01769nas a2200241   4500000000100000000000100001008004100002260001200043653002300055653004500078653004300123653004500166100001600211700002100227700001800248700001700266245010800283856009600391300001000487490000600497520101000503022001401513       2019                            d  c06/201910aGenetic Algorithms10aMulti-Objective Genetic Algorithm (MOGA)10aDoubly Fed Induction Generator  (DFIG)10aSquirrel Cage Induction Generator (SCIG)1 aSalah Kamel1 aFrancisco Jurado1 aAhmed Elkasem1 aAhmed Rashad00aOptimal Performance of Doubly Fed Induction Generator Wind Farm Using Multi-Objective Genetic Algorithm  uhttps://www.ijimai.org/journal/sites/default/files/files/2019/04/ijimai_5_5_6_pdf_17227.pdf  a48-530 v53 aThe 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.  a1989-1660