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

Zijun Zhang, Qiang Zhou, Andrew Kusiak

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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.

Original languageEnglish (US)
Article number6626554
Pages (from-to)228-236
Number of pages9
JournalIEEE Transactions on Sustainable Energy
Volume5
Issue number1
DOIs
StatePublished - Jan 2014
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

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