IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-031-95099-5_13.html
   My bibliography  Save this book chapter

Smart Grid Stability Analysis with Interpretable Machine Learning and Deep Learning Models

Author

Listed:
  • Shamanta Sharmi Sristy

    (Hajee Mohammad Danesh Science and Technology University)

  • Iftekharul Islam

    (Hajee Mohammad Danesh Science and Technology University)

  • Mahmudul Hasan

    (Deakin University
    Hajee Mohammad Danesh Science and Technology University)

  • Md. Motiur Rahman Tareq

    (Hajee Mohammad Danesh Science and Technology University)

  • Kanij Fatema

    (Hajee Mohammad Danesh Science and Technology University
    Hajee Mohammad Danesh Science and Technology University)

Abstract

Ensuring the stability of modern power grids is paramount to achieving sustainable energy systems, particularly as renewable energy sources become more integrated. This study focuses on predicting smart grid stability using machine learning (ML), deep learning (DL) algorithms, and Explainable AI (XAI) methods to ensure model interpretability. Among the ML models, SVM outperformed others with an accuracy of 97.8%, while Multilayer Perceptron achieved the highest accuracy of 97.7% among DL models. Explainable AI techniques, particularly SHAP was employed to interpret model predictions, revealing key factors such power load (g3) and reaction times (tau1) significantly influence grid stability. Our approach ensures the grid’s stability by addressing fluctuations and system imbalances in real time. The study’s findings offer practical guidance for enhancing power grid performance, which advances the more general objectives of energy resilience and efficiency in the incorporation of renewable energy sources.

Suggested Citation

  • Shamanta Sharmi Sristy & Iftekharul Islam & Mahmudul Hasan & Md. Motiur Rahman Tareq & Kanij Fatema, 2025. "Smart Grid Stability Analysis with Interpretable Machine Learning and Deep Learning Models," International Series in Operations Research & Management Science,, Springer.
    • Handle: RePEc:spr:isochp:978-3-031-95099-5_13
    DOI: 10.1007/978-3-031-95099-5_13
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:isochp:978-3-031-95099-5_13. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.