Abstract
The aging state of the oil-paper insulation system inside the transformer is an crucial factor affecting the safe operation of the transformer. Considering that oil-paper insulation samples are difficult to obtain in large quantities, this paper proposes a method of aging diagnosis of oil-paper insulation based on Raman spectroscopy with extended data. Firstly, oil-paper insulation samples are obtained by accelerated thermal aging test and then tested by Raman spectroscopy. Elman neural network is then used to simulate the real spectrum of the sample to obtain a large number of simulated spectra. Finally, all the Raman spectra obtained were used to build a diagnostic model for the aging of oil-paper insulation. The results of the study show that the obtained simulation spectra are valid and the oil-paper insulation aging diagnosis model established in this paper can evaluate the aging state of transformers. This paper develops a new way to solve small samples in oil-paper insulation diagnostic model, and at the same time lays the foundation for further quantitative estimate of the aging state of transformers.
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