Optimization of wind power and its variability with a computational intelligence approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number6626554
Pages (from-to)228-236
Journal / PublicationIEEE Transactions on Sustainable Energy
Volume5
Issue number1
Online published9 Oct 2013
Publication statusPublished - Jan 2014

Abstract

An optimization model is presented for maximizing the generation of wind power while minimizing its variability. In the optimization model, data-driven approaches are used to model the wind-power generation process based on industrial data. A new constraint is developed for governing the data-driven wind-power generation model based on physics and statistical process control theory. Since the wind-power model is nonparametric, computational intelligence algorithms are utilized to solve the optimization model. Computer experiments are designed to compare the performance of computational intelligence algorithms. The improvement in the generated wind power and its variability is demonstrated with the computational results. © 2013 IEEE.

Research Area(s)

  • Artificial immune system, Data mining, Evolutionary algorithm, Particle swarm optimization, Wind turbine control, Wind-power optimization