<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the increased complexity of power systems and the high integration of smart meters, advanced sensors, and high-level communication infrastructures within the modern power grids, the collected data becomes enormous and requires fast computation and outstanding analyzing methods under normal conditions. However, under abnormal conditions such as faults, the challenges dramatically increase. Such faults require timely and accurate fault detection, identification, and location approaches for guaranteeing their desired performance. This paper proposes two machine learning approaches based on the binary classification to improve the process of fault detection in smart grids. Besides, it presents four machine learning models trained and tested on real and modern fault detection data set designed by the Technical University of Ostrava. Many evaluation measures are applied to test and compare these approaches and models. Moreover, receiver operating characteristic curves are utilized to prove the applicability and validity of the proposed approaches. Finally, the proposed models are compared to previous studies to confirm their superiority.</p>
</abstract>

With the increased complexity of power systems and the high integration of smart meters, advanced sensors, and high-level communication infrastructures within the modern power grids, the collected data becomes enormous and requires fast computation and outstanding analyzing methods under normal conditions. However, under abnormal conditions such as faults, the challenges dramatically increase. Such faults require timely and accurate fault detection, identification, and location approaches for guaranteeing their desired performance. This paper proposes two machine learning approaches based on the binary classification to improve the process of fault detection in smart grids. Besides, it presents four machine learning models trained and tested on real and modern fault detection data set designed by the Technical University of Ostrava. Many evaluation measures are applied to test and compare these approaches and models. Moreover, receiver operating characteristic curves are utilized to prove the applicability and validity of the proposed approaches. Finally, the proposed models are compared to previous studies to confirm their superiority.

Fault detection in power grids based on improved supervised machine learning binary classification

Electrical-Electronics Engineering Department

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

With the increased complexity of power systems and the high integration of smart meters, advanced sensors, and high-level communication infrastructures within...

Fault detection in power grids based on improved supervised machine learning binary classification

Publié en ligne: 20 nov. 2021

Pages: 315 - 322

Reçu: 27 juil. 2021

DOI: https://doi.org/10.2478/jee-2021-0044

Mots clés
fault detection, smart grids, machine learning, binary classification

© 2021 Mohammed Wadi, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fault detection in power grids based on improved supervised machine learning binary classification

Publié en ligne: 20 nov. 2021

Pages: 315 - 322

Reçu: 27 juil. 2021

DOI: https://doi.org/10.2478/jee-2021-0044

Mots clésfault detection, smart grids, machine learning, binary classification

© 2021 Mohammed Wadi, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
fault detection, smart grids, machine learning, binary classification