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

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Dergiye katkıMakalebilirkişi

11 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Makale numarası6641
DergiEnergies
Hacim15
Basın numarası18
DOI'lar
Yayın durumuYayınlandı - Eyl 2022

Bibliyografik not

Publisher Copyright:
© 2022 by the authors.

Parmak izi

Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid' araştırma başlıklarına git. Birlikte benzersiz bir parmak izi oluştururlar.

Alıntı Yap