Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 26, Issue 4, Pages 2084-2092Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2011.2120632
Keywords
Power transmission; principal component analysis; statistics; time series; wind energy
Categories
Funding
- Commission for Energy Regulation
- Bord Gais Energy
- Bord na Mona Energy
- Cylon Controls
- EirGrid
- ESB Energy International
- ESB Energy Solutions
- ESB Networks
- Gaelectric
- Siemens
- SSE Renewables
- SWS Energy
- Viridian Power Energy
- Sustainable Energy Authority of Ireland through Irish Research Council for Science Engineering and Technology
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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.
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