IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i22p7680-d1284091.html
   My bibliography  Save this article

Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks

Author

Listed:
  • Ahmed Sami Alhanaf

    (Department of Computer Engineering, Yildiz Technical University, Istanbul 34220, Turkey)

  • Hasan Huseyin Balik

    (Department of Computer Engineering, Faculty of Engineering, Istanbul Aydin University, Istanbul 34295, Turkey)

  • Murtaza Farsadi

    (Department of Computer Engineering, Faculty of Engineering, Istanbul Aydin University, Istanbul 34295, Turkey)

Abstract

Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed generators, conventional relaying devices face challenges in managing dynamic fault currents. Various deep neural network algorithms have been proposed for fault detection, classification, and location. This study introduces innovative fault detection methods using Artificial Neural Networks (ANNs) and one-dimension Convolution Neural Networks (1D-CNNs). Leveraging sensor data such as voltage and current measurements, our approach outperforms contemporary methods in terms of accuracy and efficiency. Results in the IEEE 6-bus system showcase impressive accuracy rates: 99.99%, 99.98% for identifying faulty lines, 99.75%, 99.99% for fault classification, and 98.25%, 96.85% for fault location for ANN and 1D-CNN, respectively. Deep learning emerges as a promising tool for enhancing fault detection and classification within smart grids, offering significant performance improvements.

Suggested Citation

  • Ahmed Sami Alhanaf & Hasan Huseyin Balik & Murtaza Farsadi, 2023. "Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks," Energies, MDPI, vol. 16(22), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7680-:d:1284091
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/22/7680/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/22/7680/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guo, Junyu & Wan, Jia-Lun & Yang, Yan & Dai, Le & Tang, Aimin & Huang, Bangkui & Zhang, Fangfang & Li, He, 2023. "A deep feature learning method for remaining useful life prediction of drilling pumps," Energy, Elsevier, vol. 282(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Swarnali Deb Bristi & Mehtar Jahin Tatha & Md. Firoj Ali & Uzair Aslam Bhatti & Subrata K. Sarker & Mehdi Masud & Yazeed Yasin Ghadi & Abdulmohsen Algarni & Dip K. Saha, 2023. "A Meta-Heuristic Sustainable Intelligent Internet of Things Framework for Bearing Fault Diagnosis of Electric Motor under Variable Load Conditions," Sustainability, MDPI, vol. 15(24), pages 1-25, December.
    2. J. N. Chandra Sekhar & Bullarao Domathoti & Ernesto D. R. Santibanez Gonzalez, 2023. "Prediction of Battery Remaining Useful Life Using Machine Learning Algorithms," Sustainability, MDPI, vol. 15(21), pages 1-28, October.
    3. Tao Yan & Jizhong Chen & Dong Hui & Xiangjun Li & Delong Zhang, 2024. "The Remaining Useful Life Forecasting Method of Energy Storage Batteries Using Empirical Mode Decomposition to Correct the Forecasting Error of the Long Short-Term Memory Model," Sustainability, MDPI, vol. 16(5), pages 1-14, February.
    4. Przemyslaw Pietrzak & Piotr Pietrzak & Marcin Wolkiewicz, 2024. "Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors," Energies, MDPI, vol. 17(2), pages 1-22, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7680-:d:1284091. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.