Issue |
E3S Web Conf.
Volume 216, 2020
Rudenko International Conference “Methodological problems in reliability study of large energy systems” (RSES 2020)
|
|
---|---|---|
Article Number | 01053 | |
Number of page(s) | 5 | |
DOI | https://doi.org/10.1051/e3sconf/202021601053 | |
Published online | 14 December 2020 |
Experimental system for continuous monitoring of overhead power lines and substations insulation
1 Kazan State Energy University, Kazan, Russia
2 PJSC “Krasnogorskij Zavod named S.A. Zverev”, Krasnogorsk, Russia
* Corresponding author: dzaripov@list.ru
This paper describes a system for monitoring the insulation condition of overhead power lines and substations based on sensors installed on towers and insulators. The sensors works on the capacitive coupling with an insulating structure and register the appearance and growing of electrical discharges near the insulators. The data from the sensors with a predetermined frequency (from 1 minute or more) are transmitted throw cellular communication to the operating service computer (smartphone) and presents as changing graphs on the screen. In this paper in operation and functional diagram of the system are proposed with described results of tests and experiments with manufactured product samples in laboratory and field conditions on an operating 110 kV line. The results of field experiments shows the advantages of the system to monitor the insulation in comparing with traditional diagnostic devices.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 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.