A method and system for monitoring gas leakage of GIS equipment online

Abstract

This invention relates to the field of power grid equipment monitoring technology, and proposes an online monitoring method and system for gas leakage in GIS equipment. The method includes: acquiring information about the GIS equipment and gas measurement data at multiple time points during operation; constructing a GIS gas volume analysis model representing the relationship between gas replenishment volume and equipment gas change parameters, based on the ideal gas equation of state and after correction by calculating correction coefficients using voltage level U, structural form S, and operating years Y; calculating the replenishment volume and leakage rate based on the gas volume analysis model, and then predicting the replenishment cycle and the replenishment duration for each replenishment operation. This invention innovatively introduces a multi-factor correction model considering voltage level, structural form, and operating years, improving the accuracy of predicting GIS equipment leakage rate and replenishment cycle based on gas volume changes, and achieving intelligent and refined equipment leakage monitoring.

Description

Technical Field

[0001] This invention relates to the field of power grid equipment monitoring technology, specifically to an online monitoring method and system for gas leakage in GIS equipment. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] As power systems evolve towards higher reliability and intelligence, gas-insulated switchgear (GIS) is widely used in power transmission and transformation systems due to its advantages such as small footprint, strong environmental adaptability, and superior insulation performance. GIS equipment uses sulfur hexafluoride (SF6) gas within a sealed metal casing as an insulating and arc-quenching medium to ensure safe operation under high-voltage conditions. To ensure the continuity and safety of system operation, real-time monitoring and lean management of the gas status of GIS equipment has become a crucial aspect of power grid asset operation and maintenance, especially given the rapid increase in the number of devices, necessitating improvements in operation and maintenance efficiency and intelligence.

[0004] Currently, gas management in GIS equipment is typically based on the ideal gas law, estimating SF6 gas replenishment cycles using parameters such as pressure and temperature. This method has significant limitations in practical applications: current analysis methods based on the ideal gas law rely solely on static parameters like pressure and temperature, making it difficult to accurately reflect the actual SF6 gas leakage state and insulation degradation process within GIS equipment in complex real-world scenarios. Especially in substation environments with large-scale equipment deployment, the lack of integrated analysis capabilities for historical operating data, equipment aging trends, and environmental factors results in low accuracy in predicting replenishment cycles and quantities, easily leading to premature or delayed replenishment, impacting maintenance efficiency and equipment safety. Current maintenance methods largely rely on manual inspections or decentralized monitoring, lacking a unified intelligent scheduling and comprehensive analysis platform. This makes it difficult to achieve global perception, prediction, and optimized scheduling of replenishment needs for multiple GIS devices, becoming a key bottleneck restricting lean maintenance management. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes an online monitoring method and system for gas leakage in GIS equipment. It innovatively introduces a multi-factor correction model that considers factors such as voltage level, structural form, and service life, thereby improving the accuracy of predicting the leakage rate and gas replenishment cycle of GIS equipment based on gas volume changes and achieving intelligent and refined monitoring of equipment leakage.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: One or more embodiments provide an online monitoring method for gas leakage in GIS equipment, comprising the following steps: Acquire information about GIS equipment and gas measurement data at multiple time points during operation; Based on the ideal gas law, a GIS gas volume analysis model is constructed to represent the relationship between gas supply and equipment gas change parameters after correction by calculating correction coefficients using voltage level U, structural form S, and service life Y. The gas replenishment volume is calculated based on the gas volume analysis model, the leakage rate is calculated, and then the gas replenishment cycle and the gas replenishment duration of each gas replenishment operation are predicted.

[0007] One or more embodiments provide an online gas leakage monitoring system for GIS equipment, comprising: The data access layer is configured to acquire data from device operation and transmit it to the data sharing center platform. The data sharing center platform is configured to perform data preprocessing and data alignment; The analysis and calculation engine is configured to perform the steps of the above-mentioned online monitoring method for gas leakage in GIS equipment, and to realize core calculations and inferences, including GIS gas volume analysis, gas replenishment cycle prediction and power outage working time analysis, and transmit the analysis results to the business application layer. The business application layer is used to display analysis results and provide decision support for operations and maintenance personnel.

[0008] One or more embodiments provide an online gas leakage monitoring system for GIS equipment, comprising: The acquisition module is configured to acquire information about the GIS equipment and gas measurement data at multiple time points during operation. The model building module is configured to construct a GIS gas volume analysis model based on the ideal gas law, after correcting the voltage level U, structural form S and operating years Y by calculating correction coefficients. This model represents the relationship between the gas supply and the equipment gas change parameters. The prediction module is configured to calculate the gas replenishment volume based on the gas volume analysis model, calculate the leakage rate, and then predict the gas replenishment cycle and the gas replenishment duration of each gas replenishment operation.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention's method considers various influencing factors during the modeling process, such as equipment voltage level, structural differences, and aging degree. By introducing correction coefficients, it enhances the model's adaptability to actual operating conditions, effectively improving the calculation accuracy of leakage rate and the reliability of gas replenishment cycle prediction. This not only avoids blind gas replenishment operations and reduces waste of maintenance resources, but also lowers the risk of equipment failure due to untimely gas replenishment. Furthermore, through the accumulation and analysis of long-term operating data, it can be used to develop data-driven predictive maintenance strategies, significantly improving the intelligent operation and maintenance level of GIS equipment.

[0010] The advantages of the present invention, as well as its additional advantages, will be described in detail in the following specific embodiments. Attached Figure Description

[0011] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute a limitation thereof.

[0012] Figure 1 This is a block diagram of an online monitoring system for gas leakage in GIS equipment according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of an online monitoring method for gas leakage in GIS equipment, according to Embodiment 2 of the present invention. Detailed Implementation

[0013] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0014] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0015] It should be noted that the terminology used herein is for describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof. It should be noted that, without conflict, the various embodiments and features within those embodiments can be combined with each other. The embodiments will now be described in detail with reference to the accompanying drawings.

[0016] Example 1 In one or more of the technical solutions disclosed in the embodiments, such as Figure 1 As shown, an online gas leakage monitoring system for GIS equipment includes: The data access layer is configured to acquire data from device operation and transmit it to the data sharing center platform. The data sharing center platform is configured to perform data preprocessing and data alignment; The analysis and calculation engine is configured to perform core calculations and inferences, including GIS gas volume analysis, gas replenishment cycle prediction, and power outage working time analysis, and transmits the analysis results to the business application layer. The business application layer is used to display analysis results and provide decision support for operations and maintenance personnel. In this embodiment, the data access layer utilizes the DataWorks tool to collect SF6 online monitoring data, equipment ledger data, and related monitoring device information from the power grid data platform, and transmits the collected data to the data sharing center platform. Within the data sharing center platform, the multi-source heterogeneous data is processed. Subsequently, the processed data is sent to the analysis engine module to perform core calculation tasks such as GIS gas volume analysis, gas replenishment cycle prediction, and power outage working time assessment. Finally, the analysis results are pushed to the business application layer through the business interface, providing operation and maintenance personnel with visualized decision support and supporting equipment status assessment and intelligent maintenance scheduling. This embodiment, by establishing a unified intelligent scheduling and comprehensive analysis platform, enables global perception, prediction, and optimized scheduling of gas replenishment needs for multiple GIS devices.

[0017] In some embodiments, the data access layer uses DataWorks tools to access data and obtain the following multi-source heterogeneous data: SF6 gas online monitoring data: collects operational status data such as gas pressure and temperature; Equipment asset ledger data: including basic information on GIS equipment such as combined electrical appliances, logic devices, and physical devices; Online monitoring device ledger: used to establish the correspondence between monitoring points and equipment units.

[0018] In some embodiments, the data sharing center platform preprocesses the data by: cleaning, format conversion, and standardizing the modeling of the incoming data to unify the data structure; and establishing mapping relationships between gas chambers, measuring devices, and equipment units to achieve data alignment. Furthermore, it also includes constructing logical device models and physical device models for accurate characterization of the structure and function of GIS equipment in subsequent analysis; The logical device model is used to record associated monitoring substation names, equipment names, device names, manufacturers, models, degassing principles, monitoring principles, monitoring types, and other physical parameters to generate a physical device ledger.

[0019] The physical device model is used to record the type of monitored equipment, the acquisition cycle, and the logic device encoding used for communication.

[0020] In some embodiments, the analysis and calculation engine includes a GIS gas volume analysis module, a gas replenishment cycle prediction module, and a power outage working time analysis module. Furthermore, the GIS gas volume analysis module is configured to perform the following procedures: Step 11: Based on the ideal gas law, establish the basic quantitative relationship between the parameters that cause changes in gas volume and the amount of gas replenishment; The ideal gas equation is: ; in, Indicates pressure, Indicates volume, Indicates the amount of substance of a gas. Indicates temperature; Based on the ideal gas equation, a fundamental quantitative relationship is established between gas volume change and gas replenishment behavior, namely, the relationship between the change in gas replenishment volume and other parameters during gas volume change. The formula is as follows: ; in, The gas supply volume is represented by R; the molar gas constant is represented by Tavg; the average temperature (K) during the gas supply period is represented by n1 and n2; the amounts of gaseous substances (mol) measured in the two measurements are represented by p1 and p2; and the gas pressures (Pa) measured in the two measurements are represented by p1 and p2.

[0021] Step 12: Obtain the voltage level U, structural type S, and service life Y of the current equipment; calculate the correction factor; improve the basic quantitative relationship based on the correction factor to obtain the corrected formula for calculating the gas replenishment volume, i.e., the GIS gas volume analysis model, as follows: ; in, Indicates the voltage level correction factor; Indicates the structural form correction factor; This indicates the adjustment factor based on the number of years of service. Specifically, obtain the current voltage level U of the device and calculate the voltage level correction factor. The formula is: ; in, It is a learnable parameter, with a value range of 0 to 1; Specifically, obtain the current device's structural form S and calculate the structural form correction coefficient. The formula is:

[0022] in, It is a learnable parameter, with a value range of 0 to 1; S represents the structural complexity index, with values ​​of {0, 1, 2, 3}, where: Shared container, S=0; Shared / separate containers, S=1; Separate boxes / shared boxes, S=2; Separate boxes, S=3; Among them, "shared enclosure" indicates that the busbars and functional units are in the same enclosure. A common busbar enclosure refers to a system where all phase (usually three-phase A / B / C) busbar conductors are housed in a common, cylindrical metal enclosure filled with an insulating gas (such as SF6).

[0023] Functional unit co-enclosure refers to the integration of all major components (circuit breakers, disconnect switches, grounding switches, current transformers, etc.) into a common metal enclosure.

[0024] "Common enclosure / separate enclosure" means that the busbars are in the same enclosure but the functional units are in separate enclosures. A common busbar enclosure refers to a system where all phase (usually three-phase A / B / C) busbar conductors are housed in a common, cylindrical metal enclosure filled with an insulating gas (such as SF6).

[0025] Functional unit compartments refer to the fact that the main components (circuit breakers, disconnect switches, grounding switches, current transformers, etc.) within a functional unit have independent, modular air chambers.

[0026] Sub-box / Common box indicates that the busbars are sub-boxed while the functional units are in the same box; Busbar enclosure refers to the installation of each phase busbar in an independent cylindrical metal casing.

[0027] Functional unit co-enclosure refers to the integration of functional components of a circuit into a common enclosure.

[0028] Sub-box / Sub-box meaning: It means that the busbar is sub-boxed and the functional units are shared by sub-boxes; Busbar enclosure refers to the installation of each phase busbar in an independent cylindrical metal casing.

[0029] Functional unit compartments refer to the use of independent air chambers for each functional component.

[0030] Specifically, the formula for obtaining the current service life Y of the equipment and the service life correction factor is as follows: ; in, The aging factor is... For the number of years of operation, These are learnable coefficients, ranging from 0 to 1; This embodiment introduces a service life correction factor into the gas replenishment volume calculation. Combined with the service life of the GIS equipment, it differentiates the treatment for equipment at different aging stages, improving the accuracy and reliability of gas replenishment prediction. Specifically, in the first 5 years of equipment operation, the sealing performance declines relatively slowly, and a linear model is used for correction; while after 5 years of operation, aging accelerates, and an exponential model is used to better reflect actual leakage characteristics. This correction mechanism reflects the changing sealing state of the equipment over time, avoiding misjudgments caused by static models, improving the system's ability to identify aging equipment and the scientific nature of gas replenishment arrangements, thus helping to optimize operation and maintenance strategies and extend equipment lifespan.

[0031] A further technical solution involves the learnable parameter in the correction coefficient of the revised gas replenishment calculation formula. , and The model can accurately calculate the gas replenishment volume by fitting learnable parameters using historical data. It supports parameter self-learning and continuously iterates the learnable parameters in the correction coefficients such as voltage, structure, and age as operating data accumulates, thereby improving prediction accuracy. Finally, it outputs a recommended gas replenishment volume that adapts to the actual condition of the equipment.

[0032] Furthermore, the gas replenishment cycle prediction module is configured to perform the following process: Step 21: Calculate the equipment leakage rate based on actual operating data such as air chamber volume, pressure changes, temperature, and time intervals; Analysis of equipment air replenishment cycle: Based on the formula for calculating the leakage rate of GIS equipment, a relationship model between the air replenishment cycle and the air replenishment volume was constructed, and the model was verified by historical data to ensure the accuracy of the calculation of leakage rate and air replenishment volume.

[0033] The formula for calculating the leakage rate of GIS equipment is: ; Where Q represents the leakage rate (mol / s); V represents the gas chamber volume (m³); and Δt represents the time interval between two measurements (s). Indicates the average temperature during the measurement period; These represent the pressure (Pa) during the first and second measurements, respectively. In the above formula, the molecule The unit is: Pa·m³ = N / m²·m³ = N·m = J; denominator The unit is: (J / (mol·K))·K·s = J·s / mol; The unit is mol / s; Step 22: Calculate the total amount of gas leakage that the equipment can tolerate; Total amount of leakage allowed The calculation formula is: ; Step 23: Calculate the gas replenishment cycle based on the allowable total gas leakage and the leakage rate. The calculation formula is as follows: ; in, Normal / rated pressure The minimum permissible pressure; The gas replenishment cycle prediction constructed in this embodiment, combined with real-time monitoring of the gas leakage rate and multiple maintenance positive coefficients, dynamically calculates the next gas replenishment time node, which can effectively connect with the power outage plan cycle and realize intelligent matching of operation and maintenance windows.

[0034] The power outage working time analysis module is configured to perform power outage working time analysis, that is, to calculate the time for each gas replenishment, so as to accurately predict the power outage gas replenishment operation time of GIS equipment. Specifically, based on the gas replenishment time calculation formula and incorporating three key dimensions—voltage level, structural type, and service life—a power outage working time prediction model was constructed. This model, trained with historical data, can dynamically optimize prediction results, significantly improving the accuracy of power outage planning.

[0035] The power outage working time prediction model, i.e., the formula for calculating the gas replenishment time of GIS equipment, is as follows: ; t_replenishment: Time required for replenishment (s), V_replenishment: Volume of gas to be replenished, P_equipment: Flow rate of the replenishment equipment.

[0036] This embodiment introduces a multi-dimensional correction to the theoretical value of gas replenishment time by incorporating a voltage coefficient kvoltage, a structural coefficient kstructure, and an operating life coefficient kyear. This effectively reduces the deviation between theoretical calculations and actual operating conditions, significantly improving the accuracy of gas replenishment time prediction. This correction mechanism can be adapted to the characteristics of GIS equipment with different voltage levels, structural forms, and operating years, making the calculated gas replenishment time more consistent with the actual operating status of the equipment. This reduces the waste of maintenance resources or equipment safety risks caused by improper gas replenishment timing, providing reliable technical support for lean gas management of GIS equipment.

[0037] Example 2 Based on Embodiment 1, this embodiment provides an online monitoring method for gas leakage in GIS equipment, configured to be implemented in an analysis and calculation engine, such as... Figure 2 As shown, it includes the following steps: Step 1: Obtain information about the GIS equipment and gas measurement data at multiple time points during operation; Step 2: Based on the ideal gas law, after correcting the voltage level U, structural form S and operating years Y by calculating correction coefficients, construct a GIS gas volume analysis model that represents the relationship between gas supply and equipment gas change parameters. Step 3: Calculate the gas replenishment volume and leakage rate based on the gas volume analysis model, and then predict the gas replenishment cycle and the gas replenishment duration of each gas replenishment operation. This method first collects basic information about the target GIS equipment, including parameters such as the equipment's voltage level (U), structural form (S), and service life (Y). Simultaneously, it collects gas measurement data at multiple time points during the equipment's operation, including key variables such as gas pressure and temperature. Then, an initial model is constructed based on the ideal gas law, and correction coefficients are introduced using the aforementioned equipment characteristic parameters (U, S, Y) to make the model more closely reflect actual operating conditions and obtain more accurate volume change data. Furthermore, based on the corrected gas volume analysis model, the gas replenishment amount for the equipment within a specific time period is calculated. By utilizing the trend of gas replenishment changes over a continuous time period, the gas leakage rate is estimated. Combined with the current state of the equipment and the prediction model, the timing of the next gas replenishment and the duration of the required replenishment operation are accurately calculated.

[0038] Compared to existing estimation methods that rely solely on air pressure and temperature parameters, this method considers multiple influencing factors during the modeling process, such as equipment voltage level, structural differences, and aging degree. By introducing correction coefficients, it enhances the model's adaptability to actual operating conditions, effectively improving the calculation accuracy of leakage rate and the reliability of gas replenishment cycle prediction. This not only avoids the blindness of gas replenishment operations and reduces the waste of maintenance resources, but also reduces the risk of equipment failure due to untimely gas replenishment. Furthermore, through the accumulation and analysis of long-term operating data, it can be used to develop data-driven predictive maintenance strategies, significantly improving the intelligent operation and maintenance level of GIS equipment.

[0039] In step 1, information about the GIS equipment and gas measurement data at multiple time points during operation are obtained; Gas measurement data at multiple time points during equipment operation, including gas quantity n, gas pressure p, and temperature T; Information about GIS equipment, including the current voltage level U, structural type S, and service life Y; Step 2, the method for constructing the GIS gas volume analysis model of the relationship between gas supply and equipment gas change parameters, includes the following steps: Step 21: Based on the ideal gas law, establish the basic quantitative relationship between the parameters that cause changes in gas volume and the amount of gas replenishment; The ideal gas equation is: ; in, Indicates pressure, Indicates volume, Indicates the amount of substance of a gas. Indicates temperature; Based on the ideal gas equation, a fundamental quantitative relationship is established between gas volume change and gas replenishment behavior, namely, the relationship between the change in gas replenishment volume and other parameters during gas volume change. The formula is as follows: ; in, The gas supply volume is represented by R; the molar gas constant is represented by Tavg; the average temperature (K) during the gas supply period is represented by n1 and n2; the amounts of gaseous substances (mol) measured in the two measurements are represented by p1 and p2; and the gas pressures (Pa) measured in the two measurements are represented by p1 and p2.

[0040] Step 22: Obtain the voltage level U, structural type S, and service life Y of the current equipment; calculate the correction factor; and improve the basic quantitative relationship based on the correction factor to obtain the corrected formula for calculating the gas supply volume. ; in, Indicates the voltage level correction factor; Indicates the structural form correction factor; This indicates the adjustment factor based on the number of years of service. Specifically, obtain the current voltage level U of the device and calculate the voltage level correction factor. The formula is: ; in, It is a learnable parameter, with a value range of 0 to 1; preferably, The value is 0.1; Specifically, obtain the current device's structural form S and calculate the structural form correction coefficient. The formula is:

[0041] in, It is a learnable parameter, with a value range of 0 to 1; S represents the structural complexity index, with values ​​of {0, 1, 2, 3}, where: Shared container, S=0; Shared / separate containers, S=1; Separate boxes / shared boxes, S=2 Boxing / Boxing, S=3 Specifically, the formula for obtaining the current service life Y of the equipment and the service life correction factor is as follows: ; in, The aging factor is... For the number of years of operation, These are learnable coefficients, ranging from 0 to 1; This embodiment introduces a service life correction factor into the gas replenishment volume calculation. Combined with the service life of the GIS equipment, it differentiates the treatment for equipment at different aging stages, improving the accuracy and reliability of gas replenishment prediction. Specifically, in the first 5 years of equipment operation, the sealing performance declines relatively slowly, and a linear model is used for correction; while after 5 years of operation, aging accelerates, and an exponential model is used to better reflect actual leakage characteristics. This correction mechanism reflects the changing sealing state of the equipment over time, avoiding misjudgments caused by static models, improving the system's ability to identify aging equipment and the scientific nature of gas replenishment arrangements, thus helping to optimize operation and maintenance strategies and extend equipment lifespan.

[0042] A further technical solution involves the learnable parameter in the correction coefficient of the revised gas replenishment calculation formula. , and The model can accurately calculate the gas replenishment volume by fitting learnable parameters using historical data. It supports parameter self-learning and continuously iterates the learnable parameters in the correction coefficients such as voltage, structure, and age as operating data accumulates, thereby improving prediction accuracy. Finally, it outputs a recommended gas replenishment volume that adapts to the actual condition of the equipment.

[0043] Furthermore, the gas replenishment cycle prediction method includes the following process: Step 21: Calculate the equipment leakage rate based on actual operating data such as air chamber volume, pressure changes, temperature, and time intervals; Analysis of equipment air replenishment cycle: Based on the formula for calculating the leakage rate of GIS equipment, a relationship model between the air replenishment cycle and the air replenishment volume was constructed, and the model was verified by historical data to ensure the accuracy of the calculation of leakage rate and air replenishment volume.

[0044] The formula for calculating the leakage rate of GIS equipment is: ; Where Q represents the leakage rate (mol / s); V represents the gas chamber volume (m³); and Δt represents the time interval between two measurements (s). Indicates the average temperature during the measurement period; These represent the pressure (Pa) during the first and second measurements, respectively. In the above formula, the molecule The unit is: Pa·m³ = N / m²·m³ = N·m = J; denominator The unit is: (J / (mol·K))·K·s = J·s / mol; The unit is mol / s; Step 22: Calculate the total amount of gas leakage that the equipment can tolerate; Total amount of leakage allowed The calculation formula is: ; Step 23: Calculate the gas replenishment cycle based on the allowable total gas leakage and the leakage rate. The calculation formula is as follows: ; in, Normal / rated pressure The minimum permissible pressure; The gas replenishment cycle prediction model constructed in this embodiment combines real-time monitoring of the gas leakage rate with multiple maintenance positive coefficients to dynamically calculate the next gas replenishment time node, which can effectively connect with the power outage plan cycle and realize intelligent matching of operation and maintenance windows.

[0045] To accurately predict the time required for gas replenishment during power outages for GIS equipment, a power outage time prediction model was constructed based on the gas replenishment time calculation formula and incorporating three key dimensions: voltage level, structural type, and service life. This model, trained with historical data, can dynamically optimize prediction results, significantly improving the accuracy of power outage planning.

[0046] Furthermore, the gas replenishment time for each gas replenishment operation, i.e., the power outage working time, is analyzed, and the formula for calculating the gas replenishment time of the GIS equipment is as follows: ; t_replenishment: Time required for replenishment (s), V_replenishment: Volume of gas to be replenished, P_equipment: Flow rate of the replenishment equipment.

[0047] This embodiment introduces a multi-dimensional correction to the theoretical value of gas replenishment time by incorporating a voltage coefficient kvoltage, a structural coefficient kstructure, and an operating life coefficient kyear. This effectively reduces the deviation between theoretical calculations and actual operating conditions, significantly improving the accuracy of gas replenishment time prediction. This correction mechanism can be adapted to the characteristics of GIS equipment with different voltage levels, structural forms, and operating years, making the calculated gas replenishment time more consistent with the actual operating status of the equipment. This reduces the waste of maintenance resources or equipment safety risks caused by improper gas replenishment timing, providing reliable technical support for lean gas management of GIS equipment.

[0048] Example 3 Based on Embodiment 1, this embodiment provides an online monitoring system for gas leakage in GIS equipment, including: an acquisition module configured to acquire information about the GIS equipment and gas measurement data at multiple time points during operation; The model building module is configured to construct a GIS gas volume analysis model based on the ideal gas law, after correcting the voltage level U, structural form S and operating years Y by calculating correction coefficients. This model represents the relationship between the gas supply and the equipment gas change parameters. The prediction module is configured to calculate the gas replenishment volume based on the gas volume analysis model, calculate the leakage rate, and then predict the gas replenishment cycle and the gas replenishment duration of each gas replenishment operation.

[0049] It should be noted that each module in this embodiment corresponds one-to-one with each step in embodiment 1, and their specific implementation process is the same, so it will not be repeated here.

[0050] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0051] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for online monitoring of gas leakage in GIS equipment, characterized in that, The steps include the following: Acquire information about GIS equipment and gas measurement data at multiple time points during operation; Based on the ideal gas law, a GIS gas volume analysis model is constructed to represent the relationship between gas supply and equipment gas change parameters after correction by calculating correction coefficients using voltage level U, structural form S, and service life Y. The gas replenishment volume is calculated based on the gas volume analysis model, the leakage rate is calculated, and then the gas replenishment cycle and the gas replenishment duration of each gas replenishment operation are predicted.

2. The method for online monitoring of gas leakage in GIS equipment as described in claim 1, characterized in that: Gas measurement data at multiple time points during equipment operation, including the amount of gas substance n and gas pressure p; Information about GIS equipment includes the current voltage level U, structural type S, and service life Y.

3. The method for online monitoring of gas leakage in GIS equipment as described in claim 1, characterized in that: The method for constructing a GIS gas volume analysis model relating gas supply volume to equipment gas change parameters includes the following steps: Based on the ideal gas law, a basic quantitative relationship is established between the parameters that cause gas volume changes and the amount of gas replenishment. Obtain the voltage level U, structural form S, and service life Y of the current equipment, calculate the correction factor, and improve the basic quantitative relationship based on the correction factor to obtain the corrected formula for calculating the gas replenishment volume, i.e., the GIS gas volume analysis model.

4. The method for online monitoring of gas leakage in GIS equipment as described in claim 1, characterized in that: The revised formula for calculating the gas supply is as follows: ； in, Indicates the voltage level correction factor; Indicates the structural form correction factor; This indicates the adjustment factor based on the service life. The gas supply volume represents the amount of gas supplied; R represents the molar gas constant; Tavg represents the average temperature during the gas supply period; n1 and n2 represent the amounts of gaseous substances measured in the two measurements, respectively; p1 and p2 represent the gas pressures measured in the two measurements, respectively.

5. The method for online monitoring of gas leakage in GIS equipment as described in claim 4, characterized in that: Obtain the current voltage level U of the device and calculate the voltage level correction factor. The formula is: ； in, These are learnable parameters.

6. The method for online monitoring of gas leakage in GIS equipment as described in claim 4, characterized in that: Obtain the current device's structural form S and calculate the structural form correction factor. The formula is: ； in, It is a learnable parameter; S represents the structural complexity index, which takes a set value; Alternatively, the current service life Y of the equipment can be obtained, and the formula for the service life correction factor is as follows: ； in, The aging factor is... For the number of years of operation, These are the learnable coefficients.

7. An online monitoring system for gas leakage in GIS equipment, characterized in that, include: The data access layer is configured to acquire data from device operation and transmit it to the data sharing center platform. The data sharing center platform is configured to perform data preprocessing and data alignment; The analysis and calculation engine is configured to execute the steps of the online monitoring method for gas leakage of GIS equipment as described in any one of claims 1-6, realize core calculation and reasoning, including GIS gas volume analysis, gas replenishment cycle prediction and power outage working time analysis, and transmit the analysis results to the business application layer. The business application layer is used to display analysis results and provide decision support for operations and maintenance personnel.

8. The online monitoring system for gas leakage in GIS equipment as described in claim 7, characterized in that: The analysis and calculation engine includes a GIS gas volume analysis module, a gas replenishment cycle prediction module, and a power outage working time analysis module; The GIS gas volume analysis module is configured to perform the following procedures: Based on the ideal gas law, a basic quantitative relationship is established between the parameters that cause gas volume changes and the amount of gas replenishment. Obtain the voltage level U, structural form S, and service life Y of the current equipment, calculate the correction factor, and improve the basic quantitative relationship based on the correction factor to obtain the corrected formula for calculating the gas replenishment volume, i.e., the GIS gas volume analysis model.

9. The online gas leakage monitoring system for GIS equipment as described in claim 7, characterized in that, The revised formula for calculating the gas supply is as follows: ； in, Indicates the voltage level correction factor; Indicates the structural form correction factor; This indicates the adjustment factor based on the service life. The gas supply volume represents the amount of gas supplied; R represents the molar gas constant; Tavg represents the average temperature during the gas supply period; n1 and n2 represent the amounts of gaseous substances measured in the two measurements, respectively; p1 and p2 represent the gas pressures measured in the two measurements, respectively.

10. An online monitoring system for gas leakage in GIS equipment, characterized in that, include: The acquisition module is configured to acquire information about the GIS equipment and gas measurement data at multiple time points during operation. The model building module is configured to construct a GIS gas volume analysis model based on the ideal gas law, after correcting the voltage level U, structural form S and operating years Y by calculating correction coefficients. This model represents the relationship between the gas supply and the equipment gas change parameters. The prediction module is configured to calculate the gas replenishment volume based on the gas volume analysis model, calculate the leakage rate, and then predict the gas replenishment cycle and the gas replenishment duration of each gas replenishment operation.

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