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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Mon, 25 May 2020 03:36:11 GMT2020-05-25T03:36:11ZModel Order Reduction of Electrical Machines with Multiple Inputs
http://hdl.handle.net/10985/12755
Model Order Reduction of Electrical Machines with Multiple Inputs
FARZAM FAR, M.; BELAHCEN, Anouar; RASILO, Paavo; CLENET, Stéphane; PIERQUIN, A.
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.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/127552017-01-01T00:00:00ZFARZAM FAR, M.BELAHCEN, AnouarRASILO, PaavoCLENET, StéphanePIERQUIN, A.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.Uncertainty propagation of iron loss from characterization measurements to computation of electrical machines
http://hdl.handle.net/10985/9492
Uncertainty propagation of iron loss from characterization measurements to computation of electrical machines
BELAHCEN, Anouar; RASILO, Paavo; NGUYEN, Thu Trang; CLÉNET, Stéphane
The aim of the research is to find out how uncertainties in the characterization of magnetic materials propagate through identification and numerical simulation to the computation of iron losses in electrical machines. Design/methodology/approach The probabilistic uncertainties in the iron losses are modelled with the spectral approach using chaos polynomials. The Sobol indices are used for the global sensitivity analysis. The machine is modelled with a 2D finite element method and the iron losses are computed with a previously developed accurate method. Findings The uncertainties propagate in different ways to the different components of losses, i.e. eddy current, hysteresis, and excess losses. The propagation is also different depending on the investigated region of the machine, i.e. Stator or rotor teeth, yokes, tooth tips. Research limitations/implications The method does not account for uncertainties related to the manufacturing process, which might result in even larger variability. Practical implications A major implication of the findings is that the identification of iron loss parameters at low frequencies does not affect the loss variability. The identification with high frequency measurement is very important for the rotor tooth tips. The variability in the excess loss parameters is of low impact. Originality/value The presented results are of importance for the magnetic material manufacturers and the electrical machine designers. The manufacturers can plan the measurement and identification procedures as to minimize the output variability of the parameters. The designers of the machine can use the result and the presented procedures to estimate the variability of their design
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/94922015-01-01T00:00:00ZBELAHCEN, AnouarRASILO, PaavoNGUYEN, Thu TrangCLÉNET, StéphaneThe aim of the research is to find out how uncertainties in the characterization of magnetic materials propagate through identification and numerical simulation to the computation of iron losses in electrical machines. Design/methodology/approach The probabilistic uncertainties in the iron losses are modelled with the spectral approach using chaos polynomials. The Sobol indices are used for the global sensitivity analysis. The machine is modelled with a 2D finite element method and the iron losses are computed with a previously developed accurate method. Findings The uncertainties propagate in different ways to the different components of losses, i.e. eddy current, hysteresis, and excess losses. The propagation is also different depending on the investigated region of the machine, i.e. Stator or rotor teeth, yokes, tooth tips. Research limitations/implications The method does not account for uncertainties related to the manufacturing process, which might result in even larger variability. Practical implications A major implication of the findings is that the identification of iron loss parameters at low frequencies does not affect the loss variability. The identification with high frequency measurement is very important for the rotor tooth tips. The variability in the excess loss parameters is of low impact. Originality/value The presented results are of importance for the magnetic material manufacturers and the electrical machine designers. The manufacturers can plan the measurement and identification procedures as to minimize the output variability of the parameters. The designers of the machine can use the result and the presented procedures to estimate the variability of their designModel Order Reduction of Electrical Machines with Multiple Inputs
http://hdl.handle.net/10985/11834
Model Order Reduction of Electrical Machines with Multiple Inputs
FARZAM FAR, Mernhaz; BELAHCEN, Anouar; RASILO, Paavo; CLENET, Stéphane; PIERQUIN, Antoine
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.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/118342017-01-01T00:00:00ZFARZAM FAR, MernhazBELAHCEN, AnouarRASILO, PaavoCLENET, 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.