Optimal Performance of Doubly Fed Induction Generator Wind Farm Using Multi-Objective Genetic Algorithm

Optimal Performance of Doubly Fed Induction Generator Wind Farm Using Multi-Objective Genetic Algorithm

The main purpose of this paper is allowing doubly fed induction generator wind farms (DFIG), which are connected to power system, to effectively participate in feeding electrical loads. The oscillation in power system is one of the challenges of the interconnection of wind farms to the grid. The mod...

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Journal Title: International Journal of Interactive Multimedia and Artificial Intelligence
First author: Ahmed H. A. Elkasem
Other Authors: Salah Kamel;
Ahmed Rashad;
Francisco Jurado
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Language: Undetermined
Get full text: https://www.ijimai.org/journal/sites/default/files/files/2019/04/ijimai_5_5_6_pdf_17227.pdf
https://www.ijimai.org/journal/node/3004
Resource type: Journal Article
Source: International Journal of Interactive Multimedia and Artificial Intelligence; Vol 5, No 5 (Year 2019).
DOI:
Publisher: Universidad Internacional de La Rioja
Usage rights: Reconocimiento (by)
Subjects: Physical/Engineering Sciences --> Computer Science, Artificial Intelligence
Abstract: The main purpose of this paper is allowing doubly fed induction generator wind farms (DFIG), which are connected to power system, to effectively participate in feeding electrical loads. The oscillation in power system is one of the challenges of the interconnection of wind farms to the grid. The model of DFIG contains several gains which need to be achieved with optimal values. This aim can be accomplished using an optimization algorithm in order to obtain the best performance. The multi-objective optimization algorithm is used to determine the optimal control system gains under several objectives. In this paper, a multi-objective genetic algorithm is applied to the DFIG model to determine the optimal values of the gains of DFIG control system. In order to point out the contribution of this work; the performance of optimized DFIG model is compared with the non-optimized model of DFIG. The results show that the optimized model of DFIG has better performance over the non-optimized DFIG model.