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.
O'Malley, Mark
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 transmissionPrincipal component analysisStatisticsTime seriesWind energy
Subject LCSH: Power transmission
Principal components analysis
Time-series analysis
Wind power
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

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