DocumentCode
270569
Title
Optimal voltage control by wind farms using data mining techniques
Author
SaÌiz-MariÌn, Elena ; Lobato, Enrique ; Egido, Ignacio
Author_Institution
Inst. for Res. & Technol. (IIT), Univ. Pontificia Comillas, Madrid, Spain
Volume
8
Issue
2
fYear
2014
fDate
Mar-14
Firstpage
141
Lastpage
150
Abstract
Owing to the rapid growth in the use of wind power, there is a need to carry out an evaluation of the frequency and voltage control of this technology. This study focuses on the voltage control of the evacuation network which connects different wind farms to the transmission network bus. The main contribution of this study is to present a novel real time algorithm which can be used as an alternative to classical techniques such as optimal power flow or artificial intelligence to determine the amount of reactive power that each wind farm should supply in order to minimise the power losses of a whole evacuation network. The optimal voltage control proposed in this study uses data mining techniques (regression rules to estimate the optimum reactive power of the wind farms and classification trees to estimate the optimum transformer taps). The methodology proposed in this study is illustrated with a study of two actual evacuation networks in the Spanish power system. The first one is representative of long feeders whereas the second one is representative of short feeders. The variability in the results of the methodology seems to be dependent on the features of the grids.
Keywords
data mining; frequency control; optimal control; power engineering computing; power generation control; power grids; power transformers; reactive power control; transmission networks; voltage control; wind power plants; Spanish power system; artiflcial intelligence; classiflcation trees; data mining techniques; evacuation network; grids; optimal power flow; optimal voltage control; optimum reactive power; optimum transformer taps; reactive power; real time algorithm; regression rules; short feeders; transmission network bus; wind farm; wind farms;
fLanguage
English
Journal_Title
Renewable Power Generation, IET
Publisher
iet
ISSN
1752-1416
Type
jour
DOI
10.1049/iet-rpg.2013.0025
Filename
6746586
Link To Document