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.