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

Files in This Item:
File Description SizeFormat 
Burke_2011_StudyPrincipleComponent.pdf292.84 kBAdobe PDFDownload
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
Copyright (published version): 2011 IEEE
Keywords: Power transmission;Principal component analysis;Statistics;Time series;Wind energy
Subject LCSH: Power transmission
Principal components analysis
Time-series analysis
Wind power
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

Show full item record

SCOPUSTM   
Citations 10

24
Last Week
0
Last month
checked on Jun 23, 2018

Page view(s) 10

185
checked on May 25, 2018

Download(s) 10

708
checked on May 25, 2018

Google ScholarTM

Check

Altmetric


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.