Laser-Induced Single-Molecule Extraction and Detection in Aqueous Poly(N-isopropylacrylamide)/1-Butanol Solutions

BiPLS-RF: A hybrid wavelength selection strategy for laser induced fluorescence spectroscopy of power transformer oil

In this paper, a method for indirect diagnosis of transformer faults based on the fluorescence spectrum and characteristic wavelength screening of transformer oil has been proposed. Specifically, a hybrid strategy (BiPLS-RF) for establishing the fluorescence spectrum feature screening of transformer oil using backward interval partial least squares (BiPLS) and random forest (RF) has been proposed. Aiming at the problem of transformer fault diagnosis, the laser induced fluorescence (LIF) spectroscopy of transformer oil in different states was first collected, and it is found that the fluorescence spectrum intensity of normal transformer oil was stronger than that of faulty transformer oil. Then the characteristic bands of the original fluorescence spectra were screened by BiPLS. It is found that when the original fluorescence spectra were divided into 15 sub-intervals, the minimum root mean squares error of cross-validation can be obtained by selecting 3 sub-intervals (including 411 wavelengths). On this basis, RF was employed to further screen the characteristic wavelengths and realized the identification of the fluorescence spectrum. It is found that in the RF model composed of 54 trees, the selected 196 characteristic wavelengths of the fluorescence spectrum can minimize the analysis error (0.56%). In addition, the selected characteristic wavelength information was fed into other common classifiers to construct a fluorescence spectrum identification model, which further proved the effectiveness of BiPLS-RF for wavelength selection for LIF spectroscopy of power transformer oil. The results show that it is feasible to use BiPLS-RF to screen the characteristic wavelength of LIF spectroscopy and apply it to transformer fault diagnosis, which provides a new solution for transformer fault diagnosis.

Selection of characteristic wavelengths using SMA for laser induced fluorescence spectroscopy of power transformer oil

As the core component of the power system, the accurate analysis of its state and fault type is very important for the maintenance and repair of the transformer. The detection method represented by the transformer oil dissolved gas has the disadvantages of complicated processing steps and high operation requirements. Here, laser induced fluorescence (LIF) spectroscopy was applied for the analysis of transformer oil. Specifically, the slime mould algorithm (SMA) was used to select the characteristic wavelengths of the transformer oil fluorescence spectrum, and on this basis, a transformer fault diagnosis model was constructed. First, samples of transformer oil in different states were collected, and the fluorescence spectrum of the transformer oil was obtained with the help of the LIF acquisition system. Then, different spectral pretreatments were performed on the original fluorescence spectra, and it was found that the pretreatment effect of Savitzky-Golay smoothing (SG) was the best. Then, SMA was used to screen the characteristic wavelengths of the fluorescence spectrum, and 137 characteristic wavelengths were screened out to realize the accurate identification of the fluorescence spectrum of the transformer oil. In addition, the advantages of SMA for feature wavelength screening of transformer oil fluorescence spectra were demonstrated by comparing with traditional feature extraction strategies using principal components analysis (PCA). The research results show that it is effective to use SMA to screen the characteristic wavelengths of the LIF spectroscopy of transformer oil and use it for transformer fault diagnosis, which is of great significance for promoting the development of transformer fault diagnosis technology.