Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Applied Power Engineering (IJAPE)
Rajasekhar, Chukka
Unknown Affiliation
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Feature transformation with ensemble learning for power grid stability in sustainable energy and industry systems Pagoti, Sirish Kumar; Kapala, Kavitha; Prasad, Thikka Rama Kanaka Durga Vara; Rajasekhar, Chukka; Pedada, Krishna Rao; Oruganti, Sai Kiran
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp298-307

Abstract

Power grids today operate under unpredictable and rapidly changing conditions, making reliable stability prediction increasingly important. This study evaluates two hybrid learning frameworks that integrate deep feature transformation with ensemble classification. In the first framework, an autoencoder (AE) is used for feature encoding before classification with extreme gradient boosting (XGBoost), while the second applies a TabTransformer (TT) followed by the same classifier. For comparison, conventional ensemble models, including random forest and standalone LightGBM, are also assessed. The models are tested on a large public dataset using stratified cross-validation and standard performance metrics. Results show that the AE-XGBoost hybrid achieves the highest performance, with a test accuracy of 97.73% and an F1-score of 0.98 for both stable and unstable states. LightGBM also performs strongly, offering consistent accuracy (95.8%) and good interpretability. In contrast, TT-XGBoost, despite its architectural novelty, achieves lower accuracy (89.4%) and struggles with unstable states. These findings highlight that model effectiveness depends not only on architectural complexity but also on the synergy between feature transformation and classification. The results provide practical insights for building dependable, confidence-aware predictive systems to support smart grid decision-making.
Co-Authors Kapala, Kavitha Oruganti, Sai Kiran Pagoti, Sirish Kumar Pedada, Krishna Rao Prasad, Thikka Rama Kanaka Durga Vara