Matlab-Simulink Coupling to Finite Element Software for Design and Analysis of Electrical Machines

Almandoz, Gaizka; Ugalde, Gaizka; Poza, Javier; Escalada, Ana Julia

doi:10.5772/46476

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Analytical methods to solve this problem seem quite complicated because representations of losses include partial differential equations [5]- [7], [19]- [26]. In this context, by investigating the papers numbered [4], [7], [8], [13], [17], [20], [22], [27], the use of a motor based on the Finite Elements Method (FEM) modelling has been chosen as the most suitable solution for this problem.…”

Section: Finite Element Analysis Of Core Losses In Pmsmmentioning

confidence: 99%

Neuro-Fuzzy Approach on Core Resistance Estimation at Loss Minimization Control of Permanent Magnet Synchronous Motor

Erdoğan¹,

Özdemir²

2016

ElAEE

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Iron losses are among the most significant losses occurring on the Permanent Magnet Synchronous Motor (PMSM). These losses consume active power and cause heat in the iron core. Due to this behavior, they can be represented by an equivalent resistance to make the computations simple. Determining the equivalent core resistance is also a major problem. Computing these lost power is very difficult especially in dynamic applications because these lost power varies by partial differential equations.This study aims to estimate the dynamic core resistance depended on inconstant operating conditions online, and compare the performance of the motor with dynamic versus fixed core resistance at the designed loss minimization algorithm. In order to obtain this estimation, firstly the finite element calculations have been made for many different operating speeds and lost power values were gathered for each speed. Then corresponding core resistance for each power value has been calculated with the dynamic model of a PMSM. Finally, a Neuro-Fuzzy estimator has been designed by computations on the gathered resistance values to estimate the core resistance for different operating conditions. At the end the obtained results are discussed with respect to feasibility of the system.

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Section: Finite Element Analysis Of Core Losses In Pmsmmentioning

confidence: 99%

Neuro-Fuzzy Approach on Core Resistance Estimation at Loss Minimization Control of Permanent Magnet Synchronous Motor

Erdoğan¹,

Özdemir²

2016

ElAEE

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“…In the simplest approach, the reduced-order models are used for the simulation of electric drives, where the machine is modeled by a set of differential equations and the electromagnetic nature is taken into account by constant coefficients (lumped parameters); for example, the fluxcurrent relation is assumed linear or replaced by a look-up table, and the power electronics inverter is replaced by an ideal sinusoidal voltage source [1][2][3][4]. This approach allows us to obtain sufficient information about the performance of the drive, but it cannot identify and investigate certain transient and harmonic effects caused by the non-sinusoidal flux density distribution, the slotting effects, and the pulse-width modulated (PWM) voltage source inverter, which are necessary for optimization of the drive and result in higher electromagnetic losses and torque ripple [5]. Moreover, it is well known that the behavior of an electrical machine becomes more nonlinear depending on the operation conditions, the effects of magnetic saturation, and cross-saturation, which should be taken into account [6].…”

Section: Introductionmentioning

confidence: 99%

Co-Simulation Analysis for Performance Prediction of Synchronous Reluctance Drives

et al. 2021

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To improve the design of electric drives and to better predict the system performance, numerical simulation has been widely employed. Whereas in the majority of the approaches, the machines and the power electronics are designed and simulated separately, to improve the fidelity, a co-simulation should be performed. This paper presents a complete coupled co-simulation model of synchronous reluctance machine (SynRel) drive, which includes the finite element model of the SynRel, the power electronics inverter, the control system, and application examples. The model of SynRel is based on a finite element model (FEM) using Simcenter MagNet. The power electronics inverter is built using PLECS Blockset, and the drive control model is built in Simulink environment, which allows for coupling between MagNet and PLECS. The proposed simulation model provides high accuracy thanks to the complete FEA-based model fed by actual inverter voltage. The comparison of the simulation results with experimental measurements shows good correspondence.

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“…Analytical methods trying to solve this problem appear quite difficult, because loss equations include partial differential equations [5][6][7][19][20][21][22][23][24]35,36]. After investigating some previous studies [4,7,8,13,17,20,22,37], the usage of a motor model developed by the finite elements method (FEM) was chosen as the most suitable solution to the problem.…”

Section: Finite Element Analysis Of Core Losses In Pmsmmentioning

confidence: 99%

“…In electric vehicle applications, motors are generally required to operate in wide speed ranges. The authors of [3][4][5][6][7][8] performed analyses on iron losses and/or equivalent core loss resistance under fixed rotor speed operation conditions. In fixed speed operations, frequency remains fixed, too.…”

Section: Introductionmentioning

confidence: 99%

Neural network approach on loss minimization control of a PMSM with core resistance estimation

Erdoğan¹,

Özdemir²

2017

Turk J Elec Eng & Comp Sci

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Abstract:Permanent magnet synchronous motors (PMSMs) are often used in industry for high-performance applications.Their key features are high power density, linear torque control capability, high efficiency, and fast dynamic response.Today, PMSMs are prevalent especially for their use in hybrid electric vehicles. Since operating the motor at high efficiency values is critically important for electric vehicles, as for all other applications, minimum loss control appears to be an inevitable requirement in PMSMs. In this study, a neural network-based intelligent minimum loss control technique is applied to a PMSM. It is shown by means of the results obtained that the total machine losses can be controlled in a way that keeps them at a minimum level. It is worth noting here that this improvement is achieved compared to the case with I d set to zero, where no minimum loss control technique is used. Within this context, hysteresis and eddy current losses are primarily obtained under certain conditions by means of a PMSM finite element model, initially developed by CEDRAT as an educational demo. A comprehensive loss model with a dynamic core resistor estimator is developed using this information. A neural network controller is then applied to this model and comparisons are made with analytical methods such as field weakening and maximum torque per ampere control techniques. Finally, the obtained results are discussed.

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Matlab-Simulink Coupling to Finite Element Software for Design and Analysis of Electrical Machines

Cited by 7 publications

References 10 publications

Neuro-Fuzzy Approach on Core Resistance Estimation at Loss Minimization Control of Permanent Magnet Synchronous Motor

Neuro-Fuzzy Approach on Core Resistance Estimation at Loss Minimization Control of Permanent Magnet Synchronous Motor

Co-Simulation Analysis for Performance Prediction of Synchronous Reluctance Drives

Neural network approach on loss minimization control of a PMSM with core resistance estimation

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