| Issue |
ITM Web Conf.
Volume 24, 2019
AMCSE 2018 - International Conference on Applied Mathematics, Computational Science and Systems Engineering
|
|
|---|---|---|
| Article Number | 03001 | |
| Number of page(s) | 5 | |
| Section | Power Systems | |
| DOI | https://doi.org/10.1051/itmconf/20192403001 | |
| Published online | 01 February 2019 | |
Machine Learning Approach Application for High-voltage Instrument Transformers Technical State Assessment
1
Ural Federal University, 620002 Mira Street 19, Russian Federation
2
Novosibirsk State Technical University, 630073 Prospekt K. Marksa, Novosibirsk, Russian Federation
* Corresponding author: lkhalyasmaa@mail.ru
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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.