Nonlinear adaptive control for wind energy conversion systems based on quasi-ARX neural networks model

Mohammad Abu Jami'In, Imam Sutrisno, Takayuki Furuzuki

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

A wind turbine, by itself, is already a fairly complex system with highly nonlinear dynamics. Wind speed and torque fluctuations can change the dynamic parameters of wind energy conversion systems (WECS), so that the parameter will be a function of time. The quasi-ARX neural networks are nonlinear models, while the multi-layer parceptron (MLP) network is an embedded system to give the unknown parameters of the regression vector. Unknown parameter is the coefficient of nonlinear autoregressive moving average (ARMA) models and consists of two parts, linear and nonlinear parts. With a quasi-ARX model as an identifier, we design an adaptive controller for WECS. Logic switch function is used to ensure the stability and control accuracy. In this paper, the objective of WECS controller is to track the maximum power point tracking (MPPT) is used to maximize the power output of the wind turbine. However, from user's point of view, there are two majors. First, quasi-ARX neural network model is used to identification and prediction of nonlinear system, and second, by using using minimum variance controller with switching law, the proposed model successfully is used to track MPPT of WECS.

本文言語English
ホスト出版物のタイトルLecture Notes in Engineering and Computer Science
出版社Newswood Limited
ページ313-318
ページ数6
2209
January
出版ステータスPublished - 2014
イベントInternational MultiConference of Engineers and Computer Scientists, IMECS 2014 - Kowloon, Hong Kong
継続期間: 2014 3 122014 3 14

Other

OtherInternational MultiConference of Engineers and Computer Scientists, IMECS 2014
国/地域Hong Kong
CityKowloon
Period14/3/1214/3/14

ASJC Scopus subject areas

  • コンピュータ サイエンス(その他)

フィンガープリント

「Nonlinear adaptive control for wind energy conversion systems based on quasi-ARX neural networks 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