| Issue |
E3S Web of Conf.
Volume 384, 2023
Rudenko International Conference “Methodological Problems in Reliability Study of Large Energy Systems” (RSES 2022)
|
|
|---|---|---|
| Article Number | 01005 | |
| Number of page(s) | 5 | |
| DOI | https://doi.org/10.1051/e3sconf/202338401005 | |
| Published online | 26 April 2023 | |
Real-time Power System Topology Recognition through Convolutional Neural Networks
Federal Research Center "Komi Scientific Center of the Ural Branch Russian Academy of Sciences", ISE and EPN, 167000, Russia
* Corresponding author: gotman@energy.komisc.ru
This paper investigates the status of a transmission line (on/off) using a 140-bus Northeast Power Coordinating Council (NPCC) test system model [1]. The application of the software package ANDES [2] to obtain a database when solving this problem in the transient process of the power system is considered. The Deep Learning Neural Networks (DLNN) [3] were proposed to solve the problem, in particular, a convolutional neural network (CNN), the input variables of which are voltage and current phasors obtained from phasor measurement units (PMU). Calculations to determine the state of lines were performed using a program developed in the Julia language using the Flux package (a machine learning library that includes functions for creating CNN models). The results of the studies are presented.
© The Authors, published by EDP Sciences, 2023
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.
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