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Health diagnostics of PV panels by means of single cell analysis of thermographic images

descriptionPublicationkeyboard_double_arrow_right Conference object , Article 01 Jun 2016 Italy Publisher:IEEEJournal:2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC)

Authors: GUERRIERO, PIERLUIGI; Cuozzo, Gerardo; DALIENTO, SANTOLO;

doi: 10.1109/eeeic.2016.7555516

handle: 11588/649354

Health diagnostics of PV panels by means of single cell analysis of thermographic images

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Abstract

This paper proposes an automated diagnostic procedure able to detect and classify malfunctioning leading to the overheating of cells embedded in solar panels. The analysis of the temperature gradients over defective cells allows to distinguish hot spots caused by shadowing from overheating caused by cell defects. Such nonintrusive approach provides an accurate and inexpensive diagnostic tool, thus reducing operation and maintenance costs and enhancing the energy yield of PV plants.

Country

Italy

Related Organizations

University Federico II of Naples
Italy

Keywords

Sustainability and the Environment, UAV, Energy Engineering and Power Technology, thermography, solar cell, hot spot, hot spot; solar cell; thermography; UAV; Energy Engineering and Power Technology; Renewable Energy, Sustainability and the Environment; Electrical and Electronic Engineering, Renewable Energy, Electrical and Electronic Engineering

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).	18
	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%