A General Model for Estimating the Laminated Steel Losses Under PWM Voltage Supply

Popescu, Mircea; Ionel, Dan M.; Boglietti, Aldo; Cavagnino, Andrea; Cossar, C.; McGilp, M.I.

doi:10.1109/tia.2010.2049810

Cited by 96 publications

(40 citation statements)

References 14 publications

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“…A few papers, including [15] and [46], investigate iron losses up to 20 kHz, both reporting an increased loss estimation error with frequency, mainly related to skin effect. The lamination skin depth δ lam is given by:…”

Section: Iron Loss Modelmentioning

confidence: 99%

High-Frequency Characterization of Losses in Fully Assembled Stators of Slotless PM Motors

Millinger

Wallmark

Soulard

2018

IEEE Trans. on Ind. Applicat.

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PostprintThis is the accepted version of a paper published in IEEE transactions on industry applications. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination. IEEE transactions on industry applicationsAccess to the published version may require subscription. N.B. When citing this work, cite the original published paper. © © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Abstract-The recent emerge of wide band-gap (WBG) power transistors enables higher switching frequencies in electrical motor drives. Their full utilization from a system point of view requires quantification of the corresponding time-harmonic motor losses. As an initial step, this paper presents a unique study of stator losses for three different commercially available non-oriented silicon-iron (SiFe) steel grades (with lamination thicknesses 0.1, 0.2 and 0.3 mm). The investigations cover a wide frequency range (10-100 kHz) at different levels of DC-bias (up to 1.6 T). Iron losses are identified from measurements on fully assembled stators, deploying a novel technique. By utilizing fully assembled stators, no additional samples are required. Manufacturing influence is inherently incorporated. Results show that measured iron losses are twice as high at 10 kHz compared to Epstein test results, which emphasizes the need to incorporate manufacturing influence on iron losses at high frequencies. The level of DC-bias is also observed to have a significant impact on iron losses (up to 30 %). Even though thinner laminations are known for reducing iron losses, the reduction is much lower than anticipated in the studied frequency range due to skin effect. Using 0.1 mm lamination gauge instead of 0.3 mm reduces losses by 50 % at 10 kHz, while the same substitution at 100 kHz only reduces losses by 30 %. Future work includes loss separation in complete converter-fed machines.

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Section: Iron Loss Modelmentioning

confidence: 99%

High-Frequency Characterization of Losses in Fully Assembled Stators of Slotless PM Motors

Millinger

Wallmark

Soulard

2018

IEEE Trans. on Ind. Applicat.

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“…3) Core Loss: The core loss arising from hysteresis, eddy current and excess loss effects is estimated using the Bertotti iron loss model, (5), [21], where f and B are the frequency and peak flux density respectively. The coefficients k h , k e and k a are derived from measured loss data provided by the electrical steel manufacturer.…”

Section: ) Electromagnetic Modelmentioning

confidence: 99%

Exploiting cloud computing in the multi-physics design and optimisation of electromagnetic devices

Simpson

Mellor

2015

2015 IEEE 16th Workshop on Control and Modeling for Power Electronics (COMPEL)

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Abstract-Cloud computing is a type of distributed computing that enables public, on-demand network (internet) access to a large pool of shared computing resources, making it easier than ever to outsource computing tasks on a pay-as-you-go basis. In this paper, a cloud computing service is used to complement an existing high-fidelity multi-physics power inductor design workflow by enabling multiple instances of the design analysis to be executed in parallel on virtual computers in a cloud computing environment. This method accelerates the design development cycle and can allow a more thorough evaluation of the design space within a short time-frame, at a moderate cost and can potentially lead to improved designs. The modified workflow is demonstrated by the design optimisation of a high-energy-density filter inductor for an automotive application.

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“…The use of pulse width modulation (PWM) and non-linear loads results in a difficulty in iron loss prediction in magnetic materials. Although many models, which are based on physical or numerical methods [1][2][3][4][5][6][7][8][9], have been proposed by different researchers, predicting iron losses in magnetic materials, when a non-sinusoidal voltage excitation is applied, has not yet been entirely solved and is still an open research field.…”

Section: Introductionmentioning

confidence: 99%

“…With various structural arrangements and magnetization inputs applied to electrical machines, estimations of iron losses based on the steel manufacturers' datasheets, which mostly are tested under sinusoidal excitations, will exhibit large deviations from the actual ones. Over the last few decades, models have been established to improve the accuracy of the Steinmetz equation for various excitations, such as MSE (modified Steinmetz equation) [4,[12][13][14], GSE (general Steinmetz equation) [15,16] and iGSE (improved general Steinmetz equation) [17]. MSE uses the change rate of excitations instead of frequency.…”

Section: Introductionmentioning

confidence: 99%

“…iGSE combines the idea of the MSE and GSE methods. Some variability and correlation of the loss coefficients are introduced, including a form factor [5,6] and equivalent frequency [12,13] coefficients for PWM excitations and a three-order polynomial representation of the hysteresis-loss multiplicative coefficient [4,14,18]. Research papers pointed out that iron loss prediction should depend on frequency, magnitude, change rate and historical values of flux density, as well as other factors.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Iron Losses in Laminated Steel with Given Non-Sinusoidal Waveforms of Flux Density

Chen

Huang

et al. 2015

Energies

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Abstract:In electrical machines, iron losses are essential for electromagnetic and thermal designs and analyses. Although many models have been proposed to predict iron losses in magnetic materials, the calculation of iron losses under non-sinusoidal excitations is still an open field. Most works concern the influences of the value, the change rate or the frequency of flux density in the frequency domain. In this paper, we propose an engineering model for predicting loss characteristics with given waveforms of flux density in the time domain. The characteristics are collected from the knowledge of the iron loss in a laminated ring-shaped transformer. In the proposed model, we derive mathematical formulas for exciting currents in terms of flux density by describing the function methods through multi-frequency tests with sinusoidal excitations. The non-linearity of the material is interpreted by branches of conductances accounting for hysteresis and eddy-current losses. Then, iron losses are calculated based on the law of conservation of energy. An experimental system was built to evaluate the magnetic properties and iron losses under sinusoidal and non-sinusoidal excitations. Actual measurement results verify the effectiveness of the proposed model.

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A General Model for Estimating the Laminated Steel Losses Under PWM Voltage Supply

Cited by 96 publications

References 14 publications

High-Frequency Characterization of Losses in Fully Assembled Stators of Slotless PM Motors

High-Frequency Characterization of Losses in Fully Assembled Stators of Slotless PM Motors

Exploiting cloud computing in the multi-physics design and optimisation of electromagnetic devices

Predicting Iron Losses in Laminated Steel with Given Non-Sinusoidal Waveforms of Flux Density

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