TY - GEN
T1 - Uncertain Fault Diagnosis of Grid-Connected PV Systems based Improved Data-Driven Paradigms
AU - Dhibil, Khaled
AU - Mansouri, Majdi
AU - Bouzrara, Kais
AU - Nounou, Hazem
AU - Nounou, Mohamed
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The main idea behind this work is to diagnose Grid-Connected Photovoltaic (PV) systems. The uncertainty was treated by using the interval-valued data representation. The main interventions are threefold: first, interval ensemble techniques based on the combination of several models (SVM, KNN, and tree) into one improved model are proposed in order to isolate the different PV systems operating modes using interval raw data. Then, feature extraction and selection steps are proposed to improve the fault diagnosis results. Therefore, the interval KPCA (IKPCA) method is performed in order to extract and select the important characteristics. The proposed techniques were used to diagnose the GCPV system under different operating modes. The results demonstrated the superiority of the proposed methods.
AB - The main idea behind this work is to diagnose Grid-Connected Photovoltaic (PV) systems. The uncertainty was treated by using the interval-valued data representation. The main interventions are threefold: first, interval ensemble techniques based on the combination of several models (SVM, KNN, and tree) into one improved model are proposed in order to isolate the different PV systems operating modes using interval raw data. Then, feature extraction and selection steps are proposed to improve the fault diagnosis results. Therefore, the interval KPCA (IKPCA) method is performed in order to extract and select the important characteristics. The proposed techniques were used to diagnose the GCPV system under different operating modes. The results demonstrated the superiority of the proposed methods.
KW - Ensemble Learning
KW - Fault Classification
KW - Fault Diagnosis
KW - Grid-Connected PV (GCPV)
KW - Kernel Principal Component Analysis (KPCA)
KW - Uncertain Systems
UR - http://www.scopus.com/inward/record.url?scp=85143807220&partnerID=8YFLogxK
U2 - 10.1109/SSD54932.2022.9955664
DO - 10.1109/SSD54932.2022.9955664
M3 - Conference contribution
AN - SCOPUS:85143807220
T3 - 2022 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022
SP - 835
EP - 840
BT - 2022 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022
Y2 - 6 May 2022 through 10 May 2022
ER -