A method for diagnosing a gas leakage fault in a gas insulated device

Abstract

This application relates to the field of power technology and discloses a gas leakage fault diagnosis technology for gas-insulated equipment. Addressing the problem that the resolution of the P20 value output by the density relay is too low to effectively diagnose minor leaks in gas-insulated equipment, this invention utilizes the law of conservation of energy to construct a heat transfer model between the gas-insulated equipment and the application environment. A recursive relationship for the internal gas temperature of the gas-insulated equipment is derived. The gas temperature obtained from the recursive model is fitted to the actual gas temperature using the least squares method to obtain the time constant of heat transfer between the gas-insulated equipment and the external environment. The P20 of the insulating gas is calculated using a sliding window averaging algorithm and a data trend fitting algorithm to eliminate high-frequency environmental interference, significantly improving the system's leakage diagnosis capability. Furthermore, the remaining safe leakage time of the gas is estimated based on the leakage rate, providing support for the scientific and efficient operation and maintenance of gas-insulated equipment.

Description

Technical Field

[0001] This invention relates to the field of power technology, specifically to a method for diagnosing internal gas leakage faults in gas-insulated equipment. Background Technology

[0002] SF6, a gas with excellent insulating and arc-quenching properties, is widely used in substation construction. Currently, the vast majority of high-voltage equipment on the market uses SF6 as its insulating gas. Since the SF6 content directly affects the insulation and arc-quenching performance of the equipment, a decrease in SF6 gas density to a certain level will lead to the loss of insulation and arc-quenching properties, causing substantial direct losses and incalculable indirect losses. Furthermore, SF6 is a greenhouse gas with extremely high chemical stability, remaining in the atmosphere for thousands of years. Its Global Warming Potential (GWP) is 23,500 times that of CO2, and it is extremely difficult to decompose in the air. Therefore, monitoring for SF6 gas leakage in gas-insulated equipment and promptly repairing and replenishing the equipment when leakage occurs has significant economic, environmental, and engineering value.

[0003] However, current methods for monitoring leaks in gas-insulated equipment mainly include optical leakage monitoring and gas density monitoring. The former primarily utilizes optical infrared imaging (OGI) or non-dispersive infrared (NDIR) sensors to monitor the leakage rate based on the spectral absorption characteristics of gas molecules. While these methods offer advantages such as intuitive and precise spatial positioning, their testing equipment is extremely expensive, highly susceptible to environmental noise interference in complex environments, and struggles to directly output a continuous "leakage rate" in terms of mass dimension. Furthermore, optical monitoring technology is essentially an external concentration monitoring method. In real-world conditions with varying outdoor wind speeds, the concentration of leaking gas is easily diluted and dispersed, making it difficult for infrared imaging and NDIR sensors to effectively identify the leak. In contrast, gas density monitoring only requires temperature and pressure sensors to monitor density, offering a significant cost advantage and making it suitable for monitoring. Therefore, substations are currently equipped with density relays containing temperature and pressure sensors to monitor the gas density inside gas-insulated equipment.

[0004] However, because density relays are installed externally on insulating equipment, they are highly susceptible to environmental fluctuations, resulting in significant density fluctuations. Current information indicates that direct monitoring using density relays yields output density values ​​with a maximum fluctuation error exceeding 4%, making it unsuitable for accurate leak detection and leak fault trend identification. It relies solely on system-set alarm thresholds and frequently generates false alarms. This method suffers from imprecise process management; by the time an alarm is issued, a significant amount of gas has already leaked, posing safety and environmental risks. Furthermore, in areas with large temperature differences, this method is prone to false alarms, leading to a significant waste of maintenance resources. Therefore, providing a high-precision, high-accuracy method for diagnosing SF6 leaks in gas-insulated equipment has become a pressing issue for those skilled in the art. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for diagnosing internal gas leakage faults in gas-insulated equipment. From the perspective of improving the accuracy of gas density monitoring, it reduces the impact of environmental variability on leakage fault diagnosis, improves the precision and accuracy of leakage fault diagnosis, ensures the safe operation of the power grid and environmental protection applications, and at the same time reduces the ineffective workload of maintenance personnel caused by false alarms and misreports of fault information.

[0006] In a first aspect, the present invention provides a method for diagnosing internal gas leakage faults in gas-insulated equipment, employing the following technical solution: A method for diagnosing internal gas leakage faults in gas-insulated equipment includes the following steps: Step 1: Collecting real-time temperature and real-time pressure data for a time-series at regular intervals; Step 2: Using the collected real-time temperature data as a basis, recursively calculating the current temperature of the insulating gas based on the heat transfer model of the gas-insulated equipment; Step 3: Normalizing the collected real-time pressure data and the calculated current temperature of the insulating gas to calculate the real-time gas density; Step 4: Setting a sliding window with a fixed number of sampling points, performing a moving average on the calculated real-time gas density to obtain a characteristic trend curve of density versus time; Step 5: Fitting the characteristic trend curve of density versus time to obtain the leakage rate of the insulating equipment, and evaluating the remaining safe leakage time of the gas based on the leakage rate of the insulating equipment.

[0007] By adopting the above technical solution, the internal insulating gas temperature is calculated using a heat transfer model of the gas-insulated equipment, and the real-time gas density is smoothed using a sliding window. Therefore, the leakage rate of the insulating equipment, which excludes the interference of ambient temperature, is obtained, and the remaining safe leakage time of the gas is assessed.

[0008] Its specific operation and compensation principle are explained step by step as follows:

[0009] Eliminating temperature measurement errors caused by heat transfer delay: Heat exchange between the insulating gas inside gas-insulated equipment and the external environment involves heat transfer resistance and gas heat capacity. When the external ambient temperature fluctuates, the temperature change on the equipment surface cannot be immediately conducted to the internal gas, resulting in a phase lag. This solution utilizes a heat transfer model based on the law of conservation of energy for recursive calculations. Using the insulating gas temperature from the previous moment as a basis, combined with the real-time temperature and acquisition period, the current temperature of the internal insulating gas is calculated.

[0010] Gas state normalization: Based on the gas state equation, this scheme performs a simultaneous normalization calculation of the real-time pressure and the insulating gas temperature derived from the heat transfer model to obtain the real-time gas density at the standard temperature, reflecting the change in the mass of the insulating gas inside the equipment.

[0011] Environmental interference filtering: Although temperature compensation can reduce errors, environmental factors still introduce residual fluctuations with a 24-hour period into the real-time gas density. This solution uses a sliding window with a fixed number of sampling points corresponding to a 24-hour period to perform a moving average of the real-time gas density, filtering out periodic environmental interference. The slope of the characteristic trend curve of density versus time obtained after processing is not affected by diurnal temperature differences and is related to the mass loss of insulating gas inside the equipment.

[0012] Leakage rate determination and lifespan prediction: Fitting analysis is performed on the characteristic trend curves of density and time, excluding periodic interference. The leakage rate of the insulating equipment is determined by the slope of the fitted curve. Combined with the safety boundary threshold, the time from gas leakage to the alarm or lockout value is calculated, thereby predicting the remaining safe leakage time of the gas.

[0013] Preferably, in step two, during the initial operation phase of the gas-insulated device and assuming no leakage in the main body 1 of the gas-insulated device, the true gas temperature is deduced from the initial gas density and the gas state equation. The current insulating gas temperature is then fitted with the true gas temperature using the least squares method to derive the heat transfer time constant between the gas-insulated device and the external environment. By employing the above technical solution, using data from the leak-free phase, the true gas temperature and the insulating gas temperature are fitted using the least squares method to determine the heat transfer time constant. This corrects model errors caused by differences in device installation location and casing material, improving the accuracy of subsequent recursive calculations of the current insulating gas temperature.

[0014] Preferably, the recursive calculation of the insulating gas temperature at the current moment specifically refers to performing a recursive calculation based on the insulating gas temperature recursion formula, using the known insulating gas temperature from the previous moment, the acquisition period, and the determined heat transfer time constant between the gas insulation device and the external environment, combined with the real-time temperature acquired at the current moment. By adopting the above technical solution, a discretized temperature recursive calculation method is constructed. The calculation process relies only on the state variables from the previous moment and the current input variables, reducing the dependence on the depth of historical data storage and meeting the real-time calculation requirements of the intelligent control module.

[0015] Preferably, in step four, the number of sampling points in the sliding window corresponds to the number of sampling points within 24 hours, used to eliminate the interference of ambient temperature fluctuations on the real-time gas density, so that the characteristic trend curve of density and time is only related to the remaining gas mass within the main body 1 of the gas insulation device. By adopting the above technical solution, the time span of the sliding window corresponds to 24 hours, and the interference of temperature fluctuations in the 24-hour period on density is eliminated by moving average, so that the output characteristic trend curve of density and time reflects the change in the mass of insulating gas.

[0016] Preferably, in step five, the least squares method is used to fit the characteristic trend curve of density and time over the past several days to obtain a fitted curve. The slope of the fitted curve is the leakage rate of the insulation equipment, and the standard pressure corresponding to the intercept of the fitted curve is the reference standard pressure. By adopting the above technical solution, the least squares method is used to linearly fit the data over multiple days, suppressing random measurement noise and transient interference, and determining the leakage rate of the insulation equipment.

[0017] Preferably, in step five, if the leakage rate of the insulation equipment exceeds the standard-specified leakage rate value, an alarm for excessive slope is issued. By adopting the above technical solution, if the leakage rate of the insulation equipment exceeds the threshold before the real-time gas density drops to the alarm value, an alarm is issued, achieving early warning.

[0018] Preferably, assessing the remaining safe leakage time of gas based on the leakage rate of the insulating equipment specifically refers to assessing the remaining safe leakage time to the alarm value and the remaining safe leakage time to the lockout value using a remaining safe leakage time assessment formula. Specifically, the remaining safe leakage time to the alarm value is obtained by dividing the difference between the gas density in the current density-time characteristic trend curve and the alarm value by the absolute value of the leakage rate of the insulating equipment; the remaining safe leakage time to the lockout value is obtained by dividing the difference between the gas density in the current density-time characteristic trend curve and the lockout value by the absolute value of the leakage rate of the insulating equipment. By adopting the above technical solution, based on the current real-time gas density, alarm value, or lockout value, and combined with the leakage rate of the insulating equipment, a specific remaining safe leakage time of gas is calculated, providing a time basis for operation and maintenance.

[0019] Preferably, the real-time pressure and the real-time temperature are collected in real time by the pressure sensor 21 and the temperature sensor 22, respectively, installed in the density relay 2 outside the gas insulation equipment body 1. By adopting the above technical solution, the pressure sensor 21 and the temperature sensor 22 are installed inside the density relay 2, without changing the internal structure of the gas insulation equipment body 1, thus ensuring the airtightness of the equipment.

[0020] Preferably, the data calculation and trend assessment in steps two through five are all completed by the intelligent control module 23 within the density relay 2, which receives the collected values ​​in real time and runs the algorithm model. The intelligent control module 23 then uploads the acquired real-time gas density, the leakage rate of the insulating equipment, and the remaining safe leakage time of the gas to the remote monitoring platform 3 via wired or wireless means. By adopting the above technical solution, the intelligent control module 23 performs heat transfer model calculation and smoothing locally, and uploads the calculated real-time gas density, leakage rate of the insulating equipment, and remaining safe leakage time of the gas to the remote monitoring platform 3, reducing data transmission volume and achieving remote monitoring.

[0021] Secondly, the present invention provides a gas leakage fault diagnosis system for gas-insulated equipment, employing the following technical solution: A gas leakage fault diagnosis system for gas-insulated equipment, used to implement the above-mentioned gas leakage fault diagnosis method for gas-insulated equipment, including a gas insulation equipment body 1, a density relay 2, a remote monitoring platform 3, a three-way valve 4, and a power supply 5; the density relay 2 includes a pressure sensor 21, a temperature sensor 22, and an intelligent control module 23; the pressure sensor 21 is sealed to the gas insulation equipment body 1 through the three-way valve 4, used to monitor the real-time pressure of the gas inside the gas insulation equipment body 1; the temperature sensor 22... Sensor 22 is used to measure the real-time temperature inside the density relay 2; the intelligent control module 23 is electrically connected to the pressure sensor 21 and the temperature sensor 22 respectively, and is used to receive the real-time pressure and the real-time temperature in real time, and obtain the real-time gas density, the characteristic trend curve of density and time, the leakage rate of the insulation equipment, and the remaining safe leakage time of the gas through an algorithm model; the remote monitoring platform 3 is communicatively connected to the intelligent control module 23, and is used to receive or process the data information and alarm information uploaded by the density relay 2 in real time; the power supply 5 is used to supply power to the equipment in the gas leakage fault diagnosis system inside the gas insulation equipment.

[0022] By adopting the above technical solution, the diagnosis of gas leakage inside gas-insulated equipment can be achieved through the coordinated cooperation between various components.

[0023] The specific components and their working principle are as follows:

[0024] Pressure monitoring: The pressure sensor 21 is connected to the gas insulation equipment body 1 through the three-way valve 4 to form a pressure transmission channel, so that the pressure sensor 21 can monitor the real-time pressure of the gas inside the gas insulation equipment body 1.

[0025] Temperature monitoring: Temperature sensor 22 is installed inside density relay 2 to measure the real-time temperature inside density relay 2. This temperature serves as the external boundary condition in the heat transfer model of gas-insulated equipment, capturing temperature changes caused by the external environment and providing a calculation basis for intelligent control module 23.

[0026] Local calculation: The intelligent control module 23 receives signals from the pressure sensor 21 and the temperature sensor 22, and performs recursive calculation of the heat transfer model, state normalization transformation and sliding window smoothing calculation locally to obtain the real-time gas density, density and time characteristic trend curves, leakage rate of insulation equipment and gas remaining safe leakage time, thereby improving the diagnostic response speed.

[0027] Remote control and monitoring: The remote monitoring platform 3 is connected to the intelligent control module 23 to receive data and alarm information uploaded by the intelligent control module 23, realize centralized monitoring of the equipment, and can send control commands to the density relay 2.

[0028] This invention provides a method for diagnosing internal gas leakage faults in gas-insulated equipment. It has the following beneficial effects:

[0029] 1. This invention does not require additional hardware; it only requires updating the program of the remote density relay in the existing gas insulation equipment. Its application method is completely consistent with the traditional remote density relay usage, and the application cost is extremely low.

[0030] 2. This invention can obtain the leakage rate and remaining safe leakage time of gas-insulated equipment, providing strong data support for accurate and scientific operation and maintenance.

[0031] 3. This invention can detect leakage problems earlier, handle leakage faults more promptly, reduce SF6 emissions, and provide more environmentally friendly support. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of a gas leakage fault diagnosis system for gas-insulated equipment according to an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of the structure of a high-precision intelligent gas density relay contact assembly in an embodiment of the present invention;

[0034] Figure 3 This is a comparative schematic diagram of the present invention;

[0035] Figure 4 This is a system flowchart of the present invention.

[0036] The components include: 1. Gas insulation equipment body; 2. Density relay; 21. Pressure sensor; 22. Temperature sensor; 23. Intelligent control module; 3. Remote monitoring platform; 4. Three-way valve; 5. Power supply. Detailed Implementation

[0037] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Please see the appendix Figure 1 - Appendix Figure 2This invention provides a gas leakage fault diagnosis system for gas-insulated equipment, comprising a gas-insulated equipment body 1, a density relay 2, a pressure sensor 21, a temperature sensor 22, an intelligent control module 23, a remote monitoring platform 3, a three-way valve 4, and a power supply 5. The pressure sensor 21 in the density relay 2 is sealed to the gas-insulated equipment body 1 via the three-way valve 4, and can monitor the gas pressure inside the gas-insulated equipment body 1 in real time. The temperature sensor 22 in the density relay 2 measures the real-time temperature inside the density relay. The intelligent control module 23 in the density relay 2 can receive and process the data collected by the pressure sensor 21 and the temperature sensor 22 in real time, and obtain the real-time density, leakage amount, leakage rate, and remaining safe leakage time of the gas inside the gas-insulated equipment body 1 through an algorithm model. The power supply 5 supplies power to the equipment within the system. The remote monitoring platform 3 is used to receive or process data and alarm information uploaded by the density relay in real time, and can also be used to control the density relay 2. Communication between the density relay 2 and the remote platform can be wired or wireless.

[0039] The working principle of a method for diagnosing internal gas leakage faults and predicting lifespan of gas-insulated equipment is as follows:

[0040] When density relay 2 is used to monitor for gas leakage inside gas-insulated equipment 1, the temperature measured by density relay 2 reflects the comprehensive ambient temperature, including air temperature and solar radiation. Function. The gas-insulated equipment is affected by the ambient temperature and undergoes heat transfer with the environment. The heat absorbed by the gas-insulated equipment 1 is used to change the internal energy of its own tank wall and the gas. The total heat capacity of the gas-insulated equipment 1 is... The total heat transfer admittance between the environment and the gas-insulated equipment is The heat transfer time constant between the air and the gas-insulated device 1 in the environment is: , The rate of change of the temperature of the insulating gas over time. Let be the temperature of the gas in gas-insulated device 1. According to the law of conservation of energy:

[0041] (1)

[0042] In practical applications, density relay 2 is used every [time period]. The real-time temperature collected is Real-time pressure is After a period of operation, temperature and pressure data form a time-related data sequence. Based on the collected temperature data, backward difference is performed on differential equation (1), and to simplify the calculation, In the calculation, a known temperature of the insulating gas is taken. replace.

[0043] (2)

[0044] The recursive formula for the temperature of the insulating gas is obtained by refining the formula.

[0045] (3)

[0046] When density relay 2 is first installed, the initial gas density P20 is known, and the gas insulation device 1 has good sealing performance in the initial stage. Under the condition that no leakage occurs, its P20 will not change during this period, and the true temperature of the gas can be deduced by using the gas state equation. .

[0047] (4)

[0048] Therefore, in the initial stage, the least squares method is used to fit the temperature of the insulating gas. Compared to actual temperature The heat transfer time constant between the gas-insulated equipment and the external environment is derived. That is, taking the temperature of the insulating gas. Compared to actual temperature The heat transfer time constant is minimized when the sum of the squares of the differences is minimized. .

[0049] (5)

[0050] Rewrite equation (3) as a recursive formula

[0051] (6)

[0052] make:

[0053]

[0054]

[0055] Then equation (6) can be expressed as:

[0056] (7)

[0057] From equation (7), we can see that the problem is transformed into a univariate linear regression problem. Solving for... To minimize the sum of squared errors, i.e.:

[0058] (8)

[0059] When equation (8) takes its minimum value, its derivative is 0.

[0060] (9)

[0061] After simplification, the time constant of gas-insulated device 1 can be obtained. .

[0062] (10)

[0063] In practical product applications, since the physical characteristics of the gas-insulated device 1 are finalized after manufacturing, its heat transfer time constant is normally a constant and will not change. Therefore, it can be fixed after solving for its value. Substituting into equation (3), we obtain the solution method for the temperature of the insulating gas.

[0064] (11)

[0065] Collect gas pressure and the estimated gas temperature Normalize the gas and solve for its real-time density.

[0066] (12)

[0067] These are real-time values, and are estimated due to the influence of diurnal temperature variations, uneven solar radiation, and wind speed changes. Real-time values ​​exhibit some fluctuation. To overcome the problem of interference from external nonlinear impacts when the leakage of gas-insulated equipment 1 is extremely small, this method sets a sliding window with a fixed number of sampling points, N, in the algorithm. The value of N corresponds to the number of sampling points for 24 hours (or other time periods). A sliding average was used to assess the leakage trend of the insulating gas.

[0068] (13)

[0069] Equation (13) yields the characteristic trend curves of density and time, which are isolated from the interference of ambient temperature and are only related to the mass of the remaining gas in the tank. To further eliminate short-term interference, the least squares method can be used to analyze the data over the past few days. The data points are fitted together, the slope of the fitted curve is k, and the reference standard pressure is... .

[0070] (14)

[0071] The slope k in equation (14) represents the leakage rate of the insulating equipment. If the leakage rate exceeds the standard limit, an alarm indicating excessive slope is triggered. During operation, the equipment's inflation value includes the rated value and the alarm value (…). ) and latch value ( When the gas pressure leaks from the rated value to the alarm pressure value, maintenance personnel must refill the equipment with gas. When the insulating gas leaks to the lockout value, the equipment will stop working, causing a large-scale power outage. To more accurately manage equipment maintenance scientifically, ensuring sufficient insulating gas and safe and reliable operation, and to implement maintenance work in a planned, scientific, and precise manner, it is essential to assess the remaining safe leakage time of the gas. , This will be very helpful.

[0072] (15-1)

[0073] (15-2)

[0074] See Figure 2 To verify the performance of the leak diagnosis model, a test platform illustrated in the diagram was constructed. The test platform mainly consists of a temperature chamber, a gas tank, a digital density meter, a flow controller, a filling valve, an SF6 gas source, an air compressor, several valves, and a data acquisition system. During the test, the gas tank was filled with SF6 gas at 0.7 MPa. A density relay was installed on the gas tank, containing both a temperature sensor and a pressure sensor. The pressure sensor monitored the gas pressure in the tank in real time, and the temperature sensor monitored the temperature within the density relay. The gas tank was also connected to a flow control valve, which simulated a GIS leak with a leakage rate set to approximately 0.00008 MPa / h. During the test, the gas tank was placed in the temperature chamber, and the temperature simulation period was a sinusoidal change over 24 hours. Data was collected every minute during the test, and the data processing was completed by the data acquisition and control system.

[0075] See Figure 3 In a sinusoidal temperature variation and leakage condition test, data was collected over 48 hours. The test results showed that the gas in the test device leaked from an initial pressure of 0.70978 MPa to 0.70552 MPa, a leakage of 0.00426 MPa, with a leakage rate of 0.000088 MPa / h. The traditional method, during the test, obtained gas density data that varied with temperature within the range of -0.00907 MPa to 0.00702 MPa, with a density variation range of 0.01609 MPa >> 0.00426 MPa. This data indicates that under operating conditions, the traditional method lacks the ability to accurately diagnose equipment leaks; it can only issue an early warning when the gas density drops to a relatively low level. Figure 3The comparison chart of P20 calculated by the traditional method and the leakage diagnosis method of this model is shown in Figure (a). Compared with the actual gas density, the residual of the gas density output by the leakage diagnosis method of this invention fluctuates within the range of -0.000025MPa to 0.000029MPa, with a residual range of only 0.000054MPa. The error is suppressed by 99.67% compared with the traditional method, and the residual standard deviation is 0.00001343MPa. According to the commonly used engineering standard 3... According to the reliability testing principle, the minimum P20 change that the system can effectively distinguish is 0.00004 MPa. This test proves that the leakage diagnosis method described in this invention has extremely high leakage diagnosis accuracy. Figure 3 The time-series characteristics of the P20 estimation residuals for different algorithms in diagnosing leakage are shown in (b).

[0076] After the gas-insulated equipment leakage diagnosis method described in this invention is implemented, leaks in gas-insulated equipment can be detected earlier, and the severity of the leak can be evaluated through data, which will greatly help the safe and reliable operation and maintenance management of the equipment. Furthermore, it offers the following benefits: First, it requires no additional hardware; it only requires updating the program of the existing remote density relay in the gas-insulated equipment, and its application method is completely consistent with the traditional remote density relay usage, resulting in extremely low application costs. Second, it can obtain the leakage rate and remaining safe leakage time of the gas-insulated equipment, providing strong data support for accurate and scientific implementation of maintenance work. Third, it enables earlier detection of leaks and more timely handling of leak faults, reducing SF6 emissions and providing more environmentally friendly support.

[0077] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for diagnosing internal gas leakage faults in gas-insulated equipment, characterized in that, Includes the following steps: Step 1: Collect real-time temperature and pressure data for the time series at regular intervals; Step 2: Based on the heat transfer model of the gas insulation equipment, the temperature of the insulating gas at the current moment is recursively calculated using the collected real-time temperature. Step 3: Normalize the collected real-time pressure and the calculated current temperature of the insulating gas, and solve for the real-time gas density; Step 4: Set a sliding window with a fixed number of sampling points, and perform a moving average on the calculated real-time gas density to obtain the characteristic trend curve of density and time; Step 5: Fit the characteristic trend curves of density and time to obtain the leakage rate of the insulation equipment, and evaluate the remaining safe leakage time of the gas based on the leakage rate of the insulation equipment.

2. The method for diagnosing internal gas leakage faults in gas-insulated equipment according to claim 1, characterized in that, In step two, during the initial operation phase of the gas insulation equipment and without leakage in the main body (1) of the gas insulation equipment, the true gas temperature is deduced from the initial gas density and the gas state equation; the temperature of the insulating gas at the current moment is fitted with the true gas temperature using the least squares method, and the heat transfer time constant between the gas insulation equipment and the external environment is derived.

3. The method for diagnosing internal gas leakage faults in gas-insulated equipment according to claim 2, characterized in that, The recursive solution for the current insulating gas temperature specifically refers to performing a recursive calculation based on the recursive formula for the insulating gas temperature, using the known insulating gas temperature from the previous moment, the acquisition period, and the determined heat transfer time constant between the gas insulation device and the external environment, combined with the real-time temperature acquired at the current moment.

4. The method for diagnosing internal gas leakage faults in gas-insulated equipment according to claim 1, characterized in that, In step four, the number of sampling points of the sliding window corresponds to the number of sampling points within 24 hours, which is used to eliminate the interference of ambient temperature fluctuations on the real-time density of the gas, so that the characteristic trend curve of density and time is only related to the mass of the remaining gas in the gas insulation device.

5. The method for diagnosing internal gas leakage faults in gas-insulated equipment according to claim 1, characterized in that, In step five, the least squares method is used to fit the characteristic trend curve of density and time over the past few days to obtain a fitted curve. The slope of the fitted curve is the leakage rate of the insulation equipment, and the standard pressure corresponding to the intercept of the fitted curve is the reference standard pressure.

6. The method for diagnosing internal gas leakage faults in gas-insulated equipment according to claim 5, characterized in that, In step five, if the leakage rate of the insulation equipment is greater than the leakage rate value specified in the standard, an alarm for excessive slope is issued.

7. A method for diagnosing internal gas leakage faults in gas-insulated equipment according to claim 5, characterized in that, The assessment of the remaining safe leakage time of gas based on the leakage rate of the insulation equipment specifically refers to assessing the remaining safe leakage time to the alarm value and the remaining safe leakage time to the lockout value based on the remaining safe leakage time assessment formula. Specifically, the remaining safe leakage time to the alarm value is obtained by dividing the difference between the gas density in the current density-time characteristic trend curve and the alarm value by the absolute value of the leakage rate of the insulation equipment; the remaining safe leakage time to the lockout value is obtained by dividing the difference between the gas density in the current density-time characteristic trend curve and the lockout value by the absolute value of the leakage rate of the insulation equipment.

8. A fault diagnosis system for internal gas leakage in gas-insulated equipment, used to implement the fault diagnosis method for internal gas leakage in gas-insulated equipment as described in any one of claims 1-7, characterized in that, The system includes a gas insulation device body (1), a density relay (2), a remote monitoring platform (3), a three-way valve (4), and a power supply (5); the density relay (2) contains a pressure sensor (21), a temperature sensor (22), and an intelligent control module (23); the pressure sensor (21) is sealed to the gas insulation device body (1) through the three-way valve (4) and is used to monitor the real-time pressure of the gas inside the gas insulation device body (1); the temperature sensor (22) is used to measure the real-time temperature inside the density relay (2); the intelligent control module (23) The remote monitoring platform (3) is electrically connected to the pressure sensor (21) and the temperature sensor (22) respectively, and is used to receive the real-time pressure and the real-time temperature in real time, and to obtain the real-time gas density, the characteristic trend curve of the density and time, the leakage rate of the insulation equipment and the remaining safe leakage time of the gas through the algorithm model; the remote monitoring platform (3) is communicatively connected to the intelligent control module (23), and is used to receive or process the data information and alarm information uploaded by the density relay (2) in real time; the power supply (5) is used to supply power to the equipment in the gas leakage fault diagnosis system inside the gas insulation equipment.

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