IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i1p243-d304859.html
   My bibliography  Save this article

Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier

Author

Listed:
  • Elhadi Aker

    (Advanced Lightning Power and Energy Research (ALPER), Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 UPM Serdang, Selangor, Malaysia)

  • Mohammad Lutfi Othman

    (Advanced Lightning Power and Energy Research (ALPER), Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 UPM Serdang, Selangor, Malaysia)

  • Veerapandiyan Veerasamy

    (Advanced Lightning Power and Energy Research (ALPER), Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 UPM Serdang, Selangor, Malaysia)

  • Ishak bin Aris

    (Advanced Lightning Power and Energy Research (ALPER), Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 UPM Serdang, Selangor, Malaysia)

  • Noor Izzri Abdul Wahab

    (Advanced Lightning Power and Energy Research (ALPER), Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 UPM Serdang, Selangor, Malaysia)

  • Hashim Hizam

    (Advanced Lightning Power and Energy Research (ALPER), Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 UPM Serdang, Selangor, Malaysia)

Abstract

This paper presents the methodology to detect and identify the type of fault that occurs in the shunt compensated static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes (NB) classifiers. To study this, the network model is designed using Matlab/Simulink. Different types of faults, such as Line to Ground (LG), Line to Line (LL), Double Line to Ground (LLG) and the three-phase (LLLG) fault, are applied at disparate zones of the system, with and without STATCOM, considering the effect of varying fault resistance. The three-phase fault current waveforms obtained are decomposed into several levels using Daubechies (db) mother wavelet of db4 to extract the features, such as the standard deviation (SD) and energy values. Then, the extracted features are used to train the classifiers, such as Multi-Layer Perceptron Neural Network (MLP), Bayes and the Naive Bayes (NB) classifier to classify the type of fault that occurs in the system. The results obtained reveal that the proposed NB classifier outperforms in terms of accuracy rate, misclassification rate, kappa statistics, mean absolute error (MAE), root mean square error (RMSE), percentage relative absolute error (% RAE) and percentage root relative square error (% RRSE) than both MLP and the Bayes classifier.

Suggested Citation

  • Elhadi Aker & Mohammad Lutfi Othman & Veerapandiyan Veerasamy & Ishak bin Aris & Noor Izzri Abdul Wahab & Hashim Hizam, 2020. "Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier," Energies, MDPI, vol. 13(1), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:243-:d:304859
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/1/243/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/1/243/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Veerapandiyan Veerasamy & Noor Izzri Abdul Wahab & Rajeswari Ramachandran & Muhammad Mansoor & Mariammal Thirumeni & Mohammad Lutfi Othman, 2018. "High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 11(12), pages 1-24, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Supanat Chamchuen & Apirat Siritaratiwat & Pradit Fuangfoo & Puripong Suthisopapan & Pirat Khunkitti, 2021. "High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm," Energies, MDPI, vol. 14(5), pages 1-18, February.
    2. Junsoo Che & Taehun Kim & Suhan Pyo & Jaedeok Park & Byeonghyeon An & Taesik Park, 2023. "Prevention of Wildfires Using an AI-Based Open Conductor Fault Detection Method on Overhead Line," Energies, MDPI, vol. 16(5), pages 1-24, March.

    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. Francinei L. Vieira & Pedro H. M. Santos & José M. Carvalho Filho & Roberto C. Leborgne & Marino P. Leite, 2019. "A Voltage-Based Approach for Series High Impedance Fault Detection and Location in Distribution Systems Using Smart Meters," Energies, MDPI, vol. 12(15), pages 1-16, August.
    2. Yu He & Xinhui Zhang & Wenhao Wu & Jun Zhang & Wenyuan Bai & Aiyu Guo & Yu Chen, 2022. "Faulty Line Selection Method Based on Comprehensive Dynamic Time Warping Distance in a Flexible Grounding System," Energies, MDPI, vol. 15(2), pages 1-16, 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:13:y:2020:i:1:p:243-:d:304859. 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.