Transformer Aging Diagnosis Method Based on Raman Spectroscopy Wavelet Packet-SPCA Feature Extraction

Abstract

Ensuring the safety and reliable operation of a power transformer is a top priority in grid work. There is an urgent need to study methods for measuring and evaluating the aging state of oil–paper insulation in older transformers. Raman spectroscopy is widely used in material detection; the spectra can represent the inner components of transformer oil and help diagnose the aging states of the transformer efficiently. In this article, thermal accelerated aging experiments were conducted, and oil–paper insulation samples with different aging states were obtained. The aging states of oil–paper insulation were tagged by the degree of polymerization (DP) value of the paper. Four wavelet packet decompositions were used for the Raman spectra, and sparse principal component analysis was used to extract the features in the wavelet packet coefficients. A 14-D feature was built and trained with a multiclassification support vector machine (SVM), and a diagnosis model for aging states was established. The results show that the accuracy of the diagnosis model reaches 94.9%. Seven samples of transformers in operation also verify the method’s effectiveness. This method has practical significance for quickly detecting the aging states of operating transformers.