Topology identification in distribution system via machine learning algorithms.
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 p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0252436 |
| _version_ | 1818436197497700352 |
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| author | Peyman Razmi Mahdi Ghaemi Asl Giorgio Canarella Afsaneh Sadat Emami |
| author_facet | Peyman Razmi Mahdi Ghaemi Asl Giorgio Canarella Afsaneh Sadat Emami |
| author_sort | Peyman Razmi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T17:04:57Z |
| format | Article |
| id | doaj.art-5ff1b27c7cd349988cff8cd25a71b85e |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T17:04:57Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-5ff1b27c7cd349988cff8cd25a71b85e2022-12-21T22:53:45ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01166e025243610.1371/journal.pone.0252436Topology identification in distribution system via machine learning algorithms.Peyman RazmiMahdi Ghaemi AslGiorgio CanarellaAfsaneh Sadat EmamiThis 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.https://doi.org/10.1371/journal.pone.0252436 |
| spellingShingle | Peyman Razmi Mahdi Ghaemi Asl Giorgio Canarella Afsaneh Sadat Emami Topology identification in distribution system via machine learning algorithms. PLoS ONE |
| title | Topology identification in distribution system via machine learning algorithms. |
| title_full | Topology identification in distribution system via machine learning algorithms. |
| title_fullStr | Topology identification in distribution system via machine learning algorithms. |
| title_full_unstemmed | Topology identification in distribution system via machine learning algorithms. |
| title_short | Topology identification in distribution system via machine learning algorithms. |
| title_sort | topology identification in distribution system via machine learning algorithms |
| url | https://doi.org/10.1371/journal.pone.0252436 |
| work_keys_str_mv | AT peymanrazmi topologyidentificationindistributionsystemviamachinelearningalgorithms AT mahdighaemiasl topologyidentificationindistributionsystemviamachinelearningalgorithms AT giorgiocanarella topologyidentificationindistributionsystemviamachinelearningalgorithms AT afsanehsadatemami topologyidentificationindistributionsystemviamachinelearningalgorithms |