A statistical based approach for fault detection and diagnosis in a photovoltaic system
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
This 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.
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
CitationGaroudja, E., harrou, F., Sun, Y., kara, K., Chouder, A., Silvestre, S. A statistical based approach for fault detection and diagnosis in a photovoltaic system. A: International Conference on Systems and Control. "2017 6th International Conference on Systems and Control (ICSC 2017): Batna, Algeria: 7-9 May 2017". Batna: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 75-80.
|batna conference paper 2017.pdf||article||878,4Kb||View/Open|