation status parameters of key circuit nodes in the utility tunnel in real time, including partial discharge signals, abnormal temperature rise, harmonic distortion rate, and the concentration of characteristic gases from the pyrolysis of insulating materials. The dynamic debris generation module is used to quantify the debris type, particle size distribution and generation rate derived from circuit anomalies based on operating status parameters, and generate dynamic debris source terms. The erosion risk analysis module is used to obtain dynamic debris source terms, combine real-time flow field simulation of debris transport process, and analyze the dynamic erosion risk of pipe wall in real time. The risk area screening module is used to couple the dynamic erosion risk with the external environmental load to assess the probability of dynamic leakage risk and screen out high-risk areas. The leak point location module is used to collect leak monitoring signals, spatially match the suspected leak areas located by the leak monitoring signals with the screened high-risk areas, and locate the leak point.

[0041] This invention also provides a storage medium, which includes stored instructions, wherein, when the instructions are executed, the device where the storage medium is located is controlled to perform the gas leak fault monitoring method for GIL pipe corridors as described above.

[0042] Please see Figure 5 The present invention also provides an electronic device, specifically including: at least one processor, at least one memory, a power supply, a communication interface, an input / output interface, and a communication bus, wherein the memory is used to store a computer program, which is loaded and executed by the processor to perform the above-described gas leak fault monitoring method for GIL pipe corridors.

[0043] In this embodiment, the power supply is used to provide operating voltage for the various hardware devices on the electronic device, the communication interface can create a data transmission channel between the electronic device and external devices, and the input / output interface is used to acquire external input data or output data to the outside world. The specific interface type can be selected according to the specific application needs, and is not specifically limited here.

[0044] In addition, the memory, as a carrier for storing resources, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored on it can include operating system, computer program, etc., and the storage method can be temporary storage or permanent storage.

[0045] The operating system is used to manage and control the various hardware devices and computer programs on the electronic equipment. It can be Windows Server, NetWare, Unix, Linux, etc. In addition to the computer programs that can be used to execute the above-mentioned gas leak fault monitoring method for GIL pipe corridors, the computer programs can also further include computer programs that can be used to complete other specific tasks.

[0046] The various embodiments in this specification are described in a progressive manner. For the same parts between embodiments, refer to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For relevant parts, refer to the description of the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of this embodiment. Those skilled in the art can understand and implement this without creative effort.

[0047] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0048] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art. The general principles defined in this invention may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring gas leak faults in GIL (Gas Intake System) tunnels, characterized in that, The specific steps include the following: S1. Real-time monitoring of the operating status parameters of key circuit nodes in the utility tunnel, including partial discharge signals, abnormal temperature rise, harmonic distortion rate, and the concentration of characteristic gases from the pyrolysis of insulating materials. S2. Based on the operating status parameters, quantify the debris type, particle size distribution and generation rate derived from circuit anomalies, and generate dynamic debris source terms; S3. Obtain dynamic debris source terms, combine with real-time flow field simulation of debris transport process, and analyze the dynamic erosion risk of pipe wall in real time; S4. Couple the dynamic erosion risk with the external environmental load to assess the probability of dynamic leakage risk and screen out high-risk areas. S5. Collect leakage monitoring signals, spatially match the suspected leakage areas located by the leakage monitoring signals with the screened high-risk areas, and locate the leakage point.

2. The gas leak fault monitoring method for GIL (Gas Intake Line) tunnels according to claim 1, characterized in that, S1 includes the following specific steps: S11. Real-time monitoring of the operating status parameters of key circuit nodes in the pipe gallery, including partial discharge, abnormal temperature rise, current harmonic distortion rate, and concentration of characteristic gases from the pyrolysis of insulating materials. S12. Filter out the operating status parameters that exceed the corresponding preset threshold, and set the node where the operating status parameter is located as the coordinate of the fault node.

3. The gas leak fault monitoring method for GIL (Gas Intake Line) tunnels according to claim 2, characterized in that, S2 includes the following specific steps: S21. Obtain the quality score of each type of debris by the proportion of the weighted association strength of each type of debris to the total weighted association strength of all debris types, and filter out the debris type corresponding to the maximum quality score. S22. Construct a particle size probability density function and obtain the probability density value of particle size occurrence through the particle size probability density function. S23. Construct a mass generation rate model, which includes a mass generation rate calculation formula, and obtain the total mass generation rate of debris through the mass generation rate calculation formula. S24. Obtain the coordinates of the fault node, debris type, total debris mass generation rate, and probability density value of particle size occurrence, and construct a dynamic debris source term vector.

4. The gas leak fault monitoring method for GIL (Gas Intake Line) tunnels according to claim 3, characterized in that, S3 includes the following specific steps: S31. Collect the gas pressure and temperature inside the pipe gallery, and obtain the gas density under high pressure using the gas density calculation formula; S32. Construct a simulation model of the debris transport process. The simulation model of the debris transport process includes the debris particle motion equation and the debris particle heat balance equation. The velocity of the particle impacting the pipe wall is obtained by integrating the debris particle motion equation to the impact time. The temperature of the particle impacting the pipe wall is obtained by integrating the debris particle heat balance equation to the impact time. S33. Construct a thermo-coupled erosion rate model, which includes a thermo-coupled erosion rate calculation formula. Obtain the pipe wall erosion risk rate through the thermo-coupled erosion rate calculation formula.

5. The gas leak fault monitoring method for GIL (Gas Inlet and Outer Limit) pipe corridors according to claim 4, characterized in that, S4 includes the following specific steps: S41. Construct a pipe wall remaining thickness model, the pipe wall remaining thickness model includes a pipe wall remaining thickness calculation formula, and obtain the effective remaining thickness of the pipe wall through the pipe wall remaining thickness calculation formula; S42. Obtain external environmental load data, which includes static membrane stress caused by soil pressure and thermal stress caused by temperature changes. Obtain the total stress of the pipe wall under the coupling of dynamic erosion risk and external environmental load through the environmental coupling stress calculation formula. S43. Construct a leakage risk probability model, which includes a leakage risk probability calculation formula. The leakage risk probability of the pipe wall under the influence of the total stress of the pipe wall is obtained through the leakage risk probability calculation formula. S44. Obtain the preset leakage risk probability threshold, filter the leakage risk probabilities that exceed the leakage risk probability threshold, and set the corresponding areas as high-risk areas.

6. The gas leak fault monitoring method for GIL (Gas Inlet and Outer Limit) pipe corridors according to claim 5, characterized in that, S5 includes the following specific steps: S51. Collect leakage monitoring signals and obtain the suspected leakage area located in real time by the leakage monitoring signals; S52. Set the overlapping area between the suspected leakage area and the high-risk area as the leakage point.

7. A gas leak fault monitoring system for GIL (Gas Intake Line) tunnels, used to implement the gas leak fault monitoring method for GIL tunnels as described in any one of claims 1-6, characterized in that, include: The operation parameter monitoring module is used to monitor the operation status parameters of key circuit nodes in the utility tunnel in real time, including partial discharge signals, abnormal temperature rise, harmonic distortion rate, and the concentration of characteristic gases from the pyrolysis of insulating materials. The dynamic debris generation module is used to quantify the debris type, particle size distribution and generation rate derived from circuit anomalies based on operating status parameters, and generate dynamic debris source terms. The erosion risk analysis module is used to obtain dynamic debris source terms, combine real-time flow field simulation of debris transport process, and analyze the dynamic erosion risk of pipe wall in real time. The risk area screening module is used to couple the dynamic erosion risk with the external environmental load to assess the probability of dynamic leakage risk and screen out high-risk areas. The leak point location module is used to collect leak monitoring signals, spatially match the suspected leak areas located by the leak monitoring signals with the screened high-risk areas, and locate the leak point.

8. A storage medium, characterized in that, The storage medium includes stored instructions, wherein, when the instructions are executed, the device containing the storage medium is controlled to perform the gas leak fault monitoring method for GIL pipe corridors as described in any one of claims 1-6.

9. An electronic device, characterized in that, It includes a memory, and one or more instructions, wherein one or more instructions are stored in the memory and configured to be executed by one or more processors as described in any one of claims 1-6 for gas leak fault monitoring of GIL pipe racks.

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