ITM Web Conf.
Volume 24, 2019AMCSE 2018 - International Conference on Applied Mathematics, Computational Science and Systems Engineering
|Number of page(s)
|01 February 2019
Machine Learning Approach Application for High-voltage Instrument Transformers Technical State Assessment
Ural Federal University, 620002 Mira Street 19, Russian Federation
2 Novosibirsk State Technical University, 630073 Prospekt K. Marksa, Novosibirsk, Russian Federation
* Corresponding author: firstname.lastname@example.org
This paper describes the possibilities of machine learning application in the tasks of technical state assessment of high-voltage instrument transformers. An analytical review of modern systems for technical state assessment of high-voltage equipment is presented, their advantages and disadvantages are described. A mathematical model of an automated system for assessing the high-voltage instrument current and voltage transformers based on gradient boosting over decision trees has been developed. The efficiency of the developed solution is proved using the example of analysis of a real distribution zone, which allows identifying the state of instrument transformers with an accuracy of 84%.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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