TY - GEN
T1 - Adaptive control of a variable-speed variable-pitch wind turbine using RBF neural network
AU - Jafarnejadsani, Hamidreza
AU - Pieper, Jeff
AU - Ehlers, Julian
PY - 2012
Y1 - 2012
N2 - To be competitive economically, various control systems are used in large scale wind turbines. These systems enable the wind turbine to work efficiently and produce the maximum power output in varying wind speed. In this paper, an adaptive control based on Radial-Basis-Function (RBF) neural network (NN) is proposed for different operation modes of variable-speed variable-pitch (VSVP) wind turbines including torque control at speeds lower than rated wind speeds, pitch control at higher wind speeds, and smooth transition between these two modes. The adaptive neural network control approximates the non-linear dynamics of the wind turbine based on input/output measurements and ensures smooth tracking of optimal tip-speed-ratio at different wind speeds. The robust NN weight updating rules are obtained using Lyapunov stability analysis. The proposed control algorithm is first tested with a simplified mathematical model of a wind turbine. Second, the validity of results is verified by simulation studies on a 5 MW wind turbine simulator.
AB - To be competitive economically, various control systems are used in large scale wind turbines. These systems enable the wind turbine to work efficiently and produce the maximum power output in varying wind speed. In this paper, an adaptive control based on Radial-Basis-Function (RBF) neural network (NN) is proposed for different operation modes of variable-speed variable-pitch (VSVP) wind turbines including torque control at speeds lower than rated wind speeds, pitch control at higher wind speeds, and smooth transition between these two modes. The adaptive neural network control approximates the non-linear dynamics of the wind turbine based on input/output measurements and ensures smooth tracking of optimal tip-speed-ratio at different wind speeds. The robust NN weight updating rules are obtained using Lyapunov stability analysis. The proposed control algorithm is first tested with a simplified mathematical model of a wind turbine. Second, the validity of results is verified by simulation studies on a 5 MW wind turbine simulator.
KW - Adaptive control
KW - Generator torque control
KW - Pitch Control
KW - RBF neural network
KW - Transient mode of operation
KW - Variable-speed variable-pitch wind turbine
KW - Varying wind speed
UR - http://www.scopus.com/inward/record.url?scp=84875622523&partnerID=8YFLogxK
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U2 - 10.1109/EPEC.2012.6474954
DO - 10.1109/EPEC.2012.6474954
M3 - Conference contribution
AN - SCOPUS:84875622523
SN - 9781467320801
T3 - 2012 IEEE Electrical Power and Energy Conference, EPEC 2012
SP - 216
EP - 222
BT - 2012 IEEE Electrical Power and Energy Conference, EPEC 2012
T2 - 2012 IEEE Electrical Power and Energy Conference, EPEC 2012
Y2 - 10 October 2012 through 12 October 2012
ER -