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

研究成果: Article査読

8 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)368-375
ページ数8
ジャーナルIEEJ Transactions on Electrical and Electronic Engineering
10
4
DOI
出版ステータスPublished - 2015 7 1

ASJC Scopus subject areas

  • 電子工学および電気工学

フィンガープリント

「Maximum power tracking control for a wind energy conversion system based on a quasi-ARX neural network model」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル