A study of principal component analysis applied to spatially distributed wind power

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dc.contributor.author Burke, Daniel J.
dc.contributor.author O'Malley, Mark
dc.date.accessioned 2012-03-27T15:20:30Z
dc.date.available 2012-03-27T15:20:30Z
dc.date.copyright 2011 IEEE en
dc.date.issued 2011-11
dc.identifier.citation IEEE Transactions on Power Systems en
dc.identifier.issn 0885-8950
dc.identifier.uri http://hdl.handle.net/10197/3543
dc.description.abstract Multivariate dimension reduction schemes could be very useful in limiting the number of random statistical variables needed to represent distributed wind power spatial diversity in transmission integration studies. In this paper, principal component analysis (PCA) is applied to the covariance matrix of distributed wind power data from existing Irish wind farms, with the eigenvector/eigenvalue analysis generating a lower number of uncorrelated alternative variables. It is shown that though uncorrelated, these wind components may not necessarily be statistically independent however. A sample application of PCA combined with multivariate probability discretization is also outlined in detail. In that case study, the capability of PCA to reduce the number and prioritize the order of the alternative statistical variables is key to potential wind power production costing simulation efficiency gains, when compared to exhaustive multiyear time series load flow investigations. en
dc.description.sponsorship Science Foundation Ireland en
dc.format.extent 299873 bytes
dc.format.extent 1072 bytes
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.language.iso en en
dc.publisher IEEE en
dc.rights © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. en
dc.subject Power transmission en
dc.subject Principal component analysis en
dc.subject Statistics en
dc.subject Time series en
dc.subject Wind energy en
dc.subject.lcsh Power transmission en
dc.subject.lcsh Principal components analysis en
dc.subject.lcsh Time-series analysis en
dc.subject.lcsh Wind power en
dc.title A study of principal component analysis applied to spatially distributed wind power en
dc.type Journal Article en
dc.internal.availability Full text available en
dc.internal.webversions Publisher's version en
dc.internal.webversions http://dx.doi.org/10.1109/TPWRS.2011.2120632 en
dc.status Peer reviewed en
dc.identifier.volume 26 en
dc.identifier.issue 4 en
dc.identifier.startpage 2084 en
dc.identifier.endpage 2092 en
dc.identifier.doi 10.1109/TPWRS.2011.2120632
dc.neeo.contributor Burke|Daniel J.|aut| en
dc.neeo.contributor O'Malley|Mark|aut| en
dc.description.admin ti, ab, ke, jo, vo, st, en - TS 20.02.12 en


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