Title :
Adaptive Control of a Wind Turbine With Data Mining and Swarm Intelligence
Author :
Kusiak, Andrew ; Zhang, Zijun
Author_Institution :
Intell. Syst. Lab., Univ. of Iowa, Iowa City, IA, USA
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.
Keywords :
adaptive control; control engineering computing; data mining; fuzzy set theory; particle swarm optimisation; power generation control; time series; wind turbines; adaptive control; data mining; data-driven time-series model; demand data; generated power data; generator torque ramp rate; historical wind speed; linear weighted objective; optimization model; particle swarm fuzzy algorithm; power maximization; power minimization; power production; swarm intelligence; wind turbine; Adaptation model; Artificial neural networks; Data models; Electricity; Predictive models; Torque; Wind turbines; Adaptive control; blade pitch angle; data mining; electricity demand simulation; generator torque; neural networks; optimization; particle swarm fuzzy algorithm; power prediction;
Journal_Title :
Sustainable Energy, IEEE Transactions on
DOI :
10.1109/TSTE.2010.2072967
