TY - JOUR
T1 - Maximum power tracking control for a wind energy conversion system based on a quasi-ARX neural network model
AU - Jami'in, Mohammad Abu
AU - Sutrisno, Imam
AU - Hu, Jinglu
N1 - Funding Information:
This research was partly supported by the Indonesian Government Scholarship with Directorate General of Higher Education, Ministry of National Education, (Beasiswa Luar Negeri DIKTI Kementrian Pendidikan dan Kebudayaan Republik Indonesia) and the Shipbuilding Institute of Polytechnic Surabaya (Politeknik Perkapalan Negeri Surabaya PPNS).
Publisher Copyright:
© 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2015/7/1
Y1 - 2015/7/1
N2 - By itself, a wind turbine is already a fairly complex system with highly nonlinear dynamics. Changes in wind speed can affect the dynamic parameters of wind turbines, thus rendering the parameters uncertain. However, we can identify the dynamics of the wind energy conversion system (WECS) online by a quasi-ARX neural network (QARXNN) model. A QARXNN presents a problem in searching for the coefficients of the regression vector (input vector). A multilayer perceptron neural network (MLPNN) is an embedded system that provides the unknown parameters used to parameterize the input vector. Fascinatingly, the coefficients of the input vector from prediction model can be set as controller parameters directly. The stability of the closed-loop controller is guaranteed by the switching of the linear and nonlinear parts of the parameters. The dynamic of WECS is derived with given parameters, and then a wind speed signal created by a random model is fed to the system causing uncertainty parameters and reducing the power that can be absorbed from wind. By using a minimum variance controller, the maximum power is tracked from WECS. From the simulation results, it is observed that the proposed controller is effective in tracking the maximum power of WECS.
AB - By itself, a wind turbine is already a fairly complex system with highly nonlinear dynamics. Changes in wind speed can affect the dynamic parameters of wind turbines, thus rendering the parameters uncertain. However, we can identify the dynamics of the wind energy conversion system (WECS) online by a quasi-ARX neural network (QARXNN) model. A QARXNN presents a problem in searching for the coefficients of the regression vector (input vector). A multilayer perceptron neural network (MLPNN) is an embedded system that provides the unknown parameters used to parameterize the input vector. Fascinatingly, the coefficients of the input vector from prediction model can be set as controller parameters directly. The stability of the closed-loop controller is guaranteed by the switching of the linear and nonlinear parts of the parameters. The dynamic of WECS is derived with given parameters, and then a wind speed signal created by a random model is fed to the system causing uncertainty parameters and reducing the power that can be absorbed from wind. By using a minimum variance controller, the maximum power is tracked from WECS. From the simulation results, it is observed that the proposed controller is effective in tracking the maximum power of WECS.
KW - Nonlinear parameter estimation
KW - Quasi-ARX neural network
KW - Switching controller
KW - Wind energy conversion system (WECS)
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U2 - 10.1002/tee.22095
DO - 10.1002/tee.22095
M3 - Article
AN - SCOPUS:84931574806
VL - 10
SP - 368
EP - 375
JO - IEEJ Transactions on Electrical and Electronic Engineering
JF - IEEJ Transactions on Electrical and Electronic Engineering
SN - 1931-4973
IS - 4
ER -