2014
DOI: 10.1016/j.epsr.2013.10.021
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Dynamic modeling and optimal control of DFIG wind energy systems using DFT and NSGA-II

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Cited by 47 publications
(29 citation statements)
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“…This algorithm is one of the most excellent multi-objective optimization evolutionary algorithms, and has been widely applied [11][12][13]. The core of NSGA-II evolutionary algorithm lies in two aspects: the fast non-dominated sorting and elitist selection strategy for individuals.…”
Section: Nsga-ii Algorithmmentioning
confidence: 99%
“…This algorithm is one of the most excellent multi-objective optimization evolutionary algorithms, and has been widely applied [11][12][13]. The core of NSGA-II evolutionary algorithm lies in two aspects: the fast non-dominated sorting and elitist selection strategy for individuals.…”
Section: Nsga-ii Algorithmmentioning
confidence: 99%
“…Here, the voltage references for the grid side converter (GSC) and rotor side converter (RSC) are calculated based on the classical vector control strategies, similar to what is presented in [27], [32]. Control of RSC is especially important for the dynamic of the system.…”
Section: Grid and Rotor Side Voltage Reference Generationmentioning
confidence: 99%
“…Dynamic model to global rotating reference frame of the DFIG system by a d (direct) and q (quardrature)-axis rotating at the angular frequency of ωs [6]. The classical d-q magnetization of the squirrel cage induction generator was modeled with non zero rotor voltage in the Park reference frame [7].Presented steady state and dynamic models and control strategies of wind turbine generators.…”
Section: Introductionmentioning
confidence: 99%