@inproceedings{bc6944edd0784c09889dad3e3c9985d8,
title = "Reduced KPCA based Ensemble Learning Approach for Fault Diagnosis of Grid-Connected PV Systems",
abstract = "The main objective of this work is to develop novel fault diagnosis techniques using ensemble learning and multivariate statistical techniques. The proposed methods are capable of identifying and classifying PV faults. First, an ensemble learning (EL) method that combines several base models is proposed. Next, kernel principal components analysis (KPCA) and reduced KPCA are proposed to extract and select the pertinent characteristics from raw data. Then, the extracted significant characteristics are transmitted to the EL model for classification purposes. The main idea behind these proposals is to provide the best accuracy and also improve the results in terms of computation time. The diagno-sis results demonstrated the efficiency of the proposed frameworks.",
keywords = "Ensemble Learning, Fault Classification, Fault Diag-nosis, Grid-Connected PV (GCPV), Machine Learning",
author = "Khaled Dhibi and Majdi Mansouri and Kais Bouzrara and Hazem Nounou and Mohamed Nounou",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022 ; Conference date: 06-05-2022 Through 10-05-2022",
year = "2022",
doi = "10.1109/SSD54932.2022.9955929",
language = "English",
series = "2022 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "841--845",
booktitle = "2022 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022",
address = "United States",
}