Document Type
Article
Publication Date
6-1-2021
Publication Title
PLoS ONE
Volume
16
First page number:
1
Last page number:
20
Abstract
This paper contributes to the literature on topology identification (TI) in distribution networks and, in particular, on change detection in switching devices' status. The lack of measurements in distribution networks compared to transmission networks is a notable challenge. In this paper, we propose an approach to topology identification (TI) of distribution systems based on supervised machine learning (SML) algorithms. This methodology is capable of analyzing the feeder's voltage profile without requiring the utilization of sensors or any other extraneous measurement device. We show that machine learning algorithms can track the voltage profile's behavior in each feeder, detect the status of switching devices, identify the distribution system's typologies, reveal the kind of loads connected or disconnected in the system, and estimate their values. Results are demonstrated under the implementation of the ANSI case study.
Disciplines
Computer Sciences | Economics
File Format
File Size
2751 KB
Language
English
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Repository Citation
Razmi, P.,
Asl, M.,
Canarella, G.,
Emami, A.
(2021).
Topology Identification in Distribution System via Machine Learning Algorithms.
PLoS ONE, 16
1-20.
http://dx.doi.org/10.1371/journal.pone.0252436