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)
Sujatha, Bhadravathi Gavirangapa
Unknown Affiliation
Electrical & Electronics Engineering

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

Found 1 Documents
Search

 1 
High impedance fault discrimination in microgrid power system using stacking ensemble approach Vinayagam, Arangarajan; Mohandas, Raman; Chindamani, Meyyappan; Sujatha, Bhadravathi Gavirangapa; Mishra, Soumya; Sundaramurthy, Arivoli
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.pp98-109

Abstract

High impedance (HI) faults in microgrid (MG) power systems are non-linear, intermittent, and have low fault current magnitudes, making them challenging to detect by typical protective systems. Consequently, it is imperative to implement a sophisticated protection system that is dependent on the precision of fault detection. In this study, a stacking ensemble classifier (SEC) is proposed to discriminate HI fault from other transients within a photovoltaic (PV) generated MG power system. The MG model is simulated with the introduction of faults and transients. The features of data set from event signals are generated using the discrete wavelet transform (DWT) technique. The dataset is used to train the individual classifiers (Naïve Bayes (NB), decision tree J48 (DTJ), and K-nearest neighbors (KNN)) at initial and meta learner in the final stage of SEC. The SEC outperforms other classification methods with respect to accuracy of classification, rate of success in detecting HI fault, and performance measures. The outcomes of the classification study conducted under standard test conditions (STC) of solar PV and the noisy environment of event signals clearly demonstrate that the SEC is more dependable and performs better than the individual base classification approaches.
Co-Authors Chindamani, Meyyappan Mohandas, Raman Soumya Mishra, Soumya Sundaramurthy, Arivoli Vinayagam, Arangarajan