Method, device, equipment and medium for life prediction of gas insulated switchgear

Abstract

The application discloses a kind of life prediction method, device, equipment and medium of gas insulated switchgear, belong to gas insulated switchgear life prediction field.The method is: from gas insulated switchgear, gas sample is collected, and laser-induced breakdown spectroscopy technology is used to excite sample and obtain NO spectrum signal data;NO spectrum intensity is calculated according to preset wavelength parameter;Further, NO spectrum intensity is input into prediction model, to predict the switch breaking number of gas insulated switchgear and assess its life;Wherein, prediction model is obtained by establishing the quantitative relationship between NO spectrum intensity and switch breaking number.Therefore, by implementing the present application, the problem that it is difficult to ensure prediction accuracy while achieving convenient on-site life prediction in the prior art can be solved.

Description

Technical Field

[0001] This invention relates to the field of life prediction for gas-insulated switchgear, and more particularly to a method, apparatus, device, and medium for predicting the life of gas-insulated switchgear. Background Technology

[0002] In the field of life prediction for gas-insulated switchgear, laser-induced breakdown spectroscopy (LIBS) is a multi-element analysis technique based on plasma emission spectroscopy. Its principle involves focusing a high-power pulsed laser onto the sample, causing the laser-irradiated area to instantaneously reach an energy density on the order of ~GW / cm², leading to rapid ablation, vaporization, and ionization of the target material surface, forming plasma. Subsequently, a spectrometer collects the characteristic radiation emitted during the plasma cooling process, and combined with the quantitative relationship between the intensity and concentration of elemental characteristic spectral lines, rapid analysis of element types and contents is achieved. Due to its advantages such as simple sample preparation, near-non-destructive testing, and rapid multi-element measurement, it has become a highly promising detection method and is widely used in fields such as steel composition analysis, soil heavy metal detection, and atmospheric environmental monitoring. Gas-insulated switchgear (GIS) is a core facility ensuring the safe operation of power systems, and its contact life directly determines the reliability of power supply. Frequent opening and closing operations of GIS switches generate high-temperature arcs, causing thermochemical decomposition of the insulating medium and generating nitrogen oxides, primarily NO. As the number of switching operations increases, NO continuously accumulates within the sealed cavity. On one hand, NO reacts with trace amounts of moisture to form nitric acid, accelerating metal corrosion and insulation degradation. On the other hand, changes in gas composition reduce insulation strength, potentially inducing partial discharge or even breakdown accidents. Therefore, real-time monitoring of NO concentration within the GIS cavity is crucial for predicting equipment lifespan and developing maintenance strategies.

[0003] Currently, GIS lifetime diagnostics primarily relies on Dissolved Gas Analysis (DGA) technology, mainly including gas chromatography (GC), electrochemical sensor monitoring, and Fourier transform infrared spectroscopy (FTIR). GC uses column separation and high-sensitivity detectors to perform qualitative and quantitative analysis of gaseous products, providing accurate results that are often used as diagnostic benchmarks. However, it requires sampling by breaking the sealed structure of the equipment, a process that can take several hours and is difficult to implement on-site. FTIR achieves rapid multi-component measurement based on the characteristic absorption of infrared spectra by molecules, making it suitable for online monitoring, but the equipment is expensive and the operation is complex. Electrochemical sensors rely on the redox reactions of specific gases on electrode surfaces for detection, offering advantages such as low cost and portability. However, they are susceptible to cross-interference from SF6 decomposition products and have relatively short sensor lifespans. Therefore, there is an urgent need for a technical solution that can ensure prediction accuracy while improving the convenience of the lifetime prediction process. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and medium for predicting the lifespan of gas-insulated switchgear, which can ensure the accuracy of prediction while improving the convenience of the lifespan prediction process.

[0005] In a first aspect, embodiments of the present invention provide a method for predicting the lifespan of a gas-insulated switchgear, comprising: A gas sample was collected from the gas-insulated switchgear, and the gas sample was excited to obtain NO spectral signal data; The NO spectral intensity corresponding to the NO spectral signal data is calculated based on the first preset wavelength parameter, the second preset wavelength parameter, and the NO spectral signal data. Based on the NO spectral intensity and the prediction model, the number of switching interruptions of the gas-insulated switchgear is predicted to obtain the number of switching interruptions of the gas-insulated switchgear, and the predicted lifespan of the gas-insulated switchgear is obtained based on the number of switching interruptions; wherein, the prediction model is obtained by establishing a quantitative relationship between the initial spectral intensity and the initial number of switching interruptions of the gas-insulated switchgear.

[0006] This application embodiment achieves accurate extraction of NO content within gas-insulated switchgear by performing laser-induced breakdown spectral analysis on gas samples and calculating NO spectral intensity based on specific wavelength parameters. Furthermore, by establishing a quantitative prediction model between NO spectral intensity and switch interruption count, the number of interruptions and remaining lifespan of the equipment can be directly and reliably predicted. This application avoids destructive sampling and complex preprocessing in traditional testing, significantly improving detection efficiency and field applicability, while effectively reducing operational complexity and equipment maintenance costs. Therefore, this embodiment solves the problem in existing technologies of difficulty in improving the convenience of lifespan prediction while ensuring accuracy.

[0007] As a preferred example of the first aspect, the step of calculating the NO spectral intensity corresponding to the NO spectral signal data based on the first preset wavelength parameter, the second preset wavelength parameter, and the NO spectral signal data includes: Several first spectral intensities are determined based on the first preset wavelength parameter and the NO spectral signal data, and the average spectral intensity is calculated based on each first spectral intensity to determine the baseline intensity. Several second spectral intensities are determined based on the second preset wavelength parameter and the NO spectral signal data, and the NO spectral intensity corresponding to the NO spectral signal data is calculated based on each second spectral intensity and the baseline intensity.

[0008] In this preferred embodiment, by extracting the baseline intensity and the target spectral intensity from the spectral signal of the gas sample, and calculating the NO spectral intensity based on the difference between the two, this application can effectively eliminate interference caused by background noise and instrument drift, improving the accuracy and stability of NO concentration extraction. By distinguishing between the baseline wavelength and the characteristic wavelength region, the selectivity and signal-to-noise ratio of the spectral analysis are enhanced, thereby providing a more reliable input data basis for subsequent lifetime prediction models.

[0009] As a preferred example of the first aspect, the step of calculating the NO spectral intensity corresponding to the NO spectral signal data based on each of the second spectral intensities and the baseline intensity includes: Subtract the corresponding baseline intensity from each of the second spectral intensities to obtain several difference values; The NO spectral intensity corresponding to the NO spectral signal data is obtained by summing the differences.

[0010] In this preferred embodiment, by employing a calculation formula that accumulates the intensity of each second spectral point and uniformly subtracts the baseline intensity, systematic integration and background correction of spectral signals from multiple measurement points are achieved. This application effectively improves the representativeness and consistency of measurement data, reduces random errors and accidental deviations that may be introduced by single-point measurements, thereby enhancing the robustness and repeatability of NO spectral intensity calculation. Through systematic signal integration and background subtraction, the accuracy and reliability of quantitative spectral analysis are improved, providing a more stable input basis for subsequent lifetime prediction models.

[0011] As a preferred example of the first aspect, the prediction model is obtained by establishing a quantitative relationship between the initial spectral intensity and the initial number of switching operations of the gas-insulated switchgear, including: Several initial gas samples with known switch opening and closing times were obtained, and the initial NO spectral intensity corresponding to each initial gas sample was determined by laser-induced breakdown spectroscopy based on each initial gas sample. The initial net NO spectral intensity for each initial gas sample is calculated based on the background spectral intensity and the initial NO spectral intensity corresponding to each initial gas sample; wherein, the background spectral intensity is obtained by setting up a blank control group; The initial net NO spectral intensity and the initial number of on / off cycles corresponding to each initial gas sample are fitted using a nonlinear fitting function to obtain fitting parameters, and the prediction model is established based on the fitting parameters.

[0012] In this preferred example, by using net NO spectral intensity data from multiple sets of samples with known break counts and combining it with nonlinear fitting to establish a quantitative relationship model, an accurate characterization of the complex correlation between the break count and NO concentration in gas-insulated switchgear is achieved. Using a blank control effectively removes system background interference, improving the purity and comparability of the modeling data. Through nonlinear fitting, the model more closely reflects the growth pattern between concentration and break count in actual physical processes, thus significantly improving the prediction accuracy and applicability of the prediction model and providing a universal quantitative basis for life assessment of equipment in different states.

[0013] As a preferred example of the first aspect, the step of fitting parameters by using a nonlinear fitting function based on the initial net NO spectral intensity corresponding to each of the initial gas samples and the initial switch on / off count corresponding to each of the initial gas samples includes: Using the initial net NO spectral intensity corresponding to each initial gas sample as the independent variable and the initial number of switch opening and closing times corresponding to each initial gas sample as the dependent variable, a nonlinear fitting function is used to fit the data to obtain the first fitting parameter, the second fitting parameter, and the third fitting parameter.

[0014] In this preferred example, by employing an exponential nonlinear fitting function to model the relationship between the net spectral intensity of NO and the number of on / off cycles, the model can better fit the saturation or acceleration trend of gas concentration increasing with the number of on / off cycles in actual physical processes, thus more accurately characterizing the nonlinear characteristics of the equipment aging process. Compared to a simple linear relationship, this model improves the prediction sensitivity and adaptability throughout the entire equipment lifecycle, especially in the mid-to-late high-concentration stage, enhancing the prediction model's ability to characterize complex actual operating conditions and its prediction reliability.

[0015] As a preferred example of the first aspect, establishing the prediction model based on the fitting parameters includes: Based on the first fitting parameter, the third fitting parameter, the background spectral intensity, and the NO spectral intensity corresponding to the NO spectral signal data, a first formula is determined, and the natural logarithm of the first formula is calculated to obtain a second formula. The prediction model is determined based on the natural logarithm of the first fitting parameter, the second formula, and the second fitting parameter.

[0016] In this preferred embodiment, a prediction model is constructed using the nonlinear fitting function and fitting parameters, enabling the model to explicitly reflect the exponential growth relationship between the net NO spectral intensity and the number of switching operations in the form of an analytical expression. When applied, this model can be used to deduce the corresponding number of switching operations from the directly measured difference between the NO spectral intensity and the background intensity, avoiding the need for repeatedly performing complex fitting processes.

[0017] As a preferred example of the first aspect, the excitation of the gas sample to obtain NO spectral signal data includes: The gas sample was excited using laser-induced breakdown spectroscopy to obtain NO molecular spectrum signals corresponding to several pulses; The NO molecular spectrum signals corresponding to each pulse are used to compose the NO spectral signal data.

[0018] In this preferred example, by employing multi-pulse laser excitation and integrating the NO molecular spectral signals corresponding to each pulse, the signal-to-noise ratio and stability of the spectral signal are effectively improved, reducing random errors and signal fluctuations that may be introduced by a single measurement. The integration of multi-pulse data enhances the representativeness and consistency of the spectral information, providing a more reliable and comprehensive input basis for subsequent quantitative analysis. This improves the repeatability of the entire detection process and the reliability of the results, laying a solid data foundation for the accurate assessment of the lifespan of gas-insulated switchgear.

[0019] In a second aspect, the present invention provides a life prediction device for gas-insulated switchgear, comprising: a data acquisition module, a data calculation module, and a life prediction module; The data acquisition module is used to collect gas samples from the gas-insulated switchgear and excite the gas samples to obtain NO spectral signal data. The data calculation module is used to calculate the NO spectral intensity corresponding to the NO spectral signal data based on the first preset wavelength parameter, the second preset wavelength parameter and the NO spectral signal data. The lifetime prediction module is used to predict the number of switching interruptions of the gas-insulated switchgear based on the NO spectral intensity and the prediction model, thereby obtaining the number of switching interruptions of the gas-insulated switchgear and the predicted lifetime of the gas-insulated switchgear based on the number of switching interruptions; wherein, the prediction model is obtained by establishing a quantitative relationship between the initial spectral intensity and the initial number of switching interruptions of the gas-insulated switchgear.

[0020] As a preferred example of the second aspect, the step of calculating the NO spectral intensity corresponding to the NO spectral signal data based on the first preset wavelength parameter, the second preset wavelength parameter, and the NO spectral signal data includes: Several first spectral intensities are determined based on the first preset wavelength parameter and the NO spectral signal data, and the average spectral intensity is calculated based on each first spectral intensity to determine the baseline intensity. Several second spectral intensities are determined based on the second preset wavelength parameter and the NO spectral signal data, and the NO spectral intensity corresponding to the NO spectral signal data is calculated based on each second spectral intensity and the baseline intensity.

[0021] As a preferred example of the second aspect, the step of calculating the NO spectral intensity corresponding to the NO spectral signal data based on each of the second spectral intensities and the baseline intensity includes: Subtract the corresponding baseline intensity from each of the second spectral intensities to obtain several difference values; The NO spectral intensity corresponding to the NO spectral signal data is obtained by summing the differences.

[0022] As a preferred example of the second aspect, the prediction model is obtained by establishing a quantitative relationship between the initial spectral intensity and the initial number of switching operations of the gas-insulated switchgear, including: Several initial gas samples with known switch opening and closing times were obtained, and the initial NO spectral intensity corresponding to each initial gas sample was determined by laser-induced breakdown spectroscopy based on each initial gas sample. The initial net NO spectral intensity for each initial gas sample is calculated based on the background spectral intensity and the initial NO spectral intensity corresponding to each initial gas sample; wherein, the background spectral intensity is obtained by setting up a blank control group; The initial net NO spectral intensity and the initial number of on / off cycles corresponding to each initial gas sample are fitted using a nonlinear fitting function to obtain fitting parameters, and the prediction model is established based on the fitting parameters.

[0023] As a preferred example of the second aspect, the step of fitting the initial NO net spectral intensity and the initial switch on / off count corresponding to each of the initial gas samples using a nonlinear fitting function to obtain fitting parameters includes: Using the initial net NO spectral intensity corresponding to each initial gas sample as the independent variable and the initial number of switch opening and closing times corresponding to each initial gas sample as the dependent variable, a nonlinear fitting function is used to fit the data to obtain the first fitting parameter, the second fitting parameter, and the third fitting parameter.

[0024] As a preferred example of the second aspect, establishing the prediction model based on the fitting parameters includes: Based on the first fitting parameter, the third fitting parameter, the background spectral intensity, and the NO spectral intensity corresponding to the NO spectral signal data, a first formula is determined, and the natural logarithm of the first formula is calculated to obtain a second formula. The prediction model is determined based on the natural logarithm of the first fitting parameter, the second formula, and the second fitting parameter.

[0025] As a preferred example of the second aspect, the excitation of the gas sample to obtain NO spectral signal data includes: The gas sample was excited using laser-induced breakdown spectroscopy to obtain NO molecular spectrum signals corresponding to several pulses; The NO molecular spectrum signals corresponding to each pulse are used to compose the NO spectral signal data.

[0026] In summary, this application's embodiments achieve accurate extraction of NO content within gas-insulated switchgear by performing laser-induced breakdown spectral analysis on gas samples and calculating NO spectral intensity based on specific wavelength parameters. Furthermore, by establishing a quantitative prediction model between NO spectral intensity and switch interruption count, the number of interruptions and remaining lifespan of the equipment can be directly and reliably predicted. This application avoids destructive sampling and complex preprocessing in traditional testing, significantly improving detection efficiency and field applicability, while effectively reducing operational complexity and equipment maintenance costs. Therefore, this embodiment solves the problem in existing technologies of difficulty in ensuring the accuracy of lifespan prediction for gas-insulated switchgear while improving the convenience of the lifespan prediction process.

[0027] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the life prediction method for gas-insulated switchgear of the present invention.

[0028] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is executed, controls the device where the computer-readable storage medium is located to perform steps such as the life prediction method for gas-insulated switchgear of the present invention. Attached Figure Description

[0029] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0030] Figure 1 A flowchart illustrating an embodiment of a life prediction method for gas-insulated switchgear provided by the present invention; Figure 2 This is a module structure diagram of an embodiment of a life prediction device for gas-insulated switchgear provided by the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] Unless otherwise defined, 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 application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0033] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0034] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0035] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0036] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0037] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; 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; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0038] Example 1 See Figure 1To address the problem in existing technologies that it is difficult to improve the convenience of life prediction while ensuring the accuracy of life prediction for gas-insulated switchgear, an embodiment of the present invention provides a method for predicting the life of gas-insulated switchgear, comprising: S1. Collect a gas sample from the gas-insulated switchgear and excite the gas sample to obtain NO spectral signal data; In a preferred embodiment, the excitation of the gas sample to obtain NO spectral signal data includes: The gas sample was excited using laser-induced breakdown spectroscopy to obtain NO molecular spectrum signals corresponding to several pulses; The NO molecular spectrum signals corresponding to each pulse are used to compose the NO spectral signal data.

[0039] Specifically, the step of collecting a gas sample from the gas-insulated switchgear and exciting the gas sample using laser-induced breakdown spectroscopy to obtain NO spectral signal data can be implemented in the following preferred manner: A fused silica transparent gas sampling chamber was set up in the low-current region of the gas-insulated switchgear. Before sampling, the sampling chamber was evacuated to a pressure of 0.1 bar, and the sample volume was 500 mL. Then, after setting up the LIBS system, the laser was focused below the surface of the chamber. After N consecutive pulses of target shooting, the spectroscopic signals of each NO molecule were collected using a spectrometer as NO spectral signal data.

[0040] S2. Calculate the NO spectral intensity corresponding to the NO spectral signal data based on the first preset wavelength parameter, the second preset wavelength parameter, and the NO spectral signal data; As a preferred embodiment, the step of calculating the NO spectral intensity corresponding to the NO spectral signal data based on the first preset wavelength parameter, the second preset wavelength parameter, and the NO spectral signal data includes: Several first spectral intensities are determined based on the first preset wavelength parameter and the NO spectral signal data, and the average spectral intensity is calculated based on each first spectral intensity to determine the baseline intensity. Several second spectral intensities are determined based on the second preset wavelength parameter and the NO spectral signal data, and the NO spectral intensity corresponding to the NO spectral signal data is calculated based on each second spectral intensity and the baseline intensity.

[0041] In a preferred embodiment, calculating the NO spectral intensity corresponding to the NO spectral signal data based on each of the second spectral intensities and the baseline intensity includes: Subtract the corresponding baseline intensity from each of the second spectral intensities to obtain several difference values; The NO spectral intensity corresponding to the NO spectral signal data is obtained by summing the differences.

[0042] Specifically, the NO spectral intensity is calculated using a spectral intensity calculation formula based on each second spectral intensity and the baseline intensity. The spectral intensity calculation formula is as follows: in, Indicates the spectral intensity of NO. Represents the intensity of the i-th second spectrum. The value represents the baseline intensity, and n is the total number of all second spectral intensities.

[0043] S3. Based on the NO spectral intensity and the prediction model, predict the number of switching interruptions of the gas-insulated switchgear to obtain the number of switching interruptions of the gas-insulated switchgear, and obtain the predicted lifespan of the gas-insulated switchgear based on the number of switching interruptions; wherein, the prediction model is obtained by establishing a quantitative relationship between the initial spectral intensity and the initial number of switching interruptions of the gas-insulated switchgear.

[0044] In a preferred embodiment, the prediction model is obtained by establishing a quantitative relationship between the initial spectral intensity and the initial number of switching operations of the gas-insulated switchgear, including: Several initial gas samples with known switch opening and closing times were obtained, and the initial NO spectral intensity corresponding to each initial gas sample was determined by laser-induced breakdown spectroscopy based on each initial gas sample. The initial net NO spectral intensity for each initial gas sample is calculated based on the background spectral intensity and the initial NO spectral intensity corresponding to each initial gas sample; wherein, the background spectral intensity is obtained by setting up a blank control group; The initial net NO spectral intensity and the initial number of on / off cycles corresponding to each initial gas sample are fitted using a nonlinear fitting function to obtain fitting parameters, and the prediction model is established based on the fitting parameters.

[0045] In a preferred embodiment, the step of fitting parameters by using a nonlinear fitting function based on the initial net NO spectral intensity corresponding to each initial gas sample and the initial number of switch on / off cycles corresponding to each initial gas sample includes: Using the initial net NO spectral intensity corresponding to each initial gas sample as the independent variable and the initial number of switch opening and closing times corresponding to each initial gas sample as the dependent variable, a nonlinear fitting function is used to fit the data to obtain the first fitting parameter, the second fitting parameter, and the third fitting parameter.

[0046] Specifically, the non-linear fitting function can be as follows: where A, B, and C are fitting parameters, is the net spectral intensity of NO, and m is the number of switch openings and closings.

[0047] As a preferred implementation, establishing the prediction model based on the fitting parameters includes: Determining a first equation based on the first fitting parameter, the third fitting parameter, the background spectral intensity, and the NO spectral intensity corresponding to the NO spectral signal data, and taking the natural logarithm of the first equation to obtain a second equation; Determining the prediction model based on the natural logarithm of the first fitting parameter, the second equation, and the second fitting parameter.

[0048] Specifically, establishing the prediction model based on the fitting parameters, where the expression of the prediction model can be as follows: where, is the number of switch openings and closings, is the NO spectral intensity corresponding to the gas sample m, is the background spectral intensity.

[0049] Specifically, to fully explain the establishment process of the prediction model, the following solution is taken as an example for illustration: Assume that the rated life of the gas-insulated switchgear to be detected is M, and the gas sampling samples for establishing the calibration curve are from the gas-insulated switchgear with known switch opening and closing times m. The number of gas sampling samples is n (n≥10). The opening and closing times m increase in a stepwise manner and are evenly distributed in the interval (0, M), that is, m1, m2,…, mn satisfy m1<m2<…<mn, and m1≈0, mn≈M, to ensure that the measurement range of the calibration curve covers the entire life cycle of the GIS.

[0050] The spectrometer for collecting the NO spectral signal includes an ultraviolet band detection module, and the ultraviolet band detection module can detect optical signals with a wavelength range of 220 nm to 250 nm.

[0051] For N consecutive pulses, the NO molecular spectral signal in the j-th pulse is , and the NO molecular spectral signal of the detection object under the current parameters , and the calculation formula is as follows: Then, select The average value of five pixels at a mid-wavelength of approximately 240 nm was used as the baseline intensity. The spectral signal with a target wavelength ∈ (232, 237) nm has a wavelength pixel value of q within this wavelength range, and the spectral intensity of the i-th pixel within this wavelength range is given by... , i=1,2,...,q.

[0052] Under the current parameters, the NO spectral intensity I of the detected object is the integral of the spectral signal at the target wavelength. Then, based on multiple sets of GIS samples with known switch opening and closing times and their corresponding NO spectral intensity data, combined with a blank control group, a quantitative relationship between NO spectral intensity and switch opening and closing times is established. The number of gas samples used to establish the calibration curve is n, and the NO spectral intensities of n detected objects under the current parameters are obtained. , , ..., The NO spectral intensity of the blank control group was... The net spectral intensity of NO in the detected object k under the current parameters. for: The number of switch openings corresponding to n detected objects is m1, m2, ..., mn. Next, based on the net spectral intensity of NO and the number of GIS switch openings, an exponential growth function is used for nonlinear fitting. Then, an inversion is performed to obtain a prediction model for predicting the number of GIS switch openings, m.

[0053] In summary, this application's embodiments achieve accurate extraction of NO content within gas-insulated switchgear by performing laser-induced breakdown spectral analysis on gas samples and calculating NO spectral intensity based on specific wavelength parameters. Furthermore, by establishing a quantitative prediction model between NO spectral intensity and switch interruption count, the number of interruptions and remaining lifespan of the equipment can be directly and reliably predicted. This application avoids destructive sampling and complex preprocessing in traditional testing, significantly improving detection efficiency and field applicability, while effectively reducing operational complexity and equipment maintenance costs. Therefore, this embodiment solves the problem in existing technologies of difficulty in ensuring the accuracy of lifespan prediction for gas-insulated switchgear while improving the convenience of the lifespan prediction process.

[0054] Example 2 like Figure 2 As shown, based on the above method embodiments, corresponding device embodiments are provided; One embodiment of the present invention provides a life prediction device for gas-insulated switchgear, comprising: a data acquisition module 21, a data calculation module 22, and a life prediction module 23; The data acquisition module 21 is used to collect gas samples in the gas-insulated switchgear and excite the gas samples to obtain NO spectral signal data. Data calculation module 22 is used to calculate the NO spectral intensity corresponding to the NO spectral signal data based on the first preset wavelength parameter, the second preset wavelength parameter and the NO spectral signal data; The lifetime prediction module 23 is used to predict the number of switching interruptions of the gas-insulated switchgear based on the NO spectral intensity and the prediction model, to obtain the number of switching interruptions of the gas-insulated switchgear, and to obtain the predicted lifetime of the gas-insulated switchgear based on the number of switching interruptions; wherein, the prediction model is obtained by establishing a quantitative relationship between the initial spectral intensity and the initial number of switching interruptions of the gas-insulated switchgear.

[0055] As a preferred embodiment, the step of calculating the NO spectral intensity corresponding to the NO spectral signal data based on the first preset wavelength parameter, the second preset wavelength parameter, and the NO spectral signal data includes: Several first spectral intensities are determined based on the first preset wavelength parameter and the NO spectral signal data, and the average spectral intensity is calculated based on each first spectral intensity to determine the baseline intensity. Several second spectral intensities are determined based on the second preset wavelength parameter and the NO spectral signal data, and the NO spectral intensity corresponding to the NO spectral signal data is calculated based on each second spectral intensity and the baseline intensity.

[0056] In a preferred embodiment, calculating the NO spectral intensity corresponding to the NO spectral signal data based on each of the second spectral intensities and the baseline intensity includes: Subtract the corresponding baseline intensity from each of the second spectral intensities to obtain several difference values; The NO spectral intensity corresponding to the NO spectral signal data is obtained by summing the differences.

[0057] In a preferred embodiment, the prediction model is obtained by establishing a quantitative relationship between the initial spectral intensity and the initial number of switching operations of the gas-insulated switchgear, including: Several initial gas samples with known switch opening and closing times were obtained, and the initial NO spectral intensity corresponding to each initial gas sample was determined by laser-induced breakdown spectroscopy based on each initial gas sample. The initial net NO spectral intensity for each initial gas sample is calculated based on the background spectral intensity and the initial NO spectral intensity corresponding to each initial gas sample; wherein, the background spectral intensity is obtained by setting up a blank control group; The initial net NO spectral intensity and the initial number of on / off cycles corresponding to each initial gas sample are fitted using a nonlinear fitting function to obtain fitting parameters, and the prediction model is established based on the fitting parameters.

[0058] In a preferred embodiment, the step of fitting parameters by using a nonlinear fitting function based on the initial net NO spectral intensity corresponding to each initial gas sample and the initial number of switch on / off cycles corresponding to each initial gas sample includes: Using the initial net NO spectral intensity corresponding to each initial gas sample as the independent variable and the initial number of switch opening and closing times corresponding to each initial gas sample as the dependent variable, a nonlinear fitting function is used to fit the data to obtain the first fitting parameter, the second fitting parameter, and the third fitting parameter.

[0059] As a preferred embodiment, establishing the prediction model based on the fitting parameters includes: Based on the first fitting parameter, the third fitting parameter, the background spectral intensity, and the NO spectral intensity corresponding to the NO spectral signal data, a first formula is determined, and the natural logarithm of the first formula is calculated to obtain a second formula. The prediction model is determined based on the natural logarithm of the first fitting parameter, the second formula, and the second fitting parameter.

[0060] In a preferred embodiment, the excitation of the gas sample to obtain NO spectral signal data includes: The gas sample was excited using laser-induced breakdown spectroscopy to obtain NO molecular spectrum signals corresponding to several pulses; The NO molecular spectrum signals corresponding to each pulse are used to compose the NO spectral signal data.

[0061] For more detailed steps and working principles of this embodiment, please refer to the relevant description in Embodiment 1, but not limited to these descriptions.

[0062] In summary, this application's embodiments achieve accurate extraction of NO content within gas-insulated switchgear by performing laser-induced breakdown spectral analysis on gas samples and calculating NO spectral intensity based on specific wavelength parameters. Furthermore, by establishing a quantitative prediction model between NO spectral intensity and switch interruption count, the number of interruptions and remaining lifespan of the equipment can be directly and reliably predicted. This application avoids destructive sampling and complex preprocessing in traditional testing, significantly improving detection efficiency and field applicability, while effectively reducing operational complexity and equipment maintenance costs. Therefore, this embodiment solves the problem in existing technologies of difficulty in ensuring the accuracy of lifespan prediction for gas-insulated switchgear while improving the convenience of the lifespan prediction process.

[0063] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the life prediction method for gas-insulated switchgear provided by any of the above-described method embodiments of the present invention.

[0064] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0065] Example 3 Based on the above-described embodiments of the life prediction method for gas-insulated switchgear, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the life prediction method for gas-insulated switchgear according to any embodiment of the present invention.

[0066] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0067] 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.

[0068] The processor can 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. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0069] Example 4 Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the life prediction method for gas-insulated switchgear described in any of the above-described method embodiments of the present invention.

[0070] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention 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, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0071] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for predicting the lifespan of a gas-insulated switchgear, characterized in that, include: A gas sample was collected from the gas-insulated switchgear, and the gas sample was excited to obtain NO spectral signal data; The NO spectral intensity corresponding to the NO spectral signal data is calculated based on the first preset wavelength parameter, the second preset wavelength parameter, and the NO spectral signal data. Based on the NO spectral intensity and the prediction model, the number of switching interruptions of the gas-insulated switchgear is predicted to obtain the number of switching interruptions of the gas-insulated switchgear, and the predicted lifespan of the gas-insulated switchgear is obtained based on the number of switching interruptions; wherein, the prediction model is obtained by establishing a quantitative relationship between the initial spectral intensity and the initial number of switching interruptions of the gas-insulated switchgear.

2. The life prediction method for gas-insulated switchgear as described in claim 1, characterized in that, The step of calculating the NO spectral intensity corresponding to the NO spectral signal data based on the first preset wavelength parameter, the second preset wavelength parameter, and the NO spectral signal data includes: Several first spectral intensities are determined based on the first preset wavelength parameter and the NO spectral signal data, and the average spectral intensity is calculated based on each first spectral intensity to determine the baseline intensity. Several second spectral intensities are determined based on the second preset wavelength parameter and the NO spectral signal data, and the NO spectral intensity corresponding to the NO spectral signal data is calculated based on each second spectral intensity and the baseline intensity.

3. The life prediction method for gas-insulated switchgear as described in claim 2, characterized in that, The step of calculating the NO spectral intensity corresponding to the NO spectral signal data based on each of the second spectral intensities and the baseline intensity includes: Subtract the corresponding baseline intensity from each of the second spectral intensities to obtain several difference values; The NO spectral intensity corresponding to the NO spectral signal data is obtained by summing the differences.

4. The life prediction method for gas-insulated switchgear as described in claim 1, characterized in that, The prediction model is obtained by establishing a quantitative relationship between the initial spectral intensity and the initial number of switching operations of the gas-insulated switchgear, including: Several initial gas samples with known switch opening and closing times were obtained, and the initial NO spectral intensity corresponding to each initial gas sample was determined by laser-induced breakdown spectroscopy based on each initial gas sample. The initial net NO spectral intensity for each initial gas sample is calculated based on the background spectral intensity and the initial NO spectral intensity corresponding to each initial gas sample; wherein, the background spectral intensity is obtained by setting up a blank control group; The initial net NO spectral intensity and the initial number of on / off cycles corresponding to each initial gas sample are fitted using a nonlinear fitting function to obtain fitting parameters, and the prediction model is established based on the fitting parameters.

5. The life prediction method for gas-insulated switchgear as described in claim 4, characterized in that, The fitting parameters are obtained by using a nonlinear fitting function to fit the initial NO net spectral intensity and the initial switch on / off count corresponding to each initial gas sample, including: Using the initial net NO spectral intensity corresponding to each initial gas sample as the independent variable and the initial number of switch opening and closing times corresponding to each initial gas sample as the dependent variable, a nonlinear fitting function is used to fit the data to obtain the first fitting parameter, the second fitting parameter, and the third fitting parameter.

6. The life prediction method for gas-insulated switchgear as described in claim 5, characterized in that, The step of establishing the prediction model based on the fitting parameters includes: Based on the first fitting parameter, the third fitting parameter, the background spectral intensity, and the NO spectral intensity corresponding to the NO spectral signal data, a first formula is determined, and the natural logarithm of the first formula is calculated to obtain a second formula. The prediction model is determined based on the natural logarithm of the first fitting parameter, the second formula, and the second fitting parameter.

7. The life prediction method for gas-insulated switchgear as described in claim 1, characterized in that, The process of exciting the gas sample to obtain NO spectral signal data includes: The gas sample was excited using laser-induced breakdown spectroscopy to obtain NO molecular spectrum signals corresponding to several pulses; The NO molecular spectrum signals corresponding to each pulse are used to compose the NO spectral signal data.

8. A life prediction device for gas-insulated switchgear, characterized in that, include: Data acquisition module, data calculation module, and lifespan prediction module; The data acquisition module is used to collect gas samples from the gas-insulated switchgear and excite the gas samples to obtain NO spectral signal data. The data calculation module is used to calculate the NO spectral intensity corresponding to the NO spectral signal data based on the first preset wavelength parameter, the second preset wavelength parameter and the NO spectral signal data. The lifetime prediction module is used to predict the number of switching interruptions of the gas-insulated switchgear based on the NO spectral intensity and the prediction model, thereby obtaining the number of switching interruptions of the gas-insulated switchgear and the predicted lifetime of the gas-insulated switchgear based on the number of switching interruptions; wherein, the prediction model is obtained by establishing a quantitative relationship between the initial spectral intensity and the initial number of switching interruptions of the gas-insulated switchgear.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein, when the processor executes the computer program, it implements the life prediction method for gas-insulated switchgear as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the life prediction method for gas-insulated switchgear as described in any one of claims 1-7.

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