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SF6 gas detection and quantitative analysis method based on support vector machine

A support vector machine and gas detection technology, applied in material excitation analysis, Raman scattering, etc., can solve the problem that the accuracy of quantitative analysis of gas detection cannot be guaranteed with high quality, so as to improve the analysis accuracy, ensure smoothness, The effect of improving accuracy

Inactive Publication Date: 2019-03-08
YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
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Problems solved by technology

Therefore, using Raman spectroscopy for SF 6 The accuracy of gas detection quantitative analysis cannot be guaranteed with high quality

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  • SF6 gas detection and quantitative analysis method based on support vector machine
  • SF6 gas detection and quantitative analysis method based on support vector machine
  • SF6 gas detection and quantitative analysis method based on support vector machine

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Embodiment Construction

[0014] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0015] as attached figure 1 As shown, the SF based on the support vector machine provided by the embodiment of the present application 6 Quantitative analysis methods for gas detection, including:

[0016] Select the spectral peak of Raman gas detection, detection pressure, detection temperature and laser intensity as input variables, and the selected SF 6 The gas concentration is the output, and the historical operation data corresponding to N groups of input variables and the corresponding SF are collected 6 The gas concentration test data is used as a training sample;

[0017] Choose a nonli...

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Abstract

The application provides an SF6 gas detection and quantitative analysis method based on a support vector machine. The method comprises the following steps: selecting graph peak, detection pressure intensity, detection temperature and laser intensity of Raman gas detection as input variables; selecting SF6 gas concentration as an output quantity, and acquiring historical operation data and SF6 gasconcentration test data corresponding to N groups of the input variables as training samples; selecting nonlinear transformation phi (.); mapping n-dimensional input and a one-dimensional output sample vector from an original space to a high-dimensional feature space, and constructing an optimal linear regression function f(x) being equal to omega phi (x) plus b in the high-dimensional feature space, wherein omega is a weight vector, b is a deviation and x is an input variable; predicting an SF6 gas detection and quantitative analysis result. According to the SF6 gas detection and quantitativeanalysis method based on the support vector machine provided by the application, the influence of external disturbing factors such as pressure intensity is considered, and the accuracy of a method for detecting the gas concentration through Raman spectrum is improved.

Description

technical field [0001] This application relates to the technical field of gas-insulated substation equipment, in particular to a support vector machine-based SF 6 Gas detection quantitative analysis method. Background technique [0002] SF 6 Gas has excellent insulating properties and is an important gas component in GIS gas-insulated substation equipment. Therefore, for SF 6 Gas detection and quantitative analysis are extra important. Using fiber optic equipment to detect SF 6 In the process of gas, the detection results will be affected by many factors, such as laser intensity, pressure, temperature, spectral peak intensity, etc. [0003] At present, Raman spectroscopy is mostly used for SF 6 Gas detection quantitative analysis. Specifically, by measuring the Raman spectrum of a gas with a known concentration, the relationship between the spectrum and the concentration is established, and then the concentration can be inferred based on the previous relationship when...

Claims

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Application Information

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IPC IPC(8): G01N21/65
CPCG01N21/65
Inventor 钱国超彭庆军王稼轩陈伟根万福马仪程志万周仿荣邹德旭黄星洪志湖刘光祺颜冰
Owner YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
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