title: ÐÑÐµÐ´Ð¸ÐºÐ°ÑÐ¸Ð²Ð½Ð°Ñ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ° ÐºÐ¾Ð½ÑÐµÐ½ÑÑÐ°ÑÐ¸Ð¹ Ð² Ð³Ð°Ð·Ð¾Ð²ÑÑ ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼Ð°ÑÐ¾ÑÐ°Ñ
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: Predictive Analytics of Concentrations in Gas Transformers
Creators: name: ÐÑÐ´ÑÐºÐ¸Ð¹, ÐÐ°Ð½Ð¸Ð¸Ð»
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: 2020
Subjects: Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ°ÑÑÐ²Ð¾ÑÐµÐ½Ð½ÑÑ Ð³Ð°Ð·Ð¾Ð²
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python
dissolved gas analysis
acceptable and maximum values
digital double
machine learning
neural network
least squares method
correlation matrix
predictive model
piecewise linear approximation

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description: This work is devoted to the development of predictive models for predicting concentrations of oxygen-containing gases in oil transformers based on the transmitted statistical arrays of technological parameters. Methods of classical analysis of dissolved gases were considered. Methods of data analysis and preprocessing, as well as machine learning methods are analyzed and applied. Based on these methods, scenarios and algorithms for predicting the studied parameters were developed and implemented. At the end of the work, conclusions are made about the work done and the results of using predictive models. Description of work sections: The first section briefly describes the subject of research and classical methods of analysis of dissolved gases; The second section discusses the mathematical methods used to develop a predictive model; The third section is dedicated to the software used; The fourth section describes the process of developing predictive models and presents the results of their work, conclusions are made.