Abstract
Monitoring the operation condition of photovoltaic (PV) systems is crucial to improving their efficiency. In this paper, an effective method to supervise the DC part of PV plants under noisy environment is provided. In fact, noisy measurements make the supervision more challenging as the feature extraction of the fault is more difficult. The designed approach merges the desirable proprieties of the discrete wavelet transform and the exponentially weighted moving average scheme to appropriately detect faults in PV system. Specifically, this approach is employed to check the residuals generated by a simulation model based on a single-diode modeling for fault detection. We evaluated the efficiency of the proposed approach on a real PV system in Algeria. Results indicated that the proposed approach has good capacity in supervising the DC part of PV plants.
| Original language | English (US) |
|---|---|
| Title of host publication | 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA) |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 594-598 |
| Number of pages | 5 |
| ISBN (Print) | 9781538659823 |
| DOIs | |
| State | Published - Dec 13 2018 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582
Acknowledgements: This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: (OSR) under Award No: OSR-2015-CRG4-2582.
