TY - GEN
T1 - An effective ensemble learning approach-based grid stability assessment and classification
AU - Massaoudi, Mohamed
AU - Abu-Rub, Haitham
AU - Refaat, Shady S.
AU - Chihi, Ines
AU - Oueslati, Fakhreddine S.
N1 - Funding Information:
This publication was made possible by NPRP grant [NPRP10-0101-170082] from the Qatar National Research Fund (a member of Qatar Foundation) and co-funding by IBERDROLA QSTP LLC. The statements made herein are solely the responsibility of the authors.
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Ensemble learning
KW - Forecasting
KW - Gradient Boosted Decision Trees (GBDT)
KW - Smart grid
KW - Stability analysis
UR - http://www.scopus.com/inward/record.url?scp=85109361117&partnerID=8YFLogxK
U2 - 10.1109/KPEC51835.2021.9446197
DO - 10.1109/KPEC51835.2021.9446197
M3 - Conference contribution
AN - SCOPUS:85109361117
T3 - 2021 IEEE Kansas Power and Energy Conference, KPEC 2021
BT - 2021 IEEE Kansas Power and Energy Conference, KPEC 2021
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 2nd Annual IEEE Kansas Power and Energy Conference, KPEC 2021
Y2 - 19 April 2021 through 20 April 2021
ER -