Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid

Yunus Yalman, Tayfun Uyanık, İbrahim Atlı, Adnan Tan, Kamil Cağatay Bayındır, Ömer Karal, Saeed Golestan*, Josep M. Guerrero

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

4 Citations (Scopus)
5 Downloads (Pure)


Power quality (PQ) problems, including voltage sag, flicker, and harmonics, are the main concerns for the grid operator. Among these disturbances, voltage sag, which affects the sensitive loads in the interconnected system, is a crucial problem in the transmission and distribution systems. The determination of the voltage sag relative location as a downstream (DS) and upstream (US) is an important issue that should be considered when mitigating the sag problem. Therefore, this paper proposes a novel approach to determine the voltage sag relative location based on voltage sag event records of the power quality monitoring system (PQMS) in the real distribution system. By this method, the relative location of voltage sag is defined by Gaussian naive Bayes (Gaussian NB) and K-nearest neighbors (K-NN) algorithms. The proposed methods are compared with support vector machine (SVM) and artificial neural network (ANN). The results indicate that K-NN and Gaussian NB algorithms define the relative location of a voltage sag with 98.75% and 97.34% accuracy, respectively.

Original languageEnglish
Article number6641
Issue number18
Publication statusPublished - Sep 2022

Bibliographical note

Funding Information:
This work was supported by The Scientific and Technological Research Council of Turkey BIDEB-2214-A International Research Fellowship Programme for PhD Students.

Publisher Copyright:
© 2022 by the authors.


  • artificial intelligence
  • distribution system
  • power quality
  • voltage sag


Dive into the research topics of 'Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid'. Together they form a unique fingerprint.

Cite this