TY - JOUR
T1 - Adaptive control of a wind turbine with data mining and swarm intelligence
AU - Kusiak, Andrew
AU - Zhang, Zijun
PY - 2011/1
Y1 - 2011/1
N2 - 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.
AB - 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.
KW - Adaptive control
KW - blade pitch angle
KW - data mining
KW - electricity demand simulation
KW - generator torque
KW - neural networks
KW - optimization
KW - particle swarm fuzzy algorithm
KW - power prediction
UR - http://www.scopus.com/inward/record.url?scp=78650396613&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-78650396613&origin=recordpage
U2 - 10.1109/TSTE.2010.2072967
DO - 10.1109/TSTE.2010.2072967
M3 - 21_Publication in refereed journal
VL - 2
SP - 28
EP - 36
JO - IEEE Transactions on Sustainable Energy
JF - IEEE Transactions on Sustainable Energy
SN - 1949-3029
IS - 1
M1 - 5560847
ER -