Adaptive control of a wind turbine with data mining and swarm intelligence

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 number5560847
Pages (from-to)28-36
Journal / PublicationIEEE Transactions on Sustainable Energy
Volume2
Issue number1
Publication statusPublished - Jan 2011
Externally publishedYes

Abstract

The framework of adaptive control applied to a wind turbine is presented. The wind turbine is adaptively controlled to achieve a balance between two objectives, power maximization and minimization of the generator torque ramp rate. An optimization model is developed and solved with a linear weighted objective. The objective weights are autonomously adjusted based on the demand data and the predicted power production. Two simulation models are established to generate demand information. The wind power is predicted by a data-driven time-series model utilizing historical wind speed and generated power data. The power generated from the wind turbine is estimated by another model. Due to the intrinsic properties of the data-driven model and changing weights of the objective function, a particle swarm fuzzy algorithm is used to solve it. © 2010 IEEE.

Research Area(s)

  • Adaptive control, blade pitch angle, data mining, electricity demand simulation, generator torque, neural networks, optimization, particle swarm fuzzy algorithm, power prediction