Raman spectrum feature extraction and diagnosis of oil-paper insulation ageing based on kernel principal component analysis

Raman spectroscopy, with its specific ability to generate a unique fingerprint-like spectrum of certain substances, has attracted much attention in diagnosing the ageing degree of oil-paper insulation. In this study, the feature extraction and ageing diagnosis methods of oil-paper insulation Raman spectroscopy data are further studied. Based on the non-linear analysis of Raman spectra of different ageing samples, kernel principal component analysis was applied to extract the spectral features, and the back-propagation neural network was used to build a diagnosis model with high diagnostic accuracy. The results show that Raman spectroscopy combined with kernel principal component analysis and the back-propagation neural network can diagnose the ageing state of oil-paper insulation, with a diagnostic accuracy of 91.43% (64/70). The proposed method provides an effective and feasible method for the ageing assessment of oil-immersed electrical equipment.

Automated summary

This study further investigated the feature extraction and ageing diagnosis methods of oil-paper insulation Raman spectroscopy data using kernel principal component analysis and back-propagation neural network. The proposed method achieves a diagnostic accuracy of 91.43% in diagnosing the ageing state of oil-paper insulation, providing an effective and feasible method for ageing assessment of oil-immersed electrical equipment.