A Lyapunov based switching control to track maximum power point of WECS

Mohammad Abu Jami'In, Takayuki Furuzuki, Eko Julianto

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

2 Citations (Scopus)

Abstract

The control system is a key technology to extract maximum energy from the incident wind. By regulating aerodynamic control, it is possible to adapt the changes in wind speed by controlling shaft speed. Thus, the turbine generator can track maximum power extracted from wind. In this paper, we propose a Lyapunov based switching control under quasi-linear ARX neural network (QARXNN) model to track maximum power of wind energy conversion system. The switching index is used to measure the stability of nonlinear controller and selects linear or nonlinear controller in order to ensure the stability. Interestingly, a simple switching law can be built utilizing the parameters of model directly. Finally, we have compared the proposed algorithm of switching controller with another algorithm. The results show that the proposed algorithm has better control performance.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3883-3888
Number of pages6
Volume2016-October
ISBN (Electronic)9781509006199
DOIs
Publication statusPublished - 2016 Oct 31
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: 2016 Jul 242016 Jul 29

Other

Other2016 International Joint Conference on Neural Networks, IJCNN 2016
CountryCanada
CityVancouver
Period16/7/2416/7/29

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Jami'In, M. A., Furuzuki, T., & Julianto, E. (2016). A Lyapunov based switching control to track maximum power point of WECS. In 2016 International Joint Conference on Neural Networks, IJCNN 2016 (Vol. 2016-October, pp. 3883-3888). [7727702] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2016.7727702