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Topology identification in distribution system via machine learning algorithms

Author

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  • Peyman Razmi
  • Mahdi Ghaemi Asl
  • Giorgio Canarella
  • Afsaneh Sadat Emami

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.

Suggested Citation

  • Peyman Razmi & Mahdi Ghaemi Asl & Giorgio Canarella & Afsaneh Sadat Emami, 2021. "Topology identification in distribution system via machine learning algorithms," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    • Handle: RePEc:plo:pone00:0252436
    DOI: 10.1371/journal.pone.0252436
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    Cited by:

    1. Wu, Zhaoyan, 2024. "Intermittent control for identifying network topology," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).

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