Benlahbib, B. and Bouchafaa, F. and Mekhilef, Saad and Bouarroudj, N. (2017) Wind Farm Management using Artificial Intelligent Techniques. International Journal of Electrical and Computer Engineering (IJECE), 7 (3). pp. 1133-1144. ISSN 2088-8708, DOI https://doi.org/10.11591/ijece.v7i3.pp1133-1144.
Full text not available from this repository.Abstract
This paper presents a comparative study between the genetic algorithm and particle swarm optimization methods to determine the optimal proportional-integral (PI) controller parameters for wind farm supervision algorithm. The main objective of this study is to obtain a rapid and stable system by tuning of the PI controller, thereby providing an excellent monitor for our wind farm by sending separate set points to all wind generators. A supervisory system controls the active and reactive power of the entire wind farm by sending out set points to all wind turbines. A machine control system ensures that the set points at the wind turbine level are reached. The entire control is added to the normal operating power reference of the wind farm established by a supervisory control. Finally the performance of the proposed algorithm is verified through MATLAB/Simulink simulation results by considering a wind farm of three doubly-fed induction generators.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | DFIG; GA; MPPT and PCC; PI controller; PSO; Wind farm supervision |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 13 Sep 2018 05:21 |
| Last Modified: | 25 Oct 2019 05:36 |
| URI: | http://eprints.um.edu.my/id/eprint/19228 |
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