A photoacoustic spectroscopy identification method and device for characteristic gases in transformer oil

Abstract

The present invention relates to a photoacoustic spectrum identification method and device for characteristic gas in transformer oil. The method comprises: obtaining multiple photoacoustic spectra of target characteristic gas at different excitation light sources, different temperatures, different concentrations and different pressures; The photoacoustic spectrum performs image enhancement and removes the spectral lines whose absorption intensity is lower than the threshold, and then sequentially performs segmental sampling, smoothing and filtering for each photoacoustic spectrum to construct a photoacoustic spectrum data set; When the photoacoustic effect occurs, the temperature, concentration, pressure, shape of the absorption line, position of the absorption line, intensity of the line, and peak absorption coefficient are mapped to a multidimensional vector; use the above features and the photoacoustic spectrum dataset to train a convolutional neural network and It is used in the identification of photoacoustic spectra. The invention combines the traditional spectrum processing method with the convolution neural network to identify the characteristic information of the characteristic gas of the photoacoustic spectrum, has fast recognition speed and high accuracy, and can adapt to various working environments.

Description

technical field [0001] The invention belongs to the field of power equipment measurement and deep learning, and in particular relates to a photoacoustic spectrum identification method and device for characteristic gases in transformer oil. Background technique [0002] Photoacoustic spectroscopy technology is a new simple, high detection sensitivity, high selectivity, large dynamic range, strong universality, and no damage to the sample analysis and testing method. The method is different. It directly measures the absorbed energy instead of measuring the projected or reflected light intensity. It is considered to be one of the best tools for detecting trace gases and is widely used in many fields. The basic principle is the photoacoustic effect. The photoacoustic effect is a photoacoustic conversion phenomenon first discovered in solids by Alexander Graham Bell (A.G.Be11), an American scientist and founder of the Bell Telephone Company in 1880. He discovered that when the s...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problems that the existing photoacoustic spectrum detection technology of gas in transformer oil cannot satisfy the problems of high detection accuracy, low failure rate and long service life at the same time, the present invention provides a photoacoustic spectrum identification method for characteristic gases in transformer oil, The method includes the following steps: obtaining multiple photoacoustic spectra of the target characteristic gas at different excitation light sources, different temperatures, different concentrations and different pressures; each band of the photoacoustic spectrum includes a single absorption band of the target characteristic gas and multiple gases. Absorption band; image enhancement is performed on each of the photoacoustic spectra and spectral lines whose absorption intensity is lower than the threshold are removed, and then the photoacoustic spectra are sequentially segmented and sampled, smoothed and filtered to construct a photoacoustic spectrum data set; from the photoacoustic spectra The shape of the absorption line, the position of the absorption line, the intensity of the line and the peak absorption coefficient of the target characteristic gas are extracted from the acoustic spectrum data set, and the temperature, concentration, pressure, The shape of the absorption line, the position of the absorption line, the intensity of the line and the peak absorption coefficient are mapped to a multidimensional vector; the photoacoustic spectrum in the photoacoustic spectrum data set is used as a sample, and the multidimensional vector is used as a label to construct a sample data set; The sample data set trains the convolutional neural network until its error is lower than the threshold and tends to be stable, and the trained convolutional neural network is obtained; the photoacoustic spectrum to be identified is input into the trained convolutional neural network, and the obtained convolutional neural network is obtained. The identification information of the target characteristic gas in the photoacoustic spectrum; the identification information includes the concentration of the target characteristic gas, the shape of the absorption line, the position of the absorption line, and the intensity of the line

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