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A study of principal component analysis applied to spatially distributed wind power
Author(s)
Date Issued
2011-11
Date Available
2012-03-27T15:20:30Z
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.
Sponsorship
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
Subject – LCSH
Power transmission
Principal components analysis
Time-series analysis
Wind power
Web versions
Language
English
Status of Item
Peer reviewed
ISSN
0885-8950
This item is made available under a Creative Commons License
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