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Lookup NU author(s): Dr Peter Davison,
Dr David Greenwood,
Dr Neal WadeORCiD
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.
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.
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
Print publication date: 28/06/2016
Online publication date: 01/09/2016
Acceptance date: 01/04/2016
Date deposited: 05/10/2016