A statistical-based approach for fault detection and diagnosis in a photovoltaic system
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2582
Online Publication Date2017-07-10
Print Publication Date2017-05
Permanent link to this recordhttp://hdl.handle.net/10754/625672
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AbstractThis paper reports a development of a statistical approach for fault detection and diagnosis in a PV system. Specifically, the overarching goal of this work is to early detect and identify faults on the DC side of a PV system (e.g., short-circuit faults; open-circuit faults; and partial shading faults). Towards this end, we apply exponentially-weighted moving average (EWMA) control chart on the residuals obtained from the one-diode model. Such a choice is motivated by the greater sensitivity of EWMA chart to incipient faults and its low-computational cost making it easy to implement in real time. Practical data from a 3.2 KWp photovoltaic plant located within an Algerian research center is used to validate the proposed approach. Results show clearly the efficiency of the developed method in monitoring PV system status.
CitationGaroudja E, Harrou F, Sun Y, Kara K, Chouder A, et al. (2017) A statistical-based approach for fault detection and diagnosis in a photovoltaic system. 2017 6th International Conference on Systems and Control (ICSC). Available: http://dx.doi.org/10.1109/ICoSC.2017.7958710.
SponsorsThis publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2015- CRG4-2582. The authors (Elyes Garoudja and Kamel Kara) thank the SET Laboratory, Department of Electronics, Faculty of Technology, University of Blida 1, Algeria, for continuous support during the study.
Conference/Event name6th International Conference on Systems and Control, ICSC 2017