Optimization of wind turbine energy and power factor with an evolutionary computation algorithm
Andrew Kusiak and
Energy, 2010, vol. 35, issue 3, 1324-1332
An evolutionary computation approach for optimization of power factor and power output of wind turbines is discussed. Data-mining algorithms capture the relationships among the power output, power factor, and controllable and non-controllable variables of a 1.5MW wind turbine. An evolutionary strategy algorithm solves the data-derived optimization model and determines optimal control settings. Computational experience has demonstrated opportunities to improve the power factor and the power output by optimizing set points of blade pitch angle and generator torque. It is shown that the pitch angle and the generator torque can be controlled to maximize the energy capture from the wind and enhance the quality of the power produced by the wind turbine with a DFIG generator. These improvements are in the presence of reactive power remedies used in modern wind turbines. The concepts proposed in this paper are illustrated with the data collected at an industrial wind farm.
Keywords: Wind turbine; Power factor; Power output; Power quality; Data mining; Neural network; Dynamic modeling; Multi-objective optimization; Evolutionary computation algorithm (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (25) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:35:y:2010:i:3:p:1324-1332
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Series data maintained by Dana Niculescu ().