Maximum power tracking control for a wind energy conversion system based on a quasi-ARX neural network model

Mohammad Abu Jami'in, Imam Sutrisno, Jinglu Hu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)368-375
Number of pages8
JournalIEEJ Transactions on Electrical and Electronic Engineering
Issue number4
Publication statusPublished - 2015 Jul 1


  • Nonlinear parameter estimation
  • Quasi-ARX neural network
  • Switching controller
  • Wind energy conversion system (WECS)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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