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

Mohammad Abu Jami'In, Imam Sutrisno, Takayuki Furuzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Engineering and Computer Science
PublisherNewswood Limited
Pages313-318
Number of pages6
Volume2209
EditionJanuary
Publication statusPublished - 2014
EventInternational MultiConference of Engineers and Computer Scientists, IMECS 2014 - Kowloon, Hong Kong
Duration: 2014 Mar 122014 Mar 14

Other

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

Fingerprint

Energy conversion
Wind power
Neural networks
Wind turbines
Controllers
Network layers
Embedded systems
Large scale systems
Nonlinear systems
Identification (control systems)
Torque
Switches

Keywords

  • Nonlinear parameter estimation
  • Quasi-ARX neural networks
  • Switching controller
  • Wind energy conversion systems (WECS)
  • Wind turbine control

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

Cite this

Jami'In, M. A., Sutrisno, I., & Furuzuki, T. (2014). Nonlinear adaptive control for wind energy conversion systems based on quasi-ARX neural networks model. In Lecture Notes in Engineering and Computer Science (January ed., Vol. 2209, pp. 313-318). Newswood Limited.

Nonlinear adaptive control for wind energy conversion systems based on quasi-ARX neural networks model. / Jami'In, Mohammad Abu; Sutrisno, Imam; Furuzuki, Takayuki.

Lecture Notes in Engineering and Computer Science. Vol. 2209 January. ed. Newswood Limited, 2014. p. 313-318.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jami'In, MA, Sutrisno, I & Furuzuki, T 2014, Nonlinear adaptive control for wind energy conversion systems based on quasi-ARX neural networks model. in Lecture Notes in Engineering and Computer Science. January edn, vol. 2209, Newswood Limited, pp. 313-318, International MultiConference of Engineers and Computer Scientists, IMECS 2014, Kowloon, Hong Kong, 14/3/12.
Jami'In MA, Sutrisno I, Furuzuki T. Nonlinear adaptive control for wind energy conversion systems based on quasi-ARX neural networks model. In Lecture Notes in Engineering and Computer Science. January ed. Vol. 2209. Newswood Limited. 2014. p. 313-318
Jami'In, Mohammad Abu ; Sutrisno, Imam ; Furuzuki, Takayuki. / Nonlinear adaptive control for wind energy conversion systems based on quasi-ARX neural networks model. Lecture Notes in Engineering and Computer Science. Vol. 2209 January. ed. Newswood Limited, 2014. pp. 313-318
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