A study of principal component analysis applied to spatially distributed wind power
Files in This Item:
|Burke_2011_StudyPrincipleComponent.pdf||292.84 kB||Adobe PDF||Download|
|Title:||A study of principal component analysis applied to spatially distributed wind power||Authors:||Burke, Daniel J.
|Permanent link:||http://hdl.handle.net/10197/3543||Date:||Nov-2011||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.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||IEEE||Journal:||IEEE Transactions on Power Systems||Volume:||26||Issue:||4||Start page:||2084||End page:||2092||Copyright (published version):||2011 IEEE||Keywords:||Power transmission; Principal component analysis; Statistics; Time series; Wind energy||Subject LCSH:||Power transmission
Principal components analysis
|DOI:||10.1109/TPWRS.2011.2120632||Other versions:||http://dx.doi.org/10.1109/TPWRS.2011.2120632||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||ERC Research Collection|
Electrical and Electronic Engineering Research Collection
Show full item record
Page view(s) 10185
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.