IEEE Access | |
Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems | |
Hazem Nounou1  Khaled Dhibi2  Kamaleldin Abodayeh2  Majdi Mansouri3  Kais Bouzrara3  Mohamed Nounou4  | |
[1] Department of Mathematical Sciences, Prince Sultan University, Riyadh, Saudi Arabia;Electrical and Computer Engineering Program, Texas A&x0026;M University at Qatar, Doha, Qatar;Research Laboratory of Automation, Signal Processing and Image, National Engineering School of Monastir, Monastir, Tunisia; | |
关键词: Uncertain systems; ensemble learning; fault diagnosis; interval-valued data; kernel principal component analysis (KPCA); grid-connected PV (GCPV); | |
DOI : 10.1109/ACCESS.2022.3167147 | |
来源: DOAJ |
【 摘 要 】
One of the most promising renewable energy technologies is photovoltaics (PV). Fault detection and diagnosis (FDD) becomes more and more important in order to guarantee high reliability in PV systems. FDD of PV systems using machine learning technique aims to develop effective models that can provide a better rate of accuracy. Recently, numerous machine learning based ensemble models have been applied in FDD using different combination techniques. Ensemble method is a tool that merges several base models in order to produce one optimal predictive model. In this study, we propose six effective Ensemble Leaning (EL)-based FDD paradigms for uncertain Grid-Connected PV systems. First, EL-based interval centers and ranges and interval upper and lower bounds techniques are proposed to deal with PV system uncertainties (current/voltage variability, noise, measurement errors,
【 授权许可】
Unknown