Raman Diagnosis Method for Thermal Aging of Insulating Paper Based on AE-LDA and Naive Bayes

In this paper, we obtain samples of thermal aging transformer insulation paper at 6 time points through an accelerated thermal aging test that simulates the actual operating state of the transformer. The Raman spectroscopy diagnosis model of transformer insulating paper aging is proposed. The model combines an autoencoder and a linear discriminant analysis algorithm to extract the characteristics of the Raman spectrum, and uses a Naive Bayes classification method to divide the aging stage of the insulating paper. Through the Raman spectroscopy diagnosis model of transformer insulation paper aging designed in this paper, the Raman spectroscopy characteristic quantity extracted by the AE-LDA method can effectively reflect the aging characteristic information of the insulating paper, and the Naive Bayes classification algorithm is used to predict. Research shows that the model has a high predictive accuracy for the assessment of the aging state of insulating paper, and can effectively diagnose the aging state of transformer oil-paper insulation. The Raman spectroscopy used in the diagnosis of the aging of insulating paper has good aging discrimination ability and good application prospects, and provides a new idea for the diagnosis of the aging state of transformer oil-paper insulation.