A study of principal component analysis applied to spatially distributed wind power
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|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||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||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||ERC Research Collection|
Electrical and Electronic Engineering Research Collection
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