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

Yunus Yalman, Tayfun Uyanık, İbrahim Atlı, Adnan Tan, Kamil Çağatay Bayındır, Ömer Karal, Saeed Golestan () and Josep M. Guerrero
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Yunus Yalman: Department of Electrical and Electronic Engineering, Ankara Yıldırım Beyazıt University, Ankara 06010, Turkey
Tayfun Uyanık: Maritime Faculty, Istanbul Technical University, Istanbul 34940, Turkey
İbrahim Atlı: Department of Computer Engineering, Ankara Yıldırım Beyazıt University, Ankara 06010, Turkey
Adnan Tan: Department of Electrical and Electronics Engineering, Çukurova University, Adana 01250, Turkey
Kamil Çağatay Bayındır: Department of Electrical and Electronic Engineering, Ankara Yıldırım Beyazıt University, Ankara 06010, Turkey
Ömer Karal: Department of Electrical and Electronic Engineering, Ankara Yıldırım Beyazıt University, Ankara 06010, Turkey
Saeed Golestan: Center for Research on Microgrids, AAU Energy, Aalborg University, 9220 Aalborg, Denmark
Josep M. Guerrero: Center for Research on Microgrids, AAU Energy, Aalborg University, 9220 Aalborg, Denmark

Energies, 2022, vol. 15, issue 18, 1-16

Abstract: 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.

Keywords: artificial intelligence; distribution system; power quality; voltage sag (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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