| Issue |
E3S Web of Conf.
Volume 216, 2020
Rudenko International Conference “Methodological problems in reliability study of large energy systems” (RSES 2020)
|
|
|---|---|---|
| Article Number | 01032 | |
| Number of page(s) | 5 | |
| DOI | https://doi.org/10.1051/e3sconf/202021601032 | |
| Published online | 14 December 2020 | |
K-Nearest Neighbors Algorithm Application in the Electrical Grid States Recognition Problems
Nizhniy Novgorod State Technical University, Russia
* Corresponding author: dimok.93@mail.ru
One of the ways to improve the reliability of electrical grids is associated with the introduction of complex and resource-intensive algorithms in intelligent electronic devices (IED) that perform the functions of relay protection and automation at substations. Simulation modelling is used to study the features of the protected object functioning and its application makes it possible to take into account the variability of electrical grid states in the formation of IED algorithms, which are characteristic of the analyzed electrical grid section. In addition, this approach makes it possible to use for short circuits detection only those information features that have a high information value in a specific problem of states recognition. Machine learning methods are advisable to use for modern relay protection algorithms implementation. One of such methods is the k-nearest neighbours method. The article substantiates the effectiveness of the method application in comparison with the conventional algorithms on the example of protection of an electrical grid section with a distributed generation source. The reported study was funded by RFBR, project number 19-38-90144.
© 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.