Cunkas, MehmetSag, TahirAslan, Mustafa2020-03-262020-03-262009978-0-79180-297-7https://hdl.handle.net/20.500.12395/234062nd International Conference on Advanced Computer Theory and Engineering (ICACTE 2009) -- SEP 25-27, 2009 -- Cairo, EGYPTIn this paper, two algorithms, Differential Evolution Algorithm (DEA) and Genetic Algorithms (GAs), are applied to the offline identification of induction motor parameters. DEA is compared with the prediction errors and the genetic algorithm via determination parameters using nameplate data like starting torque, breakdown torque, and full-load torque in two different cases. Consequently, it is seen that DEA can be find more precise parameter values than the genetic algorithm and especially convergences to global optimum not to be stuck local optimum.eninfo:eu-repo/semantics/closedAccessInduction motorParameter identificationGenetic AlgorithmsDifferential evolution algorithmConference Object777784N/AWOS:000271545700094N/A