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 |
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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 |
巻 | 2209 |
版 | 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月 12 → 2014 3月 14 |
Other
Other | International MultiConference of Engineers and Computer Scientists, IMECS 2014 |
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国/地域 | Hong Kong |
City | Kowloon |
Period | 14/3/12 → 14/3/14 |
ASJC Scopus subject areas
- コンピュータ サイエンス(その他)