A method and system for judging operation fault types of SF6 gas switchgear

Abstract

This invention provides a method and system for determining the operational fault type of SF6 gas switchgear. The method includes obtaining the volume fractions of different types of perfluoroalkane gases in the operating SF6 gas switchgear at different time periods and obtaining the corresponding ratios of perfluoroalkane gas volume fractions; calculating the mean, standard deviation, and goodness index of the corresponding volume fraction ratios using a Weibull function; obtaining the shape parameters of the Weibull function distribution and the corresponding confidence intervals using the mean and standard deviation, thereby obtaining the Weibull probability distribution function and probability density graph; combining the Weibull probability distribution function, probability density graph, and goodness index to confirm whether the sample distribution conforms to the Weibull function distribution; and then using the shape parameters and the corresponding confidence intervals to complete the determination of the operational fault type of the SF6 gas switchgear. This method effectively improves the detection rate of SF6 switchgear fault types.

Description

Technical Field

[0001] This invention belongs to the field of power engineering technology and relates to a method and system for judging the type of operational faults in SF6 gas switchgear. Background Technology

[0002] As a core component of the power grid, SF6 gas-insulated switchgear requires regular latent fault diagnosis to effectively reduce the threat of latent faults to the safe and stable operation of the switchgear and save economic costs. In recent years, the diagnosis and analysis of latent faults in SF6 switchgear have mainly developed methods such as X-ray image network diagnosis, mechanical fault vibration detection, partial discharge measurement, acoustic imaging, ultra-high frequency partial discharge detection, particle filtering and negative selection algorithm, fuzzy hierarchical analysis, and SF6 decomposition component analysis. Most existing fault type detection methods require disassembly and inspection, which is complex and time-consuming. Furthermore, some methods establish functional relationships based on probability theory and statistics to determine the service life and failure rate of the equipment; however, research on applying these methods to fault type identification remains lacking. Summary of the Invention

[0003] To address the problems existing in the prior art, the present invention provides a method and system for determining the operational fault type of SF6 gas switchgear, thereby solving the technical problem that the equipment needs to be disassembled and the operation is complicated when determining the operational fault type of SF6 gas switchgear.

[0004] This invention is achieved through the following technical solution:

[0005] A method for determining the type of operational fault in SF6 gas switchgear includes the following steps:

[0006] S1: Obtain the volume fraction of different types of perfluoroalkane gases in the SF6 gas switchgear during different time periods, and obtain the ratio of the volume fraction of different types of perfluoroalkane gases in different time periods. Use the ratio of the volume fraction of different types of perfluoroalkane gases in different time periods as the sample distribution. Calculate the mean, standard deviation, and goodness of fit of the volume fraction ratio of different types of perfluoroalkane gases in different time periods using the Weibull function.

[0007] S2: Obtain the shape parameters of the Weibull function distribution and the confidence intervals corresponding to the shape parameters through the mean and standard deviation, thereby obtaining the Weibull probability distribution function and probability distribution density graph;

[0008] S3: Combining the Weibull probability distribution function, probability distribution density graph, and fitness index, confirm whether the sample distribution conforms to the distribution of the Weibull function, and then use the shape parameters and the confidence intervals corresponding to the shape parameters to complete the judgment of the SF6 gas switchgear operation fault type.

[0009] Preferably, the perfluoroalkane gas is CF4 or C3F8.

[0010] Preferably, the shape parameters of the Weibull function distribution and the corresponding confidence intervals are obtained using the maximum likelihood estimation method.

[0011] Preferably, the type of SF6 gas switchgear malfunction is determined by the overlap of the shape parameters and the confidence intervals corresponding to the shape parameters.

[0012] Preferably, the correspondence between the confidence interval of the shape parameter and the fault type is as follows:

[0013] When the confidence interval of the shape parameter is (1.2560, 1.6842), the fault type is overheating of insulating components; when the confidence interval of the shape parameter is (2.2670, 2.7056), the fault type is abnormal heating of metal components and overheating of insulating components; when the confidence interval of the shape parameter is (10.928, 15.491), the fault type is abnormal arc erosion and overheating of insulating components.

[0014] Preferably, in step S1, the volume fraction ratio of perfluoroalkane gas in the SF6 gas switchgear in operation is obtained by gas chromatography analysis.

[0015] Preferably, different types of perfluoroalkane gases are obtained from the arc-extinguishing chamber of the SF6 gas switchgear in operation.

[0016] A fault type determination system for SF6 gas switchgear includes:

[0017] Data acquisition module: The data acquisition module is used to acquire the volume fraction of different types of perfluoroalkane gases in the SF6 gas switchgear in operation at different time periods, and to obtain the ratio of the volume fraction of different types of perfluoroalkane gases at different time periods. The ratio of the volume fraction of different types of perfluoroalkane gases at different time periods is used as the sample distribution. The mean, standard deviation and goodness index of the volume fraction ratio of different types of perfluoroalkane gases at different time periods are calculated by using the Weibull function.

[0018] Data processing module: The data processing module is used to obtain the shape parameters of the Weibull function distribution and the confidence intervals corresponding to the shape parameters through the mean and standard deviation, thereby obtaining the Weibull probability distribution function and probability distribution density graph;

[0019] Result output module: The result output module is used to combine the Weibull probability distribution function, probability distribution density graph and fitness index to confirm whether the sample distribution approximately conforms to the distribution of the Weibull function, and then use the shape parameter and the confidence interval corresponding to the shape parameter to complete the judgment of the SF6 gas switchgear operation fault type, and output the judged fault type.

[0020] A terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the above-described method when executing the computer program.

[0021] A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the above-described method.

[0022] Compared with the prior art, the present invention has the following beneficial technical effects:

[0023] A method for determining the operational fault type of SF6 gas switchgear is disclosed. This method analyzes the components and volume fractions of perfluoroalkane (SF6) after decomposition and applies mathematical algorithms based on probability theory and mathematical statistics to obtain the mean, standard deviation, shape parameter, and corresponding confidence interval of the Weibull distribution of different types of perfluoroalkane gases at different time periods. The method then uses a fitness index, shape parameter, and corresponding confidence interval to determine the operational fault type of the SF6 gas switchgear, effectively improving the detection rate of SF6 switchgear fault types. In this method, the mean and standard deviation intuitively reflect the corresponding characteristics of the random event, and the fitness index effectively measures the degree of fit of the Weibull distribution function to the sample experiment, thus improving the accuracy of fault type determination. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating a method for determining the operational fault type of SF6 gas switchgear according to the present invention.

[0026] Figure 2This is a schematic diagram of a module for determining the operating fault type of an SF6 gas switchgear according to the present invention;

[0027] Figure 3 This is a probability distribution diagram of the latent fault state fitted by the Weibull distribution in Embodiment 2 of the present invention;

[0028] Figure 4 This is a probability distribution diagram of the latent fault state fitted by the Weibull distribution in Embodiment 3 of the present invention;

[0029] Figure 5 This is a probability distribution diagram of the latent fault state fitted by the Weibull distribution in Embodiment 4 of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0031] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0032] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0033] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0034] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0035] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0036] The present invention will now be described in further detail with reference to the accompanying drawings:

[0037] Example 1

[0038] like Figure 1 As shown, a method for determining the operational fault type of SF6 gas switchgear includes the following steps:

[0039] S1: The volume fractions of different types of perfluoroalkane gases in the SF6 gas switchgear during different time periods were obtained by gas chromatography analysis, and the ratios of the volume fractions of different types of perfluoroalkane gases at different time periods were obtained; the mean, standard deviation, and goodness of fit of the ratios of the volume fractions of different types of perfluoroalkane gases at different time periods were calculated by Weibull function; the perfluoroalkane gases were CF4 and C3F8.

[0040] S2: By using the mean and standard deviation, and combining the maximum likelihood estimation method, the shape parameters of the Weibull function distribution and the confidence intervals corresponding to the shape parameters are obtained, thereby obtaining the Weibull probability distribution function and probability distribution density graph;

[0041] S3: Combining the Weibull probability distribution function, probability distribution density graph, and fitness index, confirm whether the sample distribution approximately conforms to the Weibull function distribution. Then, use the shape parameters and their corresponding confidence intervals to determine the fault type of the SF6 gas switchgear. Specifically, the type of SF6 gas switchgear fault is determined by the overlap of the shape parameters and their corresponding confidence intervals. The correspondence between the confidence intervals of the shape parameters and the fault types is as follows: when the confidence interval of the shape parameters is (1.2560, 1.6842), the fault type is overheating of insulating components; when the confidence interval of the shape parameters is (2.2670, 2.7056), the fault type is abnormal heating of metal components and overheating of insulating components; when the confidence interval of the shape parameters is (10.928, 15.491), the fault type is abnormal arc erosion and overheating of insulating components.

[0042] This invention provides a method for judging operational faults in SF6 gas switchgear based on the Weibull function. Specifically, the technical solution involves periodically or irregularly detecting the volume fractions of perfluoroalkane (PF4) and C3F8 gases in the operating SF6 gas switchgear to obtain their volume fraction ratios. Using the Weibull function, the sample mean, sample standard deviation, and goodness-of-fit index of the CF4-C3F8 volume fraction ratio in the equipment are calculated. The maximum likelihood estimation method is used to estimate the confidence interval (95%) under the corresponding parameters (i.e., shape parameters) and typical values ​​(i.e., shape parameters) of the Weibull distribution, resulting in the corresponding probability distribution function and probability density graph. The latent fault type in the operating equipment is determined by the overlap between the Weibull function γ-point estimation (shape parameter) and the confidence interval, based on the CF4-C3F8 volume fraction ratio γ-point estimation (shape parameter) and the confidence interval. The fault types that can be determined by the Weibull function γ-point estimation (shape parameter) and confidence interval are: Type A - Overheating of insulating components; Type B - Abnormal heating of metal components + overheating of insulating components; Type C - Abnormal arc erosion + overheating of insulating components. The confidence intervals for the γ-point estimates of the Weibull distribution corresponding to the three fault types mentioned above are as follows: Class A: (1.2560, 1.6842); Class B: (2.2670, 2.7056); Class C: (10.928, 15.491). The sample mean is the average of the volume fraction ratios of CF4 and C3F8 across multiple detection results, and the variance is the degree of deviation between the random variable and the mean. A larger variance indicates greater data fluctuation; a smaller variance indicates less data fluctuation. A larger goodness index value (i.e., closer to 1) indicates a better fit to the Weibull function distribution result. If it equals 1, it means that the frequency of fault occurrence is completely consistent with the estimated probability distribution.

[0043] This invention employs the Weibull function, a typical fault distribution function, as a method for determining the fault type of SF6 switchgear. This method uses mathematical algorithms based on probability theory and mathematical statistics to analyze the concentration fraction ratios of characteristic decomposition products corresponding to faults in operating equipment, providing reliable theoretical support for engineering applications. Compared with the closest existing technology, the beneficial effects of this invention are as follows: This technology provides a new approach for the engineering application of characteristic SF6 decomposition product analysis. Furthermore, this method is not limited by the number of samplings or the sampling interval; the γ-point estimation and confidence interval can be updated synchronously with changes in sampling time and number of samplings, providing an efficient judgment tool for latent fault analysis and safe operation of high-voltage SF6 equipment. SF6 decomposition component analysis is less affected by external environmental interference, has high sensitivity, and good accuracy, making it one of the most promising methods for detecting the operating status of SF6 switchgear. Perfluoroalkane CF4 is mainly produced by the fluorination of elemental C deposited on the surface of copper-tungsten contacts, while C3F8 mainly comes from the corrosion and cracking process of polytetrafluoroethylene (PTFE), the nozzle insulation material. Clearly identifiable sources of these molecules make them ideal for diagnosing latent faults in SF6 switchgear. However, in the fault diagnosis process of SF6 switchgear, the volume fraction of perfluoroalkane (PFOA) exhibits complex and variable characteristics in different fault processes. Simply comparing volume fraction changes is insufficient to quickly and accurately determine the true nature of the fault. Probability theory and statistics, a mathematical discipline that studies the statistical regularities of random phenomena and random data processing techniques, have extremely wide applications. By applying the basic principles and methods of probability theory and statistics, establishing a functional model of the relationship between the volume fraction ratio of PFOA in SF6 decomposition products and latent faults will comprehensively improve the fault detection rate of SF6 switchgear and assist in determining the equipment's service life. Common functions used for equipment fault distribution analysis include: exponential distribution, Weibull distribution, normal distribution, and log-normal distribution. The Weibull distribution can effectively characterize the fault features of SF6 switchgear. The distribution function of the Weibull distribution is: Its probability density function is: The distribution function F(X) can completely describe the statistical regularity of a random variable. The probability density function f(x) is a function that describes the probability that the output value of a time random variable will appear near a certain point x. The data obtained represents the probability of the event occurring within that interval. Since the event is certain to occur throughout the entire interval, the result for the entire interval must be 1, satisfying the condition...

[0044] Where x is a variable, and γ and θ are both positive constants. γ, the shape parameter, determines the basic shape of the distribution density curve. The scaling parameter θ amplifies or reduces the curve. Therefore, if a high-voltage SF6 switchgear experiences the same type of fault as equipment with a known latent fault, the volume fraction ratio of perfluoroalkane may have a similar shape on the Weibull function curve. Thus, the unknown fault type is determined by the overlap between the parameter γ and the confidence interval. Furthermore, the parameter γ can also represent the failure characteristics of the equipment. 0 < γ < 1: Early failure, occurring in the initial stage of the equipment's lifespan; the failure rate decreases rapidly as the equipment's operating time increases. γ = 1: The failure rate remains constant and can be approximated as a constant. Random failure, with a low failure rate, relatively stable, and multiple causes of failure. γ = 1.5: Early wear failure. The failure rate continuously increases, initially at the fastest rate. γ = 2: Stable wear failure; the risk of wear failure continuously increases during the equipment's lifespan. 3 ≤ γ ≤ 4: Rapid wear failure. γ > 10: Very rapid wear failure, the final stage of the equipment's lifespan. Furthermore, while the probability distribution function and probability density function of fault types can be used to describe the occurrence of random events, they are, to a certain extent, difficult to intuitively reflect the corresponding characteristics of the random event. Therefore, the introduction of mean and variance effectively characterizes the numerical features of a random event. In practical applications, standard deviation (the arithmetic square root of variance) is often used instead of variance. For a continuous random variable x, if its probability density function f(x) is known, then its expected value E(x) and variance D(x) are respectively: and In addition, the fitness index can be used to measure how well the Weibull distribution function fits the sample experiment; its value is between 0 and 1.

[0045] like Figure 2 The diagram shown is a module connection diagram of an SF6 gas switchgear operation fault type judgment system according to the present invention, including:

[0046] Data acquisition module: The data acquisition module is used to acquire the volume fraction of different types of perfluoroalkane gases in the SF6 gas switchgear in operation at different time periods, and to obtain the ratio of the volume fraction of different types of perfluoroalkane gases at different time periods; the mean, standard deviation and fitness index of the volume fraction ratio of different types of perfluoroalkane gases at different time periods are calculated by Weibull function.

[0047] Data processing module: The data processing module is used to obtain the shape parameters of the Weibull function distribution and the confidence intervals corresponding to the shape parameters through the mean and standard deviation, thereby obtaining the Weibull probability distribution function and probability distribution density graph;

[0048] Result output module: The result output module is used to combine the Weibull probability distribution function, probability distribution density graph and fitness index to confirm whether the sample distribution approximately conforms to the distribution of the Weibull function, and then use the shape parameter and the confidence interval corresponding to the shape parameter to complete the judgment of the SF6 gas switchgear operation fault type, and output the judged fault type.

[0049] Additionally, a schematic diagram of a terminal device according to an embodiment of the present invention is provided. This terminal device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0050] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0051] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0052] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0053] The memory can be used to store the computer program and / or module. The processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0054] If the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0055] Example 2

[0056] Step 1: Connect the vacuum sampling bottle to the arc-extinguishing chamber of the SF6 gas switchgear, collect the gas, and analyze the collected actual operating gas by gas chromatography. Determine the volume fraction ratio of the two gases based on the peak areas of CF4 and C3F8.

[0057] Step 2: Collect data multiple times at different time periods to obtain several sets of ratios, as shown in Table 1.

[0058] Table 1 shows the volume fraction ratios of CF4 and C3F8 obtained at different time periods in Example 2.

[0059]

[0060] Step 3: Using the volume fraction ratios of all CF4 and C3F8 in the table as a sample, apply the definition of the statistic, input the Weibull function calculation program, and calculate the sample mean of the data as 1.2291 and the sample standard deviation as 0.11601.

[0061] Step 4: Set up the Weibull function calculation program and use the maximum likelihood estimation method to estimate the corresponding parameters of the Weibull distribution and the confidence intervals under typical values, as shown in Table 2.

[0062] Table 2 shows the corresponding parameters and confidence intervals for the Weibull distribution in Example 2.

[0063]

[0064] Step 5: Further input the setting program to obtain the Weibull probability distribution function and probability distribution density graph corresponding to the fault type, and obtain the Weibull distribution function's fitness index for this type of fault as 0.98977.

[0065] The Weibull distribution fitted to the probability distribution under latent fault conditions in this embodiment is shown in [reference needed]. Figure 3 , Figure 3 The asterisk (*) represents the experimental distribution function, and its goodness of fit with the theoretical distribution of the Weibull function (the continuous curve on the left, i.e., the distribution function graph). The standard deviation of all test data is 0.11601, indicating small data fluctuations. The goodness of fit of the Weibull function is 0.98977, close to 1, indicating that the volume fraction ratio of CF4 and C3F8 in the currently operating equipment conforms to the distribution characteristics of the Weibull function, and this function can be used to determine faults. Under this operating condition, the obtained γ point estimate is 1.4545, and the suitable confidence interval is (1.2560, 1.6842), indicating that the operating equipment has a latent Type A fault.

[0066] Example 3

[0067] Step 1: Connect the vacuum sampling bottle to the arc-extinguishing chamber of the SF6 gas switchgear, collect the gas, and analyze the collected actual operating gas by gas chromatography. Determine the volume fraction ratio of the two gases based on the peak areas of CF4 and C3F8.

[0068] Step 2: Collect data multiple times at different time periods to obtain several sets of ratios, as shown in Table 3:

[0069] Table 3 shows the volume fraction ratios of CF4 and C3F8 obtained at different time periods in Example 3.

[0070]

[0071] Step 3: Using the volume fraction ratios of all CF4 and C3F8 in the table as a sample, apply the definition of the statistic, input the Weibull function calculation program, and calculate the sample mean of the data as 2.172 and the sample standard deviation as 0.13088.

[0072] Step 4: Set up the Weibull function calculation program and use the maximum likelihood estimation method to estimate the corresponding parameters of the Weibull distribution and the confidence intervals under typical values, as shown in Table 4.

[0073] Table 4 shows the corresponding parameters and confidence intervals for the Weibull distribution in Example 3.

[0074]

[0075] Step 5: Further input the setting program to obtain the Weibull probability distribution function and probability distribution density graph corresponding to the fault type, and obtain the Weibull distribution function's fitness index for this type of fault as 0.99305.

[0076] The Weibull distribution fitted to the probability distribution under latent fault conditions in this embodiment is shown in [reference needed]. Figure 4 , Figure 4 The asterisk (*) represents the experimental distribution function, and its goodness of fit with the theoretical distribution of the Weibull function (the continuous curve on the left, i.e., the graph of the distribution function). The standard deviation of all test data is 0.13088, indicating small data fluctuations. The goodness of fit of the Weibull function is 0.99305, close to 1, indicating that the volume fraction ratio of CF4 and C3F8 in the currently operating equipment conforms to the distribution characteristics of the Weibull function, and this function can be used to determine faults. Under this operating condition, the obtained γ point estimate is 2.3047, and the suitable confidence interval is (2.2670, 2.7056), indicating that there is a latent Type B fault in the operating equipment.

[0077] Example 4

[0078] Step 1: Connect the vacuum sampling bottle to the arc-extinguishing chamber of the SF6 gas switchgear, collect the gas, and analyze the collected actual operating gas by gas chromatography. Determine the volume fraction ratio of the two gases based on the peak areas of CF4 and C3F8.

[0079] Step 2: Collect data multiple times at different time periods to obtain several sets of ratios, as shown in Table 5 below:

[0080] Table 5 shows the volume fraction ratios of CF4 and C3F8 obtained at different time periods in Example 4.

[0081]

[0082]

[0083] Step 3: Using the volume fraction ratios of all CF4 and C3F8 in the table as a sample, apply the definition of the statistic, input the set Weibull function calculation program, and calculate the sample mean of the data as 14.6428 and the sample standard deviation as 0.40491.

[0084] Step 4: Set up the Weibull function calculation program and use the maximum likelihood estimation method to estimate the corresponding parameters of the Weibull distribution and the confidence intervals under typical values, as shown in Table 6.

[0085] Table 6 shows the corresponding parameters and confidence intervals for typical values ​​of the Weibull distribution in Example 4.

[0086]

[0087] Step 5: Further input the setting program to obtain the Weibull probability distribution function and probability distribution density graph corresponding to the fault type, and obtain the Weibull distribution function's fitness index for this type of fault as 0.96405.

[0088] The Weibull distribution fitted to the probability distribution under latent fault conditions in this embodiment is shown in [reference needed]. Figure 5 , Figure 5 The asterisk (*) represents the experimental distribution function, and its goodness of fit with the theoretical distribution of the Weibull function (the continuous curve on the left, i.e., the distribution function graph). The standard deviation of all test data is 0.40491, indicating data fluctuation. The goodness of fit of the Weibull function is 0.96405, slightly lower than that in Examples 1 and 2, but still close to 1, indicating that the volume fraction ratio of CF4 and C3F8 in the currently operating equipment conforms to the distribution characteristics of the Weibull function, and this function can be used to determine faults. Under this condition, the obtained γ point estimate is 13.0108, and the suitable confidence interval is (10.928, 15.491), indicating that the operating equipment has a latent Class C fault.

[0089] The above are merely preferred embodiments of the present invention and are not intended to limit the present 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.

Claims

1. A method for determining the type of operational fault in SF6 gas switchgear, characterized in that, Includes the following steps: S1: Obtain the volume fraction of different types of perfluoroalkane gases in the SF6 gas switchgear during different time periods, and obtain the ratio of the volume fraction of different types of perfluoroalkane gases in different time periods. Use the ratio of the volume fraction of different types of perfluoroalkane gases in different time periods as the sample distribution. Calculate the mean, standard deviation, and goodness of fit of the volume fraction ratio of different types of perfluoroalkane gases in different time periods using the Weibull function. S2: Obtain the shape parameters of the Weibull function distribution and the confidence intervals corresponding to the shape parameters through the mean and standard deviation, thereby obtaining the Weibull probability distribution function and probability distribution density graph; S3: Combining the Weibull probability distribution function, probability distribution density graph and fitness index, confirm whether the sample distribution conforms to the distribution of the Weibull function, and then use the shape parameters and the confidence intervals corresponding to the shape parameters to complete the judgment of the SF6 gas switchgear operation fault type; The perfluoroalkane gas is CF4 and C3F8; The correspondence between the confidence interval of the shape parameter and the fault type is as follows: When the confidence interval of the shape parameter is (1.2560, 1.6842), the fault type is overheating of insulating components; when the confidence interval of the shape parameter is (2.2670, 2.7056), the fault type is abnormal heating of metal components and overheating of insulating components; when the confidence interval of the shape parameter is (10.928, 15.491), the fault type is abnormal arc erosion and overheating of insulating components.

2. The method for determining the operational fault type of SF6 gas switchgear according to claim 1, characterized in that, The shape parameters and corresponding confidence intervals of the Weibull function distribution are obtained using the maximum likelihood estimation method.

3. The method for determining the operational fault type of SF6 gas switchgear according to claim 1, characterized in that, The type of SF6 gas switchgear malfunction is determined by the shape parameters and the degree of overlap of the confidence intervals corresponding to the shape parameters.

4. The method for determining the operational fault type of SF6 gas switchgear according to claim 1, characterized in that, In step S1, the volume fraction ratio of perfluoroalkane gas in the SF6 gas switchgear in operation is obtained by gas chromatography analysis.

5. The method for determining the operational fault type of SF6 gas switchgear according to claim 1, characterized in that, Different types of perfluoroalkane gases are obtained from the arc-extinguishing chamber of the SF6 gas switchgear in operation.

6. A fault type determination system for SF6 gas switchgear, characterized in that, The steps for implementing the method for determining the operational fault type of an SF6 gas switchgear according to any one of claims 1 to 5 include: Data acquisition module: The data acquisition module is used to acquire the volume fraction of different types of perfluoroalkane gases in the SF6 gas switchgear in operation at different time periods, and to obtain the ratio of the volume fraction of different types of perfluoroalkane gases at different time periods. The ratio of the volume fraction of different types of perfluoroalkane gases at different time periods is used as the sample distribution. The mean, standard deviation and goodness index of the volume fraction ratio of different types of perfluoroalkane gases at different time periods are calculated by using the Weibull function. Data processing module: The data processing module is used to obtain the shape parameters of the Weibull function distribution and the confidence intervals corresponding to the shape parameters through the mean and standard deviation, thereby obtaining the Weibull probability distribution function and probability distribution density graph; Result output module: The result output module is used to combine the Weibull probability distribution function, probability distribution density graph and fitness index to confirm whether the sample distribution conforms to the distribution of the Weibull function, and then use the shape parameter and the confidence interval corresponding to the shape parameter to complete the judgment of the SF6 gas switch equipment operation fault type, and output the judged fault type.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-5.

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