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dc.contributor.authorSaulo, M.J, V. Siyoi, S. Kariuki
dc.date.accessioned2015-10-23T07:52:10Z
dc.date.available2015-10-23T07:52:10Z
dc.date.issued2014
dc.identifier.citationV. Siyoi, S. Kariuki, M. J. Saulo. Power Swing Prediction for Out-of-Step Mitigation. International Journal of Energy and Power Engineering. Special Issue: Electrical Power Systems Operation and Planning. Vol. 4, No. 2-1, 2015, pp. 63-72. doi: 10.11648/j.ijepe.s.2015040201.16en_US
dc.identifier.issn2326-957X
dc.identifier.issn2326-960X
dc.identifier.urihttp://hdl.handle.net/123456789/5254
dc.descriptionThe Original publication is available at http://www.sciencepublishinggroup.com/j/ijepeen_US
dc.description.abstractThis paper explored the possibility of accurately predicting the classification of developing power swings. The notion of machine learning was employed, and tested the application of Decision Tree (DT) algorithms to wide area power system protection schemes. The novelty of the designed Wide Area Protection (WAP) scheme was portrayed by the WAP’s ability to adaptively and accurately predict the classification of developing successive power swings. DTs being a Data Mining (DM) technique, a transient stability analysis was performed on an IEEE 39 bus test system in Dig SILENT®. The learning sample from the Phasor Measurement Unit (PMU) data was organized and stored in a data base in Microsoft Excel® 2010. The CART analysis and DT model design was done using Salford Predictive Modeller-CART® v6, trial licence. The results of this investigation were quite accurate and gave DT algorithms ‘thumbs-up’ in terms of classification prediction.en_US
dc.description.sponsorshipTechnical University of Mombasaen_US
dc.language.isoenen_US
dc.publisherInternational Journal of Energy and Power Engineeringen_US
dc.subjectDecision Treesen_US
dc.subjectPower Swingen_US
dc.subjectOut-of-Stepen_US
dc.subjectWide Area Protectionen_US
dc.titlePower swing prediction for out-of-step mitigationen_US
dc.typeArticleen_US


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