title: ÐÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð´Ð»Ñ ÑÐµÐ¿Ð»Ð¾Ð²Ð¸Ð·Ð¸Ð¾Ð½Ð½Ð¾Ð¹ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸ ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼Ð°ÑÐ¾ÑÐ½Ð¾Ð³Ð¾ Ð²Ð²Ð¾Ð´Ð°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Neural network model for thermal imaging diagnostics of transformer bushing
Creators: name: ÐÑÐµÑÐºÐ¾, Ð¯ÑÐ¾ÑÐ»Ð°Ð²
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ° Ð²ÑÑÐ¾ÐºÐ¾Ð²Ð¾Ð»ÑÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ
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Ð¼ÐµÑÐ¾Ð´Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð°
ÑÐµÐ¿Ð»Ð¾Ð²Ð¸Ð·Ð¸Ð¾Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ
Ñ ÑÐ¾Ð¼Ð°ÑÐ¾Ð³ÑÐ°ÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð½Ð°Ð»Ð¸Ð·
Ð¼Ð°Ð³Ð½Ð¸ÑÐ½ÑÐµ Ð¿Ð¾Ð»Ñ ÑÐ°ÑÑÐµÑÐ½Ð¸Ñ
Ð°ÐºÑÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ ÐºÐ¾Ð»ÐµÐ±Ð°Ð½Ð¸Ñ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ðµ Ð¸Ð·Ð¾Ð»ÑÑÐ¸Ð¸
high-voltage equipment diagnostics
finite element modelin
analysis methods
thermal imaging inspection
chromatographic analysis
scattered magnetic fields
acoustic vibrations
neural networks
machine learning
insulation condition

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description: This work is devoted to the study of high-voltage equipment diagnostic methods and the construction of a neural network model for transformer bushing diagnostics. The tasks that were solved in the research: 1. Study of high-voltage equipment diagnostic methods. 2. Identification of the advantages and disadvantages of thermal imaging diagnostics. 3. Study of the finite element method. 4. Development of a neural network model. As part of the thesis, research was carried out in the field of high-voltage equipment diagnostics and the creation of a finite element model of a transformer bushing. Modern diagnostic methods have been studied in detail, including the analysis of the electrical state, dielectric properties, vibration, scattered magnetic fields and acoustic vibrations, as well as chromatographic analysis of dissolved gases and thermal imaging inspection. In the second part of the work, the task of creating a finite element model of a transformer bushing was solved, followed by the construction of a neural network model to determine the quantitative characteristics of the insulation state. The results of the work confirm the effectiveness of using modern diagnostic methods and the combination of finite element modeling with machine learning in the study of the state of the transformer bushing insulator.Â