Research Article | OPEN ACCESS
A Comparative Study about the Effectiveness of Observers and Bayesian Belief Networks for the Fault Detection and Isolation in Power Electronics
1, 2Abbass Zein Eddine, 1Iyad Zaarour, 2Francois Guerin, 1Abbas Hijazi and 2Dimitri Lefebvre
1Lebanese University, Hadat, Beiruth 1003, Lebanon
2GREAH-Université Le Havre, 25 rue Philippe Lebon, 76058 Le Havre Cedex, France
Research Journal of Applied Sciences, Engineering and Technology 2017 1:10-28
Received: May ‎13, ‎2016 | Accepted: July ‎18, ‎2016 | Published: January 15, 2017
Abstract
The aim of this study is to highlight the capabilities of Bayesian Belief Network (BBN) in the domain of Fault Detection and Isolation (FDI) in DC/DC converter. Reliable electrical supplying systems are those which can provide continuously electrical energy to the consumers. This continuity requires fault free processes during all the phases of energy production, transfer and conversion. In order to achieve a fault free process it is mandatory to have an FDI system that holds on the faulty cases. In this study a Bayesian Naive Classifier (BNC) structure was selected and used as a first attempt to use BBNs for DC/DC power converter FDI.
Keywords:
Bayesian na, DC/DC power converter, fault detection and isolation, proportional observer,
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Competing interests
The authors have no competing interests.
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This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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