IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i23p4726-d1285309.html
   My bibliography  Save this article

A Data-Driven Convolutional Neural Network Approach for Power Quality Disturbance Signal Classification (DeepPQDS-FKTNet)

Author

Listed:
  • Fahman Saeed

    (Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Sultan Aldera

    (Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Mohammad Alkhatib

    (Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Abdullrahman A. Al-Shamma’a

    (Electrical Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Hassan M. Hussein Farh

    (Electrical Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

Abstract

Power quality disturbance (PQD) signal classification is crucial for the real-time monitoring of modern power grids, assuring safe and reliable operation and user safety. Traditional power quality disturbance signal classification approaches are sensitive to noise, feature selection, etc. This study introduces a novel approach utilizing a data-driven convolutional neural network (CNN) to improve the effectiveness of power quality disturbance signal classification. Deep learning has been successfully used in various fields of recognition, yielding promising outcomes. Deep learning is often characterized as a complex system, with its filters and layers being determined through empirical investigations. A deep learning model was developed for the purpose of classifying PQDs, with the aim of narrowing down the search for unidentified PQDs to a specific problem domain. This approach demonstrates a high level of efficiency in accelerating the process of recognizing PQDs among a vast database of PQDs. In order to automatically identify the number of filters and the number of layers in the model in a PQD dataset, the proposed model uses pyramidal clustering, the Fukunaga–Koontz transform, and the ratio of the between-class scatter to the within-class scatter. The suggested model was assessed using the synthetic dataset generated, with and without the presence of noise. The proposed models outperformed both well-known pre-trained models and state-of-the-art PQD classification techniques in terms of classification accuracy.

Suggested Citation

  • Fahman Saeed & Sultan Aldera & Mohammad Alkhatib & Abdullrahman A. Al-Shamma’a & Hassan M. Hussein Farh, 2023. "A Data-Driven Convolutional Neural Network Approach for Power Quality Disturbance Signal Classification (DeepPQDS-FKTNet)," Mathematics, MDPI, vol. 11(23), pages 1-15, November.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:23:p:4726-:d:1285309
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/23/4726/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/23/4726/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fahman Saeed & Muhammad Hussain & Hatim A. Aboalsamh, 2022. "Automatic Fingerprint Classification Using Deep Learning Technology (DeepFKTNet)," Mathematics, MDPI, vol. 10(8), pages 1-17, April.
    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. Florin Leon & Mircea Hulea & Marius Gavrilescu, 2022. "Preface to the Special Issue on “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning”," Mathematics, MDPI, vol. 10(10), pages 1-4, May.

    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:jmathe:v:11:y:2023:i:23:p:4726-:d:1285309. 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.