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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yunus Yalman & Tayfun Uyanık & İbrahim Atlı & Adnan Tan & Kamil Çağatay Bayındır & Ömer Karal & Saeed Golestan & Josep M. Guerrero, 2022. "Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid," Energies, MDPI, vol. 15(18), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6641-:d:912210
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    References listed on IDEAS

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    Cited by:

    1. Yunus Yalman & Tayfun Uyanık & Adnan Tan & Kamil Çağatay Bayındır & Yacine Terriche & Chun-Lien Su & Josep M. Guerrero, 2022. "Implementation of Voltage Sag Relative Location and Fault Type Identification Algorithm Using Real-Time Distribution System Data," Mathematics, MDPI, vol. 10(19), pages 1-13, September.
    2. Tayfun Uyanık & Yunus Yalman & Özcan Kalenderli & Yasin Arslanoğlu & Yacine Terriche & Chun-Lien Su & Josep M. Guerrero, 2022. "Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel," Mathematics, MDPI, vol. 10(22), pages 1-21, November.
    3. Michele Zanoni & Riccardo Chiumeo & Liliana Tenti & Massimo Volta, 2023. "What Else Do the Deep Learning Techniques Tell Us about Voltage Dips Validity? Regional-Level Assessments with the New QuEEN System Based on Real Network Configurations," Energies, MDPI, vol. 16(3), pages 1-24, January.

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