The Experts below are selected from a list of 438117 Experts worldwide ranked by ideXlab platform
Marcello Pucci - One of the best experts on this subject based on the ideXlab platform.
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State-Space Space-Vector Model of the Induction Motor Including Magnetic Saturation and Iron Losses
IEEE Transactions on Industry Applications, 2019Co-Authors: Marcello PucciAbstract:This paper proposes a space-Vector dynamic Model of the rotating induction motor (RIM) taking into consideration both the magnetic saturation and the iron losses expressed in a state form. Such a Model tries to improve some issues of the previously developed dynamic Models. The main original aspects of the proposed Model are the following: the magnetic saturation of the iron core has been described on the basis of both current versus flux and flux versus current functions; the Model is based on the complete T space-Vector electrical scheme of the RIM; two different state formulations have been proposed; it includes the iron losses, separating them in hysteresis and eddy current ones; and it includes the effect of the load on the magnetic saturation. The proposed Model has been implemented in numerical simulation in MATLAB/Simulink environment and validated experimentally on a suitably developed test set-up based on a low-power RIM. The proposed Model has been exploited also to simulate the behavior of a high-power RIM.
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state space space Vector Model of the induction motor including magnetic saturation and iron losses
European Conference on Cognitive Ergonomics, 2017Co-Authors: Marcello PucciAbstract:This paper proposes a space-Vector dynamic Model of the Induction Motor (IM) taking into consideration the iron losses in a state form, trying to improve some issues of the previously developed dynamic Models. The main original aspects of the proposed Model are the following: 1) it is based on the complete T electrical scheme of the IM, 2) it is written in a complete state form, involving in the formulation the entire set of state variables useful in FOC, 3) it includes the iron losses, separating them in hysteresis and eddy current ones, 4) it includes the effect of the stator leakage flux on the magnetic saturation, accounting for part of the effect of the load on the saturation. The proposed Model has been implemented in numerical simulation in Matlab®-Simulink® environment and validated experimentally on a suitably developed test set-up.
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state space Vector Model of linear induction motors
IEEE Transactions on Industry Applications, 2014Co-Authors: Marcello PucciAbstract:This paper presents the state space-Vector dynamic Model of the linear induction motor (LIM) taking into consideration the dynamic end effects. Starting from the space-Vector equivalent circuit of the LIM, the complete set of space-Vector equations has been deduced. Afterward, first the so-called voltage and current flux Models have been written, from which the complete state space-Vector representation has been given. The complete thrust expression including the end-effect braking force has also been introduced in the Model. The complete state space-Vector Model, electromagnetic and mechanical part, has been implemented in numerical simulation and has been validated by comparing the results with those obtainable with a finite-element analysis and experiments. Results show that, even with a machine with a limited presence of the dynamic end effects, the adoption of a this Model permits a better estimation of both the electric quantities, e.g., inductor current, and the mechanical quantities, e.g., linear speed, with respect to the classic rotating induction motor Model.
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state space Vector Model of linear induction motors
European Conference on Cognitive Ergonomics, 2012Co-Authors: Marcello PucciAbstract:This paper presents the state space-Vector Model of the LIM taking into consideration the so-called end effects. Starting from the space-Vector equivalent circuit of the LIM, the complete set of space-Vector equations have been deduced. Afterwards, firstly the so-called voltage and current flux Models have been written, from which the complete state space-Vector Model has been deduced. This state space-Vector Model has been implemented in numerical simulation and validated with experimental results. Results confirm the correctness of the proposed Model which can, in perspective, be exploited for control purposes.
R. Touzi - One of the best experts on this subject based on the ideXlab platform.
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Target Scattering Decomposition in Terms of Roll-Invariant Target Parameters
IEEE Transactions on Geoscience and Remote Sensing, 2007Co-Authors: R. TouziAbstract:The Kennaugh-Huynen scattering matrix con-diagonalization is projected into the Pauli basis to derive a new scattering Vector Model for the representation of coherent target scattering. This Model permits a polarization basis invariant representation of coherent target scattering in terms of five independent target parameters, the magnitude and phase of the symmetric scattering type introduced in this paper, and the maximum polarization parameters (orientation, helicity, and maximum return). The new scattering Vector Model served for the assessment of the Cloude-Pottier incoherent target decomposition. Whereas the Cloude-Pottier scattering type alpha and entropy H are roll invariant, beta and the so-called target-phase parameters do depend on the target orientation angle for asymmetric scattering. The scattering Vector Model is then used as the basis for the development of new coherent and incoherent target decompositions in terms of unique and roll-invariant target parameters. It is shown that both the phase and magnitude of the symmetric scattering type should be used for an unambiguous description of symmetric target scattering. Target helicity is required for the assessment of the symmetry-asymmetry nature of target scattering. The symmetric scattering type phase is shown to be very promising for wetland classification in particular, using polarimetric Convair-580 synthetic aperture radar data collected over the Ramsar Mer Bleue wetland site to the east of Ottawa, Ontario, Canada
Mahmod Reza Sahebi - One of the best experts on this subject based on the ideXlab platform.
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incoherent target scattering decomposition of polarimetric sar data based on Vector Model roll invariant parameters
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Hossein Aghababaee, Mahmod Reza SahebiAbstract:Cameron's coherent decomposition is reconsidered in this paper for incoherent target decomposition. Characterizations of the minimum and maximum symmetric scattering components are considered in a unified Model and projected into the Pauli basis to derive a new scattering Vector Model for the representation of coherent target scattering. Some roll-invariant parameters can be derived from the defined scattering Vector Model, and the classification method that Cameron developed for operational use of his decomposition can be performed using these parameters. The proposed Model is compared with Touzi's target scattering Vector Model (TSVM), and it is revealed that the parameters of the TSVM are not unique and are invariant to the orientation angle of the polarization ellipse of the maximum copolarization backscattering return, which is descriptive of the polarization, whereas the extracted parameters from the proposed scattering Vector are invariant to the orientation of the symmetry axis of the maximum symmetric component of the scatterer, which is descriptive of the target. From the implementation using Uninhabited Aerial Vehicle Synthetic Aperture Radar and Experimental Synthetic Aperture Radar images, the advantages and efficiency of the proposed scattering Vector Model are demonstrated.
Hongmei Li - One of the best experts on this subject based on the ideXlab platform.
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Continuous Voltage Vector Model-Free Predictive Current Control of Surface Mounted Permanent Magnet Synchronous Motor
IEEE Transactions on Energy Conversion, 2019Co-Authors: Yanan Zhou, Hongmei LiAbstract:To reduce the current ripples of finite control set Model predictive control, this paper proposes a continuous voltage Vector Model-free predictive current control method for the surface mounted permanent magnet synchronous motor (SMPMSM). In the proposed method, the continuous phase and the amplitude of the voltage Vector are optimized in a sequence based on their uncoupling feature, and then the steady-state current fluctuation can be reduced. Only six active voltage Vectors are enumerated, and the optimal phase is obtained by establishing the Lagrange interpolation polynomial between the cost function and the phase of voltage Vectors. Then, the amplitude of the voltage Vector with the optimal phase is optimized based on the principle of minimizing the cost function, and the optimal voltage Vector is synthesized by a three-Vector method. In addition, the ultra-local Model of the SMPMSM drive system from the previous study is used to improve the robustness of the proposed method. The current dynamic and steady-state responses are demonstrated through the simulation and experimental results.
Hossein Aghababaee - One of the best experts on this subject based on the ideXlab platform.
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incoherent target scattering decomposition of polarimetric sar data based on Vector Model roll invariant parameters
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Hossein Aghababaee, Mahmod Reza SahebiAbstract:Cameron's coherent decomposition is reconsidered in this paper for incoherent target decomposition. Characterizations of the minimum and maximum symmetric scattering components are considered in a unified Model and projected into the Pauli basis to derive a new scattering Vector Model for the representation of coherent target scattering. Some roll-invariant parameters can be derived from the defined scattering Vector Model, and the classification method that Cameron developed for operational use of his decomposition can be performed using these parameters. The proposed Model is compared with Touzi's target scattering Vector Model (TSVM), and it is revealed that the parameters of the TSVM are not unique and are invariant to the orientation angle of the polarization ellipse of the maximum copolarization backscattering return, which is descriptive of the polarization, whereas the extracted parameters from the proposed scattering Vector are invariant to the orientation of the symmetry axis of the maximum symmetric component of the scatterer, which is descriptive of the target. From the implementation using Uninhabited Aerial Vehicle Synthetic Aperture Radar and Experimental Synthetic Aperture Radar images, the advantages and efficiency of the proposed scattering Vector Model are demonstrated.