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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 Wade

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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.

For re-use rights please refer to the publisher's terms and conditions.


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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