A general formula for prediction of iron losses under nonsinusoidal voltage waveform

Amar, M.; Kaczmarek, Robert

doi:10.1109/20.406552

Cited by 99 publications

(42 citation statements)

References 18 publications

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“…Analytical models have been proposed to investigate these components that require the identification of parameters. These are dependant on the chemical and physical characteristics of the considered material [25][26][27][28]. Therefore, for sinusoidal supply, the hysteresis losses can be approximated by the following well known equation proposed by Steinmetz [29], where B max is the peak value of the flux density, f the frequency and α the Steinmetz coefficient.…”

Section: A Iron Losses Separation Approachmentioning

confidence: 99%

Stochastic Modeling of Soft Magnetic Properties of Electrical Steels: Application to Stators of Electrical Machines

2012

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To take account of the uncertainties introduced on the soft magnetic materials properties (magnetic behavior law, iron losses) during the manufacturing process, the present work deals with the stochastic modeling of the magnetic behavior law B-H and iron losses of claw pole stator generator. Twenty eight (28) samples of slinky stator (SS) coming from the same production chain have been investigated. The used approaches are similar to those used in mechanics. The accuracy of existing anhysteretic models has been tested first using cross validation techniques. The well known iron loss separation model has been implemented to take into account the variability of the losses. Then, the Multivariate Gaussian distribution is chosen to model the variability and dependencies between identified parameters, for both behavior law and iron loss models. The developed stochastic models allow predicting a 98% confidence interval for the considered samples.

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Section: A Iron Losses Separation Approachmentioning

confidence: 99%

Stochastic Modeling of Soft Magnetic Properties of Electrical Steels: Application to Stators of Electrical Machines

2012

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“…The model separates losses into hysteresis, eddy-current and excess losses. Many attempts have been made to incorporate for harmonic losses into the Bertotti model [42]- [47]. In [44], the authors present a model for prediction of losses under arbitrary input using the Preisach model for improved estimation of minor loops, validated up to 2 kHz switching frequency.…”

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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“…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%

“…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%

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 formula for prediction of iron losses under nonsinusoidal voltage waveform

Cited by 99 publications

References 18 publications

Stochastic Modeling of Soft Magnetic Properties of Electrical Steels: Application to Stators of Electrical Machines

Stochastic Modeling of Soft Magnetic Properties of Electrical Steels: Application to Stators of Electrical Machines

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

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

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