IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1956-d1070229.html
   My bibliography  Save this article

An Automated and Interpretable Machine Learning Scheme for Power System Transient Stability Assessment

Author

Listed:
  • Fang Liu

    (State Grid Sichuan Economic Research Institute, Chengdu 610041, China)

  • Xiaodi Wang

    (State Grid Sichuan Economic Research Institute, Chengdu 610041, China)

  • Ting Li

    (State Grid Sichuan Economic Research Institute, Chengdu 610041, China)

  • Mingzeng Huang

    (Engineering Research Center of Power Transmission and Transformation Technology of Ministry of Education, Hunan University, Changsha 410082, China)

  • Tao Hu

    (Engineering Research Center of Power Transmission and Transformation Technology of Ministry of Education, Hunan University, Changsha 410082, China)

  • Yunfeng Wen

    (Engineering Research Center of Power Transmission and Transformation Technology of Ministry of Education, Hunan University, Changsha 410082, China)

  • Yunche Su

    (State Grid Sichuan Economic Research Institute, Chengdu 610041, China)

Abstract

Many repeated manual feature adjustments and much heuristic parameter tuning are required during the debugging of machine learning (ML)-based transient stability assessment (TSA) of power systems. Furthermore, the results produced by ML-based TSA are often not explainable. This paper handles both the automation and interpretability issues of ML-based TSA. An automated machine learning (AutoML) scheme is proposed which consists of auto-feature selection, CatBoost, Bayesian optimization, and performance evaluation. CatBoost, as a new ensemble ML method, is implemented to achieve fast, scalable, and high performance for online TSA. To enable faster deployment and reduce the heavy dependence on human expertise, auto-feature selection and Bayesian optimization, respectively, are introduced to automatically determine the best input features and optimal hyperparameters. Furthermore, to help operators understand the prediction of stable/unstable TSA, an interpretability analysis based on the Shapley additive explanation (SHAP), is embedded into both offline and online phases of the AutoML framework. Test results on IEEE 39-bus system, IEEE 118-bus system, and a practical large-scale power system, demonstrate that the proposed approach achieves more accurate and certain appropriate trust solutions while saving a substantial amount of time in comparison to other methods.

Suggested Citation

  • Fang Liu & Xiaodi Wang & Ting Li & Mingzeng Huang & Tao Hu & Yunfeng Wen & Yunche Su, 2023. "An Automated and Interpretable Machine Learning Scheme for Power System Transient Stability Assessment," Energies, MDPI, vol. 16(4), pages 1-16, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1956-:d:1070229
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1956/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/4/1956/
    Download Restriction: no
    ---><---

    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:4:p:1956-:d:1070229. 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: 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.