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Data mining of remote monitored stand–alone Solar PV Systems for State of Health Estimation

Lookup NU author(s): Dr Peter Davison, Dr David Greenwood, Dr Neal WadeORCiD

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2016.

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Abstract

In this paper, we use data mining techniques and formulate suitable assessment metrics to derive estimates of the State of Health (SOH) of stand-alone solar home systems. Data is provided from a company with significant numbers of such systems in Africa. The systems in question contain a PV panel, lead-acid battery and a series of DC loads. Data mining allows us to not only estimate the SOH of the battery, but also infer the health of other system components.

Publication metadata

Author(s): Davison PJ, Greenwood DM, Wade NS

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE PES PowerAfrica

Year of Conference: 2016

Pages: 194-198

Print publication date: 28/06/2016

Online publication date: 01/09/2016

Acceptance date: 01/04/2016

Date deposited: 05/10/2016

Publisher: IEEE

URL: http://dx.doi.org/10.1109/PowerAfrica.2016.7556599

DOI: 10.1109/PowerAfrica.2016.7556599

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