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Detection of increased series losses in PV arrays using Fuzzy Inference Systems

descriptionPublicationkeyboard_double_arrow_right Conference object , Contribution for newspaper or weekly magazine , Article , Other literature type , Part of book or chapter of book 01 Jun 2012 Denmark, Australia Publisher:IEEEJournal:2012 38th IEEE Photovoltaic Specialists Conference

Authors: Spataru, Sergiu; Sera, Dezso; Kerekes, Tamas; Teodorescu, Remus;

doi: 10.1109/pvsc.2012.6317658

Detection of increased series losses in PV arrays using Fuzzy Inference Systems

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Abstract

There are well-defined methods to measure the (increased) series resistance of PV panels in controlled laboratory conditions. However, the presence of various irradiance levels and partial shadows, in case of an outdoor installation, may affect the series resistance estimation. This paper focuses on the estimation of increased series resistance in outdoor conditions in various irradiance levels and partial shadow conditions. A diagnostic method that is able to detect an increase in series resistance based on fuzzy classifiers is presented and experimentally validated.

Countries

Denmark, Australia

Related Organizations

Aalborg University Library (AUB)
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Aalborg University
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Aalborg University Library (AUB) Aalborg Universitet Research Portal
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Aalborg University
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Keywords

Fault diagniosis, series losses, condition monitoring, fault diagnosis, Fault Detection, fault detection, Fuzzy System, classification algorithms, fuzzy systems, Photovoltaic power systems, photovoltaic systems

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	34
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%