Optical spectral methods in the ultraviolet and visible regions can be used to develop transformer oil control technologies based on deep learning neural network models. The aim of the research is to identify informative spectral ranges of luminescent diagnostics for the automation system for monitoring the characteristics and parameters of transformer oil using deep learning neural networks. Measurements of the spectral characteristics of pure and spent transformer oil in the range of 180-700 nm were carried out on a diffraction spectrofluorimeter "Fluorat-02-Panorama". A qualitative and quantitative difference in the excitation spectra has been established: for waste oil, the spectra are shifted to the right and reduced by about four times to the maximum. The excitation maxima are located at wavelengths of 300, 322, 370 nm for pure and 388, 416 and 486 nm for waste oil. The photoluminescence spectra of pure oil at 300 nm excitation are a superposition of at least three curves, the largest of which has a maximum at 382 nm. For excitation of 370 nm, the spectrum is significantly wider and has maxima at wavelengths of 387, 405, 433-439 and 475-479 nm. The photoluminescence spectra of used oil are several times lower and have maxima at 446, 483 and 520-540 nm. The established excitation and luminescence ranges will be used when creating a methodology and installing quality control parameters of transformer oil during its operation. A deep learning neural network model based on the use of a self-organizing Kohonen map was also developed, which made it possible to predict the spectral characteristics of excitation based on the photoluminescence flow of transformer oil and, as a result, to determine the efficiency of the described method in industry through a decision-making system.
deep learning neural networks, predictive logic, neuro-fuzzy modeling, transformer oil, excitation spectra, photoluminescence spectra, ultraviolet and visible range