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SAM collecte, emmagasine, indexe, archive, et diffuse du matériel de recherche en format numérique.Sat, 24 Mar 2018 10:19:00 GMT2018-03-24T10:19:00ZModel Order Reduction of Electrical Machines with Multiple Inputs
http://hdl.handle.net/10985/11834
FARZAM FAR, Mernhaz; BELAHCEN, Anouar; RASILO, Pavo; CLENET, Stéphane; PIERQUIN, Antoine
Transactions on Industrial Applications
In this paper, proper orthogonal decomposition method is employed to build a reduced-order model from a high-order nonlinear permanent magnet synchronous machine
model with multiple inputs. Three parameters are selected as the multiple inputs of the machine. These parameters are terminal current, angle of the terminal current, and rotation angle. To produce the lower-rank system, snapshots or instantaneous system states are projected onto a set of orthonormal basis functions with small dimension. The reduced model is then validated by comparing the vector potential, flux
density distribution, and torque results of the original model, which indicates the capability of using the proper orthogonal decomposition method in the multi-variable input problems. The developed methodology can be used for fast simulations of
the machine.
Wed, 01 Mar 2017 00:00:00 GMThttp://hdl.handle.net/10985/118342017-03-01T00:00:00ZFARZAM FAR, MernhazBELAHCEN, AnouarRASILO, PavoCLENET, StéphanePIERQUIN, AntoineIn this paper, proper orthogonal decomposition method is employed to build a reduced-order model from a high-order nonlinear permanent magnet synchronous machine
model with multiple inputs. Three parameters are selected as the multiple inputs of the machine. These parameters are terminal current, angle of the terminal current, and rotation angle. To produce the lower-rank system, snapshots or instantaneous system states are projected onto a set of orthonormal basis functions with small dimension. The reduced model is then validated by comparing the vector potential, flux
density distribution, and torque results of the original model, which indicates the capability of using the proper orthogonal decomposition method in the multi-variable input problems. The developed methodology can be used for fast simulations of
the machine.