TY - GEN
T1 - Fault detection in photovoltaic systems using machine learning technique
AU - Attouri, Khadija
AU - Hajji, Mansour
AU - Mansouri, Majdi
AU - Harkat, Mohamed Faouzi
AU - Kouadri, Abdelmalek
AU - Nounou, Hazem
AU - Nounou, Mohamed
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7/20
Y1 - 2020/7/20
N2 - To ensure high reliability of the Grid-Connected Photovoltaic (GCPV) systems, promptly faults detection, diagnosis and automatic process monitoring are essential tools to keep the PV and the grid network under optimal functioning. Regardless of fault types, incipient faults are usually more difficult to detect and accurately isolate. As an alternative and effective method, the principal components analysis (PCA) is proposed to extract and select more relevant features and support vector machines (SVM) technique is applied to quickly detect the faults that occur in a GCPV system. The T2 and squared weighted errors (SWE) statistics, generally used as fault detection indices, are appropriately extracted and selected within the PCA framework. Both of these features are fed to a SVM classifier handling the incipient fault detection. This task is carried out on a simulated GCPV operating under maximum power point trackers (MPPT) and matching realistic outdoor to demonstrate the effectiveness and robustness of the proposed technique.
AB - To ensure high reliability of the Grid-Connected Photovoltaic (GCPV) systems, promptly faults detection, diagnosis and automatic process monitoring are essential tools to keep the PV and the grid network under optimal functioning. Regardless of fault types, incipient faults are usually more difficult to detect and accurately isolate. As an alternative and effective method, the principal components analysis (PCA) is proposed to extract and select more relevant features and support vector machines (SVM) technique is applied to quickly detect the faults that occur in a GCPV system. The T2 and squared weighted errors (SWE) statistics, generally used as fault detection indices, are appropriately extracted and selected within the PCA framework. Both of these features are fed to a SVM classifier handling the incipient fault detection. This task is carried out on a simulated GCPV operating under maximum power point trackers (MPPT) and matching realistic outdoor to demonstrate the effectiveness and robustness of the proposed technique.
KW - Grid-connected PV systems
KW - Machine learning (ML)
KW - fault detection
KW - feature extraction and selection
KW - principal component analysis (PCA)
UR - http://www.scopus.com/inward/record.url?scp=85103003870&partnerID=8YFLogxK
U2 - 10.1109/SSD49366.2020.9364094
DO - 10.1109/SSD49366.2020.9364094
M3 - Conference contribution
AN - SCOPUS:85103003870
T3 - Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020
SP - 207
EP - 212
BT - Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020
Y2 - 20 July 2020 through 23 July 2020
ER -