@inproceedings{4035e60ed78b44fb830d68b39c977517,
title = "An effective ensemble learning approach-based grid stability assessment and classification",
abstract = "This article proposes an accurate Stacking Ensemble Classifier (SEC) for decentral Smart Grid control Stability Prediction. The proposed S E C consists of stacking two base classifiers; specifically, extreme Gradient Boosting machine (XGBoost) and Categorical boosting (Catboost), and one meta-classier, Light Gradient Boosting Machine (LGBM). The proposed technique shows an excellent ability to classify the grid instabilities using a supervised learning approach accurately. Extensive experiments have been conducted, demonstrating the superiority of the proposed S E C model over multiple benchmarks. In summary, this paper's main contributions consist of 1) proposing a new model-based ensemble learning 2) tailoring an efficient data-driven technique for grid stability detection and classification. Numerical results are to validate the proposed model's high effectiveness.",
keywords = "Ensemble learning, Forecasting, Gradient Boosted Decision Trees (GBDT), Smart grid, Stability analysis",
author = "Mohamed Massaoudi and Haitham Abu-Rub and Refaat, \{Shady S.\} and Ines Chihi and Oueslati, \{Fakhreddine S.\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2nd Annual IEEE Kansas Power and Energy Conference, KPEC 2021 ; Conference date: 19-04-2021 Through 20-04-2021",
year = "2021",
doi = "10.1109/KPEC51835.2021.9446197",
language = "English",
series = "2021 IEEE Kansas Power and Energy Conference, KPEC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE Kansas Power and Energy Conference, KPEC 2021",
address = "United States",
}