Fault diagnosis in low voltage smart distribution grids using gradient boosting trees

Nikolaos Sapountzoglou, Jesus Lago, Bertrand Raison

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
29 Downloads (Pure)

Abstract

In this paper, a gradient boosting tree model is proposed to detect, identify and localize single-phase-to-ground and three-phase faults in low voltage (LV) smart distribution grids. The proposed method is based on gradient boosting trees and considers branch-independent input features to be generalizable and applicable to different grid topologies. Particularly, as it is shown, the method can be estimated in a specific grid topology and be employed in a different one. To test the algorithm, the method is evaluated in a simulated real LV distribution grid of Portugal. In this case study, different fault resistances, fault locations and hours of the day are considered. In detail, the algorithm is evaluated at eighteen fault resistance values between 0.1 and 1000 Ω; similarly, nine fault locations are considered within each one of the 32 sectors of the grid and the faults are simulated across different hours of a day. The developed algorithm showed promising results in both out-of-sample branch and fault resistance data especially for fault detection, demonstrating a maximum fault detection error of 0.72%.

Original languageEnglish
Article number106254
Number of pages12
JournalElectric Power Systems Research
Volume182
DOIs
Publication statusPublished - 2020

Keywords

  • Fault detection
  • Fault diagnosis
  • Fault identification
  • Gradient boosting trees
  • Low voltage distribution system
  • Machine learning

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