Non-destructive aging evaluation of transformer insulation oil via Raman spectroscopy and ensemble learning with KPCA feature extraction

Abstract

Transformer insulating oil aging critically impacts power system reliability. This study develops a non-destructive aging evaluation method using Raman spectroscopy with kernel principal component analysis (KPCA) and ensemble learning. Raman spectral data were obtained through accelerated thermal aging experiments and a spectral detection platform; subsequently, the data were preprocessed using Moving Average Sliding, Savitzky-Golay, and Gaussian filtering. Then, Raman features were extracted using KPCA with four kernel functions (Linear, Polynomial, Gaussian and Sigmoid), and evaluation performance was compared using a decision tree; eventually, four weak classifiers (DT, LDA, SVM, and BPNN) were integrated to construct the final ensemble learning evaluation model. Results showed Gaussian filtering achieved the highest signal-to-noise ratio (35.23 dB); Gaussian kernel KPCA yielded the best feature extraction, achieving 96.88 % average accuracy; and the BPNN ensemble learning evaluation model delivered the highest accuracy of 99.6 %. In addition to verifying the benefits of KPCA in feature extraction and the robustness of the model, this study conducted a comparative test with traditional principal component analysis (PCA) methods and introduced various types and intensities of noise into the test set. The study found that the model can effectively evaluate the aging state of transformer insulating oil and has high anti-interference capabilities, providing a new method for improving transformer operating status monitoring.