Characterization and Prediction of Magnetic Losses in Soft Magnetic Composites Under Distorted Induction Waveform

Barrière, Olivier de la; Appino, C.; Fiorillo, F.; Ragusa, Carlo Stefano; Lécrivain, M.; Rocchino, L.; Ahmed, Hamid Ben; Gabsi, Mohamed; Mazaleyrat, F.; Lobue, Martino

doi:10.1109/tmag.2012.2218614

Cited by 52 publications

(27 citation statements)

References 18 publications

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“…c) The accuracy of the model (8) is much better than the models (5), (6) and (7) with the help of variable coefficients.…”

Section: Iron Loss Models Under Constant Temperaturementioning

confidence: 98%

“…The excess loss coefficient can be obtained by the curve fitting of the test results. The iron loss model can be expressed as: (6) where and are the electrical conductivity and the thickness of the lamination. is the time period.…”

Section: Iron Loss Models Under Constant Temperaturementioning

confidence: 99%

“…On the other hand, the prediction accuracy keeps good and stable in the whole test range with the help of variable coefficients. (5), (6) AND (7) Parameters for iron loss model (5) 3.25×10 -2 6.67×10 -5 5.95×10 -4 2 Parameters for iron loss model (6)…”

Section: Iron Loss Models Under Constant Temperaturementioning

confidence: 99%

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Evaluation of Iron Loss Models in Electrical Machines

Zhu

Xue

Chu

et al. 2019

IEEE Trans. on Ind. Applicat.

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In this paper, more than ten different iron loss models are experimentally evaluated, which cover alternating and rotating fields, influence of temperature, DC bias flux density and distorted flux density due to PWM inverter. Iron loss models considering alternating fields are evaluated by the measured results of a lamination ring specimen. The iron loss model considering rotating field and non-sinusoidal field are evaluated by the measured results of an electrical machine under different conditions. The iron loss models considering temperature influence are also evaluated by thermal analyses and experimental tests. Based on these comprehensive investigations, the iron loss models having the best prediction accuracy for each case are identified.Index terms --Electrical machine, Iron loss, Thermal analysis.Z.Q. Zhu (M'90-SM'00-F'09) received the B.Eng. and M.Sc. degrees in electrical engineering from Zhejiang University, . His major research interests include design and application of electric machine and drive system.Jun Peng received the B.Eng. degree in mechanical design

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“…c) The accuracy of the model (8) is much better than the models (5), (6) and (7) with the help of variable coefficients.…”

Section: Iron Loss Models Under Constant Temperaturementioning

confidence: 98%

Section: Iron Loss Models Under Constant Temperaturementioning

confidence: 99%

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Evaluation of Iron Loss Models in Electrical Machines

Zhu

Xue

Chu

et al. 2019

IEEE Trans. on Ind. Applicat.

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“…7 shows the and at low frequency and high frequency, respectively. By applying and to (9) and (10) respectively, the temperature dependent coefficients can be then obtained. Fig.…”

Section: B Modelling Of Temperature Dependent Coefficientsmentioning

confidence: 99%

A New Iron Loss Model for Temperature Dependencies of Hysteresis and Eddy Current Losses in Electrical Machines

et al. 2018

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Abstract-In this paper, the different temperature dependencies of hysteresis and eddy current losses of non-oriented Si-steel laminations are investigated. The measured iron loss results show that both the hysteresis and eddy current losses vary linearly with temperature between 40°C to 100°C, a typical temperature range of electrical machines. Varying rates of hysteresis and eddy current losses with the temperature are different and fluctuate with flux density and frequency. Based on this, an improved iron loss model which can consider temperature dependencies of hysteresis and eddy current losses separately is developed. Based on the improved iron loss model, the temperature influence on the iron loss can be fully considered by measuring iron losses at only two different temperatures. The investigation is experimentally validated by both the tests based on a ring specimen and an electrical machine.Index Terms-iron loss, eddy current loss, hysteresis loss, temperature dependency, electrical machines . In [29], only the temperature dependency of the eddy current loss is considered while the hysteresis loss is assumed to be not influenced by the temperature. In [30], the temperature influence on the total iron loss is simply modeled by introducing an equivalent temperature dependent coefficient which is a mix of temperature influences on both the hysteresis and eddy current losses. However in [27] and [28], it has shown experimentally that the hysteresis and eddy current losses have different temperature dependencies.The aim of this paper is to develop an iron loss model which can consider the temperature dependencies of the hysteresis and the eddy current losses separately. The iron losses at different flux density, frequency and temperature in non-oriented Si-steel laminations are measured firstly by the ring specimen test as will be described in Section II. In Section III, the accuracy of existing iron loss model having variable

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“…The iron loss model can be expressed as (8) By applying (8) to the iron loss calculation, the predicted iron loss values vary with the temperature. The temperature influence on iron loss can be partly considered.…”

Section: Iron Loss Prediction Considering Temperature Coefficient Of mentioning

confidence: 99%

Iron loss calculation considering temperature influence in non‐oriented steel laminations

Xue¹,

Chu

Zhu³

et al. 2016

IET Science, Measurement & Technology

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In this paper, the temperature influence on iron loss of non-oriented steel laminations is investigated. The iron loss variation under different flux densities, frequencies and temperatures is systematically measured and analysed by testing two typical non-oriented steel laminations, V300-35A and V470-50A. The iron loss variation with temperature is almost linear in the typical operating temperature range of electrical machines. Furthermore, the varying rate of iron loss with temperature varies with flux density and frequency. A coefficient which can fully consider the temperature influence is introduced to the existing iron loss model to improve the iron loss prediction accuracy. The predicted and measured results show that the temperature influence on the iron loss can be effectively considered by utilizing the improved model, i.e. the prediction accuracy of the improved iron loss model remains constant, even when the temperature varies significantly. A potential simplification of this improved model is also discussed in this paper.

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Characterization and Prediction of Magnetic Losses in Soft Magnetic Composites Under Distorted Induction Waveform

Cited by 52 publications

References 18 publications

Evaluation of Iron Loss Models in Electrical Machines

Evaluation of Iron Loss Models in Electrical Machines

A New Iron Loss Model for Temperature Dependencies of Hysteresis and Eddy Current Losses in Electrical Machines

Iron loss calculation considering temperature influence in non‐oriented steel laminations

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